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Optimized Conflation of Authoritative and Crowd-Sourced Geographic Data: Creating an Integrated Bike Map

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Information Fusion and Intelligent Geographic Information Systems (IF&IGIS'17)

Part of the book series: Lecture Notes in Geoinformation and Cartography ((LNGC))

Abstract

A complete and accurate geographic dataset is critical for relevant analysis and decision-making. This chapter proposes a four-step geographic data-conflation system: preprocessing, automatic conflation, evaluation, and manual adjustments. The automatic-conflation component uses an optimization approach to find matched features and a rubber-sheeting approach to complete spatial transformation. This system was tested using two bikeway datasets in Los Angeles County, California, from an authoritative source (Los Angeles County Metropolitan Transportation Authority) and an open source (OpenStreetMap). While bikeways that are already in both datasets are improved in terms of positional accuracy and attribute completeness, the conflated bikeway dataset also integrates complementary data in either of the input datasets. Experiments demonstrate the advantages of using crowd-sourced data to improve official bikeway data, which is important for building and maintaining high-quality bicycle-infrastructure datasets. The framework described in this chapter can be adapted to conflate other types of data themes.

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Authors and Affiliations

  1. Department of Geography, California State University, 1250 Bellflower Blvd, Long Beach, CA, 90840, USA

    Linna Li & Jose Valdovinos

Authors
  1. Linna Li
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  2. Jose Valdovinos
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Corresponding author

Correspondence to Linna Li .

Editor information

Editors and Affiliations

  1. SPIIRAS Hi Tech Research and Development Office Ltd., St. Petersburg, c.St-Peterburg, Russia

    Vasily Popovich

  2. Department for Urbanism, Transport, Environment & Information Society, Central European Institute of Technology - CEIT ALANOVA gemeinnützige GmbH , Schwechat, Austria

    Manfred Schrenk

  3. Department of Geography and Earth Sciences, The University of North Carolina, Charlotte, North Carolina, USA

    Jean-Claude Thill

  4. Naval Academy Research Institute , Brest Naval, Finistère, France

    Christophe Claramunt

  5. Shanghai Maritime University, Shanghai, Shanghai, China

    Tianzhen Wang

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Li, L., Valdovinos, J. (2018). Optimized Conflation of Authoritative and Crowd-Sourced Geographic Data: Creating an Integrated Bike Map. In: Popovich, V., Schrenk, M., Thill, JC., Claramunt, C., Wang, T. (eds) Information Fusion and Intelligent Geographic Information Systems (IF&IGIS'17). Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-59539-9_17

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