{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:01:49Z","timestamp":1760058109617,"version":"build-2065373602"},"reference-count":115,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T00:00:00Z","timestamp":1742428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research funding for the 2024 Green seedling Program of the Human Resources and Social Security Department of Guangxi Zhuang Autonomous Region, China","award":["60203038919630213","S202427060300167","6020303891920","6020303891924","602021239447"],"award-info":[{"award-number":["60203038919630213","S202427060300167","6020303891920","6020303891924","602021239447"]}]},{"name":"Guangxi Innovation and Entrepreneurship Training Program for College Students","award":["60203038919630213","S202427060300167","6020303891920","6020303891924","602021239447"],"award-info":[{"award-number":["60203038919630213","S202427060300167","6020303891920","6020303891924","602021239447"]}]},{"name":"Nanning Normal University Demonstration Modern Industrial College","award":["60203038919630213","S202427060300167","6020303891920","6020303891924","602021239447"],"award-info":[{"award-number":["60203038919630213","S202427060300167","6020303891920","6020303891924","602021239447"]}]},{"name":"Nanning Normal University Characteristic Undergraduate College Construction and College Teaching Quality and Reform Engineering Project\u2014Undergraduate Education and Teaching Key Project","award":["60203038919630213","S202427060300167","6020303891920","6020303891924","602021239447"],"award-info":[{"award-number":["60203038919630213","S202427060300167","6020303891920","6020303891924","602021239447"]}]},{"name":"Nanning Normal University Doctoral Research Startup Project","award":["60203038919630213","S202427060300167","6020303891920","6020303891924","602021239447"],"award-info":[{"award-number":["60203038919630213","S202427060300167","6020303891920","6020303891924","602021239447"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In view of the challenges brought by a complex environment, diverse data sources and urban development needs, our study comprehensively reviews the application of algorithms in urban residential vacancy rate observation. First, we explore the definition and measurement of urban residential vacancy rate, pointing out the difficulties in accurately defining vacant houses and obtaining reliable data. Then, we introduce various algorithms such as traditional statistical learning, machine learning, deep learning and ensemble learning, and analyze their applications in vacancy rate observation. The traditional statistical learning algorithm builds a prediction model based on historical data mining and analysis, which has certain advantages in dealing with linear problems and regular data. However, facing the high nonlinear relationships and complexity of the data in the urban residential vacancy rate observation, its prediction accuracy is difficult to meet the actual needs. With their powerful nonlinear modeling ability, machine learning algorithms have significant advantages in capturing the nonlinear relationships of data. However, they require high data quality and are prone to overfitting phenomenon. Deep learning algorithms can automatically learn feature representation, perform well in processing large amounts of high-dimensional and complex data, and can effectively deal with the challenges brought by various data sources, but the training process is complex and the computational cost is high. The ensemble learning algorithm combines multiple prediction models to improve the prediction accuracy and stability. By comparing these algorithms, we can clarify the advantages and adaptability of different algorithms in different scenarios. Facing the complex environment, the data in the observation of urban residential vacancy rate are affected by many factors. The unbalanced urban development leads to significant differences in residential vacancy rates in different areas. Spatiotemporal heterogeneity means that vacancy rates vary in different geographical locations and over time. The complexity of data affected by various factors means that the vacancy rate is jointly affected by macroeconomic factors, policy regulatory factors, market supply and demand factors and individual resident factors. These factors are intertwined, increasing the complexity of data and the difficulty of analysis. In view of the diversity of data sources, we discuss multi-source data fusion technology, which aims to integrate different data sources to improve the accuracy of vacancy rate observation. The diversity of data sources, including geographic information system (GIS) (Geographic Information System) data, remote sensing images, statistics