Social Media Meets Big Urban Data: A Case Study of Urban Waterlogging Analysis

Abstract

With the design and development of smart cities, opportunities as well as challenges arise at the moment. For this purpose, lots of data need to be obtained. Nevertheless, circumstances vary in different cities due to the variant infrastructures and populations, which leads to the data sparsity. In this paper, we propose a transfer learning method for urban waterlogging disaster analysis, which provides the basis for traffic management agencies to generate proactive traffic operation strategies in order to alleviate congestion. Existing work on urban waterlogging mostly relies on past and current conditions, as well as sensors and cameras, while there may not be a sufficient number of sensors to cover the relevant areas of a city. To this end, it would be helpful if we could transfer waterlogging. We examine whether it is possible to use the copious amounts of information from social media and satellite data to improve urban waterlogging analysis. Moreover, we analyze the correlation between severity, road networks, terrain, and precipitation. Moreover, we use a multiview discriminant transfer learning method to transfer knowledge to small cities. Experimental results involving cities in China and India show that our proposed framework is effective.

Positive deviance, big data, and development: A systematic literature review

“…PDs in this case would be “sink” areas with very high potential for transmission due to high travel inflows from source areas, yet having only a small number of reported cases. Understanding and identifying the measures and the factors that were behind this deviance could provide valuable insights into successful disease control for other infected areas . Urban Resilience/Planning: In Zhang et al (), factors affecting waterlogging in one city were used to predict waterlogging in another city using satellite imagery, precipitation meteorological data, terrain data, and road maps. Positive deviance could be used to investigate why certain areas (PDs) within the same city experience less frequent waterlogging than others, and using those factors (eg, infrastructure and road networks) for better urban planning . Academic Research: In Surjandari et al (), Indonesian scholars' publications indexed in Scopus were analysed to map their primary research themes and advise on a nationwide research roadmap.…”

“…For example, sensor networks were used to monitor the spatio‐temporal distribution of greenhouse gas emissions in China (Tang, Yang, & Zhang, ). There is also a study (Zhang, Chen, Chen, & Chen, ) that combined sensor data, satellite images, and meteorological data with social media for the analysis of urban waterlogging disasters (where drainage systems are unable to cope). Physical data were used to observe and understand waterlogging, and social media data (ie, tracking words) were used to identify qualitative features of waterlogging incidents.…”

“…This approach was able to overcome the data sparsity issue, through training models in data‐rich areas to make predictions in data‐poor areas. Inference models were also used to predict water precipitation and potential for waterlogging based on climate data and road and terrain maps (Zhang et al, ). On the other hand, forecasting models utilize trend analysis and pattern recognition techniques to predict what will happen in the future, based on what happened in the past.…”

Structured Knowledge Base as Prior Knowledge to Improve Urban Data Analysis

“…In recent years, many applications have been proposed for different scenarios of urban data analysis, including transportation, the environment, energy, society, the economy, and public safety and security [12][13][14][15][16][17][18]. For example, a number of researchers have studied the store placement problem by focusing on various techniques, such as multiple regression discriminate analysis, spatial interaction models, and so on [19].…”

Quantitative Association between Nighttime Lights and Geo-Tagged Human Activity Dynamics during Typhoon Mangkhut

“…Social media data were also combined with remote sensing imagery to improve transportation damage assessment during the 2013 Colorado floods [27,28] and to estimate and forecast rainfall and runoff [29,30]. A transfer learning framework was proposed for urban waterlogging analysis using social media and satellite data [31]. A Bayesian statistical model was developed to estimate the probability of flood inundation using social media data, optical remote sensing and high-resolution terrain data [32].…”