Handbook of Research Methods and Applications in Spatially Integrated Social Science
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Handbook of Research Methods and Applications in Spatially Integrated Social Science

Edited by Robert Stimson

The chapters in this book provide coverage of the theoretical underpinnings and methodologies that typify research using a Spatially Integrated Social Science (SISS) approach. This insightful Handbook is intended chiefly as a primer for students and budding researchers who wish to investigate social, economic and behavioural phenomena by giving explicit consideration to the roles of space and place. The majority of chapters provide an emphasis on demonstrating applications of methods, tools and techniques that are used in SISS research, including long-established and relatively new approaches.
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Chapter 14: Classification for visualizing data: integrating multiple attributes and space for choropleth display

Tung-Kai Shyy, Imran Azeezullah, Irfan Azeezullah, Robert J. Stimson and Alan T. Murray


It is common for researchers in the social sciences to be concerned with the distributional aspects of social phenomena – such as rates of unemployment, levels of household income and types of housing tenure – across spatial units that comprise a city, state or nation, and to seek to visualize variations in the patterns of such socio-spatial data in the form of a map. Commonly that involves classifying data to produce a choropleth map. In this chapter we review a number of classification approaches that are commonly used – especially by geographers – to generate map displays of socio-spatial datasets at point and polygon levels. We also discuss the development of a new methodology and tool for enhancing classification through the categorization of data to produce an improved capacity for choropleth display of data in a map. The chapter first discusses standard categorization routines such as equal interval, quantile and natural breaks, and the Location Quotient (LQ) which is a benchmarked approach to categorization. Performances among the various classification approaches may be compared by considering the total within-group variance (TWGV) and the total within-group difference (TWGD), the measure structured in the median clustering objective.

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