data, social media data and urban grid management data, requires integration in format, scale, precision and spatiotemporal resolution through data preprocessing, standardization and normalization. The multi-source data fusion algorithm should not only have the ability of intelligent feature extraction and related analysis, but also deal with the uncertainty and redundancy of data to adapt to the dynamic needs of urban development. We also elaborate on the optimization methods of algorithms for different data sources. Through this study, we find that algorithms play a vital role in improving the accuracy of vacancy rate observation and enhancing the understanding of urban housing conditions. Algorithms can handle complex spatial data, integrate diverse data sources, and explore the social and economic factors behind vacancy rates. In the future, we will continue to deepen the application of algorithms in data processing, model building and decision support, and strive to provide smarter and more accurate solutions for urban housing management and sustainable development.<\/jats:p>","DOI":"10.3390\/a18030174","type":"journal-article","created":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T07:59:54Z","timestamp":1742457594000},"page":"174","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Algorithms Facilitating the Observation of Urban Residential Vacancy Rates: Technologies, Challenges and Breakthroughs"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8376-1694","authenticated-orcid":false,"given":"Binglin","family":"Liu","sequence":"first","affiliation":[{"name":"School of Geography and Planning, Nanning Normal University, Nanning 530001, China"},{"name":"Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education, Nanning Normal University, Nanning 530001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weijia","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Geography and Planning, Nanning Normal University, Nanning 530001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8705-566X","authenticated-orcid":false,"given":"Weijiang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Engineering, City University of Hong Kong, Hong Kong 999077, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Southwest University, Chongqing 400715, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nini","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Architecture and Built Environment, University of Nottingham, Ningbo 315154, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"05020020","DOI":"10.1061\/(ASCE)UP.1943-5444.0000612","article-title":"Identification of \u201cgrowth\u201d and \u201cshrinkage\u201d pattern and planning strategies for shrinking cities based on a spatial perspective of the Pearl River Delta Region","volume":"146","author":"Lang","year":"2020","journal-title":"J. Urban Plan. Dev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105784","DOI":"10.1016\/j.ecolind.2019.105784","article-title":"Evolution process analysis of urban metabolic patterns and sustainability assessment in western China, a case study of Xining city","volume":"109","author":"Fan","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.landusepol.2017.07.005","article-title":"Strategic adjustment of land use policy under the economic transformation","volume":"74","author":"Liu","year":"2018","journal-title":"Land Use Policy"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"107533","DOI":"10.1016\/j.eiar.2024.107533","article-title":"How does rapid urban construction land expansion affect the spatial inequalities of ecosystem health in China? Evidence from the country, economic regions and urban agglomerations","volume":"106","author":"Wei","year":"2024","journal-title":"Environ. Impact Assess. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"100047","DOI":"10.1016\/j.sftr.2021.100047","article-title":"Data-driven smart sustainable cities of the future: An evidence synthesis approach to a comprehensive state-of-the-art literature review","volume":"3","author":"Bibri","year":"2021","journal-title":"Sustain. Futur."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107218","DOI":"10.1016\/j.landusepol.2024.107218","article-title":"Carbon surplus or carbon deficit under land use transformation in China?","volume":"143","author":"Li","year":"2024","journal-title":"Land Use Policy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1006\/juec.2000.2187","article-title":"Rental housing markets, the incidence and duration of vacancy, and the natural vacancy rate","volume":"49","author":"Gabriel","year":"2001","journal-title":"J. Urban Econ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104431","DOI":"10.1016\/j.landurbplan.2022.104431","article-title":"Estimating housing vacancy rates at block level: The example of Guiyang, China","volume":"224","author":"Shi","year":"2022","journal-title":"Landsc. Urban Plan."