scholarly journals PEMODELAN KASUS DIARE DI PROVINSI BALI DENGAN METODE ANALISIS REGRESI SPASIAL

2021 ◽  
Vol 10 (2) ◽  
pp. 95
Author(s):  
I.G.A. DIAH SULASIH ◽  
MADE SUSILAWATI ◽  
NI LUH PUTU SUCIPTAWATI
Keyword(s):  
Regression Analysis ◽  
Spatial Dependence ◽  
Spatial Regression ◽  
Spatial Effect ◽  
Model Estimation ◽  
Spatial Error Model ◽  
Spatial Error ◽  
Bowel Movements ◽  
Spatial Regression Analysis ◽  
The Relationship

Diarrhea is a disease that occurs due to changes in the frequency of bowel movements and can cause death. In 2018, 115.889 cases of diarrhea were found in Bali Province. Information on the relationship between locations indicates the spatial effect in the model. Model estimation was done by using spatial regression analysis. This study aims to determine what factors influence diarrhea cases in Bali Province. The results show that the number of diarrhea cases in a district is influenced by the surrounding districts. This is reinforced in the Moran’s I test which shows spatial dependence. In the analysis of the Spatial Error Model (SEM), it was obtained that the value of  was 57,69% and the variables that significantly affected diarrhea cases in Bali Province were population density and sanitation facilities

scholarly journals Analisis Regresi Spatial Durbin Model (SDM) untuk Pemodelan Kemiskinan Provinsi Jawa Timur Tahun 2017

2019 ◽  
Vol 12 (1) ◽  
pp. 40-46
Author(s):  
Nur Roudlotul Hidayah ◽  
Artanti Indrasetianingsih
Keyword(s):  
Regression Analysis ◽  
Spatial Regression ◽  
Statistical Technique ◽  
Predictor Variables ◽  
Poor People ◽  
Spatial Durbin Model ◽  
Classic Problem ◽  
Almost All ◽  
Spatial Regression Analysis ◽  
The Relationship

Regression is a statistical technique used to describe the relationship between response variables with one or more predictor variables. The development of classical regression analysis that is influenced by the effects of space or location of a region is called spatial regression analysis. The purpose of this study is to conduct Spatial Durbin Model (SDM) regression analysis for poverty modeling in East Java in 2017. Poverty is a classic problem that occurs in almost all countries and is multidimensional, which is related to social, economic, cultural and other aspects. In 2017, poverty in East Java declined compared to the previous year. Therefore it is necessary to identify the factors that influence poverty. The variables used are the percentage of poor people as the response variable (Y) and predictor variables including Education does not finish elementary school (X1), Literacy Rate age 15 -55 years (X2), informal sector workers (X3), unemployment rate open (X4), household users of land as the widest floor (X5), and households using improper sanitation (X6), and households using drinking water sources are not feasible (X7).    Regresi merupakan teknik statistik yang digunakan untuk menggambarkan hubungan antara variabel respon dengan satu atau lebih variabel prediktor. Pengembangan dari analisis regresi klasik yang dipengaruhi oleh efek ruang atau lokasi wilayah disebut analisis regresi spasial. Tujuan dari penelitian ini adalah untuk melakukan analisis regresi Spatial Durbin Model (SDM) untuk pemodelan kemiskinan di Jawa Timur tahun 2017. Kemiskinan merupakan masalah klasik yang terjadi hampir diseluruh negara dan bersifat multidimensional, dimana berkaitan dengan aspek sosial, ekonomi, budaya dan aspek lainnya. Pada tahun 2017, kemiskinan di Jawa Timur mengalami penurunan jika dibandingkan dengan tahun sebelumnya. Oleh karena itu perlu dilakukan identifikasi faktor-faktor yang berpengaruh terhadap kemiskinan. Variabel yang digunakan yaitu persentase penduduk miskin sebagai variabel respon (Y) dan variabel prediktor antara lain Pendidikan tidak tamat SD (X1), Angka Melek Huruf  (AHM) usia 15 -55 tahun (X2), pekerja sektor informal (X3), tingkat pengangguran terbuka (X4), rumah tangga pengguna tanah sebagai lantai terluas (X5), dan rumah tangga pengguna sanitasi tidak layak (X6), dan Rumah tangga pengguna sumber air minum tidak layak (X7).

scholarly journals Spatial Modeling for Poverty: The Comparison of Spatial Error Model and Geographic Weighted Regression

2021 ◽  
Vol 12 (1) ◽  
pp. 237-251
Author(s):  
Achi Rinaldi ◽  
Yuni Susianto ◽  
Budi Santoso ◽  
Wahyu Kusumaningtyas
Keyword(s):  
Spatial Modeling ◽  
Spatial Regression ◽  
Evaluation Criteria ◽  
Spatial Models ◽  
Error Model ◽  
Weighted Regression ◽  
Spatial Error Model ◽  
Spatial Error ◽  
Explanatory Variables ◽  
The Relationship

