A spatiotemporal analysis of participatory sensing data "tweets" and extreme climate events toward real-time urban risk management

Yoshiki Yamagata, Daisuke Murakami, Gareth W. Peters, Tomoko Matsui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Real-time urban climate monitoring provides useful information that can be utilized to help monitor and adapt to extreme events, including urban heatwaves. Typical approaches to the monitoring of cli-mate data include the acquisition of weather station monitoring and also remote sensing via satellite sensors. However, climate monitoring stations are very often distributed spatially in a sparse manner, and consequently, this has a significant impact on the ability to reveal exposure risks due to extreme climates at an intra-urban scale (e.g., street level). Additionally, such traditional remote sensing data sources are typically not received and analyzed in real-time which is often required for adaptive urban management of climate extremes, such as sudden heatwaves. Fortunately, recent social media, such as Twitter, furnishes real-time and high-resolution spatial information that might be useful for climate condition estimation. The objective of this study is utilizing geo-tagged tweets (participatory sensing data) for urban tem-perature analysis. We first detect tweets relating hotness (hot-tweets). Then, we study relationships between monitored temperatures and hot-tweets via a statistical model framework based on copula modelling methods. We demonstrate that there are strong relationships between "hot-tweets" and tem-peratures recorded at an intra-urban scale, that we reveal in our analysis of Tokyo city and its suburbs. Subsequently, we then investigate the application of "hot-tweets" informing spatio-temporal Gaussian process interpolation of temperatures as an application example of "hot-tweets". We utilize a combina-tion of spatially sparse weather monitoring sensor data, infrequently available MODIS remote sensing data and spatially and temporally dense lower quality geo-tagged twitter data. Here, a spatial best linear unbiased estimation (S-BLUE) technique is applied. The result suggests that tweets provide some useful auxiliary information for urban climate assessment. Lastly, effectiveness of tweets toward a real-time urban risk management is discussed based on the analysis of the results.

Original languageEnglish
Title of host publicationProceedings of CUPUM 2015
EditorsJoseph Ferreira Jr., Robert Goodspeed
PublisherCUPUM
ISBN (Electronic)9780692474341
Publication statusPublished - 2015
Event14th International Conference on Computers in Urban Planning and Urban Management 2015 - Cambridge, United States
Duration: 7 Jul 201510 Jul 2015

Conference

Conference14th International Conference on Computers in Urban Planning and Urban Management 2015
Abbreviated titleCUPUM 2015
CountryUnited States
CityCambridge
Period7/07/1510/07/15

ASJC Scopus subject areas

  • Environmental Engineering
  • Geography, Planning and Development
  • Urban Studies
  • Ecology
  • Civil and Structural Engineering

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