{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T07:46:22Z","timestamp":1768808782057,"version":"3.49.0"},"reference-count":37,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,9,24]],"date-time":"2021-09-24T00:00:00Z","timestamp":1632441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>This paper proposes a GIS-based field model for hot-spot extraction based on POI data and analyzes the use intensity of functional areas by using Tencent location data to identify and describe the morphological characteristics and dynamic use intensity of facilities in urban functional areas. Taking the four districts of Jinan City Center as an example, we used the generalized symmetric structure spectrum and digital field-based hierarchical geo-information Tupu to extract facility hot spots. Tencent location data were then applied to quantify differences in the use intensity of functional areas between workday and weekend, as well as between daytime and nighttime. Finally, refined research on functional areas was realized from a dynamic point of view. Results showed that (1) the generalized symmetric structure spectrum and digital field-based hierarchical geo-information Tupu can identify and express the characteristics of the spatial distribution and hierarchical structures of urban facility hot spots at the horizontal and vertical levels, respectively; (2) overall, the distribution of all types of functional areas presents the characteristics of \u201ccircular structures,\u201d which form a spatial pattern of \u201cmulti-center\u201d groups and \u201csingle\/mixed\u201d functional areas; (3) aside from residential facilities, green space and square land facilities have the highest use intensity; this finding highlights the tourism characteristics of Jinan. Low-use intensity areas are distributed at the periphery of the four districts, while high-use intensity areas, the functional type of which is mainly business facilities, are mainly distributed around the urban area. These results are helpful to the development strategy of the city\u2019s efforts to adapt to economic change and provide a scientific basis for the functional orientation of Jinan City.<\/jats:p>","DOI":"10.3390\/ijgi10100640","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T04:55:33Z","timestamp":1632718533000},"page":"640","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Functional Area Recognition and Use-Intensity Analysis Based on Multi-Source Data: A Case Study of Jinan, China"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1142-6405","authenticated-orcid":false,"given":"Mingyang","family":"Yu","sequence":"first","affiliation":[{"name":"College of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8763-5452","authenticated-orcid":false,"given":"Jingqi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China"}]},{"given":"Yongqiang","family":"Lv","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China"}]},{"given":"Huaqiao","family":"Xing","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China"}]},{"given":"Huimeng","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,24]]},"reference":[{"key":"ref_1","first-page":"81","article-title":"Identification and Classification of Wuhan Urban Districts Based on POI","volume":"43","author":"Kang","year":"2018","journal-title":"J. Geomat."},{"key":"ref_2","first-page":"68","article-title":"Quantitative Identification and Visualization of Urban Functional Area Based on POI Data","volume":"41","author":"Chi","year":"2016","journal-title":"J. Geomat."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1080\/13658816.2014.977905","article-title":"Crowdsourcing urban form and function","volume":"29","author":"Crooks","year":"2015","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_4","first-page":"413","article-title":"Identifying City Functional Areas Using Taxi Trajectory Data","volume":"35","author":"Wu","year":"2018","journal-title":"J. Geomat. Sci. Technol."},{"key":"ref_5","first-page":"115","article-title":"A Research on Spatial Distribution Pattern of Urban Catering Industry in Kunming Based on POI Data","volume":"44","author":"Shan","year":"2019","journal-title":"J. Kunming Univ. Sci. Technol."},{"key":"ref_6","first-page":"86","article-title":"Spatial pattern and influencing factors of urban vitality in the middle reaches of the Yangtze River","volume":"29","author":"Mao","year":"2020","journal-title":"World Reg. Stud."},{"key":"ref_7","first-page":"691","article-title":"Analysis of Spatial Economic Structure of Northeast China Cities Based on Points of Interest Big Data","volume":"40","author":"Xue","year":"2020","journal-title":"Sci. Geogr. Sin."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1111\/tgis.12289","article-title":"Extracting urban functional regions from points of interest and human activities on location-based social networks","volume":"21","author":"Gao","year":"2017","journal-title":"Trans. GIS"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gil, E., Ahn, Y., and Kwon, Y. (2020). Tourist Attraction and Points of Interest (POIs) Using Search Engine Data: Case of Seoul. Sustainability, 12.","DOI":"10.3390\/su12177060"},{"key":"ref_10","first-page":"1202","article-title":"Study on Spatial Distribution of Modern Sweet Diet and its Impact Factors in China based on Big Data from Internet","volume":"22","author":"Yao","year":"2020","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_11","first-page":"710","article-title":"Spatial Pattern and Influencing Factors of Retailing Industries in Xi\u2019an Based on POI Data","volume":"40","author":"Gao","year":"2020","journal-title":"Sci. Geogr. Sin."},{"key":"ref_12","first-page":"1320","article-title":"Identifying Mixed Functions of Urban Public Service Facilities in Beijing by Cumulative Opportunity Accessibility Method","volume":"22","author":"Zhan","year":"2020","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_13","first-page":"13","article-title":"Tourists\u2019 digital footprint in cities: Comparing big data sources","volume":"66","year":"2017","journal-title":"Tour. Manag."},{"key":"ref_14","unstructured":"Luo, S., Liu, Y., Gao, S., and Wang, P. (2020). Quantitative identification of urban functional areas based on spatial grid. Bull. Surv. Mapp., 214\u2013217."