scholarly journals Autocorrelation of Spatial Based Dengue Hemorrhagic Fever Cases in Air Putih Area, Samarinda City

2020 ◽  
Vol 12 (2) ◽  
pp. 78
Author(s):  
Syamsir Syamsir ◽  
Dwi Murdaningsih Pangestuty
Keyword(s):  
Spatial Autocorrelation ◽  
Health Center ◽  
Dengue Hemorrhagic Fever ◽  
Control Program ◽  
Hemorrhagic Fever ◽  
Moran’S I ◽  
Autocorrelation Analysis ◽  
Index Method ◽  
Moran's I ◽  
The Impact

Introduction: Dengue Hemorrhagic Fever (DHF) is the disease that spread quickly in tropical and subtropical regions. DHF can spread quickly because the dengue virus is transmitted through the Aedes aegypti and Aedes albopictus into the human body. One of the provinces that felt the impact of the dengue outbreak was East Kalimantan, especially Samarinda City. Efforts to prevent dengue have been attempted by health center officials in Samarinda City. The cause has not yet been effective in controlling DHF programs in Samarinda City because there is no mapping of DHF vulnerable areas. This study aims to map the pattern of DHF distribution in the working area of the health center to maximize the implementation of the DHF control program. Methods: The population in this study were all DHF sufferers registered at the Air Putih Health Center in 2018. Withdrawal samples using total sampling techniques. The analysis used in this study is spatial autocorrelation analysis by Moran’s I. The Moran Index method is used to determine the autocorrelation of the distribution of DHF cases. Result and Discussion: The results of the autocorrelation analysis showed a Z score <-Z α/2, meaning Ho was rejected. This shows that there is spatial autocorrelation in the distribution of DHF in the Health Center. Based on the Moran’s I value (Moran’s I = -0.045850) which has a negative value indicates that the distribution of DHF in the working area of the Health Center tends to spread or dispersed. Conclusion: This study concludes that the more cases of DHF in a densely populated area, the greater the chance of spatial autocorrelation. The closeness between DHF cases can form spatial autocorrelation with the dispersed category.

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 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 METODE SPATIAL DURBIN MODEL UNTUK ANALISIS DEMAM BERDARAH DENGUE DI KABUPATEN BANTUL

2021 ◽  
Vol 3 (2) ◽  
pp. 1
Author(s):  
Marthin Luter Laia ◽  
Rahmat Deswanto ◽  
Erma Shofi Utami ◽  
Rokhana Dwi Bekti
Keyword(s):  
Aedes Albopictus ◽  
Dengue Hemorrhagic Fever ◽  
Spatial Model ◽  
Hemorrhagic Fever ◽  
Medical Personnel ◽  
Health Facilities ◽  
Moran’S I ◽  
Spatial Durbin Model ◽  
Moran's I ◽  
Spatial Autoregressive

Dengue Hemorrhagic Fever (DHF) is an infectious disease caused by the dengue virus which is transmitted through the bite of the Aedes aegepty and Aedes albopictus mosquitoes which are widespread in homes and public places throughout the territory of Indonesia. The high number of DHF cases in Bantul Regency, Indonesia is an indication that eradication of Aedes aegepty mosquitoes and Aedes albopictus mosquitoes has not succeeded in the Bantul Regency. Spatial Regression is an analysis that evaluates the relationship between one variable with several other variables by providing spatial effects in several locations that are the center of observation. Three type of models are Spatial Autoregressive Model (SAR), Spatial Error Model (SEM), and Spatial Durbin Model (SDM). This study uses secondary data in 2017 in Bantul Regency, Special Region of Yogyakarta, Indonesia. The dependent variable is DHF cases and the independent variables are medical personnel and health facilities in each sub-district. The spatial model used is SDM. Based on Moran’s I test, there was a spatial autocorrelation about DHF among sub-district, so the spatial model can be used. The durbin spatial model gives the result that all estimation parameters in SDM model have  P value less than α = 5%, so that medical personnel and health facilities significantly affect dengue cases in Bantul Regency. Keywords: dengue hemorrhagic fever, moran’s I test, spatial durbin model. 

scholarly journals Spatial Analysis of Dengue Hemorrhagic Fever based on Influencing Factors in Jombang, 2014–2018

2021 ◽  
Vol 9 (1) ◽  
pp. 79
Author(s):  
Retno Tri Hastuti ◽  
Lucia Yovita Hendrati
Keyword(s):  
Multivariate Analysis ◽  
Population Density ◽  
Dengue Hemorrhagic Fever ◽  
Healthy Lifestyle ◽  
Hemorrhagic Fever ◽  
Moran’S I ◽  
Bivariate Analysis ◽  
Analysis Tool ◽  
Moran's I ◽  
Healthy Homes

Background: Jombang District is an endemic area of dengue hemorrhagic fever (DHF). Purpose: The aim of this study was to spatially analyze various factors simultaneously (multivariate analysis) in relation to the incidence of DHF in Jombang District during the period 2014–2018. The factors studied were population density, larvae free index, rainfall, coverage of healthy homes, and healthy lifestyle coverage. Methods: The research was conducted as an observational study with an ecology research design. The data were secondary data from the Health Office and Statistic Central Bureau of Jombang District. The population consisted of 21 sub-districts in Jombang District in 2014–2018. The sample used the total population. The data analysis tool used in this study was GeoDa regression Moran's I software. Results: The bivariate analysis showed that there was a correlation between larvae free index (p = 0.04), healthy lifestyle coverage (p = 0.02), rainfall intensity (p = 0.20), population density (p = 0.07), and coverage of healthy houses (p = 0.22) with DHF incidence. According to Moran's I for spatial dependence (multivariate analysis), showed that there was a correlation between all the variables and DHF (p = 0.03). Conclusions: The variables of larvae free index and healthy lifestyle coverage related to the Incidence Rate (IR) of DHF cases. There was no correlation between IR and variable population density, rainfall, or coverage of healthy homes. Various spatial factors are simultaneously related to IR, even though only two variables are shown to be related to IR in the bivariate analysis.

