Spatial patterns of genetic variability in Italian chestnut (Castanea sativa)

1990 ◽  
Vol 68 (9) ◽  
pp. 1962-1967 ◽  
Author(s):  
M. Pigliucci ◽  
S. Benedettelli ◽  
F. Villani
Keyword(s):  
Multivariate Analysis ◽  
Genetic Variation ◽  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Castanea Sativa ◽  
The Other ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Migration Flow ◽  
Selection For

The spatial patterns of genetic variation for 15 loci in 18 Italian populations of chestnut were analyzed. Multivariate analysis and spatial autocorrelation analysis showed two clinal variations, one in a west–east direction, the other in a north–south direction. There was a nonrandom spatial pattern of at least three alleles and a marked heterogeneity among populations of many others. An explanation is proposed in terms of migration flow for the majority of the polymorphisms and of selection for the three autocorrelated alleles. However, anthropic interferences may also be important. Key words: chestnut, electrophoresis, spatial autocorrelation, correspondence analysis, genetic boundaries, discriminant analysis.

scholarly journals SPATIAL AUTOCORRELATION ANALYSIS OF HOUSING DISTRIBUTION IN JOHOR BAHRU

2021 ◽  
Vol 19 (17) ◽  
Author(s):  
Nur Asyikin Mohd Sairi ◽  
Burhaida Burhan ◽  
Edie Ezwan Mohd Safian
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Geographic Location ◽  
Housing Unit ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Moran's I ◽  
High Degree ◽  
Global Spatial Autocorrelation ◽  
Autocorrelation Method

Geographic location naturally generates spatial patterns that are either clustered, dispersed, or random. Moreover, Tobler’s First Law of Geography is essentially a testable assumption in the concept where geographic location matters and one method for quantifying Tobler’s law of geography is through measures of spatial autocorrelation. Therefore, the purpose of this study is to identify the spatial patterns of housing distribution in Johor Bahru through the spatial autocorrelation method. The result of the global spatial autocorrelation analysis demonstrates a high degree of clustering within the housing distribution, as well as the identification of a clustered pattern with a highly positive Moran’s I value of 0.995207. Following that, the LISA cluster map successfully identified individual clusters of each housing unit with their neighbours through the red and blue colours displayed on the map, as well as revealing home buyers’ preferences for a property in each location.

scholarly journals Risk Assessment and Factors Associated with Lung Cancer using GIS in Mae Ka Subdistrict, Muang District, Phayao Province, Thailand

2021 ◽  
pp. 21-33
Keyword(s):  
Lung Cancer ◽  
Spatial Autocorrelation ◽  
Incidence Rate ◽  
Spatial Patterns ◽  
Spatial Association ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Cross Sectional ◽  
Major Health Problem ◽  
Factors Associated

Cancer is a major health problem in the developing countries. Variations of its incidence rate among geographical areas are due to various contributing factors. This study was performed to assess the spatial patterns of lung cancer incidence in the Mae Ka subdistrict, Muang district, Phayao province, based on lung cancer registry data and to determine geographical clusters. In this cross-sectional study, the cases of lung cancer were recorded from 2015 to 2020. Crude incidence rate was estimated based on age groups and sex in the province of the Mae Ka subdistrict. It uses spatial autocorrelation analysis (SAA) techniques to provide insight into the patterns, in terms of their geographical distributions and hotspot identification. Spatial autocorrelation analysis was performed in measuring the geographic patterns and clusters using GIS. In addition, local indicators of spatial association (LISA) and kernel density (KD) estimation were used to detect lung cancer hotspots using data at village level. Factors associated with the incidence of lung cancer was analyzed for behavior risk factors. Analysis of the spatial distribution of lung cancer shows significant differences from year to year and between different areas. The hotspot maps showed spatial trend patterns of lung cancer diffusion. Villages in the northern part revealed higher incidence. Furthermore, the spatial patterns during the years 2015, 2017 and 2019 were found to represent spatially clustered patterns, both at global and local scales. However, a clear spatial autocorrelation is observed, which can be of grate interest and importance to researchers for future epidemiological studies, and to policymakers for applying preventive measures.

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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