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Understanding and predicting
urban dynamics
through new forms of
geo-social data
The SocialGlass system
and its applicatio...
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data
00
Take-home message
G...
Outline 01
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data
Geo-social ...
02
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data
Geo-social data
Sou...
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Geo-social data
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data
Sou...
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Geo-social data
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data
The...
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Geo-social data
…and the present
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of ge...
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New methods & tools
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data...
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New methods & tools
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data...
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Anatomy of a tweet
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data
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The SocialGlass system
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social d...
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The SocialGlass system
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social d...
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The SocialGlass system
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social d...
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The SocialGlass system
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social d...
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Applications
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data
Durati...
To what extent can a large-scale event influence
the activity patterns of different geo-demographic groups?
Achilleas Psyl...
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Amsterdam Light Festival 2015
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-s...
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Amsterdam Light Festival 2015
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-s...
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Amsterdam Light Festival 2015
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-s...
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Applications
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data
Durati...
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SAIL 2015
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data
To what e...
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SAIL 2015
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data
Human act...
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SAIL 2015
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data
Stad in B...
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SAIL 2015
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data
Visitors’...
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Applications
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data
Durati...
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Regionalisation & POI location prediction
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new fo...
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Regionalisation & POI location prediction
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new fo...
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Regionalisation & POI location prediction
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new fo...
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Regionalisation & POI location prediction
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new fo...
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Outlook
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data
What’s next...
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Take-home message (…continued)
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-...
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Relevant Publications
Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social da...
Understanding and predicting urban dynamics through new forms of geo-social data: The SocialGlass system and its applications
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Understanding and predicting urban dynamics through new forms of geo-social data: The SocialGlass system and its applications

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The recent emergence of new forms of geo-social data, deriving from social media, sensors, and mobile phones, calls for an update to the methodological toolbox of social sciences. The new methods and tools need to harmonise with the inherent characteristics and challenges of the emerging data sources. This talk demonstrates how SocialGlass, a web-based system for (real-time) urban analytics, helps improve the understanding of human dynamics in modern-day cities, by capitalising on new geo-social data and pioneering data science techniques. Emphasis is on real-world applications, regarding social area analysis, crowd dynamics during large-scale events, and location prediction of new urban functions across different cities.

Presentation at the Centre for BOLD (Big, Open & Linked Data) Cities anniversary meet-up | Erasmus University Rotterdam -- May 29, 2017

Published in: Data & Analytics
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Understanding and predicting urban dynamics through new forms of geo-social data: The SocialGlass system and its applications

