Geocomputation: A Practical Primer (2015)

CONCLUSION

THE FUTURE OF APPLIED GEOCOMPUTATION

Chris Brunsdon and Alex Singleton

This book has presented 18 chapters, arranged into the thematic groupings describing how the world looks through visualisation and exploratory data analysis; how movements in space can be represented across a variety of spatial and temporal scales; how geographical decisions can be made through the application of spatial algorithms; how geocomputational techniques can be used to explain spatial processes; and finally, how new methods of enabling interactions are impacting upon the field of geocomputation. Inevitably there will be overlap, and chapters could potentially be arranged in multiple ways. As such, in this final chapter of the book, we draw a series of themes together from the chapters and place these within the context of our speculations about those emerging and future directions in applied geocomputational research.

Open data, tools and reproducibility through open practices in science

An increasingly important theme for the future of geocomputation will be reflected in a more open paradigm of conducting research (Brunsdon and Singleton, Chapter 15), comprising a tighter coupling of data, software, analytical methods and final presentations. Sui (2014) usefully places such developments within a broader anatomy of GIS (Longley et al., 2005), and a number of themes related to open practices of science are echoed within this book.

Contemporary GIS adopt either an open source or closed source model (Steiniger and Bocher, 2009), with open source applications available in a source code format within the public domain, and under a number of licences that permit different permutations of reuse, adaptation and redistribution. Such software is prevalently available at no cost, and development work is often completed by a community of programmers, although some have debated the extent to which open source software development is a group effort (Krishnamurthy, 2002). An introduction to open source GIS software was presented by O’Brien (Chapter 17), and some specific applications were illustrated with the statistical programming language R (Cheshire and Lovelace, Chapter 1; Alexiou and Singleton, Chapter 8; Spielman and Folch, Chapter 9; and Brunsdon and Comber, Chapter 16) and Python (Lewis, Chapter 10; Rey, Chapter 14). However, it was interesting to note that the use of open source tools was not universal in this book; for example, others adopted either traditional GIS software such as ArcGIS from ESRI (Tomintz, Clarke and Alfadhli, Chapter 11); and other software such as MATLAB (Rohde and Corcoran, Chapter 7) or Excel (Morrissey, Chapter 13). Some authors developed bespoke software applications for their work (Batty, Chapter 3; Harland and Birkin, Chapter 5), with still others developing open source tools that aim at supporting a broad community of users, techniques and application areas. For example, Crooks (Chapter 4) introduces the MASON multiagent simulation library, and Rey (Chapter 14) describes PySAL, which provides a set of Python tools that implement a variety of spatial analytical methods. Contemporary geocomputation is reflected by a hybridisation of software tools, both in terms of where functionality is situated (e.g. spatial analysis functions within databases), and in terms of licensing frameworks (e.g. commercial GIS providers making some content open source). We would argue that such trends and cross-fertilisation will continue. However, as open source software becomes more mature, these will also likely have an impact on the traditional markets of commercial GIS and their implementation of geocomputational functionality.

Open data represents a general movement towards public data being released with less restrictive licences, enabling a new paradigm in data sharing and reuse. Within a UK context, the term ‘open data’ has an explicit definition, and relates to those data that have been released under the Open Government Licence for Public Sector Information (http://www.nationalarchives.gov.uk/doc/open-government-licence/). However, this term is also used more flexibly to refer to those other data that are free from reuse restrictions or financial cost of acquisition. Alexiou and Singleton (Chapter 8) gave an example of using open data in the development of a geodemographic classification. We postulate that open data will grow in prevalence over the next decade, with support building as a result of enhanced economic benefits derived from the exploitation of new business models, alongside both perceived and measured societal gains through applications linked to education, policing, the environment, health and well-being. Many of the platforms currently used to disseminate open data can be considered in their infancy, and the structure of such data released has been variable in terms of scale (e.g. anonymised individual vs. aggregated data) and format (e.g. XML, CSV, Excel). We argue that geocomputation will play a role in enhancing such cyberinfrastructure, enabling more flexible dissemination through the openness of formats, data linkage, disclosure control, sharing, visualisation and web-based services coupling to third-party applications.

As outlined by Brunsdon and Singleton (Chapter 15), reproducible research is enabled when work is published, and full details about the methods and data used to obtain the reported results are transparent. In much current social science research this is ensured through the peer review process; however, we argue that such mechanisms should be tightened in the future. As Brunsdon and Singleton (Chapter 15) outlined, and was illustrated in the assembly of a number of other chapters for this book (e.g. Cheshire and Lovelace, Chapter 1; Brunsdon and Comber, Chapter 16; and Spielman and Folch, Chapter 9), data, methods and geocomputational analysis can be embedded in analytical workflows linking to written interpretation. Examples within this book included the use of Sweave, a tool for embedding R code into LATEX, which is a document processing language. We believe that such tighter coupling will begin to appear as criteria for submission to some social science journals, as is becoming common practice in a number of science publications. There are of course geocomputational challenges, for example, related to data with restrictive licences, how to manage stochastic models and, more generally, how analytical work involving high-performance computing could be reproduced.

