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28 Case Selection for Case‐Study Analysis: Qualitative and Quantitative Techniques

John Gerring is Professor of Political Science, Boston University.

• Published: 02 September 2009
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This article presents some guidance by cataloging nine different techniques for case selection: typical, diverse, extreme, deviant, influential, crucial, pathway, most similar, and most different. It also indicates that if the researcher is starting from a quantitative database, then methods for finding influential outliers can be used. In particular, the article clarifies the general principles that might guide the process of case selection in case-study research. Cases are more or less representative of some broader phenomenon and, on that score, may be considered better or worse subjects for intensive analysis. The article then draws attention to two ambiguities in case-selection strategies in case-study research. The first concerns the admixture of several case-selection strategies. The second concerns the changing status of a case as a study proceeds. Some case studies follow only one strategy of case selection.

Case ‐study analysis focuses on one or several cases that are expected to provide insight into a larger population. This presents the researcher with a formidable problem of case selection: Which cases should she or he choose?

In large‐sample research, the task of case selection is usually handled by some version of randomization. However, in case‐study research the sample is small (by definition) and this makes random sampling problematic, for any given sample may be wildly unrepresentative. Moreover, there is no guarantee that a few cases, chosen randomly, will provide leverage into the research question of interest.

In order to isolate a sample of cases that both reproduces the relevant causal features of a larger universe (representativeness) and provides variation along the dimensions of theoretical interest (causal leverage), case selection for very small samples must employ purposive (nonrandom) selection procedures. Nine such methods are discussed in this chapter, each of which may be identified with a distinct case‐study “type:” typical, diverse, extreme, deviant, influential, crucial, pathway, most‐similar , and most‐different . Table 28.1 summarizes each type, including its general definition, a technique for locating it within a population of potential cases, its uses, and its probable representativeness.

While each of these techniques is normally practiced on one or several cases (the diverse, most‐similar, and most‐different methods require at least two), all may employ additional cases—with the proviso that, at some point, they will no longer offer an opportunity for in‐depth analysis and will thus no longer be “case studies” in the usual sense ( Gerring 2007 , ch. 2 ). It will also be seen that small‐ N case‐selection procedures rest, at least implicitly, upon an analysis of a larger population of potential cases (as does randomization). The case(s) identified for intensive study is chosen from a population and the reasons for this choice hinge upon the way in which it is situated within that population. This is the origin of the terminology—typical, diverse, extreme, et al. It follows that case‐selection procedures in case‐study research may build upon prior cross‐case analysis and that they depend, at the very least, upon certain assumptions about the broader population.

In certain circumstances, the case‐selection procedure may be structured by a quantitative analysis of the larger population. Here, several caveats must be satisfied. First, the inference must pertain to more than a few dozen cases; otherwise, statistical analysis is problematic. Second, relevant data must be available for that population, or a significant sample of that population, on key variables, and the researcher must feel reasonably confident in the accuracy and conceptual validity of these variables. Third, all the standard assumptions of statistical research (e.g. identification, specification, robustness) must be carefully considered, and wherever possible, tested. I shall not dilate further on these familiar issues except to warn the researcher against the unreflective use of statistical techniques. 1 When these requirements are not met, the researcher must employ a qualitative approach to case selection.

The point of this chapter is to elucidate general principles that might guide the process of case selection in case‐study research, building upon earlier work by Harry Eckstein, Arend Lijphart, and others. Sometimes, these principles can be applied in a quantitative framework and sometimes they are limited to a qualitative framework. In either case, the logic of case selection remains quite similar, whether practiced in small‐ N or large‐ N contexts.

Before we begin, a bit of notation is necessary. In this chapter “ N ” refers to cases, not observations. Here, I am concerned primarily with causal inference, rather than inferences that are descriptive or predictive in nature. Thus, all hypotheses involve at least one independent variable ( X ) and one dependent variable ( Y ). For convenience, I shall label the causal factor of special theoretical interest X   1 , and the control variable, or vector of controls (if there are any), X   2 . If the writer is concerned to explain a puzzling outcome, but has no preconceptions about its causes, then the research will be described as Y‐centered . If a researcher is concerned to investigate the effects of a particular cause, with no preconceptions about what these effects might be, the research will be described as X‐centered . If a researcher is concerned to investigate a particular causal relationship, the research will be described as X   1 / Y‐centered , for it connects a particular cause with a particular outcome. 2   X ‐ or Y ‐centered research is exploratory; its purpose is to generate new hypotheses. X   1 / Y‐centered research, by contrast, is confirmatory/disconfirmatory; its purpose is to test an existing hypothesis.

1 Typical Case

In order for a focused case study to provide insight into a broader phenomenon it must be representative of a broader set of cases. It is in this context that one may speak of a typical‐case approach to case selection. The typical case exemplifies what is considered to be a typical set of values, given some general understanding of a phenomenon. By construction, the typical case is also a representative case.

Some typical cases serve an exploratory role. Here, the author chooses a case based upon a set of descriptive characteristics and then probes for causal relationships. Robert and Helen Lynd (1929/1956) selected a single city “to be as representative as possible of contemporary American life.” Specifically, they were looking for a city with

1) a temperate climate; 2) a sufficiently rapid rate of growth to ensure the presence of a plentiful assortment of the growing pains accompanying contemporary social change; 3) an industrial culture with modern, high‐speed machine production; 4) the absence of dominance of the city's industry by a single plant (i.e., not a one‐industry town); 5) a substantial local artistic life to balance its industrial activity …; and 6) the absence of any outstanding peculiarities or acute local problems which would mark the city off from the midchannel sort of American community. ( Lynd and Lynd 1929/1956 , quoted in Yin 2004 , 29–30)

After examining a number of options the Lynds decided that Muncie, Indiana, was more representative than, or at least as representative as, other midsized cities in America, thus qualifying as a typical case.

This is an inductive approach to case selection. Note that typicality may be understood according to the mean, median, or mode on a particular dimension; there may be multiple dimensions (as in the foregoing example); and each may be differently weighted (some dimensions may be more important than others). Where the selection criteria are multidimensional and a large sample of potential cases is in play, some form of factor analysis may be useful in identifying the most‐typical case(s).

However, the more common employment of the typical‐case method involves a causal model of some phenomenon of theoretical interest. Here, the researcher has identified a particular outcome ( Y ), and perhaps a specific X   1 / Y hypothesis, which she wishes to investigate. In order to do so, she looks for a typical example of that causal relationship. Intuitively, one imagines that a case selected according to the mean values of all parameters must be a typical case relative to some causal relationship. However, this is by no means assured.

Suppose that the Lynds were primarily interested in explaining feelings of trust/distrust among members of different social classes (one of the implicit research goals of the Middletown study). This outcome is likely to be affected by many factors, only some of which are included in their six selection criteria. So choosing cases with respect to a causal hypothesis involves, first of all, identifying the relevant parameters. It involves, secondly, the selection of a case that has a “typical” value relative to the overall causal model; it is well explained. Cases with untypical scores on a particular dimension (e.g. very high or very low) may still be typical examples of a causal relationship. Indeed, they may be more typical than cases whose values lie close to the mean. Thus, a descriptive understanding of typicality is quite different from a causal understanding of typicality. Since it is the latter version that is more common, I shall adopt this understanding of typicality in the remainder of the discussion.

From a qualitative perspective, causal typicality involves the selection of a case that conforms to expectations about some general causal relationship. It performs as expected. In a quantitative setting, this notion is measured by the size of a case's residual in a large‐ N cross‐case model. Typical cases lie on or near the regression line; their residuals are small. Insofar as the model is correctly specified, the size of a case's residual (i.e. the number of standard deviations that separate the actual value from the fitted value) provides a helpful clue to how representative that case is likely to be. “Outliers” are unlikely to be representative of the target population.

Of course, just because a case has a low residual does not necessarily mean that it is a representative case (with respect to the causal relationship of interest). Indeed, the issue of case representativeness is an issue that can never be definitively settled. When one refers to a “typical case” one is saying, in effect, that the probability of a case's representativeness is high, relative to other cases. This test of typicality is misleading if the statistical model is mis‐specified. And it provides little insurance against errors that are purely stochastic. A case may lie directly on the regression line but still be, in some important respect, atypical. For example, it might have an odd combination of values; the interaction of variables might be different from other cases; or additional causal mechanisms might be at work. For this reason, it is important to supplement a statistical analysis of cases with evidence drawn from the case in question (the case study itself) and with our deductive knowledge of the world. One should never judge a case solely by its residual. Yet, all other things being equal, a case with a low residual is less likely to be unusual than a case with a high residual, and to this extent the method of case selection outlined here may be a helpful guide to case‐study researchers faced with a large number of potential cases.

By way of conclusion, it should be noted that because the typical case embodies a typical value on some set of causally relevant dimensions, the variance of interest to the researcher must lie within that case. Specifically, the typical case of some phenomenon may be helpful in exploring causal mechanisms and in solving identification problems (e.g. endogeneity between X   1 and Y , an omitted variable that may account for X   1   and Y , or some other spurious causal association). Depending upon the results of the case study, the author may confirm an existing hypothesis, disconfirm that hypothesis, or reframe it in a way that is consistent with the findings of the case study. These are the uses of the typical‐case study.

2 Diverse Cases

A second case‐selection strategy has as its primary objective the achievement of maximum variance along relevant dimensions. I refer to this as a diverse‐case method. For obvious reasons, this method requires the selection of a set of cases—at minimum, two—which are intended to represent the full range of values characterizing X   1 , Y , or some particular X   1 / Y relationship. 3

Where the individual variable of interest is categorical (on/off, red/black/blue, Jewish/Protestant/Catholic), the identification of diversity is readily apparent. The investigator simply chooses one case from each category. For a continuous variable, the choices are not so obvious. However, the researcher usually chooses both extreme values (high and low), and perhaps the mean or median as well. The researcher may also look for break‐points in the distribution that seem to correspond to categorical differences among cases. Or she may follow a theoretical hunch about which threshold values count, i.e. which are likely to produce different values on Y .

Another sort of diverse case takes account of the values of multiple variables (i.e. a vector), rather than a single variable. If these variables are categorical, the identification of causal types rests upon the intersection of each category. Two dichotomous variables produce a matrix with four cells. Three trichotomous variables produce a matrix of eight cells. And so forth. If all variables are deemed relevant to the analysis, the selection of diverse cases mandates the selection of one case drawn from within each cell. Let us say that an outcome is thought to be affected by sex, race (black/white), and marital status. Here, a diverse‐case strategy of case selection would identify one case within each of these intersecting cells—a total of eight cases. Things become slightly more complicated when one or more of the factors is continuous, rather than categorical. Here, the diversity of case values do not fall neatly into cells. Rather, these cells must be created by fiat—e.g. high, medium, low.

It will be seen that where multiple variables are under consideration, the logic of diverse‐case analysis rests upon the logic of typological theorizing—where different combinations of variables are assumed to have effects on an outcome that vary across types ( Elman 2005 ; George and Bennett 2005 , 235; Lazarsfeld and Barton 1951 ). George and Smoke, for example, wish to explore different types of deterrence failure—by “fait accompli,” by “limited probe,” and by “controlled pressure.” Consequently, they wish to find cases that exemplify each type of causal mechanism. 4

Diversity may thus refer to a range of variation on X or Y , or to a particular combination of causal factors (with or without a consideration of the outcome). In each instance, the goal of case selection is to capture the full range of variation along the dimension(s) of interest.

Since diversity can mean many things, its employment in a large‐ N setting is necessarily dependent upon how this key term is defined. If it is understood to pertain only to a single variable ( X   1 or Y ), then the task is fairly simple. A categorical variable mandates the choice of at least one case from each category—two if dichotomous, three if trichotomous, and so forth. A continuous variable suggests the choice of at least one “high” and “low” value, and perhaps one drawn from the mean or median. But other choices might also be justified, according to one's hunch about the underlying causal relationship or according to natural thresholds found in the data, which may be grouped into discrete categories. Single‐variable traits are usually easy to discover in a large‐ N setting through descriptive statistics or through visual inspection of the data.

Where diversity refers to particular combinations of variables, the relevant cross‐ case technique is some version of stratified random sampling (in a probabilistic setting) or Qualitative Comparative Analysis (in a deterministic setting) ( Ragin 2000 ). If the researcher suspects that a causal relationship is affected not only by combinations of factors but also by their sequencing , then the technique of analysis must incorporate temporal elements ( Abbott 2001 ; Abbott and Forrest 1986 ; Abbott and Tsay 2000 ). Thus, the method of identifying causal types rests upon whatever method of identifying causal relationships is employed in the large‐ N sample.

Note that the identification of distinct case types is intended to identify groups of cases that are internally homogeneous (in all respects that might affect the causal relationship of interest). Thus, the choice of cases within each group should not be problematic, and may be accomplished through random sampling or purposive case selection. However, if there is suspected diversity within each category, then measures should be taken to assure that the chosen cases are typical of each category. A case study should not focus on an atypical member of a subgroup.

Indeed, considerations of diversity and typicality often go together. Thus, in a study of globalization and social welfare systems, Duane Swank (2002) first identifies three distinctive groups of welfare states: “universalistic” (social democratic), “corporatist conservative,” and “liberal.” Next, he looks within each group to find the most‐typical cases. He decides that the Nordic countries are more typical of the universalistic model than the Netherlands since the latter has “some characteristics of the occupationally based program structure and a political context of Christian Democratic‐led governments typical of the corporatist conservative nations” ( Swank 2002 , 11; see also Esping‐Andersen 1990 ). Thus, the Nordic countries are chosen as representative cases within the universalistic case type, and are accompanied in the case‐study portion of his analysis by other cases chosen to represent the other welfare state types (corporatist conservative and liberal).

Evidently, when a sample encompasses a full range of variation on relevant parameters one is likely to enhance the representativeness of that sample (relative to some population). This is a distinct advantage. Of course, the inclusion of a full range of variation may distort the actual distribution of cases across this spectrum. If there are more “high” cases than “low” cases in a population and the researcher chooses only one high case and one low case, the resulting sample of two is not perfectly representative. Even so, the diverse‐case method probably has stronger claims to representativeness than any other small‐ N sample (including the standalone typical case). The selection of diverse cases has the additional advantage of introducing variation on the key variables of interest. A set of diverse cases is, by definition, a set of cases that encompasses a range of high and low values on relevant dimensions. There is, therefore, much to recommend this method of case selection. I suspect that these advantages are commonly understood and are applied on an intuitive level by case‐study researchers. However, the lack of a recognizable name—and an explicit methodological defense—has made it difficult for case‐study researchers to utilize this method of case selection, and to do so in an explicit and self‐conscious fashion. Neologism has its uses.

3 Extreme Case

The extreme‐case method selects a case because of its extreme value on an independent ( X   1 ) or dependent ( Y ) variable of interest. Thus, studies of domestic violence may choose to focus on extreme instances of abuse ( Browne 1987 ). Studies of altruism may focus on those rare individuals who risked their lives to help others (e.g. Holocaust resisters) ( Monroe 1996 ). Studies of ethnic politics may focus on the most heterogeneous societies (e.g. Papua New Guinea) in order to better understand the role of ethnicity in a democratic setting ( Reilly 2000–1 ). Studies of industrial policy often focus on the most successful countries (i.e. the NICS) ( Deyo 1987 ). And so forth. 5

Often an extreme case corresponds to a case that is considered to be prototypical or paradigmatic of some phenomena of interest. This is because concepts are often defined by their extremes, i.e. their ideal types. Italian Fascism defines the concept of Fascism, in part, because it offered the most extreme example of that phenomenon. However, the methodological value of this case, and others like it, derives from its extremity (along some dimension of interest), not its theoretical status or its status in the literature on a subject.

The notion of “extreme” may now be defined more precisely. An extreme value is an observation that lies far away from the mean of a given distribution. This may be measured (if there are sufficient observations) by a case's “Z score”—the number of standard deviations between a case and the mean value for that sample. Extreme cases have high Z scores, and for this reason may serve as useful subjects for intensive analysis.

For a continuous variable, the distance from the mean may be in either direction (positive or negative). For a dichotomous variable (present/absent), extremeness may be interpreted as unusual . If most cases are positive along a given dimension, then a negative case constitutes an extreme case. If most cases are negative, then a positive case constitutes an extreme case. It should be clear that researchers are not simply concerned with cases where something “happened,” but also with cases where something did not. It is the rareness of the value that makes a case valuable, in this context, not its positive or negative value. 6 Thus, if one is studying state capacity, a case of state failure is probably more informative than a case of state endurance simply because the former is more unusual. Similarly, if one is interested in incest taboos a culture where the incest taboo is absent or weak is probably more useful than a culture where it is present or strong. Fascism is more important than nonfascism. And so forth. There is a good reason, therefore, why case studies of revolution tend to focus on “revolutionary” cases. Theda Skocpol (1979) had much more to learn from France than from Austro‐Hungary since France was more unusual than Austro‐Hungary within the population of nation states that Skocpol was concerned to explain. The reason is quite simple: There are fewer revolutionary cases than nonrevolutionary cases; thus, the variation that we explore as a clue to causal relationships is encapsulated in these cases, against a background of nonrevolutionary cases.

Note that the extreme‐case method of case selection appears to violate the social science folk wisdom warning us not to “select on the dependent variable.” 7 Selecting cases on the dependent variable is indeed problematic if a number of cases are chosen, all of which lie on one end of a variable's spectrum (they are all positive or negative), and if the researcher then subjects this sample to cross‐case analysis as if it were representative of a population. 8 Results for this sort of analysis would almost assuredly be biased. Moreover, there will be little variation to explain since the values of each case are explicitly constrained.

However, this is not the proper employment of the extreme‐case method. (It is more appropriately labeled an extreme‐ sample method.) The extreme‐case method actually refers back to a larger sample of cases that lie in the background of the analysis and provide a full range of variation as well as a more representative picture of the population. It is a self‐conscious attempt to maximize variance on the dimension of interest, not to minimize it. If this population of cases is well understood— either through the author's own cross‐case analysis, through the work of others, or through common sense—then a researcher may justify the selection of a single case exemplifying an extreme value for within‐case analysis. If not, the researcher may be well advised to follow a diverse‐case method, as discussed above.

By way of conclusion, let us return to the problem of representativeness. It will be seen that an extreme case may be typical or deviant. There is simply no way to tell because the researcher has not yet specified an X   1 / Y causal proposition. Once such a causal proposition has been specified one may then ask whether the case in question is similar to some population of cases in all respects that might affect the X   1 / Y relationship of interest (i.e. unit homogeneous). It is at this point that it becomes possible to say, within the context of a cross‐case statistical model, whether a case lies near to, or far from, the regression line. However, this sort of analysis means that the researcher is no longer pursuing an extreme‐case method. The extreme‐case method is purely exploratory—a way of probing possible causes of Y , or possible effects of X , in an open‐ended fashion. If the researcher has some notion of what additional factors might affect the outcome of interest, or of what relationship the causal factor of interest might have with Y , then she ought to pursue one of the other methods explored in this chapter. This also implies that an extreme‐case method may transform into a different kind of approach as a study evolves; that is, as a more specific hypothesis comes to light. Useful extreme cases at the outset of a study may prove less useful at a later stage of analysis.

4 Deviant Case

The deviant‐case method selects that case(s) which, by reference to some general understanding of a topic (either a specific theory or common sense), demonstrates a surprising value. It is thus the contrary of the typical case. Barbara Geddes (2003) notes the importance of deviant cases in medical science, where researchers are habitually focused on that which is “pathological” (according to standard theory and practice). The New England Journal of Medicine , one of the premier journals of the field, carries a regular feature entitled Case Records of the Massachusetts General Hospital. These articles bear titles like the following: “An 80‐Year‐Old Woman with Sudden Unilateral Blindness” or “A 76‐Year‐Old Man with Fever, Dyspnea, Pulmonary Infiltrates, Pleural Effusions, and Confusion.” 9 Another interesting example drawn from the field of medicine concerns the extensive study now devoted to a small number of persons who seem resistant to the AIDS virus ( Buchbinder and Vittinghoff 1999 ; Haynes, Pantaleo, and Fauci 1996 ). Why are they resistant? What is different about these people? What can we learn about AIDS in other patients by observing people who have built‐in resistance to this disease?

