scholarly journals Influence of overweight and obesity on the diabetes in the world on adult people using spatial regression

2016 ◽  
Vol 2 (3) ◽  
pp. 149
Author(s):  
Tuti Purwaningsih ◽  
Baharudin Machmud
Keyword(s):  
Spatial Model ◽  
Spatial Regression ◽  
Overweight And Obesity ◽  
Spatial Error Model ◽  
Spatial Error ◽  
Obesity And Overweight ◽  
The World ◽  
Level Of Significance ◽  
Global Nutrition ◽  
Level Order

This research discussed about the case of diabetes, overweight, and obesity which aimed to determine the factors that most affect the number of adult people with Diabetes from Obesity and Overweight in the world and looking for the best spatial model to make predictions in the next period. This research based on data WHO in 2015 from The 2016 Global Nutrition Report. At 5% level of significance for 2015, factor that influence diabetes is obesity and the most excellent spatial model used in the analysis is Spatial Error Model (SEM) that use Weight Level Order 1 and has R2 value 81.82%.

scholarly journals Identification of Factors Affecting Tuberculosis in West Java using Spatial Modeling

2021 ◽  
pp. 9-17
Author(s):  
Yusma Yanti ◽  
Septian Rahardiantoro
Keyword(s):  
Spatial Modeling ◽  
Spatial Model ◽  
Alcoholic Beverages ◽  
Spatial Error Model ◽  
West Java ◽  
Spatial Error ◽  
Factors Affecting ◽  
The World ◽  
Spatial Autoregressive ◽  
Better Than

Tuberculosis (TB) is an infectious disease caused by the bacillus Mycobacterium tuberculosis. In 2017 WHO records there are 1.7 billion TB sufferers in the world. Whereas in the same year TB sufferers in Indonesia reached 421 thousand cases and 10 thousand of them were in the province of West Java. In this study, the factors that suspected to influence TB include poverty, population density and malnutrition were analyzed by looking at the spatial aspects. In addition to these factors, smoking and consuming alcoholic beverages can also trigger TB. The method used was Spatial Autoregressive Model (SARM), Spatial Error Model (SEM), and Generalized Spatial Model (GSM), then the best model is chosen based on the best criteria of lagrange multiplayer test. The result indicated that SEM performed better than others, with the following significant variables were malnutrition and unemployment factor.

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 The Effect of Land Use/Cover Change on Soil Erosion Change by Spatial Regression in Changwu County on the Loess Plateau in China

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1209
Author(s):  
Shichuan Yu ◽  
Fei Wang ◽  
Mei Qu ◽  
Binhou Yu ◽  
Zhong Zhao
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Spatial Regression ◽  
Ordinary Least Squares ◽  
Spatial Error Model ◽  
The Loess Plateau ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Geographically Weighted Regression Model ◽  
Lag Model

Changwu County is a typical soil and water loss area on the Loess Plateau. Soil erosion is an important ecological process, and the impact of land use/cover change on soil erosion has received much attention. The present study used remote sensing images of the study area in 1987, 1997, 2007, and 2017 to analyze the land use/cover change (LULCC), and the RUSLE model was applied to estimate the soil erosion in different times. We exploited the Sankey diagram to visualize the spatiotemporal changes in land use/cover and soil erosion. We planned to obtain the most suitable model by comparing the application of different spatial regression models (Geographically weighted regression model, Spatial lag model, Spatial error model) and Ordinary least squares in LULCC and soil erosion changes. The results revealed that land use/cover has significantly changed in the last 30 years. From 1987 to 1997, cropland expansion came mainly from planted land and orchards, which transformed 68.99 km2 and 64.93 km2, respectively. In 1997–2007, the planted land increase was mainly through the conversion of cropland. In 2007–2017, the increase in orchard area came mainly from cropland. The forest land increase was mainly from the planted land. Soil erosion in Changwu County was dominated by slight erosion and light erosion, although the area of slight erosion and light erosion continued to decrease. The annual average soil erosion increased, which was estimated at 977.84 ton km−2 year−1, 1305.17 ton km−2 year−1, 1310.60 ton km−2 year−1, and 1891.46 ton km−2 year−1 in 1987, 1997, 2007, and 2017, respectively. These amounts of transformation mainly occurred when slight erosion was converted to light erosion, light erosion was converted to moderate erosion, and moderate erosion was converted to light and severe erosion. The Spatial lag model and Spatial error model have higher accuracy than the Geographically weighted regression model and Ordinary least squares when fitting the effect of LULCC and soil erosion change, where the accuracy exceeded 0.62 in different periods.

scholarly journals Income Absolute Beta-Convergence of NUTS 3 Level Regions in New EU Member States before and During a Crisis

