Autokorelasi Spasial Untuk Analisis Pola Pengawasan Kawasan Lindung  Di Kota Ambon Maluku

Simatauw, Anna; Sediyono, Eko; Prasetyo, Sri Yulianto Joko

doi:10.34148/teknika.v8i1.144

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…Hal tersebut berbeda dengan periode julidesember memiliki nilai I < E[I] yang menyatakan bahwa membentuk pola dispersi. 28,29 Hasil Autokorelasi Spasial menggunakan Differential Moran's I bahwa p-Value > 0,05 sehingga tidak terjadi autokorelasi spasiotemporal global dan nilai I < E[I] yang menyatakan tidak terbentuk pola kluster. 30 Analisis selanjutnya dengan mengetahui autokorelasi spasial secara lokal pada setiap periode dengan nilai LISA yang disajikan pada gambar 3 berikut.…”

Section: Gambar 2 Klasifikasi Wilayah Dengan Kategori Natural Breaksunclassified

Autokorelasi Spasial ISPA pada Balita di Wilayah Kerja Puskesmas Mentaya Seberang Kabupaten Kotawaringin Timur

Rifaldi,

Cahyani,

Rahmah

et al. 2024

afiasi

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Infeksi Saluran Pernapasan Akut (ISPA) merupakan salah satu penyakit menular yang memicu kematian pada balita di Indonesia. Prevalensi ISPA di wilayah kerja Puskesmas Mentaya Seberang berada di ambang batas prevalensi Kabupaten Kotawaringin Timur. Pencegahan ISPA dapat dikaji melalui epidemiologi spasial menggunakan analisis autokorelasi spasial. Tujuan penelitian ini adalah menganalisis autokorelasi spasial ISPA pada balita di wilayah kerja Puskesmas Mentaya Seberang Kabupaten Kotawaringin Timur. Metode penelitian ini merupakan rancangan studi ekologi menggunakan metode spasial seperti Moran’s I, Differential Moran’s I, Local Indicator of Spasial Autocorrelation (LISA) dan Getis Ord Gi* melalui aplikasi GeoDa. Unit analisis spasial yang digunakan pada tingkat rukun tetangga (RT). Hasil penelitian menunjukkan tidak terdapat autokorelasi spasial global ISPA pada balita, Adanya autokorelasi spasial lokal dengan 2 wilayah High-Low pada periode januari – juni sedangkan pada periode juli – desember terdapat 1 wilayah High-Low dan 1 wilayah Low-High. Tidak ditemukan hotspot dan coldspot ISPA pada balita di wilayah kerja Puskesmas Mentaya Seberang.

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Section: Gambar 2 Klasifikasi Wilayah Dengan Kategori Natural Breaksunclassified

Autokorelasi Spasial ISPA pada Balita di Wilayah Kerja Puskesmas Mentaya Seberang Kabupaten Kotawaringin Timur

Rifaldi,

Cahyani,

Rahmah

et al. 2024

afiasi

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“…Metode spasial yang bisa digunakan adalah metode autokorelasi spasial baik global maupun lokal. Autokorelasi spasial digunakan untuk mengetahui korelasi antar lokasi apakah saling berkaitan atau tidak [6]. Uji autokorelasi spasial global dapat mendeteksi autokorelasi secara global, sedangkan autokorelasi lokal dapat mendeteksi autokorelasi secara lokal dimana masing-masing lokasi dapat terlihat jelas gambaran penyebaran datanya.…”

Section: Pendahuluanunclassified

Analisis Autokorelasi Spasial Global dan Lokal Pada Data Kemiskinan Provinsi Bali

Lestari

Brata²,

Anhar³

et al. 2023

Jambura J. Math

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Poverty is a crucial problem that often occurs in Indonesia, including in the province of Bali. The poverty rate rate in Bali has increased significantly over the last 3 years, so further analysis is needed so that this poverty rate can be reduced. The pattern of poverty between regions is different because it depends on the geographical and socio-cultural conditions in each area. The effect of location on poverty cases can be identified by global and local spatial autocorrelation methods. This study aimed to analyze the spatial autocorrelation of poverty data in Bali Province using the global autocorrelation test with the Moran’s and Geary’s C indices as well as the local spatial autocorrelation test with the Local Indicator of Spatial Association (LISA) and Getis-Ord G to obtain an overview of the spatial distribution of poverty data. Based on the global autocorrelation test, it is concluded that using Moran’s index there is a negative spatial autocorrelation in the 2020-2022 data for a=10%. Similar results were also obtained when using the Geary’s C test. In the local autocorrelation test using LISA, it was found that districts had negative spatial autocorrelation, namely in 2020 Buleleng Regency (high-low) and Klungkung (low-low), for 2021 there is Buleleng Regency (high-low) and Jembrana (low-low), while for 2022 only Buleleng Regency (high-low) has negative spatial autocorrelation. For local autocorrelation testing with the Getis-Ord G test, it was found that there were no districts/cities that showed spatial grouping or that there was no spatial autocorrelation locally.

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“…Positive spatial autocorrelation and negative spatial autocorrelation are two potential outputs of autocorrelated data. Positive spatial autocorrelation suggests that close places with comparable values cluster together, whereas negative spatial autocorrelation implies that nearby locations with differing values disperse [8].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Mapping on Bayesian Spatio-Temporal CAR Localized for Poverty in Sulawesi Island, Indonesia

Sukarna,

Kurnia,

Sadik

2023

Advances in Computer Science Research

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Spatial modeling can identify locations with high or low risk of disease effect, but it cannot explain the temporal shift in risk, which may be as relevant or more important. As a result, mapping modeling should consider both geographical and temporal components. Some research has utilized Bayesian Spatio-Temporal Conditional Autoregressive (BST CAR) models. However, no research has been conducted on using BST CAR Localized model for poverty on Sulawesi Island, Indonesia. This research aims to find the best BST CAR localized model for poverty in 81 regencies/cities on Sulawesi Island. The BST CAR localized model with different number of clusters G=2, G=3, and G=5 was used to model the relative risk (RR) of poverty in each of 81 regencies and cities. The results suggest that BST CAR Localized with G=2 is the best model for modeling the relative risk of poverty on Sulawesi Island. Variables such as Gender Development Index (IPG), Women's Income Contribution (SPP), Adjusted Per Capita Expenditure (PKD), and Human Development Index (IPM) have a significant impact on poverty. SPP has a positive influence on poverty, while the other three components have a negative impact.

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Autokorelasi Spasial Untuk Analisis Pola Pengawasan Kawasan Lindung Di Kota Ambon Maluku

Cited by 4 publications

References 5 publications

Autokorelasi Spasial ISPA pada Balita di Wilayah Kerja Puskesmas Mentaya Seberang Kabupaten Kotawaringin Timur

Autokorelasi Spasial ISPA pada Balita di Wilayah Kerja Puskesmas Mentaya Seberang Kabupaten Kotawaringin Timur

Analisis Autokorelasi Spasial Global dan Lokal Pada Data Kemiskinan Provinsi Bali

Modeling and Mapping on Bayesian Spatio-Temporal CAR Localized for Poverty in Sulawesi Island, Indonesia

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