Geographies of transport II

Abstract

Geographical scholarship on transport has been boosted by the emergence of big data and advances in the analysis of complex networks in other disciplines, but these developments are a mixed blessing. They allow transport as object of analysis to exist in new ways and raise the profile of geography in interdisciplinary spaces dominated by physics and complexity science. Yet, they have also brought back concerns over the privileging of generality over particularity. This is because they have once more made acceptable and even normalized a focus on supposedly universal laws that explain the functioning of mobility systems and on space and time independent explanations of hierarchies, inequalities and vulnerabilities in transport systems and patterns. Geographical scholarship on transport should remain open to developments in big data and network science but would benefit from more critical reflexivity on the limitations and the historical and geographical situatedness of big data and on the conceptual shortcomings of network science. Big data and network analysis need to be critiqued and re-appropriated, and examples of how this can be done are starting to emerge. Openness, critique and re-appropriation are especially important in a context where transport geography decentralizes away from its Euro-American core, and the development pathways of transport and mobility in localities beyond that core deserve their own, unique explanations.

Keywords

big data complexity science methodology network analysis physics transport vulnerability

I Introduction

Big data, network science and rapid developments in (geo)computation have impacts on many parts of the discipline, but particularly on geographical scholarship on transport. This is in part because of transport geography’s historically close ties with the Quantitative Revolution, post-positivist quantitative geography and GIScience (Taaffe and Gauthier, 1994; Hanson, 2000; Goetz et al., 2009). Equally relevant is the observation that transport is a theme around which a renewed round of interesting cross-disciplinary conversations between geographers, physicists and mathematicians has come into being. Where geographical scholarship on transport has been plugged into broader disciplinary debates through engagement with questions around inequality, anthropogenic climate change and health (Schwanen, 2016), ongoing experimentation with big data and network analysis draws the field into interdisciplinary developments around data and methodology.

These methodological developments are to be welcomed because of what philosopher of science Isabelle Stengers (2000, 2005) would understand as their generative character: they enable transport and mobilities to exist in new ways that are recognized and accepted as valid across disciplinary constituencies. At the same time, caution needs to be exercised for epistemological reasons. There is the risk that generality – regularities, laws, basic principles, time and space independent causes, as well as a privileging of simplicity, parsimony and standardization – comes to trump particularity in the form of place and time specificity, uniqueness, singularity and also local knowledge again. The tension between the two runs through the post-Second World War history of the discipline, from Schaefer’s critique of Hartshorne’s geography (Hartshorne, 1939; Schaefer, 1953; Guelke, 1977) to current debates in urban geography over planetary urbanization, assemblage urbanism and the ‘worlding’ of cities (Derickson, 2015; Peck, 2015), and geographical scholarship on transport is no stranger to it. Generality has longer been privileged in transport geography than in many other sectors of the discipline, but with philosophical, methodological and theoretical diversification in recent decades (Goetz et al., 2009; Ng et al., 2014; Cidell and Prytherch, 2015) a more balanced relationship has emerged within geographical scholarship on transport. Dualistically opposing the general and the particular is fraught with difficulties and a gross simplification. Nonetheless, balancing and somehow integrating generality and particularity benefits transport geography in light of the deep divisions over the ontological status of mobility within the broader discipline (Kwan and Schwanen, 2016) and because epistemologies and methodologies that privilege one over the other are one-sided and unnecessarily partial. The challenge, then, for geographers interested in transport is to remain open to, engage with, critique, re-appropriate and redefine interdisciplinary developments in data and methods.

II Big data: Between laws and contextualization

At the time that critical geographers have begun to debate and assess the merits and limitations of big data (Graham and Shelton, 2013; Kitchin and Lauriault, 2015; Kwan, 2016), colleagues in transport geography engaged in empirical research utilizing data of high volume, velocity and variety (cf. Laney, 2001). The majority of empirical studies to date rely on global positioning system (GPS) and radio frequency identification (RFID) technologies that enable Lagrangian tracking – the production of sequences of time-stamped spatial positions (Purves et al., 2014) – of, for instance, mobile phone users (Yuan et al., 2012; Sagl et al., 2014; Oksanen et al., 2015) and taxis (Y Liu et al., 2012; Hu et al., 2014; Manley et al., 2015; X Liu et al., 2015; Zheng et al., 2015). There is also rapid growth in research that relies upon Eulerian tracking of movements along fixed objects such as mobile phone towers (Demissie et al., 2013), entrance gates to urban rail and bus systems (Cats et al., 2015; Zhou et al., 2015) and bike sharing docking stations (Corcoran et al., 2014; O’Brien et al., 2014; Vogel et al., 2014). Yet, from an interdisciplinary perspective, geographers are not leading in transport research with big data: Shoval and colleagues’ (2014) result that only a very small proportion (<10%) of scientific articles on transport and tracking technologies published in 1995–2012 were (co)authored by geographers can probably be extended to big data analysis.

