{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T02:02:28Z","timestamp":1769911348556,"version":"3.49.0"},"reference-count":53,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T00:00:00Z","timestamp":1632960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning","award":["2020B121202019"],"award-info":[{"award-number":["2020B121202019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Numerous studies have been devoted to uncovering the characteristics of resident density and urban mobility with multisource geospatial big data. However, little attention has been paid to the internal diversity of residents such as their occupations, which is a crucial aspect of urban vibrancy. This study aims to investigate the variation between individual and interactive influences of built environment factors on occupation mixture index (OMI) with a novel GeoDetector-based indicator. This study first integrated application (App) use and mobility patterns from cellphone data to portray residents\u2019 occupations and evaluate the OMI in Guangzhou. Then, the mechanism of OMI distribution was analyzed with the GeoDetector model. Next, an optimized GeoDetector-based index, interactive effect variation ratio (IEVR) was proposed to quantify the variation between individual and interactive effects of factors. The results showed that land use mixture was the dominating factor, and that land use mixture, building density, floor area ratio, road density affected the OMI distribution directly. Some interesting findings were uncovered by IEVR. The influences of cultural inclusiveness and metro accessibility were less important in factor detector result, while they were found to be the most influential in an indirect way interacting with other built environment factors. The results suggested that both \u201chardware facilities\u201d (land use mixture, accessibility) and \u201csoft facilities\u201d (cultural inclusiveness) should be considered in planning a harmonious urban employment space and sustainable city.<\/jats:p>","DOI":"10.3390\/ijgi10100659","type":"journal-article","created":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T10:55:40Z","timestamp":1633085740000},"page":"659","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Spatial Distribution and Mechanism of Urban Occupation Mixture in Guangzhou: An Optimized GeoDetector-Based Index to Compare Individual and Interactive Effects"],"prefix":"10.3390","volume":"10","author":[{"given":"Xingdong","family":"Deng","sequence":"first","affiliation":[{"name":"Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China"},{"name":"Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China"},{"name":"Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0398-4255","authenticated-orcid":false,"given":"Feng","family":"Gao","sequence":"additional","affiliation":[{"name":"Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China"},{"name":"Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shunyi","family":"Liao","sequence":"additional","affiliation":[{"name":"Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China"},{"name":"Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China"},{"name":"Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guanfang","family":"Cai","sequence":"additional","affiliation":[{"name":"Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China"},{"name":"Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"142591","DOI":"10.1016\/j.scitotenv.2020.142591","article-title":"The varying driving forces of urban land expansion in China: Insights from a spatial-temporal analysis","volume":"766","author":"Wu","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.scitotenv.2019.01.039","article-title":"Understanding urban expansion combining macro patterns and micro dynamics in three southeast Asian megacities","volume":"660","author":"Xu","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"146586","DOI":"10.1016\/j.scitotenv.2021.146586","article-title":"Using nighttime light data to identify the structure of polycentric cities and evaluate urban centers","volume":"780","author":"Yang","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.cities.2018.12.008","article-title":"Spatiotemporal distribution characteristics and mechanism analysis of urban population density: A case of Xi\u2019an, Shaanxi, China","volume":"86","author":"Li","year":"2019","journal-title":"Cities"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5673","DOI":"10.1080\/01431161.2010.496806","article-title":"Improving the housing-unit method for small-area population estimation using remote-sensing and GIS information","volume":"31","author":"Deng","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1376","DOI":"10.1038\/srep01376","article-title":"Unique in the crowd: The privacy bounds of human mobility","volume":"3","author":"Hidalgo","year":"2013","journal-title":"Sci. Rep."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/S1361-9209(98)00010-8","article-title":"Public transportation access","volume":"3","author":"Murray","year":"1998","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1061\/(ASCE)0733-947X(1998)124:4(368)","article-title":"Urban bus transit route network design using genetic algorithm","volume":"124","author":"Pattnaik","year":"1998","journal-title":"J. Transp. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.landurbplan.2016.12.001","article-title":"Delineating urban functional areas with building-level social media data: A dynamic time warping (DTW) distance based k-medoids method","volume":"160","author":"Chen","year":"2016","journal-title":"Landsc. Urban Plan"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, X., Gao, F., Liao, S., Zhou, F., Cai, G., and Li, S. (2021). Portraying Citizens\u2019 Occupations and Assessing Urban Occupation Mixture with Mobile Phone Data: A Novel Spatiotemporal Analytical Framework. