Assessing urban streets network vulnerability against earthquake using GIS - Case study: 6th zone of Tehran

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Abstract

Great earthquakes cause huge damages to human life. Street networks vulnerability makes the rescue operation to encounter serious difficulties especially at the first 72 hours after the incident. Today, physical expansion and high density of great cities, due to narrow access roads, large distance from medical care centers and location at areas with high seismic risk, will lead to a perilous and unpredictable situation in case of the earthquake. Zone # 6 of Tehran, with 229,980 population (3.6% of city population) and 20 km2 area (3.2% of city area), is one of the main municipal zones of Tehran (Iran center of statistics, 2006). Major land-uses, like ministries, embassies, universities, general hospitals and medical centers, big financial firms and so on, manifest the high importance of this region on local and national scale. In this paper, by employing indexes such as access to medical centers, street inclusion, building and population density, land-use, PGA and building quality, vulnerability degree of street networks in zone # 6 against the earthquake is calculated through overlaying maps and data in combination with IHWP method and GIS. This article concludes that buildings alongside the streets with high population and building density, low building quality, far to rescue centers and high level of inclusion represent high rate of vulnerability, compared with other buildings. Also, by moving on from north to south of the zone, the vulnerability increases. Likewise, highways and streets with substantial width and low building and population density hold little values of vulnerability.

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APA

Rastegar, A. (2017). Assessing urban streets network vulnerability against earthquake using GIS - Case study: 6th zone of Tehran. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 42, pp. 455–462). International Society for Photogrammetry and Remote Sensing. https://doi.org/10.5194/isprs-archives-XLII-4-W4-455-2017

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