Abstract
In this paper we present a study on areas of habitation in St. Petersburg, Russia, which are actively used and perceived by city dwellers as coherent units. The motivation behind the study is to define generic urban areas formed by actual user experience and different from administrative division to improve urban management of the city territory. We employ mixed methods approach to account both for users’ practices in urban space, based on analysis of check-in data, and users’ perception of urban space, based on analysis of mental maps. The clustering algorithm is based on spatial and social proximity indexes and has been validated through the results of the mental mapping survey. The dataset of check-ins is retrieved from VKontakte social network, the most popular one for St. Petersburg and for Russia, and comprises 6128 venues with 763079 check-ins collected for December 2017–February 2018 time period. The mental mapping has been conducted within 39 users of different age and gender, representing different areas of the city under study. We compare the borders of the areas of habitation with the map of administrative division, consider functional load of the areas in different areas of the city, define environmental factors which form the borders, give suggestions on how knowledge on areas of habitation could inform and improve urban management practice.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Results can be accessed online at spblivehoods.github.io.
References
Hanson, J., Hillier, B.: The architecture of community: some new proposals on the social consequences of architectural and planning decisions. Architecture et Comportement/Architecture and Behaviour 3, 251–273 (1987)
Westerink, J., Haase, D., Bauer, A., Ravetz, J., Jarrige, F., Aalbers, C.B.E.M.: Dealing with sustainability trade-offs of the compact city in peri-urban planning across European city regions. Eur. Plan. Stud. 21, 473–497 (2013)
Campari, I.: Uncertain boundaries in urban space. Geog. Objects Indeterminate Boundaries 2, 57–69 (1996)
Hollenstein, L., Purves, R.: Exploring place through user-generated content: Using Flickr tags to describe city cores. J. Spat. Inf. Sci. 2010, 21–48 (2010)
Davies, C., Holt, I., Green, J., Harding, J., Diamond, L.: User needs and implications for modelling vague named places. Spat. Cogn. Comput. 9, 174–194 (2009)
Lynch, K.: The Image of the City, vol. 11. MIT Press, Cambridge (1960)
Egenhofer, M.J., Mark, D.M.: Naive geography. In: Frank, A.U., Kuhn, W. (eds.) COSIT 1995. LNCS, vol. 988, pp. 1–15. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-60392-1_1
Goodchild, M.F.: Citizens as sensors: the world of volunteered geography. GeoJournal 69, 211–221 (2007)
Ratti, C., et al.: Redrawing the map of Great Britain from a network of human interactions. PLoS ONE 5, e14248 (2010)
Reades, J., Calabrese, F., Ratti, C.: Eigenplaces: analysing cities using the space–time structure of the mobile phone network. Environ. Plan. 36, 824–836 (2009)
Girardin, F., Calabrese, F., Fiore, F.D., Ratti, C., Blat, J.: Digital footprinting: uncovering tourists with user-generated content (2008)
Liu, Y., Sui, Z., Kang, C., Gao, Y.: Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data. PLoS ONE 9, e86026 (2014)
Ying, J.J.-C., Lu, E.H.-C., Kuo, W.-N., Tseng, V.S.: Urban point-of-interest recommendation by mining user check-in behaviors. In: Proceedings of the ACM SIGKDD International Workshop on Urban Computing (2012)
Ferrari, L., Rosi, A., Mamei, M., Zambonelli, F.: Extracting urban patterns from location-based social networks. In: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks (2011)
Gao, S., Liu, Y., Wang, Y., Ma, X.: Discovering spatial interaction communities from mobile phone data. Trans. GIS 17, 463–481 (2013)
Schoier, G., Borruso, G.: Individual movements and geographical data mining. clustering algorithms for highlighting hotspots in personal navigation routes. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2011. LNCS, vol. 6782, pp. 454–465. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21928-3_32
Eagle, N., Pentland, A.S.: Eigenbehaviors: identifying structure in routine. Behav. Ecol. Sociobiol. 63, 1057–1066 (2009)
Huang, Y., Zhang, L., Zhang, P.: A framework for mining sequential patterns from spatio-temporal event data sets. IEEE Trans. Knowl. Data Eng. 20, 433–448 (2008)
Cranshaw, J., Schwartz, R., Hong, J., Sadeh, N.: The livehoods project: utilizing social media to understand the dynamics of a city. In: Sixth International AAAI Conference on Weblogs and Social Media (2012)
Shelton, T., Poorthuis, A., Zook, M.: Social media and the city: Rethinking urban socio-spatial inequality using user-generated geographic information. Landscape Urban Plan. 142, 198–211 (2015)
Phung, D., Adams, B., Venkatesh, S.: Computable social patterns from sparse sensor data. In: Proceedings of the First International Workshop on Location and the Web (2008)
Ferrari, L., Mamei, M.: Discovering daily routines from Google latitude with topic models. In: 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops) (2011)
Farrahi, K., Gatica-Perez, D.: Discovering routines from large-scale human locations using probabilistic topic models. ACM Trans. Intell. Syst. Technol. (TIST) 2, 3 (2011)
Acknowledgements
This research is financially supported by The Russian Science Foundation, Agreement #17-71-30029 with co-financing of Bank Saint Petersburg.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Nenko, A., Koniukhov, A., Petrova, M. (2019). Areas of Habitation in the City: Improving Urban Management Based on Check-in Data and Mental Mapping. In: Chugunov, A., Misnikov, Y., Roshchin, E., Trutnev, D. (eds) Electronic Governance and Open Society: Challenges in Eurasia. EGOSE 2018. Communications in Computer and Information Science, vol 947. Springer, Cham. https://doi.org/10.1007/978-3-030-13283-5_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-13283-5_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-13282-8
Online ISBN: 978-3-030-13283-5
eBook Packages: Computer ScienceComputer Science (R0)