Pattern-mining approach for conflating crowdsourcing road networks with POIs

International Journal of Geographical Information Science

Volumn 29, Issue 5, 2015, Pages 786-805

  Yang, Bisheng ^a   Zhang, Yunfei ^a  

^a WUHAN UNIVERSITY   (China)

Author keywords

conflation; crowdsourcing; data enrichment; pattern mining; road networks

Indexed keywords

DATA MINING; DATA QUALITY; DATA SET; GRAPHICAL METHOD; NETWORK ANALYSIS; PATTERN RECOGNITION; SPATIAL ANALYSIS; SPATIAL DATA;

EID: 84930575139     PISSN: 13658816     EISSN: 13623087     Source Type: Journal    
DOI: 10.1080/13658816.2014.997238     Document Type: Article

References (43)

1. B.Adams, and M.Gahegan,, 2014. Emerging data challenges for next-generation spatial data [online]. In: The proceeding of 2014 LOCATE, 7–9 April, Canberra, 118–129.
2. M.Al-Bakri, and D.Fairbairn,, 2012. Assessing similarity matching for possible integration of feature classifications of geospatial data from official and informal sources. International Journal of Geographical Information Science, 26 (8), 1437–1456. doi:10.1080/13658816.2011.636012
3. M.Butenuth,, et al., 2007. Integration of heterogeneous geospatial data in a federated database. ISPRS Journal of Photogrammetry and Remote Sensing, 62 (5), 328–346. doi:10.1016/j.isprsjprs.2007.04.003
4. C.-C.Chen,, C.A.Knoblock,, and C.Shahabi,, 2006. Automatically conflating road vector data with orthoimagery. GeoInformatica, 10 (4), 495–530. doi:10.1007/s10707-006-0344-6
5. H.Du,, et al., 2012. Geospatial information integration for authoritative and crowd sourced road vector data. Transactions in GIS, 16 (4), 455–476. doi:10.1111/j.1467-9671.2012.01303.x
6. M.Duckham, and M.Worboys,, 2005. An algebraic approach to automated geospatial information fusion. International Journal of Geographical Information Science, 19 (5), 537–557. doi:10.1080/13658810500032339
7. S.Gao,, et al., 2014. Constructing gazetteers from volunteered big geo-data based on Hadoop. Computers, Environment and Urban Systems. doi:10.1016/j.compenvurbsys.2014.02.004.
8. M.Goetz, and A.Zipf,, 2013. The evolution of geo-crowdsourcing: bringing volunteered geographic information to the third dimension. In: D.Sui, S.Elwood, and M.Goodchild, eds. Crowdsourcing geographic knowledge. Dordrecht: Springer, 139–159.
9. C.-H.González,, C.López-Vázquez,, and M.-Á.Bernabé,, 2013. Ephemeral conflation. The Cartographic Journal, 50 (1), 43–48. doi:10.1179/1743277412Y.0000000014
10. T.Hastie,, 1984. Principal Curves and Surfaces. Thesis (PhD). Stanford University.
11. C.Heipke,, 2010. Crowdsourcing geospatial data. ISPRS Journal of Photogrammetry and Remote Sensing, 65 (6), 550–557. doi:10.1016/j.isprsjprs.2010.06.005
12. A.K.Jain,, 2010. Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31 (8), 651–666. doi:10.1016/j.patrec.2009.09.011
13. B.Jiang,, 2013. Volunteered geographic information and computational geography: new perspectives. In: D.Sui, S.Elwood, and M.Goodchild, eds. Crowdsourcing geographic knowledge. Dordrecht: Springer, 125–138.
14. B.Jiang, and C.Claramunt,, 2004. Topological analysis of urban street networks. Environment and Planning B: Planning and Design, 31, 151–162. doi:10.1068/b306
15. B.Kégl,, et al., 2000. Learning and design of principal curves. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (3), 281–297. doi:10.1109/34.841759
16. J.Li,, et al., 2014a. Mining trajectory data and geotagged data in social media for road map inference. Transactions in GIS. doi:10.1111/tgis.12072.
17. Q.Li,, et al., 2014b. Polygon-based approach for extracting multilane roads from OpenStreetMap urban road networks. International Journal of Geographical Information Science. doi:10.1080/13658816.2014.915401.
18. X.Liu,, T.Ai,, and Y.Liu,, 2009. Road density analysis based on skeleton partitioning for road generalization. Geo-spatial Information Science, 12 (2), 110–116. doi:10.1007/s11806-009-0012-8
19. W.Mackaness, and G.Edwards,, 2002. The importance of modelling pattern and structure in automated map generalisation. In: Joint workshop on multi-scale representations of spatial data, 7–8 July, Ottawa, ON, 1–11.
20. G.McKenzie,, K.Janowicz,, and B.Adams,, 2014. A weighted multi-attribute method for matching user-generated Points of Interest. Cartography and Geographic Information Science, 41 (2), 125–137. doi:10.1080/15230406.2014.880327
21. P.Mooney,, H.Sun,, and L.Yan,, 2011. VGI as a dynamically updating data source in location-based services in urban environments. In: Proceedings of the 2nd international workshop on Ubiquitous crowdsouring, 18 September, Beijing, 13–16.
