Study on the spatial heterogeneity of the POI quality in OpenStreetMap

The volunteered geographic information(VGI) becomes more and more popolar with the general public and scientific community. In this background, OpenStreetMap (OSM) has developed rapidly as one of the most popular VGI projects. As a kind of important elements on the web map, Points of Interest (POIs) play an important role in the navigation and other fields. However, the data quality of POI in OSM can vary strongly.In order to find out the spatial heterogeneity of the POI quality in OSM, in this paper,we analyse the data quality of POIs in OSM from three aspects: positional accuracy, data completeness and topological consistency at first. And then we explore the relationship between the data quality of POIs in OSM and the local characteristics based on geographically weighted regression(GWR), and to find out the main influence factors of POIs quality in OSM. The results show that the distribution of data contributors plays an important influence on the data quality of POIs in OSM. Besides, from the data completeness of POIs, it can be find that the OSM is still new thing in China and it may be need more contributors to enrich the OSM data in China.

[1]  Alexander Zipf,et al.  Fine-resolution population mapping using OpenStreetMap points-of-interest , 2014, Int. J. Geogr. Inf. Sci..

[2]  Guillaume Touya,et al.  Assessing Crowdsourced POI Quality: Combining Methods Based on Reference Data, History, and Spatial Relations , 2017, ISPRS Int. J. Geo Inf..

[3]  H. Akaike.  A new look at the statistical model identification , 1974 .

[4]  Stéphane Roche,et al.  Quantifying the Significance of Semantic Landmarks in Familiar and Unfamiliar Environments , 2015, COSIT.

[5]  Alexander Zipf,et al.  Defining Fitness-for-Use for Crowdsourced Points of Interest (POI) , 2016, ISPRS Int. J. Geo Inf..

[6]  Steven P. Jackson,et al.  Assessing the impact of demographic characteristics on spatial error in volunteered geographic information features , 2015 .

[7]  M. Goodchild.  Citizens as sensors: the world of volunteered geography , 2007 .

[8]  Francisco C. Pereira,et al.  Mining point-of-interest data from social networks for urban land use classification and disaggregation , 2015, Comput. Environ. Urban Syst..

[9]  M. Haklay.  How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets , 2010 .

[10]  Shiliang Su,et al.  Spatially non-stationary response of ecosystem service value changes to urbanization in Shanghai, China , 2014 .

[11]  Chris Brunsdon,et al.  Geographically Weighted Regression: The Analysis of Spatially Varying Relationships , 2002 .

[12]  S. Fotheringham,et al.  Geographically weighted regression : modelling spatial non-stationarity , 1998 .