Jobs-housing Balance Research Articles

Study on the Influence of the Built Environment and Personal Attributes on Commuting Distance: A Case Study of the Tianjin Central Area Divided by TAZ Units

Long commuting distances pose a significant challenge for many large cities, undermining the principles of sustainable urban development. The factors influencing urban commuting distances among residents are complex and necessitate hierarchical analysis. This study uses Tianjin, one of China’s four municipalities, as a case study, employing transportation analysis zones (TAZ) as research units. We classify these units based on resident and working populations, extracting multiple built environment and personal attribute factors to establish a model that examines the influence of the job–housing balance. The analysis identifies 12 sub-items across two categories of influencing factors, with correlations tested through spatial analysis and linear regression. We found 28 positive associations and 35 negative associations. Notably, the job–housing relationship for the working population was generally more sensitive to changes than that of the resident population. At the TAZ level, personal attributes exerted a more significant influence on the job–housing balance than built environment factors, with commuting mode, life stage, age, and income level notably affecting commuting distances.

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.

Commuting behaviors response to living and working built environment: Dissecting interaction effects from varied supply and demand masses

Most developed megacities have experienced poly-centralization and suburbanization, leading to job-housing mismatches and negative outcomes such as increased commuting distances and frequencies. Recognizing the scarcity of structural analysis concerning the attractiveness of commuting's geographic endpoints, this study examines the diverse impact of the built environment at living and working locations on commuting flows, considering various supply-demand masses and distance levels. Utilizing a Gradient Boosting Decision Tree (GBDT) model, the study highlights the importance of job-housing ratio, POI diversity, and subway station proximity in employment locations, while informal area rates and subway proximity remain significant in residential locations. A three-dimensional analysis further indicates that achieving a perfect job-housing balance in megacities remains a dream, with each area presenting unique challenges requiring tailored solutions beyond a one-size-fits-all approach. Potential policies, such as planning large residential areas in the inner suburbs and upgrading convenient infrastructure in the outer suburbs, are proposed. Furthermore, with the uncovered distinct interaction effects of built environment on commuting behavior across various masses and distances, common perceptions related to mixed-use land and the role of informal settlement, are critically challenged. This extends our understanding of sustainable urban system design and provides references for planning policies.

Jobs-Housing Balance Dimension in the New Resettlement Areas Affected by the Hydropower Construction Project Case of: Samavkhyxay Village, Xieng Ngeun District, Luang Prabang Province

This study was conducted in Samackhysay Village, Xiengngun District, Luang Prabang Province, emphasizing the dimension of jobs-housing balance in the new resettlement areas of residents affected by the hydropower construction project and the resettlement process. 655 households have been moved from 10 villages. The methodology of the study employed a questionnaire with 250 households. The quantitative and qualitative approaches have been utilized with Binary Logistic Model in order to examine the relationships between housing and workplaces/balancing dimensions. Results of this study revealed that after the resettlement was completed, there are some positive and negative impacts illustrated, for instance, the community-level infrastructure has been updated with roads, schools, clinics, electricity, and water supply for the positive manners. Regarding the negative image, people are hard adapting to the new environment, the shortage of agricultural land and workplaces have been located far away from their residences, and their expenses increased with their longer trips to workplaces. The results also confirmed that the imbalance factor between jobs-housing is a crucial dimension of the resettlement of new locations. The Logistic Model revealed some critical issues regarding the inequalities of ethnicity, location of the original village, level of education, occupation, distance to the workplace, income, and conditions of homes and vehicles. The obvious change is that new occupations in the new resettlement area are workers compared to farmers. The outcomes of the study can be used as a guideline for creating a balance between housing and workplaces, especially the allocation of agricultural areas and the provision of occupations that are appropriate for the people to create a balance, to become the model village for the development and sustainability in the future.

Green commuting within the x-minute city: Towards a systematic evaluation of its feasibility

