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Multi-Scale Analysis of Local Labour Market Areas and Job-Housing Balance Through Complex Network Methods - Kropp and Schwengler (2016) highlight that functional regions offer distinct advantages over administrative boundaries, as they more accurately capture the spatial distribution of economic activities through strong internal interactions and limited external connections. Among these, Local Labour Market Areas (LLMAs), defined by commute-to-work patterns, serve as key analytical units for understanding travel behaviour and economic agglomeration.
This study delineates LLMAs at multiple scales and analyses job-housing balance patterns within the city. By addressing the limitations of conventional geographic scales, it aims to uncover hidden commuting patterns and provide insights for improving urban job-housing distribution. In particular, outflow-dominated communities—where a significant proportion of residents commute elsewhere for work—are identified as critical areas and candidates for in depth analysis of excess commuting (Kanaroglou et al., 2015). Their connectivity with more self-contained communities serves as a diagnostic tool for detecting labour market imbalances and informing policies on job redistribution, initiatives for affordable housing, and/or transport infrastructure improvements.
Following Tolbert and Sizer’s (1987) definition, a local labour market is an area where most residents both live and work, with minimal cross-boundary commuting, aligning with Goodman’s (1970) principle that such markets should exhibit high intra-market movement. Our study conceptualises commuting as a network linking residential and workplace US Census blocks (blocks herein). A modularity-based community detection method is applied to delineate LLMAs boundaries, with residential blocks as origins, workplace blocks as destinations, and commuting flows as network links. Modularity algorithms contains parameters, called resolution parameters, that can be adjusted when comparing observed spatial distributions to random patterns. By adjusting resolution parameters, which control the inclusion of additional nodes, the study compares LLMAs delineations at different scales, offering a nuanced perspective on commuting activity and spatial organisation. Internal and external flow distributions further characterise communities, distinguishing self-contained areas from those with high outbound commuting. This characterization is further enhanced by classifying places based on fundamental excess commuting indices and benchmarks to study the sensitivity of these indicators for different LLMA delineations.
Santa Barbara County serves as the first case study, with journey-to-work OD data from LODES at the block level. Analysis of commuting flow distributions across the county, reveals the evolving composition of LLMAs across different scales, reflecting the complexity of commuting networks. Findings indicate that modularity-based network analysis effectively identifies LLMAs, while resolution parameters help uncover their multi-scale structure. Additionally, significant variations in inflow and outflow distributions highlight disparities in job-housing balance. The multi-scale approach enhances understanding of inter-block relationships, shedding light on how some areas contribute to equilibrium while others exacerbate excess commuting. The study also facilitates further discussion on observing functional regions through the lens of industry sectors and assessing inequalities in LLMAs accessibility for disadvantaged commuting groups.
