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The New Oxford Handbook of Economic Geography

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14 How Geography Shapes—and Is Shaped by—the Internet

Shane Greenstein ([email protected]) is the MBA Class of 1957 Professor of Business Administration and co-chair of the HBS Digital Initiative at Harvard Business School. He is also co-director of the Program on the Economics of Digitization at the National Bureau ↵of Economic Research. His research spans issues of strategy, regulation, history, marketing, information systems, and organization design. He has published more than fifty journal articles on economics of enterprise IT, technological competition in computing, and the commercialization of the Internet infrastructure, and has written or edited nine books. His recent book, How the Internet Became Commercial: Innovation, Privatization, and the Birth of a New Network, traces the evolution of the Internet from government ownership to privatization to the commercial Internet, showing how interplay between government and private industry transformed the Internet. He has served as the President of the International Organization Society, and has been a member of the editorial board of Telecommunications Policy, Research Policy, and other prominent academic journals.

Chris Forman ([email protected]) is the Peter and Stephanie Nolan Professor of Applied Economics and Management at the Charles H. Dyson School of Applied Economics and Management at Cornell University. His research interests include the geography of IT use, electronic commerce, diffusion of IT innovations, and IT strategy. He has published widely on issues related to innovation in enterprise IT, including the business process innovation that accompanies enterprise IT investment within firms, as well as the strategies of enterprise IT suppliers. He currently serves as a Department Editor at Management Science, and previously served as Senior Editor at Information Systems Research.

Avi Goldfarb ([email protected]) is Ellison Professor of Marketing at Rotman School of Management, University of Toronto. He serves as Senior Editor at Marketing Science. His research focuses on understanding the opportunities and challenges of the digital economy. He has published a number of articles in areas of economics, marketing, statistics, computing, and law, and is co-editor of two books, including Economic Analysis of the Digital Economy.

Published: 05 February 2018
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The literature on the geographical implications of the Internet are reviewed, both those studying the adoption and use of the Internet, as well as those examining the Internet’s economic consequences for productivity, wealth, innovation, trade, and consumer behavior. The chapter emphasizes that the Internet reduces three key interrelated economic frictions: communication costs, transportation costs, and search costs. The impact of reducing these frictions varies across locations because it depends on three factors that vary locally: preferences, the availability of substitutes, and the availability of complements. Thus, the diffusion of the Internet benefits some locations more than others. The chapter concludes by discussing directions for future research.

Introduction

In the mid-1990s the inter-networking infrastructure and protocols of the research community became available to private users. Known colloquially as the ‘Internet’, and newly enhanced with the additional functionality of the World Wide Web, the Internet became adopted widely and spread quickly. The new network grew at an astonishing rate, and transformed economic activity across a breadth of sectors. The Internet almost defied historical precedent for combining rapid diffusion with large economic impact, generating large and impatient investments by households and businesses, and catalysing major restructuring across a large range of industries ( Greenstein, 2015 ).

What consequences did the spread of the Internet have for the geographical location of economic activity? The precise economic effects defy simple description. Offline, distance creates a variety of economic frictions and, at first glance, the Internet reduces these frictions to be more similar across locations. However, because the strength of these frictions differs across places, the marginal impact of removing them will differ. To put it plainly, the Internet has different economic consequences in distinct locations because local factors shape the impact. Specifically, the Internet reduces the importance of three interrelated location-level frictions in economic transactions: communication costs, transportation costs, and search costs.

Communication costs are lower on the Internet because it is inexpensive to communicate with others, whether they are in the same building or across the world. The cost of sending and receiving email and other forms of digital communication are the same, irrespective of the distance between the sender and receiver.

Transportation costs are lower on the Internet in two ways. Firstly, for goods that can be digitized, distribution is nearly free. Delivering physical newspapers to people’s doors requires a costly operation to transport the physical object; once digital infrastructure is in place, delivering digital news has almost zero marginal cost. Music, books, video, and other information-based goods also benefit from near-zero distribution costs. Secondly, for items that cannot be digitized, online interactions reduce the need for travel. A consumer does not need to travel when ordering an item to be shipped. Digital conferencing can reduce the need for physical travel.

The reduction in search costs on the Internet follows directly from lower transportation and communication costs. If communication costs and transportation costs fall, then it is easier to compare potential choices before making a selection. These choices can not only be physical goods, but they can also be business decisions such as outsourcing and hiring. Economic models show that lower search costs have distinct implications from the general models of lower communication and transportation costs.

We begin with the hypothesis that the Internet had a variety of effects across locations because the impact of lower communication, transportation, and search costs varies by location, depending on three main factors: local preferences, the availability of substitutes, and the availability of complements. The principal goal of this chapter is to show the manifest ways in which research has documented the scope of changes. We build on prior reviews of the interaction of online and offline, especially Forman (2014) and also Goldfarb (2012) and Lieber and Syverson (2012). Zook’s chapter in this book (Chapter 30 ) complements our approach and highlights how the Internet has affected the geography of global capital flows, reducing the importance of distance in some ways, and increasing it in others.

We begin by discussing models about whether and how the Internet might decrease the importance of geographical factors in economic activity. This is followed by an exploration of how location has affected the incentives for firms and consumers to adopt Internet technology. The following sections then, in turn, discuss the consequences of Internet adoption for the geography of wealth and productivity, innovation, consumer purchasing, and globalization and trade. We conclude with some directions for future research.

Death of Distance?

Perhaps the most commonly discussed framework is the ‘death of distance’ model popularized by Cairncross’s (1997) book of the same title and Friedman’s (2005) The World is Flat . This model assumes that the Internet is a substitute for other communication, transportation (or distribution), and search technologies. Electronic communication therefore implies that agents will rely less on offline communication channels (especially face-to-face), and substitute into digital communication. Similarly, Internet distribution replaces physical transportation of goods and Internet search replaces physical search. A key implication of these models is that cities become less important as the Internet diffuses: there is a decrease in the relative value of agglomeration benefits related to low communication, transportation, and search costs within the city.

One implicit assumption for such models is that the costs of using the electronically enabled channel will fall for all types of communication, transportation, and search equally: they do not highlight the comparative advantage that one channel may enjoy over others in certain types of communication.

In search, some attributes may be easier to appreciate online than others ( Lal and Sarvary, 1999 ; Brown and Goolsbee, 2002 ). In transportation, some goods may be easier to ship than others ( Lal and Sarvary, 1999 ). Even in communication, a large body of literature has shown the unique advantages that face-to-face interaction has over other forms of communication, 1 particularly in communicating certain kinds of tacit or ‘sticky’ knowledge (e.g. von Hippel, 1998 ). For example, while information technology (IT) may be an effective means of coordinating ongoing projects and collaborations, it may be less effective as a means of establishing new partnerships or collaborative relationships (e.g. Gaspar and Glaeser, 1998 ; Charlot and Duranton, 2006 ; Glaeser and Ponzetto, 2007 ). In this way, rich offline communication might make online communication more valuable, suggesting complementarity between face-to-face and digital communication ( Gaspar and Glaeser, 1998 ).

Adoption and Use of the Internet

Geographical variance in the adoption of IT produces variance in reduction of search, distribution, and especially communication costs. That is because adoption of the Internet relies on complementary inputs such as broadband service or expertise in implementing IT systems, which varies across locations, creating variance in the net benefits to adoption. As a result, the diffusion of the Internet did not have the same impact on all locations.

Internet Adoption Among Firms

In Forman et al. (2005) , we demonstrated that the costs and gross benefits of technology adoption may vary significantly across geographical locations, often in quite different ways. For example, the highest-value users of Internet technology may reside in rural areas and small cities, because of the potential for the Internet to reduce the costs of economic isolation. However, the costs of adoption may also be highest in such regions because of the absence of key complementary inputs such as broadband service, skilled labour, and third-party services. We showed that the net benefits of adoption for firms in 2000 were decreasing in location size for basic Internet technologies that reduced the costs of economic isolation. Further, because implementation is straightforward, the frictions associated with adoption costs do not vary much across locations. In contrast, the net benefits of adoption are increasing in location size for advanced technologies that require extensive adaptation and co-invention ( Bresnahan and Greenstein, 1996 ) to be used successfully. This is particularly true for establishments in small, single-establishment firms that are unable to rely on IT skills and other complementary inputs that may reside elsewhere in the organization and that rely on external markets to facilitate implementation of new IT ( Forman et al., 2008 ). Thus, the net benefits of Internet-enabled frontier services are frequently higher in urban areas.