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10060392"},{"key":"ref_11","first-page":"434","article-title":"A commuting spectrum analysis of the jobs\u2013housing balance and self-containment of employment with mobile phone location big data","volume":"45","author":"Zhou","year":"2018","journal-title":"Environ. Plan. B"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1007\/s11116-020-10094-z","article-title":"Understanding the modifiable areal unit problem and identifying appropriate spatial unit in jobs\u2013housing balance and employment self-containment using big data","volume":"48","author":"Zhou","year":"2020","journal-title":"Transportation"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.jtrangeo.2017.12.006","article-title":"Spatial variation of self-containment and jobs-housing balance in Shenzhen using cellphone big data","volume":"68","author":"Zhou","year":"2018","journal-title":"J. Transp. Geogr."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.physa.2015.06.032","article-title":"Uncovering urban human mobility from large scale taxi GPS data","volume":"438","author":"Tang","year":"2015","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1007\/s11116-020-10086-z","article-title":"Hailing a change: Comparing taxi and ridehail service quality in Los Angeles","volume":"48","author":"Brown","year":"2021","journal-title":"Transportation"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"102774","DOI":"10.1016\/j.trd.2021.102774","article-title":"Equitable? Exploring ridesourcing waiting time and its determinants","volume":"93","author":"Yang","year":"2021","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.jocs.2015.04.021","article-title":"Measuring variability of mobility patterns from multiday smart-card data","volume":"9","author":"Zhong","year":"2015","journal-title":"J. Comput. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"102580","DOI":"10.1016\/j.cities.2019.102580","article-title":"The varying patterns of rail transit ridership and their relationships with fine-scale built environment factors: Big data analytics from Guangzhou","volume":"99","author":"Li","year":"2020","journal-title":"Cities"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"102631","DOI":"10.1016\/j.jtrangeo.2019.102631","article-title":"Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China","volume":"82","author":"Li","year":"2020","journal-title":"J. Transp. Geogr."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1080\/15568318.2018.1429696","article-title":"Understanding the usages of dockless bike sharing in Singapore","volume":"12","author":"Shen","year":"2018","journal-title":"Int. J. Sustain. Transp."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/13658816.2020.1863410","article-title":"Understanding the modifiable areal unit problem in dockless bike sharing usage and exploring the interactive effects of built environment factors","volume":"35","author":"Gao","year":"2021","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Gao, F., Li, S., Tan, Z., Zhang, X., Lai, Z., and Tan, Z. (2021). How Is Urban Greenness Spatially Associated with Dockless Bike Sharing Usage on Weekdays, Weekends, and Holidays?. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10040238"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"111020","DOI":"10.1016\/j.envres.2021.111020","article-title":"Ridership exceedance exposure risk: Novel indicators to assess PM2.5 health exposure of bike sharing riders","volume":"197","author":"Cao","year":"2021","journal-title":"Environ. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"102974","DOI":"10.1016\/j.jtrangeo.2021.102974","article-title":"Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen, China","volume":"91","author":"Li","year":"2021","journal-title":"J. Transp. Geogr."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, C., Hu, J., Dai, Z., Fan, Z., and Wu, Z. (2020). Understanding Individual Mobility Pattern and Portrait Depiction Based on Mobile Phone Data. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9110666"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1080\/13875868.2014.984300","article-title":"Spatio-temporal analytics for exploring human mobility patterns and urban dynamics in the mobile age","volume":"15","author":"Gao","year":"2014","journal-title":"Spat. Cogn. Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"114049","DOI":"10.1088\/1748-9326\/abbd62","article-title":"Exploring spatiotemporal variation characteristics of exceedance air pollution risk using social media big data","volume":"15","author":"Cao","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"109813","DOI":"10.1016\/j.envres.2020.109813","article-title":"Explicit Spatializing Heat-Exposure Risk and Local Associated Factors by coupling social media data and automatic meteorological station data","volume":"188","author":"Cao","year":"2020","journal-title":"Environ. Res."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, B., Meng, B., Wang, J., Chen, S., and Liu, J. (2021). Perceiving Residents\u2019 Festival Activities Based on Social Media Data: A Case Study in Beijing, China. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10070474"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Jiang, W., Wang, Y., Xiong, Z., Song, X., Long, Y., and Cao, W. (2021). Detecting Urban Events by Considering Long Temporal Dependency of Sentiment Strength in Geotagged Social Media Data. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10050322"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Han, S., Liu, C., Chen, K., Gui, D., and Du, Q. (2021). A Tourist Attraction Recommendation Model Fusing Spatial, Temporal, and Visual Embeddings for Flickr-Geotagged Photos. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10010020"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, Z., Ma, D., Pang, R., Xie, F., Zhang, J., and Sun, D. (2020). Research Progress and Development Trend of Social Media Big Data (SMBD): Knowledge Mapping Analysis Based on CiteSpace. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9110632"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.trc.2009.04.011","article-title":"Daily rhythms of suburban commuters\u2019movements in the Tallinn metropolitan area: Case study with mobile positioning data","volume":"18","author":"Ahas","year":"2010","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.cities.2017.09.007","article-title":"City dynamics through Twitter: Relationships between land use and spatiotemporal demographics","volume":"72","year":"2018","journal-title":"Cities"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"103109","DOI":"10.1016\/j.cities.2021.103109","article-title":"Exploring the relationship between functional urban polycentricity and the regional characteristics of human mobility: A multi-view analysis in the Tokyo metropolitan area","volume":"111","author":"Liu","year":"2021","journal-title":"Cities"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"102985","DOI":"10.1016\/j.jtrangeo.2021.102985","article-title":"A multi-view of the daily urban rhythms of human mobility in the Tokyo metropolitan area","volume":"91","author":"Liu","year":"2021","journal-title":"J. Transp. Geogr."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.cities.2018.01.017","article-title":"Check-in behaviour and spatio-temporal vibrancy: An exploratory analysis in Shenzhen, China","volume":"77","author":"Wu","year":"2019","journal-title":"Cities"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"101428","DOI":"10.1016\/j.compenvurbsys.2019.101428","article-title":"Portraying the spatial dynamics of urban vibrancy using multisource urban big data","volume":"80","author":"Tu","year":"2020","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Guo, X., Chen, H., and Yang, X. (2021). An Evaluation of Street Dynamic Vitality and Its Influential Factors Based on Multi-Source Big Data. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10030143"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"102389","DOI":"10.1016\/j.cities.2019.102389","article-title":"Exploring the relationship between landscape characteristics and urban vibrancy: A case study using morphology and review data","volume":"95","author":"Meng","year":"2019","journal-title":"Cities"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Fu, R., Zhang, X., Yang, D., Cai, T., and Zhang, Y. (2021). The Relationship between Urban Vibrancy and Built Environment: An Empirical Study from an Emerging City in an Arid Region. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18020525"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"34","DOI":"10.2307\/141867","article-title":"Delimiting the CBD","volume":"30","author":"Murphy","year":"1954","journal-title":"Econ. Geogr."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"145","DOI":"10.2307\/142299","article-title":"The functional bases of the central-place hierarchy","volume":"34","author":"Berry","year":"1958","journal-title":"Econ. Geogr."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1111\/j.1467-8306.1960.tb00359.x","article-title":"The hierarchy of central functions within the city","volume":"50","author":"Carol","year":"1960","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"136","DOI":"10.2307\/143042","article-title":"The structure of central place systems","volume":"47","author":"Preston","year":"1971","journal-title":"Econ. Geogr."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1111\/j.1435-5597.1960.tb01710.x","article-title":"A Theory of The Urban Land Market","volume":"6","author":"Alonso","year":"2005","journal-title":"Pap. Reg. Sci."},{"key":"ref_47","unstructured":"Fotheringham, A.S., Brunsdon, C., and Charlton, M. (2003). Geographically Weighted Regression, John Wiley & Sons, Limited."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1791","DOI":"10.1068\/a36247","article-title":"A spatial filtering specification for the autologistic model","volume":"36","author":"Griffith","year":"2004","journal-title":"Environ. Plan. A"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1080\/13658810802443457","article-title":"Geographical Detector\u2014Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China","volume":"24","author":"Wang","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.ecolind.2016.02.052","article-title":"A measure of spatial stratified heterogeneity","volume":"67","author":"Wang","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.envsoft.2012.01.015","article-title":"Environmental health risk detection with GeogDetector","volume":"33","author":"Wang","year":"2012","journal-title":"Environ. Model. Softw."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Wu, Z., and Tarolli, P. (2021). Investigating the Role of Green Infrastructure on Urban WaterLogging: Evidence from Metropolitan Coastal Cities. Remote Sens., 13.","DOI":"10.3390\/rs13122341"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"103043","DOI":"10.1016\/j.jtrangeo.2021.103043","article-title":"Exploring both home-based and work-based jobs-housing balance by distance decay effect","volume":"93","author":"Zheng","year":"2021","journal-title":"J. Transp. Geogr."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/10\/659\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:08:13Z","timestamp":1760166493000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/10\/659"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,30]]},"references-count":53,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["ijgi10100659"],"URL":"https:\/\/doi.org\/10.3390\/ijgi10100659","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,30]]}}}