Research on the Method of Urban Jobs-Housing Space Recognition Combining Trajectory and POI Data

With the gradual emergence of the separation and dislocation of urban jobs-housing space, rational planning of urban jobs-housing space has become the core issue of national land-spatial planning. To study the existing relationship between workspaces and living spaces, a new method to identify jobs-housing space is proposed, which not only considers the static spatial distribution of urban public facilities but also identifies the jobs-housing space by analyzing the real mobility characteristics of people from a humanistic perspective. This method provides a new framework for the identification of urban jobs-housing space by integrating point-of-interest (POI) and trajectory data. The method involves three steps: Firstly, based on the trajectory data, we analyze the characteristics of the dynamic flow of passengers in the grid and construct the living factors and working factors to identify the distribution of jobs-housing space. Secondly, we reclassify the POIs to calculate the category ratios of different types of POIs in the grid to identify the jobs-housing space. Finally, an OR operation is performed on the results obtained by the two methods to obtain the final recognition result. We selected Haikou City as the experimental area to verify the method proposed in this paper. The experimental results show that the recognition accuracy of the travel flow model is 72.43%, the POI quantitative recognition method’s accuracy is 74.94%, and the accuracy of the method proposed in this paper is 85.90%, which is significantly higher than the accuracy of the previous two methods. Therefore, the method proposed here can serve as a reference for subsequent research on urban jobs-housing space.

[1]  Xun Liang,et al.  Delineating Mixed Urban "Jobs-Housing" Patterns at a Fine Scale by Using High Spatial Resolution Remote-Sensing Imagery , 2020, Complex..

[2]  Xudong Liu,et al.  Identification of Urban Functional Regions in Chengdu Based on Taxi Trajectory Time Series Data , 2020, ISPRS Int. J. Geo Inf..

[3]  Meijie Jia,et al.  Urban Jobs-Housing Zone Division Based on Mobile Phone Data , 2019, BlockSys.

[4]  Jing Zhang,et al.  Quantitative Identification of Urban Functions with Fishers' Exact Test and POI Data Applied in Classifying Urban Districts: A Case Study within the Sixth Ring Road in Beijing , 2019, ISPRS Int. J. Geo Inf..

[5]  Li Sun,et al.  Identification of Urban Functional Regions Based on Floating Car Track Data and POI Data , 2019 .

[6]  C. Miao,et al.  Identifying Spatial Patterns of Retail Stores in Road Network Structure , 2019, Sustainability.

[7]  Tao Cheng,et al.  A high-precision heuristic model to detect home and work locations from smart card data , 2018, Geo spatial Inf. Sci..

[8]  Fahui Wang,et al.  Using points-of-interest data to estimate commuting patterns in central Shanghai, China , 2018, Journal of Transport Geography.

[9]  Heng Wei,et al.  Detecting home location and trip purposes for cardholders by mining smart card transaction data in Beijing subway , 2018 .

[10]  Krzysztof Janowicz,et al.  The Effect of Regional Variation and Resolution on Geosocial Thematic Signatures for Points of Interest , 2017, AGILE Conf..

[11]  Jiangping Zhou,et al.  Jobs-housing balance and development zones in China: a case study of Suzhou Industry Park , 2017 .

[12]  Deng Yu,et al.  The spatial pattern and influence factors of urban expansion: A case study of Beijing , 2015 .

[13]  Marta C. González,et al.  Origin-destination trips by purpose and time of day inferred from mobile phone data , 2015 .

[14]  Francisco C. Pereira,et al.  Mining point-of-interest data from social networks for urban land use classification and disaggregation , 2015, Comput. Environ. Urban Syst..

[15]  Jean-Claude Thill,et al.  Combining smart card data and household travel survey to analyze jobs-housing relationships in Beijing , 2013, Comput. Environ. Urban Syst..

[16]  Margaret Martonosi,et al.  Identifying Important Places in People's Lives from Cellular Network Data , 2011, Pervasive.

[17]  R. Ahas,et al.  Daily rhythms of suburban commuters' movements in the Tallinn metropolitan area: Case study with mobile positioning data , 2010 .

[18]  Weiping Wu Migrant Intra-urban Residential Mobility in Urban China , 2006 .

[19]  Selima Sultana Job/Housing Imbalance and Commuting Time in the Atlanta Metropolitan Area: Exploration of Causes of Longer Commuting Time , 2002 .

[20]  J. Levine Rethinking Accessibility and Jobs-Housing Balance , 1998 .

[21]  Wang De,et al.  Employment space of residential quarters in Shanghai: An exploration based on mobile signaling data , 2020 .

[22]  Xiaochun Huang,et al.  Characteristics of jobs-housing spatial distribution in Beijing based on mobile phone signaling data , 2020, Progress in Geography.

[23]  Liu Wang-ba Urban Residents' Home-work Space and Commuting Behavior in Guangzhou , 2014 .

[24]  Zhu Chao-hong Characteristics of jobs-housing spatial organization in Lanzhou City , 2012 .

[25]  Peng Ping Housing Suburbanization and Employment Spatial Mismatch in Beijing , 2007 .