• Journal of Geo-information Science
  • Vol. 22, Issue 1, 100 (2020)
Qingfeng GUAN1、1, Shuliang REN1、1, Yao YAO1、1、2、2、*, Xun LIANG1、1, Jianfeng ZHOU1、1, Zehao YUAN1、1, and Liangyang DAI1、1
Author Affiliations
  • 1School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China
  • 1中国地质大学(武汉)地理与信息工程学院,武汉 430078
  • 2Alibaba Group, Hangzhou 311121, China
  • 2阿里巴巴集团,杭州 311121
  • show less
    DOI: 10.12082/dqxxkx.2020.190406 Cite this Article
    Qingfeng GUAN, Shuliang REN, Yao YAO, Xun LIANG, Jianfeng ZHOU, Zehao YUAN, Liangyang DAI. Revealing the Behavioral Patterns of Different Socioeconomic Groups in Cities with Mobile Phone Data and House Price Data[J]. Journal of Geo-information Science, 2020, 22(1): 100 Copy Citation Text show less
    References

    [1] 中华人民共和国国家统计局. 2010年第六次全国人口普查主要数据公报(第1号)[R].2011. [ National Bureau of statistics of the People's Republic of China. Bulletin of main data of the sixth national population census in 2010 (no. 1)[R].2011. ] [ National Bureau of statistics of the People's Republic of China. Bulletin of main data of the sixth national population census in 2010 (no. 1)[R].2011. ]

    [2] 段成荣, 吕利丹, 邹湘江. 当前我国流动人口面临的主要问题和对策——基于2010年第六次全国人口普查数据的分析[J]. 人口研究, 2013,37(2):17-24. [ Duan CR, Lv LD, Zou XJ. Major Challenges for China's Floating Population and Policy Suggestions:An Analysis of the 2010 Population Census Data.[J]. Population Research, 2013,37(2):17-24. ] [ Duan C R, Lv L D, Zou X J. Major Challenges for China's Floating Population and Policy Suggestions:An Analysis of the 2010 Population Census Data.[J]. Population Research, 2013,37(2):17-24. ]

    [3] Malaria on the move: Human population movement and malaria transmission[J]. Emerging Infectious Diseases, 6, 103-109(2000).

    [4] 牛方曲, 王芳. 城市土地利用——交通集成模型的构建与应用[J]. 地理学报, 2018,73(2):380-392. [ Niu FQ, WangF. Modelling urban spatial impacts of land-use/transport policies[J]. Acta Geographica Sinica, 2018,73(2):380-392. ] [ Niu F Q, Wang F. Modelling urban spatial impacts of land-use/transport policies[J]. Acta Geographica Sinica, 2018,73(2):380-392. ]

    [5] et alClimate change: Migration as adaptation[J]. Nature, 478, 447-449(2011).

    [6] The size, scale, and shape of cities[J]. Science, 319, 769-771(2008).

    [7] The real-time city? Big data and smart urbanism[J]. GeoJournal, 79, 1-14(2014).

    [8] Crowd-based urban characterization: Extracting crowd behavioral patterns in urban areas from twitter[J]. Pers Ubiquit Comput, 17, 605-620(2013).

    [9] The travel-activity patterns of urban residents: Dimensions and relationships to sociodemographic characteristics[J]. Economic Geography, 57, 332-347(1981).

    [10] Gender and urban activity patterns in uppsala, sweden[J]. Geographical Review, 70, 291(1980).

    [11] The influence of socioeconomic characteristics, land use and travel time considerations on mode choice for medium and longer distance trips[J]. Journal of Transport Geography, 14, 327-341(2006).

    [12] Travel patterns and environmental effects now and in the future: Implications of differences in energy consumption among socio-economic groups[J]. Ecological Economics, 30, 405-417(1999).

    [13] Ethnic differences in activity spaces: A study of out-of-home nonemployment activities with mobile phone data[J]. Annals of The Association of American Geographers, 104, 542-559(2014).

    [14] et alUnderstanding aggregate human mobility patterns using passive mobile phone location data: A home-based approach[J]. Transportation, 42, 625-646(2015).