Analisis Perubahan Tata Guna Lahan di Kabupaten Bantul Menggunakan Metode Global Moran’s I

2017 ◽  
Vol 8 (4) ◽  
Author(s):  
Matheus Supriyanto Rumetna ◽  
Eko Sediyono ◽  
Kristoko Dwi Hartomo
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Spatial Autocorrelation ◽  
Land Use Changes ◽  
Geographical Information ◽  
Moran’S I ◽  
Complete Spatial Randomness ◽  
Moran's I ◽  
Global Moran’S I ◽  
Spatial Randomness

Abstract. Bantul Regency is a part of Yogyakarta Special Province Province which experienced land use changes. This research aims to assess the changes of shape and level of land use, to analyze the pattern of land use changes, and to find the appropriateness of RTRW land use in Bantul District in 2011-2015. Analytical methods are employed including Geoprocessing techniques and analysis of patterns of distribution of land use changes with Spatial Autocorrelation (Global Moran's I). The results of this study of land use in 2011, there are thirty one classifications, while in 2015 there are thirty four classifications. The pattern of distribution of land use change shows that land use change in 2011-2015 has a Complete Spatial Randomness pattern. Land use suitability with the direction of area function at RTRW is 24030,406 Ha (46,995406%) and incompatibility of 27103,115 Ha or equal to 53,004593% of the total area of Bantul Regency.Keywords: Geographical Information System, Land Use, Geoprocessing, Global Moran's I, Bantul Regency. Abstrak. Analisis Perubahan Tata Guna Lahan di Kabupaten Bantul Menggunakan Metode Global Moran’s I. Kabupaten Bantul merupakan bagian dari Provinsi Daerah Istimewa Yogyakarta yang mengalami perubahan tata guna lahan. Penelitian ini bertujuan untuk mengkaji perubahan bentuk dan luas penggunaan lahan, menganalisis pola sebaran perubahan tata guna lahan, serta kesesuaian tata guna lahan terhadap RTRW yang terjadi di Kabupaten Bantul pada tahun 2011-2015. Metode analisis yang digunakan antara lain teknik Geoprocessing serta analisis pola sebaran perubahan tata guna lahan dengan Spatial Autocorrelation (Global Moran’s I). Hasil dari penelitian ini adalah penggunaan tanah pada tahun 2011, terdapat tiga puluh satu klasifikasi, sedangkan pada tahun 2015 terdapat tiga puluh empat klasifikasi. Pola sebaran perubahan tata guna lahan menunjukkan bahwa perubahan tata guna lahan tahun 2011-2015 memiliki pola Complete Spatial Randomness. Kesesuaian tata guna lahan dengan arahan fungsi kawasan pada RTRW adalah seluas 24030,406 Ha atau mencapai 46,995406 % dan ketidaksesuaian seluas 27103,115 Ha atau sebesar 53,004593 % dari total luas wilayah Kabupaten Bantul. Kata Kunci: Sistem Informasi Georafis, tata guna lahan, Geoprocessing, Global Moran’s I, Kabupaten Bantul.

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.

scholarly journals Approaches to measuring the relevance of geographical dimension of societa inequalities

Geografie ◽  
2009 ◽  
Vol 114 (1) ◽  
pp. 52-65 ◽  
Author(s):  
Pavlína Netrdová ◽  
Vojtěch Nosek
Keyword(s):  
Spatial Autocorrelation ◽  
Demographic Data ◽  
Moran’S I ◽  
Theil Index ◽  
Moran's I ◽  
Similar Information ◽  
Index Decomposition ◽  
Geographical Dimension

The article focuses on geographical dimension of societal inequalities, especially on approaches to its analysing. Two distinct methods of analysing the relative geographical inequality are utilized: Theil index decomposition and spatial autocorrelation measured by Moran’s I coefficient. Both employed methods should bring, in theory, very similar information. This fact is explored empirically by comparing both methods and by their application on detailed economic, social and demographic data on municipalities in Czechia. Conclusions, predominantly of epistemological nature, are intended to assess advantages and limitations of individual methods and their possible application in practice.

scholarly journals Financing of Dengue Hemorrhagic Fever Control Program in Banda Aceh City

2018 ◽  
Vol 7 (1) ◽  
Author(s):  
Elli, Nufara ◽  
Ali a , Ghufron Mukti ◽  
Tri Baskoro T. Satoto Mail
Keyword(s):  
Dengue Hemorrhagic Fever ◽  
Control Program ◽  
Hemorrhagic Fever ◽  
Banda Aceh
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