2005—2020年城市研究中机器学习方法应用进展与综述

曾文菁; ZENG Wenjing; 周恺; ZHOU Kai; 熊益群; XIONG Yiqun

doi:10.48014/cgsr.20220711001

2005—2020年城市研究中机器学习方法应用进展与综述

Advances and Review of Machine Learning Applications in Urban Studies from 2005 to 2020

中国地理科学评论 2023年第1卷第1期页码[16-30] 下载全文[3.8MB] [HTML]

摘要

机器学习作为实现数据挖掘和问题预测的新方法, 近年来在城市研究各领域中获得广泛使用, 本研究对相关文献进行阶段性总结。首先, 本文从数据的类型与选择及预处理出发, 介绍了各类机器学习算法的性质特点和适用性, 利用Citespace对2005—2020年以来机器学习与城市研究的交叉领域、热点、前沿和趋势做出分析。其次, 研究重点对近五年相关文献中的有监督机器学习算法应用情况进行研读, 从城市交通、城市生态、自然地理学、人文地理学四个主要领域展开综述, 并解读城市研究中无监督学习、半监督学习及强化学习方法的初步探索。最后, 文章总结了机器学习方法的优势, 提出未来应当挖掘各类机器学习方法在城市研究多领域多视角中的应用潜力, 把握智能技术方法与城市研究高效结合的前沿趋势。

Abstract

Machine learning, as a new method for data mining and problem prediction, has been widely used in various fields of urban studies in recent years, which requires a periodical summary of relevant literature. Start with data types, selection and preprocessing, this paper introduces the characteristics and applicability of various machine learning algorithms, and analyzes the cross-fields, hot spots, frontiers and trends of machine learning and urban studies from 2005 to 2020 by using Citespace. Second, focusing on the application of supervised machine learning algorithms from relevant literature in the past five years, a review is made from four main aspects including urban traffic, urban ecology, physical geography, human geography, and the tentative explorations of unsupervised learning, semi-supervised learning and reinforcement learning method in urban studies are unscrambled as well. Finally, the advantages of machine learning methods are summarized, and it's proposed that the application potential of various machine learning methods in multiple fields and perspectives of urban research should be explored in the future, and the cutting-edge trend of efficient combination of intelligent technology and methods with urban research should be grasped.

DOI	10.48014/cgsr.20220711001
文章类型	综述性论文
收稿日期	2022-07-12
接收日期	2022-08-10
出版日期	2023-03-28
关键词	机器学习, 城市研究, 数据, 有监督学习, 研究综述
Keywords	Machine learning, urban studies, data, supervised learning, research review
作者	曾文菁, 周恺^*, 熊益群
Author	ZENG Wenjing, ZHOU Kai^*, XIONG Yiqun
所在单位	湖南大学建筑与规划学院, 长沙 410000
Company	School of Architecture and Planning, Hunan University, Changsha 410000, China
浏览量	877
下载量	403
基金项目	国家自然科学基金项目“城市收缩治理的理论模型、国际比较和关键规划领域研究”(项目号:52078197)
参考文献	[1] 张润, 王永滨. 机器学习及其算法和发展研究[J]. 中国传媒大学学报(自然科学版), 2016, 23(02): 10-18, 24. https://doi.org/10.3969/j.issn.1673-4793.2016.02.002 [2] 闫友彪, 陈元琰. 机器学习的主要策略综述[J]. 计算机应用研究, 2004,(07): 4-10, 13. https://doi.org/10.3969/j.issn.1001-3695.2004.07.002 [3] 陈康, 向勇, 喻超. 大数据时代机器学习的新趋势[J]. 电信科学, 2012, 28(12): 88-95. https://doi.org/10.3969/j.issn.1000-0801.2012.12.014 [4] 韩京宇, 徐立臻, 董逸生. 数据质量研究综述[J]. 计算机科学, 2008, 35(2): 1-5, 12. https://doi.org/10.3969/j.issn.1002-137X.2008.02.001 [5] 熊中敏, 郭怀宇, 吴月欣. 缺失数据处理方法研究综述[J]. 计算机工程与应用, 2021, 57(14): 27-38. https://doi.org/10.3778/j.issn.1002-8331.2101-0187 [6] 刘星毅, 农国才. 几种不同缺失值填充方法的比较[J]. 南宁师范高等专科学校学报, 2007,(03): 148-150. https://doi.org/10.3969/j.issn.1674-8891.2007.03.049 [7] 陈娟, 王献雨, 罗玲玲, 等. 缺失值填补效果: 机器学习与统计学习的比较[J]. 统计与决策, 2020, 36(17): 28-32. https://doi.org/10.13546/j.cnki.tjyjc.2020.17.006 [8] 王和勇, 樊泓坤, 姚正安, 等. 不平衡数据集的分类方法研究[J]. 计算机应用研究, 2008, 25(5): 1301- 1303, 1308. https://doi.org/10.3969/j.issn.1001-3695.2008.05.006 [9] 杨明, 尹军梅, 吉根林. 不平衡数据分类方法综述[J]. 