scholarly journals Pemetaan dan Analisis Faktor yang Mempengaruhi Persentase Usaha E-Commerce di Indonesia

2021 ◽  
Vol 9 (2) ◽  
pp. 130-135
Author(s):  
Wahyuni Windasari ◽  
Tuti Zakiyah
Keyword(s):  
Weighted Regression ◽  
Geographical Weighted Regression

Perkembangan teknologi dan informasi membawa dampak pada pertumbuhan E-Commerce di Indonesia. Sebagai pasar E-Commerce besar di ASEAN, usaha E-Commerce di Indonesia masih terpusat di Pulau Jawa dan Sumatera. Hal ini mengindikasikan masih belum meratanya usaha E-Commerce di Indonesia. Pada penelitian ini dibahas terkait ada tidaknya pengaruh faktor spasial atau kewilayahan pada persentase usaha E-Commerce di Indonesia. Metode yang digunakan adalah Geographical Weighted Regression (GWR). Hasil analisis mengklasifikasikan 34 provinsi di Indonesia menjadi lima kelompok berdasarkan model signifikan yaitu (1) Enam provinsi di Indonesia signifikan terhadap pertumbuhan ekonomi, (2) Sembilan provinsi signifikan terhadap keahlian di bidang TIK, (3) Dua provinsi signifikan terhadap keahlian di bidang TIK dan ketersediaan BTS, (4) Tiga provinsi signifikan terhadap keahlian di bidang TIK dan pertumbuhan ekonomi, (5) Empat belas provinsi di Indonesia tidak signifikan terhadap variabel prediktor yang digunakan pada penelitian ini.

scholarly journals Dynamical spatial model of heavy metals in Kendari bay using Bayesian geographical weighted regression

2021 ◽  
Vol 1899 (1) ◽  
pp. 012107
Author(s):  
Mukhsar ◽  
Alrum Armid ◽  
Fahmiati ◽  
Ryuichi Shinjo ◽  
Dewi Rukmayanti Rustan ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Spatial Model ◽  
Weighted Regression ◽  
Geographical Weighted Regression

scholarly journals KETIMPANGAN PEMBANGUNAN WILAYAH DI PROVINSI KALIMANTAN TENGAH (KAJIAN PADA KABUPATEN KOTAWARINGIN TIMUR DAN PEMEKARANNYA)

2019 ◽  
Vol 21 (2) ◽  
pp. 253
Author(s):  
Luthfian Riza Sanjaya ◽  
Endriatmo Soetarto ◽  
Andrea Emma Pravitasari
Keyword(s):  
Regional Development ◽  
Policy Development ◽  
Research Area ◽  
Regional Inequality ◽  
Weighted Regression ◽  
Dominant Factor ◽  
Central Kalimantan ◽  
Geographical Weighted Regression ◽  
Development Plans ◽  
Developing Area

Inequality of regional development is a common problem faced by all provinces in Indonesia, not least the Province of Central Kalimantan. Regional inequality stems from factors that should be minimized. The aims of this research were  1)to tanalyze the trend of regional development inequality in Central Kalimantan Province and the factors that influence it 2) to analyze the hierarchy of regional development in Kotawaringin Timur (Kotim) Regency and developing area  3) to analyze regional development inequality and influenced factors using Geographical Weighted Regression (GWR).4) to prepare regional directives and policy development plans. The method used are Williamson's Index, Multiple Regression with Unbalanced Panel, weighted Skalogram, and GWR. The results of this study indicated  that the level of inequality in Central Kalimantan Province tends to decrease with RHGU as the most dominant factor. The regional development of the area at the sub-district level showed spatial patterns which indicated an imbalance between the northern and southern regions. The GWR resulted also shows that RHGU is still the dominant factor with the same influence throughout the research area. Thus, the urgency of agrarian reform in plantation areas is important to be implemented immediately.

scholarly journals Spatial distribution and geographical heterogeneity factors associated with poor consumption of foods rich in vitamin A among children age 6–23 months in Ethiopia: Geographical weighted regression analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252639
Author(s):  
Sofonyas Abebaw Tiruneh ◽  
Dawit Tefera Fentie ◽  
Seblewongel Tigabu Yigizaw ◽  
Asnakew Asmamaw Abebe ◽  
Kassahun Alemu Gelaye
Keyword(s):  
Spatial Distribution ◽  
Vitamin A ◽  
Weighted Regression ◽  
Rural Residence ◽  
Wealth Status ◽  
Geographical Weighted Regression ◽  
Dietary Consumption ◽  
Tropical Area ◽  
Weighted Regression Analysis ◽  
Geographical Weighted Regression Analysis

