灾害风险视角下的城市安全评估及其驱动机制分析——以滁州市中心城区为例

doi:10.31497/zrzyxb.20210914

Abstract

Abstract:

The frequent occurrence of natural and man-made disasters in the process of urbanization has seriously restricted the survival and safe development of cities. We used Chuzhou central city, one of the representative disaster prone areas, as a case study to measure safety risk. We constructed a safety risk index system, based on a typical Pressure-State-Response (PSR) conceptual framework. We then used coupling indication model to compound the risk assessment results of each single disaster, and quantitatively evaluated the distribution characteristic of urban safety risk. Finally, we used geographic detector to analyse the impact mechanism of the driving indicators of urban safety risk. We found that: (1) The proportions of urban safety risk grades from low to high were 2.49%, 8.71%, 41.08%, 30.47% and 17.25%, respectively, with medium risk dominating the study area. Spatially, the safety risk of central area was higher than that of the surrounding areas, that is to say, it showed a declining trend from the central city to the suburbs. (2) The order of the explanatory power intensity of the driving indicators on the safety risk was as follows: population density (0.404) > GDP density (0.402) > building disaster tolerance (0.095) > vegetation coverage (0.078) > road network density (0.013) > risk of land use type (0.012), and the explanatory power of the results was enhanced by interaction of factors. The research will provide a new theoretical perspective and framework for urban safety risk assessment, and help for disaster risk management in high risk areas.

Key words: urban safety risk, PSR framework, coupling indication model, geographic detector, driving mechanism, Chuzhou central city

YANG Hai-feng, ZHAI Guo-fang. Spatial assessment and driving mechanism of urban safety from the perspective of disaster risk:A case study of Chuzhou central city[J].JOURNAL OF NATURAL RESOURCES, 2021, 36(9): 2368-2381.

Figures/Tables 13

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数据类型	数据来源	精度	网址
人口密度	World pop	100 m、2015年	https://www.worldpop.org/
GDP	中国资源环境数据云平台	1 km、2015年	http://www.resdc.cn/
气象数据	国家气象科学数据共享服务平台	2000—2015年	http://data.cma.cn/
遥感植被数据	地理空间数据云	250 m、2015年	http://www.gscloud.cn/
DEM	地理空间数据云	30 m	http://www.gscloud.cn/
基础地理信息数据	安徽省城乡规划设计研究院	行政边界、土地利用、道路、避难点、火灾点以及医疗、消防点等数据

目标层	准则层	各灾害类型指标层
目标层	准则层	地震	地质灾害	洪涝	高温	火灾
滁州市中心城区安全风险	压力	地震震级分区A1	地质灾害易发性B1	多年平均降雨量C1	夏季舒适度指数D1	与重大危险源距离E1
			地质灾害强度B2	距河距离C2		消防警情数量E2
	状态	人口密度A2	人口密度B3	人口密度C3	人口密度D2	人口密度E3
		建筑抗震能力A3	坡度B4	海拔高度C4	植被覆盖度D3	消防安全用地等级E4
			植被覆盖度B5	地形起伏度C5	土地利用类型D4	建筑密度E5
			地质岩土类型B6	河网密度C6
	响应	地均GDP A4	地均GDP B7	地均GDP C7	地均GDP D5	地均GDP E6
		路网密度A5	路网密度B8	路网密度C8	医疗设施D6	路网密度E7
		公共空间密度A6		防洪设施C9		消防力量覆盖程度E8
		避难场所可达性 A7				消防供水能力E9

地震			地质灾害			洪涝			高温			火灾
指标	属性	权重	指标	属性	权重	指标	属性	权重	指标	属性	权重	指标	属性	权重
A1	+	0.305	B1	+	0.232	C1	+	0.179	D1	+	0.341	E1	+	0.133
A2	+	0.087	B2	+	0.200	C2	-	0.122	D2	+	0.222	E2	+	0.217
A3	定性	0.171	B3	+	0.083	C3	+	0.038	D3	-	0.124	E3	+	0.027
A4	-	0.042	B4	+	0.085	C4	-	0.122	D4	定性	0.128	E4	定性	0.075
A5	-	0.137	B5	-	0.041	C5	+	0.072	D5	-	0.066	E5	+	0.135
A6	-	0.132	B6	定性	0.173	C6	-	0.125	D6	-	0.119	E6	-	0.047
A7	-	0.126	B7	-	0.057	C7	-	0.027				E7	-	0.115
			B8	-	0.129	C8	-	0.135				E8	-	0.114
						C9	-	0.180				E9	-	0.157