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, Z., Du, Y., Yi, J., Liang, F., Ma, T., and Pei, T. (2019). Quantitative Association between Nighttime Lights and Geo-Tagged Human Activity Dynamics during Typhoon Mangkhut. Remote Sens., 11.","DOI":"10.3390\/rs11182091"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1080\/17538947.2019.1645894","article-title":"Quantitative estimates of collective geo-tagged human activities in response to typhoon Hato using location-aware big data","volume":"13","author":"Liu","year":"2020","journal-title":"Int. J. Digit. Earth"},{"key":"ref_17","first-page":"477","article-title":"Exploring urban functional areas based on multi-source data: A case study of Beijing","volume":"40","author":"Yang","year":"2021","journal-title":"Geogr. Res."},{"key":"ref_18","first-page":"1378","article-title":"Network Kernel Density Estimation for the Analysis of Facility POI Hotspots","volume":"44","author":"Yu","year":"2015","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_19","first-page":"788","article-title":"Identification of Polycentric Urban Structure of Central Chongqing Using Points of Interest Big Data","volume":"33","author":"Duan","year":"2018","journal-title":"J. Nat. Resour."},{"key":"ref_20","first-page":"354","article-title":"Hotspot discovery and its spatial pattern analysis for catering service in cities based on field model in GIS","volume":"39","author":"Zhang","year":"2020","journal-title":"Geogr. Res."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Min, J., Liu, C., and Li, Y. (2021). Hotspot Detection and Spatiotemporal Evolution of Catering Service Grade in Mountainous Cities from the Perspective of Geo-Information Tupu. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10050287"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1578","DOI":"10.1111\/tgis.12663","article-title":"Inference method for cultural diffusion patterns using a field model","volume":"24","author":"Zhang","year":"2020","journal-title":"Trans. GIS"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhou, X., and Huang, Y. (2020). Analysis of Spatial Interaction between Different Food Cultures in South and North China: Practices from People\u2019s Daily Life. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9020068"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jiang, S., Zhang, H., Wang, H., Zhou, L., and Tang, G. (2021). Using Restaurant POI Data to Explore Regional Structure of Food Culture Based on Cuisine Preference. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10010038"},{"key":"ref_25","first-page":"109","article-title":"Spatial Data Analysis and Visualization of Urban POI","volume":"43","author":"Chi","year":"2020","journal-title":"Geomat. Spat. Inf. Technol."},{"key":"ref_26","first-page":"38","article-title":"Identifying Urban Functional Regions Based on POI Data and Spatial Analysis of Main Transit Hubs","volume":"42","author":"Zhao","year":"2019","journal-title":"Geomat. Spat. Inf. Technol."},{"key":"ref_27","first-page":"119","article-title":"Research on function identification and distribution characteristics of Wuhan supported by big data","volume":"45","author":"Li","year":"2020","journal-title":"Sci. Surv. Mapp."},{"key":"ref_28","first-page":"140","article-title":"Urban Research Using Points of Interest Data in China","volume":"41","author":"Zhang","year":"2021","journal-title":"Sci. Geogr. Sin."},{"key":"ref_29","first-page":"973","article-title":"Extracting hierarchical landmarks from urban POI data","volume":"15","author":"Zhao","year":"2011","journal-title":"J. Remote Sens."},{"key":"ref_30","first-page":"692","article-title":"Recognition of Urban Polycentric Structure Based on Spatial Aggregation Characteristics of POI Elements: A Case of Zhengzhou City","volume":"56","author":"Li","year":"2020","journal-title":"Acta Sci. Nat. Univ. Pekin."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yi, D., Yang, J., Liu, J., Liu, Y., and Zhang, J. (2019). Quantitative Identification of Urban Functions with Fishers\u2019 Exact Test and POI Data Applied in Classifying Urban Districts: A Case Study within the Sixth Ring Road in Beijing. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8120555"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hu, Y., and Han, Y. (2019). Identification of Urban Functional Areas Based on POI Data: A Case Study of the Guangzhou Economic and Technological Development Zone. Sustainability, 11.","DOI":"10.3390\/su11051385"},{"key":"ref_33","unstructured":"Chen, S., Yue, T., and Li, H. (2000). Studies on Geo-Informatic Tupu and its application. Geogr. Res., 337\u2013343."},{"key":"ref_34","first-page":"197","article-title":"Brief Analysis of Traffic Congestion in Jinan\u2014Take Tokyo for Reference","volume":"43","author":"Li","year":"2020","journal-title":"Heilongjiang Jiaotong Keji"},{"key":"ref_35","first-page":"35","article-title":"Discussion on Current Traffic Situation and Reconstruction Measures","volume":"17","author":"Han","year":"2020","journal-title":"Urban. Archit."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Tiboni, M., Rossetti, S., Vetturi, D., Torrisi, V., Botticini, F., and Schaefer, M.D. (2021). Urban Policies and Planning Approaches for a Safer and Climate Friendlier Mobility in Cities: Strategies, Initiatives and Some Analysis. Sustainability, 13.","DOI":"10.3390\/su13041778"},{"key":"ref_37","first-page":"313","article-title":"Land-Use and Transport integration polices and real estate values. The development of a GIS methodology and the application to Naples (Italy)","volume":"12","author":"Carpentieri","year":"2019","journal-title":"TeMA-J. Land Use Mobil. Environ."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/10\/640\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:04:41Z","timestamp":1760166281000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/10\/640"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,24]]},"references-count":37,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["ijgi10100640"],"URL":"https:\/\/doi.org\/10.3390\/ijgi10100640","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,24]]}}}