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通过在城市地区应用空间建模预防与极端自然地面变形事件相关的灾害(厄瓜多尔基多)。

Prevention of Disasters Related to Extreme Natural Ground Deformation Events by Applying Spatial Modeling in Urban Areas (Quito, Ecuador).

机构信息

Geology Department, External Geodynamics Area, Faculty of Sciences, University of Salamanca, Plaza Merced s/n, 37008 Salamanca, Spain.

出版信息

Int J Environ Res Public Health. 2020 Jan 24;17(3):753. doi: 10.3390/ijerph17030753.

DOI:10.3390/ijerph17030753
PMID:31991618
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7037894/
Abstract

Synthetic Aperture Radar Interferometry (InSAR) is a spatial technique based on obtaining the phase differences of two radar images, acquired by a satellite from separate orbits and at different times, to obtain a ground displacement image of a study area, This image is called interferogram. On the other hand, space syntax is a technique within architecture that is applied to quantify and describe the level of ease of population movement through any urban space in a city. It analyzes the flow, transit, displacement, accessibility and concentration of the population in areas of basic services, health, security, commerce and entertainment. What would happen if an earthquake greater than 6 or 7 Moment Magnitude-Mw occurs in these areas of intense concentration of the population that are in buildings constructed on intense deformations of the land? With respect to the seismic risk in the city of Quito, many studies related to seismic risks have been published, but there are no studies that relate the deformation of the land (INSAR) with the space syntax, so this article presents a new vision in the joint application of these tools, a useful vision for urban planners and designers, considering the occurrence of a major earthquake in areas of buildings that are located on intense land deformations and have high population concentrations. This study has been prepared in two phases: in the first phase, the built-up areas concentrated in the greatest terrain deformations by accumulated displacement obtained using the APS estimation & multitemporal analysis by PSI-InSAR time series analysis methodology and Sentinel 1A and 1B satellite images were categorized. In the second phase, through the space syntax's theory and the use of DepthmapX, the movement patterns and traffic flows of the population were determined by means of graphs of spaces interconnected by streets (axial maps), to predict the spatial behavior of humans and its concentration in the mentioned sites. Finally, the results were integrated, determining the degree of exposure of the population found in built areas with high to very high displacement and an intense population concentration.

摘要

合成孔径雷达干涉测量(InSAR)是一种基于获取两颗卫星从不同轨道、不同时间获取的两幅雷达图像相位差的空间技术,以获取研究区域的地面位移图像,该图像称为干涉图。另一方面,空间句法是一种应用于量化和描述城市中任何一个城市空间的人口流动便利性的建筑技术。它分析了基本服务、健康、安全、商业和娱乐区域的人口流动、过境、位移、可达性和集中程度。如果在人口高度集中的这些地区发生大于 6 或 7 级地震会怎样?这些人口高度集中的地区位于建在土地剧烈变形上的建筑物中?关于基多市的地震风险,已经发表了许多与地震风险相关的研究,但没有研究将土地变形(InSAR)与空间句法联系起来,因此本文提出了这些工具联合应用的新视角,这对城市规划师和设计师来说是一个有用的视角,考虑到在位于土地剧烈变形且人口高度集中的建筑物区域发生大地震的情况。这项研究分两个阶段进行:在第一阶段,使用 APS 估计和多时序分析,根据 PSI-InSAR 时间序列分析方法和 Sentinel 1A 和 1B 卫星图像获取的累积位移,对集中在最大地形变形的建成区进行分类。在第二阶段,通过空间句法理论和 DepthmapX 的使用,通过街道(轴向图)互联的空间的图形确定了人口的流动模式和交通流量,以预测提到的站点中的人类空间行为及其集中程度。最后,将结果进行整合,确定在高至非常高位移和人口高度集中的建成区中发现的人口的暴露程度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/31f5f203b423/ijerph-17-00753-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/ebd7a7730b4c/ijerph-17-00753-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/1d731089b19b/ijerph-17-00753-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/87c1134234af/ijerph-17-00753-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/505d1f611826/ijerph-17-00753-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/8d5faf4894a7/ijerph-17-00753-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/1397f8c9985e/ijerph-17-00753-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/28bad9e05bd2/ijerph-17-00753-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/f34e2a3ca811/ijerph-17-00753-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/3ba8d33eb1b2/ijerph-17-00753-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/25dda6e0593c/ijerph-17-00753-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/bd0ee2483e77/ijerph-17-00753-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/c22b77266026/ijerph-17-00753-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/7f0136fde66e/ijerph-17-00753-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/31f5f203b423/ijerph-17-00753-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/ebd7a7730b4c/ijerph-17-00753-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/1d731089b19b/ijerph-17-00753-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/87c1134234af/ijerph-17-00753-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/505d1f611826/ijerph-17-00753-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/8d5faf4894a7/ijerph-17-00753-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/1397f8c9985e/ijerph-17-00753-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/28bad9e05bd2/ijerph-17-00753-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/f34e2a3ca811/ijerph-17-00753-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/3ba8d33eb1b2/ijerph-17-00753-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/25dda6e0593c/ijerph-17-00753-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/bd0ee2483e77/ijerph-17-00753-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/c22b77266026/ijerph-17-00753-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/7f0136fde66e/ijerph-17-00753-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a39d/7037894/31f5f203b423/ijerph-17-00753-g014.jpg

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