Inspecting Transport–Land Synergy in Transit-Oriented Station Areas from the Perspective of Jobs–Housing Relationship Typology: A Case Study of the Highest-Density Built-Up Zone of Shenzhen, China

Jobs–housing balance is an important goal of urban sustainable development, and Transit-Oriented Development (TOD) is widely regarded as an effective pathway to optimize the jobs–housing relationship. However, station-area-scale jobs–housing studies remain limited, and existing jobs–housing typologies have not been linked to regulable TOD characteristics, leaving jobs–housing optimization without a basis for differentiated regulation. Using 72 built subway station areas in Shenzhen’s Density Zone 1 as samples, this study draws on three dimensions, namely transport supply (Node), land use (Place), and jobs–housing, and applies hierarchical clustering, multiple linear regression, and Lasso regression to examine jobs–housing typologies, land use and building distribution characteristics, and the effects of transport supply and land use on the jobs–housing relationship in TOD station areas. Regression analysis uses jobs–housing value, a weighted score derived from the employment–residential area ratio (JH1) and the employment–residential population ratio (JH2), as the dependent variable. The main findings are as follows. (1) TOD station areas can be classified into four types, which differ significantly in land use and building distribution; planning strategies should therefore be differentiated according to the characteristics of each type. (2) The four types exhibit a concentric spatial structure comprising an employment-oriented core, a jobs–housing balanced middle ring, and a residential-oriented periphery, with the mean betweenness centrality increasing gradually, indicating that TOD intensity rises in tandem with subway network hub status. (3) Across the full sample, the interaction term between transport supply and land use is significantly and positively associated with jobs–housing value, a result supported by spatial econometric robustness checks; the main effects of Node and Place are significantly negative only in ordinary least squares. (4) Subway line direction, bus stop density, floor area ratio, and maximum planned floor area ratio are positively associated with jobs–housing value, whereas shared bike density, POI density, total road length, and building mixing entropy are negatively associated with it. This study provides a quantitative basis and actionable planning pathways for the differentiated regulation of the jobs–housing relationship in TOD station areas.

Authors

Institutions

Publication Details

Journal
Land
Published
2026-09-29
DOI
https://doi.org/10.3390/land15101834
Primary Topic
Urban Transport and Accessibility
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Inspecting Transport–Land Synergy in Transit-Oriented Station Areas from the Perspective of Jobs–Housing Relationship Typology: A Case Study of the Highest-Density Built-Up Zone of Shenzhen, China

Hao Geng, Jingyi Zhang, Yusong Zhu, Zhitao Zhong et al.
Land
Urban Transport and Accessibility
article

Inspecting Transport–Land Synergy in Transit-Oriented Station Areas from the Perspective of Jobs–Housing Relationship Typology: A Case Study of the Highest-Density Built-Up Zone of Shenzhen, China

Hao Geng, Jingyi Zhang, Yusong Zhu, Zhitao Zhong, Fang Liu
article en

Abstract

Jobs–housing balance is an important goal of urban sustainable development, and Transit-Oriented Development (TOD) is widely regarded as an effective pathway to optimize the jobs–housing relationship. However, station-area-scale jobs–housing studies remain limited, and existing jobs–housing typologies have not been linked to regulable TOD characteristics, leaving jobs–housing optimization without a basis for differentiated regulation. Using 72 built subway station areas in Shenzhen’s Density Zone 1 as samples, this study draws on three dimensions, namely transport supply (Node), land use (Place), and jobs–housing, and applies hierarchical clustering, multiple linear regression, and Lasso regression to examine jobs–housing typologies, land use and building distribution characteristics, and the effects of transport supply and land use on the jobs–housing relationship in TOD station areas. Regression analysis uses jobs–housing value, a weighted score derived from the employment–residential area ratio (JH1) and the employment–residential population ratio (JH2), as the dependent variable. The main findings are as follows. (1) TOD station areas can be classified into four types, which differ significantly in land use and building distribution; planning strategies should therefore be differentiated according to the characteristics of each type. (2) The four types exhibit a concentric spatial structure comprising an employment-oriented core, a jobs–housing balanced middle ring, and a residential-oriented periphery, with the mean betweenness centrality increasing gradually, indicating that TOD intensity rises in tandem with subway network hub status. (3) Across the full sample, the interaction term between transport supply and land use is significantly and positively associated with jobs–housing value, a result supported by spatial econometric robustness checks; the main effects of Node and Place are significantly negative only in ordinary least squares. (4) Subway line direction, bus stop density, floor area ratio, and maximum planned floor area ratio are positively associated with jobs–housing value, whereas shared bike density, POI density, total road length, and building mixing entropy are negatively associated with it. This study provides a quantitative basis and actionable planning pathways for the differentiated regulation of the jobs–housing relationship in TOD station areas.

LandVol. 15(10)
Macau University of Science and Technology (MO), Urban Planning & Design Institute of Shenzhen (China) (CN), Southeast University (CN)
Sustainable cities and communities
Openalex Percentile: Top 7%
Urban Transport and Accessibility
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.