A Spatiotemporal Analysis of Participatory Sensing Data 'Tweets' and Extreme Climate Events Toward Real-Time Urban Risk Management

This manuscript was presented in the 14th International Conference on Computers in Urban Planning and Urban Management (CUPUM 2015).

34 Pages Posted: 5 Jun 2017

See all articles by Yoshiki Yamagata

Yoshiki Yamagata

University of Tsukuba

Daisuke Murakami

University of Tsukuba - Graduate School of Systems and Information Engineering

Gareth Peters

Department of Actuarial Mathematics and Statistics, Heriot-Watt University; University College London - Department of Statistical Science; University of Oxford - Oxford-Man Institute of Quantitative Finance; London School of Economics & Political Science (LSE) - Systemic Risk Centre; University of New South Wales (UNSW) - Faculty of Science

Tomoko Matsui

Independent

Date Written: 2015

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 climate 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 realtime 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 temperature 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 temperatures 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 combination 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.

Keywords: heat wave, twitter

Suggested Citation

Yamagata, Yoshiki and Murakami, Daisuke and Peters, Gareth and Matsui, Tomoko, A Spatiotemporal Analysis of Participatory Sensing Data 'Tweets' and Extreme Climate Events Toward Real-Time Urban Risk Management (2015). This manuscript was presented in the 14th International Conference on Computers in Urban Planning and Urban Management (CUPUM 2015)., Available at SSRN: https://ssrn.com/abstract=2980468 or http://dx.doi.org/10.2139/ssrn.2980468

Yoshiki Yamagata

University of Tsukuba ( email )

Tsukuba University , Ibaraki Ken
Tsukuba, Ibaraki 305-8573, Ibaraki 3050006
Japan

Daisuke Murakami

University of Tsukuba - Graduate School of Systems and Information Engineering ( email )

Ibaraki, 305-8573
Japan

Gareth Peters (Contact Author)

Department of Actuarial Mathematics and Statistics, Heriot-Watt University ( email )

Edinburgh Campus
Edinburgh, EH14 4AS
United Kingdom

HOME PAGE: http://garethpeters78.wixsite.com/garethwpeters

University College London - Department of Statistical Science ( email )

1-19 Torrington Place
London, WC1 7HB
United Kingdom

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

University of Oxford Eagle House
Walton Well Road
Oxford, OX2 6ED
United Kingdom

London School of Economics & Political Science (LSE) - Systemic Risk Centre ( email )

Houghton St
London
United Kingdom

University of New South Wales (UNSW) - Faculty of Science ( email )

Australia

Tomoko Matsui

Independent ( email )

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