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.

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This short paper links 2 areas of big data science in the context of GIScience: inferential analysis and visualisation. It discusses ideas around integration and analysis of large, spatial referenced datasets and considers how results of these can best be visualised. It advocates a critical approach to big data visualization and warns of the inherent dangers of simply identifying patterns, whether through data mining, modeling or visualization. It adds to ongoing debates by suggesting techniques and rubrics, possibly even hinting at a manifesto.