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Spatial Distribution Characteristics of Residents’ Emotions Based on Sina Weibo Big Data: A Case Study of Nanjing

  • Feng Zhen
  • Jia TangEmail author
  • Yingxue Chen
Chapter
Part of the Advances in Geographic Information Science book series (AGIS)

Abstract

Many urban planning approaches have emphasized the need to be people oriented. With the fast development of cities, more attention should be given to the perception and spatial experiences of the residents. Following urban planning theories and studies on environmental quality, infrastructure allocation, and planning, research on residents’ emotions has drawn increasing research attention. Nanjing City of China, is used as an example for applying Sina Weibo big data, a new type of data, to extract real-time emotions and their corresponding geo-locations of residents. This study uses ArcGIS software to analyze the spatial distribution characteristics of residents’ emotions in the overall city and in different types of places of Nanjing. Grid statistics are used to provide evidence to optimize urban space development.

Keywords

Big data Residents’ emotions Spatial distribution characteristics Nanjing 

References

  1. Blumen, O. (2002). Criss-crossing boundaries: Ultraorthodox Jewish women go to work. Gender Place and Culture: A Journal of Feminist Geography, 9(2), 133–151.CrossRefGoogle Scholar
  2. Chen, Y. X., & Zhen, F. (2014). Further investigation into urban spatial function organization based on residents; activity data: A case study of Nanjing. Urban Planning Forum, 5, 72–78.Google Scholar
  3. Colls, R. (2006). Outsize/outside: Bodily bignesses and the emotional experiences of British women shopping for clothes. Gender Place and Culture, 13(5), 529–545.CrossRefGoogle Scholar
  4. Crewe, B., Warr, J., et al. (2014). The emotional geography of prison life. Theoretical Criminology, 18(1), 56–74.CrossRefGoogle Scholar
  5. Fielding, S. (2000). Children’s geographies and the primary school. In S. L. Holloway & G. Valentine (Eds.), Children’s geographies: Playing, living, learning (p. 199). London: Routledge.Google Scholar
  6. Hallman, B. C., & Benbow, S. M. P. (2007). Family leisure, family photography and zoos: Exploring the emotional geographies of families. Social & Cultural Geography, 8(6), 871–888.CrossRefGoogle Scholar
  7. Heimtun, B. (2010). The holiday meal: Eating out alone and mobile emotional geographies. Leisure Studies, 29(2), 175–192.CrossRefGoogle Scholar
  8. Hemming, P. J. (2007). Renegotiating the primary school: children’s emotional geographies of sport, exercise and active play. Children’s Geographies, 5(4), 353–371.CrossRefGoogle Scholar
  9. Hogertz C. (2010). WALK21 Cheltenham (UK); Emotions of the urban pedestrian: Sensory mapping. In Pedestrians’ quality needs (pp. 31–52).Google Scholar
  10. Kwan, M. P. (2007). Affecting geospatial technologies: Toward a feminist politics of emotion. The Professional Geographer, 59(1), 22–34.CrossRefGoogle Scholar
  11. Lüscher, P., & Weibel, R. (2013). Exploiting empirical knowledge for automatic delineation of city centres from large-scale topographic databases. Computers Environment and Urban Systems, 37(1), 18–34.CrossRefGoogle Scholar
  12. Miller, J. C. (2014). Malls without stores (MwS): The affectual spaces of a Buenos Aires shopping mall. Transactions of the Institute of British Geographers, 39(1), 14–25.CrossRefGoogle Scholar
  13. Moisi, D. (2010). The geopolitics of emotion: How cultures of fear, humiliation, and hope are reshaping the world. New York: Random House LLC.Google Scholar
  14. Philo, C., & Parr, H. (2000). Institutional geographies: Introductory remarks. Geoforum, 31(4), 513–521.CrossRefGoogle Scholar
  15. Stieglitz, S., & Dang-Xuan, L. (2013). Emotions and information diffusion in social media—Sentiment of Microblogs and sharing behaviour. Journal of Management Information Systems, 29(4), 217–248.CrossRefGoogle Scholar
  16. Tang, G. A. (2006). ArcGIS spatial analysis experiment course. Beijing: Science Press.Google Scholar
  17. Tumasjan, A., Sprenger, T. O., et al. (2011). Election forecasts with Twitter: How 140 characters reflect the political landscape. Social Science Computer Review, 29(4), 402–418.CrossRefGoogle Scholar
  18. Williams, P., Hubbard, P., et al. (2001). Consumption, exclusion and emotion: The social geographies of shopping. Social & Cultural Geography, 2(2), 203–220.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.School of Architecture and Urban PlanningNanjing UniversityNanjingChina
  2. 2.Shanghai Urban Planning & Verifying CenterShanghaiChina

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