The chapters in this book have presented a wide range of application areas in GIS for human geography and related social sciences. We hope we have illustrated how GIS can help shed light on important spatial variations in the incidence of activities and how additional spatial analysis can help understand those patterns further. The fact that we have included many examples of work being undertaken for, or on behalf of, key public and private sector organisations, we believe helps to drive home the fact that GIS represents a very public facing, applied perspective for the discipline as a whole. In an era of increasing academic accountability, this part of geography can at least point to many successes in forging partnerships with both business and planning organisations to help solve so-called real-world problems (see Birkin et al. (2014a) for more discussion on their perceived benefits of applied GIS and spatial analysis in geography and academia).
However, we recognise that GIS technology moves on and new data sets are appearing with the potential to transform some of the application areas we have reviewed, especially round the area of social media. Indeed, some might argue that this should now be an important application area in its own right. In making some observations about the future of GIS we briefly explore some key issues around the area of GIS and social media in this final chapter.
The first interpretation of GIS and social media is the increasing use of GIS by organisations involved in journalism. The news reports seen on television, plus many newspaper articles, are increasingly using GIS to help present those news stories to the general public. Sometimes these maps are provided in an interactive framework, with accompanying data sets (on related websites) for viewers to plot variables relating, perhaps, to their home locations or own personal interests (see Herzog (2003) for a good review and Murray and Tong (2009) for a broader examination of GIS applications seen in the media). ESRI (2007) give the example of the partnership between CBS in the US and themselves, with the former increasingly using a version of ArcGIS on their regular news and current affairs bulletins. For example, they quote Al Oritz of CBS in relation to their coverage of the US elections in 2004: ‘The GIS mapping and data system allowed us, for the first time, to show county-level election results integrated with demographic data using 3D digital maps … The system enriched the presentation of results for our viewers.’
Second, is the increasing use of online mapping tools to provide the general public with more interactive GIS-style mapping. Hudson-Smith et al. (2009) review a range of online mapping products (Figure 14.1).
Figure 14.1 A comparison of different public domain map systems with respect to detail and added content
Source: Hudson-Smith et al. (2009)
As Hudson-Smith et al. (2009) and Batty et al. (2010) explain, many of these mapping systems allow users to tag new information to their maps. They note, for example, that Google have made available an application program interface (API) that lets users embed their maps into third-party applications. Through such interactive mapping interested parties can communicate with each other, often helping to add to maps using so-called ‘volunteered information’ (Goodchild, 2007). Sui and Goodchild (2011), for example, give the examples of people adding their maps of the ‘location of Bin Laden’s death, Google Earth mashups of critical sites using data posted on WikiLeaks and tracking the diffusion of BP’s oil spill in the Gulf of Mexico’ (see also the excellent text of Sui et al. (2012) which explores the notion of volunteered information and online mapping in far more detail). Such volunteered information is seen by many as an important new source of data to be used in GIS although others offer more caution over its reliability and therefore use (Goodchild and Li, 2012; Batty et al., 2010). This type of information has also been referred to as ‘crowdsourcing’. This phrase has different definitions in different disciplines but in a GIS context it means information provided by groups of individuals (initial information and then response data to key questions or debates) which is then shared with all other users within the GIS portal. Collecting data through maps has also been supplemented by the increasing availability of GIS on tablets and smartphones which can make them ideal also for data collection. In the past, for increased accuracy, smartphone users could connect their phone to an accurate GPS device using applications such as Bluetooth. Nowadays, most smartphones and tablets contain increasingly accurate GPS recorders which help to collect information on location more routinely (and can be used for analysis in the field also – for example, retail location planners exploring demographic or drive times via GIS on a tablet while in the field).
Third, in addition to the increasing use of online mapping, the public is increasingly volunteering information through data provided through social media websites such as Twitter and Facebook. These social media sites are now generating enormous levels of traffic, and hence potential new data. Twitter, for example, claimed to have around 500 million tweets per day globally in 2014. Again these media sites allow interested parties to communicate with others, and show the interactions between key events of the day and the reactions of the public. These networks can also be used by media or government agencies to relay vital information back to the public, especially in the event of emergencies (see discussion below). Sadly, not all of these data are georeferenced. However, Stefanidis et al. (2013) note that much of Twitter data, for example, can provide ‘geospatial footprints’ in the form of where the tweet originates or references in the context to geographic entities.
