Contents lists available at ScienceDirect Journal of Transport Geography journal homepage: www.elsevier.com/locate/jtrangeo A geographically weighted regression approach to investigating the spatially varied built-environment effects on community opportunity Chih-Hao Wang a,⁎ , Na Chen b a Department of Geography and City and Regional Planning, California State University, Fresno, Fresno, CA 93740, USA b City and Regional Planning, The Ohio State University, Columbus, OH 43210, USA ARTICLE INFO Keywords: Spatial statistics GWR Land-use and transportation planning Community opportunity Social equity ABSTRACT Geographically weighted regression (GWR) has been increasingly used to better understand the spatially varied relationships between socioeconomic outcomes and policy investments. In this study, a community opportunity index (COI) is computed for Columbus, Ohio, using a set of socioeconomic factors. An ordinary regression and GWR model are estimated to account for the global and local effects of land uses and capital investments re- spectively, while controlling for socio-demographical characteristics. The global results indicate that the com- munity opportunity increases as the distance from the city center increases, while The Ohio State University (OSU) has higher positive spillover-effect on near communities than on distant ones. However, the local results appear a local spatially inverse relationship in the areas adjacent to the international airport (CMH), indicating the existence of negative externalities. With the advantage of visualizing spatial variations, the GWR results suggest that the most effective location for allocating future developments is in eastern Columbus, where shows a clustering of higher COI premiums of a percentage change in residential and commercial uses. A variety of spatial variations is found among different capital-investment effects. Therefore, local characteristics require consideration when allocating additional public facilities. Finally, the GWR results reveal the existence of spatial mismatch that socially disadvantaged groups (e.g. black population, other minorities, single-parent families, and zero-vehicle households) tend to reside in vulnerable communities. These local results provide a new perspective on land-use and transportation planning to help shape a fair community opportunity framework for the future. 1. Introduction Geographical mapping of community opportunity can help examine social inequity, using a spatially aggregated index comprised of a set of socioeconomic factors. This idea is based on the concept of “neigh- borhood effects” that where people live influences their socioeconomic outcomes (Acevedo-Garcia et al., 2004; Jencks and Mayer, 1990). Using GIS, a community opportunity index can be computed by combining a set of un-weighted standardized neighborhood factors. The term “community opportunity” in the present study refers to the physical and socioeconomic outcomes of a residential community. This approach has been applied for visualizing the spatial opportunity and deprivation in many America's cities (Powell, 2007; Reece and Gambhir, 2008). However, the geographical mapping approach does not explicate how the community opportunity is influenced by social and institutional mechanisms. Particularly, the physical setting of land uses and capital investments (i.e. the built environment) was often overlooked in the past research (Sampson et al., 2002). In addition, it is necessary to apply a spatial statistical approach to better understand the spatial variations of the built-environment effects (Du and Mulley, 2012; Mulley, 2014). Geographically weighted regression (GWR) has in- creasingly attracted attention on studying such spatially varied re- lationships over a geographical area (Brunsdon et al., 1996; Paez, 2006; Tu, 2011). Through understanding the local built-environment effects, land-use and transportation planning can help shape a fair community development framework to remedy spatial disparities. In this study, a community opportunity index (COI) was computed and mapped for Columbus, Ohio. Census tracts were selected as the geographic units. An ordinary regression model was estimated to ac- count for the global built-environment effects, while controlling for socio-demographical factors. A GWR model was also estimated to in- vestigate the spatial variations of the built-environment effects. The explanatory variables were characterized into four groups, including metropolitan-location effects, land uses, capital investments, and socio- demographical characteristics. This study will add to the existing lit- erature by examining the local built-environment effects on community opportunity. In the future, the spatially varied relationships can be used to develop an optimization-modeling framework that would facilitate http://dx.doi.org/10.1016/j.jtrangeo.2017.05.011 Received 4 December 2016; Received in revised form 5 May 2017; Accepted 28 May 2017 ⁎ Corresponding author at: Department of Geography and City and Regional Planning, California State University, Fresno, 2555 E San Ramon M/S SB69, Fresno, CA 93740, USA. E-mail addresses: cwang@csufresno.edu (C.-H. Wang), chen.2572@osu.edu (N. Chen). Journal of Transport Geography 62 (2017) 136–147 Available online 14 June 2017 0966-6923/ © 2017 Elsevier Ltd. All rights reserved. MARK