Quantitative geography III: Future challenges and challenging futures

Abstract

In the previous two reports in this series, we discussed the history and current status of quantitative geography. In this final report, we focus on the future. We argue that quantitative geographers are most helpful when we can simplify difficult problems using our distinct domain expertise. To do this, we must clarify the theory underpinning core conceptual problems in quantitative geography. Then, we examine the social forces that are shaping the future of quantitative geography. We conclude with criteria for how quantitative geography might succeed in addressing these challenges.

Emphasising spatial structure in geosocial media data using spatial amplifier filtering

“…The proposed filtering approach helps to overcome a significant hurdle in the non-aggregated and thus fine-grained geographical analysis of geosocial media data. Many geosocial media analyses are conducted in aggregated form, but this poses the modifiable areal unit problem (MAUP) (Openshaw, 1984), a longstanding but still unsolved challenge in the spatial sciences (Wolf et al., 2020). The MAUP not only raises problems related to the arbitrariness of aggregation units.…”

For Geographical Network Analysis

“…Although recently there have been some technical advances in resolving this scale problem through better zoning methods and algorithms, Wolf et al . (2020, p. 3) argue that ‘we cannot escape the MAUP by drawing more or better areal units: we must make stronger theories about what context, exposure, or place does’. With very few convincing answers on how to select the ‘right’ scale of analysis, these methodological challenges might be best addressed with some combination of theory‐driven deductive reasoning and data‐driven exploratory analysis – an approach to nodalisation pursued by Poorthuis and van Meeteren (2021).…”

The spatial structure debate in spatial interaction modeling: 50 years on

“…Meanwhile, in the ESF literature, spatial autocorrelation is often distinguished as local distance effects in contrast to global distance effects that are captured by distances between locations (Griffith 2007(Griffith , 2009a(Griffith , 2011Griffith and Paelinck 2018). An effect that occurs locally and globally implies a multiscale process that potentially varies over space (Wolf et al 2018). However, there has yet to be a comparison or incorporation of local SI models within the context of ESFs, which could be used to shed light on the nature of scale in SI models rather than a priori assuming process stationarity.…”

“…Ultimately, the recommendations put forth here to move the SI modeling research agenda forward are a call for more specific theories and a better understanding of the connection between theories, models, and the interpretations extracted from them. As quantitative human geography progresses, the incorporation of best practices for scientific code development, a culture of data-sharing, and the establishment of common task frameworks could also go a long way toward broadening and aligning overlapping yet sometimes diverging branches of inquiry within the SI modeling paradigm (Wolf et al, 2020).…”