  1. 1. Understanding and predicting urban dynamics through new forms of geo-social data The SocialGlass system and its applications Dr Achilleas Psyllidis A.Psyllidis [at] tudelft [dot] nl
  2. 2. Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data 00 Take-home message Geo-social data are a goldmine of knowledge about cities that we cannot afford ignoring New forms of geo-social data call for an update to the methodological toolbox of urban studies to be continued…
  3. 3. Outline 01 Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data Geo-social data New methods & tools The SocialGlass system Applications Outlook
  4. 4. 02 Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data Geo-social data Sources of geo-social data • Census • GPS • Geo-portals, Spatial data infrastructures • Cell phones • Location-based social networks (e.g. Foursquare) • Geo-enabled social media (e.g. Twitter, Instagram etc.)
  5. 5. 03 Geo-social data Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data Sources of geo-social data • Census • GPS • Geo-portals, Spatial data infrastructures • Cell phones • Location-based social networks (e.g. Foursquare) • Geo-enabled social media (e.g. Twitter, Instagram etc.) Traditional New
  6. 6. 04 Geo-social data Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data The past… • Data scarcity • Limited official resources (e.g. censuses, surveys) • Large volumes, yet infrequently updated • Limited storage and processing • [+] Structured datasets Spatial analysis methods were developed in this context…
  7. 7. 05 Geo-social data …and the present Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data • Data richness • Variety of sources • Near real-timeupdates • Abundant storage and processing • [—] Spontaneous and unstructured datasets …and are still in use Need for an update to the methodological toolbox
  8. 8. 06 New methods & tools Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data An updated toolbox needs to capitalise on… • High spatial & temporal resolution • Ease of access (e.g. through APIs) • Multiple information layers (e.g. spatial,temporal, social etc.)
  9. 9. 07 New methods & tools Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data …and tackle… • Biases (representational, contextual, functional etc.) • Complexity,diversity & multidimensionality • Very large volumes Human Computation Machine Learning Distributed Computing How?
  10. 10. 09 Anatomy of a tweet Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data Age Gender Nationality User info Place of residence Date Type of activity Place of activity (Location) Social information
  11. 11. 10 The SocialGlass system Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data • Scalable web-based system* • (Real-time)urban analytics & geo-visualisation** • Used by IBM and the Municipalities of Amsterdam, Paris, Adelaide • 10+ Research Exhibitions & Demonstrations (http://www.social-glass.org)*Bocconi et al.(2015) **Psyllidis et al. (2015a, b)
  12. 12. 11 The SocialGlass system Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data SocialGlass: choropleth map of prevalent social activities, as inferred from Instagram (http://www.social-glass.org)
  13. 13. 12 The SocialGlass system Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data SocialGlass: activity patterns of Amsterdam residents (ALF 2015) (http://www.social-glass.org)
  14. 14. 13 The SocialGlass system Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data SocialGlass: Flows of residents (http://www.social-glass.org)
  15. 15. 14 Applications Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data Duration of experiment Scale SAIL 2015 ALF 2015 Regionalisation & POI Location Prediction 1 Week 1 Month 3 Months 1 City 3 Cities
  16. 16. To what extent can a large-scale event influence the activity patterns of different geo-demographic groups? Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data 15 Amsterdam Light Festival 2015
  17. 17. 16 Amsterdam Light Festival 2015 Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data Residents’ activity appears balanced over time and dispersed across space throughout the monitoring period*. *Psyllidis (2016)
  18. 18. 17 Amsterdam Light Festival 2015 Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data Tourists’ activity tends to cluster around central areas of the city and shows fluctuations over time*. *Psyllidis (2016)
  19. 19. 18 Amsterdam Light Festival 2015 Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data *Psyllidis (2016) Local Moran’s I cluster mapsof social activity before, during, and after ALF Moran’s Icluster map (normalised POIlocations) Discrepancy between the clustering of urban functions and the clustering of activities*.
  20. 20. 19 Applications Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data Duration of experiment Scale SAIL 2015 ALF 2015 Regionalisation & POI Location Prediction 1 Week 1 Month 3 Months 1 City 3 Cities
  21. 21. 20 SAIL 2015 Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data To what extent can social media microposts be used to estimate the density of attendees in a large-scale event?
  22. 22. 21 SAIL 2015 Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data Human activity patterns during SAIL 2015.
  23. 23. 22 SAIL 2015 Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data Stad in Balans. Karla Gutierrez, Eric van der Kooij
  24. 24. 23 SAIL 2015 Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data Visitors’ home countries Dutch residents’ homes Crowd dashboard: Flow and route choice, Visitors’ density (number of people / m²), Speed and visit duration. SocialGlass real-time analytics: Geo-location mapping, Human mobility and dynamics, Visitors’ demographics. Approach: Density estimation strategies inspired by pedestrian flow theory
  25. 25. 24 Applications Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data Duration of experiment Scale SAIL 2015 ALF 2015 Regionalisation & POI Location Prediction 1 Week 1 Month 3 Months 1 City 3 Cities
  26. 26. 25 Regionalisation & POI location prediction Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data How could we detect neighborhoods of uniform social interaction and predict new POI locations?
  27. 27. 26 Regionalisation & POI location prediction Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data Age Gender Hour Topic Social Category Venue Category Weekday All Clusters Multidimensional clusters of social interaction in Amsterdam (Component planes and Hierarchical GeoSOM)*. *Psyllidis et al. (2017)
  28. 28. 27 Regionalisation & POI location prediction Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data Residents’ clusterLeisure cluster Tourists’ cluster Arabic language clusterTransport / Nightlife cluster Work-related activities cluster *Psyllidis et al. (2017)
  29. 29. 28 Regionalisation & POI location prediction Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data *Psyllidis et al. (2017) Event spaceCafé Gym RestaurantHotel Tram stop Estimates of appropriate locations for various types of new POIs, based on a Factorisation Machine model*.
  30. 30. 29 Outlook Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data What’s next? • SocialGlass as a service • Future applications: • Evidence-based policy for vulnerable youth (H2020, NWA) • Data-driven policy for ageing populations in cities (H2020 – EU/China) • Detection, prediction, and semantic enrichment of traffic incidents
  31. 31. 30 Take-home message (…continued) Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data Geo-social data are a goldmine of knowledge about cities that we cannot afford ignoring New forms of geo-social data call for an update to the methodological toolbox of urban studies that combines machine learning and spatial data science with human computation and user modelling
  32. 32. 31 Relevant Publications Achilleas PsyllidisUnderstanding and predicting urban dynamics through new forms of geo-social data Psyllidis, A., Yang., J., Bozzon, A. (2017). Using MachineLearning on TwitterData to Regionalize Social Interactions and Predict New POI Locations. PLoS ONE (underreview). Psyllidis, A. (2016). RevisitingUrban Dynamics through Social UrbanData:Methods and tools fordata integration, visualization, and exploratory analysis to understand the spatiotemporal dynamics of human activity in cities. PhDdissertation. A+BE|Architecture and the Built Environment, Delft. doi: http://dx.doi.org/10.7480/abe.2016.18 Psyllidis, A., Bozzon, A., Bocconi, S., & Bolivar, C. T. (2015a). Harnessing Heterogeneous Social Datato Explore, Monitor, and Visualize Urban Dynamics. In: Ferreira J Jr, Goodspeed R (eds) Planning Support Systems and Smart Cities: Proceedings of the 14th International Conference on Computers in Urban Planning and Urban Management (CUPUM 2015). MIT, Cambridgre, MA, USA,pp. 239-1 — 239-22. Psyllidis, A., Bozzon, A., Bocconi, S., & Bolivar, C. T. (2015b). A Platform forUrban Analytics and SemanticIntegration in City Planning. In: Celani G, Moreno Sperling D, Franco JMS (eds)Computer-Aided Architectural Design Futures – NewTechnologies and the Future of the Built Environment: 16th International Conference(CAADFutures 2015) – Selected Papers. LNCS, CCIS 527, Springer, Berlin Heidelberg, pp. 21—36. doi: http://dx.doi.org/10.1007/978-3-662-47386-3_2 Bocconi, S., Bozzon, A., Psyllidis, A., & Bolivar, C. T. (2015). SocialGlass: A Platform forUrban Analytics and Decision-making Through Heterogeneous Social Data. In: Gangemi A, Leonardi S, Panconesi A (eds) 24th International Word Wide Web Conference (WWW 2015). ACM, New York, NY, pp. 175—178. doi: http://dx.doi.org/10.1145/2740908.2742826

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