Likewise, in psychology and sociology case studies may be comprised of deviant (in the social sense) persons or groups. In economics, case studies may consist of countries or businesses that overperform (e.g. Botswana; Microsoft) or underperform (e.g. Britain through most of the twentieth century; Sears in recent decades) relative to some set of expectations. In political science, case studies may focus on countries where the welfare state is more developed (e.g. Sweden) or less developed (e.g. the United States) than one would expect, given a set of general expectations about welfare state development. The deviant case is closely linked to the investigation of theoretical anomalies. Indeed, to say deviant is to imply “anomalous.” 10

Note that while extreme cases are judged relative to the mean of a single distribution (the distribution of values along a single variable), deviant cases are judged relative to some general model of causal relations. The deviant‐case method selects cases which, by reference to some (presumably) general relationship, demonstrate a surprising value. They are “deviant” in that they are poorly explained by the multivariate model. The important point is that deviant‐ness can only be assessed relative to the general (quantitative or qualitative) model. This means that the relative deviant‐ness of a case is likely to change whenever the general model is altered. For example, the United States is a deviant welfare state when this outcome is gauged relative to societal wealth. But it is less deviant—and perhaps not deviant at all—when certain additional (political and societal) factors are included in the model, as discussed in the epilogue. Deviance is model dependent. Thus, when discussing the concept of the deviant case it is helpful to ask the following question: Relative to what general model (or set of background factors) is Case A deviant?

Conceptually, we have said that the deviant case is the logical contrary of the typical case. This translates into a directly contrasting statistical measurement. While the typical case is one with a low residual (in some general model of causal relations), a deviant case is one with a high residual. This means, following our previous discussion, that the deviant case is likely to be an un representative case, and in this respect appears to violate the supposition that case‐study samples should seek to reproduce features of a larger population.

However, it must be borne in mind that the primary purpose of a deviant‐case analysis is to probe for new—but as yet unspecified—explanations. (If the purpose is to disprove an extant theory I shall refer to the study as crucial‐case, as discussed below.) The researcher hopes that causal processes identified within the deviant case will illustrate some causal factor that is applicable to other (more or less deviant) cases. This means that a deviant‐case study usually culminates in a general proposition, one that may be applied to other cases in the population. Once this general proposition has been introduced into the overall model, the expectation is that the chosen case will no longer be an outlier. Indeed, the hope is that it will now be typical , as judged by its small residual in the adjusted model. (The exception would be a circumstance in which a case's outcome is deemed to be “accidental,” and therefore inexplicable by any general model.)

This feature of the deviant‐case study should help to resolve questions about its representativeness. Even if it is not possible to measure the new causal factor (and thus to introduce it into a large‐ N cross‐case model), it may still be plausible to assert (based on general knowledge of the phenomenon) that the chosen case is representative of a broader population.

5 Influential Case

Sometimes, the choice of a case is motivated solely by the need to verify the assumptions behind a general model of causal relations. Here, the analyst attempts to provide a rationale for disregarding a problematic case or a set of problematic cases. That is to say, she attempts to show why apparent deviations from the norm are not really deviant, or do not challenge the core of the theory, once the circumstances of the special case or cases are fully understood. A cross‐case analysis may, after all, be marred by several classes of problems including measurement error, specification error, errors in establishing proper boundaries for the inference (the scope of the argument), and stochastic error (fluctuations in the phenomenon under study that are treated as random, given available theoretical resources). If poorly fitting cases can be explained away by reference to these kinds of problems, then the theory of interest is that much stronger. This sort of deviant‐case analysis answers the question, “What about Case A (or cases of type A)? How does that, seemingly disconfirming, case fit the model?”

Because its underlying purpose is different from the usual deviant‐case study, I offer a new term for this method. The influential case is a case that casts doubt upon a theory, and for that reason warrants close inspection. This investigation may reveal, after all, that the theory is validated—perhaps in some slightly altered form. In this guise, the influential case is the “case that proves the rule.” In other instances, the influential‐case analysis may contribute to disconfirming, or reconceptualizing, a theory. The key point is that the value of the case is judged relative to some extant cross‐case model.

A simple version of influential‐case analysis involves the confirmation of a key case's score on some critical dimension. This is essentially a question of measurement. Sometimes cases are poorly explained simply because they are poorly understood. A close examination of a particular context may reveal that an apparently falsifying case has been miscoded. If so, the initial challenge presented by that case to some general theory has been obviated.

However, the more usual employment of the influential‐case method culminates in a substantive reinterpretation of the case—perhaps even of the general model. It is not just a question of measurement. Consider Thomas Ertman's (1997) study of state building in Western Europe, as summarized by Gerardo Munck. This study argues

that the interaction of a) the type of local government during the first period of statebuilding, with b) the timing of increases in geopolitical competition, strongly influences the kind of regime and state that emerge. [Ertman] tests this hypothesis against the historical experience of Europe and finds that most countries fit his predictions. Denmark, however, is a major exception. In Denmark, sustained geopolitical competition began relatively late and local government at the beginning of the statebuilding period was generally participatory, which should have led the country to develop “patrimonial constitutionalism.” But in fact, it developed “bureaucratic absolutism.” Ertman carefully explores the process through which Denmark came to have a bureaucratic absolutist state and finds that Denmark had the early marks of a patrimonial constitutionalist state. However, the country was pushed off this developmental path by the influence of German knights, who entered Denmark and brought with them German institutions of local government. Ertman then traces the causal process through which these imported institutions pushed Denmark to develop bureaucratic absolutism, concluding that this development was caused by a factor well outside his explanatory framework. ( Munck 2004 , 118)

Ertman's overall framework is confirmed insofar as he has been able to show, by an in‐depth discussion of Denmark, that the causal processes stipulated by the general theory hold even in this apparently disconfirming case. Denmark is still deviant, but it is so because of “contingent historical circumstances” that are exogenous to the theory ( Ertman 1997 , 316).

Evidently, the influential‐case analysis is similar to the deviant‐case analysis. Both focus on outliers. However, as we shall see, they focus on different kinds of outliers. Moreover, the animating goals of these two research designs are quite different. The influential‐case study begins with the aim of confirming a general model, while the deviant‐case study has the aim of generating a new hypothesis that modifies an existing general model. The confusion stems from the fact that the same case study may fulfill both objectives—qualifying a general model and, at the same time, confirming its core hypothesis.

Thus, in their study of Roberto Michels's “iron law of oligarchy,” Lipset, Trow, and Coleman (1956) choose to focus on an organization—the International Typographical Union—that appears to violate the central presupposition. The ITU, as noted by one of the authors, has “a long‐term two‐party system with free elections and frequent turnover in office” and is thus anything but oligarchic ( Lipset 1959 , 70). As such, it calls into question Michels's grand generalization about organizational behavior. The authors explain this curious result by the extraordinarily high level of education among the members of this union. Michels's law is shown to be true for most organizations, but not all. It is true, with qualifications. Note that the respecification of the original model (in effect, Lipset, Trow, and Coleman introduce a new control variable or boundary condition) involves the exploration of a new hypothesis. In this instance, therefore, the use of an influential case to confirm an existing theory is quite similar to the use of a deviant case to explore a new theory.

In a quantitative idiom, influential cases are those that, if counterfactually assigned a different value on the dependent variable, would most substantially change the resulting estimates. They may or may not be outliers (high‐residual cases). Two quantitative measures of influence are commonly applied in regression diagnostics ( Belsey, Kuh, and Welsch 2004 ). The first, often referred to as the leverage of a case, derives from what is called the hat matrix . Based solely on each case's scores on the independent variables, the hat matrix tells us how much a change in (or a measurement error on) the dependent variable for that case would affect the overall regression line. The second is Cook's distance , a measure of the extent to which the estimates of all the parameters would change if a given case were omitted from the analysis. Cases with a large leverage or Cook's distance contribute quite a lot to the inferences drawn from a cross‐case analysis. In this sense, such cases are vital for maintaining analytic conclusions. Discovering a significant measurement error on the dependent variable or an important omitted variable for such a case may dramatically revise estimates of the overall relationships. Hence, it may be quite sensible to select influential cases for in‐depth study.

Note that the use of an influential‐case strategy of case selection is limited to instances in which a researcher has reason to be concerned that her results are being driven by one or a few cases. This is most likely to be true in small to moderate‐sized samples. Where N is very large—greater than 1,000, let us say—it is extremely unlikely that a small set of cases (much less an individual case) will play an “influential” role. Of course, there may be influential sets of cases, e.g. countries within a particular continent or cultural region, or persons of Irish extraction. Sets of influential observations are often problematic in a time‐series cross‐section data‐set where each unit (e.g. country) contains multiple observations (through time), and hence may have a strong influence on aggregate results. Still, the general rule is: the larger the sample, the less important individual cases are likely to be and, hence, the less likely a researcher is to use an influential‐case approach to case selection.

6 Crucial Case

Of all the extant methods of case selection perhaps the most storied—and certainly the most controversial—is the crucial‐case method, introduced to the social science world several decades ago by Harry Eckstein. In his seminal essay, Eckstein (1975 , 118) describes the crucial case as one “that must closely fit a theory if one is to have confidence in the theory's validity, or, conversely, must not fit equally well any rule contrary to that proposed.” A case is crucial in a somewhat weaker—but much more common—sense when it is most, or least, likely to fulfill a theoretical prediction. A “most‐likely” case is one that, on all dimensions except the dimension of theoretical interest, is predicted to achieve a certain outcome, and yet does not. It is therefore used to disconfirm a theory. A “least‐likely” case is one that, on all dimensions except the dimension of theoretical interest, is predicted not to achieve a certain outcome, and yet does so. It is therefore used to confirm a theory. In all formulations, the crucial‐case offers a most‐difficult test for an argument, and hence provides what is perhaps the strongest sort of evidence possible in a nonexperimental, single‐case setting.

Since the publication of Eckstein's influential essay, the crucial‐case approach has been claimed in a multitude of studies across several social science disciplines and has come to be recognized as a staple of the case‐study method. 11 Yet the idea of any single case playing a crucial (or “critical”) role is not widely accepted among most methodologists (e.g. Sekhon 2004 ). (Even its progenitor seems to have had doubts.)

Let us begin with the confirmatory (a.k.a. least‐likely) crucial case. The implicit logic of this research design may be summarized as follows. Given a set of facts, we are asked to contemplate the probability that a given theory is true. While the facts matter, to be sure, the effectiveness of this sort of research also rests upon the formal properties of the theory in question. Specifically, the degree to which a theory is amenable to confirmation is contingent upon how many predictions can be derived from the theory and on how “risky” each individual prediction is. In Popper's (1963 , 36) words, “Confirmations should count only if they are the result of risky predictions ; that is to say, if, unenlightened by the theory in question, we should have expected an event which was incompatible with the theory—and event which would have refuted the theory. Every ‘good’ scientific theory is a prohibition; it forbids certain things to happen. The more a theory forbids, the better it is” (see also Popper 1934/1968 ). A risky prediction is therefore one that is highly precise and determinate, and therefore unlikely to be achieved by the product of other causal factors (external to the theory of interest) or through stochastic processes. A theory produces many such predictions if it is fully elaborated, issuing predictions not only on the central outcome of interest but also on specific causal mechanisms, and if it is broad in purview. (The notion of riskiness may also be conceptualized within the Popperian lexicon as degrees of falsifiability .)

These points can also be articulated in Bayesian terms. Colin Howson and Peter Urbach explain: “The degree to which h [a hypothesis] is confirmed by e [a set of evidence] depends … on the extent to which P(eČh) exceeds P (e) , that is, on how much more probable e is relative to the hypothesis and background assumptions than it is relative just to background assumptions.” Again, “confirmation is correlated with how much more probable the evidence is if the hypothesis is true than if it is false” ( Howson and Urlbach 1989 , 86). Thus, the stranger the prediction offered by a theory—relative to what we would normally expect—the greater the degree of confirmation that will be afforded by the evidence. As an intuitive example, Howson and Urbach (1989 , 86) offer the following:

If a soothsayer predicts that you will meet a dark stranger sometime and you do in fact, your faith in his powers of precognition would not be much enhanced: you would probably continue to think his predictions were just the result of guesswork. However, if the prediction also gave the correct number of hairs on the head of that stranger, your previous scepticism would no doubt be severely shaken.

While these Popperian/Bayesian notions 12 are relevant to all empirical research designs, they are especially relevant to case‐study research designs, for in these settings a single case (or, at most, a small number of cases) is required to bear a heavy burden of proof. It should be no surprise, therefore, that Popper's idea of “riskiness” was to be appropriated by case‐study researchers like Harry Eckstein to validate the enterprise of single‐case analysis. (Although Eckstein does not cite Popper the intellectual lineage is clear.) Riskiness, here, is analogous to what is usually referred to as a “most‐ difficult” research design, which in a case‐study research design would be understood as a “least‐likely” case. Note also that the distinction between a “must‐fit” case and a least‐likely case—that, in the event, actually does fit the terms of a theory—is a matter of degree. Cases are more or less crucial for confirming theories. The point is that, in some circumstances, a paucity of empirical evidence may be compensated by the riskiness of the theory.

The crucial‐case research design is, perforce, a highly deductive enterprise; much depends on the quality of the theory under investigation. It follows that the theories most amenable to crucial‐case analysis are those which are lawlike in their precision, degree of elaboration, consistency, and scope. The more a theory attains the status of a causal law, the easier it will be to confirm, or to disconfirm, with a single case. Indeed, risky predictions are common in natural science fields such as physics, which in turn served as the template for the deductive‐nomological (“covering‐law”) model of science that influenced Eckstein and others in the postwar decades (e.g. Hempel 1942 ).

A frequently cited example is the first important empirical demonstration of the theory of relativity, which took the form of a single‐event prediction on the occasion of the May 29, 1919, solar eclipse ( Eckstein 1975 ; Popper 1963 ). Stephen Van Evera (1997 , 66–7) describes the impact of this prediction on the validation of Einstein's theory.

Einstein's theory predicted that gravity would bend the path of light toward a gravity source by a specific amount. Hence it predicted that during a solar eclipse stars near the sun would appear displaced—stars actually behind the sun would appear next to it, and stars lying next to the sun would appear farther from it—and it predicted the amount of apparent displacement. No other theory made these predictions. The passage of this one single‐case‐study test brought the theory wide acceptance because the tested predictions were unique—there was no plausible competing explanation for the predicted result—hence the passed test was very strong.

The strength of this test is the extraordinary fit between the theory and a set of facts found in a single case, and the corresponding lack of fit between all other theories and this set of facts. Einstein offered an explanation of a particular set of anomalous findings that no other existing theory could make sense of. Of course, one must assume that there was no—or limited—measurement error. And one must assume that the phenomenon of interest is largely invariant; light does not bend differently at different times and places (except in ways that can be understood through the theory of relativity). And one must assume, finally, that the theory itself makes sense on other grounds (other than the case of special interest); it is a plausible general theory. If one is willing to accept these a priori assumptions, then the 1919 “case study” provides a very strong confirmation of the theory. It is difficult to imagine a stronger proof of the theory from within an observational (nonexperimental) setting.

In social science settings, by contrast, one does not commonly find single‐case studies offering knockout evidence for a theory. This is, in my view, largely a product of the looseness (the underspecification) of most social science theories. George and Bennett point out that while the thesis of the democratic peace is as close to a “law” as social science has yet seen, it cannot be confirmed (or refuted) by looking at specific causal mechanisms because the causal pathways mandated by the theory are multiple and diverse. Under the circumstances, no single‐case test can offer strong confirmation of the theory ( George and Bennett 2005 , 209).

However, if one adopts a softer version of the crucial‐case method—the least‐likely (most difficult) case—then possibilities abound. Indeed, I suspect that, implicitly , most case‐study work that makes a positive argument focusing on a single case (without a corresponding cross‐case analysis) relies largely on the logic of the least‐ likely case. Rarely is this logic made explicit, except perhaps in a passing phrase or two. Yet the deductive logic of the “risky” prediction is central to the case‐study enterprise. Whether a case study is convincing or not often rests on the reader's evaluation of how strong the evidence for an argument might be, and this in turn—wherever cross‐ case evidence is limited and no manipulated treatment can be devised—rests upon an estimation of the degree of “fit” between a theory and the evidence at hand, as discussed.

Lily Tsai's (2007) investigation of governance at the village level in China employs several in‐depth case studies of villages which are chosen (in part) because of their least‐likely status relative to the theory of interest. Tsai's hypothesis is that villages with greater social solidarity (based on preexisting religious or familial networks) will develop a higher level of social trust and mutual obligation and, as a result, will experience better governance. Crucial cases, therefore, are villages that evidence a high level of social solidarity but which, along other dimensions, would be judged least likely to develop good governance, e.g. they are poor, isolated, and lack democratic institutions or accountability mechanisms from above. “Li Settlement,” in Fujian province, is such a case. The fact that this impoverished village nonetheless boasts an impressive set of infrastructural accomplishments such as paved roads with drainage ditches (a rarity in rural China) suggests that something rather unusual is going on here. Because her case is carefully chosen to eliminate rival explanations, Tsai's conclusions about the special role of social solidarity are difficult to gainsay. How else is one to explain this otherwise anomalous result? This is the strength of the least‐likely case, where all other plausible causal factors for an outcome have been minimized. 13

Jack Levy (2002 , 144) refers to this, evocatively, as a “Sinatra inference:” if it can make it here, it can make it anywhere (see also Khong 1992 , 49; Sagan 1995 , 49; Shafer 1988 , 14–6). Thus, if social solidarity has the hypothesized effect in Li Settlement it should have the same effect in more propitious settings (e.g. where there is greater economic surplus). The same implicit logic informs many case‐study analyses where the intent of the study is to confirm a hypothesis on the basis of a single case.

Another sort of crucial case is employed for the purpose of dis confirming a causal hypothesis. A central Popperian insight is that it is easier to disconfirm an inference than to confirm that same inference. (Indeed, Popper doubted that any inference could be fully confirmed, and for this reason preferred the term “corroborate.”) This is particularly true of case‐study research designs, where evidence is limited to one or several cases. The key proviso is that the theory under investigation must take a consistent (a.k.a. invariant, deterministic) form, even if its predictions are not terrifically precise, well elaborated, or broad.

As it happens, there are a fair number of invariant propositions floating around the social science disciplines (Goertz and Levy forthcoming; Goertz and Starr 2003 ). It used to be argued, for example, that political stability would occur only in countries that are relatively homogeneous, or where existing heterogeneities are mitigated by cross‐cutting cleavages ( Almond 1956 ; Bentley 1908/1967 ; Lipset 1960/1963 ; Truman 1951 ). Arend Lijphart's (1968) study of the Netherlands, a peaceful country with reinforcing social cleavages, is commonly viewed as refuting this theory on the basis of a single in‐depth case analysis. 14

Granted, it may be questioned whether presumed invariant theories are really invariant; perhaps they are better understood as probabilistic. Perhaps, that is, the theory of cross‐cutting cleavages is still true, probabilistically, despite the apparent Dutch exception. Or perhaps the theory is still true, deterministically, within a subset of cases that does not include the Netherlands. (This sort of claim seems unlikely in this particular instance, but it is quite plausible in many others.) Or perhaps the theory is in need of reframing; it is true, deterministically, but applies only to cross‐ cutting ethnic/racial cleavages, not to cleavages that are primarily religious. One can quibble over what it means to “disconfirm” a theory. The point is that the crucial case has, in all these circumstances, provided important updating of a theoretical prior.

Heretofore, I have treated causal factors as dichotomous. Countries have either reinforcing or cross‐cutting cleavages and they have regimes that are either peaceful or conflictual. Evidently, these sorts of parameters are often matters of degree. In this reading of the theory, cases are more or less crucial. Accordingly, the most useful—i.e. most crucial—case for Lijphart's purpose is one that has the most segregated social groups and the most peaceful and democratic track record. In these respects, the Netherlands was a very good choice. Indeed, the degree of disconfirmation offered by this case study is probably greater than the degree of disconfirmation that might have been provided by other cases such as India or Papua New Guinea—countries where social peace has not always been secure. The point is that where variables are continuous rather than dichotomous it is possible to evaluate potential cases in terms of their degree of crucialness .

Note that the crucial‐case method of case‐selection, whether employed in a confirmatory or disconfirmatory mode, cannot be employed in a large‐ N context. This is because an explicit cross‐case model would render the crucial‐case study redundant. Once one identifies the relevant parameters and the scores of all cases on those parameters, one has in effect constructed a cross‐case model that confirms or disconfirms the theory in question. The case study is thenceforth irrelevant, at least as a means of decisive confirmation or disconfirmation. 15 It remains highly relevant as a means of exploring causal mechanisms, of course. Yet, because this objective is quite different from that which is usually associated with the term, I enlist a new term for this technique.

