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Estimation of Spatio-Temporal Missing Data for Expanding Urban LOD

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Book cover Semantic Technology (JIST 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10055))

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Abstract

The illegal parking of bicycles has been an urban problem in Tokyo and other urban areas. We have sustainably built a Linked Open Data (LOD) relating to the illegal parking of bicycles (IPBLOD) to support the problem solving by raising social awareness. Also, we have estimated and complemented the temporally missing data to enrich the IPBLOD, which consisted of intermittent social-sensor data. However, there are also spatial missing data where a bicycle might be illegally parked, and it is necessary to estimate those data in order to expand the areas. Thus, we propose and evaluate a method for estimating spatially missing data. Specifically, we find stagnation points using computational fluid dynamics (CFD), and we filter the stagnation points based on popularity stakes that are calculated using Linked Data. As a result, a significant difference in between the baseline and our approach was represented using the chi-square test.

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Notes

  1. 1.

    https://developers.google.com/places/?hl=en.

  2. 2.

    https://developer.foursquare.com/.

  3. 3.

    http://www.soumu.metro.tokyo.jp/30english/index-en.htm.

  4. 4.

    http://www.jma.go.jp/jma/indexe.html.

  5. 5.

    http://www.ohsuga.is.uec.ac.jp/sparql.

  6. 6.

    http://virtuoso.openlinksw.com/.

  7. 7.

    http://www.ohsuga.is.uec.ac.jp/bicycle/dataset.html.

  8. 8.

    http://www.cs.waikato.ac.nz/ml/weka/.

  9. 9.

    http://www.gsi.go.jp/ENGLISH/index.html.

  10. 10.

    http://www.esri.com/software/arcgis/arcgis-for-desktop.

  11. 11.

    http://www.airflowanalyst.com/en/index.php.

  12. 12.

    http://developer.yahoo.co.jp/webapi/map/openlocalplatform/v1/reversegeocoder.html.

  13. 13.

    http://www.dublinked.ie/.

  14. 14.

    http://ec.europa.eu/eurostat/web/cities.

  15. 15.

    http://unstats.un.org/unsd/default.htm.

  16. 16.

    http://www.census.gov/.

  17. 17.

    https://www.flickr.com/.

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Acknowledgments

This work was supported by JSPS KAKENHI Grant Numbers 16K12411, 16K00419, 16K12533.

Author information

Authors and Affiliations

  1. Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan

    Shusaku Egami, Takahiro Kawamura & Akihiko Ohsuga

  2. Japan Science and Technology Agency, Tokyo, Japan

    Takahiro Kawamura

Authors
  1. Shusaku Egami
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  2. Takahiro Kawamura
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  3. Akihiko Ohsuga
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Corresponding author

Correspondence to Shusaku Egami .

Editor information

Editors and Affiliations

  1. Information Technology, Monash University, Melbourne, Victoria, Australia

    Yuan-Fang Li

  2. Computer Science and Technology, Nanjing University, Nanjing, China

    Wei Hu

  3. Computer Science, National University of Singapore, Singapore, Singapore

    Jin Song Dong

  4. University of Huddersfield, Huddersfield, United Kingdom

    Grigoris Antoniou

  5. Information and Communication Technology, Griffith University, Brisbane, Queensland, Australia

    Zhe Wang

  6. ISTD, Singapore University of Technology and Design, Singapore, Singapore

    Jun Sun

  7. Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Yang Liu

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Egami, S., Kawamura, T., Ohsuga, A. (2016). Estimation of Spatio-Temporal Missing Data for Expanding Urban LOD. In: Li, YF., et al. Semantic Technology. JIST 2016. Lecture Notes in Computer Science(), vol 10055. Springer, Cham. https://doi.org/10.1007/978-3-319-50112-3_12

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  • DOI: https://doi.org/10.1007/978-3-319-50112-3_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50111-6

  • Online ISBN: 978-3-319-50112-3

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