Linking Job/Housing Balance, Land Use Mix and Commute to Work

With gas prices rising rapidly, many people have started to believe that it has become imperative to reduce their vehicle miles travelled. Land use patterns have been found culpable of contributing to the extra VMT driven by the average. As such, urban planners have employed many strategies to attempt to reduce this portion of VMT. For example, research shows that smart growth in the form of mixed-use compact development results in a better match of jobs and housing since it brings trip origins and destinations closer, thereby making work trips shorter.

This research uses spatial modeling in GIS and Multiple Linear regression/ANOVA in SPSS to analyze the link between job-housing (J/H) mismatch, land use mix and worker commute flows. The study examines J/H imbalance within a travel catchment area using a 7-mile buffer from the centroid of each census tract in Dallas County, Texas. Moreover, it uses jobs, workers local economic and community data in the form of Local Employment Dynamics, Longitudinal Employer-Household Dynamics and Quarterly Workforce Indicators provided by the US Census Bureau to carry out area profile, area comparison, distance/direction, destination, inflow/outflow and paired area analysis for workers place of work and residential distributions in Dallas county. This analysis is linked in Geographical Information Systems to the land use map, which is classified as an entropy index. The GIS results present a spatial picture of labor- shed, commute-shed, job-housing balanced and imbalanced areas by relating the land use mix and commute flows of workers in Dallas County. Moreover, MLR regression model in SPSS shows that Land use mix, Job/housing balance and housing affordability are significant predictors of mean travel time to work. This strategic tool developed through Target Area Analysis and Hot Spot Analysis will act as a guideline for land use planners to understand the regional growth complexities related to work flows. The analytical model developed can also be deployed to direct land development patterns, which will ultimately improve the quality of life, halt urban sprawl, lower costs to businesses and commuters and produce related positive externalities.