Whither Spatial Statistics?

After a good half-century of quantitative developments in geography and geographic developments in statistics, Nelson takes stock of the field of spatial statistics by asking eminent spatial statisticians (statistical geopgraphers?) for their take on how the field has developed, current and future challenges and seminal works that should be on any quantitative geographer’s bookshelf.  She synthesizes the researchers’ responses to get at the broader trends characterizing spatial statistics.

A key shift discussed by Nelson is the state of advances in methodology vis-à-vis data availability and size. As spatial statistics grew in tandem with the Quantitative Revolution in the mid-20th century, geostatistical methods were in many ways ahead of the available data and technology: computers and automated data management technologies were still nascent, limiting the quantity of data that could be analyzed to what could be managed by hand or by using punchcard systems.  Meanwhile, data collection and organization was onerous and typically manual.  Today, we have the opposite problem: we have TONS of processing power to perform complex calculations, and programming languages to implement new methods easily, so much so that our technology is now ahead of most conventional methods of spatial analysis.  Of particular importance is the new problem of how to work with big data, which may provide more comprehensive samples (even data for the entire population!), in a finer temporal resolution and a richer detail than ever before.

Rising up to the challenges presented by big data and stagnant methods will be paramount for the continued relevance of spatial statistics into the future.  However, today’s cohort of geography students may be falling behind the curve in their technical ability to respond to these challenges.  Mathematics and computer science, absolutely crucial to working in advanced spatial statistics, are receiving less and less of a focus in our Geography departments (though this is starting to change with computer science, specifically in relation to GIS curricula).  Indeed, the very core of quantitative methodology in geography has been shaken by the Cultural Turn.  While qualitative approaches are doubtlessly important to a rich understanding of geographical processes, there is a risk that geographers will lose their quantitative toolkit in a policy context where, increasingly, ‘numbers talk’.  Spatial statistics itself may also suffer if geographers are unable to bring their nuanced views of spatial considerations to the table.

When thinking about these issues, I come back to Nelson’s Figure 1, the Haggett view of progress in geography and helical time. It is still an open question whether we are on the cusp of a second Quantitative Revolution in geography, or whether spatial statistics and geographic thought will continue to drift away from each other, with potentially dire consequences.


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