南京师范大学学报(工程技术版), 2008,(04): 7-12. https://doi.org/10.3969/j.issn.1672-1292.2008.04.002 [10] 李艳霞, 柴毅, 胡友强, 等. 不平衡数据分类方法综述[J]. 控制与决策, 2019, 34(04): 673-688. https://doi.org/10.13195/j.kzyjc.2018.0865 [11] 刘莉, 徐玉生, 马志新. 数据挖掘中数据预处理技术综述[J]. 甘肃科学学报, 2003,(01): 117-119. https://doi.org/10.3969/j.issn.1004-0366.2003.01.027 [12] 胡洁. 高维数据特征降维研究综述[J]. 计算机应用研究, 2008,(09): 2601-2606. https://doi.org/10.3969/j.issn.1001-3695.2008.09.009 [13] 张丽新. 高维数据的特征选择及基于特征选择的集成学习研究[D]. 北京: 清华大学, 2004. [14] Naqa I E, Murphy M J. What Is machine learning? [M]. //Naqa I E, Li R, Murphy M J. Machine learning in radiation oncology. Cham, Switzerland: Springer International Publishing, 2015: 3-11. https://doi.org/10.1007/978-3-319-18305-3_1 [15] 杨剑锋, 乔佩蕊, 李永梅, 等. 机器学习分类问题及算法研究综述[J]. 统计与决策, 2019, 35(06): 36-40. https://doi.org/10.13546/j.cnki.tjyjc.2019.06.008 [16] 殷瑞刚, 魏帅, 李晗, 等. 深度学习中的无监督学习方法综述[J]. 计算机系统应用, 2016, 25(08): 1-7. https://doi.org/10.15888/j.cnki.csa.005283 [17] 梁吉业, 高嘉伟, 常瑜. 半监督学习研究进展[J]. 山西大学学报(自然科学版), 2009, 32(04): 528-534. https://doi.org/10.13451/j.cnki.shanxi.univ(nat.sci.).2009.04.030 [18] 高阳, 陈世福, 陆鑫. 强化学习研究综述[J]. 自动化学报, 2004(01): 86-100. https://doi.org/10.16383/j.aas.2004.01.011 [19] 黄炳强. 强化学习方法及其应用研究[D]. 上海: 上海交通大学, 2007. [20] Lu W Z, Wang W J. Potential assessment of the “support vector machine” method in forecasting ambient air pollutant trends[J]. Chemosphere, 2005, 59(5): 693-701. https://doi.org/10.1016/j.chemosphere.2004.10.032 [21] Chang N B, Vannah B, Yang Y J. Comparative sensor fusion between hyperspectral and multispectral satellite sensors for monitoring microcystin distribution in lake Erie[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2426-2442. https://doi.org/10.1109/jstars.2014.2329913 [22] Tan K, Jin X, Plaza A, et al. Automatic change detection in high-resolution remote sensing images by using a multiple classifier system and spectral-spatial features [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(8): 3439-3451. https://doi.org/10.1109/jstars.2016.2541678 [23] Mallinis G, Mitsopoulos L, Beltran E, et al. Assessing wildfire risk in cultural heritage properties using high spatial and temporal resolution satellite imagery and spatially explicit fire simulations: The case of Holy Mount Athos, Greece[J]. Forest, 2016, 7(2): 46. https://doi.org/10.3390/f7020046 [24] Anandakumar R, Nidamanuri R R, Rishnan R. A supervoxel- based spectro-spatial approach for 3D urban point cloud labeling[J]. International Journal of Remote Sensing, 2016, 37(17): 4172-4200. https://doi.org/10.1080/01431161.2016.1211348 [25] 徐睿, 梁循, 齐金山, 等. 极限学习机前沿进展与趋势[J]. 计算机学报, 2019, 42(07): 1640-1670. https://doi.org/10.11897/SP.J.1016.2019.01640 [26] Yaseen Z M, Jaafar O, Deo R C, et al. Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq[J]. Journal of Hydrology, 2016, 542: 603-614. https://doi.org/10.1016/j.jhydrol.2016.09.035 [27] Kariminia S, Shamshirband S, Motamedi S, et al. A systematic extreme learning machine approach to analyze visitors' thermal comfort at a public urban space[J]. Renewable and Sustainable Energy Reviews, 2016, 58: 751-760. https://doi.org/10.1016/j.rser.2015.12.321 [28] Ivana B P, Vukadinovi A, Radosavljevi J M, et al. Forecasting of outdoor thermal comfort index in urban open spaces: The Nis fortress case study[J]. Thermal Science, 2016, 20(5): 1531-1539. https://doi.org/10.2298/TSCI16S5531B [29] Hao X, Zhang G G, Ma S. Deep learning[J]. International Journal of Semantic Computing, 2016, 10(03): 417-439. https://doi.org/10.1142/S1793351X16500045 [30] 程政. 