Handbooks of Research Methods and Applications series
Edited by Robert Stimson
Chapter 14: Classification for visualizing data: integrating multiple attributes and space for choropleth display
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.
You are not authenticated to view the full text of this chapter or article.
Elgaronline requires a subscription or purchase to access the full text of books or journals. Please login through your library system or with your personal username and password on the homepage.
Non-subscribers can freely search the site, view abstracts/ extracts and download selected front matter and introductory chapters for personal use.
Your library may not have purchased all subject areas. If you are authenticated and think you should have access to this title, please contact your librarian.