PROGRESS IN GEOGRAPHY ›› 2012, Vol. 31 ›› Issue (8): 1024-1031.doi: 10.11820/dlkxjz.2012.08.005

• Original Articles • Previous Articles     Next Articles

Spatial Heterogeneity Analysis of Household Residential Location Choice: A Case Study in Orange County, Florida, USA

YU Linjun1,2, Sun Danfeng2, PENG Zhongren3, ZHANG Jian4   

  1. 1. Institute of Remote Sensing Applications, CAS, Beijing 100101, China;
    2. College of Resources and Environmental Sciences, China Agriculture University, Beijing 100193, China;
    3. Department of Urban and Regional Planning, University of Florida, Gainesville, FL 32611-5706, USA;
    4. Nanjing Institute of Geography and Limnology, CAS, Nanjing 210008, China
  • Received:2012-01-01 Revised:2012-03-01 Online:2012-08-25 Published:2012-08-25

Abstract: Households’behaviors of choosing residential locations are spatial in nature. Therefore, spatial effects cannot be disregarded when molding household residential location choice. Spatial correlation and spatial heterogeneity are two aspects of spatial effect. Several studies regarding spatial effect in household location choice modeling have been conducted. However, the existing studies mainly focused on spatial autocorrelation effect in household location choices, where the spatial heterogeneity effect was ignored. Conventional residential location choice models are typically based on the assumption of spatial homogeneity, assuming that the impacts of an influencing factor are constant across space. Therefore, spatial heterogeneity effect is ignored in traditional residential location choice models. In this study, Orange County, FL, USA was used as a case study. Census Block Groups (CBG) in the study area were grouped into five sets, where hot spot analysis (Getis-Ord Gi*) in ArcGIS was employed for location alternatives grouping because it enables spatial locations to be geographically grouped based on one non-spatial attribute. Finally, we used seven multinomial logit model based residential location choices models in the five sub-regions of the study area and at two spatial scales (CBG scale and CBG set scale), respectively. The estimation results provided evidences that both location-dependent and scale-dependent variations exist in the impacts of the influencing factors on household residential location choice. At the higher scale (CBG set scale), zonal attributes are the main influencing factors. At the lower scale (CBG scale), however, the interaction variables between household characters and zonal attributes play a leading role. Variable effects vary significantly across regions at the CBG scale. For example, the interactive variable between zonal Black population density and the Black ethnicity dummy variable of a household is only significant in two regions out of all the five regions. Therefore, spatial heterogeneity is a significant characteristic of the impacts of influencing factors on household residential location choice. Although the study area is located in USA, the conclusion is meaningful for future studies which use regions in China as cases.

Key words: Florida, USA, household location choice, multinomial logit model, spatial effect, spatial heterogeneity, spatial homogeneity