Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1080/13658816.2013.830728 | International Journal of Geographical Information Science |
Keywords | Field | DocType |
matching node,associated road network,road network,geometric-based approach,heterogeneous road network,vgi pois,heterogeneous polygonal data set,official mapping agency,poi connectivity graph,professional road network,matching | Information system,Geospatial analysis,Spatial analysis,Affine transformation,Data mining,Computer science,Volunteered geographic information,GIS applications,Artificial intelligence,Point of interest,Probabilistic logic,Machine learning | Journal |
Volume | Issue | ISSN |
28 | 1 | 1365-8816 |
Citations | PageRank | References |
9 | 0.50 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bisheng Yang | 1 | 308 | 33.15 |
| Yunfei Zhang | 2 | 43 | 4.34 |
| Feng Lu | 3 | 11 | 1.27 |