The analysis of big data on transport raises all kinds of new issues for transport geography. The sheer volume of such data makes the statistical and econometric techniques on which the interdisciplinary field of transport studies is disproportionally dependent lose meaningfulness. An interesting response to this challenge by geographers has come in the form of geovisualization and geovisual analytics (Sagl et al., 2014; Corcoran et al., 2014; Tao et al., 2014). Corcoran and colleagues, for instance, have used ‘flow-comaps’ – a mapping technique in which the display of origin-destination flows is conditioned upon other variables such as time of day and precipitation level – to explore big data on usage of urban buses and public bike sharing schemes. A second concern relates to data availability (Purves et al., 2014; Kitchin and Lauriault, 2015). Not only has the release of big data for academic research at the level of individuals or discrete vehicles been relatively rare to date, academics who are well endowed with computational capability and close links with the often commercial owners of data have been most successful in negotiating access to new data. Existing patterns of inequality and privilege across transport geography research groups are unlikely to be reduced in the big data era.

Yet, for a sub-discipline that has always struggled with moving beyond the quantitative revolution of the 1960s, the greatest concern is that ‘the prevalence of the big data meme might lead to a new scientistic, positivist, and quantitative turn’ (Graham and Shelton, 2013: 257; Wyly, 2014) that privileges generality over particularity. This is especially so because the so-called big data revolution coincides with – and indeed enables – a reinvigorated interest in social physics and the uncovering of supposedly space and time invariant ‘laws’ to explain transport and urban systems. There now exists a vibrant literature in physics and complexity science across a wide range of journals, including Nature and Science, that seemingly verifies the predictability of human mobility (Song et al., 2010); develops parsimonious models that capture the fat-tailed distributions of individuals’ travel distances, activity space characteristics and frequented locations (González et al., 2009; Noulas et al., 2012; Schneider et al., 2013); and that generalizes the well-known gravity model in order to ‘capture fundamental decision mechanisms that, directly or indirectly, are relevant to a wide span of mobility and transport-driven processes’ (Simini et al., 2012: 97). Recent work also seeks to derive universal principles about urban structure from big data on mobility (Roth et al., 2011; Louail et al., 2015).

Many geographers may ‘roll [their] eyes in exasperation’ because the recent social physics literature disregards ‘decades of geographical research’ and ‘lacks even in its own terms’ (O’Sullivan and Manson, 2015: 717). Indeed, it often denies the fundamental importance of context, situation and place, and there is little sign of the reflexivity that is customary in post-positivist geography. Reinvigorated social physics research nonetheless influences geographical scholarship on transport and mobility because non-geographers now submit social physics papers to Journal of Transport Geography ¹ and presumably other geography journals, and because geographers seek to participate in the interdisciplinary discussions on human mobility laws. These geographers qualify the overemphasis on the general in almost all of the social physics literature by drawing attention to the role of trip purpose (Zheng et al., 2015); age, gender, the difference between week and weekend days (Yuan et al., 2012); and geographical heterogeneity of the study area (Y Liu et al., 2012).² While not directly engaging with the social physics literature, other work suggests that the aforementioned predictability of human mobility may partly reflect the choice of indicator: mobile phone data from Harbin, Paris and Tallinn suggest substantial differences in the social organization of time between and within those urban areas (Ahas et al., 2015). Still, the very fact that geographers engage with the literature on mobility laws suggests that physicists’ geography envy (O’Sullivan and Manson, 2015) is paralleled – and possibly trumped – by a more familiar, long-standing awe of physics.

It is against this background that Kwan’s (2016) critique of big data on transport and mobility becomes significant. She calls for critical reflexivity in two respects: transport geographers should critically analyse ‘algorithmic uncertainty’ – the effects of the widespread deployment of algorithms on the generation, processing and analysis of big data – and be mindful of the ‘omissions, exclusions, and marginalizing power of big data’ on transport and more generally. Almost offhand she notes that information on social identity (gender, race/ethnicity, etc.) or embodiment and lived experience is absent from most big datasets. Thus, a wholehearted embrace of big data risks propelling geographical research on transport and mobility back to a time when individuals were unmarked by processes of gendering, racialization, etc., and – as Rose (1993) and, in different ways, De Certeau (1984) argued so powerfully – devoid of embodiment and sentience. Attempts by transport and time geographers to avoid the reduction of individuals to the space-time trajectories they draw (Schwanen, 2006; Kwan, 2007; McQuoid and Dijst, 2012; Berg et al., 2014) risk being outstripped.

