scholarly journals Autokorelasi Spasial Kemiskinan dan Luas Lahan Pertanian di Kabupaten Mesuji

2021 ◽  
Vol 5 (2) ◽  
pp. 121-134
Author(s):  
Tasha Adiza
Keyword(s):  
Spatial Autocorrelation ◽  
Agricultural Land ◽  
Research Area ◽  
Poor People ◽  
Spatial Autocorrelation Analysis ◽  
Land Area ◽  
Autocorrelation Analysis ◽  
Cluster Group ◽  
The Relationship ◽  
High Cluster

This research aims to examine the spatial analysis autocorrelation to determine the pattern of relationships or correlations between locations (observations). In the case of the percentage of poverty in Mesuji Regency and the influence of agricultural land area, this method will provide important information in analyzing the relationship between the characteristics of poverty between regions. Therefore, in this study, a spatial autocorrelation analysis was carried out on the percentage of population poverty data in 2017. The methods used were the Morans I test and the Local Indicator of Spatial Autocorrelation (LISA). The results of the spatial autocorrelation of poverty among 7 sub-districts in Mesuji Regency in 2017 are spatially clustered. Poverty grouping occurs where there are sub-districts that have almost the same observational value as sub-districts that are located close to each other or neighbors.There is one grouping based on the level of poverty, which consists of one high-high cluster, namely Panca Jaya District. low-low cluster group. While the high-low outliers and low-high-outliers categories were not found in the inter-district research area in Mesuji Regency. Variable Agricultural land area has a negative and significant effect on the percentage of poor people in Mesuji Regency in 7 Districts in a statistical model, increasing agricultural land will decrease the percentage of the poor.

scholarly journals Autokorelasi Spasial untuk Identifikasi Pola Hubungan Kemiskinan di Jawa Timur

2012 ◽  
Vol 3 (1) ◽  
pp. 217
Author(s):  
Rokhana Dwi Bekti
Keyword(s):  
Spatial Analysis ◽  
Spatial Autocorrelation ◽  
Moran’S I ◽  
Poor People ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Moran's I ◽  
Relationship Of ◽  
The Relationship

Spatial autocorrelation is a spatial analysis to determine the relationship pattern or correlation among some locations (observation). On the poverty case of East Java, this method will provide important information for analyze the relationship of poverty characteristics in each district or cities. Therefore, in this research performed spatial autocorrelation analysis on the data of East Java’s poverty. The method used is moran's I test and Local Indicator of Spatial Autocorrelation (LISA). The analysis showed that by the moran's I test, there is spatial autocorrelation found in the percentage of poor people amount in East Java, both in 2006 and 2007. While by LISA, obtained the conclusion that there is a significant grouping of district or cities.

scholarly journals ANALISIS POLA SPASIAL PERSEBARAN KAWASAN LAHAN PERTANIAN PANGAN BERKELANJUTAN DI KABUPATEN KARAWANG

2020 ◽  
Vol 12 (02) ◽  
pp. 161
Author(s):  
Ranti Marinda ◽  
Santun R.P. Sitorus ◽  
Didit Okta Pribadi
Keyword(s):  
Economic Growth ◽  
Spatial Autocorrelation ◽  
Agricultural Land ◽  
Autocorrelation Analysis ◽  
Sustainable Food ◽  
Pattern Distribution ◽  
The Relationship ◽  
Autocorrelation Method ◽  
Spatial Plan

Kabupaten Karawang merupakan contoh wilayah yang menghadapi dualisme peran sebagai hinterland 2 kawasan metropolitan (Jabodetabek dan Cekungan Bandung) sekaligus sebagai salah satu lumbung padi nasional. Penetapan rencana tata ruang dan penetapan luasan serta lokasi Lahan Pertanian Pangan Berkelanjutan (LP2B) telah dilakukan untuk merespon dualisme peran tersebut. Penetapan Kawasan LP2B menjadi menarik untuk diteliti, khususnya terkait persebaran pola spasialnya melalui metode autokorelasi spasial. Penelitian ini bertujuan untuk dapat menunjukkan pola hubungan atau korelasi antarlokasi, serta menganalisis faktor-faktor pendorong terjadinya korelasi tersebut. Analisis autokorelasi spasial yang dilakukan menghasilkan kesimpulan bahwa terdapat autokorelasi spasial bersifat positif dengan pola sebaran mengelompok (clustered), yang didefinisikan dalam 2 tipologi hubungan pengelompokan yaitu high-high dan low-low. Hubungan yang terjadi pada persebaran luasan Kawasan LP2B ini membuktikan adanya pengaruh rencana tata ruang dalam mengatur fungsi kawasan di Kabupaten Karawang. Penetapan Kawasan LP2B telah mengadaptasi perkembangan kutub-kutub pertumbuhan ekonomi non-pertanian secara keruangan, yang disesuaikan dengan penggunaan lahan saat ini.Kata kunci: autokorelasi spasial, kutub pertumbuhan ekonomi, LISA, LP2B, Moran’sKarawang Regency faces dualism as a hinterland of 2 metropolitans area (Jabodetabek and Cekungan Bandung), as well as a national rice barn. Determination of the spatial plan and determination of the extent and location of the distribution of Sustainable Food Agricultural Land (LP2B) has been carried out to respond the role dualism. The determination of LP2B area is interesting to study, especially in relation to the spatial pattern distribution through the spatial autocorrelation method. This study aims to be able to show the pattern of relationships or correlations between locations, and analyze the driving factors of correlation. Spatial autocorrelation analysis concluded that there is a positive spatial autocorrelation with clustered patterns, which are defined in 2 typologies of grouping relationships namely high-high and low-low. The relationship that occurred in the distribution of LP2B area proved the influence of spatial plan in regulating the function of area in Karawang Regency. Establishment of the LP2B Area adapted non-agricultural economic growth poles, which are adapted to current land use.Key words: spatial autocorrelation, economic growth poles, LISA, LP2B, Moran’s

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.

Sign in / Sign up

Export Citation Format

Share Document