Using data on domain name registrations, Kolko (2000) and Moss and Townsend (1997) also document cases in which adoption of Internet technology increases location size. Because domain name registrations are based on the business location of the registration rather than that of the Internet service provider (ISP), they can be a useful measure of the location of Internet adoption; however, they are limited because some firms have Internet but no domain name and because domain names are typically only associated with the headquarters of multi-establishment firms, which are disproportionately in larger cities ( Aarland et al., 2007 )

The availability of complements necessary for Internet adoption appear to be increasing in location size. For example, the supply of third-party outsourcing firms that are used in IT implementations are increasing in location size, as are the use of outsourcing services ( Arora and Forman, 2007 ; Ono, 2007 ). As outsourcing firms frequently play a key role in the implementation of frontier Internet services, such variance in local outsourcing supply is likely to influence adoption costs. Further, a rich literature has demonstrated variance in the supply of broadband providers and the quality of the ISPs across locations (e.g. Greenstein, 2000 ; Downes and Greenstein, 2002 ; Prieger, 2003 ; Flamm, 2005 ; Zook, 2005 ). Such variance may influence the costs of technical inputs such as broadband service and, more broadly, Internet connectivity.

IT Adoption Among Individuals

A variety of studies has shown variance in the extent of individual Internet adoption across urban and rural (and small city) regions (e.g. Hindman, 2000 ; Wellman et al., 2001 ; Mills and Whitacre, 2003 ; Agarwal et al., 2009 ). Much of the urban–rural variance appears as a result of other factors that may be correlated with location, such as income, age, and education (e.g. Hindman, 2000 ; Mills and Whitacre, 2003 ).

Even after controlling for these demographic factors, location characteristics may play an independent role on adoption because of differences in the availability of complements such as the local supply of Internet service. Owing to low fixed costs of entry and the widespread availability of entrepreneurs from related industries, dial-up ISPs quickly entered most small geographical markets relatively soon after the commercialization of the Internet, providing access at competitive rates in all but the smallest markets ( Downes and Greenstein, 2002 ; Greenstein, 2015 ). In contrast, owing to differing economics of deployment, differences in the supply of broadband providers have persisted ( Prieger, 2003 ; Flamm, 2005 ; Zook, 2005 ; Grubesic, 2012 ).

Spillovers have also likely shaped the adoption of Internet technology among individuals. For example, Agarwal et al. (2009) have shown that adoption is correlated among users in the same metropolitan statistical area. Their results support the existence of network effects, particularly among those located in regions with high housing density and those within a dense network of social interactions. The approach of Agarwal et al. (2009) builds upon earlier work by Goolsbee and Klenow (2002) , who found evidence that spillovers related to Internet use influenced the adoption of personal computers. Goldfarb (2006) also provides evidence that local spillovers influenced individual adoption of the Internet: the impact of prior university attendance on Internet use is much higher for people who attended university in the mid-1990s (and people who live with them) than for others.

Consequences of the Internet for Wealth and Productivity

A small literature has focused on the implications of IT investment for local economic outcomes, shedding light on whether the diffusion of the Internet has contributed to convergence or divergence in income. One view supporting convergence stresses that the diffusion of the Internet led to employment growth and wage gains in regions that have low populations and are economically isolated; that is, by lowering communication costs, the Internet contributes most to economic growth in regions that are not well off. An alternative view stresses divergence; that is, that the Internet disproportionately benefits regions that have large cities with highly skilled populations who already have high incomes.

Research in this area is, in part, motivated by work on the broader literature on enterprise IT that has emphasized the value of complementary labour and information inputs to achieving value from IT systems (e.g. Bresnahan and Greenstein, 1996 ; Bresnahan et al., 2002 ; Bloom et al., 2012 ). As was highlighted in the previous section, the supply of some of these complements is distributed heterogeneously across locations. The returns to IT investment may be greater in large cities owing to Marshallian externalities. In addition, theories of skill-biased technical change have suggested that IT investment is complementary with skilled labour (e.g. Katz and Autor, 1999 ; Autor et al., 2003 ), which is found in larger quantities within cities. Further, the productivity benefits of IT investment have been shown to be systematically higher for a subset of ‘IT-intensive’ industries that have typically had long-lived IT capital investments (e.g. Stiroh, 2002 ; Jorgenson et al., 2005 ), and the productivity benefits of IT investments among IT-intensive firms have been found to be particularly strong in large cities (e.g. Henderson, 2003 ). The presence of IT-intensive industries can indirectly increase the supply of other complements. As a result, firms’ response to new IT will be likely be non-uniform across regions, leading to divergence.

Forman et al. (2012) examined the relationship between investment in advanced Internet technology and local wage growth across 2743 counties in the USA. We identified a puzzle in the relationship between Internet investment and local wages. On average, the impact of advanced Internet investment on local wages is small and on employment is non-existent. However, investment in the Internet is correlated with wage and employment growth in about 6 per cent of US counties, representing 42 per cent of the US population. These counties were already well-off prior to 1995, with high income, large populations, high skills, and concentrated IT use. These well-off counties averaged 28 per cent wage growth from 1995 to 2000 (unweighted by population), while all counties averaged just 20 per cent wage growth over this period. The Internet exacerbates regional wage inequality, explaining over half the additional wage growth experienced by the 6 per cent of counties that were already well-off. This is the pay-off puzzle: only a few counties experienced wage growth, despite widespread Internet investment.

Dranove et al. (2014) demonstrate a similar result in exploring the effects of health information technology on hospital costs between 1996 and 2009. Hospitals in IT-intensive locations experienced declines in costs three years after adoption, while hospitals in other locations experienced an increase in costs even after six years. These differences appear to be driven by agglomeration of IT employment in other hospitals.

There is less empirical evidence on convergence. One example is Kolko (2007) , who finds that service industries that trade with one another are more likely to collocate within the same zip code if IT use is high, but less likely to collocate in the same state if IT use is high. One potential interpretation of this result is that IT reduces the costs of transporting services output over long distances.

Formal theory has provided some insights into other ways that the Internet may reshape the geography of production through changes to communication patterns. For example, Gaspar and Glaeser (1998) present a model and some empirical evidence that show that Internet-enabled communications may not only substitute for some face-to-face interactions but also increase the demand for face-to-face interactions that cannot easily be performed electronically. This can make cities even more appealing, particularly for industries or activities where ideas are complex and difficult to communicate electronically.

Lower communication costs may enable some industries like manufacturing to move out of cities. However, innovative ideas-producing industries that involve frequent exchange of complex knowledge will likely continue to rely on face-to-face interactions in cities, and these industries will likely continue to be agglomerated ( Glaeser and Ponzetto, 2007 ). Such industry-level variance has been used to explain the resurgence of cities with high concentrations of innovative industries, like New York and San Francisco, and for the decline of traditional manufacturing centres, such as Detroit and Cleveland. Similarly, the costs of performing headquarters and support functions have historically been lower in cities owing to the propensity of such functions to outsource business services ( Aarland et al., 2007 ), while some production activities can take place outside of big cities. If Internet-enabled communications costs fall so may the costs of managing multiple firm locations. As a result, we may observe both an increasing number of multi-unit organizations and a specialization of cities by functional area ( Duranton and Puga, 2005 ).

Much work has investigated the role of upgrades to Internet access in the form of broadband technologies. Several papers have also investigated the relationship between local broadband availability and local economic outcomes (e.g. Gillett et al., 2006 ; Crandall et al., 2007 ; Van Gaasbeck et al., 2007 ; Kolko, 2012 ). These have consistently found a positive relationship on average between local broadband availability and local employment growth. However, as Kolko (2012) notes, the majority of these studies show correlation, not causality—it is difficult to identify empirically whether broadband provision leads to employment growth or whether regions with high employment growth have more broadband providers. Further, none of these studies examine heterogeneity in outcomes in region; in particular, whether broadband availability disproportionately benefits large or small cities or urban or rural regions.

Overall, the evidence suggests Internet technology has the biggest economic effect on larger cities, although there are particular situations in which small cities and rural areas do benefit.

Consequences of the Internet for Innovation

Lower communications costs can influence the productivity of scientists, as well as shape the localization of innovation. 2 This line of research is important because understanding what influences the productivity of individual scientists is inherently important for firms and the science of science policy. Further, researchers have long observed that innovative activity is localized (e.g. Jaffe et al., 1993 ), and there is an important public policy question about the extent to which recent declines to communication costs can enable more geographically dispersed innovation (e.g. Macher and Mowery, 2008 ). If the diffusion of the Internet has differing implications for the geographical concentration of innovation compared with the geographical concentration of production, this suggests important changes in the geographical concentration of the value chain over the very long term ( Forman et al., 2015 ).

