[1] et alEvaluating the effectiveness of urban growth boundaries using human mobility and activity records[J]. Cities, 46, 76-84(2015).
[2] et alInferring trip purposes and uncovering travel patterns from taxi trajectory data[J]. Cartography and Geographic Information Science, 43, 103-114(2016).
[3] et alSocial sensing: A new approach to understanding our socioeconomic environments[J]. Annals of the Association of American Geographers, 105, 512-530(2015).
[4] 郑宇. 城市计算概述[J]. 武汉大学学报·自然科学版, 2015,40(1):1-13. [ ZhengY. Introduction to urban computing[J]. Geomatics and Information Science of Wuhan University, 2015,40(1):1-13. ] [ Zheng Y.Introduction to urban computing[J]. Geomatics and Information Science of Wuhan University, 2015,40(1):1-13. ]
[5] Exploring the spatiotemporal structure of dynamic urban space using metro smart card records[J]. Computers, Environment and Urban Systems, 64, 169-183(2017).
[7] et alUnderstanding intra-urban trip patterns from taxi trajectory data[J]. Journal of Geographical Systems, 14, 463-483(2012).
[8] et alStreet as a big geo-data assembly and analysis unit in urban studies: A case study using Beijing taxi data[J]. Applied Geography, 86, 152-164(2017).
[9] 龙瀛, 张宇, 崔承印. 利用公交刷卡数据分析北京职住关系和通勤出行[J]. 地理学报, 2012,67(10):1339-1352. [ LongY, ZhangY, CUI CY. Identifying commuting pattern of Beijing using bus smart card data[J]. Acta Geographica Sinica, 2012,67(10):1339-1352.] [ Long Y, Zhang Y, CUI C Y. Identifying commuting pattern of Beijing using bus smart card data[J]. Acta Geographica Sinica,2012,67(10):1339-1352.]
[10] et alA trajectory clustering approach based on decision graph and data field for detecting hotspots[J]. International Journal of Geographical Information Science, 31, 1101-1127(2016).
[11] et alDetecting arbitrarily shaped clusters in origin-destination flows using ant colony optimization[J]. International Journal of Geographical Information Science, 33, 134-154(2018).
[12] et alDetecting the dynamics of urban structure through spatial network analysis[J]. International Journal of Geographical Information Science, 28, 2178-2199(2014).
[13] Revealing travel patterns and city structure with taxi trip data[J]. Journal of Transport Geography, 43, 78-90(2015).
[14] Identifying spatial interaction patterns of vehicle movements on urban road networks by topic modelling[J]. Computers, Environment and Urban Systems, 74, 50-61(2019).
[15] et alUnderstanding the representativeness of mobile phone location data in characterizing human mobility indicators[J]. ISPRS International Journal of Geo-information, 6, 7(2017).
[16] 杨喜平, 方志祥. 移动定位大数据视角下的人群移动模式及城市空间结构研究进展[J]. 地理科学进展, 2018,37(7):18-27. [ Yang XP, Fang ZX. Recent progress in studying human mobility and urban spatial structure based on mobile location big data[J]. Progress in Geography, 2018,37(7):18-27. ] [ Yang X P, Fang Z X. Recent progress in studying human mobility and urban spatial structure based on mobile location big data[J]. Progress in Geography, 2018,37(7):18-27. ]
[17] et alDo different datasets tell the same story about urban mobility-a comparative study of public transit and taxi usage[J]. Journal of Transport Geography, 70, 78-90(2018).
[18] 龙瀛, 孙立君, 陶遂. 基于公共交通智能卡数据的城市研究综述[J]. 城市规划学刊, 2015,223(3):70-77. [ LongY, Sun LJ, TaoS. A review of urban studies based on transit smart card data[J]. Urban Planning Journal, 2015,223(3):70-77. ] [ Long Y, Sun L J, Tao S.A review of urban studies based on transit smart card data[J]. Urban Planning Journal, 2015,223(3):70-77. ]
[19] Tests for rank correlation coefficients[J]. Biometrika, 48, 29-40(1961).
[20] The analysis of spatial association by use of distance statistics[J]. Geographical Analysis, 24, 189-206(1992).
[21] Finding and evaluating community structure in network[J]. Physical Review E, 69, 026113(2004).
[22] [M]. Geographically weighted regression: the analysis of spatially varying relationships(2002).