Introduction Vitamin A deficiency is a major public health problem in poor societies. Dietary consumption of foods rich in vitamin A was low in Ethiopia. This study aimed to assess the spatial distribution and spatial determinants of dietary consumption of foods rich in vitamin A among children aged 6–23 months in Ethiopia. Methods Ethiopian 2016 demographic and health survey dataset using a total of 3055 children were used to conduct this study. The data were cleaned and weighed by STATA version 14.1 software and Microsoft Excel. Children who consumed foods rich in vitamin A (Egg, Meat, Vegetables, Green leafy vegetables, Fruits, Organ meat, and Fish) at least one food item in the last 24 hours were declared as good consumption. The Bernoulli model was fitted using Kuldorff’s SaTScan version 9.6 software. ArcGIS version 10.7 software was used to visualize spatial distributions for poor consumption of foods rich in vitamin A. Geographical weighted regression analysis was employed using MGWR version 2.0 software. A P-value of less than 0.05 was used to declare statistically significant predictors spatially. Results Overall, 62% (95% CI: 60.56–64.00) of children aged 6–23 months had poor consumption of foods rich in vitamin A in Ethiopia. Poor consumption of foods rich in vitamin A highly clustered in Afar, eastern Tigray, southeast Amhara, and the eastern Somali region of Ethiopia. Spatial scan statistics identified 142 primary spatial clusters located in Afar, the eastern part of Tigray, most of Amhara and some part of the Oromia Regional State of Ethiopia. Children living in the primary cluster were 46% more likely vulnerable to poor consumption of foods rich in vitamin A than those living outside the window (RR = 1.46, LLR = 83.78, P < 0.001). Poor wealth status of the household, rural residence and living tropical area of Ethiopia were spatially significant predictors. Conclusion Overall, the consumption of foods rich in vitamin A was low and spatially non-random in Ethiopia. Poor wealth status of the household, rural residence and living tropical area were spatially significant predictors for the consumption of foods rich in vitamin A in Ethiopia. Policymakers and health planners should intervene in nutrition intervention at the identified hot spot areas to reduce the poor consumption of foods rich in vitamin A among children aged 6–23 months.

scholarly journals Spatial modeling for COVID-19 analysis: An Indian case study

2020 ◽  
Vol 8 (S1) ◽  
pp. 19-32
Author(s):  
Iyyanki M ◽  
Prisilla J ◽  
Kandle S
Keyword(s):  
Spatial Distribution ◽  
Linear Regression ◽  
Spatial Data ◽  
Spatial Modeling ◽  
Spatial Models ◽  
Least Square ◽  
Weighted Regression ◽  
Ordinary Least Square ◽  
Geographical Weighted Regression

The coronavirus disease 2019 (COVID-19) outbreak in India from January 31, 2020, onwards to June 15, 2020, has reached confirmed cases over 3,32,424 that are being reported. The aim of this study is to predict and explore the spatial distribution of COVID-19 data of India using three models – geographical weighted regression (GWR), generalized linear regression (GLR), and ordinary least square (OLS). In this paper, the swift rise in COVID-19 cases is experiential after the lockdown period. This is explored using ArcGIS on the confirmed case of June 15, 2020, as the response with the explanatory of COVID-19 cases, i.e March 15, 2020, April 7, April 12, May 12, and June 1, 2020. The confirmed cases of the dataset is classified into three cases ie. case-1: June 15, 2020, vs March 15 and April 7, 2020; case-2: June 15, 2020 vs April 12, May 12 and June 1, 2020; and case-3: June 15, 2020 Vs all dates mentioned in discussion Hence, the prediction using GWR gave the much closer values for June 16, 2020. AICc of GWR (618.9038) was found to have the minimum value over GLR and OLS models. The day-wise increase and samples tested per day in twelve different states is analyzed using STATA. The number of testing varies with states to states, depending on the population and testing labs available. The percentage for each slope is achieved as m1 (-5.714 %), m2 (39.393%), m3 (6.521%) and m4 (46.938%). Keywords: COVID-19; GIS; spatial data; spatial models; testing samples

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