Effects of neighborhood types & socio-demographics on activity space

Abstract

This paper examines the relationships between socio-demographic characteristics, travel time, the built environment and resulting average activity spaces for all activities and non-work activities separately using data from the 2012 Northeast Ohio Regional Travel Survey. Multiple regression models are developed to analyze these relationships at individual level. First K-means cluster analysis is conducted to create seven neighborhood types based on five built environment variables. These new neighborhood types are used as discrete explanatory variables to explain average activity spaces, while controlling for travel time, individual and household features, access to transit facilities and the job-population balance. The modeling results indicate that residential location characteristics have significant influences on activity spaces. People living in places away from suburban and rural areas and with a high mix of population and employment tend to have smaller activity spaces. Moreover, this study finds out that while the effects of some explanatory variables (such as age and gender) vary for all activities and non-work activities, socially disadvantaged people (such as the elderly and low income households) generally experience smaller activity spaces.