Most research in this area has examined how IT investments have influenced research productivity and collaboration patterns in the academe. Research in this area provides ambiguous predictions, reflecting not only differences in research design, but also differences in setting, ranging from economics ( Hammermesh and Oster, 2002 ; Rosenblatt and Mobious, 2004), life sciences ( Winkler et al., 2009 ; Ding et al., 2010 ), and engineering ( Agrawal and Goldfarb, 2008 ) to other disciplines ( Jones et al., 2008 ).

Several authors have examined time-series trends in research collaborations. Such research generally finds that co-authorship has risen over time across a wide variety of fields ( Hammermesh and Oster, 2002 ; Jones et al., 2008 ), and co-authorship has increasingly spanned university boundaries ( Jones et al., 2008 ) and metropolitan areas ( Hammermesh and Oster, 2002 ).

While research has found that co-authorship has increased, it has also found that the trend has been increasingly towards segmentation or ‘balkanization’ of research collaboration. If agents have preferences to collaborate with those with similar preferences and if IT lowers the costs of collaborating across distance, than the diffusion of new IT will lead to a ‘balkanization’ of communities and that within-group separation may increase even while the costs of distant collaborations decline ( Rosenblatt and Mobius, 2004 ; Van Alstyne and Brynjolfsson, 2005 ).

Research has found a direct empirical link between digital communication and the rise in research collaboration. Agrawal and Goldfarb (2008) examines how BITNET adoption influenced the likelihood of collaboration among engineering scientists in universities. They find that BITNET disproportionately increased collaboration between top-tier and middle-tier universities, particularly among those that were collocated. They argue that this result may reflect gains from trade, perhaps through the increased use of underutilized research equipment or increased specialization. Research on life scientists has also shown that investments in the Internet are associated with an increase in researcher productivity ( Winkler et al., 2009 ; Ding et al., 2010 ), and disproportionately aids researchers in lower tier institutions ( Winkler et al., 2009 ) and female scientists ( Ding et al., 2010 ).

Forman and van Zeebroeck (2012) examine commercial innovation, demonstrating that Internet adoption leads to a significant increase in the likelihood of research collaborations among inventors within the same firm in geographically dispersed locations, but no increase in collaborations among collocated inventors. That is, their data support the view that Internet adoption led to a decline in the costs of coordinating distant research.

Forman et al. (2015) examined whether the Internet increased or decreased the geographical concentration of invention. This can be framed much like the prior work on wages and productivity: convergence or divergence. The results generally favour the view that the Internet worked against the concentration of invention. In particular, while invention became more geographically concentrated over this period, this is not true for the counties that were leaders in business Internet adoption.

Consequences of the Internet for Consumer Behaviour

Electronic commerce can benefit consumers through lower prices (surveyed in Baye et al., 2006 ), lower search costs and greater product selection among online merchants ( Brynjolfsson et al., 2003 ), and better convenience (e.g. Sinai and Waldfogel, 2004 ; Forman et al., 2009 ). These benefits are likely to be particularly salient for consumers in small local markets, where the number of retailers is likely to be low and where consumers with idiosyncratic or minority preferences may be underserved (e.g. George and Waldfogel, 2003 ; Waldfogel, 2003 ). In addition, consumers with relatively unusual preferences may benefit, even if they live in densely populated areas ( Sinai and Waldfogel, 2004 ; Choi and Bell, 2011 ).

The Internet reduces distribution costs for a variety of consumer goods. If distribution is digital, as for music, news, and (increasingly) movies and books, then the marginal cost of distribution is near zero, across all locations. Even when distribution still requires shipping, these costs fall, and they likely fall more for relatively isolated individuals. They also fall more for some goods than other goods ( Ellison and Ellison, 2009 ). For example, distribution costs remain high online for big-ticket items such as automobiles (Overby and Forman, 2014 ).

The fall in distribution costs is likely to improve the welfare of economically isolated consumers. Balasubramanian (1998) structured the main consequences of lower distribution costs in his model of consumer channel choice in commodity markets. In the model, consumers trade-off between the fixed disutility costs of buying online (e.g. shipping time or the inability to view the product physically) and the transportation costs of buying online (in addition to the price differences between the two channels). A variety of empirical papers has documented the usefulness of this framework on understanding geographical patterns in online purchasing. Forman et al. (2009) show substitution between online and offline stores: when a Walmart, Barnes and Noble, or Borders store opens offline, local sales on Amazon change. Brynjolfsson et al. (2009) show substitution between online and offline channels in apparel. By lowering distribution costs, electronic commerce lowers the sum of purchase price plus transportation costs for consumers.

Lower distribution costs online also enable a wider selection of products, particularly for geographically isolated customers. Online retailers have the potential to offer a much greater product selection than what can be carried by any physical store ( Brynjolfsson et al., 2003 ; Anderson, 2006 ) and new search tools make it easier to find niche products. As a result, consumer propensity to purchase such ‘niche’ or ‘long tail’ products online is greater in online than offline channels ( Zentner et al., 2013 ) and is insensitive to local offline supply ( Brynjolfsson et al., 2009 ; Forman et al., 2009 ).

Electronic commerce can improve offline options if information about offline products is offered online. This can lead to either lower average prices or less price dispersion, or both. For example, the increasing use of electronic commerce in wholesale car auctions led to price convergence across geographically dispersed auction sites: buyers increasingly shifted from using high-price to low-price auction sites because they could more easily observe prices in other locations (Overby and Forman, 2014 ). Competition from the online channel also shifted the market structure of offline industries such as travel agencies, bookstores, and new-car dealers towards larger lower-cost establishments ( Goldmanis et al., 2009 ).

While growing evidence shows that consumers use the Internet to lower the costs of economic isolation, there also remains significant evidence that location significantly shapes how consumers behave online (see Bell, 2014 for a review). Consumers are more likely to visit websites that are hosted by firms geographically proximate to them. One reason is that a lot of online content is local ( Sinai and Waldfogel, 2004 ); further, consumer tastes are spatially correlated and vary significantly across regions (e.g. Jank and Kannan, 2005 ). As a result, consumers may self-select into websites that tailor to their preferences; this is particularly true for taste-dependent digital products such as music, games, and pornography ( Blum and Goldfarb, 2006 ). Consumers have also shown a preference to trade with others who are geographically proximate to them. Again, this is, in part, because many products are heavily taste dependent or consumed locally ( Hortacsu et al., 2009 ).

In addition to reductions in communication, distribution, and search costs, there are regulatory reasons why consumer use of the Internet varies by location. For example, Internet purchasing is higher in places with higher sales taxes ( Goolsbee, 2000 ; Ellison and Ellison, 2009 ; Anderson et al., 2010 ; Einav et al., 2014 ). Internet advertising is more expensive and more effective in places with restrictions on offline advertising ( Goldfarb and Tucker, 2011a ). Copyright can add costs to international distribution ( Aguiar and Waldfogel, 2014 ). Also, regulation of the Internet varies across locations. For example, privacy regulation and Net neutrality regulation vary across countries ( Goldfarb and Tucker, 2011b ; Lee and Wu, 2009 ).

The interplay of consumer behaviour between the online and offline worlds is particularly important to understanding mobile Internet behaviour. Consumer behaviour on the mobile Internet differs from that on the traditional fixed-line Internet: some types of search costs are higher and consumers are even more likely to browse on websites that are geographically proximate ( Ghose et al., 2013 ). Research has emphasized how mobile advertising can influence demand for local products and services ( Ghose et al., 2014 ; Luo et al., 2014 ).

Consequences of the Internet for Globalization and Trade

A wealth of anecdotal evidence has been used to argue that IT investments, by reducing coordination costs, have facilitated the globalization of economic activity (e.g. Friedman, 2005 ). However, systematic empirical evidence on the link between Internet investment and globalization is relatively rare. One exception is the work of Freund and Weinhold ( 2002 , 2004 ), who examine the association between IT investment and trade in goods and services and find that increases in web hosts are associated with an increase in goods exports and an even greater climb in services exports. They argue that the Internet will have a greater impact on trade volume in services as many services can now be transported costlessly. In other words, trade in goods is likely to increase because of lower search and communication costs, while trade in services is likely to increase even more because transportation costs also fall through digital distribution.

Recent work has sought to understand the implications of IT investments for global supply chains. Fort (2014) finds that IT investments lead to the fragmentation of production in industries where production specifications are most commonly formalized in electronic formats. However, Fort’s evidence also suggests that electronic communication lowers the coordination costs of production fragmentation disproportionately more for domestic than for foreign sourcing. Fort argues this latter result may reflect a need for skilled suppliers. Country-level IT adoption is also associated with an increase in north–south vertical mergers, particularly for industries with low ‘routineness’; this may suggest that IT investments will facilitate monitoring of distant enterprises ( Basco and Mestieri, 2014 ).