城市道路短时车流量预测模型研究[D]. 合肥: 中国科学技术大学, 2016. [31] 金玮. 基于周期性分量提取的城市快速路交通流短时预测理论与方法研究[D]. 北京: 北京交通大学, 2017. [32] Liu Z Y, Liu Y, Meng Q, et al. A tailored machine learning approach for urban transport network flow estimation[J]. Transportation Research Part C: Emerging Technologies, 2019, 108: 130-150. https://doi.org/10.1016/j.trc.2019.09.006 [33] Li L C, Qu X, Zhang J, et al. Traffic incident detection based on extreme machine learning[J]. Journal of Applied Science and Engineering, 2017, 20(4): 409-416. https://doi.org/10.6180/jase.2017.20.4.01 [34] 李春晓. 城市轨道交通突发事件下乘客路径选择行为建模与仿真[D]. 北京: 北京交通大学, 2017. [35] Bratsas C, Koupidis K, Salanova J M, et al. A comparison of machine learning methods for the prediction of traffic speed in urban places[J]. Sustainability, 2020, 12(1): 142. https://doi.org/10.3390/su12010142 [36] Liu Z D, Li Z J, Wu K, et al. Urban traffic prediction from mobility data using deep learning[J]. IEEE Network, 2018, 32(4): 40-46. https://doi.org/10.1109/mnet.2018.1700411 [37] Fan Z Y, Liu C, Cai D J, et al. Research on black spot identification of safety in urban traffic accidents based on machine learning method[J]. Safety Science, 2019, 118: 607-616. https://doi.org/10.1016/j.ssci.2019.05.039 [38] Peppa M V, Bell D, Komar T, et al. Urban traffic flow analysis based on deep learning car detection from CCTV image series[J]. Remote Sensing and Spatial Information Sciences, 2018, 42(4): 499-506. https://doi.org/10.5194/isprs-archives-xlii-4-499-2018 [39] Du B W, Peng H, Wang S Z, et al. Deep irregular convolutional residual LSTM for urban traffic passenger flows prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(3): 972-985. https://doi.org/10.1109/tits.2019.2900481 [40] Bacciu D, Carta A, Gnesi S, et al. An experience in using machine learning for short-term predictions in smart transportation systems[J]. Journal of Logical and Algebraic Methods in Programming, 2017, 87: 52-66. https://doi.org/10.1016/j.jlamp.2016.11.002 [41] Zhou X L, Wang M S, Li D Y. Bike-sharing or taxi? Modeling the choices of travel mode in Chicago using machine learning[J]. Journal of Transport Geography, 2019, 79: 102479. https://doi.org/10.1016/j.jtrangeo.2019.102479 [42] Aqib M, Mehmood R, Alzahrani A, et al. Rapid transit systems: Smarter urban planning using big data, inmemory computing, deep learning, and GPUs[J]. Sustainability, 2019, 11(10): 2736. https://doi.org/10.3390/su11102736 [43] Sabouria S, Brewer S, Ewing R. Exploring the relationship between ride-sourcing services and vehicle ownership, using both inferential and machine learning approaches[J]. Landscape and Urban Planning, 2020, 198: 103797. https://doi.org/10.1016/j.landurbplan.2020.103797 [44] Salcedo-Sanz S, Deo R C, Carro-Calvo L, et al. Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms[J]. Theoretical and Applied Climatology, 2016, 125: 13-25. https://doi.org/10.1007/s00704-015-1480-4 [45] Hung C H, Knudby A, Xu Y M, et al. A comparison of urban heat islands mapped using skin temperature, air temperature, and apparent temperature(Humidex), for the greater Vancouver area[J]. Science of the Total Environment, 2016, 544, 15: 929-938. https://doi.org/10.1016/j.scitotenv.2015.12.021 [46] Shaban K B, Kadri A, Rezk E. Urban air pollution monitoring system with forecasting models[J]. IEEE Sensors