7 Pathway Case

One of the most important functions of case‐study research is the elucidation of causal mechanisms. But which sort of case is most useful for this purpose? Although all case studies presumably shed light on causal mechanisms, not all cases are equally transparent. In situations where a causal hypothesis is clear and has already been confirmed by cross‐case analysis, researchers are well advised to focus on a case where the causal effect of X   1 on Y can be isolated from other potentially confounding factors ( X   2 ). I shall call this a pathway case to indicate its uniquely penetrating insight into causal mechanisms. In contrast to the crucial case, this sort of method is practicable only in circumstances where cross‐case covariational patterns are well studied and where the mechanism linking X   1 and Y remains dim. Because the pathway case builds on prior cross‐case analysis, the problem of case selection must be situated within that sample. There is no standalone pathway case.

The logic of the pathway case is clearest in situations of causal sufficiency—where a causal factor of interest, X   1 , is sufficient by itself (though perhaps not necessary) to account for Y 's value (0 or 1). The other causes of Y , about which we need make no assumptions, are designated as a vector, X   2 .

Note that wherever various causal factors are substitutable for one another, each factor is conceptualized (individually) as sufficient ( Braumoeller 2003 ). Thus, situations of causal equifinality presume causal sufficiency on the part of each factor or set of conjoint factors. An example is provided by the literature on democratization, which stipulates three main avenues of regime change: leadership‐initiated reform, a controlled opening to opposition, or the collapse of an authoritarian regime ( Colomer 1991 ). The case‐study format constrains us to analyze one at a time, so let us limit our scope to the first one—leadership‐initiated reform. So considered, a causal‐pathway case would be one with the following features: (a) democratization, (b) leadership‐initiated reform, (c) no controlled opening to the opposition, (d) no collapse of the previous authoritarian regime, and (e) no other extraneous factors that might affect the process of democratization. In a case of this type, the causal mechanisms by which leadership‐initiated reform may lead to democratization will be easiest to study. Note that it is not necessary to assume that leadership‐initiated reform always leads to democratization; it may or may not be a deterministic cause. But it is necessary to assume that leadership‐initiated reform can sometimes lead to democratization on its own (given certain background features).

Now let us move from these examples to a general‐purpose model. For heuristic purposes, let us presume that all variables in that model are dichotomous (coded as 0 or 1) and that the model is complete (all causes of Y are included). All causal relationships will be coded so as to be positive: X   1 and Y covary as do X   2 and Y . This allows us to visualize a range of possible combinations at a glance.

Recall that the pathway case is always focused, by definition, on a single causal factor, denoted X   1 . (The researcher's focus may shift to other causal factors, but may only focus on one causal factor at a time.) In this scenario, and regardless of how many additional causes of Y there might be (denoted X   2 , a vector of controls), there are only eight relevant case types, as illustrated in Table 28.2 . Identifying these case types is a relatively simple matter, and can be accomplished in a small‐ N sample by the construction of a truth‐table (modeled after Table 28.2 ) or in a large‐ N sample by the use of cross‐tabs.

Notes : X   1 = the variable of theoretical interest. X   2 = a vector of controls (a score of 0 indicates that all control variables have a score of 0, while a score of 1 indicates that all control variables have a score of 1). Y = the outcome of interest. A–H = case types (the N for each case type is indeterminate). G, H = possible pathway cases. Sample size = indeterminate.

Assumptions : (a) all variables can be coded dichotomously (a binary coding of the concept is valid); (b) all independent variables are positively correlated with Y in the general case; ( c ) X   1 is (at least sometimes) a sufficient cause of Y .

Note that the total number of combinations of values depends on the number of control variables, which we have represented with a single vector, X   2 . If this vector consists of a single variable then there are only eight case types. If this vector consists of two variables ( X   2a , X   2b ) then the total number of possible combinations increases from eight (2 3 ) to sixteen (2 4 ). And so forth. However, none of these combinations is relevant for present purposes except those where X   2a and X   2b have the same value (0 or 1). “Mixed” cases are not causal pathway cases, for reasons that should become clear.

The pathway case, following the logic of the crucial case, is one where the causal factor of interest, X   1 , correctly predicts Y while all other possible causes of Y (represented by the vector, X   2 ) make “wrong” predictions. If X   1 is—at least in some circumstances—a sufficient cause of Y , then it is these sorts of cases that should be most useful for tracing causal mechanisms. There are only two such cases in Ta b l e 28.2—G and H. In all other cases, the mechanism running from X   1 to Y would be difficult to discern either because X   1 and Y are not correlated in the usual way (constituting an unusual case, in the terms of our hypothesis) or because other confounding factors ( X   2 ) intrude. In case A, for example, the positive value on Y could be a product of X   1 or X   2 . An in‐depth examination of this case is not likely to be very revealing.

Keep in mind that because the researcher already knows from her cross‐case examination what the general causal relationships are, she knows (prior to the case‐ study investigation) what constitutes a correct or incorrect prediction. In the crucial‐ case method, by contrast, these expectations are deductive rather than empirical. This is what differentiates the two methods. And this is why the causal pathway case is useful principally for elucidating causal mechanisms rather than verifying or falsifying general propositions (which are already more or less apparent from the cross‐case evidence). Of course, we must leave open the possibility that the investigation of causal mechanisms would invalidate a general claim, if that claim is utterly contingent upon a specific set of causal mechanisms and the case study shows that no such mechanisms are present. However, this is rather unlikely in most social science settings. Usually, the result of such a finding will be a reformulation of the causal processes by which X   1 causes Y —or, alternatively, a realization that the case under investigation is aberrant (atypical of the general population of cases).

Sometimes, the research question is framed as a unidirectional cause: one is interested in why 0 becomes 1 (or vice versa) but not in why 1 becomes 0. In our previous example, we asked why democracies fail, not why countries become democratic or authoritarian. So framed, there can be only one type of causal‐pathway case. (Whether regime failure is coded as 0 or 1 is a matter of taste.) Where researchers are interested in bidirectional causality—a movement from 0 to 1 as well as from 1 to 0—there are two possible causal‐pathway cases, G and H. In practice, however, one of these case types is almost always more useful than the other. Thus, it seems reasonable to employ the term “pathway case” in the singular. In order to determine which of these two case types will be more useful for intensive analysis the researcher should look to see whether each case type exhibits desirable features such as: (a) a rare (unusual) value on X   1 or Y (designated “extreme” in our previous discussion), (b) observable temporal variation in X   1 , ( c ) an X   1 / Y relationship that is easier to study (it has more visible features; it is more transparent), or (d) a lower residual (thus indicating a more typical case, within the terms of the general model). Usually, the choice between G and H is intuitively obvious.

Now, let us consider a scenario in which all (or most) variables of concern to the model are continuous, rather than dichotomous. Here, the job of case selection is considerably more complex, for causal “sufficiency” (in the usual sense) cannot be invoked. It is no longer plausible to assume that a given cause can be entirely partitioned, i.e. rival factors eliminated. However, the search for a pathway case may still be viable. What we are looking for in this scenario is a case that satisfies two criteria: (1) it is not an outlier (or at least not an extreme outlier) in the general model and (2) its score on the outcome ( Y ) is strongly influenced by the theoretical variable of interest ( X   1 ), taking all other factors into account ( X   2 ). In this sort of case it should be easiest to “see” the causal mechanisms that lie between X   1 and Y .

Achieving the second desiderata requires a bit of manipulation. In order to determine which (nonoutlier) cases are most strongly affected by X   1 , given all the other parameters in the model, one must compare the size of the residuals for each case in a reduced form model, Y = Constant + X   2 + Res reduced , with the size of the residuals for each case in a full model, Y = Constant + X   2 + X   1 + Res full . The pathway case is that case, or set of cases, which shows the greatest difference between the residual for the reduced‐form model and the full model (ΔResidual). Thus,

Note that the residual for a case must be smaller in the full model than in the reduced‐ form model; otherwise, the addition of the variable of interest ( X   1 ) pulls the case away from the regression line. We want to find a case where the addition of X   1 pushes the case towards the regression line, i.e. it helps to “explain” that case.

As an example, let us suppose that we are interested in exploring the effect of mineral wealth on the prospects for democracy in a society. According to a good deal of work on this subject, countries with a bounty of natural resources—particularly oil—are less likely to democratize (or once having undergone a democratic transition, are more likely to revert to authoritarian rule) ( Barro 1999 ; Humphreys 2005 ; Ross 2001 ). The cross‐country evidence is robust. Yet as is often the case, the causal mechanisms remain rather obscure. In order to better understand this phenomenon it may be worthwhile to exploit the findings of cross‐country regression models in order to identify a country whose regime type (i.e. its democracy “score” on some general index) is strongly affected by its natural‐research wealth, all other things held constant. An analysis of this sort identifies two countries— the United Arab Emirates and Kuwait—with high Δ Residual values and modest residuals in the full model (signifying that these cases are not outliers). Researchers seeking to explore the effect of oil wealth on regime type might do well to focus on these two cases since their patterns of democracy cannot be well explained by other factors—e.g. economic development, religion, European influence, or ethnic fractionalization. The presence of oil wealth in these countries would appear to have a strong independent effect on the prospects for democratization in these cases, an effect that is well modeled by general theory and by the available cross‐case evidence.

To reiterate, the logic of causal “elimination” is much more compelling where variables are dichotomous and where causal sufficiency can be assumed ( X   1 is sufficient by itself, at least in some circumstances, to cause Y ). Where variables are continuous, the strategy of the pathway case is more dubious, for potentially confounding causal factors ( X   2 ) cannot be neatly partitioned. Even so, we have indicated why the selection of a pathway case may be a logical approach to case‐study analysis in many circumstances.

The exceptions may be briefly noted. Sometimes, where all variables in a model are dichotomous, there are no pathway cases, i.e. no cases of type G or H (in Table 28.2 ). This is known as the “empty cell” problem, or a problem of severe causal multicollinearity. The universe of observational data does not always oblige us with cases that allow us to independently test a given hypothesis. Where variables are continuous, the analogous problem is that of a causal variable of interest ( X   1 ) that has only minimal effects on the outcome of interest. That is, its role in the general model is quite minor. In these situations, the only cases that are strongly affected by X   1 —if there are any at all—may be extreme outliers, and these sorts of cases are not properly regarded as providing confirmatory evidence for a proposition, for reasons that are abundantly clear by now.

Finally, it should be clarified that the identification of a causal pathway case does not obviate the utility of exploring other cases. One might, for example, want to compare both sorts of potential pathway cases—G and H—with each other. Many other combinations suggest themselves. However, this sort of multi‐case investigation moves beyond the logic of the causal‐pathway case.

8 Most‐similar Cases

The most‐similar method employs a minimum of two cases. 16 In its purest form, the chosen pair of cases is similar in all respects except the variable(s) of interest. If the study is exploratory (i.e. hypothesis generating), the researcher looks for cases that differ on the outcome of theoretical interest but are similar on various factors that might have contributed to that outcome, as illustrated in Table 28.3 (A) . This is a common form of case selection at the initial stage of research. Often, fruitful analysis begins with an apparent anomaly: two cases are apparently quite similar, and yet demonstrate surprisingly different outcomes. The hope is that intensive study of these cases will reveal one—or at most several—factors that differ across these cases. These differing factors ( X   1 ) are looked upon as putative causes. At this stage, the research may be described by the second diagram in Table 28.3 (B) . Sometimes, a researcher begins with a strong hypothesis, in which case her research design is confirmatory (hypothesis testing) from the get‐go. That is, she strives to identify cases that exhibit different outcomes, different scores on the factor of interest, and similar scores on all other possible causal factors, as illustrated in the second (hypothesis‐testing) diagram in Table 28.3 (B) .

The point is that the purpose of a most‐similar research design, and hence its basic setup, often changes as a researcher moves from an exploratory to a confirmatory mode of analysis. However, regardless of where one begins, the results, when published, look like a hypothesis‐testing research design. Question marks have been removed: (A) becomes (B) in Table 28.3 .

As an example, let us consider Leon Epstein's classic study of party cohesion, which focuses on two “most‐similar” countries, the United States and Canada. Canada has highly disciplined parties whose members vote together on the floor of the House of Commons while the United States has weak, undisciplined parties, whose members often defect on floor votes in Congress. In explaining these divergent outcomes, persistent over many years, Epstein first discusses possible causal factors that are held more or less constant across the two cases. Both the United States and Canada inherited English political cultures, both have large territories and heterogeneous populations, both are federal, and both have fairly loose party structures with strong regional bases and a weak center. These are the “control” variables. Where they differ is in one constitutional feature: Canada is parliamentary while the United States is presidential. And it is this institutional difference that Epstein identifies as the crucial (differentiating) cause. (For further examples of the most‐similar method see Brenner 1976 ; Hamilton 1977 ; Lipset 1968 ; Miguel 2004 ; Moulder 1977 ; Posner 2004 .)

X   1 = the variable of theoretical interest. X   2 = a vector of controls. Y = the outcome of interest.

Several caveats apply to any most‐similar analysis (in addition to the usual set of assumptions applying to all case‐study analysis). First, each causal factor is understood as having an independent and additive effect on the outcome; there are no “interaction” effects. Second, one must code cases dichotomously (high/low, present/absent). This is straightforward if the underlying variables are also dichotomous (e.g. federal/unitary). However, it is often the case that variables of concern in the model are continuous (e.g. party cohesion). In this setting, the researcher must “dichotomize” the scoring of cases so as to simplify the two‐case analysis. (Some flexibility is admissible on the vector of controls ( X   2 ) that are “held constant” across the cases. Nonidentity is tolerable if the deviation runs counter to the predicted hypothesis. For example, Epstein describes both the United States and Canada as having strong regional bases of power, a factor that is probably more significant in recent Canadian history than in recent American history. However, because regional bases of power should lead to weaker parties, rather than stronger parties, this element of nonidentity does not challenge Epstein's conclusions. Indeed, it sets up a most‐difficult research scenario, as discussed above.)

In one respect the requirements for case control are not so stringent. Specifically, it is not usually necessary to measure control variables (at least not with a high degree of precision) in order to control for them. If two countries can be assumed to have similar cultural heritages one needn't worry about constructing variables to measure that heritage. One can simply assert that, whatever they are, they are more or less constant across the two cases. This is similar to the technique employed in a randomized experiment, where the researcher typically does not attempt to measure all the factors that might affect the causal relationship of interest. She assumes, rather, that these unknown factors have been neutralized across the treatment and control groups by randomization or by the choice of a sample that is internally homogeneous.

The most useful statistical tool for identifying cases for in‐depth analysis in a most‐ similar setting is probably some variety of matching strategy—e.g. exact matching, approximate matching, or propensity‐score matching. 17 The product of this procedure is a set of matched cases that can be compared in whatever way the researcher deems appropriate. These are the “most‐similar” cases. Rosenbaum and Silber (2001 , 223) summarize:

Unlike model‐based adjustments, where [individuals] vanish and are replaced by the coefficients of a model, in matching, ostensibly comparable patterns are compared directly, one by one. Modern matching methods involve statistical modeling and combinatorial algorithms, but the end result is a collection of pairs or sets of people who look comparable, at least on average. In matching, people retain their integrity as people, so they can be examined and their stories can be told individually.

Matching, conclude the authors, “facilitates, rather than inhibits, thick description” ( Rosenbaum and Silber 2001 , 223).

In principle, the same matching techniques that have been used successfully in observational studies of medical treatments might also be adapted to the study of nation states, political parties, cities, or indeed any traditional paired cases in the social sciences. Indeed, the current popularity of matching among statisticians—relative, that is, to garden‐variety regression models—rests upon what qualitative researchers would recognize as a “case‐based” approach to causal analysis. If Rosenbaum and Silber are correct, it may be perfectly reasonable to appropriate this large‐ N method of analysis for case‐study purposes.

As with other methods of case selection, the most‐similar method is prone to problems of nonrepresentativeness. If employed in a qualitative fashion (without a systematic cross‐case selection strategy), potential biases in the chosen case must be addressed in a speculative way. If the researcher employs a matching technique of case selection within a large‐ N sample, the problem of potential bias can be addressed by assuring the choice of cases that are not extreme outliers, as judged by their residuals in the full model. Most‐similar cases should also be “typical” cases, though some scope for deviance around the regression line may be acceptable for purposes of finding a good fit among cases.

X   1 = the variable of theoretical interest. X   2a–d = a vector of controls. Y = the outcome of interest.

9 Most‐different Cases

A final case‐selection method is the reverse image of the previous method. Here, variation on independent variables is prized, while variation on the outcome is eschewed. Rather than looking for cases that are most‐similar, one looks for cases that are most‐ different . Specifically, the researcher tries to identify cases where just one independent variable ( X   1 ), as well as the dependent variable ( Y ), covary, while all other plausible factors ( X   2a–d ) show different values. 18

The simplest form of this two‐case comparison is illustrated in Table 28.4 . Cases A and B are deemed “most different,” though they are similar in two essential respects— the causal variable of interest and the outcome.

As an example, I follow Marc Howard's (2003) recent work, which explores the enduring impact of Communism on civil society. 19 Cross‐national surveys show a strong correlation between former Communist regimes and low social capital, controlling for a variety of possible confounders. It is a strong result. Howard wonders why this relationship is so strong and why it persists, and perhaps even strengthens, in countries that are no longer socialist or authoritarian. In order to answer this question, he focuses on two most‐different cases, Russia and East Germany. These two countries were quite different—in all ways other than their Communist experience— prior to the Soviet era, during the Soviet era (since East Germany received substantial subsidies from West Germany), and in the post‐Soviet era, as East Germany was absorbed into West Germany. Yet, they both score near the bottom of various cross‐ national indices intended to measure the prevalence of civic engagement in the current era. Thus, Howard's (2003 , 6–9) case selection procedure meets the requirements of the most‐different research design: Variance is found on all (or most) dimensions aside from the key factor of interest (Communism) and the outcome (civic engagement).

What leverage is brought to the analysis from this approach? Howard's case studies combine evidence drawn from mass surveys and from in‐depth interviews of small, stratified samples of Russians and East Germans. (This is a good illustration, incidentally, of how quantitative and qualitative evidence can be fruitfully combined in the intensive study of several cases.) The product of this analysis is the identification of three causal pathways that, Howard (2003 , 122) claims, help to explain the laggard status of civil society in post‐Communist polities: “the mistrust of communist organizations, the persistence of friendship networks, and the disappointment with post‐communism.” Simply put, Howard (2003 , 145) concludes, “a great number of citizens in Russia and Eastern Germany feel a strong and lingering sense of distrust of any kind of public organization, a general satisfaction with their own personal networks (accompanied by a sense of deteriorating relations within society overall), and disappointment in the developments of post‐communism.”

The strength of this most‐different case analysis is that the results obtained in East Germany and Russia should also apply in other post‐Communist polities (e.g. Lithuania, Poland, Bulgaria, Albania). By choosing a heterogeneous sample, Howard solves the problem of representativeness in his restricted sample. However, this sample is demonstrably not representative across the population of the inference, which is intended to cover all countries of the world.

More problematic is the lack of variation on key causal factors of interest— Communism and its putative causal pathways. For this reason, it is difficult to reach conclusions about the causal status of these factors on the basis of the most‐different analysis alone. It is possible, that is, that the three causal pathways identified by Howard also operate within polities that never experienced Communist rule.

Nor does it seem possible to conclusively eliminate rival hypotheses on the basis of this most‐different analysis. Indeed, this is not Howard's intention. He wishes merely to show that whatever influence on civil society might be attributed to economic, cultural, and other factors does not exhaust this subject.

My considered judgment is that the most‐different research design provides minimal leverage into the problem of why Communist systems appear to suppress civic engagement, years after their disappearance. Fortunately, this is not the only research design employed by Howard in his admirable study. Indeed, the author employs two other small‐ N cross‐case methods, as well as a large‐ N cross‐country statistical analysis. These methods do most of the analytic work. East Germany may be regarded as a causal pathway case (see above). It has all the attributes normally assumed to foster civic engagement (e.g. a growing economy, multiparty competition, civil liberties, a free press, close association with Western European culture and politics), but nonetheless shows little or no improvement on this dimension during the post‐ transition era ( Howard 2003 , 8). It is plausible to attribute this lack of change to its Communist past, as Howard does, in which case East Germany should be a fruitful case for the investigation of causal mechanisms. The contrast between East and West Germany provides a most‐similar analysis since the two polities share virtually everything except a Communist past. This variation is also deftly exploited by Howard.

I do not wish to dismiss the most‐different research method entirely. Surely, Howard's findings are stronger with the intensive analysis of Russia than they would be without. Yet his book would not stand securely on the empirical foundation provided by most‐different analysis alone. If one strips away the pathway‐case (East Germany) and the most‐similar analysis (East/West Germany) there is little left upon which to base an analysis of causal relations (aside from the large‐ N cross‐national analysis). Indeed, most scholars who employ the most‐different method do so in conjunction with other methods. 20 It is rarely, if ever, a standalone method. 21

Generalizing from this discussion of Marc Howard's work, I offer the following summary remarks on the most‐different method of case analysis. (I leave aside issues faced by all case‐study analyses, issues that are explored in Gerring 2007 .)

