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

TL;DR

This paper explores using geo-tagged Twitter data to enhance real-time urban climate monitoring and risk management, especially for heatwaves, by analyzing relationships between tweets and temperature data and applying statistical models.

Contribution

It introduces a novel approach of integrating participatory sensing via Twitter with traditional climate data for intra-urban temperature analysis and risk assessment.

Findings

Strong correlation between hot-tweets and recorded temperatures

Twitter data improves spatial interpolation of urban temperatures

Tweets provide valuable auxiliary information for real-time climate risk management

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 weather station monitoring and remote sensing. 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. Additionally, 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 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. 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 and spatially and temporally dense lower quality twitter data. Here, a spatial best linear unbiased estimation 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 results.