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"He, M., Xu, Y., and Li, N. (2020). Population Spatialization in Beijing City Based on Machine Learning and Multisource Remote Sensing Data. Remote Sens., 12.","DOI":"10.3390\/rs12121910"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.cities.2019.05.006","article-title":"Ghost cities of China: Identifying urban vacancy through social media data","volume":"94","author":"Williams","year":"2019","journal-title":"Cities"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.compchemeng.2019.04.003","article-title":"Advances and opportunities in machine learning for process data analytics","volume":"126","author":"Qin","year":"2019","journal-title":"Comput. Chem. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"287","DOI":"10.32479\/ijeep.14683","article-title":"New Insights into the research landscape on the application of artificial intelligence in sustainable smart cities: A bibliometric mapping and network analysis approach","volume":"13","author":"Zaidi","year":"2023","journal-title":"Int. J. Energy Econ. Policy"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bibri, S.E., and Bibri, S.E. (2020). The compact city paradigm and its centrality in sustainable urbanism in the era of big data revolution: A comprehensive state-of-the-art literature review. Advances in the Leading Paradigms of Urbanism and Their Amalgamation: Compact Cities, Eco\u2013Cities, and Data\u2013Driven Smart Cities, Springer.","DOI":"10.1007\/978-3-030-41746-8"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Alamri, S. (2024). The Geospatial Crowd: Emerging Trends and Challenges in Crowdsourced Spatial Analytics. ISPRS Int. J. Geo-Inf., 13.","DOI":"10.3390\/ijgi13060168"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"771","DOI":"10.3390\/smartcities5030040","article-title":"The metaverse as a virtual form of smart cities: Opportunities and challenges for environmental, economic, and social sustainability in urban futures","volume":"5","author":"Allam","year":"2022","journal-title":"Smart Cities"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1177\/10780874211025451","article-title":"Understanding Urban Retail Vacancy","volume":"58","author":"Talen","year":"2021","journal-title":"Urban Aff. Rev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1080\/02673037.2013.760029","article-title":"Housing Vacancy and the Shrinking City: Trends and Policies in the UK and the City of Liverpool","volume":"28","author":"Couch","year":"2013","journal-title":"Hous. Stud."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1108\/IJHMA-03-2022-0035","article-title":"The impact of apartment vacancies on nearby housing rents over multiple time periods: Application of smart meter data","volume":"16","author":"Baba","year":"2022","journal-title":"Int. J. Hous. Mark. Anal."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"107883","DOI":"10.1016\/j.resconrec.2024.107883","article-title":"Trade-offs and synergies pattern evolution of ecosystem structure-resilience-activity-services (SRAS) in the Belt and Road Initiative region","volume":"211","author":"Wei","year":"2024","journal-title":"Resour. Conserv. Recycl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4929","DOI":"10.1007\/s10462-022-10286-2","article-title":"AI-big data analytics for building automation and management systems: A survey, actual challenges and future perspectives","volume":"56","author":"Himeur","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1016\/j.apenergy.2018.11.002","article-title":"Reinforcement learning for demand response: A review of algorithms and modeling techniques","volume":"235","author":"Nagy","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1108\/IJCHM-09-2016-0540","article-title":"Use of dynamic pricing strategies by Airbnb hosts","volume":"30","author":"Gibbs","year":"2018","journal-title":"Int. J. Contemp. Hosp. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Nocca, F. (2017). The Role of Cultural Heritage in Sustainable Development: Multidimensional Indicators as Decision-Making Tool. Sustainability, 9.","DOI":"10.3390\/su9101882"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"102730","DOI":"10.1016\/j.cities.2020.102730","article-title":"Urban regeneration: Community engagement process for vacant land in declining cities","volume":"102","author":"Kim","year":"2020","journal-title":"Cities"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1177\/0894439315611103","article-title":"Smart cities governance: The need for a holistic approach to assessing urban participatory policy making","volume":"34","author":"Castelnovo","year":"2016","journal-title":"Soc. Sci. Comput. Rev."