Public crowdsensing of heat waves by social media data

Valentina Grasso,Gianni Pantaleo,Paolo Nesi,Marco Morabito,Alfonso Crisci

doi:10.5194/asr-14-217-2017

Valentina Grasso, Gianni Pantaleo + Show 3 more

Open Access

https://doi.org/10.5194/asr-14-217-2017

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Abstract

Abstract. Investigating on society-related heat wave hazards is a global issue concerning the people health. In the last two decades, Europe experienced several severe heat wave episodes with catastrophic effects in term of human mortality (2003, 2010 and 2015). Recent climate investigations confirm that this threat will represent a key issue for the resiliency of urban communities in next decades. Several important mitigation actions (Heat-Health Action Plans) against heat hazards have been already implemented in some WHO (World Health Organization) European region member states to encourage preparedness and response to extreme heat events. Nowadays, social media (SM) offer new opportunities to indirectly measure the impact of heat waves on society. Using the crowdsensing concept, a micro-blogging platform like Twitter may be used as a distributed network of mobile sensors that react to external events by exchanging messages (tweets). This work presents a preliminary analysis of tweets related to heat waves that occurred in Italy in summer 2015. Using TwitterVigilance dashboard, developed by the University of Florence, a sample of tweets related to heat conditions was retrieved, stored and analyzed for main features. Significant associations between the daily increase in tweets and extreme temperatures were presented. The daily volume of Twitter users and messages revealed to be a valuable indicator of heat wave impact at the local level, in urban areas. Furthermore, with the help of Generalized Additive Model (GAM), the volume of tweets in certain locations has been used to estimate thresholds of local discomfort conditions. These city-specific thresholds are the result of dissimilar climatic conditions and risk cultures.

Highlights

Use of social media (SM) during emergencies to communicate timely information has become a practice in the last years
Not all social media data are effectively available for research purposes due to platform policies which limit the access to messages
To monitor Twitter activity related to extreme temperature conditions we created a “Heat” monitoring channel on TwitterVigilance platform based on a set of keywords and hashtags semantically related to heat conditions

Summary

Introduction

Use of social media (SM) during emergencies to communicate timely information has become a practice in the last years. Twitter mining represents an indirect form of crowdsensing with no explicit engagement of people into data collection In this case user-generated contents, like tweets, are used for a second purpose in what may be seen as a mobile Crowdsensing (Guo et al, 2014). Based on the Eurostat database (http://ec.europa.eu/eurostat/statistics-explained/ index.php/Population_and_social_conditions), while a higher proportion of the elderly population of the EU-28 countries lived in rural regions, those who were in urban regions were more likely to be living alone The latter is a well-known risk factor for heat-related mortality (Naughton et al, 2002; Semenza et al, 1996). In particular the following aspects are investigated: verify the usefulness of SM as social indicators of thermal impacts on the population; perform a data-driven estimation of city-specific thresholds of apparent temperature associated with a peak in volumes of tweets, that may be used as a quantitative risk assessment for each location

Research design and methodology

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