2016 ◽  
Vol 16 (2) ◽  
pp. 151-162 ◽  
Author(s):  
Paweł Folfas
Keyword(s):  
Spatial Model ◽  
Ordinary Least Squares ◽  
Spatial Error Model ◽  
Spatial Error ◽  
Member States ◽  
Absolute Income ◽  
Eu Member States ◽  
Spatial Dependencies ◽  
Beta Convergence ◽  
New Eu Member States

Abstract This paper is aimed at answering the question of whether absolute income (GDP per capita) beta-convergence exists in the case of regions in new EU Member States before the period of 2000–2008 and during the 2008–2011 crisis. The sample consists of 211 regions (NUTS 3-level) of Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovenia and Slovakia. The research is based on econometric models, namely on the spatial lagged model (SLM), the spatial error model (SEM) and the Durbin spatial model which contrary to the ordinary least squares the (OLS) model include possible spatial dependencies. The SLM and SEM models as well as the Durbin spatial model detect the absolute income beta-convergence on the level of about 1% during the years 2000–2008. Additionally, models do not confirm the existence of absolute income beta-convergence during the crisis of 2008–2011. SLM models (which offer the most reliable findings) find a spatial correlation (measured by the rho-parameter) at a level of 0.75 during 2000–2008 and 0.35 during 2008–2011. Thus, absolute income beta-convergence in the case of NUTS 3 regions in 10 new EU Member States existed only in the pre-crisis period and this period is characterized by much stronger spatial dependencies than the period of 2008–2011.

The Effect of Socioeconomic and Environmental Factors on Obesity

2021 ◽  
Vol 12 (4) ◽  
pp. 58-74
Author(s):  
Ortis Yankey ◽  
Prince M. Amegbor ◽  
Marcellinus Essah
Keyword(s):  
Environmental Factors ◽  
Regression Models ◽  
Spatial Regression ◽  
Positive Association ◽  
Error Model ◽  
Spatial Error Model ◽  
Significant Positive Association ◽  
Spatial Error ◽  
Obesity Rate ◽  
Spatial Regression Models

This paper examined the effect of socio-economic and environmental factors on obesity in Cleveland (Ohio) using an OLS model and three spatial regression models: spatial error model, spatial lag model, and a spatial error model with a spatially lagged response (SEMSLR). Comparative assessment of the models showed that the SEMSLR and the spatial error models were the best models. The spatial effect from the various spatial regression models was statistically significant, indicating an essential spatial interaction among neighboring geographic units and the need to account for spatial dependency in obesity research. The authors also found a statistically significant positive association between the percentage of families below poverty, Black population, and SNAP recipient with obesity rate. The percentage of college-educated had a statistically significant negative association with the obesity rate. The study shows that health outcomes such as obesity are not randomly distributed but are more clustered in deprived and marginalized neighborhoods.

scholarly journals MEMODELKAN ANGKA GIZI BURUK DI PROVINSI BALI DENGAN PENDEKATAN REGRESI SPASIAL

2021 ◽  
Vol 10 (2) ◽  
pp. 103
Author(s):  
ANAK AGUNG ISTRI AYU PRATAMI ◽  
I KOMANG GDE SUKARSA ◽  
NI LUH PUTU SUCIPTAWATI ◽  
I PUTU EKA NILA KENCANA
Keyword(s):  
Birth Weight ◽  
Exclusive Breastfeeding ◽  
Spatial Regression ◽  
Geographic Location ◽  
Regression Method ◽  
Spatial Error Model ◽  
Spatial Autoregressive Model ◽  
Spatial Error ◽  
Regression Methods ◽  
Spatial Autoregressive

Nutritional problems in toddler are still a serious problem in various districts/cities in Indonesia. The case of malnutrition in Bali Province vary in many regions and hypothesized to be influenced by geographic location, which is often known as spatial heterogeneity. To overcome this problem, a spatial regression method is used on this research. This study aims to model the factors that are hypothesized affect malnourished toddlers in Bali Province using spatial regression methods, i.e. spatial autoregressive model (SAR) and spatial error model (SEM). Both models have 5 predictors variable, i.e. the percentage of toddlers aged between 6 - 59 months who received vitamin A, the percentage of babies with low birth weight (LBW), the percentage of households with clean and healthy living behavior (PHBS), the percentage of children under five receiving exclusive breastfeeding, and the percentage of toddler health services, which are obtained from Bali Provincial Health Office. The results showed SEM method produced smaller AIC value and higher , with  and  AIC values ??of 96.24% and 60.84, respectively.

scholarly journals SPATIAL ANALYSIS OF RELATIONSHIP BETWEEN COVID-19 CASES AND GLOBALISATION IN EUROPE

2020 ◽  
pp. 255-265
Author(s):  
Yılmaz Toktaş
Keyword(s):  
Spatial Analysis ◽  
The Netherlands ◽  
Global Scale ◽  
Error Model ◽  
European Countries ◽  
Spatial Error Model ◽  
Spatial Error ◽  
The World ◽  
The Uk ◽  
Positive Effect

