Study on Urban Spatial Function Mixture and Individual Activity Space From the Perspectives of Resident Activity

The research on the relationship between residents’ daily activities and urban spatial structure is of considerable significance to urban planning engineering and the organization of urban functions. However, little research considers the perspective of micro-spatial scale or resident perception. The increasing user-generated activity check-in data in social networks provides a database for this research. In this study, we first divided the urban space into nine functions that satisfy the residents’ activities, then used the small-scale grid to divide the city blocks and used information entropy to evaluate the mixed degree of land use functions. We then introduced the latent Dirichlet allocation (LDA) topic model to identify 15 mixed patterns of land use functions and each spatial unit’s topic distribution. Moreover, the JS divergence index was employed to measure spatial units’ similarity, fit the distance-activity intensity decay curve, and studied the influence of the individual spatial function distribution choice. We demonstrate that in urban space, residents’ daily activities mold the blending of urban area functions and shift single-function urban planning to mixed-use, consisting of single-function dominant and multi-function mixed. Besides, the functional complementarity between the activity units weakens the distance attenuation effect of the activity-space interaction intensity to some extent. The research on the interaction between active space and spatial activities expect to support the combination of urban land use types, the layout of facilities, and the guidance of residents’ activities.

[1]  D. Harvey,et al.  The Condition of Postmodernity. An Enquiry into the Origins of Cultural Change (an excerpt) , 1991, Journal of Economic Sociology.

[2]  Xiao Zhou,et al.  Cultural investment and urban socio-economic development: a geosocial network approach , 2017, Royal Society Open Science.

[3]  Mei-Po Kwan,et al.  Investigating commuting flexibility with GPS data and 3D geovisualization: a case study of Beijing, China , 2013 .

[4]  A. Kellerman,et al.  The Constitution of Society : Outline of the Theory of Structuration , 2015 .

[5]  Donghai Liu,et al.  The Intuitionistic Fuzzy Linguistic Cosine Similarity Measure and Its Application in Pattern Recognition , 2018, Complex..

[6]  Michel Beigbeder,et al.  Correlation between textual similarity and quality of LDA topic model results , 2019, 2019 13th International Conference on Research Challenges in Information Science (RCIS).

[7]  Kazutoshi Sumiya,et al.  Exploring urban characteristics using movement history of mass mobile microbloggers , 2010, HotMobile '10.

[8]  J M Bailey,et al.  SUBSIDISED PUBLIC TRANSPORT AND THE DEMAND FOR TRAVEL. THE SOUTH YORKSHIRE EXAMPLE , 1983 .

[9]  Harvey J. Miller,et al.  User‐centred time geography for location‐based services , 2004 .

[10]  Hui Xiong,et al.  Discovering Urban Functional Zones Using Latent Activity Trajectories , 2015, IEEE Transactions on Knowledge and Data Engineering.

[11]  R. Golledge,et al.  Spatial Behavior: A Geographic Perspective , 1996 .

[12]  Yong Gao,et al.  Analyzing Relatedness by Toponym Co‐Occurrences on Web Pages , 2014, Trans. GIS.

[13]  Zhenjiang Shen,et al.  Geospatial Analysis to Support Urban Planning in Beijing , 2015 .

[14]  Christa Hubers,et al.  ICT and temporal fragmentation of activities: An analytical framework and initial empirical findings , 2008 .

[15]  Jean-Claude Thill,et al.  Visual Data Mining in Spatial Interaction Analysis with Self-Organizing Maps , 2009 .

[16]  Franziska Hoffmann,et al.  Human Activity Patterns In The City Things People Do In Time And In Space , 2016 .

[17]  Dick Ettema,et al.  Fragmentation of work activity as a multi-dimensional construct and its association with ICT, employment and sociodemographic characteristics , 2010 .

[18]  Mark Moberg The Condition of Postmodernity: An Enquiry into the Origins of Cultural Change. DAVID HARVEY , 1994 .

[19]  Satish V. Ukkusuri,et al.  Urban activity pattern classification using topic models from online geo-location data , 2014 .

[20]  Fahui Wang,et al.  Urban land uses and traffic 'source-sink areas': Evidence from GPS-enabled taxi data in Shanghai , 2012 .

[21]  B. Lenntorp,et al.  Time-geography – at the end of its beginning , 1999 .

[22]  Yu Liu,et al.  Human mobility patterns in different communities: a mobile phone data-based social network approach , 2015, Ann. GIS.

[23]  Daqing Zhang,et al.  Measuring social functions of city regions from large-scale taxi behaviors , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[24]  Wei Tu,et al.  Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns , 2017, Int. J. Geogr. Inf. Sci..