触发灾害	被触发灾害
触发灾害	地震	地质	洪涝	火灾	高温
地震	—	1	1	1	0
地质	0	—	1	0	0
洪涝	0	1	—	0	0
火灾	0	0	0	—	0
高温	0	0	0	1	—

关系表达式	交互作用关系类型
P(X_i∩X_j)>P(X_i)+P(X_j)	非线性增强
P(X_i∩X_j)>max[P(X_i), P(X_j)]	双线性增强
P(X_i∩X_j)=P(X_i)+P(X_j)	相互独立
P(X_i∩X_j)<min[P(X_i), P(X_j)]	非线性减弱
min[P(X_i), P(X_j)]<P(X_i∩X_j)<max[P(X_i), P(X_j)]	单线性减弱

Spatial assessment and driving mechanism of urban safety from the perspective of disaster risk:A case study of Chuzhou central city

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风险等级	地震	地质	洪涝	高温	火灾	综合风险
低	15.70	6.64	10.89	14.24	35.81	2.49
较低	16.33	37.74	25.95	21.22	30.29	8.71
中	22.62	16.12	24.33	15.65	19.21	41.08
较高	35.41	26.12	20.58	23.66	10.30	30.47
高	9.94	13.38	18.25	25.23	3.77	17.25

X_i∩X_j	P(X_i∩X_j)	判断	交互关系作用类型
人口密度（0.404）∩地均GDP（0.402）	0.407	P(X_i∩X_j)>max[P(X_i), P(X_j)]	双线性增强
人口密度（0.404）∩用地类型风险（0.012）	0.427	P(X_i∩X_j)>P(X_i)+P(X_j)	非线性增强
人口密度（0.404）∩植被覆盖度（0.078）	0.429	P(X_i∩X_j)>max[P(X_i), P(X_j)]	双线性增强
人口密度（0.404）∩路网密度（0.013）	0.409	P(X_i∩X_j)>max[P(X_i), P(X_j)]	双线性增强
人口密度（0.404）∩建筑承灾能力（0.095）	0.422	P(X_i∩X_j)>max[P(X_i), P(X_j)]	双线性增强
地均GDP（0.402）∩用地类型风险（0.012）	0.424	P(X_i∩X_j)>P(X_i)+P(X_j)	非线性增强
地均GDP（0.402）∩植被覆盖度（0.078）	0.430	P(X_i∩X_j)>max[P(X_i), P(X_j)]	双线性增强
地均GDP（0.402）∩路网密度（0.013）	0.408	P(X_i∩X_j)>max[P(X_i), P(X_j)]	双线性增强
地均GDP（0.402）∩建筑承灾能力（0.095）	0.421	P(X_i∩X_j)>max[P(X_i), P(X_j)]	双线性增强
用地类型风险（0.012）∩植被覆盖度（0.078）	0.131	P(X_i∩X_j)>P(X_i)+P(X_j)	非线性增强
用地类型风险（0.012）∩路网密度（0.013）	0.029	P(X_i∩X_j)>P(X_i)+P(X_j)	非线性增强
用地类型风险（0.012）∩建筑承灾能力（0.095）	0.114	P(X_i∩X_j)>P(X_i)+P(X_j)	非线性增强
植被覆盖度（0.078）∩路网密度（0.013）	0.107	P(X_i∩X_j)>P(X_i)+P(X_j)	非线性增强
植被覆盖度（0.078）∩建筑承灾能力（0.095）	0.150	P(X_i∩X_j)>max[P(X_i), P(X_j)]	双线性增强
路网密度（0.013）∩建筑承灾能力（0.095）	0.108	P(X_i∩X_j)>P(X_i)+P(X_j)	非线性增强

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