Analyzing the multiscale patterns of jobs-housing balance and employment self-containment by different income groups using LEHD data: A case study in Cincinnati metropolitan area

Achieving a balanced jobs-housing relationship has been treated as a solution to relieve traffic congestion and air pollution. Jobs-housing ratio and employment self-containment (ESC) are applied to quantify different levels of jobs-housing balance. Though appropriated methods have been analyzed to explore jobs-housing relationship at multiple fixed scales, scale dependency problem remains unsolved and resulted in discord about jobs-housing relationship. Furthermore, the differences of the jobs-housing balance and ESC by various income groups received little attention. This paper proposed a K-medoids clustering method to explore the aggregate multi-scale patterns of jobs-housing ratio and ESC by higher, medium, and lower-income groups using 2016 longitudinal employment household dynamics (LEHD) data. To alleviate the scale effect, individual workers and jobs were simulated by Monte Carlo approach according to LEHD data provided within each block. Afterwards, we analyzed the jobs-housing relationship within multi-scale units by different aggregated groups. We found jobs-housing balance had a positive effect on ESC, while the effect varied by different income groups. Jobs-housing balance affects ESC more for lower-income group when the searching radius is <4 km, while beyond 4 km, jobs-housing balance has more effect on ESC of higher-income group. ESC of medium-income group is least affected. Employing the K-medoids clustering method, we classified the four patterns of jobs-housing and the two patterns of ESC for each income group. We further identified jobs-housing matched and mismatched areas through eight joint patterns of jobs-housing balance and ESC. Our work contributes to understanding of jobs-housing balance and commuting by scale variations and income.