},{"key":"ref_26","first-page":"1","article-title":"Reconceptualising housing emptiness beyond vacancy and abandonment","volume":"23","author":"Caramaschi","year":"2022","journal-title":"Int. J. Hous. Policy"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Joo, H., Lee, S., Kang, S.-J., and Kim, S.-Y. (2022). Vacant house characteristics by use area and their application to sustainable community. Appl. Sci., 12.","DOI":"10.3390\/app122110696"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1002\/2475-8876.12088","article-title":"Factors and tendencies of housing abandonment: An analysis of a survey of vacant houses in Kawaguchi City, Saitama","volume":"2","author":"Baba","year":"2019","journal-title":"Jpn. Arch. Rev."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1068\/c12215","article-title":"State intervention in vacant residential properties: An evaluation of empty dwelling management orders in England","volume":"33","author":"Henderson","year":"2015","journal-title":"Environ. Plan. C Gov. Policy"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ma, W., Jiang, G., Zhou, T., and Zhang, R. (2022). Mixed land uses and community decline: Opportunities and challenges for mitigating residential vacancy in peri-urban villages of China. Front. Environ. Sci., 10.","DOI":"10.3389\/fenvs.2022.887988"},{"key":"ref_31","unstructured":"Chen, S. (2021). Multi-Domain Multi-Objective Optimisation of Urban District Environmental Performance. [Doctoral Dissertation, University of Sheffield]."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, Q., Sun, Z., and Li, W. (2023). Effects of COVID-19 on residential planning and design: A scientometric analysis. Sustainability, 15.","DOI":"10.3390\/su15032823"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1007\/s11769-020-1171-7","article-title":"Spatial identification of housing vacancy in China","volume":"31","author":"Pan","year":"2021","journal-title":"Chin. Geogr. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"He, W., and Chen, M. (2024). Advancing urban life: A systematic review of emerging technologies and artificial intelligence in urban design and planning. Buildings, 14.","DOI":"10.3390\/buildings14030835"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Khan, I., Zedadra, O., Guerrieri, A., and Spezzano, G. (2024). Occupancy prediction in iot-enabled smart buildings: Technologies, methods, and future directions. Sensors, 24.","DOI":"10.3390\/s24113276"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yue, W., Chen, Y., Zhang, Q., and Liu, Y. (2019). Spatial explicit assessment of urban vitality using multi-source data: A case of Shanghai, China. Sustainability, 11.","DOI":"10.3390\/su11030638"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"119253","DOI":"10.1016\/j.techfore.2018.03.024","article-title":"Big data analytics: Computational intelligence techniques and application areas","volume":"153","author":"Iqbal","year":"2020","journal-title":"Technol. Forecast. Soc. Chang."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"103248","DOI":"10.1016\/j.apgeog.2024.103248","article-title":"The non-linear dynamics of South Australian regional housing markets: A machine learning approach","volume":"166","author":"Soltani","year":"2024","journal-title":"Appl. Geogr."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yu, D., and Fang, C. (2023). Urban remote sensing with spatial big data: A review and renewed perspective of urban studies in recent decades. Remote Sens., 15.","DOI":"10.3390\/rs15051307"},{"key":"ref_40","first-page":"508","article-title":"The smart future for sustainable development: Artificial intelligence solutions for sustainable urbanization","volume":"33","year":"2024","journal-title":"Sustain. Dev."},{"key":"ref_41","unstructured":"Ratner, B. (2017). Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Chapman and Hall\/CRC."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"102817","DOI":"10.1016\/j.ssresearch.2022.102817","article-title":"Knowledge Discovery: Methods from data mining and machine learning","volume":"110","author":"Shu","year":"2022","journal-title":"Soc. Sci. Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"121243","DOI":"10.1016\/j.renene.2024.121243","article-title":"A review of machine learning approaches for electric vehicle energy consumption modelling in urban transportation","volume":"234","author":"Zhang","year":"2024","journal-title":"Renew. Energy"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"8368","DOI":"10.1109\/TII.2024.3371990","article-title":"Novel Two-Stream Deep Slow and Nonstationary Fast Feature Extraction for Chemical Process Soft Sensing Application","volume":"20","author":"Wang","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1214\/ss\/1009213726","article-title":"Statistical Modeling: The Two Cultures","volume":"16","author":"Breiman","year":"2001","journal-title":"Stat. