Advertisement

The Effect of Regional Variation and Resolution on Geosocial Thematic Signatures for Points of Interest

  • Grant McKenzieEmail author
  • Krzysztof Janowicz
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Computational models of place are a key component of spatial information theory and play an increasing role in research ranging from spatial search to transportation studies. One method to arrive at such models is to extract knowledge from user-generated content e.g., from texts, tags, trajectories, pictures, and so forth. Over the last years, topic modeling techniques such as latent Dirichlet allocation (LDA) have been studied to reveal linguistic patterns that characterize places and their types. Intuitively, people are more likely to describe places such as Yosemite National Park in terms of hiking, nature, and camping than cocktail or dancing. The geo-indicativeness of non-georeferenced text does not only apply to place instances but also place types, e.g., state parks. While different parks will vary greatly with respect to their landscape and thus human descriptions, the distribution of topics common to all parks will differ significantly from other types of places, e.g., night clubs. This aggregation of topics to the type level creates thematic signatures that can be used for place categorization, data cleansing and conflation, semantic search, and so on. To make full use of these signatures, however, requires a better understanding of their intra-type variability as regional differences effect the predictive power of the signatures. Intuitively, the topic composition for place types such as store and office should be less effected by regional differences than the topic composition for types such as monument and mountain. In this work, we approach this regional variability hypothesis by attempting to prove that all place types are aspatial with respect to their thematic signatures. We reject this hypothesis by comparing the signature similarities of 316 place types between major cities in the U.S. We then select the most and least varying place types and compare them to thematic signatures from regions outside of the U.S. Finally, we explore the effects of LDA topic resolution on differences between and within place types.

Keywords

Semantic signature Point of interest Place type Geosocial Topic modeling 

References

  1. Adams B, Janowicz K (2012). On the geo-indicativeness of non-georeferenced text. In: ICWSM, pp 375–378Google Scholar
  2. Adams B, McKenzie G, Gahegan M (2015) Frankenplace: interactive thematic mapping for ad hoc exploratory search. In: 24th international world wide web conference, IW3C2Google Scholar
  3. Arun R, Suresh V, Madhavan CV, Murthy MN (2010) On finding the natural number of topics with latent Dirichlet allocation: some observations. In: Advances in knowledge discovery and data mining. Springer, pp 391–402Google Scholar
  4. Bao J, Zheng Y, Wilkie D, Mokbel M (2015) Recommendations in location-based social networks: a survey. GeoInformatica 19(3):525–565CrossRefGoogle Scholar
  5. Basso KH (1996) Wisdom sits in places: landscape and language among the Western Apache. UNM PressGoogle Scholar
  6. Bentler PM, Bonett DG (1980) Significance tests and goodness of fit in the analysis of covariance structures. Psychol Bull 88(3):588CrossRefGoogle Scholar
  7. Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993–1022Google Scholar
  8. Cheng C, Yang H, Lyu MR, King I (2013) Where you like to go next: successive point-of-interest recommendation. In: IJCAI, vol 13, pp 2605–2611Google Scholar
  9. Cheng Z, Caverlee J, Lee K (2010) You are where you tweet: a content-based approach to geo-locating twitter users. In: Proceedings of the 19th ACM international conference on information and knowledge management. ACM, pp 759–768Google Scholar
  10. Cresswell T (2014) Place: an introduction. WileyGoogle Scholar
  11. Gao H, Tang J, Hu X, Liu H (2013) Exploring temporal effects for location recommendation on location-based social networks. In: Proceedings of the 7th ACM conference on recommender systems. ACM, pp 93–100Google Scholar
  12. Graham M, Zook M (2013) Augmented realities and uneven geographies: exploring the geolinguistic contours of the web. Environ Plan A 45(1):77–99CrossRefGoogle Scholar
  13. Griffiths TL, Steyvers M (2004) Finding scientific topics. Proc Natl Acad Sci 101(suppl 1):5228–5235CrossRefGoogle Scholar
  14. Hecht BJ, Gergle D (2010) On the localness of user-generated content. In: Proceedings of the 2010 ACM conference on computer supported cooperative work. ACM, pp 229–232Google Scholar
  15. Hollenstein L, Purves R (2010) Exploring place through user-generated content: using flickr tags to describe city cores. J Spat Inf Sci 1(1):21–48Google Scholar
  16. Hu B, Ester M (2013) Spatial topic modeling in online social media for location recommendation. In: Proceedings of the 7th ACM conference on recommender systems. ACM, pp 25–32Google Scholar
  17. Johnstone B (2004) Place, globalization, and linguistic variation. Sociolinguist Var Crit Reflect 65–83Google Scholar
  18. Kang M (2010) The managed hand: race, gender, and the body in beauty service work. University of California PressGoogle Scholar
  19. Kendall MG, Smith BB (1939) The problem of m rankings. Ann Math Stat 10(3):275–287CrossRefGoogle Scholar
  20. Kinsella S, Murdock V, O’Hare N (2011) I’m eating a sandwich in glasgow: modeling locations with tweets. In: Proceedings of the 3rd international workshop on search and mining user-generated contents. ACM, pp 61–68Google Scholar
  21. Lin J (1991) Divergence measures based on the shannon entropy. IEEE Trans Inf Theory 37(1):145–151CrossRefGoogle Scholar
  22. McCallum AK (2002) Mallet: a machine learning for language toolkitGoogle Scholar
  23. McKenzie G, Janowicz K, Gao S, Gong L (2015a) How where is when? on the regional variability and resolution of geosocial temporal signatures for points of interest. Comput Environ Urban Syst 54:336–346Google Scholar
  24. McKenzie G, Janowicz K, Gao S, Yang J-A, Hu Y (2015b) POI pulse: a multi-granular, semantic signatures-based information observatory for the interactive visualization of big geosocial data. Cartogr Int J Geogr Inf Geovis 50:71–85Google Scholar
  25. Mülligann C, Janowicz K, Ye M, Lee W-C (2011) Analyzing the spatial-semantic interaction of points of interest in volunteered geographic information. In: Spatial information theory. Springer, Berlin, pp 350–370Google Scholar
  26. Scellato S, Noulas A, Mascolo C (2011) Exploiting place features in link prediction on location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 1046–1054Google Scholar
  27. Shaw B, Shea J, Sinha S, Hogue A (2013) Learning to rank for spatiotemporal search. In: Proceedings of the sixth ACM international conference on web search and data mining. ACM, pp 717–726Google Scholar
  28. Stefanidis A, Crooks A, Radzikowski J (2013) Harvesting ambient geospatial information from social media feeds. GeoJournal 78(2):319–338CrossRefGoogle Scholar
  29. Tanasescu V, Jones CB, Colombo G, Chorley MJ, Allen SM, Whitaker RM (2013) The personality of venues: places and the five-factors (‘big five’) model of personality. In: 2013 fourth international conference on computing for geospatial research and application (COM. Geo). IEEE, pp 76–81Google Scholar
  30. Tuan Y-F (1991) Language and the making of place: a narrative-descriptive approach. Ann Assoc Am Geogr 81(4):684–696CrossRefGoogle Scholar
  31. Ye M, Janowicz K, Mülligann C, Lee W-C (2011) What you are is when you are: the temporal dimension of feature types in location-based social networks. In: Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, pp 102–111Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Geographical SciencesUniversity of MarylandCollege ParkUSA
  2. 2.STKO Lab, Department of GeographyUniversity of CaliforniaSanta BarbaraUSA

Personalised recommendations