scholarly journals Spatial autocorrelation analysis of Covid-19 cases in South Kalimantan, Indonesia

2021 ◽  
Vol 2106 (1) ◽  
pp. 012005
Author(s):  
O N Amaliah ◽  
Y Sukmawaty ◽  
D S Susanti

Abstract Coronavirus Disease 2019 (COVID-19) is a new coronavirus that was discovered in Wuhan, China, at the end of 2019. In March 2020, the outbreak extended throughout the world, including Indonesia and one of its provinces, South Kalimantan. This rapid expansion should belinked to people’s mobility between regions, hence the linkage across regions must be examined. In South Kalimantan Province, the purpose of this research is to evaluate the distribution and relationship across regions in terms of the number of positive COVID-19 cases, the number of additional positive COVID-19 cases, and the number of COVID-19 patients under treatment. The spatial autocorrelation analysis with the Moran Index and Local Indicator of Spatial Autocorrelation (LISA) tests were used to determine the spatial autocorrelation between what and what using what data/where the data obtained? from March 22 to September 30, 2020. Based on the results of the Moran Index test, it is known that there is a spatial autocorrelation in the number of cases, the number of additional cases and the number of positive confirmed COVID-19 patients in treatment between one region and another neighboring area. While the results of the LISA Index test show that Balangan Regency, Hulu Sungai Tengah Regency, Hulu Sungai Utara Regency, Banjarmasin City, Tabalong Regency and Banjar Regency affect the level of COVID-19 cases in their respective neighboring areas. Therefore, there is a need for policies to control community mobility in those spatially correlated areas and increase testing and tracing to control the spread of COVID-19 cases in South Kalimantan Province.

Author(s):  
Lin Lei ◽  
Anyan Huang ◽  
Weicong Cai ◽  
Ling Liang ◽  
Yirong Wang ◽  
...  

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.


1991 ◽  
Vol 69 (3) ◽  
pp. 547-551 ◽  
Author(s):  
Chang Yi Xie ◽  
Peggy Knowles

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.


Sign in / Sign up

Export Citation Format

Share Document