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1214\/10-STS330","article-title":"To Explain or to Predict?","volume":"25","author":"Shmueli","year":"2010","journal-title":"Stat. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"153","DOI":"10.2991\/ijcis.d.200129.001","article-title":"Urban real estate market early warning based on support vector machine: A case study of Beijing","volume":"13","author":"Wang","year":"2020","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2208","DOI":"10.1109\/TITS.2023.3327266","article-title":"A hybrid visualization model for knowledge mapping: Scientometrics, SAOM, and SAO","volume":"25","author":"Xiao","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1177\/10780874211065014","article-title":"Using emerging hot spot analysis to explore spatiotemporal patterns of housing vacancy in ohio metropolitan statistical areas","volume":"59","author":"Morckel","year":"2021","journal-title":"Urban Aff. Rev."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"104537","DOI":"10.1016\/j.landusepol.2020.104537","article-title":"Analyzing driving factors of land values in urban scale based on big data and non-linear machine learning techniques","volume":"94","author":"Ma","year":"2020","journal-title":"Land Use Policy"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Baldominos, A., Blanco, I., Moreno, A.J., Iturrarte, R., Bern\u00e1rdez, \u00d3., and Afonso, C. (2018). Identifying real estate opportunities using machine learning. Appl. Sci., 8.","DOI":"10.20944\/preprints201810.0297.v1"},{"key":"ref_52","first-page":"1","article-title":"A survey on deep learning: Algorithms, techniques, and applications","volume":"51","author":"Pouyanfar","year":"2018","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"53040","DOI":"10.1109\/ACCESS.2019.2912200","article-title":"Review of deep learning algorithms and architectures","volume":"7","author":"Shrestha","year":"2019","journal-title":"IEEE Access"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Latif, J., Xiao, C., Imran, A., and Tu, S. (2019, January 30\u201331). Medical imaging using machine learning and deep learning algorithms: A review. Proceedings of the IEEE 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur, Pakistan.","DOI":"10.1109\/ICOMET.2019.8673502"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"902","DOI":"10.1016\/j.rser.2017.02.085","article-title":"A review on time series forecasting techniques for building energy consumption","volume":"74","author":"Deb","year":"2017","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"e1249","DOI":"10.1002\/widm.1249","article-title":"Ensemble learning: A survey","volume":"8","author":"Sagi","year":"2018","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s11704-019-8208-z","article-title":"A survey on ensemble learning","volume":"14","author":"Dong","year":"2019","journal-title":"Front. Comput. Sci."},{"key":"ref_58","first-page":"3","article-title":"Modelling urban change with cellular automata: Contemporary issues and future research directions","volume":"45","author":"Liu","year":"2019","journal-title":"Prog. Hum. Geogr."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Thakuriah, P., Tilahun, N., and Zellner, M. (2017). Big data and urban informatics: Innovations and challenges to urban planning and knowledge discovery. Seeing Cities Through Big Data: Research, Methods and Applications in Urban Informatics, Springer.","DOI":"10.1007\/978-3-319-40902-3"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.compenvurbsys.2018.03.004","article-title":"Using machine learning and small area estimation to predict building-level municipal solid waste generation in cities","volume":"70","author":"Kontokosta","year":"2018","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_61","first-page":"1135","article-title":"The impact of housing vacancy rates on house prices","volume":"42","author":"Wang","year":"2020","journal-title":"Resour. Sci."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Van, H.T.K., Ha, T.V., Asada, T., and Arimura, M. (2022). Vacancy Dwellings Spatial Distribution\u2014The Determinants and Policy Implications in the City of Sapporo, Japan. Sustainability, 14.","DOI":"10.3390\/su141912427"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1111\/1468-2427.12101","article-title":"How does (n\u2019t) Urban Shrinkage get onto the Agenda? Experiences from L eipzig, L iverpool, G enoa and B ytom","volume":"38","author":"Bernt","year":"2014","journal-title":"Int. J. Urban Reg. Res."