Let us begin with a methodological obstacle that is faced by both Millean styles of analysis—the necessity of dichotomizing every variable in the analysis. Recall that, as with most‐similar analysis, differences across cases must generally be sizeable enough to be interpretable in an essentially dichotomous fashion (e.g. high/low, present/absent) and similarities must be close enough to be understood as essentially identical (e.g. high/high, present/present). Otherwise the results of a Millean style analysis are not interpretable. The problem of “degrees” is deadly if the variables under consideration are, by nature, continuous (e.g. GDP). This is a particular concern in Howard's analysis, where East Germany scores somewhat higher than Russia in civic engagement; they are both low, but Russia is quite a bit lower. Howard assumes that this divergence is minimal enough to be understood as a difference of degrees rather than of kinds, a judgment that might be questioned. In these respects, most‐different analysis is no more secure—but also no less—than most‐similar analysis.

In one respect, most‐different analysis is superior to most‐similar analysis. If the coding assumptions are sound, the most‐different research design may be quite useful for eliminating necessary causes . Causal factors that do not appear across the chosen cases—e.g. X   2a–d in Table 28.4 —are evidently unnecessary for the production of Y . However, it does not follow that the most‐different method is the best method for eliminating necessary causes. Note that the defining feature of this method is the shared element across cases— X   1 in Table 28.4 . This feature does not help one to eliminate necessary causes. Indeed, if one were focused solely on eliminating necessary causes one would presumably seek out cases that register the same outcomes and have maximum diversity on other attributes. In Table 28.4 , this would be a set of cases that satisfy conditions X   2a–d , but not X   1 . Thus, even the presumed strength of the most‐different analysis is not so strong.

Usually, case‐study analysis is focused on the identification (or clarification) of causal relations, not the elimination of possible causes. In this setting, the most‐ different technique is useful, but only if assumptions of causal uniqueness hold. By “causal uniqueness,” I mean a situation in which a given outcome is the product of only one cause: Y cannot occur except in the presence of X . X is necessary, and in some situations (given certain background conditions) sufficient, to cause Y . 22

Consider the following hypothetical example. Suppose that a new disease, about which little is known, has appeared in Country A. There are hundreds of infected persons across dozens of affected communities in that country. In Country B, located at the other end of the world, several new cases of the disease surface in a single community. In this setting, we can imagine two sorts of Millean analyses. The first examines two similar communities within Country A, one of which has developed the disease and the other of which has not. This is the most‐similar style of case comparison, and focuses accordingly on the identification of a difference between the two cases that might account for variation across the sample. A second approach focuses on communities where the disease has appeared across the two countries and searches for any similarities that might account for these similar outcomes. This is the most‐different research design.

Both are plausible approaches to this particular problem, and we can imagine epidemiologists employing them simultaneously. However, the most‐different design demands stronger assumptions about the underlying factors at work. It supposes that the disease arises from the same cause in any setting. This is often a reasonable operating assumption when one is dealing with natural phenomena, though there are certainly many exceptions. Death, for example, has many causes. For this reason, it would not occur to us to look for most‐different cases of high mortality around the world. In order for the most‐different research design to effectively identify a causal factor at work in a given outcome, the researcher must assume that X   1 —the factor held constant across the diverse cases—is the only possible cause of Y (see Table 28.4 ). This assumption rarely holds in social‐scientific settings. Most outcomes of interest to anthropologists, economists, political scientists, and sociologists have multiple causes. There are many ways to win an election, to build a welfare state, to get into a war, to overthrow a government, or—returning to Marc Howard's work—to build a strong civil society. And it is for this reason that most‐different analysis is rarely applied in social science work and, where applied, is rarely convincing.

If this seems a tad severe, there is a more charitable way of approaching the most‐different method. Arguably, this is not a pure “method” at all but merely a supplement, a way of incorporating diversity in the sub‐sample of cases that provide the unusual outcome of interest. If the unusual outcome is revolutions, one might wish to encompass a wide variety of revolutions in one's analysis. If the unusual outcome is post‐Communist civil society, it seems appropriate to include a diverse set of post‐Communist polities in one's sample of case studies, as Marc Howard does. From this perspective, the most‐different method (so‐called) might be better labeled a diverse‐case method, as explored above.

10 Conclusions

In order to be a case of something broader than itself, the chosen case must be representative (in some respects) of a larger population. Otherwise—if it is purely idiosyncratic (“unique”)—it is uninformative about anything lying outside the borders of the case itself. A study based on a nonrepresentative sample has no (or very little) external validity. To be sure, no phenomenon is purely idiosyncratic; the notion of a unique case is a matter that would be difficult to define. One is concerned, as always, with matters of degree. Cases are more or less representative of some broader phenomenon and, on that score, may be considered better or worse subjects for intensive analysis. (The one exception, as noted, is the influential case.)

Of all the problems besetting case‐study analysis, perhaps the most persistent— and the most persistently bemoaned—is the problem of sample bias ( Achen and Snidal 1989 ; Collier and Mahoney 1996 ; Geddes 1990 ; King, Keohane, and Verba 1994 ; Rohlfing 2004 ; Sekhon 2004 ). Lisa Martin (1992 , 5) finds that the overemphasis of international relations scholars on a few well‐known cases of economic sanctions— most of which failed to elicit any change in the sanctioned country—“has distorted analysts view of the dynamics and characteristics of economic sanctions.” Barbara Geddes (1990) charges that many analyses of industrial policy have focused exclusively on the most successful cases—primarily the East Asian NICs—leading to biased inferences. Anna Breman and Carolyn Shelton (2001) show that case‐study work on the question of structural adjustment is systematically biased insofar as researchers tend to focus on disaster cases—those where structural adjustment is associated with very poor health and human development outcomes. These cases, often located in sub‐Saharan Africa, are by no means representative of the entire population. Consequently, scholarship on the question of structural adjustment is highly skewed in a particular ideological direction (against neoliberalism) (see also Gerring, Thacker, and Moreno 2005) .

These examples might be multiplied many times. Indeed, for many topics the most‐studied cases are acknowledged to be less than representative. It is worth reflecting upon the fact that our knowledge of the world is heavily colored by a few “big” (populous, rich, powerful) countries, and that a good portion of the disciplines of economics, political science, and sociology are built upon scholars' familiarity with the economics, political science, and sociology of one country, the United States. 23 Case‐study work is particularly prone to problems of investigator bias since so much rides on the researcher's selection of one (or a few) cases. Even if the investigator is unbiased, her sample may still be biased simply by virtue of “random” error (which may be understood as measurement error, error in the data‐generation process, or as an underlying causal feature of the universe).

There are only two situations in which a case‐study researcher need not be concerned with the representativeness of her chosen case. The first is the influential case research design, where a case is chosen because of its possible influence on a cross‐case model, and hence is not expected to be representative of a larger sample. The second is the deviant‐case method, where the chosen case is employed to confirm a broader cross‐case argument to which the case stands as an apparent exception. Yet even here the chosen case is expected to be representative of a broader set of cases—those, in particular, that are poorly explained by the extant model.

In all other circumstances, cases must be representative of the population of interest in whatever ways might be relevant to the proposition in question. Note that where a researcher is attempting to disconfirm a deterministic proposition the question of representativeness is perhaps more appropriately understood as a question of classification: Is the chosen case appropriately classified as a member of the designated population? If so, then it is fodder for a disconfirming case study.

If the researcher is attempting to confirm a deterministic proposition, or to make probabilistic arguments about a causal relationship, then the problem of representativeness is of the more usual sort: Is case A unit‐homogeneous relative to other cases in the population? This is not an easy matter to test. However, in a large‐ N context the residual for that case (in whatever model the researcher has greatest confidence in) is a reasonable place to start. Of course, this test is only as good as the model at hand. Any incorrect specifications or incorrect modeling procedures will likely bias the results and give an incorrect assessment of each case's “typicality.” In addition, there is the possibility of stochastic error, errors that cannot be modeled in a general framework. Given the explanatory weight that individual cases are asked to bear in a case‐study analysis, it is wise to consider more than just the residual test of representativeness. Deductive logic and an in‐depth knowledge of the case in question are often more reliable tools than the results of a cross‐case model.

In any case, there is no dispensing with the question. Case studies (with the two exceptions already noted) rest upon an assumed synecdoche: The case should stand for a population. If this is not true, or if there is reason to doubt this assumption, then the utility of the case study is brought severely into question.

Fortunately, there is some safety in numbers. Insofar as case‐study evidence is combined with cross‐case evidence the issue of sample bias is mitigated. Indeed, the suspicion of case‐study work that one finds in the social sciences today is, in my view, a product of a too‐literal interpretation of the case‐study method. A case study tout court is thought to mean a case study tout seul . Insofar as case studies and cross‐case studies can be enlisted within the same investigation (either in the same study or by reference to other studies in the same subfield), problems of representativeness are less worrisome. This is the virtue of cross‐level work, a.k.a. “triangulation.”

11 Ambiguities

Before concluding, I wish to draw attention to two ambiguities in case‐selection strategies in case‐study research. The first concerns the admixture of several case‐ selection strategies. The second concerns the changing status of a case as a study proceeds.

Some case studies follow only one strategy of case selection. They are typical , diverse , extreme , deviant , influential , crucial , pathway , most‐similar , or most‐different research designs, as discussed. However, many case studies mix and match among these case‐selection strategies. Indeed, insofar as all case studies seek representative samples, they are always in search of “typical” cases. Thus, it is common for writers to declare that their case is, for example, both extreme and typical; it has an extreme value on X   1 or Y but is not, in other respects, idiosyncratic. There is not much that one can say about these combinations of strategies except that, where the cases allow for a variety of empirical strategies, there is no reason not to pursue them. And where the same cases can serve several functions at once (without further effort on the researcher's part), there is little cost to a multi‐pronged approach to case analysis.

The second issue that deserves emphasis is the changing status of a case during the course of a researcher's investigation—which may last for years, if not decades. The problem is acute wherever a researcher begins in an exploratory mode and proceeds to hypothesis‐testing (that is, she develops a specific X   1 / Y proposition) or where the operative hypothesis or key control variable changes (a new causal factor is discovered or another outcome becomes the focus of analysis). Things change. And it is the mark of a good researcher to keep her mind open to new evidence and new insights. Too often, methodological discussions give the misleading impression that hypotheses are clear and remain fixed over the course of a study's development. Nothing could be further from the truth. The unofficial transcripts of academia— accessible in informal settings, where researchers let their guards down (particularly if inebriated)—are filled with stories about dead‐ends, unexpected findings, and drastically revised theory chapters. It would be interesting, in this vein, to compare published work with dissertation prospectuses and fellowship applications. I doubt if the correlation between these two stages of research is particularly strong.

Research, after all, is about discovery, not simply the verification or falsification of static hypotheses. That said, it is also true that research on a particular topic should move from hypothesis generating to hypothesis‐testing. This marks the progress of a field, and of a scholar's own work. As a rule, research that begins with an open‐ended ( X ‐ or Y ‐centered) analysis should conclude with a determinate X   1 / Y hypothesis.

The problem is that research strategies that are ideal for exploration are not always ideal for confirmation. The extreme‐case method is inherently exploratory since there is no clear causal hypothesis; the researcher is concerned merely to explore variation on a single dimension ( X or Y ). Other methods can be employed in either an open‐ ended (exploratory) or a hypothesis‐testing (confirmatory/disconfirmatory) mode. The difficulty is that once the researcher has arrived at a determinate hypothesis the originally chosen research design may no longer appear to be so well designed.

This is unfortunate, but inevitable. One cannot construct the perfect research design until (a) one has a specific hypothesis and (b) one is reasonably certain about what one is going to find “out there” in the empirical world. This is particularly true of observational research designs, but it also applies to many experimental research designs: Usually, there is a “good” (informative) finding, and a finding that is less insightful. In short, the perfect case‐study research design is usually apparent only ex post facto .

There are three ways to handle this. One can explain, straightforwardly, that the initial research was undertaken in an exploratory fashion, and therefore not constructed to test the specific hypothesis that is—now—the primary argument. Alternatively, one can try to redesign the study after the new (or revised) hypothesis has been formulated. This may require additional field research or perhaps the integration of additional cases or variables that can be obtained through secondary sources or through consultation of experts. A final approach is to simply jettison, or de‐emphasize, the portion of research that no longer addresses the (revised) key hypothesis. A three‐case study may become a two‐case study, and so forth. Lost time and effort are the costs of this downsizing.

In the event, practical considerations will probably determine which of these three strategies, or combinations of strategies, is to be followed. (They are not mutually exclusive.) The point to remember is that revision of one's cross‐case research design is normal and perhaps to be expected. Not all twists and turns on the meandering trail of truth can be anticipated.

12 Are There Other Methods of Case Selection?

At the outset of this chapter I summarized the task of case selection as a matter of achieving two objectives: representativeness (typicality) and variation (causal leverage). Evidently, there are other objectives as well. For example, one wishes to identify cases that are independent of each other. If chosen cases are affected by each other (sometimes known as Galton's problem or a problem of diffusion), this problem must be corrected before analysis can take place. I have neglected this issue because it is usually apparent to the researcher and, in any case, there are no simple techniques that might be utilized to correct for such biases. (For further discussion of this and other factors impinging upon case selection see Gerring 2001 , 178–81.)

I have also disregarded pragmatic/logistical issues that might affect case selection. Evidently, case selection is often influenced by a researcher's familiarity with the language of a country, a personal entrée into that locale, special access to important data, or funding that covers one archive rather than another. Pragmatic considerations are often—and quite rightly—decisive in the case‐selection process.

A final consideration concerns the theoretical prominence of a particular case within the literature on a subject. Researchers are sometimes obliged to study cases that have received extensive attention in previous studies. These are sometimes referred to as “paradigmatic” cases or “exemplars” ( Flyvbjerg 2004 , 427).

However, neither pragmatic/logistical utility nor theoretical prominence qualifies as a methodological factor in case selection. That is, these features of a case have no bearing on the validity of the findings stemming from a study. As such, it is appropriate to grant these issues a peripheral status in this chapter.

One final caveat must be issued. While it is traditional to distinguish among the tasks of case selection and case analysis, a close look at these processes shows them to be indistinct and overlapping. One cannot choose a case without considering the sort of analysis that it might be subjected to, and vice versa. Thus, the reader should consider choosing cases by employing the nine techniques laid out in this chapter along with any considerations that might be introduced by virtue of a case's quasi‐experimental qualities, a topic taken up elsewhere ( Gerring 2007 , ch. 6 ).

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Gujarati (2003) ; Kennedy (2003) . Interestingly, the potential of cross‐case statistics in helping to choose cases for in‐depth analysis is recognized in some of the earliest discussions of the case‐study method (e.g. Queen 1928 , 226).

This expands on Mill (1843/1872 , 253), who wrote of scientific enquiry as twofold: “either inquiries into the cause of a given effect or into the effects or properties of a given cause.”

This method has not received much attention on the part of qualitative methodologists; hence, the absence of a generally recognized name. It bears some resemblance to J. S. Mill's Joint Method of Agreement and Difference ( Mill 1843/1872 ), which is to say a mixture of most‐similar and most‐different analysis, as discussed below. Patton (2002 , 234) employs the concept of “maximum variation (heterogeneity) sampling.”

More precisely, George and Smoke (1974 , 534, 522–36, ch. 18 ; see also discussion in Collier and Mahoney 1996 , 78) set out to investigate causal pathways and discovered, through the course of their investigation of many cases, these three causal types. Yet, for our purposes what is important is that the final sample includes at least one representative of each “type.”

For further examples see Collier and Mahoney (1996) ; Geddes (1990) ; Tendler (1997) .

Traditionally, methodologists have conceptualized cases as having “positive” or “negative” values (e.g. Emigh 1997 ; Mahoney and Goertz 2004 ; Ragin 2000 , 60; 2004 , 126).

Geddes (1990) ; King, Keohane, and Verba (1994) . See also discussion in Brady and Collier (2004) ; Collier and Mahoney (1996) ; Rogowski (1995) .

The exception would be a circumstance in which the researcher intends to disprove a deterministic argument ( Dion 1998 ).

Geddes (2003 , 131). For other examples of casework from the annals of medicine see “Clinical reports” in the Lancet , “Case studies” in Canadian Medical Association Journal , and various issues of the Journal of Obstetrics and Gynecology , often devoted to clinical cases (discussed in Jenicek 2001 , 7). For examples from the subfield of comparative politics see Kazancigil (1994) .

For a discussion of the important role of anomalies in the development of scientific theorizing see Elman (2003) ; Lakatos (1978) . For examples of deviant‐case research designs in the social sciences see Amenta (1991) ; Coppedge (2004) ; Eckstein (1975) ; Emigh (1997) ; Kendall and Wolf (1949/1955) .

For examples of the crucial‐case method see Bennett, Lepgold, and Unger (1994) ; Desch (2002) ; Goodin and Smitsman (2000) ; Kemp (1986) ; Reilly and Phillpot (2003) . For general discussion see George and Bennett (2005) ; Levy (2002) ; Stinchcombe (1968 , 24–8).

A third position, which purports to be neither Popperian or Bayesian, has been articulated by Mayo (1996 , ch. 6 ). From this perspective, the same idea is articulated as a matter of “severe tests.”

It should be noted that Tsai's conclusions do not rest solely on this crucial case. Indeed, she employs a broad range of methodological tools, encompassing case‐study and cross‐case methods.

See also the discussion in Eckstein (1975) and Lijphart (1969) . For additional examples of case studies disconfirming general propositions of a deterministic nature see Allen (1965); Lipset, Trow, and Coleman (1956) ; Njolstad (1990) ; Reilly (2000–1) ; and discussion in Dion (1998) ; Rogowski (1995) .

Granted, insofar as case‐study analysis provides a window into causal mechanisms, and causal mechanisms are integral to a given theory, a single case may be enlisted to confirm or disconfirm a proposition. However, if the case study upholds a posited pattern of X/Y covariation, and finds fault only with the stipulated causal mechanism, it would be more accurate to say that the study forces the reformulation of a given theory, rather than its confirmation or disconfirmation. See further discussion in the following section.

Sometimes, the most‐similar method is known as the “method of difference,” after its inventor ( Mill 1843/1872 ). For later treatments see Cohen and Nagel (1934) ; Eggan (1954) ; Gerring (2001 , ch. 9 ); Lijphart (1971 ; 1975) ; Meckstroth (1975) ; Przeworski and Teune (1970) ; Skocpol and Somers (1980) .

For good introductions see Ho et al. (2004) ; Morgan and Harding (2005) ; Rosenbaum (2004) ; Rosenbaum and Silber (2001) . For a discussion of matching procedures in Stata see Abadie et al. (2001) .

The most‐different method is also sometimes referred to as the “method of agreement,” following its inventor, J. S. Mill (1843/1872) . See also De Felice (1986) ; Gerring (2001 , 212–14); Lijphart (1971 ; 1975) ; Meckstroth (1975) ; Przeworski and Teune (1970) ; Skocpol and Somers (1980) . For examples of this method see Collier and Collier (1991/2002) ; Converse and Dupeux (1962) ; Karl (1997) ; Moore (1966) ; Skocpol (1979) ; Yashar (2005 , 23). However, most of these studies are described as combining most‐similar and most‐different methods.

In the following discussion I treat the terms social capital, civil society, and civic engagement interchangeably.

E.g. Collier and Collier (1991/2002) ; Karl (1997) ; Moore (1966) ; Skocpol (1979) ; Yashar (2005 , 23). Karl (1997) , which affects to be a most‐different system analysis (20), is a particularly clear example of this. Her study, focused ostensibly on petro‐states (states with large oil reserves), makes two sorts of inferences. The first concerns the (usually) obstructive role of oil in political and economic development. The second sort of inference concerns variation within the population of petro‐states, showing that some countries (e.g. Norway, Indonesia) manage to avoid the pathologies brought on elsewhere by oil resources. When attempting to explain the constraining role of oil on petro‐states, Karl usually relies on contrasts between petro‐states and nonpetro‐states (e.g. ch. 10 ). Only when attempting to explain differences among petro‐states does she restrict her sample to petro‐states. In my opinion, very little use is made of the most‐different research design.

