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Open Geospatial Data Contribution Towards Sentiment Analysis Within the Human Dimension of Smart Cities

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Open Source Geospatial Science for Urban Studies

Abstract

In recent years, there is a widespread growth of smart cities. These cities aim to increase the quality of life for its citizens, making living in an urban space more attractive, livelier, and greener. In order to accomplish these goals, physical sensors are deployed throughout the city to oversee numerous features such as environmental parameters, traffic, and the resource consumption. However, this concept lacks the human dimension within an urban context, not reflecting how humans perceive their environment and the city’s services. In this context there is a need to consider sentiment analysis within a smart city as a key element toward coherent decision making, since it is important not only to assess what people are doing, but also, why they are behaving in a certain way. In this sense, this work aims to assemble tools and methods that can collect, analyze and share information, based on User Generated spatial Content and Open Source Geospatial Science. The emotional states of citizens were sensed through social media data sources (Twitter), by extracting features (location, user profile information and tweet content by using the Twitter Streaming API) and applying machine learning techniques, such as natural language processing (Tweepy 3.0, Python library), text analysis and computational linguistics (Textblob, Python library). With this approach we are capable to map abstract concepts like sentiment while linking both quantitative and qualitative analysis in human geography. This work would lead to understand and evaluate the “immaterial” and emotional dimension of the city and its spatial expression, where location-based social networks, can be established as pivotal geospatial data sources revealing the pulse of the city.

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References

  1. Nam, T., Pardo, T.A.: Conceptualizing smart city with dimensions of technology, people, and institutions. The Proceedings of the 12th Annual International Conference on Digital Government Research.Challenging Times—dg.o ’11, p 282 (2011)

    Google Scholar 

  2. Kehoe, M., Nesbitt, P.: Smarter cities series : a foundation for understanding IBM smarter cities. IBM J. Res. Dev. (2010)

    Google Scholar 

  3. Caragliu, A., Del Bo, C., Nijkamp, P.: Smart Cities in Europe. J. Urban Technol. 18(2), 65–82 (2011)

    Article  Google Scholar 

  4. Lathia, N., Quercia, D., Crowcroft, J.: The hidden image of the city: sensing community well-being from urban mobility. Pervasive Comput., 1–8 (2012)

    Google Scholar 

  5. Resch, B., Summa, A., Sagl, G., Zeile, P., Exner, J.: Urban emotions—geo-semantic emotion extraction from technical sensors, human sensors and crowdsourced data. In: Gartner, G., Haosheng, H. (eds.) Progress in Location-Based Services, pp. 199–212. Springer International Publishing, Switzerland (2014)

    Google Scholar 

  6. Exner, J.: Smart planning & smart cities. In: Corp 2014, vol. 8, pp. 603–610

    Google Scholar 

  7. Exner, J., Zeile, P., Streich, B.: Monitoring laboratory spatial planning: new benefits and potentials for urban planning through the use of urban sensing, geo- and mobile web. In: 12th International Conference on Computers in Urban Planning and Urban Management (CUPUM), pp. 1–18 (2011)

    Google Scholar 

  8. Roche, S.: Geographic information science I:Why does a smart city need to be spatially enabled? Prog. Hum. Geogr. 38(5), 0309132513517365-, Feb 2014

    Google Scholar 

  9. Washburn, D., Sindhu, U.: Helping CIOs Understand ‘Smart City’ Initiatives. Massachusetts , Cambridge (2009)

    Google Scholar 

  10. Giffinger, R.: Smart cities ranking of European medium-sized cities, Vienna, Austria (2007)

    Google Scholar 

  11. Appleyard, B., et al.: Smart Cities: Solutions for China’s Rapid Urbanization, New York (2007)

    Google Scholar 

  12. Hall, R.E., Bowerman, B., Braverman, J., Taylor, J., Todosow, H.: The vision of a smart city. In: 2nd International Life Extension Technology Workshop, p. 7 (2000)

    Google Scholar 

  13. Harrison, C., et al.: Foundations for smarter cities. IBM J. Res. Dev. 54(4), 1–16 (2010)

    Article  Google Scholar 

  14. Rios, P.: Creating ‘The Smart City’. University of Detroit Mercy (2012)

    Google Scholar 

  15. Partridge, H.: Developing a human perspective to the digital divide in the smart city. In: ALIA 2004 Biennial Conference, Challenging Ideas, p. 7 (2004)

    Google Scholar 

  16. Schipperijn, J., Stigsdotter, U.K., Randrup, T.B., Troelsen, J.: Influences on the use of urban green space—a case study in dense, Denmark. Urban For. Urban Green. 9(1), 25–32 (2010)

    Article  Google Scholar 

  17. Naqvi, N., Shiv, B., Bechara, A.: The role of emotion in decision making a cognitive neuroscience perspective. Curr. Dir. Psychol. Sci. 15(5) (2006)

