Spatial-Temporal Analysis of Human Dynamics on Urban Land Use Patterns Using Social Media Data by Gender

The relationship between urban human dynamics and land use types has always been an important issue in the study of urban problems in China. This paper used location data from Sina Location Microblog (commonly known as Weibo) users to study the human dynamics of the spatial-temporal characteristics of gender differences in Beijing’s Olympic Village in June 2014. We applied mathematical statistics and Local Moran’s I to analyze the spatial-temporal distribution of Sina Microblog users in 100 m × 100 m grids and land use patterns. The female users outnumbered male users, and the sex ratio ( S R varied under different land use types at different times. Female users outnumbered male users regarding residential land and public green land, but male users outnumbered female users regarding workplace, especially on weekends, as the S R on weekends ( S R was 120.5) was greater than that on weekdays ( S R was 118.8). After a Local Moran’s I analysis, we found that High–High grids are primarily distributed across education and scientific research land and residential land; these grids and their surrounding grids have more female users than male users. Low–Low grids are mainly distributed across sports centers and workplaces on weekdays; these grids and their surrounding grids have fewer female users than male users. The average number of users on Saturday was the highest value and, on weekends, the number of female and male users both increased in commercial land, but male users were more active than female users ( S R was 110).

[1]  Eugenio Cesario,et al.  Trajectory Pattern Mining for Urban Computing in the Cloud , 2017, IEEE Transactions on Parallel and Distributed Systems.

[2]  Liu Jinquan Fine Grid Dynamic Features of Population Distribution in Shenzhen , 2010 .

[3]  Zhaohui Wu,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 1 Land-Use Classification Using Taxi GPS Traces , 2022 .

[4]  조현,et al.  Analysis of social network user behavior in Collaborative Tagging System for enhancing recommendation quality = 추천 성능 향상을 위한 협업적 태깅 시스템의 소셜 네트워크 사용자 행위에 관한 분석 , 2012 .

[5]  Xing Xie,et al.  Mining correlation between locations using human location history , 2009, GIS.

[6]  关健,et al.  Research on Spatial-Temporal Characteristics of Scenic Tourist Activity Based on Sina Microblog:A Case Study of Nanjing Zhongshan Mountain National Park , 2015 .

[7]  Alex Erath,et al.  Estimating Dynamic Workplace Capacities by Means of Public Transport Smart Card Data and Household Travel Survey in Singapore , 2013 .

[8]  Cui Chengyin,et al.  Identifying Commuting Pattern of Beijing Using Bus Smart Card Data , 2012 .

[9]  WU Jiansheng,et al.  Spatio-temporal Dynamics and Driving Mechanisms of Resident Trip in Small Cities , 2017 .

[10]  X Zhou Spatial Analysis of Dynamic Data - Identifying City Center , 2014 .

[11]  Long Yin A Review of Urban Studies Based on Transit Smart Card Data , 2015 .

[12]  Wei Yang,et al.  POI Information Enhancement Using Crowdsourcing Vehicle Trace Data and Social Media Data: A Case Study of Gas Station , 2018, ISPRS Int. J. Geo Inf..

[13]  高晓路,et al.  Estimation of urban population at daytime and nighttime and analyses of their spatial pattern: A case study of Haidian District , Beijing , 2013 .

[14]  Wang Yao-li,et al.  A Review of Human Mobility Research Based on Location Aware Devices , 2011 .

[15]  G. Lebon The Crowd: A Study of the Popular Mind , 2003 .

[16]  Xing Xie,et al.  Mining Individual Life Pattern Based on Location History , 2009, 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware.

[17]  Domenico Talia,et al.  Mining human mobility patterns from social geo-tagged data , 2016, Pervasive Mob. Comput..

[18]  Chaogui Kang,et al.  Social Sensing: A New Approach to Understanding Our Socioeconomic Environments , 2015 .

[19]  LI Cheng-ming,et al.  Spatial Continuous Surface Model of Population Density , 2003 .