Advertisement

Data Quality of Points of Interest in Selected Mapping and Social Media Platforms

  • Hartwig H. Hochmair
  • Levente Juhász
  • Sreten Cvetojevic
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

A variety of location based services, including navigation, geo-gaming, advertising, and vacation planning, rely on Point of Interest (POI) data. Mapping platforms and social media apps oftentimes host their own geo-datasets which leads to a plethora of data sources from which POIs can be extracted. Therefore it is crucial for an analyst to understand the nature of the data that are available on the different platforms, their purpose, their characteristics, and their data quality. This study extracts POIs for seven urban regions from seven mapping and social media platforms (Facebook, Foursquare, Google, Instagram, OSM, Twitter, and Yelp). It analyzes the POI data quality regarding coverage, point density, content classification, and positioning accuracy, and also examines the spatial relationship (e.g. segregation) between POIs from different platforms.

Keywords

VGI Crowd-sourcing Point of interest Data quality 

References

  1. Barron C, Neis P, Zipf A (2014) A comprehensive framework for intrinsic OpenStreetMap quality analysis. Trans GIS 18(6):877–895CrossRefGoogle Scholar
  2. Cvetojevic S, Juhász L, Hochmair HH (2016) Positional accuracy of twitter and instagram images in urban environments. GI_Forum 1:191–203Google Scholar
  3. Dixon PM (2002) Ripley’s K function. In: El-Shaarawi AH, Piegorsch WW (eds) Encyclopedia of environmetrics. Wiley, ChichesterGoogle Scholar
  4. Duckham M, Winter S, Robinson M (2010) Including landmarks in routing instructions. J Location Based Serv 4(1):28–52CrossRefGoogle Scholar
  5. Fan H, Zipf A, Fu Q, Neis P (2014) Quality assessment for building footprints data on OpenStreetMap. Int J Geogr Inf Sci 28(14):700–719CrossRefGoogle Scholar
  6. Gröchenig S, Brunauer R, Rehrl K (2014) Estimating completeness of VGI datasets by analyzing community activity over time periods. In: Huerta J, Schade S, Granell C (eds) Connecting a digital Europe through location and place. Lecture notes in geoinformation and cartography. Springer, Berlin, pp 3–18Google Scholar
  7. Hastings JT (2008) Automated conflation of digital gazetteer data. Int J Geogr Inf Sci 22(10):1109–1127CrossRefGoogle Scholar
  8. Heipke C (2010) Crowdsourcing geospatial data. ISPRS J Photogramm Remote Sens 65:550–557CrossRefGoogle Scholar
  9. Hochmair HH, Zielstra D (2013) Development and completeness of points of interest in free and proprietary data sets: a Florida case study. In: Jekel T, Car A, Strobl J, Griesebner G (eds), GI_Forum 2013. Creating the GISociety. Wichmann, Berlin, pp 39–48Google Scholar
  10. Jackson SP, Mullen W, Agouris P, Crooks A, Croitoru A, Stefanidis A (2013) Assessing completeness and spatial error of features in volunteered geographic information. ISPRS Int J Geo-Inf 2:507–530CrossRefGoogle Scholar
  11. Juhász L, Hochmair HH (2016) Cross-linkage between Mapillary street level photos and OSM edits. In: Sarjakoski T, Santos MY, Sarjakoski T (eds) Geospatial data in a changing world: selected papers of the 19th AGILE conference on geographic information science. Lecture notes in geoinformation and cartography. Springer, Berlin, pp 141–156Google Scholar
  12. Juhász L, Hochmair HH (2017) Where to catch ‘em all?’—a geographic analysis of Pokémon Go locations. Geo-spat Inf Sci 20(3):241–251CrossRefGoogle Scholar
  13. Juhász L, Rousell A, Arsanjani JJ (2016) Technical guidelines to extract and analyze VGI from different platforms. Data 1(3):15CrossRefGoogle Scholar
  14. Li L, Xing X, Xia H, Huang X (2016) Entropy-weighted instance matching between different sourcing points of interest. Entropy 18(2):45CrossRefGoogle Scholar
  15. Lim KH, Chan J, Leckie C, Karunasekera S (2015) Personalized tour recommendation based on user interests and points of interest visit durations. In: 24th international joint conference on artificial intelligence (IJCAI 2015), Buenos Aires, BrazilGoogle Scholar
  16. McKenzie G, Janowicz K, Adams B (2014) A weighted multi-attribute method for matching user-generated points of interest. Cartogr Geogr Inf Sci 41(2):125–137CrossRefGoogle Scholar
  17. Mülligann C, Janowicz K, Ye M, Lee W-C (2011) Analyzing the spatial-semantic interaction of points of interest in volunteered geographic information. In: Egenhofer MJ, Giudice NA, Moratz R, Worboys MF (eds) Conference on spatial information theory (COSIT 2011). LNCS 6899. Springer, Berlin, pp 350–370Google Scholar
  18. Nothegger C, Winter S, Raubal M (2004) Computation of the salience of features. Spat Cogn Comput 4:113–136Google Scholar
  19. O’Sullivan D, Unwin DJ (2010) Geographic information analysis, 2nd edn. Wiley, Hoboken, New JerseyCrossRefGoogle Scholar
  20. Rösler R, Liebig T (2013) Using data from location based social networks for urban activity clustering. In: Vandenbroucke D, Bucher B, Crompvoets J (eds) Geographic information science at the heart of Europe. Lecture notes in geoinformation and cartography. Springer, BerlinGoogle Scholar
  21. Senaratne H, Mobasheri A, Ali AL, Capineri C, Haklay M (2017) A review of volunteered geographic information quality assessment methods. Int J Geogr Inf Sci 31(1):138–167CrossRefGoogle Scholar
  22. Zhao B, Sui DZ (2017) True lies in geospatial big data: detecting location spoofing in social media. Ann GIS 23(1):1–14CrossRefGoogle Scholar
  23. Zielstra D, Hochmair HH (2013) Positional accuracy analysis of Flickr and Panoramio images for selected world regions. J Spat Sci 58(2):251–273CrossRefGoogle Scholar
  24. Zielstra D, Hochmair HH, Neis P (2013) Assessing the effect of data imports on the completeness of OpenStreetMap—A United States case study. Trans GIS 17(3):315–334Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Hartwig H. Hochmair
    • 1
  • Levente Juhász
    • 1
  • Sreten Cvetojevic
    • 1
  1. 1.Geomatics ProgramUniversity of FloridaDavieUSA

Personalised recommendations