Utilizing Geospatial Analysis of U.S. Census Data for Studying the Dynamics of Urbanization and Land Consumption

34 Pages Posted: 22 Jan 2016 Last revised: 26 Jan 2016

Date Written: January 22, 2016

Abstract

Geographically referenced US census data provide a large amount of information about the extent of urbanization and land consumption. Population count, the number of housing units and their vacancy rates, and demographic and economic parameters such as racial composition and household income, and their change over time, can be examined at different levels of geographic resolution to observe patterns of urban flight, suburbanization, reurbanization, and sprawl. This paper will review the literature on prior application of census data in a geospatial setting. It will identify strengths and weaknesses and address methodological challenges of census-based approaches to the study of urbanization. To this end, a detailed overview of the geographic structure of U.S. Census data and its evolution is provided. Ecological Fallacies and the Modifiable Areal Unit Problem (MAUP) are discussed and the Population Weighted Density as a more robust alternative to crude population density is introduced. Of special interest will be literature comparing and/or integrating census data with alternative methodologies, e.g. based on Remote Sensing. The general purpose of this paper is to lay the groundwork for the optimal use of high resolution census data in studying urbanization in the United States.

Keywords: Population Density, Population Weighted Density, Census Geographies, Urbanization, Land Consumption, Sprawl, Land Use Efficiency, LULC, GIS, City Limits, Urban Extent, Built Environment, Urban Form, Areal Interpolation, Scale, Spatial Scale, Dasymmetric Mapping, Ecological Fallacy, MAUP

JEL Classification: R11, R14

Suggested Citation

Menninger, Toni, Utilizing Geospatial Analysis of U.S. Census Data for Studying the Dynamics of Urbanization and Land Consumption (January 22, 2016). Available at SSRN: https://ssrn.com/abstract=2720293 or http://dx.doi.org/10.2139/ssrn.2720293

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