Abstract
Urban heatwaves increase residential health risks. Identifying urban residential sensitivity to heatwave risks is an important prerequisite for mitigating the risks through urban planning practices. This research proposes a new paradigm for urban residential sensitivity to heatwave risks based on social media Big Data, and describes empirical research in five megacities in China, namely, Beijing, Nanjing, Wuhan, Xi’an and Guangzhou, which explores the application of this paradigm to real-world environments. Specifically, a method to identify urban residential sensitive to heatwave risks was developed by using natural language processing (NLP) technology. Then, based on remote sensing images and Weibo data, from the perspective of the relationship between people (group perception) and the ground (meteorological temperature), the relationship between high temperature and crowd sensitivity in geographic space was studied. Spatial patterns of the residential sensitivity to heatwaves over the study area were characterized at fine scales, using the information extracted from remote sensing information, spatial analysis, and time series analysis. The results showed that the observed residential sensitivity to urban heatwave events (HWEs), extracted from Weibo data (Chinese Twitter), best matched the temporal trends of HWEs in geographic space. At the same time, the spatial distribution of observed residential sensitivity to HWEs in the cities had similar characteristics, with low sensitivity in the urban center but higher sensitivity in the countryside. This research illustrates the benefits of applying multi-source Big Data and intelligent analysis technologies to the understand of impacts of heatwave events on residential life, and provide decision-making data for urban planning and management.
Highlights
We did not have access to daily weather data for all pixels in these cities; we defined the pixel as “High” if the temperature of the merged land surface temperature (LST) dataset in this pixel was greater than or equal to 37 ◦ C (we integrated the air–ground temperature differences caused by various urban environmental factors, and determined a value of 2 ◦ C as the most reasonable [61]; as we described above, 35 ◦ C is the temperature of heatwave events (HWEs) defined by the National Meteorological Information Centre (CMA))
From the temporal correlation between HWEs and Weibo data complaining about high temperature, we can determine that the two trends show the same characteristics, but when a hot weather process ends, urban residential sensitivity to HWEs will continue
This study used natural language processing (NLP) technology to extract the residential sensitivity to HWEs from Weibo data
Summary
The Fifth Report of the Intergovernmental Panel on Climate Change (IPCC) confirmed that human activities, especially greenhouse gases emitted by fossil fuels, have made climate warming an irreversible fact. Urban heatwave events (HWEs) have become increasingly frequent, and are listed as extreme weather events [1]. Following the current emissions scenario, the frequency of population exposure to HWEs in the Northern. Hemisphere will be 4–8 times higher than in 2010 by the end of the century [2]. HWEs lead to urban resource shortage and ecological environment deterioration, and directly affect residential work and life, and even threaten human health [3,4,5,6,7,8].
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