scholarly journals Using Spatial Pattern Analysis to Explore the Relationship between Vulnerability and Resilience to Natural Hazards

2021 ◽  
Vol 18 (11) ◽  
pp. 5634
Author(s):  
Chien-Hao Sung ◽  
Shyue-Cherng Liaw
Keyword(s):  
Spatial Pattern ◽  
Spatial Autocorrelation ◽  
Natural Hazards ◽  
Socioeconomic Development ◽  
Spatial Pattern Analysis ◽  
Spatial Difference ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Mountain Areas ◽  
Urban Regions

This research aims to explore the spatial pattern of vulnerability and resilience to natural hazards in northeastern Taiwan. We apply the spatially explicit resilience-vulnerability model (SERV) to quantify the vulnerability and resilience to natural hazards, including flood and debris flow events, which are the most common natural hazards in our case study area due to the topography and precipitation features. In order to provide a concise result, we apply the principal component analysis (PCA) to aggregate the correlated variables. Moreover, we use the spatial autocorrelation analysis to analyze the spatial pattern and spatial difference. We also adopt the geographically weighted regression (GWR) to validate the effectiveness of SERV. The result of GWR shows that SERV is valid and unbiased. Moreover, the result of spatial autocorrelation analysis shows that the mountain areas are extremely vulnerable and lack enough resilience. In contrast, the urban regions in plain areas show low vulnerability and high resilience. The spatial difference between the mountain and plain areas is significant. The topography is the most significant factor for the spatial difference. The high elevation and steep slopes in mountain areas are significant obstacles for socioeconomic development. This situation causes consequences of high vulnerability and low resilience. The other regions, the urban regions in the plain areas, have favorable topography for socioeconomic development. Eventually, it forms a scenario of low vulnerability and high resilience.

Spatial analysis of rural drinking water supply in China

Water Policy ◽  
2014 ◽  
Vol 17 (3) ◽  
pp. 441-453 ◽  
Author(s):  
Hongxing Li ◽  
Qi Zhang ◽  
Weiwei Li ◽  
Qing Luo ◽  
Kaitai Liu ◽  
...  
Keyword(s):  
Drinking Water ◽  
Water Supply ◽  
Spatial Pattern ◽  
Spatial Autocorrelation ◽  
Drinking Water Supply ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Rural Water Supply ◽  
Rural Water ◽  
Rural Drinking Water Supply

A spatial autocorrelation analysis method was employed to process the spatial change of rural water supply over the past 19 years in the People's Republic of China. Statistical analyses indicate great achievements in rural water supply construction. Two main indices describing rural drinking water supply status, the Rural Popularization Rate of Tap Water and the Rural Popularization Rate of Water Improvement Beneficiaries, were found to be spatially auto-correlated. The Global Moran's I of the latter decreased generally, and local spatial autocorrelation analysis showed that the regional gap of rural water supply infrastructure is declining. The main factors affecting the spatial pattern of rural water supply were analyzed through the mean centre method. Our research shows that the spatial pattern of economic development and government investment has had a decisive role in the formation and evolution of rural water supply.

scholarly journals Spatial Clustering of ILI in Yunnan Province, China, Based on a Geographical Information System

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Xia Xiao ◽  
Chunrui Luo ◽  
Xiaoxiao Song ◽  
Wei Liu ◽  
Le Cai ◽  
...  
Keyword(s):  
Information System ◽  
Spatial Pattern ◽  
Spatial Autocorrelation ◽  
Yunnan Province ◽  
Spatial Clustering ◽  
Sentinel Surveillance ◽  
Geographical Information ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Cross Border

This research explored the spatial pattern of ILI in one poorer and numerous cross-border-mobility-populations in China. A spatial autocorrelation analysis, "Local" and "Global", "Moran" I, carried out in Yunnan province for 5-year sentinel surveillance data. Four counties shown high susceptible to ILI, which maybe result from poorer surrounding districts or be neighboring with Vietnam or/and Laos.

scholarly journals Spatial and Temporal Analysis of Lung Cancer in Shenzhen, 2008–2018

2020 ◽  
Vol 18 (1) ◽  
pp. 26
Author(s):  
Lin Lei ◽  
Anyan Huang ◽  
Weicong Cai ◽  
Ling Liang ◽  
Yirong Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Spatial Autocorrelation ◽  
Incidence Rate ◽  
Cancer Incidence ◽  
Southern China ◽  
Percentage Change ◽  
Spatial Autocorrelation Analysis ◽  
Lung Cancer Incidence ◽  
Annual Percentage Change ◽  
Autocorrelation Analysis

Lung cancer is the most commonly diagnosed cancer in China. The incidence trend and geographical distribution of lung cancer in southern China have not been reported. The present study explored the temporal trend and spatial distribution of lung cancer incidence in Shenzhen from 2008 to 2018. The lung cancer incidence data were obtained from the registered population in the Shenzhen Cancer Registry System between 2008 and 2018. The standardized incidence rates of lung cancer were analyzed by using the joinpoint regression model. The Moran’s I method was used for spatial autocorrelation analysis and to further draw a spatial cluster map in Shenzhen. From 2008 to 2018, the average crude incidence rate of lung cancer was 27.1 (1/100,000), with an annual percentage change of 2.7% (p < 0.05). The largest average proportion of histological type of lung cancer was determined as adenocarcinoma (69.1%), and an increasing trend was observed in females, with an average annual percentage change of 14.7%. The spatial autocorrelation analysis indicated some sites in Shenzhen as a high incidence rate spatial clustering area. Understanding the incidence patterns of lung cancer is useful for monitoring and prevention.

Spatial genetic substructure within natural populations of jack pine (Pinus banksiana)

1991 ◽  
Vol 69 (3) ◽  
pp. 547-551 ◽  
Author(s):  
Chang Yi Xie ◽  
Peggy Knowles
Keyword(s):  
Spatial Autocorrelation ◽  
Pinus Banksiana ◽  
Pollen Dispersal ◽  
Natural Populations ◽  
Jack Pine ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Long Distance ◽  
Genetic Substructure ◽  
Good Agreement

Spatial autocorrelation analysis was used to investigate the geographic distribution of allozyme genotypes within three natural populations of jack pine (Pinus banksiana Lamb.). Results indicate that genetic substructuring within these populations is very weak and the extent differs among populations. These results are in good agreement with those inferred from mating-system studies. Factors such as the species' predominantly outbreeding system, high mortality of selfs and inbreds prior to reproduction, long-distance pollen dispersal, and the absence of strong microhabitat selection may be responsible for the observed weak genetic substructuring. Key words: jack pine, Pinus banksiana, genetic substructure, allozyme, spatial autocorrelation analysis.

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