{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T18:36:06Z","timestamp":1770921366833,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T00:00:00Z","timestamp":1770854400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42161066"],"award-info":[{"award-number":["42161066"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42371463"],"award-info":[{"award-number":["42371463"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004775","name":"Key Project of Natural Science Foundation of Gansu Province","doi-asserted-by":"publisher","award":["24JRRA224"],"award-info":[{"award-number":["24JRRA224"]}],"id":[{"id":"10.13039\/501100004775","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Most machine learning (ML)-based facility location studies utilize uniform grid partitioning, often overlooking spatial heterogeneity. This limitation can compromise the validity and practical applicability of the resulting site selections. In response to this issue, this paper uses fire stations as the research subject and proposes a location prediction method that considers the heterogeneous characteristics within cities. Firstly, the Gaussian Mixture Model (GMM) is adopted based on the Point of Interest (POI) data to determine the clustering centres of the study area. Secondly, a Voronoi diagram is constructed to divide the study area reasonably. Then, a comprehensive feature matrix is constructed by integrating multi-source spatial data and five machine learning models: Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) and Logistic Regression (LR). These are then used for training and evaluation. Finally, the GBDT model with the best performance in terms of both the F1 score and the AUC value was selected to predict the location of fire stations in Chengguan District, Lanzhou City. The results demonstrate the GBDT model\u2019s effectiveness in identifying the rationale behind existing fire station locations and predicting potential new locations. It predicts 12 suitable locations for new fire stations, and the suitability of these predicted locations is validated by comparing them with the existing fire station locations, 8 of which are in the same block as existing fire stations in Chengguan District. Adding micro fire stations at four new predicted locations would improve response efficiency. The results of the feature importance analysis show that road accessibility is the primary factor affecting fire station location selection. This study\u2019s proposed method effectively enhances the reasonableness of fire station site selection and provides a basis for planning fire stations in new urban areas in the future.<\/jats:p>","DOI":"10.3390\/ijgi15020076","type":"journal-article","created":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T17:42:13Z","timestamp":1770918133000},"page":"76","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Location Prediction of Urban Fire Station Based on GMM Clustering and Machine Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6206-251X","authenticated-orcid":false,"given":"Xiaomin","family":"Lu","sequence":"first","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China"},{"name":"Key Laboratory of Science and Technology in Surveying & Mapping, Lanzhou Jiaotong University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Gansu Earthquake Agency, Lanzhou 730000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2792-3425","authenticated-orcid":false,"given":"Haowen","family":"Yan","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China"},{"name":"Key Laboratory of Science and Technology in Surveying & Mapping, Lanzhou Jiaotong University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4408-456X","authenticated-orcid":false,"given":"Haoran","family":"Song","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China"},{"name":"Key Laboratory of Science and Technology in Surveying & Mapping, Lanzhou Jiaotong University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China"},{"name":"Key Laboratory of Science and Technology in Surveying & Mapping, Lanzhou Jiaotong University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China"},{"name":"Key Laboratory of Science and Technology in Surveying & Mapping, Lanzhou Jiaotong University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Na","family":"He","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China"},{"name":"Key Laboratory of Science and Technology in Surveying & Mapping, Lanzhou Jiaotong University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Han, B., Hu, M., Zheng, J., and Tang, T. (2021). Site selection of fire stations in large cities based on actual spatiotemporal demands: A case study of Nanjing City. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10080542"},{"key":"ref_2","unstructured":"National Fire and Rescue Administration (2025, November 02). National Fire and Rescue Administration Held a Regular Press Conference. [EB\/OL]. (24 January 2025) [2 March 2025]. Available online: https:\/\/mp.weixin.qq.com\/s\/R99uMjUoShaoQwvFL7qHWQ."},{"key":"ref_3","first-page":"40","article-title":"A Review of the Discrete Facility Location Problem","volume":"11","author":"Fei","year":"2006","journal-title":"Int. J. Plant Eng. Manag."