Geometric-based approach for integrating VGI POIs and road networks

Integrating heterogeneous spatial data is a crucial problem for geographical information systems (GIS) applications. Previous studies mainly focus on the matching of heterogeneous road networks or heterogeneous polygonal data sets. Few literatures attempt to approach the problem of integrating the point of interest (POI) from volunteered geographic information (VGI) and professional road networks from official mapping agencies. Hence, the article proposes an approach for integrating VGI POIs and professional road networks. The proposed method first generates a POI connectivity graph by mining the linear cluster patterns from POIs. Secondly, the matching nodes between the POI connectivity graph and the associated road network are fulfilled by probabilistic relaxation and refined by a vector median filtering (VMF). Finally, POIs are aligned to the road network by an affine transformation according to the matching nodes. Experiments demonstrate that the proposed method integrates both the POIs from VGI and the POIs from official mapping agencies with the associated road networks effectively and validly, providing a promising solution for enriching professional road networks by integrating VGI POIs.

[1]  K. Mcdougall The potential of citizen volunteered spatial information for building SDI , 2009 .

[2]  Thomas Devogele,et al.  Matching Networks with Different Levels of Detail , 2008, GeoInformatica.

[3]  Rafael Schirru,et al.  Matching Points of Interest from Different Social Networking Sites , 2012, KI.

[4]  Catriel Beeri,et al.  Location‐based algorithms for finding sets of corresponding objects over several geo‐spatial data sets , 2010, Int. J. Geogr. Inf. Sci..

[5]  Krzysztof Janowicz,et al.  Analyzing the Spatial-Semantic Interaction of Points of Interest in Volunteered Geographic Information , 2011, COSIT.

[6]  Peter Mooney,et al.  Using OSM for LBS – An Analysis of Changes to Attributes of Spatial Objects , 2012 .

[7]  David Bernstein,et al.  Some map matching algorithms for personal navigation assistants , 2000 .

[8]  Yan Shi,et al.  A density-based spatial clustering algorithm considering both spatial proximity and attribute similarity , 2012, Comput. Geosci..

[9]  Yehoshua Sagiv,et al.  Ad hoc matching of vectorial road networks , 2013, Int. J. Geogr. Inf. Sci..

[10]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[11]  Soumaya Louhichi,et al.  A density based algorithm for discovering clusters with varied density , 2014, 2014 World Congress on Computer Applications and Information Systems (WCCAIS).

[12]  D. Levinson Network Structure and City Size , 2012, PloS one.

[13]  Alan Saalfeld,et al.  Conflation Automated map compilation , 1988, Int. J. Geogr. Inf. Sci..

[14]  Michael W. Dobson,et al.  VGI as a Compilation Tool for Navigation Map Databases , 2013 .

[15]  James M. Keller,et al.  Relaxation-Based Point Feature Matching for Vector Map Conflation , 2011, Trans. GIS.

[16]  Anil K. Jain Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..

[17]  Timothy C. Matisziw,et al.  Inferring network paths from point observations , 2012, Int. J. Geogr. Inf. Sci..

[18]  Bisheng Yang,et al.  A probabilistic relaxation approach for matching road networks , 2013, Int. J. Geogr. Inf. Sci..

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

[20]  Monika Sester,et al.  GEOMETRICAL ADJUSTMENT TOWARDS THE ALIGNMENT OF VECTOR DATABASES , 2012 .

[21]  David Fairbairn,et al.  Assessing similarity matching for possible integration of feature classifications of geospatial data from official and informal sources , 2012, Int. J. Geogr. Inf. Sci..

[22]  W. Mackaness,et al.  Lecture Notes in Geoinformation and Cartography , 2006 .

[23]  Kiyun Yu,et al.  Detecting conjugate-point pairs for map alignment between two polygon datasets , 2011, Comput. Environ. Urban Syst..

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

[25]  Ickjai Lee,et al.  Multi-Level Clustering and its Visualization for Exploratory Spatial Analysis , 2002, GeoInformatica.

[26]  Sagi Dalyot,et al.  Automatic georeferencing of non-geospatially referenced provisional cadastral maps , 2012 .

[27]  Gérard G. Medioni,et al.  Curvature-Augmented Tensor Voting for Shape Inference from Noisy 3D Data , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Christian Heipke,et al.  Crowdsourcing geospatial data , 2010 .

[29]  Glen Hart,et al.  Geospatial Information Integration for Authoritative and Crowd Sourced Road Vector Data , 2012, Trans. GIS.

[30]  David L. Tulloch Crowdsourcing geographic knowledge: volunteered geographic information (VGI) in theory and practice , 2014, Int. J. Geogr. Inf. Sci..

[31]  Jong-Ha Lee,et al.  Topology Preserving Relaxation Labeling for Nonrigid Point Matching , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Meng Zhang,et al.  Methods and Implementations of Road-Network Matching , 2009 .

[33]  Marcus Götz,et al.  Towards generating highly detailed 3D CityGML models from OpenStreetMap , 2013, Int. J. Geogr. Inf. Sci..

[34]  Monika Sester,et al.  Mutual Linear Feature Matching and Alignment Designed for Geometric Accuracy Enhancement of Graphical Cadastral Datasets , 2010 .