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Yang, P., and Pan, J. (2022). Estimating housing vacancy rate using nightlight and POI: A case study of main urban area of Xi\u2019an City, China. Appl. Sci., 12.","DOI":"10.3390\/app122312328"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1111\/j.1467-9787.2006.00480.x","article-title":"Economic fundamentals in local housing markets: Evidence from U.S. metropolitan regions","volume":"46","author":"Hwang","year":"2006","journal-title":"J. Reg. Sci."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Zhao, S., Li, W., Zhao, K., and Zhang, P. (2021). Change characteristics and multilevel influencing factors of real estate inventory\u2014Case studies from 35 Key Cities in China. Land, 10.","DOI":"10.3390\/land10090928"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Hou, J. (2021, January 18\u201320). The Evaluation of the Health of Chinese Real Estate Market: Empirical Research based on index clustering and AHP. Proceedings of the 2021 1st International Conference on Control and Intelligent Robotics, Guangzhou, China.","DOI":"10.1145\/3473714.3473845"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","volume":"1","author":"Rudin","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"116601","DOI":"10.1016\/j.apenergy.2021.116601","article-title":"Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives","volume":"287","author":"Himeur","year":"2021","journal-title":"Appl. Energy"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Camero, A., Toutouh, J., Stolfi, D.H., and Alba, E. (2018, January 10\u201315). Evolutionary deep learning for car park occupancy prediction in smart cities. Proceedings of the Learning and Intelligent Optimization: 12th International Conference, LION 12, Kalamata, Greece. Revised Selected Papers 12.","DOI":"10.1007\/978-3-030-05348-2_32"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"4729","DOI":"10.1007\/s00521-020-05531-0","article-title":"Multi-source data fusion for economic data analysis","volume":"33","author":"Li","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_72","first-page":"04022003","article-title":"Multi-source data fusion for urban vacancy rate prediction in Shenzhen","volume":"148","author":"Li","year":"2022","journal-title":"J. Urban Plan. Dev."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1080\/01621459.2022.2096038","article-title":"Measuring Housing Vitality from Multi-source Big Data and Machine Learning","volume":"117","author":"Zhou","year":"2022","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_74","first-page":"1234","article-title":"Adaptive algorithms for dynamic vacancy rate prediction in new urban districts: A case study of Xiong\u2019an","volume":"6","author":"Liu","year":"2023","journal-title":"Smart Cities"},{"key":"ref_75","unstructured":"Canziani, A., Paszke, A., and Culurciello, E. (2016). An analysis of deep neural network models for practical applications. arXiv."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Miikkulainen, R., Liang, J., Meyerson, E., Rawal, A., Fink, D., Francon, O., Raju, B., Shahrzad, H., Navruzyan, A., and Duffy, N. (2024). Evolving deep neural networks. Artificial Intelligence in the Age of Neural Networks and Brain Computing, Academic Press.","DOI":"10.1016\/B978-0-323-96104-2.00002-6"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1459107","DOI":"10.1155\/2020\/1459107","article-title":"Generative adversarial network technologies and applications in computer vision","volume":"2020","author":"Jin","year":"2020","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Zhong, X., Fricker, P., Yu, F., Tan, C., and Pan, Y. (2022, January 13\u201316). A Discussion on an Urban Layout Workflow Utilizing Generative Adversarial Network (GAN)\u2014With a focus on automatized labeling and dataset acquisition. Proceedings of the eCAADe 2022: International Conference on Education and Research in Computer Aided Architectural Design in Europe, Leuven, Belgium.","DOI":"10.52842\/conf.ecaade.2022.2.583"},{"key":"ref_79","first-page":"1","article-title":"Geological remote sensing interpretation using deep learning feature and an adaptive multisource data fusion network","volume":"60","author":"Han","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"100198","DOI":"10.1016\/j.egyai.2022.100198","article-title":"Machine learning and deep learning methods for enhancing building energy efficiency and indoor environmental quality\u2014A review","volume":"10","author":"Tien","year":"2022","journal-title":"Energy AI"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"113436","DOI":"10.1016\/j.enbuild.2023.113436","article-title":"Advanced controls on energy reliability, flexibility and occupant-centric control for smart and energy-efficient buildings","volume":"297","author":"Liu","year":"2023","journal-title":"Energy Build."