While systematic evidence linking IT investments to changes in trade or the structure of global supply chains are relatively rare, researchers have claimed that time series trends in each of these variables may be influenced, in part, by IT investments. For example, Blinder (2006) and others have argued that what can be traded has been steadily increasing over time. While traditionally any good that can be shipped and placed in a box has been considered tradable, IT has changed the sets of services that can be delivered at a distance ( Blinder, 2006 ).

Some services work cannot ever be done at a distance—hairdressers need hair to cut and janitors cannot be in another continent to do their job ( Blinder, 2006 ). However, there are a number of occupations at the margin, and recent research has attempted to identify which kinds of work presently represent tradable services or soon will. The methods used to answer this question vary widely. One approach is to examine the geographical concentration of an industry to identify whether its outputs are likely to be tradable ( Jensen and Kletzer, 2005 ). A more common method has been to construct an index of whether work is routine or can be codified using descriptions of work from references like the US Dictionary of Occupational Titles. Work that is more routine or can be more easily codified can, for example, be more easily performed at a distance (e.g. Mithas and Whitaker, 2007 ; Autor and Dorn, 2013 ; Basco and Mestieri, 2014 ). Overall, research in this area demonstrates that many occupations and forms of work remain non-tradable, although the margin between tradable and non-tradable is likely changing over time, and IT investments are increasing the potential for transactions to span great distances and country boundaries.

Directions for Future Research

This chapter has summarized the literature on the importance of geography in understanding the causes and consequences of the diffusion of the Internet. It has emphasized that the Internet reduces three key interrelated economic frictions: communication costs, transportation costs, and search costs. The impact of reducing these frictions varies across locations. It depends on three factors that vary locally: preferences, the availability of substitutes, and the availability of complements. Thus, a reduction in communications cost benefits some locations more than others. Overall, the existing literature that stresses ‘the death of distance’ is too simplistic; offline differences across locations leads to heterogeneous impacts of Internet technology, and many offline factors mediate how the diffusion of the Internet shapes the location of economic activity.

This literature is far from complete. A number of open questions remain, particularly with respect to the broad economic consequences of Internet adoption across geographies and with respect to how these consequences might change as the technology changes.

In terms of the broad geographical consequences, one key open puzzle is why the Internet has only increased economic outcomes in a small number of locations. Despite the promise of a death of distance, the Internet has thus far exacerbated differences in income across locations. One promising avenue to study this puzzle comes from microdata on the wage profiles of individuals, either from US census data ( Vilhuber and McKinney, 2014 ) or from online resume and career histories (e.g. Agrawal and Tambe, 2016 ). A second open puzzle is how the Internet has affected trade in services. The key challenge to study this question is that services often do not cross borders in easily measured ways. In the absence of systematic internationally representative data, one promising avenue might be to do case studies of particular firms (or even government agencies) that provide access to their email and web browsing. One source of such information is legal proceedings, as with the release of the Enron emails. A related approach identifies the role of strong and weak institutions for policing piracy across countries ( Athey and Stern, 2015 ).

Technological change will also continue to provide new questions. Much of the research in this chapter has focused on the causes and economic consequences of broadband adoption. Smartphones and the mobile Internet are already changing the geography of consumer behaviour and are likely to affect firms and non-profits. Social media provide the opportunity to meet, and keep in touch with, friends from around the world, yet most social networks remain highly local ( Agrawal et al., 2015 ). Zook’s chapter (Chapter 30 ) provides case studies of how new technologies such as Bitcoin can rework global capital flows.

We foresee the prospects for research about how new applications that make use of broadband, smartphones, and social media affect economic activity. New Internet applications do not diffuse evenly across geography and such uneven diffusion provides quasi-natural experiments for understanding the impact of the Internet on local economic activity. For example, Craigslist did not deploy across US geography all at once, and that enabled research to analyse its impact on newspaper advertising ( Seamans and Zhu, 2014 ). In a similar spirit, ride-sharing services such as Uber build off the use of smartphones, and have not entered every city at the same time, enabling research to examine how its deployment affects activities, such as drunk driving ( Greenwood and Wattal, forthcoming ).

A variety of other technologies are on the horizon that may also someday influence the location of economic activity. These technologies will provide new puzzles to study and deeper understanding of the interaction between digital communication and economic geography.

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Lower search costs also have important implications for innovation, but less so for the geography of innovation. In particular, McCabe and Snyder (2015) show that lower online search costs through online referencing and JSTOR lead to increased citations to past work.

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Research Article

Does Tobler’s first law of geography apply to internet attention? A case study of the Asian elephant northern migration event

Roles Conceptualization, Data curation, Formal analysis, Writing – original draft, Writing – review & editing

Affiliation School of Geographical Sciences, Fujian Normal University, Fuzhou, China

Roles Data curation, Formal analysis, Methodology

Roles Funding acquisition, Project administration, Supervision

Affiliation School of Geography and Remote Sensing, Guangzhou University, Guangzhou, China

Roles Funding acquisition, Project administration, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations School of Geographical Sciences, Fujian Normal University, Fuzhou, China, Institute of Geography, Fujian Normal University, Fuzhou, China, Key Laboratory for Humid Subtropical Eco-Geographical Processes of the Ministry, Fujian Normal University, Fuzhou, China

Boming Zheng,
Xijie Lin,
Duo Yin,

Published: March 1, 2023
https://doi.org/10.1371/journal.pone.0282474
Peer Review
Reader Comments

One of the basic assumptions of spatial theory is formulated in Waldo Tobler’s first law of geography: "everything is related to everything else, but near things are more related than distant things." However, as internet space is a complex virtual space independent of the real world, whether this law is applicable to things in the internet space remains to be explored in depth. Therefore, this study takes the event of Asian elephant northern migration as an example, attempts to investigate the issue of the applicability of Tobler’s first law of geography to internet attention by integrating geographic methods such as spatial visualization, spatial correlation analysis, and Geo-detector. The results show that Tobler’s first law of geography does not fully apply to internet attention, which does not decay with increasing distance. Geographical distance, within certain boundaries, is influenced by "identity" and "relevance", and still plays a large role in internet attention. However, once the boundaries are exceeded, the impact of geographic distance on internet attention is weakened by the intervention of influencing factors such as the degree of information technology, population, and the strength of news media publicity. Overall, the strength of news media publicity has the greatest impact on internet attention. And when it interacts with geographic proximity, it has the most significant effect on internet attention.

Citation: Zheng B, Lin X, Yin D, Qi X (2023) Does Tobler’s first law of geography apply to internet attention? A case study of the Asian elephant northern migration event. PLoS ONE 18(3): e0282474. https://doi.org/10.1371/journal.pone.0282474

Editor: Wei Tu, Shenzhen University, CHINA

Received: September 17, 2022; Accepted: February 16, 2023; Published: March 1, 2023

Copyright: © 2023 Zheng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This research was funded by National Social Science Foundation of China, grant number 18BJL126. National Natural Science Foundation of China, grant number 41901173. The funders' contributions to the paper were mainly in supervision, funding acquisition, and project administration.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Since the 1990s, human society has gradually moved from the industrial age to the information age [ 1 ]. The rapid spread of the internet makes information dissemination no longer limited by time and space, and extensive information is spreading rapidly and instantly. The China Internet Development Report (2021) shows that the number of Chinese Internet users reached 989 million by the end of 2020, and access to all kinds of information has become increasingly prevalent. Traditional geographical laws such as "distance decay" and "diminishing benefits" are defined in this context as "spatial and temporal compression" [ 2 ], and pessimistic arguments including "death of distance" [ 3 ] and "the end of geography" [ 4 ] frequently emerge in academic circles. To respond to the challenges of the information age and explore the laws of space and time, the research framework of geography has been widened into sub-disciplines, including information geography [ 5 ], communication geography [ 6 , 7 ], telecommunication geography [ 8 ], cyberspace geography [ 9 ], virtual geography [ 10 ], and media geography [ 11 ]. The aim is to explore the characteristics and laws of the territorial system of human-land relations from a geographic perspective in the information age, thereby expanding the research scope of geography [ 12 ]. In this context, this study attempts to investigate the applicability of Tobler’s first law of geography to internet attention (An indicator to measure the active search behavior of internet users based on their subjective information needs, which characterizes the attention orientation of the public) in the hope of enriching and improving the theoretical perception of distance decay in the internet era.

