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Mining Trajectory Data and Geotagged Data in Social Media for Road Map Inference
Author(s) -
Li Jun,
Qin Qiming,
Han Jiawei,
Tang LuAn,
Lei Kin Hou
Publication year - 2015
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/tgis.12072
Subject(s) - inference , social media , computer science , trajectory , data mining , road map , data science , information retrieval , geography , artificial intelligence , cartography , world wide web , physics , astronomy
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 C hina, and geotagged text data from Flickr and G oogle Maps/Earth, validates the effectiveness of this method in inferring road maps.