},{"key":"ref_4","first-page":"551","article-title":"Practice of optimizing the layout of urban fire stations by integrating multiple factors","volume":"43","author":"Wang","year":"2024","journal-title":"Fire Sci. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1007\/s12061-023-09502-5","article-title":"Spatial location optimization of fire stations with traffic status and urban functional areas","volume":"16","author":"Chen","year":"2023","journal-title":"Appl. Spat. Anal. Policy"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/s10708-025-11304-w","article-title":"Advanced GIS and fuzzy logic integration for strategic fire station placement in Yanbu Industrial City, Saudi Arabia","volume":"90","author":"Almuqataf","year":"2025","journal-title":"GeoJournal"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1186\/1476-072X-13-42","article-title":"Selecting the optimal healthcare centers with a modified P-median model: A visual analytic perspective","volume":"13","author":"Jia","year":"2014","journal-title":"Int. J. Health Geogr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2128","DOI":"10.1016\/j.cor.2013.02.019","article-title":"Robust vertex p-center model for locating urgent relief distribution centers","volume":"40","author":"Lu","year":"2013","journal-title":"Comput. Oper. Res."},{"key":"ref_9","first-page":"277","article-title":"Multi-objective location model research and application in the city emergency logistics based on different product materials","volume":"63","author":"Liu","year":"2011","journal-title":"Appl. Mech. Mater."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1111\/j.1435-5597.1983.tb00807.x","article-title":"Generalized coverage models and public facility location","volume":"53","author":"Church","year":"1983","journal-title":"Pap. Reg. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1287\/opre.12.3.450","article-title":"Optimum locations of switching centers and the absolute centers and medians of a graph","volume":"12","author":"Hakimi","year":"1964","journal-title":"Oper. Res."},{"key":"ref_12","unstructured":"Shang, C.D. (2021). Research on Layout Optimization of Fire Rescue Station Based on Urban Fire Risk Assessment. [Master\u2019s Thesis, Hebei Normal University]."},{"key":"ref_13","first-page":"26","article-title":"New fire station location problem based on AHP and set coverage model","volume":"13","author":"Xu","year":"2019","journal-title":"Inf. Comput."},{"key":"ref_14","first-page":"172","article-title":"Optimization of fire stations services in Minna metropolis using maximum covering location model (MCLM)","volume":"3","author":"Adesina","year":"2017","journal-title":"J. Appl. Sci. Environ. Sustain."},{"key":"ref_15","first-page":"2452","article-title":"An intelligent site selection approach for public service facilities coupled with improved graph attention network and deep reinforcement learning","volume":"26","author":"Wang","year":"2024","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1134\/S1990478921040128","article-title":"Discrete facility location in machine learning","volume":"15","author":"Vasilyev","year":"2021","journal-title":"J. Appl. Ind. Math."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Vargas-Santiago, M., Le\u00f3n-Velasco, D.A., Jim\u00e9nez, R.M., and Morales-Rosales, L.A. (2023). Complementing solutions for facility location optimization via video game crowdsourcing and machine learning approach. Appl. Sci., 13.","DOI":"10.3390\/app13084884"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.eswa.2016.11.025","article-title":"Hybrid evolutionary algorithm with extreme machine learning fitness function evaluation for two-stage capacitated facility location problems","volume":"71","author":"Guo","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sharma, D.P., Beigi-Mohammadi, N., Geng, H., Dixon, D., Madro, R., Emmenegger, P., Tobar, C., Li, J., and Leon-Garcia, A. (2024). Statistical and machine learning models for predicting fire and other emergency events. arXiv.","DOI":"10.1109\/ACCESS.2024.3390089"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, Y. (2018). Optimization on fire station location selection for fire emergency vehicles using K-means algorithm. Proceedings of the 3rd International Conference on Advances in Materials, Mechatronics and Civil Engineering (ICAMMCE 2018), Hangzhou, China, 13\u201315 April 2018, Atlantis Press.","DOI":"10.2991\/icammce-18.2018.71"},{"key":"ref_21","first-page":"24","article-title":"Classification of the fire station requirement using machine learning algorithms","volume":"11","year":"2019","journal-title":"Int. J. Inf. Technol. Comput. Sci."},{"key":"ref_22","unstructured":"Gao, J. (2020). Study on Fire Characteristics and Optimization of the Distribution of Urban Fire Station in Urban Area. [Master\u2019s Thesis, Xi\u2019an University of Science and Technology]."},{"key":"ref_23","unstructured":"Benliu News (2025, November 02). A Total of 607 Fires Occurred in Lanzhou City in the First 11 Months. Available online: https:\/\/news.sina.cn\/sa\/2005-12-07\/detail-ikknscsi8439510.d.html."