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 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 Autokorelasi Spasial Demam Berdarah Dengue di Kecamatan Samarinda Utara, Kota Samarinda

2020 ◽  
Vol 19 (2) ◽  
pp. 119-126
Author(s):  
Syamsir Syamsir ◽  
Andi Daramusseng ◽  
Rudiman Rudiman
Keyword(s):  
Public Health ◽  
Spatial Autocorrelation ◽  
Health Center ◽  
Dengue Hemorrhagic Fever ◽  
Hemorrhagic Fever ◽  
Cluster Sampling ◽  
Spatial Autocorrelation Analysis ◽  
Z Score ◽  
Autocorrelation Analysis ◽  
Moran's I

Latar belakang: Demam Berdarah Dengue (DBD) masih menjadi masalah kesehatan masyarakat. Indonesia menjadi salah satu negara yang setiap tahunnya ditemukan kasus DBD. Program pengendalian DBD masih kurang maksimal karena puskesmas belum mampu memetakan wilayah rentan DBD. Penelitian ini bertujuan untuk mengetahui pola sebaran DBD di Kecamatan Samarinda Utara dengan menggunakan autokorelasi spasial.Metode: Penelitian ini dilaksanakan di kelurahan yang berada pada wilayah kerja Puskesmas Lempake, Kecamatan Samarinda Utara. Sampel penelitian dipilih berdasarkan metode cluster sampling. Berdasarkan kriteria jumlah kasus tertinggi maka kelurahan di Kecamatan Samarinda Utara yang representatif untuk dijadikan cluster pada penelitian ini yaitu kelurahan yang berada pada wilayah kerja Puskesmas Lempake. Analisis yang digunakan pada penelitian ini yaitu Spatial Autocorrelation Analysis dengan menggunakan metode Moran’s I. Spatial Autocorrelation Analysis digunakan untuk mengetahui apakah terdapat hubungan antar titik dan arah hubungannya (postif atau negatif).Hasil: Nilai Z-score atau Z hitung = 3,651181 dengan nilai kritis (Z α/2) sebesar 2,58. Ini menunjukkan bahwa Z-score > Z α/2 (3,6511 > 2,58) sehingga Ho ditolak. Terdapat autokorelasi spasial pada sebaran kasus DBD di wilayah kerja Puskesmas Lempake. Sebaran kasus DBD di wilayah kerja Puskesmas Lempake termasuk kategori clustered atau berkelompok pada lokasi tertentu. Moran’s Index (I) = 0,124420 artinya I > 0. Ini menunjukkan bahwa pola sebaran DBD di wilayah kerja Puskesmaas Lempake merupakan autokorelasi positif.    Simpulan: Pola sebaran kasus DBD di Kecamatan Samarinda Utara yaitu clustered. Autokorelasi spasial yang dihasilkan yaitu autokorelasi positif.  ABSTRACTTitle: Spatial Autocorrelation of Dengue Hemorrhagic Fever  in North Samarinda district, Samarinda CityBackground: Dengue Hemorrhagic Fever (DHF) is still a public health problem. Indonesia is one of the countries where DHF cases are found every year. The DHF control program is still less than optimal because the public health center has not been able to map the DHF vulnerable areas. This study aims to determine the pattern of DHF distribution in the District of North Samarinda by using spatial autocorrelation.Method: This research was conducted in a village located in the working area of the Lempake Health Center, Samarinda Utara district. The research sample was chosen based on the cluster sampling method. Based on the criteria for the highest number of cases, the representative village to be clustered in this study are the village within the working area of the Lempake Health Center. The analysis used in this study is spatial autocorrelation nalysis using the Moran’s I. Spatial autocorrelation Analysis method is used to determine whether there is a relationship between the point and direction of the relationship (positive or negative).Result: Z-score or Z count = 3.651181 with a critical value (Z α / 2) of 2.58. This shows that Z-score> Z α / 2 (3.6511> 2.58) so that Ho is rejected. There is a spatial autocorrelation in the distribution of dengue cases in the working area of the Lempake Health Center. The distribution of dengue cases in the working area of Lempake Health Center is classified as clustered or grouped in certain locations. Moran’s Index (I) = 0.124420 means I> 0. This shows that the pattern of DHF distribution in the work area of Lempake Health Center is a positive autocorrelation.Conclusion: The pattern of distribution of dengue cases in the District of North Samarinda is clustered. The resulting spatial autocorrelation is positive autocorrelation. 

