A new idea of detecting and identifying hidden danger space in jinan under epidemic situation
Topics:
Keywords: space syntax, pandemic, Getis-Ord Gi* index, GIS, POI
Abstract Type: Virtual Paper Abstract
Authors:
Qimeng Ren, Northeast Forestry University
Ming Sun, Northeast Forestry University
,
,
,
,
,
,
,
,
Abstract
The pandemic like coronavirus has been raging, which caused huge security risks and panic. Countries adopted traditional case data screening methods to mitigate the spread of the epidemic. However, these methods still have many limitations. We must find new ways to check the hidden dangers of the epidemic. The spatial layout may lead to the concentration of crowd, so the epidemic situation is more likely to occur. This paper attempts to analyze the spatial traffic network layout and POI data. Taking the main urban area of Jinan City, China as an example, we used the method of combining spatial syntax and local Getis-Ord Gi* index to propose a new idea to detect the hidden danger space of urban agglomeration under the epidemic. Thus, we analyzed the hot regions that are easy to gather people, and investigated the regions with high risk of epidemic. The research results showed that the road network in the main urban area of Jinan has obvious hot spots. They are located in scenic area area, school area, commercial complex area, community life circle area, transportation hub area and high-tech industry area, and the potential hazards of spatial agglomeration extend around the main axis. Topography, water, greening and parks could effectively relieve the potential abnormal epidemic. In general, the analysis results have been verified and the new idea could more accurately identify the urban agglomeration hidden danger space, which provides a practical reference for urban planning departments to investigate the key control areas of the epidemic.
A new idea of detecting and identifying hidden danger space in jinan under epidemic situation
Category
Virtual Paper Abstract
Description
Submitted by:
Qimeng Ren
772659841@qq.com
This abstract is part of a session. Click here to view the session.
| Slides