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

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.

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

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.

Analisis Pengaruh Alokasi DAK Masing-Masing Bidang Terhadap Tingkat Kemiskinan di Kabupaten Pandeglang dan Kabupaten Lebak

Fiscal decentralization is one of the government’s strategies to improve people’s welfare. The fiscal decentralization policy instrument that can directly affect the quality of local government spending is The Specific Allocation Fund (DAK). The DAK management in the financial aspect has a few problems, nonoptimal regional government performance and mismatched allocation and government needs. Proposal based The Specific Allocation Fund (DAK) hopefully could adjust the development priorities determined by regional conditions, government needs, and undeveloped villages with high-level poverty. Pandeglang and Lebak Regency are the region with the highest poverty level in Banten Province. This study aims to analyze the DAK effect of each sector on poverty in Pandeglang and Lebak Regency. The analysis used Geographical Weighted Regression (GWR) with DAK data for each field in 2018 and poverty data in 2019. The result showed that DAK variables in education, health, and agriculture tended not to reduce poverty rates. The DAK variable in the housing and settlement sector, the marine and fisheries sector, the tourism sector, and the market sector tended to reduce poverty. At the same time, the DAK variable in Road, sanitation, and village funds tended to reduce poverty levels in most districts. Based on the study, the poverty alleviation program in each district is adjusted to each of the DAK sectors that are influential. However, the result indicated that the adjustment of DAK sectors influenced the poverty alleviation program in every district in Pandeglang and Lebak Regency.

Do Air Pollutants as Well as Meteorological Factors Impact Corona Virus Disease 2019 (COVID-19)? Evidence From China Based on the Geographical Perspective

The COVID-19 is still a huge challenge that seriously threatens public health globally. Previous studies focused on the influence of air pollutants and probable meteorological parameters on confirmed COVID-19 infections via epidemiological methods. Whereas, the findings of relations between possible variables and COVID-19 incidences using geographical perspective were scarce. In the present study, data concerning confirmed COVID-19 cases and possible affecting factors were collected for 325 cities across China up to May 27, 2020. The Geographical Weighted Regression (GWR) model was introduced to explore the impact of probable determinants on confirmed COVID-19 incidences. Some results were obtained. AQI, PM2.5, and PM10 demonstrated significantly positive impacts on COVID-19 during the most study period with the majority lag group (P＜0.05). Nevertheless, the relation of temperature with COVID-19 was significantly negative (P＜0.05). Especially, CO exhibited a negative effect on COVID-19 in most study period with the majority lag group. The impacts of each possible determinant on COVID-19 represented significantly spatial heterogeneity. The obvious influence of the majority of possible factors on COVID-19 was mainly detected during the after lockdown period with the lag 21 group. Although the COVID-19 spreading has been effectively controlled by tough measures taken by the Chinese government, the study findings remind us to address the air pollution issues persistently for protecting human health.

Study on Spatial Imbalance and Determinants of E-Commerce Development in Zhejiang, China

As a world-famous and well-developed e-commerce region, the development of e-commerce in Zhejiang province has always attracted people’s wide attention. Based on publicly available e-commerce transaction-related data, basic geographic data, and regional economic and social development data, we use the Gini coefficient to measure the imbalance of e-commerce development in Zhejiang province during 2017–2019. With the help of spatial analyst tools in ArcGIS desktop, the cluster and outlier analysis method is used to study the spatial pattern of e-commerce development in the province at the district or county-level city scale. To explore the causes of spatial aggregation and imbalance of e-commerce in Zhejiang province quantitatively, the paper proposes a geographical weighted regression (GWR) model with 15 economic and social development-related indicators. GWR and ordinary least squares (OLS) analysis indicate that 5 of the 15 selected indicators are highly related to the development of regional e-commerce development in Zhejiang, China.

Geographical Weighted Regression of Risk Factor of Stunting in Malang Regency, Indonesia

Stunting has become a global concern. The incidence of stunting in the world contributes to 15% of under-five mortality, with 55 million children losing their health, and it is estimated to reduce the country's GDP level by up to 7%. In Indonesia, the incidence of stunting has become one of the main health problems that need to be solved immediately. Malang Regency is one of the districts in East Java Province that has received the spotlight regarding this problem. This research examined the risk factors of stunting in Malang Regency through Geographically Weighted Regression (GWR). GWR was carried out to calculate the correlation between predetermined demographic, health, and economic variables, which were assumed to influence risk factors of stunting. GWR allocation and model examinations are important in understanding risk factors of stunting in the study of disease transmission in the investigation zone. Based on GWR analysis, the research shows that only four (4) sub-variables were significant: the number of poor people, level of education, number of health facilities, and access to health facilities. We also found that Lawang, Gondanglegi, and Turen districts have high-risk areas to stunting. Therefore, within this study that correlates to government policy to decrease or eliminate stunting incidents, districts belonging to the high-risk class should be prioritized or concerned. Moreover, based on LISA, some districts are affected by the risk factors of stunting from the surrounding districts with higher stunting potential value such as Gondanglegi and Pagelaran Districts.