This was recognized, at least implicitly, by Mill (1843/1872 , 258–9). Skepticism has been echoed by methodologists in the intervening years (e.g. Cohen and Nagel 1934 , 251–6; Gerring 2001 ; Skocpol and Somers 1980 ). Indeed, explicit defenses of the most‐different method are rare (but see De Felice 1986 ).

Another way of stating this is to say that X is a “nontrivial necessary condition” of Y .

Wahlke (1979 , 13) writes of the failings of the “behavioralist” mode of political science analysis: “It rarely aims at generalization; research efforts have been confined essentially to case studies of single political systems, most of them dealing …with the American system.”

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Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles (2018)

Chapter: chapter 4 - case study site selection.

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

46 To study and find the conditions amenable to dedicating lanes for CAV users, the team con- ducted a modeling and simulation-based study of CAV driver behavior on DLs on a selected set of diverse case-study sites. This chapter details the process that was used to select the case study sites based on the project objectives. The team identified a set of evaluation criteria to assess the case study sites. Figure 4.1 presents the overall approach to identifying and selecting the case study sites used for modeling CACC DLs. As shown in Figure 4.1, a set of initial candidate case study sites was created based on the team members’ extensive experience with modeling managed lanes and CACC applications. Evalua- tion criteria pertaining to case study site characteristics, managed lane characteristics, and CAV modeling feasibility were developed to down-select these case study sites to two or three that could help define guidelines for agency use in determining whether their specific applications would merit lane dedication. Case study site characteristics include features that define their operational and geographic characteristics, demand, modes, ITS strategies, and the existence of managed lanes. Managed lane characteristics include the features of the existing (or proposed) managed lane facility. These characteristics include operational rules, priority conditions, allowable modes, and access features. The team used CAV modeling feasibility to rank the test sites. 4.1 Initial List of Candidate Sites The team analyzed nine case study sites that were available for use in the modeling effort. Each candidate site represented a simulation-based corridor model for which a managed lane facility existed or had been proposed. The map in Figure 4.2 shows the initial candidate case study sites. Because of map scaling, some candidate test beds overlap (i.e., the candidate sites in St. Paul and Minneapolis, Minnesota, and in Maryland and Northern Virginia). Table 4.1 shows the preliminary list of case study sites that were evaluated and assessed for their effectiveness in achieving the project goals. The next section presents a brief description of the geographic and modeling characteristics of these candidate case study sites. 4.1.1 I-66 Corridor, Northern Virginia The candidate site on the I-66 corridor in Fairfax, Virginia, starts from the outside of the Capital Beltway (I-495) and extends for 13 miles as a 4-lane freeway segment that includes an HOV lane on the left-most lane and stretches to the west all the way through the interchange with US-29 to SR-234 (see Figure 4.3). C H A P T E R 4 Case Study Site Selection

Case Study Site Selection 47 This suburban test site includes six interchanges and two dedicated on-and-off ramps for an HOV lane that is separated from the GPLs. The average distance between interchanges is approximately 1.2 miles, yielding 0.6 miles and 2 miles of minimum and maximum interchange spacing, respec- tively. The test site experiences recurring congestion caused by high directional daily demand every weekday for the eastbound lanes (i.e., toward Washington, D.C.) during the a.m. peak and the west- bound lanes (i.e., toward Fairfax, Virginia) during the p.m. peak. Between 2:00 p.m. and 8:00 p.m., traffic volumes of the test bed range from 900 vphpl to 2,100 vphpl and include approximately Initial List of Case Study Sites Feasibility of Modeling CAV Applications Characteristics of the Managed Lane Facility Characteristics of the Case Study Sites Selected Case Study Sites Figure 4.1. Selection process for the case study sites. Source: NCHRP 20‐102(08) project team; base map from www.HERE.com. Figure 4.2. Initial candidate case study site mapping.

48 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles 1,500 vphpl of peak HOV traffic volumes. This simulation model is currently available in the U.S.DOT’s Open Source Application Development Portal for academic/research use. Based on field observations, the existing simulation model includes a traffic stream with varying vehicle compositions (FHWA Class 4 and above). The existing freeway deploys several ITS strategies along the corridor—hard shoulder running, lane use control signals, VMS, and advanced ramp metering. US-29 is a parallel arterial and an alternate route to I-66 and is acces- sible via the six interchanges included in the existing model. Currently, the parallel roadway is not included in the simulation model. The I-66 managed lanes operate as far-left, single-lane, time-of-day HOV-2 lanes in both eastbound and westbound directions. User-type restrictions along the existing HOV-2 lanes allow only vehicle classes with two or more vehicle occupancy requirements. The existing managed HOV-2 lanes operate on a time-of-day basis with restrictions applying during a.m. and p.m. peak periods on weekdays. No physical barrier separates the managed HOV-2 lanes from the mixed-use lanes. Currently, only double solid white lane markings are used to No. Case Study Corridor Location Length of Corridor Freeway Average Annual Daily Traffic (AADT) Range 1 I- 66 Northern Virginia 13 150,000–160,000 2 US 101 San Mateo, California 8.5 200,000–250,000 3 I-15 San Diego, California 22 250,000–300,000 4 I-35 MnPASS Lanes St. Paul, Minnesota 15 39,000–125,000 5 I-94 St. Paul–Minneapolis, Minnesota 14 132,000–179,000 6 I-290 Managed Lanes Chicago, Illinois 14.5 159,000–211,000 7 I-75 HOV Lanes Detroit, Michigan 18.5 105,000–180,000 8 I-270 Corridor Maryland 26 175,000–270,000 9 I-95 Express Lanes Miami, Florida 20 94,000–260,000 Table 4.1. Initial candidate case study sites. Source: NCHRP 20-102(08) project team; base map data © 2018 Google. Figure 4.3. I-66 case study site coverage.

Case Study Site Selection 49 separate the lanes and to indicate no lane changing and no access. Access points between the dedicated HOV-2 lanes and the mixed-use lanes are permitted only along areas with dashed lane striping. The I-66 also has hard shoulder running lanes on the far-most right lanes in both directions. These lanes operate from 5:30 a.m. to 11:00 a.m. in the eastbound direction and from 2:00 p.m. to 8:00 p.m. in the westbound direction. Lane utilization is indicated via VMS, which show a green arrow for permitted use and a red cross for closed for use unless exiting. The simulation case study was developed and calibrated using the PTV Vissim micro- simulation software, which allows external API-based control of simulation components, including driver behavior, making it a good candidate for CAV modeling. Driver behavior was calibrated to replicate field-observed corridor travel time, speed, and traffic volume. Freeway speed and volume information for the case study site are available, in 5-minute inter- vals and classified by lanes, via the FHWA’s Saxton Transportation Operation Laboratory (U.S.DOT 2018). The existing ITS strategies along the corridor were included in the traffic simulation model. 4.1.2 US-101 Corridor, San Mateo, California The US-101 case study site is located within the County of San Mateo, California, and stretches from Redwood City to the City of Burlingame. The length of the modeled US-101 freeway facil- ity is approximately 8.5 miles, with a parallel arterial, El Camino Real (SR-82), of similar length. Drivers can divert to the parallel arterial via seven possible interchanges. The extent and coverage of the US-101 corridor model is illustrated in Figure 4.4. Source: NCHRP 20-102(08) project team; base map from www.openstreetmap.org. Figure 4.4. US-101 case study site coverage.

50 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles 4.1.3 I-15 Corridor, San Diego, California The I-15 case study site is made up of a 22-mile stretch of the I-15 corridor facility and associ- ated parallel arterials. It extends north-to-south from the interchange with SR-78, just below the City of Escondido, California, to the interchange with Balboa Avenue, approaching San Diego, California. This facility is shown in Figure 4.5. The corridor passes through a suburban area. A network of arterials runs concurrent with the I-15 freeway, and drivers on the Interstate are able to divert via 18 possible interchanges, including Pomerado Road and Ted Williams Parkway. The I-15 corridor has a ramp metering information system and traffic light synchroniza- tion, both used for an active traffic demand management system. Speed and volume detectors are located throughout the freeway. Existing ITS strategies, specific to active traffic demand management systems, were included in the existing model. Within the limits of the simulation model, congestion during peak periods has been recorded to be approximately 50% higher than Source: NCHRP 20-102(08) project team; base map © San Diego Geographic Information Source (SanGIS) 2015, accessed at San Diego Association of Governments (SANDAG) website (www.sandag.org). Legend ICM Network Figure 4.5. I-15 case study site coverage.

Case Study Site Selection 51 off-peak hours in the peak direction. The measured daily VMT varies from the average value of all days observed by no more than a 10% margin. The simulation model includes varying heavy vehicle percentages within the traffic stream by time of day. No transit vehicles were included in the model. The I-15 freeway also includes express lanes that are separated by a concrete median barrier. These lanes are located between the GPLs northbound and southbound. The median barriers are moveable to manage congestion during peak hours. The standard lane configuration is two northbound lanes and two southbound lanes. This configuration can be changed to three south- bound lanes and one northbound lane to mediate peak-hour traffic demand. Currently, these are the only two-lane configuration choices available. The managed lane facility operates as HOT lanes using distance-based dynamic pricing. Motorcycles and all vehicles with two or more occupants can access the express lanes with no charge. SOVs also are allowed to access the express lanes, but pay a fee. Heavy vehicles (Class 4 and above) are restricted from the express lane facility. Designated ramp entrances and exits to this facility exist to and from SR-163, the I-15 south GPLs, and the I-15 north GPLs. There are two entrances and two exit flyover access ramps near the center of the express lanes facility granting direct access to and from SR-56. There are six access points each in the northbound and southbound directions between the express lanes and the GPLs. The simulation case study site was developed and calibrated using Aimsun microsimulation software. This software allows external API-based control of simulation components, including driver behavior, making it a good candidate for CAV modeling. Driver behavior was calibrated to replicate field-observed corridor travel time, speed, and traffic volume. Roadway speed and volume data were available through the Caltrans Performance Measurement System (PeMS) by specific days with precision of 1-minute intervals (Caltrans 2018). The detector data also classi- fies the traffic volume by lanes. 4.1.4 I-35E MnPass Lanes, St. Paul, Minnesota The I-35 MnPASS lanes that pass through the dense urban area of St. Paul, Minnesota, also represent conditions for assessing feasibility of dedicating lanes to CAVs. On the northern half of the 15-mile study corridor, the managed lane freeway transitions to suburban and rural struc- tures. The freeway corridor also contains a system-to-system interchange with I-694 where the two freeways run concurrently for approximately 1 mile. This facility is shown in Figure 4.6. Existing calibrated models in both CORSIM and Vissim formats are owned by the Minnesota Department of Transportation (Minnesota DOT). Traffic count and speed data detection by lane is archived daily. For this study, traffic count and speed data along the ramps and mainline were obtained through Minnesota DOT’s Regional Traffic Management Center detector data (Minnesota DOT 2017). Turning-movement counts at the ramp terminals, which were available from previous signal retiming projects at most of the study area interchanges, also were used in the model. This corridor experiences typical a.m. and p.m. peak-hour commuter demand and mild to moderate congestion, with the southbound traffic experiencing heavier demand during the a.m. peak and the northbound traffic experiencing greater demand during the p.m. peak Outside of these commuter rush hours, demand drops off considerably, and traffic moves under free-flow conditions. Alternate arterial routes exist (i.e., US-61), and even the regional freeway network provides alternate routes; however, these regional routes were not included in the scope of this microsimulation project. Traffic on the corridor is largely commuter traffic and mostly consists

52 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles of passenger vehicles with a small proportion made up of commercial trucks. On this corridor, transit is not significant enough to affect operations greatly; therefore, transit was not explicitly modeled. Existing ITS strategies include VMS, ramp metering, and traffic speed/volume detec- tors feeding to the traffic management centers. The existing managed lane facility is present on the southern half of the study corridor (south of I-694) and consists of a single lane in each direction. Studies are being performed to assess the feasibility of expanding the current facility to the north. HOV, transit vehicles, and motorcycles can use the current facility at no charge, whereas SOVs pay to use the facility. Large commercial vehicles (with more than two axles and weighing more than 26,000 pounds) are restricted from the managed lane during peak hours but can use the managed lane during non-peak travel times. The facility is currently operated as a managed lane during commuter rush and as a GPL during off-peak hours. A solid double white line indicates that access to the managed lane is restricted. Frequent access areas, which also are major weaving areas, are indicated with striping. Buses are permitted to run along the shoulders of I-35E in the northbound and southbound direction for an approximate 2-mile stretch north of I-694 and an approximate 3-mile stretch south of I-694. Buses can use the outside shoulder along these stretches of I-35E when congestion slows travel speeds to 35 mph or slower. Buses using the shoulder may only exceed adjacent general traffic speeds by 15 mph. Source: NCHRP 20-102(08) project team; base map data © 2018 Google. Legend Study Area Interchange Figure 4.6. I-35E case study site coverage.

Case Study Site Selection 53 Driver behavior was calibrated in CORSIM per Minnesota DOT guidelines. Vissim models were calibrated as well to replicate existing travel speeds and congestion levels during the a.m. and p.m. peak-hour rush periods. 4.1.5 I-94 Managed Lanes (Proposed), Minneapolis, MN The proposed I-94 managed lane facility will be in a dense urban area between Minneapo- lis and St. Paul, Minnesota, and will include system interchanges with I-35W and I-35E (see Figure 4.7). The length of the facility included in the simulation model is approximately 14 miles, extending from I-394 on the west to US-61 on the east, with 32 interchanges modeled. Traffic on the existing corridor is largely commuter traffic and mostly is made up of passenger vehicles with a smaller proportion of commercial trucks. Transit is not a significant enough component of this corridor to impact operations greatly; therefore, transit was not explicitly modeled. The corridor experiences typical a.m. and p.m. peak-hour commuter demand and moderate to heavy congestion during these peak periods. Alternate arterial routes are available with lim- ited river crossings, but the alternate routes were not included in the microsimulation modeling of this project. The facility currently has VMS, ramp metering, and traffic speed/volume detec- tors. The proposed project is to construct an expansion of the managed lane (MnPASS) system to include this corridor. The managed lane would be a single lane in each direction. Buses would be permitted to run along the shoulders of I-94 between Highway 280 and Downtown St. Paul. As with other bus shoulder-running applications in the area, buses would be permitted to use the outside shoulder when congestion slows travel speeds to 35 mph or slower, with bus speeds limited to no more than 15 mph faster than the adjacent general-purpose traffic. The proposed facility would be operated as a HOT lane, allowing free access to high-occupancy passenger cars, transit vehicles, and motorcycles. SOVs would be able to access this facility with a fee. Heavy vehicles are restricted from access to the facility. The proposed operating rules involve time-of-day plans, operating each managed lane as a mixed-use lane during off-peak hours while operating it as a HOT lane during peak periods. Driver behavior was calibrated in CORSIM per Minnesota DOT guidelines. Source: NCHRP 20-102(08) project team; base map data © 2018 Google. Figure 4.7. I-94 case study site coverage.

54 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles The existing calibrated models were in CORSIM format and owned by the Minnesota DOT, which presented significant challenges to modeling CAV behavior due to limitations in uti- lizing external API to code varying driver behaviors. Traffic count and speed data along the ramps and mainline were obtained through Minnesota DOT’s Regional Traffic Management Center detector data (Minnesota DOT 2017). Traffic count detection is by lane and is archived daily. Turning-movement counts at the ramp terminals were available from previous signal reti- ming projects at most of the study area interchanges. At interchanges where turning-movement counts were not available, new turning-movement counts were collected. 4.1.6 I-290 Managed Lanes, Chicago, Illinois The I-290 facility runs through a dense urban area in Chicago and metropolitan communi- ties to the west of downtown Chicago (see Figure 4.8). A managed lane facility, which includes a single lane in each direction, is proposed on this corridor. The length of the facility included in the simulation model is approximately 14.5 miles, extending from I-294 on the west to I-90 on the east, with 21 interchanges modeled. The corridor experiences typical a.m. and p.m. peak- hour commuter demand and heavy congestion during these 2- to 3-hour peak periods. Outside of these peaks, traffic demand reduces enough to allow for free-flow operations along I-290. There are no restrictions on transit or heavy vehicles on the existing facility and, due to left-side entrance/exit ramps along the corridor, commercial heavy vehicles can utilize all lanes. Alternate arterial routes are available (Roosevelt Road being the primary alternate route), but the alternate routes were not included in the microsimulation modeling of this project. Traffic on the I-290 case study corridor is largely commuter traffic, consisting mostly of pas- senger vehicles with a smaller proportion of commercial trucks. Transit is not a significant com- ponent of this modeled corridor. Commuter rail is present, running immediately adjacent to Source: NCHRP 20-102(08) project team; base map data © 2018 Google. Figure 4.8. I-290 case study site coverage.

Case Study Site Selection 55 I-290 and within the I-290 median for the eastern half of the study corridor; however, due to limited interaction with the freeway, transit—including commuter rail—was not included in the microsimulation modeling. VMS are present indicating travel time along the corridor. Ramp metering, closed-circuit television (CCTV), and traffic speed/volume detectors also are present on the corridor. The operating rules proposed for this managed lane facility are HOV and HOT time-of-day restrictions by which the facility operates as a managed lane during peak hours and as a GPL during off-peak hours. Access to and from the managed lane is proposed to be indicated using dashed white line pavement striping only, and restricted (no) access is to be indicated with a solid double white line. Existing calibrated models in the Vissim model format were owned by the Illinois Department of Transportation (Illinois DOT). Traffic count and speed data along the ramps and mainline were used for calibration of the models and obtained from the Illinois DOT’s detector database. Traffic count detection is by lane and is archived daily. Turning-movement counts at the ramp terminals were collected at most of the study area interchanges as part of the project. 4.1.7 I-75 HOV Lanes, Detroit, Michigan The I-75 freeway corridor is based in a dense urban area immediately north of the Detroit city limits. This area includes a major system interchange with I-696. The length of the facility included in the simulation model is approximately 6 miles, extending from M-102 on the south end to 12 Mile Road on the north end, with five interchanges included (see Figure 4.9). The microsimula- tion model was prepared for detailed analysis of a subarea of a larger (18.5-mile) corridor being studied for the addition of a managed lane (from M-102 on the south end to M-59 on the north end). The corridor experiences typical a.m. (southbound) and p.m. (northbound) peak-hour commuter demand and moderate congestion during these peak periods. Outside of these peaks, traffic demand reduces enough to allow for free-flow operations along I-75 during most of the day. A managed lane facility with a single lane in each direction was proposed for immediate construction. The construction would require widening along the corridor for the addition of this lane in each direction. The proposed managed lane facility would extend 12.5 miles, from SR-59 to approximately 12 Mile Road. This would be the first managed lane facility along the freeway system in Michigan. Operational rules for this managed lane facility would be based on a time-of-day HOV restriction, by which the facility would operate as a managed lane during peak hours and as a GPL during off-peak hours. Alternate arterial routes are available, with Woodward Avenue being the primary alternate route; however, the alternate routes were not included in the microsimulation modeling of this project. Traffic on this corridor is largely commuter traffic, consisting mostly of passenger vehicles with a smaller proportion of commercial trucks. Transit is not a significant component of this modeled corridor. Existing ITS strategies and technologies along the corridor include VMS, CCTV, and traffic-count stations. Areas providing access to and from the managed lane are proposed to be indicated by dashed white line striping only. Restricted areas (with no access to or from the managed lane) are pro- posed to be indicated by a solid double white line. The entire corridor (18.5 miles) was given a macroscopic Highway Capacity Manual analysis of the basic freeway segments, merge/diverge areas, and weave areas. A more detailed micro- simulation analysis was conducted for a 7-mile section containing the system interchange with I-696 and proposed ramp-braiding alternatives. The microsimulation was conducted in Vissim (Version 6), and traffic count data was obtained from the Michigan Department of

56 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles Transportation (Michigan DOT) traffic count web portal for ramps and mainline counts along the corridor (Michigan DOT 2018). The model was set up as a ramp and mainline model only. Full interchange operations were not modeled. Speed and congestion data used for calibration were obtained from the Regional Integrated Transportation Information System (RITIS) maintained by the University of Maryland (CATT Lab 2018). 4.1.8 I-270 Corridor, Maryland I-270 is a 34.7-mile auxiliary Interstate Highway that travels between I-495 (the Capital Belt- way) just north of Bethesda, in Montgomery County, Maryland, and I-70 in the city of Frederick in Frederick County, Maryland. The corridor consists of a 32.60-mile main line plus a 2.10-mile spur that provides access to and from southbound I-495 (see Figure 4.10). Most of the southern part of the route in Montgomery County passes through suburban areas around Rockville and Gaithersburg. This portion of I-270 is up to 12 lanes wide and consists of a local-express lane configuration as well as HOV lanes that are in operation during peak travel times. North of the Gaithersburg area, the road continues through the northern part of Montgomery County as a 6- to 8-lane highway with an HOV lane in the northbound direction only. Farther north, I-270 continues through rural areas into Frederick County and toward the city of Frederick as a 4-lane freeway. The modeled length is approximately 26 miles. Source: NCHRP 20-102(08) project team; base map data © 2018 Google. Figure 4.9. I-75 case study site coverage.