Some brief illustrations of the use of Twitter data are useful here and can be related back to our earlier chapters. First let us examine their potential use in retail analysis, discussed in Chapter 8. Twitter data can provide interesting information relating to consumer movements within cities in relation to choice of shopping centres and offer greater insight into multi-purpose trip making. Location models in retail planning often rely on limited data on consumer movements to calibrate trip-making models. GIS can incorporate Twitter data to plot where consumers have travelled from to get to a shopping centre and help that calibration process. They can also be used to examine where consumers have been prior to visiting a shopping centre – i.e. whether the trips have been made from home, work or some other location in the daily cycle of movement around the city (see Birkin et al. (2014b, 2017) and Lovelace et al. (2016) for more details in relation to retail use of Twitter data and Arribas-Bel et al. (2015) for the implications of Twitter data for understanding general movements across cities).
Second, Twitter data could be useful for health applications (building on the material of Chapter 9). Many public health organisations are already monitoring tweets for any mention of illness or disease (and the spread of such disease in a geographical context). Paul and Drezde (2011), for example, consider a broad range of public health applications seen on Twitter, examining over one and a half million health-related tweets, and discover mentions of over a dozen ailments, including allergies, obesity and insomnia. An interesting application has recently begun at the University of Leeds to look at the geography of food poisoning through the discussion on Twitter/Facebook of ill-health induced by bad experiences of meals in cafés, restaurants or bars (Oldroyd, forthcoming).
Third, many applications of GIS and social media have used Twitter data to explore reactions to natural disasters (relevant especially to emergency planning, discussed in Chapter 10). The common methodology here is to use Twitter data to identify and localise the impact areas of major events (event monitoring). This information could also have implications for emergency planning and real-time evacuation procedures (see interesting applications in Crooks et al., 2013; Crooks and Wise, 2013; Stefanidis et al., 2013; Goodchild and Glennon, 2010; Middleton et al., 2014).
Not surprisingly, technical advances have allowed the development of new GIS and related software packages to handle the increase in social media data. Tsou et al. (2013), for example, have designed new mapping software to analyse Twitter data by searching on key words and then producing ‘geospatial fingerprints’ of matters related to the variables associated with keywords (see also Batty et al., 2010). Their application area is based around political geography. They showed that it was possible to map the popularity of different electoral candidates across the US by an exploration of complimentary tweets towards those candidates. These geospatial fingerprints showed a strong correlation to the geography of the final election results. Thus, this type of analysis could be useful for forecasting the results of future elections, especially at the small-area level.
The increased availability of social media has also been associated with the development of a new information era in GIS and spatial analysis – that of ‘big data’. Big data are not only social media data: they can also come from public and private organisations – perhaps not data typically in the public domain in the past. So retailers, for example, might make data on sales available for academics to explore spatial variations in diet and nutrition, as seen through the bundles of foods bought by customers in different parts of cities or regions. We have already seen some examples of this type of data in the earlier chapters. For example, in Chapter 12, we referred to the data being provided by the transit companies in Brisbane, Australia, allowing researchers to map and analyse vast quantities of (hourly) public transport data across the city.
Big data (and especially social media data) have inevitably raised new and additional concerns over accuracy and ethics. These concerns are not new in the history of GIS. Pickles (1995, 1999) was one of the first to look at the ethical use of data in GIS and geo-demographics. For big data there are now a plethora of studies re-exploring these issues, along with important concerns over data accuracy (e.g. Kitchin, 2014; Goodchild and Li, 2012; Graham and Shelton, 2013; Xu et al., 2013; Dalton and Thatcher, 2015; Thatcher, 2014). All users of big data should at least be aware of these wider debates.
All of these developments are likely to increase the ease of access to GIS technology and perhaps increase the number of ways in which data can be captured and shared. However, wherever the data come from in the future we hope that the presentation of what GIS can do in different areas of the social sciences that we have offered in this book (with the related practical examples) will still be a valuable learning tool for students and practitioners alike, especially those new to the discipline.
All website URLs accessed 30 May 2017.
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