Tobler’s first law of geography states that everything is related to everything else, but near things are more related to each other [ 13 ]. This law mainly explains the frictional effect of distance on spatial interactions and considers that the strength of spatial interactions decreases as the distance between locations increases [ 14 ]. In the real world, many things and activities exist in a specific macro or micro space, and will undergo some spatial variation with the migration of time. Because of this, Tobler’s first law of geography has long had a significant impact on many natural and humanities fields such as ecology [ 15 – 17 ], biology [ 17 , 18 ], tourism [ 19 , 20 ], transportation [ 21 , 22 ], and behavior [ 23 ]. Bjorholm took American palms (Arecaceae) as an example, assess the extent to which Tobler’s first law applies to species richness and species composition. To shed light on the mechanisms driving distance decays in community structure and quantify the relative contribution of geographic distance per se and environmental changes as drivers of spatial turnover in species richness and composition [ 15 ]. Siewert demonstrated the great spatial variability of subsurface soil properties in permafrost topography and argued that permafrost does not conform to Tobler’s first law [ 18 ]. Lee examined the relationship between distance and destination choice of international leisure traveller activity in Hong Kong over a decade, clarified the relationship between time variation and distance decay [ 19 ]. Gao analyzed the Spatial heterogeneity in distance decay of using bike sharing in Shanghai [ 21 ]. Hammond examined the fit of logarithmic, negative exponential, and quadratic decay functions to the distribution of the distances travelled to offend by a sample of 70 prolific burglars from the UK, to bring to light the possible psychological and behavioural processes inherent in offending distance decay [ 23 ]. However, in the current era of highly developed information technology, does Tobler’s first law of geography apply to virtual space? In recent years, empirical efforts attempting to contribute to this debate have utilized emerging data sources derived from location-based services [ 24 , 25 ]. Online social networks [ 26 – 31 ] and mobile communication networks [ 32 – 38 ] are drawing particular attention from researchers. For example, Liu proposed a method for capturing "relatedness between geographical entities" based on the co-occurrences of their names on web pages [ 26 ]. Yuan explored the spatial decay effect in mass media and location-based social media [ 31 ]. Gao discovered that a high correlation exists between phone users’ movements in physical space and phone-call interaction in cyberspace [ 34 ]. Kang identified the distance decay effect in intercity mobile communications of China using a subnet data set [ 35 ]. But few relevant studies have been conducted based on internet attention. Therefore, this paper attempts to discuss this issue based on the perspective of internet attention.

A review of the literature reveals that existing studies on internet attention tend to be based on Google Trends (frequency of searches and related statistics for a keyword on the Google platform) or the Baidu Index (a data-sharing platform based on Baidu’s massive internet user behavior data, based on which keyword search trends can be studied and thus insights can be gained into the changing needs of internet users). Scholars have used Google Trends and the Baidu Index to research issues such as the correlation between crowding levels and flu outbreaks [ 39 ], virus forecasting [ 40 ], the relationship between economic growth and ecology [ 41 ], the image of geographic locations in virtual environments [ 42 ], sales forecasting [ 43 ], tourism demand forecasting [ 44 – 46 ], and tourism destination selection behavior [ 47 ]. Numerous fields such as medicine, economics, ecology, society, and marketing are involved. Through the existing research, it can be found that the perspective based on internet attention is conducive to observing the flow direction of information in cyberspace and highlighting its spatial differentiation characteristics, so as to summarize and extract the hidden geographic laws.

In summary, this paper attempts to take the Asian elephant northern migration event in southwest China, which has attracted widespread attention at home and abroad, as a case study. It has the characteristics of immediacy, high interest, and a long time span, which can reflect spatial and temporal differences and induce geographic patterns. Using the Baidu index to obtain the internet attention index for the event from April 16 to August 5, 2021, in all provinces (municipalities and autonomous regions). Subsequently, we use spatial visualization and spatial autocorrelation to explore its spatial and temporal evolution characteristics and introduce the geo-detector method to analyze the influencing factors. We tried to discuss the question: Does Tobler’s first law of geography apply to internet attention? This study helps to further expand the research outreach of information geography and strengthen the academic community’s knowledge of the applicability of Tobler’s first law of geography.

Methods and data

Space autocorrelation..

Geo-detector.

We also applied the interaction detection function in the geo-detector to identify whether two different influencing factors X s together enhance or weaken the explanation of the dependent variable compared to a single factor, as shown in Table 1 .

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https://doi.org/10.1371/journal.pone.0282474.t001

Study subject.

The Asian elephant northern migration event began on April 16, 2021, when 17 wild Asian elephants from the Mengyangzi Reserve in Xishuangbanna, Yunnan Province left their original habitat and migrated northward through eight counties in the three prefectures of Yuxi, Honghe, and Kunming in Yunnan Province. According to data released by the provincial command of Yunnan’s northward Asian elephant herd safety prevention work, to monitor the dynamics of Asian elephant herds and ensure the safety of humans and elephants, the local authorities of Yunnan Province dispatched more than 25,000 police officers and staff, 973 drones, and more than 15,000 emergency vehicles. More than 150,000 people were evacuated, and nearly 180 tons of food were put out for the elephants. The migration disrupted the normal order of day-to-day life and production in the region, causing huge economic losses. This has been the most damaging wildlife incident in recent years in China. Naturally, it has become a hot topic for public attention and opinion at local and global scales.

Data source.

Internet attention data for the Asian elephant northern migration event were obtained from the Baidu index. First, the search key phrase is formulated by combining three fields: the first field is the "region" field, i.e. the place where the northern migration of Asian elephants occurred, "Yunnan"; the second field is the "subject" field, i.e. the subject of the northern migration of Asian elephants, such as "Asian elephants" and "elephant herd"; the third field is the "behavior" field, i.e. the behavior carried out by the subject, such as "northern migration". Second, the search time was clarified. According to information released by the provincial command of Yunnan’s northward Asian elephant herd safety prevention work, the herd originally lived in the Xishuangbanna Mengyangzi Reserve, where they first moved to Mojiang County, Pu’er City, in December 2020, but remained within their traditional habitat. The start of the northward migration of the herd outside of its traditional habitat occurred on April 16, 2021. Therefore, April 16, 2021, was set as the starting date of data collection; the data were collected in periods of "weeks", with the deadline being the week when the data collection was completed. Search phrases were constructed and the search time was specified to achieve accurate collection of internet attention data and avoid noisy data. Ultimately, we collected internet attention data for a total of 16 weeks from April 16 to August 5, 2021.

The data for each influencing factor was derived from the China Statistical Yearbook, China Rural Statistical Yearbook, China Tertiary Industry Statistical Yearbook, 31 provinces (municipalities and autonomous regions), national economic and social development statistical bulletins, Baidu maps, and other online materials.

Spatial and temporal characteristics

In this part of the study, we try to observe whether the internet attention of a region is stronger when it is closer to the region where the event took place by summarizing the temporal and spatial characteristics.

Analysis of the time-series characteristics

Based on the internet attention statistics of the elephants’ northward migration event, we plotted the temporal evolution of the national and sub-regional events (east, central, and west) to analyze their temporal characteristics. As shown in Fig 1 , the time series evolution curves of the whole country (east, central, and west regions) show a "single peak" pattern of rising and then falling, which can be distinguished into three stages: latent, intense, and receding. The latent stage of attention lasted from the first week to the fifth week. At this stage, due to the initial migration of Asian elephants to the north, the event had not yet aroused widespread public concern (apart from the professional departments); the internet attention curve also did not show large fluctuations, showing stable and low characteristics. With the progression of the event, internet attention began to enter the critical stage. Starting from the 6th week, the major mainstream official media (People’s Daily, Xinhua, CCTV News, etc.), short videos (Tik Tok, Kwai, etc.), and portals (Weibo, Baidu, Tencent, etc.) issued many reports and propaganda on the event, which quickly aroused extensive discussions amongst the populace with the internet attention continuing to escalate the trend that continued until the 8th week (when it reached its peak). After the 9th week, the fervor of the event gradually subsided across all media, frequency of publicity reports decreased, and the internet attention curve showed a downward trend, thus entering the stage of receding attention. Comparing the fervent and receding stages, we found that the receding curve was smoother and lasted longer than the upward curve, indicating that the development of online public opinion quickly climbs to a peak over a short period of time, while the receding speed of public opinion is slower and lasts longer. In addition, it can also be discovered that the values of internet attention at all time points of the fervent and recession stages are greater than those of the latent stage, indicating that the publicity and coverage of events by mainstream social media have a value-added diffusion effect on internet attention, which is most evident during the intensive period of media coverage and continues to exist during the recession stage when media attention shifts.

https://doi.org/10.1371/journal.pone.0282474.g001

In addition, the Asian elephant northward migration event occurred in the western region. However, the highest internet attention was in the east, followed by that in the west, and lowest in the center, which does not conform to Tobler’s first law of geography. This may be related to the higher level of economic development and more developed information network in the eastern region. We can also speculate that in the era of information, the rapid spread of the internet will largely overcome the obstacle of geographical and spatial distance for information access and dissemination.

