Mining trajectory data and geotagged data in social media for road map inference

Jun Li, Qiming Qin, Jiawei Han, Lu An Tang, Kin Hou Lei

Research output: Contribution to journalArticlepeer-review

Abstract

As mapping is costly and labor-intensive work, government mapping agencies are less and less willing to absorb these costs. In order to reduce the updating cycle and cost, researchers have started to use user generated content (UGC) for updating road maps; however, the existing methods either rely heavily on manual labor or cannot extract enough information for road maps. In view of the above problems, this article proposes a UGC-based automatic road map inference method. In this method, data mining techniques and natural language processing tools are applied to trajectory data and geotagged data in social media to extract not only spatial information - the location of the road network - but also attribute information - road class and road name - in an effort to create a complete road map. A case study using floating car data, collected by the National Commercial Vehicle Monitoring Platform of China, and geotagged text data from Flickr and Google Maps/Earth, validates the effectiveness of this method in inferring road maps.

Original languageEnglish (US)
Pages (from-to)1-18
Number of pages18
JournalTransactions in GIS
Volume19
Issue number1
DOIs
StatePublished - Feb 1 2015

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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