This study aims to analyze poverty using spatial models. The researchers also compared the Spatial Error Model (SEM) and Geographically Weighted Regression (GWR). The comparison of the two models was based on the estimation evaluation criteria and the constructed spatial associations. Spatial regression is considered very appropriate to be used to model the relationship pattern between poverty and explanatory variables when the observed data has a spatial effect caused by the proximity between the observation areas. The spatial dependence of errors on observational data can be overcome using SEM, while the effect of heterogeneity of spatial variance can overcome using GWR.

scholarly journals ANALISIS REGRESI SPASIAL DAN POLA PENYEBARAN PADA KASUS DEMAM BERDARAH DENGUE (DBD) DI PROVINSI JAWA TENGAH

2017 ◽  
Vol 10 (2) ◽  
pp. 95
Author(s):  
Inna Firindra Fatati ◽  
Hari Wijayanto ◽  
Agus M. Sholeh
Keyword(s):  
Spatial Regression ◽  
Spatial Models ◽  
Hemorrhagic Fever ◽  
Spatial Effect ◽  
Health Centers ◽  
Central Java ◽  
Local Moran ◽  
Spatial Regression Analysis ◽  
Different Characteristics ◽  
Central Java Province

Dengue Hemorrhagic Fever (DHF) is one of the diseases that threaten human health. The cases of dengue fever in the district / city certainly has different characteristics, geographic condition, the potential of the region, health facilities, as well as other matters that lie behind them. Based on local moran index values are visualized through thematic maps, some area adjacent quadrant tends to be in the same group. There are two significant quadrant in describing the pattern of spread of dengue cases namely quadrant high-high and lowlow. This indicates a spatial effect on the number of dengue cases, so that the spatial regression analysis. Based on the value of  and AIC, autoregressive spatial models (SAR) is good enough to be used in modeling the number of dengue cases in the province of Central Java. Factors that influence the number of dengue cases Central Java province in 2015 is the number of health centers per 1000 population, the number of polindes per 1000 population, population density (X3), percentage of people with access to drinking water sustainable decent (X6), the percentage of water quality net free of bacteria, fungi and chemicals (X7), and the number of facilities protected springs (X8).

scholarly journals Multilevel and spatial analyses of childhood malnutrition in Uganda: examining individual and contextual factors

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Prince M. Amegbor ◽  
Zhaoxi Zhang ◽  
Rikke Dalgaard ◽  
Clive E. Sabel
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Dependence ◽  
Contextual Factors ◽  
Spatial Regression ◽  
Climatic Conditions ◽  
Cross Sectional ◽  
Mixed Effect ◽  
Spatial Error ◽  
Under Five ◽  
Diurnal Temperature

AbstractIn this study, we examine the concepts of spatial dependence and spatial heterogeneity in the effect of macro-level and micro-level factors on stunting among children aged under five in Uganda. We conducted a cross-sectional analysis of 3624 Ugandan children aged under five, using data from the 2016 Ugandan Demographic and Health Survey. Multilevel mixed-effect analysis, spatial regression methods and multi-scale geographically weight regression (MGWR) analysis were employed to examine the association between our predictors and stunting as well as to analyse spatial dependence and variability in the association. Approximately 28% of children were stunted. In the multilevel analysis, the effect of drought, diurnal temperature and livestock per km2 on stunting was modified by child, parent and household factors. Likewise, the contextual factors had a modifiable effect on the association between child’s sex, mother’s education and stunting. The results of the spatial regression models indicate a significant spatial error dependence in the residuals. The MGWR suggests rainfall and diurnal temperature had spatial varying associations with stunting. The spatial heterogeneity of rainfall and diurnal temperature as predictors of stunting suggest some areas in Uganda might be more sensitive to variability in these climatic conditions in relation to stunting than others.

SPATIAL REGRESSION ANALYSIS FOR MODELING THE SPATIAL VARIATION IN HIGHWAY CONSTRUCTION COSTS

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Minsoo Baek ◽  
Baabak Ashuri
Keyword(s):  
Regression Analysis ◽  
Spatial Variation ◽  
Spatial Regression ◽  
Highway Construction ◽  
Price Volatility ◽  
Construction Costs ◽  
Construction Market ◽  
Spatial Regression Analysis ◽  
Transportation Agencies ◽  
Geographical Locations

Price volatility in wages, materials, and equipment has a significant impact on highway construction costs. As the construction market and economy have experienced dynamic changes in prices, the price volatility becomes less predictable. In addition, various levels of the price volatility in different market locations aggravate the prediction. Thus, in developing highway construction costs, transportation agencies should consider geographical location of construction projects and market conditions of the locations. Transportation agencies face significant uncertainties in price volatility across different geographical locations. This volatility may not be uniformly distributed across different geographical locations due to changes in the availability of local contractors, materials, equipment, and labor. The objective of this research is to develop statistical models that are capable to explain spatial variations in submitted unit prices for asphalt line items in highway projects considering local market condition factors. Historical bid data used in this research consist of resurfacing and widening projects let in the state of Georgia, the United States, between 2008 and 2015. The methodology of this research is a spatial regression analysis to explain the spatial variation in the submitted unit prices for asphalt line items. The findings of this research indicate that volatility in submitted bid prices is not uniformly distributed across different geographical locations within the same transportation agency. The contribution to the body of knowledge of this research is an improved understanding of the role of local construction market and macroeconomic conditions to explain geographic variability in construction costs.

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