[1] 姜亚莉,蔡心田,丁振兴. 基于空间分析方法的商业选址研究——以珠海市香洲区为例[J]. 测绘与空间地理信息, 2014,37(12):131-133.
[2] 王静远,李超,熊璋,等. 以数据为中心的智慧城市研究综述[J]. 计算机研究与发展, 2014,51(2):239-259.
[3] 郑宇. 城市计算概述[J]. 武汉大学学报(信息科学版), 2015,40(1):1-13.
[4] SANTOS F, ALMEIDA A, MARTINS C, et al. Using POI functionality and accessibility levels for delivering personalized tourism recommendations[C]// World Conference on Information Systems and Technologies. Springer, 2017:539-548.
[5] 任星怡,宋美娜,宋俊德. 基于用户签到行为的兴趣点推荐[J]. 计算机学报, 2017(1):28-51.
[6] LI M Y, KWAN M P, WANG F H, et al. Using points-of-interest data to estimate commuting patterns in central Shanghai, China[J]. Journal of Transport Geography, 2018,72:201-210.
[7] 薛冰,肖骁,李京忠,等. 基于POI大数据的城市零售业空间热点分析——以辽宁省沈阳市为例[J]. 经济地理, 2018,38(5):36-43.
[8] ZHAI W, BAI X B, SHI Y, et al. Beyond Word2vec: An approach for urban functional region extraction and identification by combining Place2vec and POIs[J]. Computers, Environment and Urban Systems, 2019,74:1-12.
[9] CUI J X, LIU F, HU J, et al. Identifying mismatch between urban travel demand and transport network services using GPS data: A case study in the fast growing Chinese city of Harbin[J]. Neurocomputing, 2016,181:4-18.
[10]陈世莉,陶海燕,李旭亮,等. 基于潜在语义信息的城市功能区识别——广州市浮动车GPS时空数据挖掘[J]. 地理学报, 2016,71(3):471-483.
[11]付鑫,杨宇,孙皓. 出租汽车出行轨迹网络结构复杂性与空间分异特征[J]. 交通运输工程学报, 2017,17(2):106-116.
[12]刘菊,许珺,蔡玲,等. 基于出租车用户出行的功能区识别[J]. 地球信息科学学报, 2018,20(11):1550-1561.
[13]罗孝羚,蒋阳升. 基于出租车运营数据和POI数据的出行目的识别[J]. 交通运输系统工程与信息, 2018,18(5):60-66.
[14]KONG Y, WU J P, XU M, et al. Charging pile siting recommendations via the fusion of points of interest and vehicle trajectories[J]. China Communications, 2017(11):35-44.
[15]MENG C S, CUI Y, HE Q, et al. Travel purpose inference with GPS trajectories, POIs, and geo-tagged social media data[C]// 2017 IEEE International Conference on Big Data. 2017:1319-1324.
[16]KRAUSE C M, ZHANG L. Short-term travel behavior prediction with GPS, land use, and point of interest data[J]. Transportation Research Part B, 2018,123:349-361.
[17]LIB Z, CAI Z L, JIANG L L, et al. Exploring urban taxi ridership and local associated factors using GPS data and geographically weighted regression[J]. Cities, 2019,87:68-86.
[18]KARAMSHUK D, NOULAS A, SCELLATO S. Geo-Spotting: Mining online location-based services for optimal retail store placement[C]// Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2013:793-801.
[19]CHEN T Y, CHEN L C, CHEN Y M. Mining location-based service data for feature construction in retail store recommendation[C]// Industrial Conference on Data Mining. Springer, 2017:68-77.
[20]MOKBEL M F, ALARABI L, BAO J, et al. MNTG: Anextensible Web-based traffic generator[C]// International Symposium on Spatial and Temporal Databases. Springer, 2013:38-55.
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