    [15] Activity patterns, socioeconomic status and urban spatial structure: what can social media data tell us?[J]. International Journal of Geographical Information Science, 30, 1873-1898(2016).

    [16] et alHuman mobility and socioeconomic status: Analysis of Singapore and Boston[J]. Computers, Environment and Urban Systems, 72, 51-67(2018).

    [17] 深圳市统计局. 深圳统计年鉴[M]. 北京: 中国统计出版社, 2018. [ Statistical yearbook of Shenzhen[M]. Beijing: China statistics press, 2018. ] [ Statistical yearbook of Shenzhen[M]. Beijing: China statistics press, 2018. ]

    [18] 林宇川, 冯健. 深圳关内关外一体化过程中的边界效应及时空演变[J]. 热带地理, 2011,31(6):580-585. [ Lin YC, FengJ. Border effect and its temporal-spatial evolution in the process of regional integration in Shenzhen[J]. Tropical Geography, 2011,31(6):580-585. ] [ Lin Y C, Feng J. Border effect and its temporal-spatial evolution in the process of regional integration in Shenzhen[J]. Tropical Geography, 2011,31(6):580-585. ]

    [19] 深圳市统计局. 深圳市2015 年全国 1%人口抽样调查主要数据公报[R].2015. [ Shenzhen statistics bureau. Shenzhen 2015 national 1% sample survey main data bulletin[R].2015. ] [ Shenzhen statistics bureau. Shenzhen 2015 national 1% sample survey main data bulletin[R]. 2015. ]

    [20] 陈刚, 李树, 陈屹立. 人口流动对犯罪率的影响研究[J]. 中国人口科学, 2009(4):52-61. [ ChenG, LiS, Chen YL. Population mobility and crime:An empirical analysis based on China's observation[J]. Chinese Journal of Population Science, 2009(4):52-61. ] [ Chen G, Li S, Chen Y L. Population mobility and crime:An empirical analysis based on China's observation[J]. Chinese Journal of Population Science, 2009(4):52-61. ]

    [21] 邵源, 宋家骅. 大城市交通拥堵管理策略与方法——以深圳市为例[J]. 城市交通, 2010,8(6):7-13. [ ShaoY, Song JH. Traffic congestion management strategies and methods in large metropolitan area: A case study in Shenzhen[J]. City Traffic, 2010,8(6):7-13. ] [ Shao Y, Song J H. Traffic congestion management strategies and methods in large metropolitan area: A case study in Shenzhen[J]. City Traffic, 2010,8(6):7-13. ]

    [22] 傅崇辉, 苏杨, 陆杰华, 等. 深圳人口与健康发展报告(2014)[R]. 2014. [ Fu CH, SuY, Lu JH, et al. Shenzhen population and health development report (2014)[R]. 2014. ] [ Fu C H, Su Y, Lu J H, et al. Shenzhen population and health development report (2014)[R]. 2014. ]

    [23] Study on the determinants and relationship between China's urban housing prices and rents. Hangzhou: Zhejiang University(2011).

    [24] The spatial differentiation and relationship between housing prices and rents: Evidence from Beijing in China[J]. Geographical Research(2019).

    [25] et alHousing price, housing rent, and rent-price ratio: Evidence from 30 cities in China[J]. Journal of Urban Planning and Development-asce, 144, 4017026(2018).

    [26] 方毅, 赵石磊. 房屋销售价格和租赁价格的关系研究[J]. 数理统计与管理, 2007,26(6):951-957. [ FangY, Zhao SL. Empirical study on the relationship of sale price and rent of house in China[J]. Application of Statistics and Management, 2007,26(6):951-957. ] [ Fang Y, Zhao S L. Empirical study on the relationship of sale price and rent of house in China[J]. Application of Statistics and Management, 2007,26(6):951-957. ]

    [27] 姚尧, 任书良, 王君毅, 等. 卷积神经网络和随机森林的城市房价微观尺度制图方法[J]. 地球信息科学学报, 21(2):36-45. [ YaoY, Ren SL, Wang JY, et al. Mapping the fine-scale housing price distribution by integrating a convolutional neural network and random forest[J]. Journal of Geo-information Science, 2019,21(2):168-177. ] [ Yao Y, Ren S L, Wang J Y, et al. Mapping the fine-scale housing price distribution by integrating a convolutional neural network and random forest[J]. Journal of Geo-information Science, 2019,21(2):168-177. ]

    [28] et alMapping fine-scale urban housing prices by fusing remotely sensed imagery and social media data[J]. Transactions in Gis, 22, 561-581(2018).