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.compenvurbsys.2018.10.006","article-title":"Location optimization of urban fire stations: Access and service coverage","volume":"73","author":"Yao","year":"2019","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_25","first-page":"49","article-title":"Research on location selection of elderly care facilities based on random forest algorithm and supply-demand relationship","volume":"32","author":"Liu","year":"2023","journal-title":"Eng. Surv. Mapp."},{"key":"ref_26","first-page":"39","article-title":"Risk level classification method for railway network data based on GMM clustering","volume":"32","author":"Shang","year":"2023","journal-title":"Railw. Comput. Appl."},{"key":"ref_27","first-page":"94","article-title":"Driving style identification based on typical working conditions and GMM algorithm","volume":"41","author":"Guo","year":"2024","journal-title":"Intern. Combust. Eng. Powerpl."},{"key":"ref_28","first-page":"1058","article-title":"Research on equipment parameter early warning based on GMM and NSET optimization algorithm","volume":"29","author":"Yuan","year":"2022","journal-title":"Control Eng. China"},{"key":"ref_29","first-page":"535","article-title":"Spatial optimization of mega-city fire station distribution based on point of interest data: A case study within the 5th Ring Road in Beijing","volume":"37","author":"Xu","year":"2018","journal-title":"Prog. Geogr."},{"key":"ref_30","first-page":"148","article-title":"Urban fire risk assessment and planning response based on multi-source data","volume":"31","author":"Wang","year":"2021","journal-title":"China Saf. Sci. J."},{"key":"ref_31","first-page":"6","article-title":"Urban fire risk evaluation and location optimization of fire station based on POI: A case study of main urban region in Wuhan","volume":"37","author":"Zhu","year":"2018","journal-title":"Areal Res. Dev."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4903","DOI":"10.1007\/s10994-022-06296-4","article-title":"A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning","volume":"113","author":"Elreedy","year":"2024","journal-title":"Mach. Learn."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"94276","DOI":"10.1109\/ACCESS.2019.2926109","article-title":"A new application of optimized random forest algorithms in intelligent fault location of rudders","volume":"7","author":"Chang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"W339","DOI":"10.1093\/nar\/gkm368","article-title":"MiPred: Classification of real and pseudo microRNA precursors using random forest prediction model with combined features","volume":"35","author":"Jiang","year":"2007","journal-title":"Nucleic Acids Res."},{"key":"ref_35","first-page":"41","article-title":"Research on intrusion foreign objects classification of contact networks based on GBDT model","volume":"14","author":"Guo","year":"2024","journal-title":"Intell. Comput. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"121746","DOI":"10.1016\/j.ijheatmasstransfer.2021.121746","article-title":"A novel way to determine transient heat flux based on GBDT machine learning algorithm","volume":"179","author":"Wu","year":"2021","journal-title":"Int. J. Heat Mass Transf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.neucom.2019.10.118","article-title":"A comprehensive survey on support vector machine classification: Applications, challenges and trends","volume":"408","author":"Cervantes","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1016\/j.asej.2020.11.011","article-title":"Extreme gradient boosting (XGBoost) model to predict groundwater levels in Selangor, Malaysia","volume":"12","author":"Osman","year":"2021","journal-title":"Ain Shams Eng. J."},{"key":"ref_39","first-page":"1","article-title":"Intrusion detection system classification using different machine learning algorithms on KDD-99 and NSL-KDD datasets\u2014A review paper","volume":"11","author":"Ravipati","year":"2019","journal-title":"Int. J. Comput. Sci. Inf. Technol."},{"key":"ref_40","first-page":"17","article-title":"Assessment of fire station service coverage based on network weighted Voronoi diagram","volume":"41","author":"Wang","year":"2025","journal-title":"Geogr. Geo-Inf. Sci."},{"key":"ref_41","first-page":"285","article-title":"Using the gradient boosting decision tree (GBDT) algorithm for a train delay prediction model considering the delay propagation feature","volume":"16","author":"Zhang","year":"2021","journal-title":"Adv. Prod. Eng. Manag."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s41651-025-00238-z","article-title":"Urban Heat Island Response to Projected Land-Use Change and Surface Energy Balance Modifications in Chongqing City","volume":"9","author":"Yeboah","year":"2025","journal-title":"China J. Geovis. Spat. Anal."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/15\/2\/76\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T17:52:35Z","timestamp":1770918755000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/15\/2\/76"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,12]]},"references-count":42,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["ijgi15020076"],"URL":"https:\/\/doi.org\/10.3390\/ijgi15020076","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,12]]}}}