The Use of Spatially Lagged Explanatory Variables for Modeling Neighborhood Amenities and Mobility in Older Adults

Tony H. Grubesic
Drexel University

Andrea L. Rosso
University of Pittsburgh

As more researchers in the socioeconomic, planning, and health sciences embrace the use of spatial data for exploring the local context of study regions, the demand for alternative (not U.S. Census Bureau) databases is increasing. In particular, information pertaining to local amenities (for example, retail, recreation, and cultural resources) or disamenities (for example, crime and pollution) can provide important details about place. The purpose of this article is to provide a brief overview of a popular alternative data source for capturing local amenities in an urban environment: the Esri Business Analyst. This article also explains and illustrates an approach for incorporating these data into a spatial analysis. We specifically highlight the use of spatially lagged explanatory variables in general linear models. In the spirit of previous contributions to the SpAM series in Cityscape, this article uses data and mirrors methods from a previously published study. In this case, we expand on the work of Rosso et al. (2013) and their recently completed analysis of neighborhood amenities and mobility for older adults in Philadelphia, Pennsylvania.