    [29] et alMining individual life pattern based on location history[C]. 2009 tenth international conference on mobile data management: Systems, services and middleware, IEEE(2009).

    [30] Trajectory data mining: An overview[J]. ACM Transactions on intelligent systems and technology, 6, 29(2015).

    [31] Understanding individual human mobility patterns[J]. Nature, 453, 779-782(2008).

    [32] et alLimits of predictability in human mobility[J]. Science, 327, 1018-1021(2010).

    [33] et alAnother tale of two cities: Understanding Human activity space using actively tracked cellphone location data[J]. Annals of the American Association of Geographers, 106, 489-502(2016).

    [34] et alUnderstanding individual daily activity space based on large scale mobile phone locationdata[C]. Geo-Computation 2015 Conference, Dallas, Texas, US(2015).

    [35] 刘瑜, 肖昱, 高松, 等. 基于位置感知设备的人类移动研究综述[J]. 地理与地理信息科学, 2011,27(4):8-13. [ LiuY, XiaoY, GaoS, et al. A review of human mobility research based on location aware devices[J]. Geography and Geo-Information Science, 2011,27(4):8-13. ] [ Liu Y, Xiao Y, Gao S, et al. A review of human mobility research based on location aware devices[J]. Geography and Geo-Information Science,2011,27(4):8-13. ]

    [36] What about people in geographic information science[R]. 2005 Re-Presenting Geographic Information Systems,[report]. John Wiley, 215-242(2005).

    [37] A general feature-based map matching framework with trajectory simplification[C]. Proceedings of the 7 th ACM SIGSPATIAL International Workshop on GeoStreaming. ACM, 7(2016).

    [38] Exploring network structure, dynamics, and function using network X, 2008[R]. Los Alamos National Lab, Los Alamos,[report]. NM (United States), 01(2008).

    [39] A note on two problems in connection with graphs[J]. Numerische Mathematics, 1, 269-271(1959).

    [40] et alIdentifying important places in people's lives from cellular network data[J], 133-151(2011).

    [41] et alChoosing the right home location definition method for the given dataset[J]. arXiv: Social and Information Networks(2015).

    [42] Does urban mobility have a daily routine? Learning from the aggregate data of mobile networks[J]. Journal of Urban Technology, 17, 41-60(2010).

    [43] 曹劲舟, 涂伟, 李清泉, 等. 基于大规模手机定位数据的群体活动时空特征分析[J]. 地球信息科学学报, 2017,19(4):467-474. [ Cao JZ, TuW, Li QQ. Spatio-temporal analysis of aggregated human activities based on massive mobile phone tracking data[J]. Journal of Geo-information Science, 2017,19(4):467-474. ] [ Cao J Z, Tu W, Li Q Q. Spatio-temporal analysis of aggregated human activities based on massive mobile phone tracking data[J]. Journal of Geo-information Science, 2017,19(4):467-474. ]

    [44] The data model concept in statistical mapping[C]. International Yearbook of Cartography(1967).

    [45] Measuring geographic concentration by means of the standard deviational ellipse[J]. American Journal of Sociology, 32, 88-94(1926).

    Qingfeng GUAN, Shuliang REN, Yao YAO, Xun LIANG, Jianfeng ZHOU, Zehao YUAN, Liangyang DAI. Revealing the Behavioral Patterns of Different Socioeconomic Groups in Cities with Mobile Phone Data and House Price Data[J]. Journal of Geo-information Science, 2020, 22(1): 100
    Download Citation