Analysis of the spatial evolution characteristics

Spatial visualization..

To present the regional differences in a more detailed way, the natural breaks (Jenks) method in the ArcGIS software was used to classify the internet attention of 31 provinces (municipalities and autonomous regions) into five levels: highest, higher, medium, lower, and lowest. Spatial visualization was used to further analyze the variation characteristics of the internet attention at the provincial level. As shown in Fig 2 , the regions with the highest internet attention were only two provinces, Yunnan and Guangdong; the regions with higher internet attention cover Sichuan, Beijing, Zhejiang, Shanghai, Jiangsu, Shandong, Henan, and other places along the east coast. The areas with intermediate internet attention were mainly located in Hubei, Hunan, Jiangxi, Anhui, and other central regions, and the areas with lower and lowest internet attention were concentrated in the northeast (Heilongjiang, Jilin, Inner Mongolia, etc.) and northwest regions (Xinjiang, Tibet, Qinghai, Gansu, Ningxia, etc.) of China. Although there were individual regions (e.g., Guangxi, Jiangxi, Ningxia, and Inner Mongolia) where the internet attention increased during the study period, the overall pattern of spatial divergence was relatively stable. We can find that Yunnan has the highest internet attention as the region where the event took place. However, Guangdong, Jiangsu, Zhejiang, Shandong, and Henan provinces, which are far away from Yunnan, have higher internet attention than Sichuan, Guangxi, and Guizhou, which are adjacent to Yunnan. This situation is not by Tobler’s first law of geography. It suggests that the spatial divergence of internet attention is not decisively influenced by geographic proximity, and is probably influenced by other factors as well.

The underlying layer is quoted from the Sky Map website ( https://www.tianditu.gov.cn/ ), the standard map review number is GS (2019) 1833.

https://doi.org/10.1371/journal.pone.0282474.g002

Yunnan was always at the top of the list, probably because it is the location of the migration. Being a direct stakeholder, the population of Yunnan was extremely interested in this event because of their "local" identity. Guangdong ranked with Yunnan as the region with the highest internet attention, partly because of its relatively large population (126 million people) and developed economy and society. Sichuan Province, which is adjacent to Yunnan Province that is rich in biodiversity, is the main habitat and reserve of China’s national treasure: giant pandas; thus, it emphasizes on the protection of giant pandas and other wild animals. Therefore, people in the region are more concerned and aware of the need for protecting wild animals. The levels of economic development, urbanization, internet penetration, and population in Beijing, Shanghai, and Jiangsu are generally high, providing a good foundation for people to browse on the internet and thus have a high level of internet attention. Regions with relatively low internet attention ratings either have a long distance between them and Yunnan Province or have characteristics such as low population, average economic development level, low urbanization level, low network penetration rate, and low education level of residents. It is easy to understand that geographic proximity still has some influence on the spatial divergence of internet attention, but it is not in a dominant factor and is also subject to the combined effect of other factors.

Spatial clustering.

GeoDa software was applied to calculate the global Moran’s I index of internet attention for the migration event ( Table 2 ). The results showed that the Moran’s I index at the time points examined showed values in the interval [0.0834, 0.1596], which were positive; however, the values were not high and tended to increase over time, passing the 5% and 10% significance tests, respectively. This reveals that internet attention within the study area exhibits a statistically significant positive spatial correlation; that is, there is a certain spatial clustering characteristic, although the positive correlation is relatively insignificant.

https://doi.org/10.1371/journal.pone.0282474.t002

To further explore the local spatial association characteristics, a Lisa agglomeration map was measured and drawn using GeoDa software ( Fig 3 ). In terms of clustering patterns, three main types of clustering patterns existed during the study period, namely: high-high clustering (high internet attention areas are adjacent to each other with small spatial differences in attribute characteristics), low-high clustering (low internet attention areas are adjacent to high internet attention areas with large spatial differences in attribute characteristics), and low-low clustering (low internet attention areas are adjacent to each other with small spatial differences in attribute characteristics). The high-high aggregation area shows a trend of "diffusion-shrinking-rediffusion" during weeks 1–10. There are only two regions in Hubei and Zhejiang in week 1, and two regions in Anhui and Fujian were added in week four; however, these two regions dropped out of a high-high aggregation area in week seven. Although the spatial distribution of the high-high aggregation area increased in the first and middle periods, relatively stable high-high aggregation areas were formed mainly around Hubei and Zhejiang provinces. During weeks 11–16, the high-high aggregation area spread again and formed agglomerative clusters, with Hunan, Hubei, Anhui, Zhejiang, and Fujian as the main members. The distribution of low-high aggregation areas differed significantly in weeks 1–10, jumping continuously in regions such as Fujian, Anhui, and Guangxi until a stable low-high aggregation area centered on Jiangxi was formed after week 13. The spatial distribution of low-low aggregation areas did not change significantly; only significant transitions in individual regions (Xinjiang, Qinghai) occurred during weeks 1–7. However, from the 10th week onwards, clusters were formed in low-lying areas including Tibet, Xinjiang, and Qinghai.

https://doi.org/10.1371/journal.pone.0282474.g003

We can observe more clearly that the high-high clustering areas are mainly concentrated on the southeast coast of China, far from Yunnan, where the event took place. This again shows that Tobler’s first law of geography does not apply to internet attention.

Factors influencing the spatio-temporal evolution

In the spatial analysis above, we initially inferred that Tobler’s first law of geography does not apply to internet attention and that the spatial divergence of internet attention is likely to be influenced by a combination of many factors. Therefore, in this part of the study, we introduce the method of Geo-detector to analyze the extent to which each factor, including geographic proximity, affects the spatial divergence of internet attention when many factors are combined.

Impact factor selection

Combining the characteristics of the Asian elephant northward migration event, drawing on relevant research results, and considering the compatibility, comprehensiveness, scientific significance, and data accessibility of the indicators, an index system affecting the spatial and temporal heterogeneity of the attention of the Asian elephant northward migration event network was constructed ( Table 3 ). The index system is explained in terms of five aspects: geographical location, regional development, ecological environment, demographic characteristics, network and publicity.

① Geographic location is represented by geographic proximity (X 1 ), and, ceteris paribus, areas closer to the center of the event (Yunnan Province) are likely to be of higher relative concern.
② Regional development covers three major detection factors: economic development level (X 2 ), industrial development level (X 3 ), and urbanization level (X 4 ). The level of economic development (X 2 ) reflects, to a certain extent, the quality of life and consumption ability of the people in the study area, and the internet penetration rate tends to be positively related to the level of economic development. The industrial development level (X 3 ) mainly refers to the development of industries such as agriculture, industry, and tourism, which may encroach on and destroy wildlife habitats, thus increasing the probability of animal incidents in the region, and the local population may pay more attention to such incidents. The level of urbanization (X 4 ) is the degree of gradual transformation of society from a traditional rural type to a modern urban type. Generally, areas with higher levels of urbanization will have better infrastructure and may have higher levels of local network construction.
③ The ecological environment consists of three major detection factors: forest coverage (X 5 ), farmland coverage (X 6 ), and the number of nature reserves (X 7 ). In areas with high forest coverage (X 5 ) and the greater the spatial overlap between the main areas inhabited by wild animals and areas where humans live and work, the more attention will be paid to such incidents. Intensive farming areas (X 6 ) generally have a higher probability of animal damage of food crops, and therefore the local population may be more concerned about animal disturbance. In areas with many nature reserves (X 7 ), the public may be more aware of wildlife protection and more concerned about the occurrence of wildlife-related incidents.
④ Demographic characteristics mainly involve three detection factors: population (X 8 ), population age structure (X 9 ), and educational attainment (X 10 ). A high population (X 8 ) facilitates the dissemination of information and reinforces the active online search behavior of the public. The age structure of the population (X 9 ) reflects the concentration of internet users in China, especially in the age range of 15–64 years. Educational attainment (X 10 ) indicates the merit of the population of a region. Those with higher educational attainment have a stronger desire to learn about new things and topical events and are more capable of understanding and sharing current events on the internet.
⑤ Network and publicity are mainly related to the degree of information technology (X 11 ) and the strength of news media publicity (X 12 ). Areas with a high degree of information technology (X 11 ) are also more rapid at searching and receiving the information dynamics of events. The strength of news media publicity (X 12 ) reflects the attention and coverage of relevant credentials, which is shown in this study through the Baidu Information Attention Index.

