Visualization of commuting areas and examination of jobs-housing balance with the adoption of excess commuting

Rapid urban growth extends the boundaries of urbanized areas and makes people spend extra travel cost on the journey to work. Scholars have examined commuting behaviors between residence and work places within a region, and suggested the conceptual theory of jobs-housing balance to evaluate if there are sufficient job opportunities for residents in the region. A quantitative measurement of excess commuting is proposed to reflect the degree of jobs-house balance. However, current studies focus on the evaluation of excess commuting and the causality of spatial restructuring between residence and workplace, the spatial extend of excess commuting in an urban region is not visually delineated. In addition, conventional studies acquire static data such as statistic data or other survey data to analyze impacts of commuting patterns on urban formation. Today the emergence of spatial big data sheds light on the causality of urban commuting from higher spatial and temporal resolutions. This study aims to examine the degree of jobs-housing balance through the calculation of excess commuting and represent the spatial extent of excess commuting of Taichung metropolitan area in Taiwan. Electronic Toll Collection (ETC) data, the spatial big data which is publicly accessible in Taiwan, is introduced as the commuting data and to calculate three key parameters of excess commuting, Tmin, Tobs, and Tmax. The three parameters are used to visualize the extent of commuting zones and examine commuting patterns of Taichung metropolitan area and observe the spatial relationship between those patterns and land-use types. The evaluation of excess commuting indicates that the degree of jobs-housing imbalance in Taichung metropolitan area is similar to Los Angeles metropolis in US. The analytical results explain how ETC data can be successfully used for the calculation of excess commuting, and visualization of the extent of commuting regions. In addition, after the cluster analysis of residential areas, it is intuitive to observe spatial correlation between hot zones of residential areas and the spatial extent of theoretical maximum commuting, and shows that the scenarios of commuting can be considered as an indicator to explain the possibility of urban expansion in the future.