Spatial Pattern Analysis Dan Spatial Autocorrelation Produk Domestik Regional Bruto (Pdrb) Sektor Industri Untuk Menggambarkan Perekonomian Penduduk Di Jawa Timur

Novitasari, Diah Ayu

doi:10.30736/ekbis.v13i1.113

Cited by 9 publications

(9 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…(D. A. Novitasari, 2015) The results show that the distribution pattern of the proportion of GRDP in East Java tends to be clustered (cluster), that is, clustered in certain districts. While the results of testing with Morans's I I show that there is no spatial autocorrelation in the data on the proportion of GRDP in East Java Province.…”

Section: The Results Of Morans's I Scatterplot and Local Indicator Of...mentioning

confidence: 94%

Economic Spatial Patterns and Human Development Index Districts and Cities in Five Southern Sumatera Provinces

Pratama

Ciptawaty

2022

pwk

View full text Add to dashboard Cite

The Spatial linkages between regions are an important part of a regional economy as an analysis of relations and interactions between regions. Regional economic studies tend to focus only on the independence of a region so that it does not consider the spatial effects that occur between one region and another. This study focuses on looking at spatial autocorrelation which will produce spatial patterns and spatial linkages between regions of the gross regional domestic product and the human development index, with the findings of seeing spatial interactions and patterns of similarities or differences in the formation of the two variables between regions. The research area focuses on 60 districts and cities in 5 provinces of Southern Sumatera with the 2015-2019 research year, the analysis method uses Geographic Information Systems with Geoda software, the results of the calculations will produce Moran’s I, LISA Significance and Clustered maps. The outcomes show that there has been a spatial relationship as certain autocorrelation of GRDP and HDI, the formation of the economy and human capital has a spatial relationship, results of Moran'I show that the positive value of the two variables has a group pattern and has a level of formation of GRDP and HDI with the same characteristics, moran scatterplot shows the similarity with the resulting region divided into 4 quadrants. The LISA cluster map and the LISA marking are the similarities in the findings of the GRDP and HDI results, but both variables have no findings of Low-high patterns.

show abstract

Section: The Results Of Morans's I Scatterplot and Local Indicator Of...mentioning

confidence: 94%

Economic Spatial Patterns and Human Development Index Districts and Cities in Five Southern Sumatera Provinces

Pratama

Ciptawaty

2022

pwk

View full text Add to dashboard Cite

show abstract

“…Moran's Index akan menginformasikan autokorelasi spasial positif jika unit tetangga memiliki nilai yang mirip dengan unit sasarn. Autokorelasi spasial akan negatif jika unit tetangga memiliki nilai yang berbeda 9 . Statistik I Moran untuk autokorelasi spasial didefinisikan sebagai:…”

Section: ) Lagrange Multiplier Test (Lm)unclassified

Analisa Faktor-Faktor Yang Mempengaruhi Prevalensi Ketidakcukupan Konsumsi Pangan Di Sumatera Barat Menggunakan Geoda

Mardison

2020

JURNAL RISET GIZI

View full text Add to dashboard Cite

Background: Daily energy requirements must be fulfilled for a healthy and active life. Prevalence of Undernourishment (PoU) is an indicator which are developed for goal. A number of factors were expected affecting PoU were tested in this study. We also tested the possibility of these factors could have a spatial correlationObjectives: The study produced a map of the spread of PoU and the factors which were influenced them (independent variables). The study need to yield the best estimation model.Method : This study produce spread map of all variables, for visible purposes, and the classical regression were made. All OLS assumption will be test. Then, SAR and SEM models will be made. Finally, the best model for this study will be chosen. GeoDa software helps all steps.Results : This study concludes PoU has positive auto correlation and growth has a negative autocorrelation. The best model which produced is the Spatial Error Model (SEM). The slow trend of PoU in West Sumatera Barat is strongly suspected due to the habit of West Sumatera residents in consuming high-calorie foods such as rendang, curry and coconut milk.

show abstract

“…Analisis yang digunakan pada penelitian ini yaitu Spatial Autocorelation Analysis dengan menggunakan metode Moran's I. Spatial Autocorelation Analysis digunakan untuk mengetahui apakah terdapat hubungan antar titik dan arah hubungannya (postif atau negatif) (15) . Moran's I dapat membantu untuk mengetahui tingkat kerentanan suatu wilayah terhadap kejadian penyakit tertentu, seperti Demam Berdarah Dengue.…”

Section: Materi Dan Metodeunclassified

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

Syamsir¹,

Daramusseng²,

Rudiman³

2020

Jurnal Kesehatan Lingkungan Indonesia

View full text Add to dashboard Cite

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.

show abstract

Spatial Pattern Analysis Dan Spatial Autocorrelation Produk Domestik Regional Bruto (Pdrb) Sektor Industri Untuk Menggambarkan Perekonomian Penduduk Di Jawa Timur

Cited by 9 publications

References 0 publications

Economic Spatial Patterns and Human Development Index Districts and Cities in Five Southern Sumatera Provinces

Economic Spatial Patterns and Human Development Index Districts and Cities in Five Southern Sumatera Provinces

Analisa Faktor-Faktor Yang Mempengaruhi Prevalensi Ketidakcukupan Konsumsi Pangan Di Sumatera Barat Menggunakan Geoda

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

Contact Info

Product

Resources

About