The modifiable areal unit problem in the relationship between jobs-housing balance and commuting distance through big and traditional data

Jobs-housing balance aims to reduce commuting distance and alleviate traffic congestion as a transport policy. However, the measurement of jobs-housing balance is affected by the spatial areal unit employed in analysis and planning. The mechanism by which commuting distance is affected by jobs-housing balance remains controversial for several reasons, including inconsistency of spatial analysis units and whether socio-demographic attributes are controlled. Cellphone data exhibit superiority in identifying the journey-to-work trips for large sample sizes and entail high spatial precision that can be integrated into different analysis units. By combining cellphone data and traditional survey data with detailed socio-demographic attributes aggregated at different analysis units, this study examines the scale and zoning issues of jobs-housing relationships in Shanghai, China. When using large analysis units, the effect of jobs-housing balance was expanded and that of different sociodemographic attributes changed variously and unpredictably. Furthermore, the uniformity of the spatial analysis unit influenced the effect of jobs-housing balance on commuting distance. This study substantiates the effect of the modifiable areal unit problem (MAUP) on the relationship between jobs-housing balance and commuting distance considering socio-demographic attributes and demonstrates that the changing rules of the effects of that balance on the commuting distance are different from those of socio-demographic attributes as the scale changes. Consequently, this work enriches the literature on the MAUP in relation to the jobs-housing balance and obtains a thorough understanding of the MAUP effects through combining big and traditional data.