Estimation of Spatio-Temporal Missing Data for Expanding Urban LOD

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10055)

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.

Keywords

Linked open data Urban problem Illegally parked bicycles 

Notes

Acknowledgments

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

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Shusaku Egami
    • 1
  • Takahiro Kawamura
    • 1
    • 2
  • Akihiko Ohsuga
    • 1
  1. 1.Graduate School of Informatics and EngineeringThe University of Electro-CommunicationsTokyoJapan
  2. 2.Japan Science and Technology AgencyTokyoJapan

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