Yet, it is important to avoid assigning seemingly essential characteristics to big data on transport and mobility. As has happened with ‘small’ GPS and mobile phone data (Kwan, 2004; Purves et al., 2014; Siła-Nowicka et al., 2015), big data on transport can be emplaced and embedded in geographical context. A promising example of this approach which merits attention from geographers working on transport is offered by Shelton and colleagues (2015). Building on research that analyses urban segregation as enacted in everyday activities and mobility patterns (Schnell and Yoav, 2001; Wong and Shaw, 2011; Silm and Ahas, 2014) and relational urban theory, these authors use Twitter data to examine socio-spatial segregation in Louisville, Kentucky. Their work shows that the particular – both place specificity and situated, local knowledge – is crucial to understanding rather than statistically summarizing the patterns and trends in big data on transport and mobility. Such data don’t speak for themselves but can be made to speak to the concerns of critical and social geography.

III Network analysis revived

With the relational turn in geography, network has become one of the discipline’s core concepts (Jessop et al., 2008). While conceptualized differently, network has been central to transport geography since the quantitative revolution: around 1960, graph theory was introduced to analyse the structure of the US highway system (Garrison, 1960) and subsequently generalized to study other ‘problems involving flows’ (Haggett and Chorley, 1969: 3). Early work focused on planar networks – collections of nodes (vertices) and links (edges) whereby the latter do not cross each other – and generated useful measures of connectivity (Kansky, 1963; Ducruet and Rodrigue, 2013) which have since been used continuously (for recent examples see Duan and Lu, 2013; C Wang et al., 2015).

In the 21st century the focus on planar networks has been complemented by sharply increased interest in non-planar networks in which edges can be crossed, so much so that network analysis is now the only methodological tool kit that is used across all branches of transport geography, from research on aviation (J Wang et al., 2011; Zhang et al., 2015) and maritime transport (Ducruet, 2016) to public transport and road networks (Rodríguez-Núñez and García-Palomares, 2014; C Wang et al., 2015) and to individual travel-activity behaviour (Arentze et al., 2012; Kowald et al., 2013). This rise in popularity is partly for substantive reasons: transport geography’s conventional preoccupation with the identification of central nodes and efficient network topologies – including the well-known hub-and-spoke structure – has been augmented with a focus on network vulnerability and resilience. Sparked by governmental concerns over terrorism, extreme weather events and natural hazards, and blackouts, studies about the consequences of node and link interdiction for transport flows at multiple geographical scales are rapidly increasing in number (Rodríguez-Núñez and García-Palomares, 2014; Jenelius and Mattsson, 2015; O’Kelly, 2015).

Developments in other disciplines are equally important in explaining the increased attention to non-planar networks: the popularization of social network analysis (Freeman, 1979, 2004; Wasserman and Faust, 1994) and its absorption into the analysis of ‘small-world’, ‘scale-free’ and ‘spatial networks’³ by physicists and complexity scientists have opened up an unprecedented set of opportunities to quantitatively examine and evaluate transport networks. Transport geographers have begun to capitalize on those opportunities, entering into dialogue with spatial and regional scientists working on other types of circulation such as communication (Grubesic et al., 2008; O’Kelly, 2015) and across disciplinary boundaries with physicists and mathematicians. Developments in physics and complexity science are shaping the geographical analysis of transport and mobility yet again.

For César Ducruet (Ducruet and Beauguitte, 2014; Ducruet, 2016) the main contributions of (transport) geography to the interdisciplinary literature on network science lie not only in the slowing down of physicists’ hard-and-fast conclusions through critical reflexivity on data quality, meaningfulness of results and policy relevance. Equally important is its role in integrating space and time into network research, and recent research on road networks in China (Wang et al., 2015), on maritime transport (Ducruet, 2016) and aviation (Zhang et al., 2015) evidences transport geography’s potential in this regard. However, questions can be raised about how beneficial the dominance of graph theory and complex network analysis is to the geographical analysis of transport. Haggett and Chorley (1969: 7) already warned that ‘the gains [that] come in higher level of abstraction, the relative ease with which large numbers of complex networks can be handled and compared, and in greater flexibility’ need to be traded against the ‘penalties [of] huge losses in relevant information’.