https://doi.org/10.1371/journal.pone.0282474.t003

Factor detection analysis

Using the Natural Breaks (Jenks) method in ArcGIS software to convert the influencing factors from continuous variables to discrete variables, and applying Geo-detector to detect the degree of influence of each influencing factor on the internet attention from two dimensions of temporal evolution and spatial differentiation, and referring to related studies, the factors that passed the 1% significance test were considered core influencing factors, and those that passed the 5% and 10% significance tests were considered important influencing factors, and the detection results are shown in Tables 4 and 5 .

https://doi.org/10.1371/journal.pone.0282474.t004

https://doi.org/10.1371/journal.pone.0282474.t005

Temporal evolution

According to the detection results in Table 4 , among the 12 detection factors, only industrial development level (X 3 ), population (X 8 ) and the strength of news media publicity (X 12 ) passed the significance test, indicating that the above three factors are the most important factors influencing the temporal evolution of internet attention. Geographical proximity (X 1 ) did not pass the significance test, indicating that it did not play a significant role.

In order of influence and importance: the strength of media publicity (X 12 ) > industrial development level (X 3 ) > population (X 8 ). Among them, the strength of news media publicity (X 12 ) passed the 1% significance test in all examined nodes, and the q-value was stronger than the other two factors, indicating that the strength of media publicity (X 12 ) was the core influencing factor. The public has initial contact and understanding of the northern migration of Asian elephant events based on media propaganda reports, and gradually develops interest and curiosity with the increasing frequency of reports and continuous disclosure of details. Finally, interest and curiosity eventually evolve into active online search behavior. The industrial development level (X 3 ) passed the significance tests of 1% at week 13, and 10% at most of the remaining time points; thus, it can be classified as an important influencing factor. The industrial development level (X 3 ) passed the significance test, indicating that the public’s concern about wildlife incidents and conservation increased with the level of industrial development. Population (X 8 ) passed the 5% significance test in week 13, and only passed the 10% significance test at most time points, so it can only be classified as a important influencing factor, which also verifies the conclusion that Guangdong is ranked as a high internet concern region above because it has a high population size.

Spatial differentiation

We divided the study area into three regions: East, Central, and West, and tested the influence factors separately to further clarify the influence of geographical proximity on the spatial divergence of internet attention. Yunnan, where the event took place, is located in the western region. Therefore, the western region is the closest to the event site, the central region is the next closest, and the eastern region is the farthest. According to the detection results in Table 5 , geographic proximity (X 1 ) passed the significance test in the western region but did not gain support in the eastern and central regions. However, the results of "3.1 Analysis of the Time-Series Characteristics" above show that the eastern region had higher internet attention than the western region, and the western region had higher internet attention than the central region. Combining the two results, it is thought that geographic distance has a positive effect on internet attention, but this effect is often limited to a certain range. When a certain threshold is exceeded, the degree of influence of geographic distance on internet attention may be weakened by the intervention of other influencing factors.

In addition, the strength of news media publicity (X 12 ) and industry development level (X 3 ) in the eastern and central regions passed the significance test. In the west, the strength of media publicity (X 12 ) also passed the significance test. The strength of media publicity (X 12 ) topped the explanatory ratings of many influencing factors in all three regions, indicating that regional differences did not weaken the effect of its on internet attention, further validating the status of the strength of media publicity (X 12 ) as a core influencing factor. The level of industrial development in the eastern and central regions of China is more developed than in the western regions; therefore, the industrial development level (X 3 ) is considerably better at explaining internet attention in the eastern and central regions.

Factor interaction

As mentioned above, the spatial variation of internet attention is influenced by the combined effect of multiple factors, so is the degree of influence of multi-factor interaction on the spatial variation of internet attention stronger than that of a single factor? Which factors had the strongest influence when interacting with each other? To answer these questions, this study used the factor interaction detection module in the geo-detector to conduct two-factor interaction detection. As shown in Tables 6 and 7 , the influence of both two-factor interactions was greater than that of the single factor, with 61% of the interaction types being nonlinearly enhanced and 39% being two-factor enhanced. Of these, population age structure (X 9 ) had the largest effect on the interaction; among the 11 factors, only one factor, the strength of news media publicity (X 12 ), had a two-factor enhancement type of interaction with population age structure, whereas the others were all non-linearly enhanced. The type of interaction between the strength of news media publicity (X 12 ) and the other 11 factors is a two-factor enhancement, and they do not promote each other much. The reason for this may be that the single factor of the strength of news media publicity (X 12 ) is too influential, resulting in a "shielding effect" and "ceiling effect". Specifically, the interaction influence of the strength of news media publicity (X 12 ) and geographic proximity (X 1 ) was 0.990, ranking first among all factors of interaction influence, indicating that the two have the strongest driving effect on the spatial divergence of internet attention when they interact together. In addition, although the strength of news media publicity (X 12 ) does not contribute significantly to other factors when interacting with them, the interaction effect is significant (all above 0.900), further verifying that the strength of news media publicity (X 12 ) is the core influencing factor affecting the spatial variation of internet attention.

https://doi.org/10.1371/journal.pone.0282474.t006

https://doi.org/10.1371/journal.pone.0282474.t007

Discussion and conclusion

Tobler’s first law of geography states that everything is related to everything else, but near things are more related to each other. It emphasizes the key role played by distance in the connection between things. Therefore, in the material world, this law has been more widely applied because there is a real spatial distance between things in the material world. However, in the current internet era, the concept of distance seems to gradually blur or even disappear in the internet space. So, does Tobler’s first law of geography still apply to things in the internet space? This question remains to be continuously explored and discussed. Therefore, this study attempts to discuss the applicability of Tobler’s first law of geography to internet attention in internet space, using the Asian elephant northern migration event as a research case. It helps to answer the pessimistic arguments about the "death of distance" and "the end of geography" in the internet era and further deepens the understanding of Tobler’s first law of geography.

We found that the rapid spread of information technology has indeed broken the distance barrier in the process of people’s active access to information to a certain extent, distance does not have a decisive influence on internet attention, and Tobler’s first law of geography does not fully apply to internet attention. Of course, this does not mean that spatial distance does not have any influence on internet attention. On the internet, regardless of the distance from the information source, the time and efficiency associated with information access are often similar. However, under the condition of relative geographical proximity, influenced by "identity" and "interest-related", the phenomenon of "distance decay" is still characterized; that is, "the closer the distance → more relevant interest → higher internet attention". Once this relative distance is exceeded and there is no direct correlation of interest other factors, such as news media coverage, population, and internet penetration, which may have a greater impact on internet attention than geographic distance.

In addition, the results of our study can also help provide a reference for internet public opinion management, because internet attention is an indicator that characterizes the active information acquisition behavior of internet users, which is a manifestation of internet public opinion. First, the evolution of internet attention shows the stages of "latent → hot spot → decline". The relevant departments need to achieve a real-time and precise understanding of the development stage of internet public opinion, whether they carry out emergency control of internet public opinion or use the hotness of internet public opinion to conduct relevant publicity activities. However, in the actual practice of internet governance, it is often a passive post-facto action rather than a proactive prevention, which is ultimately due to the lack of knowledge of the regularity of internet public opinion, making it difficult to carry out quantitative predictions. Therefore, there is an urgent need to strengthen the ability to monitor and judge internet public opinions. Mainstream media reports on the event have a value-added diffusion effect on internet attention; this effect is most obvious during the intensive period of mainstream media reports, even if the effect is reduced with the shift of mainstream media attention. Mainstream media reports are a core factor in influencing internet attention; when it interact with geographical proximity, its influence is even more powerful. Thus, in terms of internet public opinion monitoring, it is necessary to pay full attention to combining the use of emerging technologies such as artificial intelligence and big data analysis and deeply integrating internet public opinion collection, data storage, and data mining systems to achieve dynamic monitoring. In terms of internet public opinion research and judgment, a special internet opinion evaluation team with keen insight and judgment should be cultivated and built to realize the scientific, standardized, and professional evaluation and prediction of internet public opinion. To achieve accurate research and judgment of internet public opinion, we must improve the ability to find opportunities from crises and strengthen the scientific application of internet public opinion. For example, in this incident of the elephant migration, Yunnan Province made full use of the internet public opinion and timely launched the slogans of "Elephants yearning for Kunming", "A place where elephants yearn for" and so on, to achieve a worldwide city branding and image building. This has also been a typical example of China’s biodiversity conservation achievements during the 15th Conference of the Parties to the Convention on Biological Diversity (CBD), which is a successful experience worth learning from. Naturally, the use of internet public opinion should also take into consideration spatial variability. During this incident, internet attention formed two high-attention areas, namely "southeast" and "southwest". For these two regions, the internet fervor and follow-up effect of this incident can be used to educate people about wildlife conservation and stimulate their awareness of the importance of biodiversity conservation.