    Google Scholar 

  18. Dolan, R.J.: Emotion, cognition, and behavior. Science 298(5596), 1191–1194 (2002)

    Article  Google Scholar 

  19. Guthier, B., Alharthi, R., Abaalkhail, R., El Saddik, A.: Detection and visualization of emotions in an affect-aware city. In: Proceedings of the 1st International Workshop on Emerging Multimedia Applications and Services for Smart Cities—EMASC ’14, pp. 23–28 (2014)

    Google Scholar 

  20. Goodchild, M.F.: Citizens as sensors: The world of volunteered geography. GeoJournal 69(4), 211–221 (2007)

    Article  Google Scholar 

  21. Stefanidis, A., Crooks, A., Radzikowski, J.: Harvesting ambient geospatial information from social media feeds. GeoJournal 78(2), 319–338 (2013)

    Article  Google Scholar 

  22. Goodchild, M.F.: Twenty years of progress: GIScience in 2010. J. Spat. Inf. Sci. 1(1), 3–20 (2010)

    Google Scholar 

  23. Roche, S.: Sensing placesʼ life to make city smarter. In: ACM SIGKDD International Workshop on Urban Computing (UrbComp 2012), 12 Aug 2012, Beijing, China (2012)

    Google Scholar 

  24. Goodchild MF (2012) Reflections and visions, final keynote presentation. In: Global Geospatial Conference, pp. 1–22 (2012)

    Google Scholar 

  25. De Oliveira, T.H.M., Painho, M.: Using ambient geographic information (AGI) in order to understand emotion & stress within smart cities. In: Proceedings of AGILE 2015—18th AGILE International Conference on Geographic Information Science (2015)

    Google Scholar 

  26. De Oliveira, T.H.M., Painho, M.: Emotion & stress mapping—assembling an ambient geographic information-based methodology in order to understand smart cities. In: Atas da 10a Conferência Ibérica de Sistemas e Tecnologias de Informação, vol. II, pp. 2–5 (2015)

    Google Scholar 

  27. Marwick AE, Boyd D (2010) I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience. New Media Soc 13(1), 114–133 (2010)

    Google Scholar 

  28. Kar, B., Ghose, R.: Is my information private? Geo-privacy in the world of social media. ceur-ws.org, no. Webster, pp. 2009–2012 (2010)

    Google Scholar 

  29. Liu, Y., Piyawongwisal, P., Handa, S., Yu, L., Xu, Y., Samuel, A.: Going beyond citizen data collection with mapster: a mobile+cloud real-time citizen science experiment. In: 2011 IEEE Seventh International Conference on e-Science Work, pp 1–6 (2011)

    Google Scholar 

  30. Crooks, A., Croitoru, A., Stefanidis, A., Radzikowski, J.: #Earthquake: Twitter as a Distributed Sensor System. Trans. GIS 17(1), 124–147 (2013)

    Article  Google Scholar 

  31. Iaconesi, S., Persico, O.: An emotional compass harvesting geo-located emotional states from user generated content on social networks and using them to create a novel experience of cities. In: Proceedings of First International Workshop on Emotion and Sentiment in Social and Expressive Media approaches Perspectives From AI (ESSEM 2013) A Work. International Conference of the Italian Association for Artificial Intelligence, vol. 1096 (2013)

    Google Scholar 

  32. Younis, E.M.G.: Sentiment analysis and text mining for social media microblogs using open source tools : an empirical study. Int. J. Comput. Appl. 112(5), 44–48 (2015)

    Google Scholar 

  33. Hossmann, T., Legendre, F.: Twitter in disaster mode: opportunistic communication and distribution of sensor data in emergencies. In: Proceedings of the 3rdExtreme Conference on Communication, No. 1, pp. 1–6 (2011)

    Google Scholar 

  34. Aramaki, E., Maskawa, S., Morita, M.: Twitter catches the flu : detecting influenza epidemics using Twitter. In: EMNLP ’11 Proceedings of Conference on Empirical Methods in Natural Language Process, pp. 1568–1576 (2011)

    Google Scholar 

  35. Marcus, A., Bernstein, M., Badar, O.: Twitinfo: aggregating and visualizing microblogs for event exploration. In: Proceedings of SIGCHI Conference Human Factors in Computing Systems (2011)

    Google Scholar 

  36. Sui, D., Goodchild, M.: The convergence of GIS and social media: challenges for GIScience. Int. J. Geogr. Inf. Sci. 25(11), 1737–1748 (2011)

    Article  Google Scholar 

  37. Weinreb, A.R.: Mapping feeling: an approach to the study of emotional response to the built environment and landscape. J. Archit. Plann. Res. 1–19 (2013)

    Google Scholar 

  38. Leshed, G., Kaye, J.: Understanding how bloggers feel: recognizing affect in blog posts. In: CHI’06 Extended Abstracts on Human Factors in Computing Systems, pp. 2–7 (2006)