With hindsight, Haggett and Chorley saw the higher levels of abstraction too easily as a gain, considering that contemporary network science tends to reify configurations of connections and struggle with understanding dynamics and change in networks. Differences in the durability of links – compare highway networks with the GPS tracked trips of individuals over a week or month – tend to be disregarded, whereas these may be critical to understanding how various types of connections and networks affect, and are affected by, the spaces and places they traverse. Different networks may not be as comparable as network science assumes, raising questions about whether a generic set of connectivity measures may be as applicable to GPS tracked movements of ships or individuals as to the more rigid and immutable road or rail networks. Temporality is also imagined in specific ways: the versions of network analysis that prevail in transport geography result in what Bergson (1913) called quantitative multiplicities – successions of mutually externalized states – that reduce time to a ‘staccato of Nows’ (Hoy, 2009: 59) or ‘series of salami slices’ (Crang, 2005: 208). As the entanglements of past, present and future characterizing change are not accommodated, dynamics in networks cannot be fully grasped. Yet, the normalization of the practically convenient indicators from network science raises barriers to the uptake in transport geography of other, philosophically more robust understandings of change in connections and networks – for instance, from non-representational theory and process philosophy – despite the benefits the latter could bring to the sub-discipline.

Haggett and Chorley’s (1969: 7) point about ‘huge losses in relevant information’ is important because network science erases heterogeneity: vertices are vertices, edges are edges, and differences among either result from connectivity patterns. Further differentiations need to be imported from ‘outside’, by linking scores on connectivity indices to attributes of the nodes, links or wider geographical context in question (Zhang et al., 2015) or segmenting the analysis on the basis of external variables (C Wang et al., 2015). This is a reasonable and productive procedure yet can have unintended consequences: it nudges geographers and transport researchers into adopting the internal network perspective that is widely favoured by physicists and complexity scientists. They may end up explaining the effects of node or link interdiction mainly or even only in terms of network topology (Jenelius and Mattsson, 2015; Reggiani et al., 2015; O’Kelly, 2015) without considering context. Pertinent questions about transport governance and politics and about the wider political economy are easily foregone: Why are apparently critical or vulnerable nodes or links located in particular sites and corridors and what has been done – or neglected – to make them more impervious to perturbations? And why have transport networks been configured as they are in the first place?

Explaining vulnerability and resilience in terms of network topology rather than by contextually embedding nodes and links generates another way of privileging generality over particularity. By encouraging internal explanations, a reliance on network science steers transport geographers towards the supposedly universal mechanisms of network development (such as, for instance, Barabási and Albert’s [1999] preferential attachment). It follows that geographical scholarship into transport network vulnerability and resilience needs to be broadened. Graph and complexity theory should be complemented by research traditions that critically unpack network formation, policy and governance aimed at mitigating interdiction, and that ask who is affected most and in what ways by both the interdiction and resilience of nodes, links and whole networks. To address such issues, transport geography can profitably draw on the wider, now burgeoning geographical literature on vulnerability and resilience (Adger, 2006; Ribot, 2011; MacKinnon and Derickson, 2012; Brown, 2014; Cretney, 2014).

IV Differentiation and decentralization

Lest this report is misunderstood, it would be patently incorrect to argue that the whole sub-discipline of transport geography is reverting towards generality. The field is instead being drawn into multiple, at times difficult-to-reconcile directions, with some practitioners (re)connecting with broader debates in Anglophone geography via the mobilities turn and others turning outwards to such disciplines as physics. Both enrich geographical scholarship on transport through conceptual, theoretical and methodological diversification, making transport as object of inquiry exist in new ways. Risks are nonetheless encountered along the way. Not only does the prospect of fragmenting pluralism (Barnes and Sheppard, 2010) loom because transport geographers may lose the ability to speak to and learn from each other as differences in ontology, epistemology and methodology increase further. Also, (transport) geography’s characteristic openness towards concepts, methods and practices from disciplines that historically have been held in greater esteem – physics and mathematics in particular – might once again lock sectors of the sub-discipline into a systematic favouring of the general over the particular for a considerable length of time, especially among transport geographers in localities and countries where the sub-discipline is growing rapidly.

On a global scale, geographical scholarship on transport is decentring away from its traditional core towards East Asia – China first and foremost – and to a lesser extent Southern and Eastern Europe, Latin America and Africa. So far this trend has reinforced the application of ‘western’⁴ concepts, tools and understandings about transport to historically understudied geographical contexts. The fact that many big datasets on transport emerge from some of these localities – notably China – and are deployed in research animated by a reinvigorated social physics and/or utilizing network analysis (Y Liu et al., 2012; Hu et al., 2014; X Liu et al., 2015; Zhong et al., 2015) signals not merely a continuing domination of western modes of thinking and tools but also a potentially entrenched privileging of generality over particularity.

Then again, there are some signs that post-colonial and/or decolonial geographical studies of transport and mobility are emerging (Kwan and Schwanen, 2016), suggesting that further differentiation in conceptualization, theory and methodology is indeed the defining trend in geographical scholarship on transport. Reconciling the general and the particular may remain a challenge but the recent influence of physics and complexity science may ultimately turn out to be only one among many.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

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