scholarly journals Spatial Autocorrelation Analysis of Tuberculosis Cases (2016-2018) In Kebumen

2019 ◽  
Author(s):  
Kurniawan Adi Pradana ◽  
Purnama Budi Santosa
Keyword(s):  
Spatial Autocorrelation ◽  
Causes Of Death ◽  
Spatial Association ◽  
Geographical Location ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Spread Pattern ◽  
Tuberculosis Disease ◽  
Case Number ◽  
Autocorrelation Method

Tuberculosis (TB) is one of the tenth highest causes of death in Indonesia and even worldwide. Tuberculosis is an infectious disease caused by an infection of Mycobacterium tuberculosis bacterium. Kebumen is one of the districts with high tuberculosis cases and tends to increase every year. Based on the high case number, it is necessary to start research that examines patterns of spread. Spatial analysis is a very useful tool to evaluate the spreading pattern of the tuberculosis disease according to its geographical location. The study aimed to spatially analyze tuberculosis spread pattern from 2016 to 2018 using the spatial autocorrelation method through Moran Index and Local Indicator of Spatial Association (LISA). The study showed that the spatial autocorrelation in the spreading patterns of tuberculosis occurred in Kebumen and had a clustered pattern because of Moran Index is positive. The results of the LISA analysis in the High-High quadrant showed that the high tuberculosis cases correlated with areas that also had high tuberculosis cases. Sixteen villages were included in the High-High quadrant.

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 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.

Spatial Autocorrelation in the Study of Neighbourhoods towards Smart Cities: Empirical Evidence from Kerman, Iran

2012 ◽  
Vol 9 (2) ◽  
pp. 1
Author(s):  
Asra Hosseini
Keyword(s):  
Spatial Autocorrelation ◽  
Urban Areas ◽  
Smart Cities ◽  
Moran’S I ◽  
Moran's I ◽  
Urban Planner ◽  
Universal Feature ◽  
Autocorrelation Method ◽  
Selection Of

From earliest cities to the present, spatial division into residential zones and neighbourhoods is the universal feature of urban areas. This study explored issue of measuring neighbourhoods through spatial autocorrelation method based on Moran's I index in respect of achieving to best neighbourhoods' model for forming cities smarter. The research carried out by selection of 35 neighbourhoods only within central part of traditional city of Kerman in Iran. The results illustrate, 75% of neighbourhoods' area in the inner city of Kerman had clustered pattern, and it shows reduction in Moran's index is associated with disproportional distribution of density and increasing in Moran's I and Z-score have monotonic relation with more dense areas and clustered pattern. It may be more efficient for urban planner to focus on spatial autocorrelation to foster neighbourhood cohesion rather than emphasis on suburban area. It is recommended characteristics of historic neighbourhoods can be successfully linked to redevelopment plans toward making city smarter, and also people's quality of life can be related to the way that neighbourhoods' patterns are defined. 

Spatial Autocorrelation in the Study of Neighbourhoods towards Smart Cities: Empirical Evidence from Kerman, Iran

2012 ◽  
Vol 9 (2) ◽  
pp. 1
Author(s):  
Asra Hosseini
Keyword(s):  
Spatial Autocorrelation ◽  
Smart Cities ◽  
Moran’S I ◽  
Moran's I ◽  
Urban Planner ◽  
Universal Feature ◽  
Autocorrelation Method ◽  
Selection Of ◽  
Monotonic Relation

From earliest cities to the present, spatial division into residential zones and neighbourhoods is the universal feature ofurban areas. This study explored issue ofmeasuring neighbourhoods through spatial autocorrelation method based on Moran's I index in respect of achieving to best neighbourhoods' model for forming cities smarter. The research carried out by selection of 35 neighbourhoods only within central part of traditional city of Kerman in Iran. The results illustrate, 75% ofneighbourhoods, area in the inner city of Kerman had clustered pattern, and it shows reduction in Moran's index is associated with disproportional distribution of density and increasing in Moran's I and Z-score have monotonic relation with more dense areas and clustered pattern. It may be more efficient for urban planner to focus on spatial autocorrelation to foster neighbourhood cohesion rather than emphasis on suburban area. It is recommended characteristics of historic neighbourhoods can be successfully linked to redevelopment plans toward making city smarter, and also people's quality of life can be related to the way that neighbourhoods' patterns are defined.

Sign in / Sign up

Export Citation Format

Share Document