Turning Big Data into big information

In addition to open data mentioned in the previous section, the other most common contemporary prefix to the word data is the term big. “Big Data” relate to those new data sources that are very large in size, making manipulation and analysis complex with traditional tools and methods. Such data are generated through a variety of mechanisms including, but not limited to earth observation satellites, transportation networks, consumer data recording and social media activity. For those engaged in geocomputation, such new data present potentially interesting new areas of research. However, it is worth noting that contemporary smaller data are in fact ‘big’ within a historical context (Graham and Shelton, 2013); and we assert that caution is needed with the term. Furthermore, accompanying the growth in use of the term ‘Big Data’, there have been some grand claims about the impact that such resources will have on a new data-intensive form of science. Hey et al. (2009) argue that science has been characterised by a series of paradigms including the empirical/descriptive, theoretical (testing models) and computational (simulation). They argue that we are entering a ‘fourth paradigm’ of science, characterised by statistical exploration and data mining. Others, and rather dangerously in our view, have speculated an ‘end of theory’, where correlation in “Big Data” is enough, relative to more traditional modelling approaches involving hypothesis testing (Anderson, 2008). While we argue that such boosterish claims should be treated with caution (see, for example, Brunsdon, 2014), we would make the pragmatic case that both traditional and new approaches offer strength, and that for problems relating to a spatial dimension, a framework of geocomputation provides much potential for integration.

However, it is clear to us that as the prevalence of Big Data grows, this will create new research challenges for geocomputation. For example, how could those models presented by Nakaya (Chapter 12) be implemented for data-rich, rather than data-poor, applications (Kitchin, 2013)? Or to what extent could the spatial and temporal data warehousing technology outlined by Miller (Chapter 6) be made efficient, robust and resilient under the constraint of such increased volumes of new data? One area that is likely to see increased activity is the area of cloud computing, referring to both distributed storage and processing provided by organizations such as Google (https://cloud.google.com/products/compute-engine/) and Amazon (http://aws.amazon.com/ec2/). To some extent, such developments are not new, and the use of high-performance computing has been one of the hallmarks of geocomputation since the 1990s (see Turton and Openshaw, 1998). However, in recent years, we have seen an evolution from traditional ‘grid’ computing infrastructure (Harris et al., 2010), perhaps housed by a single or group of institutions, to those in the ‘cloud’, a typically more distributed assemblage of computers often run by commercial organisations. Services comprise a mixture of storage and processing, including new technologies such as graphics processing units, and billing is flexible to meet a range of potential usage scenarios. Furthermore, for services such as ec2 from Amazon, operating system images are available for running both commercial (e.g. https://aws.amazon.com/solutions/global-solution-providers/esri/) and open source GIS tools within the cloud (http://boundlessgeo.com/2010/09/opengeo-suite-community-edition-on-amazon-web-services/); or more generic operating system images enabling simple deployment of bespoke software. Such advances are making high-performance computing more accessible through ease of access and cost, and as such we would anticipate the utility of such services will likely be exploited more explicitly by the geocomputation community in the future. However, there are constraints that require further research, such as how sensitive data might be managed within such settings where the place of storage and computation is not necessarily known.

Fast versus slow temporal dynamics and the measurement and conceptualizations of place

Activities on the earth’s surface are situated in both space and time, and in this book a variety of the authors tackle such dynamics across a range of spatial and temporal scales from the aggregate (e.g. Batty, Chapter 3; Crooks, Chapter 4; Morrissey, Chapter 13) to the personal and/or micro scale (Torrens, Chapter 2; Rohde and Corcoran, Chapter 7; Harland and Birkin, Chapter 5). In some sense, these examples begin to address those concerns raised by Goodchild (2013) who discusses how the use of maps as a conceptual framework for most GIS software has resulted in time being considered in only very course resolution: the distribution of a population from a census, or the location and extent of physical features such as a mountain range. The examples presented within this book consider dynamics at varying geographic and temporal scales. However, this should perhaps not be surprising, and indeed Longley (1998: 3) notes: ‘[t]he hallmarks of geocomputation are those of research-led applications which emphasise process over form, dynamics over statics, and interaction over passive response.’