In cities, carbon emissions associated with commuting transport is large and significant. This study integrates data about the jobs-housing relationship, road network configurations, public transport availability, and real-time traffic conditions during peak hours to evaluate the commuting feasibility and performance of green travel modes (walking, cycling and public transport) and explore the potential determining factors. In the context of the x-minute city, the green travel commuting feasibility (GTCF) indicator measures the percentage of the working population who can commute via green travel modes within specific x-minute thresholds. 15, 30, 45 and 60 min have been considered. In comparison, the car travel commuting feasibility (CTCF) indicator is developed to evaluate the corresponding commuting performance by car. Nanshan district of Shenzhen in China is taken as a case study. Results show that distinct gaps exist between GTCF and CTCF. Public transport performs well only for long-distance (> 8 km) commuting trips, and cycling does well for short (< 3 km) and medium-distance trips. Geographically, areas with large differences of GTCF and CTCF are identified for improving green travel modes with priority. Potential factors influencing GTCF are explored with a regression model and case-based analysis. Smaller street blocks, bus route realignment and better jobs-housing balance should be targeted. Designing cycling short-cuts and public transport routes that avoid traffic jams are also recommended to promote green commuting. The findings demonstrate that real-time trip-planning information is of great value in evaluating the commuting feasibility of multimodal travel and identifying influential factors for achieving the x-minute city.

Exploring the relationship between jobs and housing in China’s megacities: the case of Guangzhou and Shenzhen

ABSTRACT The jobs-housing imbalance is a critical problem in many megacities. However, prevalent research tends to focus on the numerical balance while overlooking the balance in affordability. Moreover, studies connecting jobs-housing relationships with employment characteristics are limited. Taking Guangzhou and Shenzhen as the cases, this study conducts a spatial autocorrelation analysis based on online jobs and housing big data to explore the spatial relationships between jobs and housing in terms of both availability and affordability as well as their associations with employment attributes. This study reveals that jobs-housing mismatches usually exist in suburban areas between urban cores and peripheries. Additionally, the percentages of job postings with different characteristics vary across different categories of jobs-housing relationships. The findings underscore the necessity of integrating job opportunities and housing in the development of suburban areas, while also advocating for enhanced housing provisions in suburban industrial zones. Furthermore, the study suggests that individuals in the private sector, lacking employer-provided accommodations, holding bachelor’s degrees, or possessing moderate to minimal work experience, should be prioritized while making housing policies as they are more susceptible to experiencing jobs-housing imbalances. These insights contribute to the policy formulation for different groups of workers, thereby fostering jobs-housing balance.

China’s New Housing Security Model: Evaluation of the Job–Housing Balance in Affordable Rental Housing, Shanghai

The Chinese government’s recent low-income housing scheme aims to tackle housing challenges faced by the urban floating population. A notable shift in this initiative is the focus on the job–housing balance. This study proposes that the spatial interaction between land designated for affordable rental housing and land for commercial facilities serves as a fundamental metric for evaluating this equilibrium, providing insights into the effectiveness of China’s nascent affordable housing efforts. Drawing on post-2021 data, when China’s revamped affordable housing policy took effect, our research examines the spatial distribution of affordable rental housing and commercial service land in Shanghai. By employing coupled coordination models and local entropy metrics, we delve into the supply equilibrium and pragmatic interrelation of these land types. Our findings reveal localized clustering in the spatial arrangement of rental and commercial land within Shanghai. Zones in the urban core exhibit a supply balance, while the peripheries display diminishing accessibility between these land types. Core urban areas have a lower supply balance but higher accessibility, whereas urban fringes face both low supply balance and low accessibility. These study outcomes have significant implications for strategic planning and the construction of affordable rental housing.

Effect of multiscale metro network-wide attributes on peak-hour station passenger and flow balancing

Analyzing the balance of station passenger and passenger flow is essential for understanding jobs-housing balance and built environment in station areas and network-wide range as well as for enhancing the efficiency of urban rail transit operations. Taking the Shanghai rail transit network as a case study, this paper defines the Multiscale Subnetwork (MSSN) based on a specific spatial scope. By extracting the network features and built-environment elements of the stations and the MSSN, this study analyzes the factors affecting the peak-hour station passenger and the imbalance of regional network passenger flow. The research suggests that the small MSSN analysis, within 6-8 km from a station, can provide valuable results from a network-wide perspective, rather than solely focusing on individual station areas or the entire network. The regional attributes of jobs-housing balance and the transportation conditions in the MSSN range have great impact on both station passengers and flow imbalance. This research provides theoretical insights for urban planners and policymakers to formulate effective strategies for urban rail transit networks.