Of course, there are some limitations of our study. First, this study analyzes the applicability of Tobler’s first law of geography to internet attention, but the internet space is a complex virtual space that also includes other things beyond internet attention, and the study of these things needs to be further explored in the future. Second, this study is a case study, and more cases need to be added to verify it in the future. Finally, this study is an indirect discussion of the applicability of Tobler’s first law of geography to internet attention based on the analysis of the spatial and temporal characteristics and influencing factors of internet attention. Future research can try to use quantitative models to establish more direct arguments for geospatial relationships.

This study explores the applicability of Tobler’s first law of geography to internet attention using the Asian elephant northern migration event as a research case. We found that Tobler’s first law of geography does not apply to internet attention, and the distance decay effect is not fully reflected in internet attention. Distance has an effect on internet attention, but this effect is often limited to a certain distance, and when a certain distance threshold is exceeded, the degree of influence of geographic proximity on internet attention is probably no longer significant due to the intervention of other influencing factors. Therefore, the influence of distance on internet attention is not decisive. The factor that really has a decisive influence on internet attention is the strength of news media publicity, and it has the greatest influence on internet attention when the strength of news media publicity interacts with geographic proximity.

Supporting information

S1 table. internet attention of provinces during week 1 to the week 16..

https://doi.org/10.1371/journal.pone.0282474.s001

S2 Table. Factors.

https://doi.org/10.1371/journal.pone.0282474.s002

Acknowledgments

The authors acknowledge the two reviewers for their valuable comments on the paper, which have helped greatly improve the paper’s quality.

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2018 Sulawesi, Indonesia Earthquake and Tsunami Case Study

What caused the Sulawesi, Indonesian earthquake and what were the effects?

On Friday 28th September 2018 a magnitude 7.5 earthquake struck Palu, on the Indonesian island of Sulawesi, just before dusk wreaking havoc and destruction across the city and triggering a deadly tsunami on its coast. The 7.5 magnitude earthquake hit only six miles from the country’s coast.

A map to show the location of Palu

The shallow tremor was more powerful than a series of earthquakes that killed hundreds on the Indonesian island of Lombok this July and August.

Palu is located on the Indonesian island of Sulawesi, 1,650 kilometres northeast of Jakarta, at the mouth of the Palu River. It is the capital of the province of Central Sulawesi, situated on a long, narrow bay .

A satellite image to show the location of Palu – Source Google Earth

The coastal city of Palu is home to 350,000 people.

Small foreshocks had been happening throughout 28th September in Palu. However, in the early evening, the Palu-Koru fault suddenly slipped, a short distance offshore and only 10km (6 miles) below the surface. This generated the 7.5 magnitude earthquake.

The impact of the earthquake was magnified because of the thick layers of sediment on which the city lies. Whereas bedrock shakes in an earthquake, sediment moves a lot more, behaving like a liquid. Poorly constructed houses cannot withstand movement of this magnitude.

Scientists don’t pay much attention to the Palu-Koru fault line, as far as tsunamis are concerned. This is because the two plates are moving past each other, not with the vertical thrust required to form a tsunami.

Scientists are still trying to work out what happened to cause the tsunami. It is possible that the earthquake caused an underwater landslide which disturbed the water or there could be inaccuracies in the identification of the type of fault.

Once the wave started moving, Palu, at the end of a narrow 10km-long bay, was a sitting duck.

Tsunamis are no danger when out at sea. But when the waves come closer to land, their base drags on the seabed causing them to rise up.

Primary Effects

The quake destroyed thousands of homes in the city, as well as an eight-storey hotel, hospital and a large department store.

More before/after comparisons from around the #PaluTsunami and #PaluEarthquake captured by @planetlabs . Included rough lat/long. Keep an eye on https://t.co/Kz73HlYmGF as they often post the sat. imagery for responders, relief agencies et al. pic.twitter.com/1Vreovjt9b — Murray Ford (@mfordNZ) October 1, 2018

At least 2256 people have been confirmed dead, with more than 10,679 injured and 1075 missing. 200,000 people were in urgent need of assistance, about a quarter of them children.

The earthquake caused widespread liquefaction , which is when soil and groundwater mix. The ground becomes very soft, similar to quicksand. It causes foundations of buildings and other structures to sink into the ground.

In the case of Palu, buildings not only collapsed but some were moved by the liquefaction. This is why it is better to build on bedrock rather than on top of the soil.

The control tower and runway at Palu’s airport also sustained damage. Commercial flights were cancelled with only humanitarian and search and rescue flights permitted.

Secondary Effects

The earthquake triggered a tsunami reaching 6 metres in height. As the tsunami approached the coast it was reported to be travelling 250mph. The damage was as extensive: the main highway was cut off by a landslide and a large bridge washed away by the tsunami wave, which hit Palu’s Talise beach and the coastal town of Donggala.

Landslides, downed communications networks and collapsed bridges have made it hard for aid workers and rescuers to reach rural areas.

Due to hospitals being damaged, people received medical treatment in the open.

Strong aftershocks hit the island the day after the earthquake.

Immediate (Short Term) Response

A tsunami warning was issued by Indonesia’s geophysics agency (BMKG) when the earthquake was detected. However, the agency lifted the warning 34 minutes after it was first issued. The closest tidal sensor to Palu is around 200km (125 miles) away. The decision to lift the tsunami warning was based on this data.

Search and rescue teams were deployed to the worst-affected areas. Around 700 army and police officers were dispatched to assist in the emergency response.

The military sent cargo planes with aid from Jakarta and other cities. However, this was slow to arrive.

A large number of charities set up appeals to raise funds to support people in the affected area. Buckingham Palace reported that the Queen had made a donation to the Disasters Emergency Committee (DEC) appeal for survivors, which raised £6m in a day when it was launched.

The RAF delivered thousands of shelter kits, solar lanterns and water purifiers to the disaster zone in addition to trucks and power generators to help get them to where they are needed.

At least 70,000 people gathered in evacuation sites across the island.

Long-term Response

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Geographies of Cyberspace: Internet, Community, Space, and Place

First Online: 05 November 2017

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Tobias Boos 4

Part of the book series: Geographies of Media ((GOM))

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This chapter “Geographies of Cyberspace” discusses the term “cyberspace” in relation to the concepts of “space”, “place”, and “community” by reviewing the academic literature from human geography, sociology, and cultural anthropology. It forms the basis for the development of a phenomenological perspective on cyberspace. Instead of presenting all possible approaches towards cyberspace, this chapter discusses only those concepts that focus on the connections between on- and offline life. It proposes that cyberspace not only includes spatial and territorial metaphors , as several human geographers have pointed out, but that it is also characterised by complex geographies, which have to be explored in order to understand recent cultural and social developments, both on- and offline.

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Boos, T. (2017). Geographies of Cyberspace: Internet, Community, Space, and Place. In: Inhabiting Cyberspace and Emerging Cyberplaces. Geographies of Media. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-58454-6_2

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14 How Geography Shapes—and Is Shaped by—the Internet

Introduction

Death of Distance?

Adoption and Use of the Internet

Internet Adoption Among Firms

IT Adoption Among Individuals

Consequences of the Internet for Wealth and Productivity

Consequences of the Internet for Innovation

Consequences of the Internet for Consumer Behaviour

Consequences of the Internet for Globalization and Trade

Directions for Future Research

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Does Tobler’s first law of geography apply to internet attention? A case study of the Asian elephant northern migration event

Introduction

Methods and data

Geo-detector.

Study subject.

Data source.

Spatial and temporal characteristics

Analysis of the time-series characteristics

Analysis of the spatial evolution characteristics

Spatial clustering.

Factors influencing the spatio-temporal evolution

Impact factor selection

Factor detection analysis

Temporal evolution

Spatial differentiation

Factor interaction

Discussion and conclusion

Supporting information

S2 Table. Factors.

Acknowledgments

2018 Sulawesi, Indonesia Earthquake and Tsunami Case Study

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