scholarly journals Changes in the spatial pattern of net earnings: Evidence from Serbia

2015 ◽  
Vol 65 (3) ◽  
pp. 351-365 ◽  
Author(s):  
Uglješa Stankov ◽  
Vanja Dragićević
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Clustering ◽  
Hot Spot ◽  
Spatial Segregation ◽  
Cold Spot ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
The North ◽  
Spatial Changes ◽  
Global And Local

Spatial autocorrelation analysis is an important method that can reveal the structure and patterns of economic spatial variables. It can be used to identify not only global spatial patterns in the country, but also characteristic locations at micro levels. In this research, we used spatial autocorrelation methodologies, including Global Moran’s I and Local Getis—Ord Gi statistics to identify the intensity of the spatial clustering of municipalities in Serbia by the level of average monthly net earnings from 2001 to 2010. We identified and mapped local clusters (hot and cold spots) by the level of average monthly net earnings for the same period. The results show that overall spatial segregation between municipalities with high and low average monthly net earnings was predominantly increasing during the investigated period. Local statistics illustrated that overall spatial segregation followed a broad north—south divide, with a concentration of municipalities with high net earnings in the north of Serbia, and low net earnings in the south. Closer inspection showed that at the beginning of the study period, there were three statistically significant hot spots in the north. As time passed, only one highly clustered hot spot remained — the Belgrade region. One cold spot retained a relatively stable position in the country’s southeast. This research shows that spatial changes of net earnings can be successfully studied with respect to statistically significant global and local spatial associations in the variables using spatial autocorrelation analysis.

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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