Destigmatizing urban villages by examining their attractiveness: Quantification evidence from Shenzhen

While existing social and political research has qualitatively discussed the stigmatization of urban villages (UVs), there's a lack of quantitative support. This study innovatively attempts to provide quantitative evidence, employing a spatial perspective for the destigmatization of UVs. We integrated population trajectory, GDP data, employment and residential points of interest, and developed a spatial gravity model to derive the spatial attractiveness index. Concurrently, we selected environmental, spatial structure, and housing economic indicators to construct a regression analysis. Our findings revealed that urban villagers exhibit a preference for shorter daily commutes, underscoring the positive role of UVs in promoting job-housing balance and providing diverse services. The high-frequency mobility of villagers accelerates the categorization of UVs and enhances intra-area circulation. We further discussed the interaction between UV renewal and its destigmatization. To counter spatial stigmatization, enhancing the spatial quality and infrastructure of UVs, as well as promoting diversified land use can reduce the public's stereotypical impressions. Moreover, prioritizing affordable housing and equitable distribution of facilities, along with fostering synergies between urban capital and UVs, can facilitate the destigmatization process. Stigmatization in informal settlements is a widespread issue. Our quantitative approaches, as well as targeted renovation and policy recommendations, can serve as a blueprint for addressing similar challenges in informal settlements globally.

Leveraging grants to coordinate land use and transportation planning for local governments: a review of national MPO programs

ABSTRACT Throughout the United States, Metropolitan Planning Organizations (MPOs) are increasingly attempting to shape local land use planning. Some MPOs have developed funding programs that redirect highway and road construction dollars to support less auto-centric community development - which we call Transportation and Land-Use Connection (TLC) programs. Based on a survey of 92 MPOs (out of 402 contacted) and a review of relevant documents, our paper aims to understand the scope and influence of these TLC programs on communities and how their impact is measured. We found that at least 24 MPOs had TLC programs. Our findings show that despite federal funding predominantly supporting these programs, state, county, local, and other regional agencies also provide support. Population size appears to be the most significant influence on an MPO’s participation in a TLC program. Most programs measure pre-implementation success through selection criteria reflecting program goals rather than direct impact measurement. For those that do measure after grants have been awarded, increased non-automobile shares tax revenue and jobs-housing balance were the most common ways success was measured. These findings can help MPOs with existing TLC programs improve formulation guidelines and assist MPOs without such programs in adopting them in their jurisdictions.

Investigating built environment and traffic flow impact on crash frequency in urban road networks

While numerous studies have examined the factors that influence crash occurrence, there remains a gap in understanding the intricate relationship between built environment, traffic flow, and crash occurrences across different spatial units. This study explores how built environment attributes, and dynamic traffic flow characteristics affect crash frequency by focusing on proposed traffic density-based zones (TDZs). Utilizing a comprehensive dataset from Greater Melbourne, Australia, this research emphasizes on the dynamic traffic flow variables and insights from the Macroscopic Fundamental Diagram model, considering parameters such as shockwave velocity and congestion index. The association between the potential influencing factors and crash frequency is examined using a random parameter negative binomial regression model. Results indicate that the data segmentation based on TDZs is instrumental in establishing a more refined crash model compared to traditional planning-based zones, as demonstrated by improved goodness-of-fit measures. Factors including density (e.g., employment density), network design (e.g., road density and highway density), land use diversity (e.g., job-housing balance and land use mixture), and public transit accessibility (e.g., bus route density) are significantly associated with crash occurrence. Furthermore, the unobserved heterogeneity effects of the shockwave velocity and congestion index on crashes are revealed. The study highlights the significance of incorporating dynamic traffic flow variables in understanding crash frequency variations across different spatial units. These findings can inform optimal real-time traffic monitoring, environmental design, and road safety management strategies to mitigate crash risks.

How does the urban built environment affect dockless bikesharing-metro integration cycling? Analysis from a nonlinear comprehensive perspective

The multi-modal combination of "dockless bikesharing (DBs) + metro" can effectively addresses the "last mile" challenge, and its importance to improve the quality of metro integration and residents' commuting efficiency is becoming increasingly prominent. As one of the important determining factors of transportation, the urban built environment directly affects the integration travel behavior of residents. To better understand this issue, it is necessary to conduct in-depth research on the influence mechanism of urban built environment on DBs-metro integration cycling (DBsMIC), and reveal its complex nonlinear characteristics and spatial heterogeneity. Therefore, taking Shenzhen as a case study, this study first introduced the OLS regression model to detect the significant built environmental impact factors of DBsMIC. Secondly, the random forest regression model was used to analyze its nonlinear influence. Finally, aiming at the problem of spatial non-stationarity of influencing effects, the spatial heterogeneity is analyzed from both global and local perspectives based on the MGWR model. The results show that the significant impact indices of the built environment of the integration cycling volume vary in different time periods. Overall, seven factors such as road and residential density have a significant impact on DBsMIC. Among them, the job-housing balance and the bus line density have a negative impact, while the rest have a positive impact. Meanwhile, the nonlinear effect of the built environment on DBsMIC is obvious, and there is an obvious marginal effect. In addition, the nonlinear impact of the built environment on DBsMIC in different time periods has spatial heterogeneity and spatial marginal effects. For example, the influence of road density shows a global positive correlation effect in multiple time periods, and the promotion effect increases from southwest to northeast. This study can provide auxiliary decision-making support for the construction of an efficient, green and sustainable urban transportation system according to local conditions.