[25]  Morton E. O'Kelly,et al.  Spatial Interaction Models:Formulations and Applications , 1988 .

[26]  Jean-Claude Thill,et al.  Combining smart card data and household travel survey to analyze jobs-housing relationships in Beijing , 2013, Comput. Environ. Urban Syst..

[27]  M. Goodchild Citizens as sensors: the world of volunteered geography , 2007 .

[28]  G. Chao New perspectives on spatial structure research in information era , 2002 .

[29]  Licia Capra,et al.  Urban Computing: Concepts, Methodologies, and Applications , 2014, TIST.

[30]  Martin Dijst,et al.  ICTs and Accessibility: An Action Space Perspective on the Impact of New Information and Communication Technologies , 2004 .

[31]  D. Walmsley,et al.  Human geography: Behavioural approaches , 1984 .

[32]  Mark J. Smith,et al.  PEDESTRIAN MOVEMENT AND THE DOWNTOWN ENCLOSED SHOPPING CENTER. , 1993 .

[33]  Mark Steyvers,et al.  Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[34]  K. Axhausen,et al.  Collecting data on leisure travel: The link between leisure contacts and social interactions , 2010 .

[35]  A. Tatem,et al.  Dynamic population mapping using mobile phone data , 2014, Proceedings of the National Academy of Sciences.

[36]  Xuegang Chen,et al.  Spatiotemporal structural evolution and regional differentiation analysis of land use in Xinjiang based on information entropy , 2010, 2010 The 2nd Conference on Environmental Science and Information Application Technology.

[37]  Yong Gao,et al.  Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data , 2013, PloS one.

[38]  Michael Batty,et al.  Inferring building functions from a probabilistic model using public transportation data , 2014, Comput. Environ. Urban Syst..

[39]  H. Miller Tobler's First Law and Spatial Analysis , 2004 .

[40]  Tim Schwanen,et al.  How fixed is fixed? Gendered rigidity of space–time constraints and geographies of everyday activities , 2008 .

[41]  Jianhua Lin,et al.  Divergence measures based on the Shannon entropy , 1991, IEEE Trans. Inf. Theory.

[42]  Håkan Forsberg Institutions, consumer habits and retail change in Sweden , 1998 .

[43]  Mei-Po Kwan,et al.  The Internet, mobile phone and space-time constraints , 2008 .

[44]  Martin A. Andresen,et al.  Obesity relationships with community design, physical activity, and time spent in cars. , 2004, American journal of preventive medicine.

[45]  Dietmar Bauer,et al.  Inferring land use from mobile phone activity , 2012, UrbComp '12.

[46]  Li Jian INTERNET,INDUSTRY CLUSTERS AND GLOBAL PRODUCTION NETWORKS:THE NEW ICTs' EFFECTS ON INDUSTRIAL SPACE ORGANIZATION , 2009 .

[47]  R. Thakur,et al.  The Group of Twenty (G20) , 2013 .

[48]  Lan Mu,et al.  GIS analysis of depression among Twitter users , 2015 .

[49]  Keith C. Clarke,et al.  Do Global Cities Enable Global Views? Using Twitter to Quantify the Level of Geographical Awareness of U.S. Cities , 2015, PloS one.

[50]  Fahui Wang,et al.  Measurement, Optimization, and Impact of Health Care Accessibility: A Methodological Review , 2012, Annals of the Association of American Geographers. Association of American Geographers.

[51]  Fahui Wang,et al.  Reconstructing Gravitational Attractions of Major Cities in China from Air Passenger Flow Data, 2001–2008: A Particle Swarm Optimization Approach , 2013 .

[52]  Bernd Resch,et al.  From Social Sensor Data to Collective Human Behaviour Patterns - Analysing and Visualising Spatio-Temporal Dynamics in Urban Environments , 2012 .

[53]  Chaogui Kang,et al.  Incorporating spatial interaction patterns in classifying and understanding urban land use , 2016, Int. J. Geogr. Inf. Sci..

[54]  Michael Batty,et al.  Detecting the dynamics of urban structure through spatial network analysis , 2014, Int. J. Geogr. Inf. Sci..

[55]  Xingjian Liu,et al.  Featured Graphic. How Mixed is Beijing, China? A Visual Exploration of Mixed Land Use , 2013 .

[56]  Xing Xie,et al.  Discovering regions of different functions in a city using human mobility and POIs , 2012, KDD.

[57]  Y. Tuan,et al.  Space and Place: The Perspective of Experience. , 1978 .

[58]  W. Fitzgerald,et al.  Urban Geography , 1949, Nature.

[59]  David M. Blei,et al.  Probabilistic topic models , 2012, Commun. ACM.

[60]  P. Haggett Locational analysis in human geography , 1967 .