Journal, 2016, 16(8): 2598-2606. https://doi.org/10.1109/jsen.2016.2514378 [47] Kök I, ŞImᶊek M U, ÖzdemIr S. A deep learning model for air quality prediction in smart cities[A]//2017 IEEE International Conference on Big Data(Big Data)[C]. Boston, MA, USA: IEEE, 2017, 1983-1990. https://doi.org/10.1109/bigdata.2017.8258144 [48] Khan Y, Chai S S. Predicting and analyzing water quality using machine learning: A comprehensive model [A]//2016 IEEE Long Island Systems, Applications and Technology Conference(LISAT)[C]. Farmingdale, NY, USA: IEEE, 2016: 1-6. https://doi.org/10.1109/lisat.2016.7494106 [49] Kranjcic N, Medak D, Župan R, et al. Machine learning methods for classification of the green infrastructure in city areas[J]. International Journal of Geo-information, 2019, 8(10): 463. https://doi.org/10.3390/ijgi8100463 [50] Labib S M. Investigation of the likelihood of green infrastructure(GI)enhancement along linear waterways or on derelict sites(DS)using machine learning[J]. Environmental Modelling and Software, 2019, 118: 146-165. https://doi.org/10.1016/j.envsoft.2019.05.006 [51] 黄烈佳, 杨鹏. 基于机器学习的武汉城市圈土地生态安全格局识别与优化策略[J]. 生态与农村环境学报, 2020, 36(07): 862-869. https://doi.org/10.19741/j.issn.1673-4831.2019.0517 [52] Molina-Gómez N I, Rodríguez-Rojas K, Calderón-Rivera D, et al. Using machine learning tools to classify sustainability levels in the development of urban ecosystems[J]. Sustainability, 2020, 12: 3326. https://doi.org/10.3390/su12083326 [53] Lu Y. The association of urban greenness and walking behavior: Using google street view and deep Learning techniques to estimate residents’ exposure to urban greenness[J]. International Journal of Environmental Research and Public Health, 2018, 15: 1576. https://doi.org/10.3390/ijerph15081576 [54] Ye Y, Richards D, Lu Y, et al. Measuring daily accessed street greenery: A human-scale approach for informing better urban planning practices[J]. Landscape and Urban Planning, 2019, 191: 103434. https://doi.org/10.1016/j.landurbplan.2018.08.028 [55] Tang Z Y, Ye Y, Jiang Z D, et al. A data-informed analytical approach to human-scale greenway planning: Integrating multi-sourced urban data with machine learning algorithms[J]. Urban Forestry & Urban Greening, 2020, 56: 126871. https://doi.org/10.1016/j.ufug.2020.126871 [56] Vilar L, Gómez I, Martínez-Vega J, et al. Multitemporal modelling of socio-economic wildfire drivers in central Spain between the 1980s and the 2000s: Comparing generalized linear models to machine learning algorithms[J]. PLoS ONE, 2016, 11(8): e0161344. https://doi.org/10.1371/journal.pone.0161344 [57] Ke Q, Tian X, Bricker J, et al. Urban pluvial flooding prediction by machine learning approaches-a case study of Shenzhen city, China[J]. Advances in Water Resources, 2020, 145: 103719. https://doi.org/10.1016/j.advwatres.2020.103719 [58] Chen J F, Li Q, Wang H M, et al. A machine learning ensemble approach based on random forest and radial basis function neural network for risk evaluation of regional flood disaster: A case study of the Yangtze River Delta, China[J]. International Journal of Environmental Research and Public Health, 2020, 17(1): 49. https://doi.org/10.3390/ijerph17010049 [59] Eini M, Kaboli H S, Rashidian M, et al. Hazard and vulnerability in urban flood risk mapping: Machine learning techniques and considering the role of urban districts[J]. International Journal of Disaster