Application of the local colocation quotient method in jobs-housing balance measurement based on mobile phone data: A case study of Nanjing City

The issue of jobs-housing balance concerns the sustainable development of cities and the well-being of residents. Conventional measurement approaches, however, often fall short due to the zoning problem (as a subproblem of the modifiable areal unit problem), leading to inconsistent and inaccurate results depending on the spatial partitioning scheme applied. This paper discusses the application and advantages of the local colocation quotient method in jobs-housing balance measurement. A case study of Nanjing, China, is selected, and mobile location data are used to obtain the jobs and housing locations of workers. Then, the adjusted jobs-workers ratio and the local colocation quotient values that reflect the degree of jobs-housing balance are calculated and compared by category. The results show that on the one hand, due to the zoning effect, when points are aggregated into spatial units, some points with different spatial characteristics are masked by the dominant value of the units; on the other hand, the local colocation quotient method can solve the zoning problem and obtain more fine-scale and accurate results, thus providing a new analytical tool and perspective for this field.

What can urban transport policy achieve? How transport-policy-invariant characteristics relate to city-level car ownership and car use

Most case and comparative studies discuss urban characteristics that can hardly be influenced by urban transport policies. However, these studies do not quantify the impact of the so-called contextual factors on car ownership and car use. This paper focuses on these contextual factors and their associations with car ownership and car use. Regression models show that contextual factors - such as city size, the share of households with children and the jobs-housing balance - explain a substantial part of the variance in car ownership and car trips in 44 German cities. Residual analyses reveal that neglecting contextual factors leads to biases in comparative studies and consequently in the evaluation of urban transport policies. Thus, there is a risk that cities with favourable contextual factors may be mistakenly viewed as successful examples of transport policy, whereas successful policies in cities with less favourable contextual factors may be overlooked.

A COMPREHENSIVE MEASUREMENT MODEL FOR JOB-HOUSING BALANCE CONSIDERING SPATIAL INTERACTIONS: A CASE STUDY IN SHANGHAI

Abstract. Assessing jobs-housing balance (JHB) is crucial for the optimization of urban spatial pattern and transportation planning. However, due to the limitations of traditional data and the inability to integrate evaluation indicators, the evaluation results may be bias, conflicting with common sense. Considering interaction of multiple factors, a Job-housing balance measurement model (JHBM-SEM) was proposed to accurately evaluate the degree of JHB in a certain area. To address the problem of multi-indicator fusion, the structural equation model is used to incorporate the advantages, limitations and complementary relationships of each evaluation indicator into the evaluation process. A function relationship is defined between factor loadings, path coefficients, and variable scores to construct the measurement model and obtain comprehensive evaluation results. In addition, by integrating multi-source spatiotemporal big data mainly based on mobile signaling data, the paper can effectively mine the current status of job-housing balance in cities, and solve problems such as low precision and granularity of traditional data. The model was validated using Shanghai as an example, and the results show that compared with classical methods, the model's results are more consistent with the real situation of regional jobhousing patterns. It can identify the pseudo-balance phenomenon in underdeveloped areas of the suburbs and make reasonable evaluations, and the intermediate process can explain the direction of the imbalance factors in the region.

Interactions between centrality and commuting costs in a mountainous city: Implications for jobs-housing relationships and land use policies

While various empirical studies have been conducted to unpack the links which exist between spatial pattern and commuting costs, the interactions remain unclear given the variations in environmental contexts and methods for identifying polycentricity. Furthermore, there has been limited attention given to mountainous metropolitan areas where the urban structure is greatly influenced by natural barriers. This paper proposes two indicators of centrality by exploring spatial distribution of urban functions and functional linkages respectively, and examines how the two centrality indicators are related to commuting costs in Chongqing, China. The findings confirm the urban structure of Chongqing as being polycentric from both morphological and functional dimensions. The modelling results suggest that both morphological centrality and functional centrality are positively related to commuting costs. The cross-level interactions of variables highlight the significant role that morphological centrality has in a mountainous area. Furthermore, the spatial mismatch of employment and residency varied in different areas of Chongqing; suggesting that there is a need for policy interventions to promote land use mix and jobs-housing balance.