Critical within this early definition of geocomputation was an emphasis on dynamics and interactivity, and although some have offered paths for integration into desktop GIS (Yu and Shaw, 2008), or extension of geographical indicators (Cheng et al., 2012), such concepts typically remain an adjunct rather than an integral component. Some have gone further; for example, Batty (2012a: 193) says that such ‘[n]ew data begets new theory’; and there is a challenge to explore how such short-term Big Data may be linked with more traditional longer-term data such as censuses or surveys. Some progress in this area was reported by Singleton and Spielman (2014) in their discussion of a requirement to shift emphasis from a variables to a contextual paradigm in the social sciences, and is illustrated in the chapters by Alexiou and Singleton (Chapter 8) and Spielman and Folch (Chapter 9) in this book. As such, we would argue that future analysis will continue to exploit the dynamics of time in addition to location, thus remaining a key facet of geocomputation research.

A related challenge for geocomputation research will be to derive new methods that address what Sui and Goodchild (2011: 1744) refer to as the ‘world of place (social media)’ rather than the ‘world of space (traditional GIS)’. This moves the ontology of place from one which focuses on Cartesian space (e.g. x and y coordinates) to something more flexible; for example, ‘London’ might be defined as a place, rather than an (x, y) pair for the centroid of the city, or the boundary of an administrative unit reflecting official extent. This differentiation in part relates to burgeoning volumes of new data created through processes of volunteered geographic information, defined as ‘the widespread engagement of large numbers of private citizens, often with little in the way of formal qualifications, in the creation of geographic information’ (Goodchild, 2007: 212). A useful disambiguation of VGI, is provided by Wilson and Graham (2013). Furthermore, there are increasing volumes of network-based data generated through social media, but also other forms of large data generation in cities such as mobility tracking through transit networks, or prospectively through RFID, or low-power Bluetooth devices; and these are also eliciting a growing relevance of network-based ontologies (Sui and Goodchild, 2011) for applied geocomputation. As such, we would argue that a further key future direction will be in the area of geographic network analysis (see O’Sullivan, 2014).

Cities and the scale of geocomputation

Modelling and simulation have a rich history of application within the context of cities, and maintain contemporary relevance (Clarke, 2013). Batty (2009, 2010, 2013) describes a new science of cities, building on developments that have emerged from the wider-complexity sciences that represent a shift in thinking about how cities function, from a concept of top-down and central ordering, through to more organic and bottom-up processes where cities structure and function are a result of the dynamic actions of millions of individual and group decisions, typically occurring with limited central control (Batty, 2012b). In such applications, it is not uncommon to create analytical frameworks that mix a range of the geocomputational methods presented within this book, for example microsimulation (Harland and Birkin, Chapter 5) to create synthetic populations, through spatial interaction models to calibrate flows (Morrissey, Chapter 13) and agent-based simulation (Crooks, Chapter 4; Torrens, Chapter 2) to manage interactions and emergence across scales and contexts.

Much of this new science is data-rich, embedding those resources discussed earlier in the context of ‘Big Data’ into modelling frameworks for either calibration or validation. Within this context, visualisation utilising tools such as those presented by Cheshire and Lovelace (Chapter 1) becomes important; as do those methods of conflating disparate data sources into meaningful indicators (Spielman and Folch, Chapter 9; Alexiou and Singleton, Chaper 8) alongside the storing of such resources within spatio-temporal databases (Miller, Chapter 6). Within this context, geocomputation in the future will not only have a role in facilitating a deeper embedding of the production of data within the fabric of cities (Dodge and Kitchin, 2005), but also play a significant role in enabling the consumption of data, and information derived through modelling, and particularly so for those services designed to engender citizen participation (Kingston, Chapter 18). Within this setting we will likely see the scale of geocomputation become more detailed (see Torrens, Chapter 2), enabled by enhanced computational power and also an increasing prevalence of data available about how individuals move within cities. Furthermore, we would argue that geocomputation will impact the scale of data consumption within cities, and will increasingly be embedded within those technologies enabling the ambient supply of spatial data and informational resources, for example, through mobile devices such as phones or augmented reality eyewear.

Concluding comments

It is always difficult to pull together a text designed to represent all activities within a field, and particularly so for one such as geocomputation where there has been such a great deal of activity since the 1990s. Perhaps the most enduring feature of geocomputation, and testament to its relevance today, is how embedded it has become in many of those activities we now consider as routine to our daily lives: for example, navigation between places utilising distributed routing facilities enabled on mobile devices. We hope that through the chapters presented, these highlight the essence of geocomputational methods, and those applied contexts in which they are implemented. In this final chapter, we have aimed to provide some of our own speculations about the areas in which we see geocomputation having future impact. It is clear to us that there are a great deal of opportunities for researchers to engage with geocomputation, and the relevance of these methods will increase, as they have done since the definition of the field in the 1990s.

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