Uncovering Spatiotemporal Patterns of Travel Flows Under Extreme Weather Events by Tensor Decomposition (Short Paper)

Extreme weather events have caused dramatic damage to human society. Human mobility is one of the important aspects that are impacted significantly by extreme weather. Currently, focus on human mobility research during extreme weather is often limited to the transport infrastructure and emergency management perspectives, lacking a systematic understanding of the spatiotemporal patterns of human travel behavior. In this research, we examine the structural changes in human mobility under the severe rainstorm that occurred on July 20th, 2021 in Zhengzhou, Henan Province, China. Innovatively applying a tensor decomposition approach to analyzing spatiotemporal flows of human movements represented by the mobile phone big data, we extract the characteristic components of human travel behaviors from the spatial and temporal dimensions, which help discover and understand the latent spatiotemporal patterns hidden in human mobility data. This study provides a new methodological perspective and demonstrates that it can be useful for uncovering latent patterns of human mobility and identifying its structural changes during extreme weather events. This is of great importance to a better understanding of the behavioral side of human mobility and its response to external shocks and has significant implications for human-focused policies in urban risk mitigation and emergency response

Measuring relative opinion from location-based social media: A case study of the 2016 U.S. presidential election

Social media has become an emerging alternative to opinion polls for public opinion collection, while it is still posing many challenges as a passive data source, such as structurelessness, quantifiability, and representativeness. Social media data with geotags provide new opportunities to unveil the geographic locations of users expressing their opinions. This paper aims to answer two questions: 1) whether quantifiable measurement of public opinion can be obtained from social media and 2) whether it can produce better or complementary measures compared to opinion polls. This research proposes a novel approach to measure the relative opinion of Twitter users towards public issues in order to accommodate more complex opinion structures and take advantage of the geography pertaining to the public issues. To ensure that this new measure is technically feasible, a modeling framework is developed including building a training dataset by adopting a state-of-the-art approach and devising a new deep learning method called Opinion-Oriented Word Embedding. With a case study of the tweets selected for the 2016 U.S. presidential election, we demonstrate the predictive superiority of our relative opinion approach and we show how it can aid visual analytics and support opinion predictions. Although the relative opinion measure is proved to be more robust compared to polling, our study also suggests that the former can advantageously complement the later in opinion prediction

A disaster-damage-based framework for assessing urban resilience to intense rainfall-induced flooding

Resilience has been widely used as a concept to analyse, understand, and improve cities' coping capacities to disasters. However, it is still a challenge to operationalise and quantify resilience. This study proposes a framework for assessing resilience to disasters based on the relationship between disaster intensity and damage rate. We use intense (short-term heavy) rainfall-induced urban flooding in Shenzhen city, one of the largest cities in China, as an example to explore the main features and transferability of the proposed resilience assessment framework. In addition, we demonstrate the usability of the proposed framework by using it to assess and compare the effectiveness of two resilience-building strategies: (1) permeable pavement transformation and (2) land vulnerability reduction. This research makes an innovative contribution through its effective disaster-damage-based approach for quantitatively evaluating urban resilience to disasters, which can support building resilience and mitigating the impact of climate change