Risk Reduction, 2020, 50: 101687. https://doi.org/10.1016/j.ijdrr.2020.101687 [60] Bialas J, Oommen T, Rebbapragada U, et al. Objectbased classification of earthquake damage from highresolution optical imagery using machine learning[J]. Journal of Applied Remote Sensing, 2016, 10(3): 036025. https://doi.org/10.1117/1.JRS.10.036025 [61] Wang X Y, Guo Y G, He J, et al. Fusion of HJ1B and ALOS PALSAR data for land cover classification using machine learning methods[J]. International Journal of Applied Earth Observation and Geoinformation, 2016, 52: 192-203. https://doi.org/10.1016/j.jag.2016.06.014 [62] Gu H Y, Li H T, Liu Z Y, et al. A semi-automatic rule set building method for urban land cover classificationbased on machine learning and human knowledge[J]. Remote Sensing and Spatial Information Sciences, 2017, 42(2)/W7: 729-732. https://doi.org/10.5194/isprs-archives-xlii-2-w7-729-2017 [63] Huang B, Zhao B, Song Y M. Urban land-use mappingusing a deep convolutional neural network with highspatial resolution multispectral remote sensing imagery[J]. Remote Sensing of Environment, 2018, 214(1): 73-86. https://doi.org/10.1016/j.rse.2018.04.050 [64] Srivastava S, Vargas-Mu~noz J E, Tui D. Understandingurban landuse from the above and ground perspectives: A deep learning, multimodal solution[J]. RemoteSensing of Environment, 2019, 228: 129-143. https://doi.org/10.1016/j.rse.2019.04.014 [65] Aburas M M, Ahamad M S S, Omar N Q. Spatio-temporalsimulation and prediction of land-use changeusing conventional and machine learning models: A review[J]. Environmental Monitoring and Assessment, 2019, 191: 205. https://doi.org/10.1007/s10661-019-7330-6 [66] Shafizadeh-Moghadam H, Asghari A, Tayyebi A. Couplingmachine learning, tree-based and statistical modelswith cellular automata to simulate urban growth[J]. Computers, Environment and Urban Systems, 2017, 64: 297-308. https://doi.org/10.1016/j.compenvurbsys.2017.04.002 [67] He J L, Li X, Yao Y, et al. Mining transition rules ofcellular automata for simulating urban expansion byusing the deep learning techniques[J]. InternationalJournal of Geographical Information Science, 2018, 32(10): 2076-2097. https://doi.org/10.1080/13658816.2018.1480783 [68] Mu L, Wang L Z, Wang Y W, et al. Urban land use andland cover change prediction via self-adaptive cellularbased deep learning with multisourced data[J]. IEEEJournal of Selected Topics in Applied Earth Observationsand Remote Sensing, 2019, 12(12): 5233-5247. https://doi.org/10.1109/jstars.2019.2956318 [69] 徐朗. 土地生态适宜性约束下的未来城市扩张优化研究[D]. 南京: 南京大学, 2019. [70] Xing W R, Qian Y H, Guan X F, et al. A novel cellularautomata model integrated with deep learning for dynamicspatio-temporal land use change simulation[J]. Computers and Geosciences, 2020, 137: 104430. https://doi.org/10.1016/j.cageo.2020.104430 [71] Dou Y Y, Liu Z F, He C Y, et al. Urban land extractionusing VIIRS nighttime light data: An evaluation ofthree popular methods[J]. Remote Sensing, 2017, 9(2): 175. https://doi.org/10.3390/rs9020175 [72] 王胜利. 深度学习在城市功能区域划分中的应用研究[D]. 成都: 电子科技大学, 2018. [73] 刘星南. 基于深度神经网络的城市边缘区界定研究[D]. 广州: 广州大学, 2020. https://doi.org/10.27040/d.cnki.ggzdu.2020.000056 [74] Guo J X, Ren H Z, Zheng Y T, et al. Identify urban areafrom remote sensing image using deep learning method[C]. IGARSS 2019- 2019 IEEE International Geoscienceand Remote Sensing Symposium, 2019: 7407-7410. https://doi.org/10.1109/igarss.2019.8898874 [75] 成方龙, 赵冠伟. 