Case Study Site Selection 57 This corridor experiences very little diversity in demand conditions and traffic patterns, and currently operates at a high level of congestion throughout most typical days. The only parallel alternate route is SR-355, an arterial corridor with signalized intersections and significant busi- ness activity. Modes of transportation included in the model were passenger cars, buses, and heavy vehicles. The parallel alternative routes were not included in the model. The model included imme- diate facilities, such as highway interchanges and immediate signalized intersections at the interchanges. The current ITS infrastructure consists of speed detectors, video monitoring infrastructure, and VMS. The Maryland DOT is in the process of procuring an innovative congestion-management upgrade project ($100 million) that could introduce a range of new technologies/strategies. The facility’s managed lane is currently a single HOV lane in each direction. HOV operating restrictions apply during the traffic peak period in the peak direction only. The HOV lane is concurrent with other lanes and is distinguished by special pavement markings. Vehicles can access the managed lane from the GPL throughout the entire facility with no restrictions. The simulation platform used to develop the model was Vissim. No existing ITS strategies were included in the existing model. 4.1.9 I-95 Express Lanes, Miami, Florida Interstate 95 (I-95) is a key component of the Interstate Highway System, running along the east coast of the country from Miami, Florida, to the U.S.-Canada border in eastern Maine. The study segment of this facility is an urban freeway with directional commuter traffic flows that runs through the densely urban cores within Miami–Dade and Broward Counties in South Florida Source: NCHRP 20-102(08) project team; base map data © 2018 Google. Figure 4.10. I-270 case study site coverage.

58 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles (see Figure 4.11). The managed lane section currently runs approximately 20 miles between Davie Road, near Downtown Ft. Lauderdale, to just north of Downtown Miami at SR-836. I-95 operates at high levels of congestion throughout most of the day, with concentrated congestion at various bottlenecks during off-peak hours while distributed throughout the facility during peak periods. The closest parallel facility is the signalized arterial of US-1/ Federal Highway/Biscayne Boulevard. Another parallel alternative route located along the northern portion of the facility is the Florida Turnpike. The various transportation modes include passenger cars, heavy vehicles, and buses. Heavy vehicles are restricted from using the express lanes. Existing ITS strategies used along the I-95 corridor include VMS, video monitoring, and various detectors. The I-95 express lanes represent a conversion from HOV to HOT operation and were imple- mented to provide more reliable trip times for corridor users. The facility allows for toll-free access for HOV3+ users and transit but requires carpools to pre-register. SOVs can access the lane by paying a toll that is assessed on a dynamic basis in response to congestion. As volumes increase, so does the price for access. Currently, the maximum rate for an SOV is $1.50 per mile or $10.50 over the full length of the express lanes. This cap may be raised if the LOS on the facility Source: NCHRP 20-102(08) project team; base map from Florida DOT (www.95express.com). Figure 4.11. I-95 case study coverage.

Case Study Site Selection 59 consistently declines below 45 mph over a 90-day period, a policy that is largely the result of the project’s initial funding through the Federal Urban Partnership Agreement. The Florida DOT estimates that about 2% to 3% of the traffic in the express lanes is travelling toll free. The managed lanes are separated from the GPLs by flexible delineator posts. The model cur- rently includes four northbound entrances, five southbound entrances, four northbound exits, and four southbound exits between the managed lanes and GPLs (see Figure 4.12). The simulation platform used to develop this network was Vissim. The Vissim modeling included the GPLs, the managed lanes, and the individual interchange operations. 4.2 Evaluation Criteria This section describes the evaluation criteria used for modeling the initial candidate case study sites. These evaluation criteria were ranked as being of low, medium, or high priority based on their relevancy in assessing DL conditions. For example, model availability is considered a high-priority criterion, whereas having a moderately sized facility is of low priority. Based on the relative importance of each evaluation factor, the team used weighted scoring when ranking case study sites. 4.2.1 Case Study Site Characteristics The team used eight evaluation criteria to identify characteristics and rank the case study sites. This evaluation included characterization of the geographic and operational conditions that exist in the test sites. 4.2.1.1 Geographic Characteristics Managed lanes generally are an urban/suburban roadway feature; hence, it is desirable that the final selected case study sites represent reasonable use of dedicated/managed lanes in or near metropolitan area conditions. Drivers in larger metropolitan areas will be more accustomed to regularly encountering recurring or nonrecurring congestion. In larger metro politan areas, congestion will tend to be more ubiquitous and bidirectional. For this criterion, the characteristics assessed reflected diverse sites that ranged from less urban to more urban in terms of number of lanes, AADT, and location. This evaluation criterion was given medium priority in the case study selection process because managed lanes are mostly an urban feature (Figure 4.13). 4.2.1.2 Availability of Data/Case Study Site Model Successful modeling of CACC in DLs depends on the model’s closeness to the real world. Hence, availability of a calibrated case study site model is of extreme importance. The team selected case study site models that were available for use in a calibrated state. To evaluate the impacts of DLs for CACC-equipped vehicles, the case study sites needed to be validated and cali- brated using historical, near real-time, and real-time data. The data had to represent a case study site’s geographic and temporal scope as well as characteristics such as existing ITS infrastructure and managed lane configurations. The availability of case study models and the associated cali- bration data was a high-priority evaluation criterion, given the importance of a fully calibrated simulation model in assessing realistic and credible benefits and sensitivity parameters of CACC application on a DL facility. The research team gave preference to models that were available in an open-source portal such as the U.S.DOT’s Open Source Application Development Portal (OSADP) or the U.S.DOT Data Repository, as well as models that were available upon request from local agencies (see Figure 4.14).

60 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles Source: NCHRP 20-102(08) project team; base map data from Florida DOT (2018). Figure 4.12. I-95 express lane configurations and access points.

Case Study Site Selection 61 Figure 4.14. Case study characterization based on model availability. Figure 4.13. Case study characterization based on geographic characteristics. 4.2.1.3 Diversity in Demand/Operational Conditions Operating demand of a corridor facility determines the operational conditions for the drivers. For this case study selection, we assess the demand in terms of traffic volumes over the entire case study site. Traffic demand for low (uncongested), medium (near capacity), and high (congested) levels will yield different traffic patterns and a wide range of cases to assess and compare their performances. Although low-demand conditions do not present challenging conditions for the deployment of CAV applications, having a variable demand would allow assessment of impacts under different saturation rates. Hence, the selection included a case study site with varying traffic demand, or multiple case study sites that represent different demands. Having differing demand conditions is important to analyze the sensitivity of DLs under various saturation rates, but the demand can be scaled easily from existing models. Therefore, this criterion was consid- ered medium priority (see Figure 4.15). 4.2.1.4 Length of Facility The length of a DL facility relative to the overall case study site is an important factor in gaug- ing its influence on the overall network, parallel corridor, and parallel arterials. The length of the facility corresponds directly to the proportion of benefits or disbenefits imposed on the assess- ment boundary, which is defined as the limits of the roadway facility that have been included in the assessment. Effects like the proportion of a given trip utilizing the DL versus not using the DL can be compared between facilities with longer DLs versus shorter DLs. At the same time, modeling the CACC application entails computationally intensive driver-behavior capture to an external interface and trajectory implementation, and larger models can become difficult to Figure 4.15. Case study characterization based on operational demand.

62 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles Figure 4.16. Case study characterization based on routing features. model. The team selected medium-sized facilities to enable full evaluation of trip-based perfor- mance measures as well as manage the computation size. This evaluation criterion was given a medium priority in the case study selection. 4.2.1.5 Availability of Alternate Routes One consideration in assessing CACC DLs’ impact on non-users is the availability of alternate routes, such as parallel arterials. The case study sites were assessed to determine whether a parallel route was explicitly included in the model and whether vehicle rerouting was possible through these alternate routes. For the case study selection, the team prioritized models that included alternate routes. Because most of the candidate models had been developed for corridor analysis, however, only a few might have included alternate parallel routes. This evaluation criterion was given a medium priority in the case study selection due to this limitation (see Figure 4.16). 4.2.1.6 Diversity in Modes Varying modes of transportation within the traffic stream composition is an important consideration due to its impact on traffic flow characteristics. Freeways and interstates in urban environments have a significant composition of heavy and transit vehicles unless heavy and/or transit vehicle access is restricted. Heavy and transit vehicles have different accel- eration and deceleration profiles compared to passenger cars. For evaluation purposes, the selected case study sites needed to have varying vehicle type composition or be restricted by time-of-day access to heavy and transit vehicles so that their impacts could be assessed. This evaluation criterion was given a medium priority in the case study selection (see Figure 4.17). 4.2.1.7 Existence of Managed Lanes Managed lanes commonly are used within urban metropolitan areas. Types of managed lanes may include HOV lanes, HOT lanes, and express toll lanes (ETLs). One important factor for consideration is the traffic and safety impacts of using several types of managed lanes on the same corridor. Other impacts include mixed use of managed lanes, which may include dedicated CACC with HOV, HOT, and ETLs. These scenarios could be compared to a scenario with com- plete conversion of existing managed lanes to dedicated CACC lanes. For this study, the research team gave preference to case study sites with existing managed lanes or where managed lanes had been proposed for deployment in the near future. This evaluation criterion was given a medium priority in the case study selection (see Figure 4.18). Figure 4.17. Case study characterization based on modal diversity.

Case Study Site Selection 63 4.2.1.8 Existence of ITS Strategies ITS strategies are implemented to maximize roadway carrying capacity and increase safety. Concurrent implementation of ITS strategies with CACC DLs may have either synergistic or conflicting effects on roadway capacity and driver safety. For example, CACC is expected to work synergistically with dynamic speed limits because it improves the string stability of CACC platoons. The research team assessed the case study sites to determine whether ITS strategies existed and were modeled in the available simulation model. These existing ITS strategies could then be screened for conditions that can cause synergies or conflicts with CACC applications. This evaluation criterion was given a low priority in the case study site selection because currently implemented ITS strategies may or may not exist in conjunction with CACC implementation. 4.2.2 Managed Lane Characteristics The existing or proposed characteristics of the managed lanes for each of the case study sites also were assessed. Specifically, the following five characteristics were used for case study site scoring: managed lane geometry, user types, operating rules, physical barrier types, and diversity in access point configurations. 4.2.2.1 Managed Lane Geometry The number of lanes available for use as managed lanes is a critical factor to assess the capacity benefits of additional lanes. Capacity impacts are an important determining factor in deciding on the implementation of additional lanes due to roadway widening or hard running shoulder uses. Addi- tional lanes mitigate the “snail” effect by which the slowest-moving vehicle in the managed lane can govern the speed of the entire lane. Additional lane design should complement the access manage- ment strategy to accommodate traffic safety and capacity due to lane changes. Case study sites with a diverse number of DLs and varying roadway geometries were preferred so that these impacts could be assessed. This evaluation criterion was given a low priority in the case study selection. 4.2.2.2 User Types Within the selected case study sites, existing managed lanes (e.g., HOT lanes, HOV lanes, and ETLs) with a mix of user-types (e.g., SOVs, HOVs, transit vehicles, and heavy vehicles) were pre- ferred. To assess the benefits and disbenefits of imposing future restrictions on current user types (e.g., through conversion of existing managed lanes to dedicated CAV lanes) the team identified case study sites with a diverse user base. Among the project objectives, a major consideration was to evaluate the feasibility of mixed lane use by CAV vehicles and non-CAV vehicles. Hence, this evaluation criterion was given a medium priority in the case study selection (see Figure 4.19). 4.2.2.3 Operating Rules Managed lanes can have a variety of operating rules to manage the facility for both operational and safety reasons. For example, time-of-day and vehicle-class access restrictions commonly are used along certain managed lane facilities. These operating rules influence traffic patterns Figure 4.18. Case study characterization based on managed lanes.

64 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles throughout the day at the imposed area. Other operating rules may include the enforcement of left-lane passing only laws, which may involve safety concerns for vehicles that must pass mul- tiple platooned vehicles to find an acceptable gap for a lane change. The team categorized the testbed operating rules as: a. Time-of-day operation, wherein lanes operate as managed lanes only during peak hours. During non-peak hours, no lanes are dedicated to special vehicle categories such as HOVs or toll-paying SOVs. b. Time-of-day pricing, wherein lanes always operate as managed lanes, but the pricing depends on the time of day and follows a schedule. This category includes managed lanes for which off-peak usage may be free. c. Dynamic congestion pricing, wherein the usage fee for the managed lanes is determined based on existing travel conditions. This evaluation criterion was given a medium priority in the case study selection because the variance in these factors is somewhat limited (see Figure 4.20). 4.2.2.4 Physical Barrier Types Managed lanes that are separated from the GPLs by physical barriers like flexible delineator posts or concrete median barriers may have different posted speed limits from the GPLs. The potential difference in speed limits distinguishes managed lanes with physical barriers from managed lanes that are separated only by pavement striping. Differences in posted speed limits will have considerable effects on roadway capacity, traffic characteristics, and driver behaviors. The impacts on driver behaviors and traffic characteristics caused by varying physical barrier separations can be assessed and compared to the impacts on driver behaviors and traffic charac- teristics at managed lanes with no physical barrier separations. Accordingly, the research team gave preference to a testbed portfolio that included varying barrier types. This evaluation crite- rion was given a medium priority in the case study selection (see Figure 4.21). 4.2.2.5 Diversity in Access Point Configurations Access points to and from the DLs have a significant impact on the roadway capacity. The frequency of available access points along a DL directly correlates to drivers’ wayfinding and Figure 4.19. Case study characterization based on managed lane user characteristics. Figure 4.20. Case study characterization based on managed lane operating rules.

Case Study Site Selection 65 access to the facility. Driver lane-changing behaviors on both GPLs and DLs will be affected by advanced knowledge of access availability. Treatments that mediate the impacts of traffic turbu- lence caused by weaving vehicles making lane changes to enter or exit the DLs also are impor- tant factors to consider. The two types of access point configurations categorized by the team were continuous access and restricted access, with the latter type defined by access point frequency, strictness of access point location, and access section length. Access point frequency and weave management treatments, such as shorter access lengths (which challenge weaving movements), could be compared to assess the treatments’ impacts on both the GPLs and DLs. This evaluation criterion was given a medium priority in the case study selection (see Figure 4.22). 4.2.3 CAV Modeling Feasibility The feasibility of modeling CAVs also represented an important set of scoring criteria. Spe- cifically, the case study site needed to be modeled in an environment that permitted model- ing of customized vehicle and driver behavior. Specific feasibility criteria considered by the research team were the possibility of external programming interface and available driver behavior calibration data. 4.2.3.1 Possibility of External Programming Interface For the purposes of this project, the simulation environment needed to allow for modeling CACC driver and automatic car-following behaviors. The environment needed to allow for the inclusion of external API or a software-in-the-loop-system, if the CACC driver behavior was not already readily available with the model. External API also was required to query and receive vehicle parameters that were not already catalogued for analysis. This evaluation criterion was given a high priority in the case study selection because modeling CACC applications without external API was not possible (see Figure 4.23). 4.2.3.2 Available Driver Behavior Calibration Data The case study sites selected would need driver behavior calibration data specific to the local environments to allow for a detailed replication of conventional local driving behavior. A case study model that closely mimicked existing driver behavior would provide for a high-fidelity rep- resentation of the case study area and better comparisons among analyzed scenarios. Assessing Figure 4.21. Case study characterization based on managed lane separation. Figure 4.22. Case study characterization based on managed lane access features.

66 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles the traffic flow performance under a mixed-use case including CAV and non-CAV was critical to determining their traffic impacts, so this criterion was given a high priority. 4.3 Selected Case Study Sites The project team used the scoring criteria discussed in the preceding sections of this chapter to score and rank the nine candidate case study sites. 4.3.1 Case Study Site Scoring The study team developed a comprehensive scoring process to rank the initial candidate case study sites and select a portfolio of case study sites that could be used to effectively model the CAV applications and determine the implications on dedicating lanes to such vehicles. The selected case study sites needed to be able to define guidelines that agencies can use to determine whether their specific applications would merit lane dedication. These guidelines should include different levels of traffic congestion, network connectivity, availability of alternate routes and modes, spacing of access/egress points, truck traffic, and traffic patterns (e.g., core focused versus dispersed). Selecting a single case study site would not be sufficient to model the diversity in conditions that needed to be assessed, whereas modeling numerous test sites would be resource-intensive. Consequently, the team used the evaluation criteria scoring process to select two case study sites. 4.3.1.1 Mapping of Evaluation Criteria The team used the evaluation criteria it had developed to identify a set of 15 parameters (see Table 4.2). The nine initial candidate testbeds were then evaluated based on these 15 parameters. For each parameter, the team identified corresponding site-specific value(s), which are shown in Table 4.2 and Table 4.3. Multiple values were selected for certain parameters that involved a mix of different values. For example, the case study from St. Paul, Minnesota involved a mix of rural, suburban, and urban geographical areas. 4.3.1.2 Scoring of Case Study Sites Once the site-specific value for each parameter had been assessed, the team scored the param- eter based on whether it was least preferred (0) to most preferred (3), as shown in Table 4.4. For example, for availability of model and data, the Northern Virginia test site received a score of 3 because the model is available as open source, whereas case study sites such as the Chicago site received a score of 2. A weighted factor to indicate the priority of that specific evaluation factor was assigned. A weight value of 1 through 3 was used for factors with priority low to high, respectively. The final score of each testbed was calculated as a sum-product of each of the evaluation scores and their corresponding weights (w). Thus, for a testbed (i), the final score (Si) was calculated as follows: ∑= ,S s wi ij jj where j represents the variable evaluation scores. Figure 4.23. Case study characterization based on modeling interface.

Northern Virginia San Mateo, California San Diego, California St. Paul, Minnesota Minneapolis, Minnesota Chicago, Illinois Detroit, Michigan Maryland Miami, Florida Ca se S tu dy S ite C ha ra ct er is tic s Characteristics Urban ● ● ● ● ● ● Suburban ● ● ● ● ● Rural ● Availability of Model and Data Open-source ● ● Available on Request ● ● ● ● ● ● ● Unavailable Demand Levels Low ● ● ● ● ● Medium ● ● ● ● ● ● ● High ● ● ● ● ● ● ● ● ● Size of Model Length (Miles) 13 8.5 22 15 14 14.5 18.5 26 20 Alternate Routing Unavailable Available, but not modeled ● ● ● ● ● ● ● Available and modeled ● ● Modal Diversity Cars ● ● ● ● ● ● ● ● ● Trucks ● ● ● ● ● ● ● ● ● Transit ● ● ● Existing ITS Strategies None Available ● ● ● ● ● ● ● ● ● Managed Lanes Existing ● ● ● ● ● Proposed ● ● ● ● Unavailable Table 4.2. Modeling feasibility given site-specific values for case study site and managed lane characteristics (Part 1 of 2).

Northern Virginia San Mateo, California San Diego, California St. Paul, Minnesota Minneapolis, Minnesota Chicago, Illinois Detroit, Michigan Maryland Miami, Florida M an ag ed L an e Ch ar ac te ri sti cs Characteristics Number of Lanes 1 2 2 1 1 1 1 1 2 User Types HOV ● ● ● ● ● ● ● ● ● HOT ● ● ● ● ● Transit ● ● ● ● ● ● ● ● ● Trucks ● Operating Rules Time of Day Operation ● ● ● ● ● ● ● ● Time of Day Pricing Congestion Pricing ● ● ● Physical Barriers None ● Lane-marking ● ● ● ● ● ● Delineators ● ● Separated Access Point Throughout ● ● Limited ● ● ● ● ● ● ● Fe as ib ili ty Modeling Platform API Unavailable ● API Available ● ● ● ● ● ● ● ● Driver Behavior Not Calibrated Calibrated ● ● ● ● ● ● ● ● ● Table 4.3. Modeling feasibility given site-specific values for case study site and managed lane characteristics (Part 2 of 2).