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Sadayuki, T., Kanayama, Y., and Arimura, T.H. (2019). Evaluating the Externality of Vacant Houses in Japan: The Case of Toshima Municipality, Tokyo, Waseda University.","DOI":"10.52324\/001c.13522"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"127476","DOI":"10.1016\/j.jhydrol.2022.127476","article-title":"A critical review of real-time modelling of flood forecasting in urban drainage systems","volume":"607","author":"Piadeh","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_84","first-page":"251","article-title":"Regional differences in the socio-economic and built-environment factors of vacant house ratio as a key indicator for spatial urban shrinkage","volume":"4","author":"Baba","year":"2017","journal-title":"Urban Reg. Plan. Rev."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Yue, X., Wang, Y., Zhao, Y., and Zhang, H. (2022). Estimation of urban housing vacancy based on daytime housing exterior images\u2014a case study of Guangzhou in China. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11060349"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1080\/02673037.2022.2119212","article-title":"Identifying housing vacancy using data on registered addresses and domestic consumption","volume":"39","author":"Flas","year":"2022","journal-title":"Hous. Stud."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Liu, X., Li, Z., Fu, X., Yin, Z., Liu, M., Yin, L., and Zheng, W. (2023). Monitoring house vacancy dynamics in the pearl river delta region: A method based on NPP-VIIRS night-time light remote sensing images. Land, 12.","DOI":"10.3390\/land12040831"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1111\/1467-9906.00007","article-title":"Predicting housing abandonment with the philadelphia neighborhood information system","volume":"25","author":"Hillier","year":"2003","journal-title":"J. Urban Aff."},{"key":"ref_89","first-page":"84","article-title":"DBSCAN algorithm based on homestay cluster identification, distribution pattern and influencing factors\u2014Take Nanjing city as an example","volume":"36","author":"Ma","year":"2021","journal-title":"Hum. Geogr."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"05021024","DOI":"10.1061\/(ASCE)UP.1943-5444.0000721","article-title":"Exploring the bidirectional relationship between urbanization and rural sustainable development in China since 2000: Panel data analysis of Chinese cities","volume":"147","author":"Li","year":"2021","journal-title":"J. Urban Plan. Dev."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wang, Y., Wu, S., and Du, Z. (2022). House Price Valuation Model Based on Geographically Neural Network Weighted Regression: The Case Study of Shenzhen, China. ISPRS Int. J. Geo Inf., 11.","DOI":"10.3390\/ijgi11080450"},{"key":"ref_92","first-page":"261","article-title":"Is impulsive behavior adaptive in harsh and unpredictable environments?","volume":"41","author":"Fenneman","year":"2020","journal-title":"A formal model."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"5943","DOI":"10.1016\/j.jksuci.2021.08.007","article-title":"Reviewing the application of machine learning methods to model urban form indicators in planning decision support systems: Potential, issues and challenges","volume":"34","author":"Tekouabou","year":"2022","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"108026","DOI":"10.1016\/j.buildenv.2021.108026","article-title":"Study on an adaptive thermal comfort model with K-nearest-neighbors (KNN) algorithm","volume":"202","author":"Xiong","year":"2021","journal-title":"Build. Environ."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1109\/TRO.2021.3094984","article-title":"Outlier-robust estimation: Hardness, minimally tuned algorithms, and applications","volume":"38","author":"Antonante","year":"2021","journal-title":"IEEE Trans. Robot."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"114284","DOI":"10.1016\/j.rser.2024.114284","article-title":"A systematic review and comprehensive analysis of building occupancy prediction","volume":"193","author":"Li","year":"2024","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1109\/TETCI.2019.2907718","article-title":"A Survey on an Emerging Area: Deep Learning for Smart City Data","volume":"3","author":"Chen","year":"2019","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Yuan, X., Dai, X., Zou, Z., He, X., Sun, Y., and Zhou, C. (2024). Multi-Source Data-Driven Extraction of Urban Residential Space: A Case Study of the Guangdong\u2013Hong Kong\u2013Macao Greater Bay Area Urban Agglomeration. Remote Sens., 16.","DOI":"10.3390\/rs16193631"},{"key":"ref_99","unstructured":"Censor, Y., Zaknoon, M., and Zaslavski, A.J. (2019). Data-compatibility of algorithms. arXiv."