分区策略与机器学习的人口分布精细化模拟[J]. 测绘科学, 2020, 45(09): 165-173. https://doi.org/10.16251/j.cnki.1009-2307.2020.09.025 [76] 肖莎. 基于机器学习的高分辨率遥感影像城市固废检测[D]. 福州: 福州大学, 2018. [77] 吕浩博. 基于深度学习的长时间序列城市制图与变化检测研究[D]. 北京: 清华大学, 2018. https://doi.org/10.27266/d.cnki.gqhau.2018.000194 [78] 郑屹, 杨俊宴. 基于大规模街景图片人工智能分析的精细化城市修补方法研究[J]. 中国园林, 2020, 36(08): 73-77. https://doi.org/10.19775/j.cla.2020.08.0073 [79] 秦和天. 基于GIS和机器学习的未来城市公园选址研究———以常州市为例[D]. 南京: 南京大学, 2020. https://doi.org/10.27235/d.cnki.gnjiu.2020.000649 [80] Lai Y, Kontokosta C E. Quantifying place: Analyzingthe drivers of pedestrian activity in dense urban environments[J]. Landscape and Urban Planning, 2018, 180: 166-178. https://doi.org/10.1016/j.landurbplan.2018.08.018 [81] Gui R Z, Chen T J, Nie H. In-depth analysis of railwayand company evolution of Yangtze River Delta withdeep learning[J]. Complexity, 2020, 2020: 5192861. https://doi.org/10.1155/2020/5192861 [82] 高梦琦. 基于机器学习的城市轨道交通客流需求预测[D]. 北京: 北京交通大学, 2020. https://doi.org/10.26944/d.cnki.gbfju.2020.003623 [83] Zhao J H, Fan W, Zhai X H. Identification of land-usecharacteristics using bicycle sharing data: A deep learningapproach[J]. Journal of Transport Geography, 2020, 82: 102562. https://doi.org/10.1016/j.jtrangeo.2019.102562 [84] 刘镇熙. 基于机器学习算法的中国城市圈层特征测度及其与产业发展的耦合研究[D]. 长沙: 湖南大学, 2019. https://doi.org/10.27135/d.cnki.ghudu.2019.003889 [85] 林豪, 江竹, 李树彬. 基于机器学习的城市快速路速度—密度关系模型[J]. 西安科技大学学报, 2020, 40(6): 1109-1116. https://doi.org/10.13800/j.cnki.xakjdxxb.2020.0623 [86] 廖自然. 基于街景图片机器学习技术的城市建筑风貌分类研究[D]. 南京: 东南大学, 2019. https://doi.org/10.27014/d.cnki.gdnau.2019.001699 [87] Ferreira D L, Nunes B A A, Campos C A V, et al. Adeep learning approach for identifying user communitiesbased on geographical preferences and its applicationsto urban and environmental planning[J]. ACMTransactions on Spatial Algorithms and Systems, 2020, 6(3): 1-24. https://doi.org/10.1145/3380970 [88] Cheng Z Y, Wang W, Lu J, et al. Classifying the trafficstate of urban expressways: A machine learning approach[J]. Transportation Research Part A: Policy andPractice, 2020, 137: 411-428. https://doi.org/10.1016/j.tra.2018.10.035 [89] 张一帆. 基于机器学习的城市轨道交通新线开行下常规公交站点客流预测研究[D]. 北京: 北京交通大学, 2020. https://doi.org/10.26944/d.cnki.gbfju.2020.002161 [90] Rhee J, Llach D C, Krishnamurti R. Context-rich urbananalysis using machine learning: A case study in Pittsburgh, PA[A]//Sousa J P, Xavier J P, Henriques G C, Architecture in the Age of the 4th Industrial Revolution-Proceedings of the 37th eCAADe and 23rd SIGra-Di Conference[C]. Porto, Portugal: University of Porto, 2019: 343-352. https://doi.org/10.52842/conf.ecaade.2019.3.343 [91] 叶宇, 仲腾, 钟秀明. 城市尺度下的建筑色彩定量化测度———基于街景数据与机器学习的人本视角分析[J]. 住宅科技, 2019, 39(05): 7-12. https://doi.org/10.13626/j.cnki.hs.2019.05.002 [92] Zhao L, Wang J D, Liu J J, et al. Routing for crowdmanagement in smart cities: A deep reinforcementlearning perspective[J]. IEEE Communications Magazine, 2019, 57(4): 88-93. https://doi.org/10.1109/mcom.2019.1800603 [93] 胡思润, 杨晓旭, 宋靖华. 基于机器学习的城市生成方法研究[J]. 智能建筑与智慧城市, 2019,(11): 106-109. https://doi.org/10.13655/j.cnki.ibci.2019.11.036
引用本文	曾文菁, 周恺, 熊益群. 2005—2020年城市研究中机器学习方法应用进展与综述[J]. 中国地理科学评论, 2023, 1(1): 16-30
Citation	ZENG Wenjing, ZHOU Kai, XIONG Yiqun. Advances and review of machine learning applications in urban studies from 2005 to 2020[J]. Chinese Geography Sciences Review, 2023, 1(1): 16-30.