基于地理标记社交媒体数据来探究不同人群城市活动的时空模式

摘要

Large-scale geotagged social media data have been increasingly used for exploring human movement patterns in cities. Challenges of this new data type, such as non-representative users and the lack of activity purposes, remain unsolved and limit its applications in exploring activity-based human patterns in cities. To deal with the above challenges, this paper proposed an analytical framework of social media data enrichment — by revealing the demographic composition of non-representative social media data users and inferring activity purposes of geotagged posts — for better exploring spatial-temporal patterns of human activity in cities. A deep learning model is employed to reveal social media users’ age and gender groups from user names, profile images, biographies, and language settings. Eight types of activity purposes are inferred from embedded geo-location by spatially joining with fine-scale building and land use data. Using Greater London as the case study, this paper explores the temporal dynamics of activity purposes with heatmaps of hourly frequency of tweets and identifies spatial differences across age and gender groups using hotspots analysis (Getis–Ord Gi* statistics). This paper demonstrates the application of geotagged social media data in identifying spatial, temporal and demographic patterns of urban activities, which potentially helps shape better place-based and age/gender-sensitive urban policies and planning decisions.

出版物
Computers, Environment and Urban Systems
 Distribution of distinct point-based geotags in Greater London.
Distribution of distinct point-based geotags in Greater London.

Highlights

  • Using large-scale geotagged social media data to understand patterns of urban activities.
  • A deep learning model is applied to infer demographic characteristics from metadata of social media users.
  • Utilising the _ne-level building uses and green space uses to infer activity purposes from geotagged social media data.
  • Revealing temporal and spatial patterns of eight types of urban activities across different age and gender groups.
牛海沣
牛海沣
副研究员

英国剑桥大学土地经济系副研究员,欧盟Horizon 2020资助项目Emotional Cities空间分析研究员。主要研究兴趣包括城市大数据挖掘、空间数据科学、地理可视化、城市感知和城市动态模拟,特别是关注如何通过结合机器学习、人工智能和城市大数据来更好地支持城市规划、政策制定和智能管理。