Spatio-Temporal Risk Assessment Process Modeling for Urban Hazard Events in Sensor Web Environment

Wang, Wei; Chuanbo, HU; Chen, Nengcheng; Xiao, Changjiang; Jia, Shaofeng

doi:10.3390/ijgi5110203

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…Spatiotemporal data analysis provides a series of important tools to solve problems such as correlation analysis of spatiotemporal data, spatiotemporal pattern analysis, and spatiotemporal prediction problems [ 44 , 55 , 56 , 57 , 58 , 59 ]. To analyze the spatio-temporal correlation between risk factors (see Table 1 ) and stroke mortality, the GTWR model was selected as the regression model.…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal Correlation Analysis of Hydraulic Fracturing and Stroke in the United States

Liu

Wang

et al. 2022

IJERPH

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Hydraulic fracturing or fracking has led to a rapid growth of oil and gas production in the United States, but the impact of fracking on public health is an important but underresearched topic. We designed a methodology to study spatiotemporal correlations between the risk of fracking and stroke mortality. An annualized loss expectancy (ALE) model is applied to quantify the risk of fracking. The geographically and temporally weighted regression (GTWR) model is used to analyze spatiotemporal correlations of stroke mortality, fracking ALE, and nine other socioeconomic- and health-related factors. The analysis shows that fracking ALE is moderately correlated with stroke mortality at ages over 65 in most states of fracking, in addition to cardiovascular disease and drug overdose being positively correlated with stroke mortality. Furthermore, the correlations between fracking ALE and stroke mortality in men appear to be higher than in women near the Marcellus Shale, including Ohio, Pennsylvania, West Virginia, and Virginia, while stroke mortality among women is concentrated in the Great Plains, including Montana, Wyoming, New Mexico, and Oklahoma. Lastly, within two kilometers of the fracking mining activity, the level of benzene in the air was found to be significantly correlated with the fracking activity in Colorado.

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Section: Methodsmentioning

confidence: 99%

Spatiotemporal Correlation Analysis of Hydraulic Fracturing and Stroke in the United States

Liu

Wang

et al. 2022

IJERPH

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“…(10) Through the real-time perception of the real world and data acquisition, dynamic data streams can be provided, which can provide a massive amount of spatio-temporal dynamic information for environmental monitoring, resource management, ecological analysis, disaster emergencies, and other applications. (11) With the development of a number of high-performance technologies represented by Hadoop, Spark, and Spark Streaming, real-time or near-real-time spatial querying and analysis have become possible. (12)(13)(14) These technologies greatly improve the computing efficiency.…”

Section: Related Workmentioning

confidence: 99%

Approach to Urban Geospatial Monitoring Combining Sensor Web and High-performance Computing Infrastructure

Zhai¹,

Liu²,

Zhu³

et al. 2023

Sensors and Materials

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Urban geospatial monitoring is a dynamic early warning in the process of urban development. It reflects urban spatial changes from a geospatial perspective. Modern urban digital governance must use spatial data infrastructures, integrate multisource data, and implement dynamic realtime monitoring in 3D space. To meet the real-time monitoring requirements of urban geographic space in 3D environments, the rapid construction of 3D scenes, the rapid processing of monitoring information, and dynamic process simulation have become key challenges. This paper presents an approach to urban geospatial monitoring that combines a sensor web and a high-performance computing infrastructure. The approach leverages Apache Spark and stream computing to transform the traditional processing algorithm into a data retrieval and analysis process supported by high-performance computing, which drives the rapid construction of 3D scenes and the dynamic calculation of sensor web observations. The effectiveness of this method is demonstrated through a case of urban waterlogging monitoring.

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“…In the last decade, the visualization platforms of above-ground geographical information have been constructed in various fields [1][2][3][4][5]. Owing to the concealment of the underground space, detecting underground geohazards on urban roads is a difficult problem across the world.…”

Section: Introductionmentioning

confidence: 99%

A Three-Dimensional Visualization Framework for Underground Geohazard Recognition on Urban Road-Facing GPR Data

Wang

Wan

Han

2020

IJGI

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The identification of underground geohazards is always a difficult issue in the field of underground public safety. This study proposes an interactive visualization framework for underground geohazard recognition on urban roads, which constructs a whole recognition workflow by incorporating data collection, preprocessing, modeling, rendering and analyzing. In this framework, two proposed sampling point selection methods have been adopted to enhance the interpolated accuracy for the Kriging algorithm based on ground penetrating radar (GPR) technology. An improved Kriging algorithm was put forward, which applies a particle swarm optimization (PSO) algorithm to optimize the Kriging parameters and adopts in parallel the Compute Unified Device Architecture (CUDA) to run the PSO algorithm on the GPU side in order to raise the interpolated efficiency. Furthermore, a layer-constrained triangulated irregular network algorithm was proposed to construct the 3D geohazard bodies and the space geometry method was used to compute their volume information. The study also presents an implementation system to demonstrate the application of the framework and its related algorithms. This system makes a significant contribution to the demonstration and understanding of underground geohazard recognition in a three-dimensional environment.

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Spatio-Temporal Risk Assessment Process Modeling for Urban Hazard Events in Sensor Web Environment

Cited by 5 publications

References 33 publications

Spatiotemporal Correlation Analysis of Hydraulic Fracturing and Stroke in the United States

Spatiotemporal Correlation Analysis of Hydraulic Fracturing and Stroke in the United States

Approach to Urban Geospatial Monitoring Combining Sensor Web and High-performance Computing Infrastructure

A Three-Dimensional Visualization Framework for Underground Geohazard Recognition on Urban Road-Facing GPR Data

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