Socioeconomic and health analysts commonly rely on areally aggregated data, in part because government regulations on confidentiality prohibit data release at the individual level. Analytical results from areally aggregated data, however, are sensitive to the modifiable areal unit problem (MAUP). Levels of aggregation as well as the arbitrary and modifiable sizes, shapes, and arrangements of zones affect the validity and reliability of findings from analyses of areally aggregated data. MAUP, long acknowledged, remains unresolved. We present an exploratory spatial data analytical approach (ESDA) to understand the scalar effects of MAUP. To characterize relationships between data aggregation structures and spatial scales, we develop a method for statistically and visually exploring the local indicators of spatial association (LISA) exhibited between a variable and itself across varying levels of aggregation. We demonstrate our approach by analyzing the across-scale relationships of aggregated 2010 median income for the State of Pennsylvania and 2005–2009 cancer diagnosis rates for the State of New York between county–tract, tract–block group, and county–block group level US census designated enumeration units. This method for understanding the relationship between MAUP and spatial scale provides guidance to researchers in selecting the most appropriate scales to aggregate, analyze, and represent data for problem-specific analyses.
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering
- Geography, Planning and Development
- Management of Technology and Innovation