22. P.Neis, and D.Zielstra,, 2014. Recent developments and future trends in volunteered geographic information research: the case of OpenStreetMap. Future Internet, 6 (1), 76–106. doi:10.3390/fi6010076
23. J.J.Ruiz,, et al., 2011. Digital map conflation: a review of the process and a proposal for classification. International Journal of Geographical Information Science, 25 (9), 1439–1466. doi:10.1080/13658816.2010.519707
24. A.Saalfeld,, 1988. Conflation automated map compilation. International Journal of Geographical Information Systems, 2 (3), 217–228. doi:10.1080/02693798808927897
25. E.Safra,, et al., 2013. Ad hoc matching of vectorial road networks. International Journal of Geographical Information Science, 27 (1), 114–153. doi:10.1080/13658816.2012.667104
26. T.Scheffler,, R.Schirru,, and P.Lehmann,, 2012. Matching points of interest from different social networking sites. In: B.Glimm and A.Krüger, eds. KI 2012: advances in artificial intelligence. Berlin: Springer, 245–248.
27. M.Sester,, et al., 2014. Integrating and generalising volunteered geographic information. In: D.Burghardt, C.Duchêne, and W.Mackaness, eds. Abstracting geographic information in a data rich world. Switzerland: Springer International Publishing, 119–155.
28. D.Sheeren,, S.Mustière,, and J.-D.Zucker,, 2009. A data‐mining approach for assessing consistency between multiple representations in spatial databases. International Journal of Geographical Information Science, 23 (8), 961–992. doi:10.1080/13658810701791949
29. W.Song,, et al., 2009. Automated geospatial conflation of vector road maps to high resolution imagery. IEEE Transactions on Image Processing, 18 (2), 388–400. doi:10.1109/TIP.2008.2008044
30. D.Sui,, M.Goodchild,, and S.Elwood,, 2013. Volunteered Geographic Information, the Exaflood, and the Growing Digital Divide. In: D.Sui, S.Elwood, and M.Goodchild, eds. Crowdsourcing Geographic Knowledge. Dordrecht: Springer, 1–12.
31. Y.Sun,, et al., 2013. Road-based travel recommendation using geo-tagged images. Computers, Environment and Urban Systems. doi:10.1016/j.compenvurbsys.2013.07.006.
32. S.Thom,, 2006. Conflict identification and representation for roads based on a Skeleton. In: Proceedings of the 12th international symposium on spatial data handling, 12–14 July, Vienna, 659–680.
33. R.C.Thomson,, 2006. The ‘stroke’ concept in geographic network generalization and analysis. In: Proceedings of the 12th international symposium on spatial data handling, 12–14 July, Vienna, 681–697.
34. G.Touya, and C.Brando-Escobar,, 2013. Detecting level-of-detail inconsistencies in Volunteered Geographic Information data sets. Cartographica: The International Journal for Geographic Information and Geovisualization, 48 (2), 134–143. doi:10.3138/carto.48.2.1836
35. G.C.Tseng,, 2007. Penalized and weighted K-means for clustering with scattered objects and prior information in high-throughput biological data. Bioinformatics, 23 (17), 2247–2255. doi:10.1093/bioinformatics/btm320
36. J.Wang,, et al., 2014. A novel approach for generating routable road maps from vehicle GPS traces. International Journal of Geographical Information Science. doi:10.1080/13658816.2014.944527
37. B.Yang,, X.Luan,, and Q.Li,, 2011. Generating hierarchical strokes from urban street networks based on spatial pattern recognition. International Journal of Geographical Information Science, 25 (12), 2025–2050. doi:10.1080/13658816.2011.570270
38. B.Yang,, Y.Zhang,, and F.Lu,, 2014. Geometric-based approach for integrating VGI POIs and road networks. International Journal of Geographical Information Science, 28 (1), 126–147. doi:10.1080/13658816.2013.830728
39. B.Yang,, Y.Zhang,, and X.Luan,, 2013. A probabilistic relaxation approach for matching road networks. International Journal of Geographical Information Science, 27 (2), 319–338. doi:10.1080/13658816.2012.683486
40. M.Zhang,, W.Yao,, and L.Meng,, 2014a. Enrichment of topographic road database for the purpose of routing and navigation. International Journal of Digital Earth, 7 (5), 411–431. doi:10.1080/17538947.2012.717110
41. X.Zhang,, et al., 2014b. Data matching of building polygons at multiple map scales improved by contextual information and relaxation. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 147–163. doi:10.1016/j.isprsjprs.2014.03.010
42. Q.Zhou, and Z.Li,, 2012. A comparative study of various strategies to concatenate road segments into strokes for map generalization. International Journal of Geographical Information Science, 26 (4), 691–715. doi:10.1080/13658816.2011.609990
43. M.Zook,, et al., 2010. Volunteered geographic information and crowdsourcing disaster relief: a case study of the Haitian earthquake. World Medical & Health Policy, 2 (2), 7–33. doi:10.2202/1948-4682.1069