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"27226","DOI":"10.1007\/s10489-023-04891-z","article-title":"The Dynamic Fusion Representation of Multi\u2014Source Fuzzy Data","volume":"53","author":"Qin","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1080\/15472450.2015.1037955","article-title":"A real-time parking prediction system for smart cities","volume":"20","author":"Vlahogianni","year":"2015","journal-title":"J. Intell. Transp. Syst."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"e847","DOI":"10.7717\/peerj-cs.847","article-title":"Research on Remote Sensing Image Extraction Based on Deep Learning","volume":"8","author":"Shun","year":"2022","journal-title":"PeerJ Comput. Sci."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1108\/JPIF-09-2019-0131","article-title":"Can digital technologies speed up real estate transactions?","volume":"38","author":"Saull","year":"2020","journal-title":"J. Prop. Invest. Financ."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1504\/IJBC.2019.101853","article-title":"Digital ledger technology-based real estate transaction mechanism and its block size assessment","volume":"1","author":"Singh","year":"2019","journal-title":"Int. J. Blockchains Cryptocurrencies"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"3671","DOI":"10.1007\/s11269-018-2012-7","article-title":"A mixed strategy based on self-organizing map for water demand pattern profiling of large-size smart water grid data","volume":"32","author":"Padulano","year":"2018","journal-title":"Water Resour. Manag."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1080\/19443994.2014.917887","article-title":"Smart water grid: The future water management platform","volume":"55","author":"Lee","year":"2015","journal-title":"Desalination Water Treat."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Public Utilities Board Singapore Pan_Ju_Khuan@ pub. gov. sg (2016). Managing the water distribution network with a Smart Water Grid. Smart Water, 1, 4.","DOI":"10.1186\/s40713-016-0004-4"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"40595","DOI":"10.1109\/ACCESS.2021.3064445","article-title":"Water leak detection survey: Challenges & research opportunities using data fusion & federated learning","volume":"9","author":"Moubayed","year":"2021","journal-title":"IEEE Access"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1007\/s12273-017-0387-7","article-title":"A preliminary investigation of water usage behavior in single-family homes","volume":"10","author":"Xue","year":"2017","journal-title":"Build. Simul."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1007\/s11121-022-01478-x","article-title":"Reaching Latinx communities with algorithmic optimization for SARS-CoV-2 testing locations","volume":"24","author":"Searcy","year":"2023","journal-title":"Prev. Sci."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Du, M., Wang, L., Zou, S., and Shi, C. (2018). Modeling the census tract level housing vacancy rate with the Jilin1-03 satellite and other geospatial data. Remote Sens., 10.","DOI":"10.3390\/rs10121920"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Park, J.-I. (2019). A Multilevel model approach for assessing the effects of house and neighborhood characteristics on housing vacancy: A case of daegu, South Korea. Sustainability, 11.","DOI":"10.3390\/su11092515"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Lee, J., Newman, G., and Lee, C. (2022). Predicting detached housing vacancy: A multilevel analysis. Sustainability, 14.","DOI":"10.3390\/su14020922"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1186\/s13677-023-00494-8","article-title":"Blockchain Based Trusted Execution Environment Architecture Analysis for Multi\u2014Source Data Fusion Scenario","volume":"12","author":"Yang","year":"2023","journal-title":"J. Cloud Comput."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1038\/s41578-022-00490-5","article-title":"Machine Learning for a Sustainable Energy Future","volume":"8","author":"Yao","year":"2023","journal-title":"Nat. Rev. Mater."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/3\/174\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:57:05Z","timestamp":1760029025000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/3\/174"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,20]]},"references-count":115,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["a18030174"],"URL":"https:\/\/doi.org\/10.3390\/a18030174","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2025,3,20]]}}}