Case Study Site Selection 69 Parameter Scoring and Criteria 1. Case Study Characteristics Geographic Characteristics 3 = Urban region. 2 = Suburban region. 1 = Rural region. Availability of Model and Data 3 = All models that were available as open-source. 2 or 1 = The score was lowered based on the increasing difficulty of obtaining the model. Demand Levels 3 = Sites that replicate low, medium, and high demand conditions. 2 or 1 = The score was lowered depending on the model’s inability to mimic certain demand conditions. Size of Model 3 = Sites between 7 miles and 14 miles in length. 2 or 1 = The score was lowered for smaller or larger sites owing to the relative increase in complexity/computational intensity of modeling CAV applications at these sites. Alternate Routing 3 = Sites with an available alternate route that also could be modeled. 2 or 1 = The score was lowered when an alternate route was not available for modeling. Modal Diversity 3 = Sites with a diverse modal set (including cars, trucks, and transit). 2 or 1 = The score was lowered when the number of modes was reduced. Existing ITS Strategies 3 = Sites with existing ITS strategies (e.g., ramp metering, hard-shoulder running, variable speed limits). 1 = Sites without existing ITS strategies.* 2. Managed Lane Characteristics Existence of Managed Lanes 3 = Sites with existing managed lanes. 2 = Sites with proposed managed lanes. 1 = Sites with no managed lanes. User Types 3 = Sites that allow all types of users in the managed lanes. 2 = Sites with some restrictions on vehicle types allowed in the managed lanes. 1 = Sites with the greatest restrictions on vehicle types allowed in the managed lanes. Operating Rules 3 = Sites with an operating rule. 1 = Sites without an operating rule.* Physical Barriers 3 = Sites with separation or barriers. 1 = Sites without separation or barriers.* Access Options 3 = Sites with limited-entry managed lanes. 1 = Sites with continuous access.* 3. CAV Modeling Feasibility CAV Modeling Ability 3 = Sites with available API. 1 = Sites without available API.* Driver Behavior 3 = Sites that can be calibrated to realistic driving behavior. 1 = Sites not calibrated to realistic driving behavior.* *Scoring for this parameter did not include a score of 2. Table 4.4. Case study site scoring criteria.

70 Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles With regard to parameters for which variety was preferred, the case study sites that represented a diverse set of values were given higher scores. For example, St. Paul, Minnesota, received a high score for demand levels because the case study site is subject to varying demand levels. These scores were generated for each parameter and a total score was assessed as shown in Table 4.5. Based on the case study site scores provided in Table 4.5, the top-ranking testbeds were: 1. Northern Virginia, and 2. San Mateo, California. Chapter 5 provides a detailed description of these two testbeds along with details on their calibration data and operational conditions in terms of traffic demand, weather conditions, and occurrence of incidents. W ei gh ts N or th er n Vi rg in ia Sa n M at eo , Ca lif or ni a Sa n Di eg o, Ca lif or ni a St . P au l, M in ne so ta M in ne ap ol is, M in ne so ta Ch ic ag o, Ill in oi s De tr oi t, M ic hi ga n M ar yl an d M ia m i, Fl or id a Ca se S tu dy S ite C ha ra ct er isti cs Geographic Characteristics 2 2 3 2 3 3 3 3 2 3 Availability of Model and Data 3 3 3 2 2 2 2 2 2 2 Demand Levels 2 3 3 2 3 3 3 3 1 1 Size of Model 2 3 3 2 2 3 2 2 1 2 Alternate Routing 2 2 3 3 2 2 2 2 2 2 Modal Diversity 2 3 3 3 2 2 2 2 2 2 Existing ITS Strategies 1 3 3 3 3 3 3 3 3 3 M an ag ed L an e Ch ar ac te ris tic s Existence of Managed Lanes 2 3 2 3 3 2 2 2 3 3 User Types 2 2 2 3 3 3 3 2 2 2 Operating Rules 2 3 3 3 3 3 3 3 3 3 Physical Barriers 2 3 3 3 3 3 3 3 3 3 Access Options 2 3 1 3 3 3 3 1 1 3 M od el in g Fe as ib ili ty CAV Modeling Ability 3 3 3 3 3 1 3 3 3 3 Driver Behavior 3 3 3 3 3 3 3 3 3 3 TOTAL CASE STUDY SITE SCORE 84 82 81 81 75 79 73 67 75 Table 4.5. Site-specific scoring matrix.

TRB’s National Cooperative Highway Research Program (NCHRP) Research Report 891: Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles identifies and evaluates opportunities, constraints, and guiding principles for implementing dedicated lanes for connected and automated vehicles. This report describes conditions amenable to dedicating lanes for users of these vehicles and develops the necessary guidance to deploy them in a safe and efficient manner. This analysis helps identify potential impacts associated with various conditions affecting lane dedication, market penetration, evolving technology, and changing demand.

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The Importance of Site Selection

• Michelle Whitham
• Catherine M. Kopil
• Tara Hastings

Applied Clinical Trials

Case study focused on quest for potential Parkinson’s biomarker highlights opportunities and challenges in site selection.

A case study focused on the quest for a potential Parkinson’s biomarker highlights the opportunities and challenges in site selection

While a comprehensive site-selection process may require a significant investment of time and resources at the outset, the benefits may outweigh the long-term risks (e.g., study delays, additional financial resources, high attrition rates, inability to meet enrollment goals). 1 Developing a checklist such as the one used by the S4 Steering Committee can facilitate the site-selection process and lead to more open lines of communication between study sponsors and sites. Sites have an opportunity to share their interest, background, and experience, and sponsors can dig deeper into questions they may have about the ways which the protocol and operating objectives align. 1

To learn more about the S4 study, see the following peer-reviewed publications:

Chahine LM, Beach TG, Seedorff N, et al. Feasibility and Safety of Multicenter Tissue and Biofluid Sampling for a-Synuclein in Parkinson’s Disease: The Systemic Synuclein Sampling Study (S4). Journal of Parkinson’s Disease, in press. 2018

Beach TG, Serrano GE, Kremer T, et al. Systemic Synuclein Sampling Study (S4). Immunohistochemical Method and Histopathology Judging for the Systemic Synuclein Sampling Study (S4). J Neuropathol Exp Neurol. 2018 Aug 13. doi: 10.1093/jnen/nly056.

Visanji NP, Mollenhauer B, Beach TG, et al. Systemic Synuclein Sampling Study (S4). The Systemic Synuclein Sampling Study: toward a biomarker for Parkinson’s disease. Biomarkers in Medicine. 2017;11(4):359-368.

• Beth Harper and David Zuckerman. “Critical Success Factors for Planning for Site Selection and Patient Recruitment Planning.” BioExecutive International (2006).

The MJFF Research Engagement Team includes James Gibaldi , MS, Associate Director; Tara Hastings , Senior Associate Director; Catherine M. Kopil , PhD, Director; Bernadette Siddiqi , MA, Associate Director; and Michelle Whitham , Research Partnerships Officer; all at The Michael J. Fox Foundation in New York, NY. To contact the MJFF Research Engagement Team, email: [email protected]

MJFF would like to acknowledge the following individuals for their contribution to the research presented in this case study: Lana Chahine, MD (Co-PI), Sherri Mosovsky, Brit Mollenhauer, MD, PhD (Co-PI), Danna Jennings, MD (ex oficio), John Seibyl, MD, Vanessa Arnedo, Lindsey Riley, Kuldip Dave, PhD,Tatiana Foroud, PhD, Thomas Beach, MD, Charles Adler, MD, PhD, Chris Coffey, PhD, Dixie Ecklund, RN and the Systemic Synuclein Sampling Study (S4); and Sarah Berk, MPH.

Faint Hopes of Dispelling the Fog Around Cross-Border Trials in Europe

A forum of prominent industry stakeholders highlights the challenges for patients in navigating the multiple legal, regulatory, and ethical barriers to cross-border participation.

What are the Most Common Barriers to Community Cancer Centers Engaging in Clinical Trials?

Survey study assessed 58 cancer centers in the United States to identify their greatest challenges.

Do Assigned Roles in Clinical Trials Affect Patient-Reported Outcomes?

Analysis of randomized clinical trial for migraine sought to determine whether there were differences between outcomes from patients assigned to roles and those as healthy controls.

Using Patient Reported Outcomes in Dermatology Trials

In part 3 of this video interview with ACT editor Andy Studna, Melissa Mooney, director, eCOA sales engineering, IQVIA sheds light on the unique challenges of dermatology trials and how clinical outcome assessments can be implemented in them.

Enabling Evidence-Based Study Endpoint Selection

Harnessing advances in digital health technologies for a more precision-measured approach.

Navigating a Digital-Era Shift to Patient-Mediated Research

The potential of personal health records to bridge the gap between research and care.

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Site Selection Analysis

Site selection analysis is a critical process that involves a comprehensive assessment of potential locations for a business operation, such as a new store, manufacturing plant, or office. It entails gathering and analyzing various factors, such as market socioeconomics, competition, transportation and infrastructure, labor availability, real estate costs, and regulatory environment. By conducting this analysis, businesses can make informed decisions about where to establish or expand their operations while considering the strengths and weaknesses of each potential area.

Commercial property analysis in a broad spectrum of fields

Quick service restaurants, drop-off / pick-up points & lockers, distribution centers, retail stores, ev charging points, shopping malls & centers, apparel & fashion, franchise chains, retail site selection (pharmacy location case).

Site selection tools are valuable resources for identifying suitable locations for retail points, including pharmacies. In this case study, we analyzed a range of criteria, such as:

• Location footfall analysis
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• Average income per capita
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• Competitors analysis (distance to competitors)

By considering these factors, we were able to determine the most favorable location for the pharmacy, which would maximize its potential for success.

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Choosing the right location for a restaurant is crucial for its success in a highly competitive industry. Site location analysis plays a significant role in maximizing footfall, capturing a target market, and leveraging existing customer overflow from nearby establishments. With the help of Kontur technology and geospatial data analysis, finding the ideal spot for a restaurant becomes a data-driven process, minimizing risks and optimizing logistics.

In this case, we analyzed the criteria:

• Population density
• Competitors analysis

This research enables the rapid identification of areas with a high population density that are currently underserved by competitors. More about Case Study

Industrial site selection (Solar farm site suitability)

Industrial site selection tools are crucial for identifying suitable locations for solar farms. To optimize energy generation and minimize risks, we analyze various criteria, such as:

• Solar irradiation
• Elevation slope
• Proximity to power lines and substations
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When performing a site selection analysis with Kontur, you can expect to receive a comprehensive report that provides insights and recommendations on the optimal location for your business operations.

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A site selection analysis may also include a competitive investigation that analyzes the strengths and weaknesses of other businesses within the area, as well as any potential threats or opportunities.

Location information

A location selection analysis typically incorporates a detailed investigation of location-based data such as covered population, demographics, competition, labor accessibility, and infrastructure.

Recommendations

After researching, a report will suggest the most suitable places for your company’s work, considering your specific requirements and objectives.

Example: A medium-sized chain of fast-food restaurants was looking to optimize and expand their presence in the city. After analyzing a set of their current locations and the ones available for acquisition, Kontur provided a ranked list with what-if analysis that allowed the chain to open a new location in the non-covered part of the city, close one of two nearby locations that were essentially fighting for the same audience, and reject an offer to acquire a restaurant that had looked good in the offer papers but did not really add any customers to the chain.

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When conducting site selection analyses, potential threats to business operations, such as environmental risks in a specific location, are also taken into account.

In general, a site selection investigation can provide valuable insights and data-driven recommendations that can assist you in making informed decisions about where to establish or extend your business operations.

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Site selection by using the multi-criteria technique-a case study of Bafra, Turkey

Affiliations.

• 1 Department of Architecture and Urban Planning, Samsun University, Kavak Vocational School, Samsun, Turkey.
• 2 Faculty of Engineering and Architecture, Department of Landscape Architecture, Kastamonu University, Kastamonu, Turkey. [email protected].
• 3 Faculty of Forestry, Department of Forest Engineering, Kastamonu University, Kastamonu, Turkey.
• 4 Faculty of Engineering and Architecture, Department of Environmental Engineering, Kastamonu University, Kastamonu, Turkey.
• PMID: 32865639
• DOI: 10.1007/s10661-020-08562-1

Population growth, which is the main source of the biggest problems of the world today, combined with migration from rural areas to urban centers, causes the urban centers to be even more concentrated. This necessitates the opening of new residential areas in many city centers, but new residential areas are mostly determined by the decisions of local authorities, who may not base their decisions on scientific data. With the wrong area selection, ordinary natural events can be potentially catastrophic. Such events can result in large numbers of casualties and material damage every year. In this study, an example of applying a method for location selection using various parameters has been realized. The study focuses on Bafra, Turkey (the study area). Risk maps were created in terms of floods and overflows; maps of regions and high-voltage power transmission lines that enjoy a protected area status; and maps of regions in terms of biocomfort suitability. As a result of the evaluation made according to these criteria, it is calculated that only 1.96% of the total working area is suitable for use as a residential area. In relevant literature studies, it was observed that the studies related to the selection of residential areas were carried out only depending on a single standard or criterion. Some suggested biocomfort, and others used vulnerability to risks such as landslide, flood, and earthquakes as their main principle. Studies based on multi-criteria were generally used for purposes such as solid waste site selection and determination of the road routes. The study aims to shed light on the multi-criteria method in an attempt to standardize it in regional planning studies and to inspire similar studies in which different criteria can be used to achieve the maximum efficiency.

Keywords: Bafra; Biocomfort; Flood; Overflow; Site selection.

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GIS-based MCDM Approach for Wind Farm Site Selection - A Case Study

Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, 5 Iroon Polytechniou, 15780 Athens, Greece

Department of Surveying and Geoinformatics Engineering, University of West Attica, Ag. Spyridonos Str., Egaleo, 12243 Athens, Greece

† The authors contributed equally to this work.

Academic Editor: Andrés Elías Feijóo Lorenzo

Collection:   Wind Energy

Received: May 08, 2021 | Accepted: July 30, 2021 | Published: August 17, 2021

Journal of Energy and Power Technology 2021 , Volume 3, Issue 3, doi: 10.21926/jept.2103039

Recommended citation: Feloni E, Karandinaki E. GIS-based MCDM Approach for Wind Farm Site Selection - A Case Study. Journal of Energy and Power Technology 2021 ; 3(3): 039; doi:10.21926/jept.2103039.

© 2021 by the authors. This is an open access article distributed under the conditions of the Creative Commons by Attribution License , which permits unrestricted use, distribution, and reproduction in any medium or format, provided the original work is correctly cited.

The objective of this study is the development and implementation of a geographic information system (GIS)-based methodology to determine suitable sites for wind farms by using multicriteria decision making (MCDM) techniques. Many eligibility criteria are considered in this approach, such as GIS analysis is performed in combination with MCDM techniques, both for the criteria weighting process (using the analytical hierarchical process) and for the standardization/classification of the criteria values into a common scale, in order to finally apply the weighted linear combination technique. The MCDM output is a suitability map, which provides the spatial distribution of the final score, showing zones ranging from non-suitable locations to highly suitable areas after categorizing them into five classes. Furthermore, three scenarios regarding the criteria selection and their hierarchy are investigated: technical, techno-economic, and techno-economic-environmental. For each of these scenarios, different criteria are assigned different levels of importance, and the corresponding results are compared and evaluated. The proposed approach is presented through a case study for the regional unit of Chania (Crete Island, Aegean Sea, Greece), for which the necessary geospatial data were collected, created, and processed. This analysis highlights the capabilities of GIS in performing site selection for onshore wind farms while considering the regulations, legislations, and other constraints.

Graphical abstract

GIS; multicriteria analysis; site selection; wind turbines; wind farm; Crete; Chania

1. Introduction

1.1 general.

1. Boyle R. Global trends in sustainable energy investment 2008: Analysis of trends and issues in the financing of renewable energy and energy efficiency. Nairobi: United Nations Environment Programme; 2008. Available from: <a href="https://wedocs.unep.org/handle/20.500.11822/8821">https://wedocs.unep.org/handle/20.500.11822/8821</a>.

2. Bertsiou MM, Theochari AP, Baltas E. Multi‐criteria analysis and geographic information systems methods for wind turbine siting in a North Aegean island. Energy Sci Eng. 2021; 9: 4-18. [ CrossRef ]

3. Directive 2001/77/EC of the European Parliament and of the Council of 27 September 2001 on the promotion of electricity produced from renewable energy sources in the internal electricity market. Official Journal of the European Union; 2001. Available from: <a href="https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32001L0077&from=en">https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32001L0077&from=en</a>.

4. Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the promotion of the use of energy from renewable sources and amending and subsequently repealing Directives 2001/77/EC and 2003/30/EC. <a name="OLE_LINK6">Official Journal of the European Union</a>; 2009. Available from: <a href="http://www.nezeh.eu/assets/media/fckuploads/file/Legislation/RED_23April2009.pdf">http://www.nezeh.eu/assets/media/fckuploads/file/Legislation/RED_23April2009.pdf</a>.

5. Directive (EU) 2018/2001 of the European Parliament and of the Council of 11 December 2018 on the promotion of the use of energy from renewable sources. Official Journal of the European Union; 2018; 21.12.2018. Available from: <a href="https://eur-lex.europa.eu/eli/dir/2018/2001/oj">https://eur-lex.europa.eu/eli/dir/2018/2001/oj</a>.

6. Capizzi F, Das A, Dauwe T, Moorkens I, Juhana R. Renewable energy in Europe—2019. Belgium: European Environmental Agency-European Topic Centre on Climate Change Mitigation and Energy; 2019; ETC/CME 2019/8.

7. Meschede H, Holzapfel P, Kadelbach F, Hesselbach J. Classification of global island regarding the opportunity of using RES. Appl Energy. 2016; 175: 251-258. [ CrossRef ]

8. Costoya X, DeCastro M, Santos F, Sousa MC, Gómez-Gesteira M. Projections of wind energy resources in the Caribbean for the 21st century. Energy. 2019; 178: 356-367. [ CrossRef ]

9. Nezhad MM, Shaik RU, Heydari A, Razmjoo A, Arslan N, Astiaso Garcia D. A SWOT analysis for offshore wind energy assessment using remote-sensing potential. Appl Sci. 2020; 10: 6398. [ CrossRef ]

10. Nezhad MM, Neshat M, Groppi D, Marzialetti P, Heydari A, Sylaios G, et al. A primary offshore wind farm site assessment using reanalysis data: A case study for Samothraki island. Renew Energy. 2021; 172: 667-679. [ CrossRef ]

11. Energypedia for collaborative knowledge exchange on renewable energy, energy access, and energy efficiency topics in developing countries: Greece Energy Situation [Internet]. Energypedia; 2020 [cited date 2021 April 11]. Available from: <a href="https://energypedia.info/wiki/Greece_Energy_Situation">https://energypedia.info/wiki/Greece_Energy_Situation</a>.

12. Katopodis T, Vlachogiannis D, Politi N, Gounaris N, Karozis S, Sfetsos A. Assessment of climate change impacts on wind resource characteristics and wind energy potential in Greece. J Renew Sust Energy. 2019; 11: 066502. [ CrossRef ]

13. Hansen HS. GIS-based multi-criteria analysis of wind farm development. Proceedings of the ScanGIS 2005: 10th Scandinavian Research Conference on Geographical Information Science;2005 June 13-15; Stockholm, Sweden. Parramatta, New South Wales: Department of Planning and Environment.

14. Manchado C, Otero C, Gómez-Jáuregui V, Arias R, Bruschi V, Cendrero A. Visibility analysis and visibility software for the optimisation of wind farm design. Renew Energy. 2013; 60: 388-401. [ CrossRef ]

15. Felber G, Stoeglehner G. Onshore wind energy use in spatial planning—a proposal for resolving conflicts with a dynamic safety distance approach. Energy Sustain Soc. 2014; 4: 1-8. [ CrossRef ]

16. Musselman A, Thomas VM, Boland N, Nazzal D. Optimizing wind farm siting to reduce power system impacts of wind variability. <a name="OLE_LINK7">Wind Energy.</a> 2019; 22: 894-907.

1.2 Case Study

The purpose of this study is to present an integrated GIS-based MCDM methodology for evaluating eligible sites for wind farm installation by considering numerous design criteria and examining various combinations of these criteria. Three scenarios regarding suitable locations determination, namely technical, techno-economic, and techno-economic-environmental, are investigated to highlight the influence of both the selected set of design criteria and the criteria weighting that is applied. The results revealed that, apart from the spatial constraints, site selection strongly depends on which criteria are chosen. Criteria weighing is also decisive in the MCDM technique. The idea behind this approach is to provide a flexible GIS-based decision making scheme through the implementation of different scenarios that consider sets of wind farm design criteria that are based on easily accessible data. This framework enables decision-makers to evaluate alternatives that best fit their priorities. The need for research and applications relevant to wind energy development is always important, especially as it concerns the NIIs’ targets to achieve energy independence.

