scholarly journals MAPPING POVERTY HOT SPOTS IN PENINSULAR MALAYSIA USING SPATIAL AUTOCORRELATION ANALYSIS

Author(s):  
M. Rafee Majid ◽  
Abdul Razak Jaffar ◽  
Noordini Che Man ◽  
Mehrdad Vaziri ◽  
Mohamed Sulemana

In September 2000 The Millennium Summit adopted the UN Millennium Declaration, committing nations to a new global partnership to reduce extreme poverty with a deadline of 2015. Eight Millennium Development Goals were formulated of which the eradication of poverty given top priority. However, Malaysia's participation with the UN in dealing with poverty, precede this when it committed itself with the United Nations Decade for the Eradication of Poverty (1997-2006) programme, which was then reinforced when the Millennium Declaration was made in 2000. Nationally, poverty eradication as well as bridging the inequality gap among the major ethnic groups and states has been the main development goal in Malaysia's development agenda since independence. In this regards, the principle of “growth with equity has been the central theme in all Malaysia's development policies and efforts since independence. Although Malaysia has made significant achievements in reducing the incidence of aggregate poverty across the country from 8.9% in 1995 down to 1.7% in 2012, there still exist pockets of poverty in the rural areas, in certain states/regions and among ethnic groups, as well as in some urban areas. This shows that formulating planning and policy implementation to eradicate poverty now needs to be more spatially focused for the implementation to be more effective. Recognising the incidence of poverty through standard statistical data tables alone is no longer adequate in formulating planning and policy implementation. Through spatial autocorrelation analysis the pattern of distribution of poverty in space over a period of time can easily be visualised and hotspots of incidence of poverty identified. This paper attempts to show how this analysis can assist in focusing efforts to eradicate poverty in Malaysia.

2016 ◽  
Vol 14 (4) ◽  
Author(s):  
M. Rafee Majid ◽  
Abdul Razak Jaffar ◽  
Noordini Che Man ◽  
Mehdrad Vaziri ◽  
Mohamed Sulemana

In September 2000 The Millennium Summit adopted the UN Millennium Declaration, committing nations to a new global partnership to reduce extreme poverty with a deadline of 2015. Eight Millennium Development Goals were formulated of which the eradication of poverty given top priority. However, Malaysia’s participation with the UN in dealing with poverty, precede this when it committed itself with the United Nations Decade for the Eradication of Poverty (1997–2006) programme, which was then reinforced when the Millennium Declaration was made in 2000. Nationally, poverty eradication as well as bridging the inequality gap among the major ethnic groups and states has been the main development goal in Malaysia’s development agenda since independence. In this regards, the principle of “growth with equity” has been the central theme in all Malaysia’s development policies and efforts since independence. Although Malaysia has made significant achievements in reducing the incidence of aggregate poverty across the country from 8.9% in 1995 down to 1.7% in 2012, there still exist pockets of poverty in the rural areas, in certain states/regions and among ethnic groups, as well as in some urban areas. This shows that formulating planning and policy implementation to eradicate poverty now needs to be more spatially focused for the implementation to be more effective. Recognising the incidence of poverty through standard statistical data tables alone is no longer adequate in formulating planning and policy implementation. Through spatial autocorrelation analysis the pattern of distribution of poverty in space over a period of time can easily be visualised and hotspots of incidence of poverty identified. This paper attempts to show how this analysis can assist in focusing efforts to eradicate poverty in Malaysia.


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.


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