Geographical Influences on Job–Housing Balance: A Study of Coastal Urban Areas in Boston

As cities expand, residents are experiencing increasing commuting distances and a growing trend of job–housing separation, which is often associated with traffic congestion, inefficiency in commuting, and air pollution. In the process of studying the urban job–housing balance, most scholars focus on exploring socio-economic indicators, overlooking the more fundamental characteristics—the geographical features and barriers of the city. This paper delves into the intricate dynamics of the job–housing balance in urban areas, focusing on the city of Boston, characterized by its unique geographic and demographic tapestry. Through the job–housing distribution data of over 3 million residents in Boston and a measurement of spatial proximity to natural barriers, we explore the impact of geographic barriers on residential and employment distributions. Our findings reveal a pronounced divergence in employees’ preferences for job and housing locations, with tracts in the margin areas showing higher aggregation of job distributions and those near geographic barriers exhibiting a low job–housing ratio (JHR) index. Using regression models, our study determined that for every 1% increase in proximity to the Atlantic Ocean on Boston’s right side, job opportunities would decrease by 0.102%, and the JHR would experience a reduction of 0.246%. Our findings prove the importance of the effects of natural barriers on the job–housing balance and provide insights into traffic congestion and the uneven distribution of housing supply prices and have significant implications for urban planning and policy formulation, particularly in coastal cities. By exploring the multifaceted nature of urban residency and employment and the role of geographical constraints therein, this paper contributes valuable perspectives for fostering equitable and sustainable urban development.

Research on Social-spatial Effects Caused by Residential Differentiation : Case Study of Typical Communities in Xi'an City

Combining the evaluation results of questionnaire and remote sensing image, the study evaluate the social-spatial effects of residential differentiation from the micro perspective of residents and the macro perspective of city in a qualitative and quantitative manner. The social-spatial effects are mainly manifested in improving the targeting of community services, hindering interaction between social groups, endangering public safety, exacerbating the unbalanced pattern of public resource and the differences in job-housing balance .

Discovering the long-term effects of COVID-19 on jobs–housing relocation

Jobs–housing relocation plays a crucial role in urban spatial restructuring and development. As the COVID-19 pandemic has dramatically altered the form of human mobility, it is likely to have affected individual relocation patterns. This study uses mobile phone signalling data from 2018 to 2022 in Beijing to examine and compare intra–urban jobs–housing relocation behaviours among commuters before and after the pandemic. This study captures the pandemic’s long–term effects via an event study design. Despite prevalent negative impressions of the pandemic, this study found that it may have unexpected positive influences on our cities. The pandemic decelerated suburbanisation in young middle–income home relocators but encouraged suburbanisation in relatively older high–income home relocators, which may have helped to preserve inner city vitality. The pandemic accelerated the decentralisation of spatial distribution of employment, which may have helped to further break the monocentric city structure. The pandemic also accelerated inverse jobs–housing separation and improved jobs-housing balance, which may have made the city greener. It is suggested that policy makers seize this opportunity to guide cities towards structural improvement and sustainability.

Examining the Effects of Built Environments and Individual Characteristics on Commuting Time under Spatial Heterogeneity: An Empirical Study in China Using HLM

A large number of studies have provided evidence regarding the factors that influence commuting time. However, few studies have explored such effects in the context of considering spatial heterogeneity across cities, which limits the generalizability of the findings. This study addresses this gap by utilizing a dataset of 113 cities in China across the years 2014, 2016, 2018, and 2020. A two-level hierarchical linear model (HLM) was developed to explore the combined effects of city-level and individual-level factors on commuting time by constructing a nested “city-individual” relationship. The results show that (1) built environments at the city level significantly impact commuting time; (2) a non-linear association between population density and commuting time (U-shaped relationship) was identified, as well as between the number of buses and commuting time (inverted U-shaped relationship); (3) the urban construction land area and road area per capita exert negative effects on commuting time; (4) the impacts of individuals’ jobs–housing balance, travel allowances, and education on commuting time vary across cities. These findings might contribute to optimizing the design of a built environment, addressing the challenge posed by longer commuting times, and providing a better understanding of the effects of individuals’ characteristics on commuting time while considering the inherent differences across cities.