    Google Scholar 

  39. Bollen, J., Mao, H., Pepe, A.: Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. ICWSM, pp. 450–453 (2011)

    Google Scholar 

  40. De Choudhury, M., Counts, S., Gamon, M.: Not all moods are created equal! exploring human emotional states in social media. In: The Sixth International AAAI Conference on Weblogs and Social Media, no. 1 (2012)

    Google Scholar 

  41. Dramstad, W.E., Tveit, M.S., Fjellstad, W.J., Fry, G.L.A.: Relationships between visual landscape preferences and map-based indicators of landscape structure. Landsc. Urban Plan. 78(4), pp. 465–474 (2006)

    Google Scholar 

  42. Novack, T., Wang, Z., Zipf, A.: A system for generating customized pleasant pedestrian routes based on openstreetmap data. Sensors (Switzerland), 18(11) (2018)

    Google Scholar 

  43. Siemer, M., Mauss, I., Gross, J.J.: Same situation–different emotions: how appraisals shape our emotions. Emotion 7(3), 592–600 (2007)

    Article  Google Scholar 

  44. Petrosillo, I., Zurlini, G., Corlianò, M.E., Zaccarelli, N., Dadamo, M.: Tourist perception of recreational environment and management in a marine protected area. Landsc. Urban Plan. 79(1), 29–37 (2007)

    Article  Google Scholar 

  45. Zheng, Y., Liu, Y., Yuan, J., Xie, X.: Urban computing with taxicabs. In: Proceedings of 13th International Conference on Ubiquitous Computing—UbiComp ’11, p. 89 (2011)

    Google Scholar 

  46. Reips, U.D., Garaizar, P.: Mining twitter: a source for psychological wisdom of the crowds. Behav. Res. Methods 43(3), 635–642 (2011)

    Article  Google Scholar 

  47. De Longueville, B., Smith, R., Luraschi, G.: OMG, from here, I can see the flames!: a use case of mining location based social networks to acquire spatio-temporal data on forest fires. In: Conference: Proceedings of the 2009 International Workshop on Location Based Social Networks, no. c, pp. 73–80 (2009)

    Google Scholar 

  48. Schneider, J., Passant, A., Groza, T., Breslin, J.G.: Argumentation 3.0: how semantic web technologies can improve argumentation. In: Computational Models of Argument Proceedings, COMMA 2010, vol. 1380 (2010)

    Google Scholar 

  49. Eriksson, B., Barford, P., Sommers, J., Nowak, R.: A learning-based approach for IP geolocation. Passiv. Act. Meas., 171–180 (2010)

    Google Scholar 

  50. Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: WWW’10 Proceedings of 19th International Conference on World Wide Web, p. 851 (2010)

    Google Scholar 

  51. Balahur, A.: Sentiment Analysis in Social Media Texts. Wassa 2013, 120–128 (2013)

    Google Scholar 

  52. Ellison, N.B., Steinfield, C., Lampe, C.: Connection strategies: social capital implications of Facebook-enabled communication practices. New Media Soc. 13(6), 873–892 (2011)

    Article  Google Scholar 

  53. Foody, G., et al.: Mapping and the Citizen Sensor, First. Ubiquity Press Ltd., London (2017)

    Book  Google Scholar 

  54. Mocnik, F.B., Mobasheri, A., Griesbaum, L., Eckle, M., Jacobs, C., Klonner, C.: A grounding-based ontology of data quality measures. J. Spat. Inf. Sci. 16(16), 1–25 (2018)

    Google Scholar 

  55. Estima, J., Painho, M.: Investigating the potential of OpenStreetMap for land use/land cover production: a case study for continental Portugal. In: Arsanjani, J., Zipf, A., Mooney, P., Helbich, M. (eds.) OpenStreetMap in GIScience. Springer International Publishing (2015)

    Google Scholar 

  56. Jonietz, D., Antonio, V., See, L., Zipf, A.: Highlighting Current Trends in Volunteered Geographic Information, pp. 1–8 (2017)

    Google Scholar 

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Author information

Authors and Affiliations

  1. NOVA IMS, Universidade NOVA de Lisboa, Lisbon, Portugal

    Tiago H. Moreira de Oliveira & Marco Painho

Authors
  1. Tiago H. Moreira de Oliveira
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  2. Marco Painho
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Correspondence to Tiago H. Moreira de Oliveira .

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  1. GIScience Research Group, Heidelberg University, Heidelberg, Germany

    Amin Mobasheri

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de Oliveira, T.H.M., Painho, M. (2021). Open Geospatial Data Contribution Towards Sentiment Analysis Within the Human Dimension of Smart Cities. In: Mobasheri, A. (eds) Open Source Geospatial Science for Urban Studies. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. https://doi.org/10.1007/978-3-030-58232-6_5

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