Abstract
Perhaps one of the most surprising arguments in this book, this chapter will make the case that quantitative researchers need to adopt critical methods (and vice versa). To understand geography and war, we need empirical analysis. But we cannot do this blindly: existing research has depended on data that do not meet the needs of conflict research. Two case studies will be presented: measuring mountains to study war and measuring levels of infrastructure development to study war. There are two reasons for choosing these case studies. First, they will demonstrate how we can move from critique to empirical analysis. Second, they will highlight two prominent areas of conflict research, which have depended on two fundamentally misleading geospatial variables.
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Notes
- 1.
Sometimes referred to as spatial econometrics, even when economic factors are absent.
- 2.
Writing on the Caucasus, Radvanyi and Muduyev (2007) argue that ‘[a]mong the shortcomings of conventional geographical determinist discourse is the frequent use of the adjective ‘mountainous’ to describe the entire region without qualification or nuance’ (Radvanyi and Muduyev 2007, 158). Similarly, Ó Tuathail (2001) used discourse analysis to argue that people’s notions of mountains were used to create the idea of the Balkans being a powderkeg.
- 3.
See Openshaw (1984).
- 4.
And yes, there are Marilyns: a prominence of 150 metres: see Dawson (1992).
- 5.
Consider again some of the arguments presented in Chap. 1. As well as making the case that the use of the word ‘mountain’ in conflict literature often lacks nuance, Radvanyi and Muduyev (2007) point out that ‘most authors who purportedly analyse “the Caucasus” are actually writing about the piedmonts, unaware of reality in the mountains proper’ (Radvanyi and Muduyev 2007, 174). Zürcher (2007) argues that different types of mountains have different types of effects in South Ossetia, Abkhazia, Nagorno-Karabakh, and Chechnya. Bohara et al. (2006) find that hilly terrain saw the most violence in Nepal; the high Himalayas do not provide an environment in which conflict can occur: as a rule of thumb, the probability of conflict is low when you need to carry an oxygen tank. Finally, Dupuy (1985) finds that even land that is generally regarded as flat can hamper tank manoeuvres. Yet Gerrard (2000) argues that there is little point in distinguishing high from low mountains. Using a simple binary, mountain definition will disregard all of these points.
- 6.
Mountain definitions often come with bias; this bias can be based on national factors. Gerrard’s definition here, then, is refreshingly free from such bias: by setting the lower threshold at 1000 m, he has declared England void of mountains: even its highest point, Scafell Pike, is only between 977 and 979 m (depending on source).
- 7.
This map, then, uses one of the ‘tricks of the cartographic trade’ described by Harley (1989): the use of colour. However, another trick it employs, not mentioned by Harley, is that of vertical exaggeration. The elevation of the terrain has been increased by a considerable scale in order that the reader is able to differentiate high areas from lower areas more easily. Put simply, the high points in Africa are not quite as high as this map suggests. This trick is used by NASA quite frequently in their renderings of mountains on other planets. Olympus Mons may indeed be the highest mountain in the solar system; however, rather inconveniently, its gradient is rather shallow. Viewed from a distance, Olympus is not as pleasingly pointy as its terrestrial cousin, Everest, despite being considerably higher. As such, NASA exaggerates the y axis, in relation to the x and z axes. The same principle has been employed in creating the map in Fig. 6.2.
- 8.
This research considers sea level to be the lowest point on Earth. Several states have part of their territories at elevations considerably lower than this: indeed, the lowest point in Africa is Lake Assal (155 metres below sea level) which is today part of Djibouti but would have been part of the territory of Ethiopia. Handling negative elevations would have required considerably more research time but would not yield a substantially different result. As such, the compromise of sea level as the lowest point was accepted.
- 9.
The minimum figure of 0 on this eight-bit scale will represent 0 metres in reality; the maximum figure of 255 on the eight-bit scale will represent 8850 metres (Everest). Accordingly, each of the squares represented in this diagram can be converted to metres by multiplying by 34.71: the top left square (197) is therefore 6837 metres above sea level.
- 10.
Fearon and Laitin look at a slightly different set of states to Collier and Hoeffler; as Gerrard’s data set was created for Collier and Hoeffler, this means that there are some gaps in the data used by Fearon and Laitin. As such, Fearon and Laitin use some interesting methodological shortcuts to fill in these gaps. Adding to the inconsistency, Collier, Hoeffler and Rohner go on to use Fearon and Laitin’s inconsistent data set.
- 11.
A review of research using the DCW to look at infrastructure is beyond the scope of this case study, but as a brief sample, researchers have used the DCW to model infrastructure in their analyses of biodiversity loss (Alkemade et al. 2009), conflict and malnutrition in Africa (Rowhani et al. 2011; Sherbinin 2011), desertification (Okayasu et al. 2010), distribution of HIV and AIDS in Africa (Kalipeni and Zulu 2012), mobility patterns in the Ivory Coast (Dixon et al. 2014), civil wars in Africa (Buhaug and Rød 2006), distribution of forest elephants (Yackulic et al. 2011), and the future of China’s infrastructure (Hou and Li 2011).
- 12.
Nelson et al. (2006) write in reference to the Vector Smart Map Level 0 (VMap0), which is the successor to the DCW. The differences between the DCW and VMap0 are essentially data formatting issues; in terms of actual road data content, they can be regarded as functionally equivalent. The specifications for VMap0 can be found at Defense Mapping Agency (1995b) and Defense Mapping Agency (1995c).
- 13.
To give an idea of the number of people who worked on the creation of the DCW, Langaas (1995, 10) points out that ‘around 200 ESRI staff took part in the project, although several of these were only part-time involved. During the production peak fifty personnel worked full time.’
- 14.
See National Imagery and Mapping Agency (1995).
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Pickering, S. (2017). Critical Quantitative Geo-Spatial Methods and War. In: Understanding Geography and War. Palgrave Macmillan, New York. https://doi.org/10.1057/978-1-137-52217-7_6
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