Chania, one of the four regional units of the island of Crete (Aegean Sea, Southern Greece), was selected as a case study. It has a population of 156585, and tourism is the most important economic activity that continues to grow. Chania borders only the regional unit of Rethymno to the east, while it is bounded in the north by the Cretan Sea and by the Mediterranean Sea in the west and south. The regional unit also includes the southernmost island of Europe: Gavdos Island. The topographical relief of the region, the existence of several settlements, monuments and protected areas, the road network of varying density, and other infrastructure, in combination with the availability of the necessary spatial data, make Chania an ideal case study to present the methodological framework and, at the same time, to incorporate the national legislation in the analysis.

2. Materials and Methods

2.1 mcdm scheme.

17. Eastman JR, Kyem PA, Toledano J. A procedure for multi-objective decision making in GIS under conditions of conflicting objectives. Proceedings of <a name="OLE_LINK8"></a><a name="OLE_LINK9">the Fourth European Conference on Geographic Information Systems</a>; 1993 March 29-April 1; Genoa, Italy.

Figure 1 Main steps of the decision-making process.

As shown in Figure 1 , the GIS-based MCDM process for wind farm site selection can be divided into several stages. After the determination of the design criteria, which are analyzed in the next paragraph, the hierarchy among them must be defined to calculate their weights. All criteria are transformed into standardized values and are combined using the weighted linear combination (WLC) method, a widely used method that is based on the factors’ weighted aggregation, to finally create a suitability map.

18. Saaty TL. A scaling method for priorities in hierarchical structures. J Math Psychol. 1977; 15: 234-281. [ CrossRef ]

19. Feloni EG, Karpouzos DK, Baltas EA. Optimal hydrometeorological station network design using GIS techniques and multicriteria decision analysis. J Hazard Toxic Radioact Waste. 2018; 22: 04018007. [ CrossRef ]

20. Rehman S, Khan SA. Multi-criteria wind turbine selection using weighted sum approach. Int J Adv Comput Sci Appl. 2017; 8: 128-132. [ CrossRef ]

21. Ali Y, Butt M, sabir M, Mumtaz U, Salman A. Selection of suitable site in Pakistan for wind power plant installation using analytic hierarchy process (AHP). J Control Decis. 2018; 5: 117-128. [ CrossRef ]

22. Al-Shabeeb AR, Al-Adamat R, Mashagbah A. AHP with GIS for a preliminary site selection of wind turbines in the North West of Jordan. Int J Earth Sci. 2016; 7: 1208. [ CrossRef ]

23. Malczewski J. GIS and multicriteria decision analysis. Hoboken, New Jersey: John Wiley & Sons; 1999.

\[ F S=\sum W i X i \tag{1} \]

where W is the weighting parameter, X is the value parameter, and FS is the FS (suitability score) for each scenario.

In case there are Boolean constraints as well, the aforementioned equation should be modified as follows:

\[ F S=\sum W i X i \times \prod C i \tag{2} \]

where C corresponds to the Boolean constraints, such as buffer zones in which wind turbine installation is not permitted.

24. Voogd JH. Multicriteria evaluation for urban and regional planning. Eindhoven, North Brabant, Netherlands: Eindhoven University of Technology; 1982.

\[ x i=\frac{F V i-F V m i n}{F \operatorname{Vmax}-F V \min } \times S R \tag{3,a} \]

\[ x i=1-\frac{F V i-F V \min }{F V \max -\text { FVmin }} \times S R \tag{3,b} \]

For linear transformation of each criterion into the range of ( 0,1), the choice between Equations (3.a) and (3.b) depends on whether the optimum value is the maximum or the minimum value of the criterion, respectively. In the GIS-based MCDM approach, this procedure is applied for each factor separately by using map algebra in a GIS environment. Furthermore, when factor values are of district types (i.e., categories, or a group of values or specific values), then either classification or reclassification is performed to transform the values to the same scale of (0,1). Based on the abovementioned steps, a decision is derived from the assessment of the composed suitability, that is, the degree to which a location belongs to a suitable or non-suitable set.

2.2 Design Criteria

25. SFSPSD-RES. Special Framework for Spatial Planning and Sustainable Development for Renewable Energy Resources. OGG 2464B′/2008; The Official Government Gazette: Athens, Greece, 2008. (In Greek).

a. Exclusion of areas demarcated for absolute nature protection that belong to the Natura 2000 network in accordance with Directive 2006/613/EC of the European Parliament: The shapefile regarding the margins of these areas is transformed into a raster file with code “0” for Natura areas and code “1” for the remaining locations of the regional unit of Chania. As previously described, code “1” denotes the cells in the raster analysis where wind farm installation is allowed.

b. Buffer zone of 1000 m from each town/settlement boundaries: This criterion is used in two ways: as a constraint to prohibit wind farm installation within a 500-m radius from each settlement and as a factor to express the need for an installation not far from the settlements for which the electricity requirements must be met as the closer the wind farms, the lower the energy loss during transmission.

c. Buffer zone of 500 m from World Heritage Sites and other monuments of major importance (e.g., monasteries), as well as from the delimited archaeological sites for protecting the cultural heritage: According to the national legislation, construction activities must be avoided near monuments so as not to change their field of view.

d. Buffer zone of 300 m from existing road networks as there is a need to easily access the road network during construction and operation; however, a neighboring area should be excluded to avoid nuisance.

Accordingly, to proceed with the implementation of the MCDM for the evaluation of eligible sites for wind farm installation, the following factors are introduced:

a. Elevation (F1): Wind potential is expected to be higher at higher altitudes; thus, this criterion shields the desire to maximize the produced energy. To express this factor, a digital elevation model provided by the National Cadastre & Mapping Agency S.A. of Greece was used. The dataset is characterized by a pixel size of 5 × 5 m, a geometric accuracy RMSE of ≤2.00 m, and an absolute accuracy of approximately 3.92 m for a 95% confidence level.

b. Slopes (F2): This is a technical factor as installing wind turbines in flat locations or in areas with mild surface slope is preferable. The raster dataset regarding slopes is derived from the first factor by using GIS techniques.

c. Wind potential (F3): To ensure maximum wind power capacity, high wind potential is required in order to maximize the energy generated by the wind turbines. The wind data is obtained from the Center for Renewable Energy Sources and Saving, which is a Greek national entity for the promotion of RESs, rational use of energy, and energy conservation. The available dataset provides the wind potential based on the average annual wind speed (in meters per second) at the height of 80 m.

26. CORINE land cover [Internet]. Copernicus; 2018 [cited 2021 April 15]. Available from: <a href="https://land.copernicus.eu/pan-european/corine-land-cover/clc2018">https://land.copernicus.eu/pan-european/corine-land-cover/clc2018</a>.

27. OpenStreetMap data for Greece [Internet]. Geofabrik Downloads; [cited 2021 March 10]. Available from: <a href="http://download.geofabrik.de/europe/greece.html">http://download.geofabrik.de/europe/greece.html</a>.

Figure 2 Buffer zones/areas of exclusion for each constraint.

Figure 3 Factors F1-F8 (standardized values).

Table 2 Values for F8 factors depending on the type of land cover.

2.3 Scenarios

In this study, the selection of the design criteria is based on the current legislation and on criteria that are commonly introduced in relevant applications. To highlight the influence of the selected criteria on suitability mapping, three scenarios are investigated. These scenarios are expressed through different sets as well as through different hierarchies among the selected criteria. In the MCDM technique, first, the collection and analysis of geospatial data, determination of the constraints, and standardization of the criteria are performed. Then, the criteria weights are calculated using the AHP method, followed by the composition of the criteria in order to produce a resulting map for each scenario.

The current methodology employs a hierarchical model to implement the problem, allowing pairwise comparisons among the criteria that are selected for each scenario, as shown in Table 3 . This table presents the pairwise comparison according to the fundamental scale of Saaty, the corresponding metric for evaluating the performance, and the factors’ weights resulting from the application of the method for each scenario.

Table 3 Pairwise comparison matrices and criteria weights for each scenario.

As shown through the values of the factors’ weights, the first scenario (S1) is a technical one that focuses on the criteria contributing to the output based on the technical specifications of a wind farm. In addition, the wind potential (which is one of the priorities in all scenarios), surface slope, and elevation are used to describe the technical scenario. The second one (S2) is the techno-economic scenario, wherein three additional factors are introduced in the WLC: distance from roads, settlements, and airports. Finally, the third scenario (S3) is a combination of technical, economic, and environmental factors; here, the focus on the consideration of all the criteria, but relatively higher importance is attributed to the criterion of distance from airports.

3. Results and Discussion

The objective of the present case study is to combine the GIS and MCDM techniques for identifying suitable sites for wind farm installation after considering a set of design criteria. This analysis shows that the degree of suitability is affected by various aspects, such as the criteria that are selected, the standardization/classification method that is applied to transform the values of each factor to a common scale for all criteria, and the weights that are attributed to each criterion, which is one of the most important steps in MCDM. Although each case study is unique and requires different criteria to be set (for instance, as part of national legislation or in accordance with specific technical requirements), the proposed framework describes the way various criteria can be introduced in a GIS-based MCDM approach. Further, for comparison, three scenarios are described and compared.

Figure 4 shows the suitability maps that are the output of the WLC among factors considered in the three scenarios ( Table 3 ). As expected, the number of criteria and their hierarchy, which influences the calculated weights, control the FS spatial distribution. How the suitability maps can be interpreted is directly linked to the FS scale; however, the classification among FS values in suitability zones influences the suitability pattern. FS takes values between 0 and 1, in accordance with the same scale of standardized factors; as such, areas with an FS value of 1 and 0 are characterized as the most suitable and non-suitable areas, respectively. For illustration purposes, five classes (suitability zones) are created:

The first one (zone A; non-suitable locations) corresponds to locations with constraints, i.e., areas that are excluded from the analysis (e.g., Natura network) or buffer zones (e.g., forests). As shown in Table 4 , the total area for zone A remains the same in all examined scenarios.

Figure 4 Suitability maps for the three scenarios.

Table 4 Percentage area per suitability zone for the three scenarios.

The other zones (i.e., B, C, D, and E) are classified to correspond to low, moderate, high, and very high suitability. However, there is no scenario indicating extended areas of very high suitability; only limited areas are defined in the technical and techno-economic scenarios. All scenarios could identify appropriate sites for wind farm installation in the eastern part of the regional unit of Chania, in a mountainous region that is characterized by rich wind potential; further, all criteria regarding proximity are satisfied.

The major scientific problem underlying this analysis is that the different concepts that are investigated through these three scenarios lead to different results. The integrated use of GIS and MCDM for RES planning and development plays an important role in providing guidelines regarding spatial planning and site evaluation, especially in the case of extensive field exploration. The capabilities of this approach include (i) the ability to combine as many criteria as required and (ii) the flexibility in attributing hierarchy among the criteria, depending on the objectives of each case study.

4. Conclusions

In this study, a GIS-based MCDM technique is proposed for assessing the degree of suitability for wind farm installation in the regional unit of Chania. The proposed technique considers restrictions, national legislations, and other factors; these factors are selected and weighted based on three scenarios that are introduced to investigate the method’s flexibility and capabilities in optimal site selection.

The nature of the design criteria, the number of criteria, and their hierarchy are the three factors that control the resulting suitability pattern. However, the GIS-based MCDM technique is vital in relevant applications, especially for preliminary suitability assessment. The decision-makers set the spatial context, and the suitability map denotes the optimal positions. The final selection from among areas with high scores is linked to other aspects, such as the available space and the distance from any points of interest. Regarding suitable areas found in the case study for the regional unit of Chania, many suitable locations with adequate areas and best locations in terms of wind resources were observed high in the mountains in large open fields; however, the spatial extent and FSs of the best areas varied for different scenarios.

This paper provides an overview of the capabilities and limitations of relevant techniques in decision-making regarding wind farm site selection; it also highlights the variation in resulting patterns when different concepts are examined. Future research on design criteria hierarchy and weighing will help in evaluating different scenarios and in extending the guidelines provided for criteria selection and hierarchy. The concept of suitability analysis introduced herein will further contribute to the evaluation of the degree of suitability of sites to changes in the criteria. Finally, further analysis of the additional spatial criteria introduced in the proposed method will allow analyzing their potential impact on the resulting suitability score describing the best locations.

Author Contributions

E.F. and E.K. conceived of the presented idea. E.K. planned and carried out the computations. E.F. verified the analytical methods, encouraged E.K. to investigate several scenarios and supervised the findings of this work. E.F. wrote the initial version of the manuscript. Both authors discussed the results and contributed to the final manuscript.

Competing Interests

The authors declare that there is no conflict of interest.

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Please note you do not have access to teaching notes, development of a dedicated process simulator for the digital twin in apparel manufacturing: a case study.

International Journal of Clothing Science and Technology

ISSN : 0955-6222

Article publication date: 16 May 2024

The purpose of this study is to introduce a dedicated simulator to automatically generate and simulate a balanced apparel assembly line, which is critical to the digital twin concept in apparel manufacturing. Given the low automation level in apparel manufacturing, this is a first step toward the implementation of a smart factory based on cyber-physical systems.

Design/methodology/approach

The mixed task assignment algorithm was implemented to automatically generate a module-based apparel assembly line in the developed simulator. To validate the developed simulator, a case study was conducted using process analysis data of technical jackets obtained from an apparel manufacturer. The case study included three scenarios: calculating the number of workers, selecting orders based on factory capacity and managing unexpected worker absences.

The developed simulator is approximately 97.2% accurate in assigning appropriate tasks to workstations using the mixed task assignment algorithm. The simulator was also found to be effective in supporting decision-making for production planning, order selection and apparel assembly line management. In addition, the module-based line generation algorithm made it easy to modify the assembly line.

Originality/value

This study contributes a novel approach to address the challenge of low automation levels in apparel manufacturing by introducing a dedicated simulator. This dedicated simulator improves the efficiency of virtual apparel assembly line generation and simulation, which distinguishes it from existing commercial simulation software.

• Task assignment algorithm
• Line balancing
• Apparel assembly line
• Automatic assembly line generation
• Apparel manufacturing
• Digital twin

Kim, M. and Kim, S. (2024), "Development of a dedicated process simulator for the digital twin in apparel manufacturing: a case study", International Journal of Clothing Science and Technology , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJCST-01-2024-0017

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Environmental Changes Are Fueling Human, Animal and Plant Diseases, Study Finds

Biodiversity loss, global warming, pollution and the spread of invasive species are making infectious diseases more dangerous to organisms around the world.

By Emily Anthes

Several large-scale, human-driven changes to the planet — including climate change, the loss of biodiversity and the spread of invasive species — are making infectious diseases more dangerous to people, animals and plants, according to a new study.

Scientists have documented these effects before in more targeted studies that have focused on specific diseases and ecosystems. For instance, they have found that a warming climate may be helping malaria expand in Africa and that a decline in wildlife diversity may be boosting Lyme disease cases in North America.

But the new research, a meta-analysis of nearly 1,000 previous studies, suggests that these patterns are relatively consistent around the globe and across the tree of life.

“It’s a big step forward in the science,” said Colin Carlson, a biologist at Georgetown University, who was not an author of the new analysis. “This paper is one of the strongest pieces of evidence that I think has been published that shows how important it is health systems start getting ready to exist in a world with climate change, with biodiversity loss.”

In what is likely to come as a more surprising finding, the researchers also found that urbanization decreased the risk of infectious disease.

The new analysis, which was published in Nature on Wednesday, focused on five “global change drivers” that are altering ecosystems across the planet: biodiversity change, climate change, chemical pollution, the introduction of nonnative species and habitat loss or change.

The researchers compiled data from scientific papers that examined how at least one of these factors affected various infectious-disease outcomes, such as severity or prevalence. The final data set included nearly 3,000 observations on disease risks for humans, animals and plants on every continent except for Antarctica.

The researchers found that, across the board, four of the five trends they studied — biodiversity change, the introduction of new species, climate change and chemical pollution — tended to increase disease risk.

“It means that we’re likely picking up general biological patterns,” said Jason Rohr, an infectious disease ecologist at the University of Notre Dame and senior author of the study. “It suggests that there are similar sorts of mechanisms and processes that are likely occurring in plants, animals and humans.”

The loss of biodiversity played an especially large role in driving up disease risk, the researchers found. Many scientists have posited that biodiversity can protect against disease through a phenomenon known as the dilution effect.

The theory holds that parasites and pathogens, which rely on having abundant hosts in order to survive, will evolve to favor species that are common, rather than those that are rare, Dr. Rohr said. And as biodiversity declines, rare species tend to disappear first. “That means that the species that remain are the competent ones, the ones that are really good at transmitting disease,” he said.

Lyme disease is one oft-cited example. White-footed mice, which are the primary reservoir for the disease, have become more dominant on the landscape, as other rarer mammals have disappeared, Dr. Rohr said. That shift may partly explain why Lyme disease rates have risen in the United States. (The extent to which the dilution effect contributes to Lyme disease risk has been the subject of debate, and other factors, including climate change, are likely to be at play as well.)

Other environmental changes could amplify disease risks in a wide variety of ways. For instance, introduced species can bring new pathogens with them, and chemical pollution can stress organisms’ immune systems. Climate change can alter animal movements and habitats, bringing new species into contact and allowing them to swap pathogens .

Notably, the fifth global environmental change that the researchers studied — habitat loss or change — appeared to reduce disease risk. At first glance, the findings might appear to be at odds with previous studies, which have shown that deforestation can increase the risk of diseases ranging from malaria to Ebola. But the overall trend toward reduced risk was driven by one specific type of habitat change: increasing urbanization.

The reason may be that urban areas often have better sanitation and public health infrastructure than rural ones — or simply because there are fewer plants and animals to serve as disease hosts in urban areas. The lack of plant and animal life is “not a good thing,” Dr. Carlson said. “And it also doesn’t mean that the animals that are in the cities are healthier.”

And the new study does not negate the idea that forest loss can fuel disease; instead, deforestation increases risk in some circumstances and reduces it in others, Dr. Rohr said.

Indeed, although this kind of meta-analysis is valuable for revealing broad patterns, it can obscure some of the nuances and exceptions that are important for managing specific diseases and ecosystems, Dr. Carlson noted.

Moreover, most of the studies included in the analysis examined just a single global change drive. But, in the real world, organisms are contending with many of these stressors simultaneously. “The next step is to better understand the connections among them,” Dr. Rohr said.

Emily Anthes is a science reporter, writing primarily about animal health and science. She also covered the coronavirus pandemic. More about Emily Anthes

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A GIS-based catastrophe approach for optimal site selection for installation of solar power plants, East Azerbaijan province case study, Iran

• Research Article
• Published: 14 May 2024

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• Marziyeh Esmaeilpour 1 &
• Mansour Kheirizadeh Arouq 2  

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Due to depletion of fossil fuels and environmental issues, renewable energy consumption is increasingly growing. Solar energy as the most abundant renewable energy source available is becoming more popular around the world. In the current study, the optimal sites for solar photovoltaic power plants in East Azerbaijan province, Northwest Iran, were investigated. A total of 17 variables were categorized into four groups: climatic, geomorphological, environmental, and access-economic. In order to integrate the variables, a model based on catastrophe theory in the context of GIS was applied. The relative importance and weight of the criteria are computed based on the internal mechanism of the catastrophic system, thus greatly reducing subjectivism and uncertainties of the decision-making process. Five optimal sites located in the western part of the province within the counties of Malekan, Bonab, Ajabshir, Shabestar, and Tabriz were identified as suitable sites for the construction of solar photovoltaic power plants, where there are ideal conditions in terms of many environmental-human variables such as high potential of solar energy, high sunshine hours, low relative humidity, suitable slope, poor vegetation, distance to protected areas, proximity to the population centers, excellent access to the roads and to the main power lines.

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The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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This paper is published as part of a research project supported by University of Maragheh Research Affairs Office, grant number 98/d/3390.

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Marziyeh Esmaeilpour

Geomorphology From University of Tabriz, Tabriz, Iran

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Marziyeh Esmaeilpour: visualization, software, formal analysis, writing—original draft preparation, writing—review and editing, funding acquisition, conceptualization, analysis and interpretation of results. Mansour Kheirizadeh: data curation, resources, formal analysis, methodology, writing—review and editing, investigation. All authors reviewed the results and approved the final version of the manuscript.

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Esmaeilpour, M., Kheirizadeh Arouq, M. A GIS-based catastrophe approach for optimal site selection for installation of solar power plants, East Azerbaijan province case study, Iran. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-33639-6

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Received : 23 December 2022

Accepted : 06 May 2024

Published : 14 May 2024

DOI : https://doi.org/10.1007/s11356-024-33639-6

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