With the globalisation process, economic, social and political structures have become more and more intertwined. Due to the current Covid-19 pandemic, it has been observed that epidemics such as Covid-19 are globalising and that they turn into pandemics on a global scale. In this study, it is suggested that, along with Covid-19’s distinctive abilities such as spreading rapidly, the fact that the world has become more mobile and integrated due to globalisation is considered to have an impact on the pandemic; thus, the effect of globalisation on Covid-19 cases in European countries was investigated through spatial analysis methods. The results of Moran’s I test carried out on Covid-19 cases in European countries suggest that there is positive autocorrelation. According to the LISA analysis results, it was found that the UK, the Netherlands, France, and Belgium not only have a higher number of Covid-19 cases, but also have been affected by the countries with a number of cases above the European mean. According to the results of Spatial Error Model designed to examine the effect of globalisation, it was found that globalisation had a slight but positive effect on Covid-19 cases in Europe.

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 Modeling Spatial Error Model (SEM) On Human Development Index (IPM) In Central Java 2018

2020 ◽  
Vol 1 (2) ◽  
pp. 48
Author(s):  
Aprilia Dwi Anggara Wati ◽  
Laelatul Khikmah
Keyword(s):  
Human Development ◽  
Human Development Index ◽  
Spatial Regression ◽  
School Enrollment ◽  
Error Model ◽  
Spatial Error Model ◽  
Spatial Error ◽  
Development Index ◽  
Central Java ◽  
Life Expectancy At Birth

The Human Development Index (HDI) is a human development index that is used to achieve the development outcomes of a region. HDI is formed by 3 basic dimensions, namely the health dimension as seen from the indicator of life expectancy at birth, the dimension of knowledge seen from a combination of indicators of average length of schooling and expectation of school years and dimensions of decent living standards as seen from the indicator of average per capita expenditure has been adjusted. The development of HDI in Central Java shows an increase every year. In 2018 the HDI figure for Central Java Province reached 71.12% and increased by 0.6% from the previous year. This is because the large HDI figures in an area are influenced by the large HDI numbers in adjacent areas. The location / area factor is thought to have a spatial dependence effect on the HDI figure. This problem can be overcome by using spatial regression by including the relationship between regions into the model. The spatial regression approach used in this study is the Spatial Error Model (SEM). The weighting matrix used in this study is Queen Contiguity (intersection between sides and corners). This study provides results that the variables that significantly influence HDI are poverty and school enrollment rates.

scholarly journals Model Regresi Spasial dan Aplikasinya dalam Menganalisis Angka Putus Sekolah Usia Wajib Belajar di Provinsi Sulawesi Selatan

2019 ◽  
Vol 1 (2) ◽  
pp. 183
Author(s):  
Wahidah Sanusi ◽  
Hisyam Ihsan ◽  
Nur Hikmayanti Syam
Keyword(s):  
Regression Model ◽  
Spatial Regression ◽  
Predictor Variable ◽  
Error Model ◽  
Drop Out ◽  
Moran’S I ◽  
Spatial Error Model ◽  
Spatial Error ◽  
Moran's I ◽  
School Drop Out

Abstrak. Penduduk Sulawesi Selatan pada kelompok pengeluaran terendah menunjukkan bahwa banyak dari mereka mengalami putus sekolah. Salah satu faktor yang mempengaruhi angka putus sekolah yaitu lokasi antar wilayah. Tujuan penelitian ini adalah untuk mengaplikasikan regresi spasial untuk memodelkan angka putus sekolah di Provinsi Sulawesi Selatan. Pengujian dependensi spasial dan pemilihan model regresi spasial dilakukan menggunakan uji Moran’s I dan Langrange Multiplier (LM). Dari hasil penelitian, kasus putus sekolah untuk tingkat SMP tidak memiliki dependensi spasial baik dalam lag maupun error dan berdasarkan model regresi klasiknya diperoleh variabel prediktor yang signifikan mempengaruhi variabel respon adalah jumlah penduduk miskin . Sedangkan untuk kasus angka putus sekolah tingkat SMA, diperoleh dependensi spasial dalam error sehingga model regresi spasial yang digunakan adalah Spatial Error Model (SEM) dan matriks pembobotnya adalah queen contiguity. Matriks pembobot tersebut menggambarkan ukuran kedekatan antar wilayah pengamatan. Hasil analisis spasial menunjukkan bahwa variabel prediktor yang signifikan mempengaruhi variabel respon adalah jumlah penduduk miskin  dan kepadatan penduduk , dengan nilai  89,78% dan AIC =  430,604.Kata Kunci: Langrange Multiplier, Moran’s I, Putus Sekolah, Regresi Spasial, Spatial Error Model (SEM).  Abstract. The population of South Sulawesi in the lowest expenditure group shows that many of them have dropped out of school. One of the factors that influence the drop out rate is location between regions. The purpose of this study was applying spatial regression to the model drop out rates in South Sulawesi Province. Spatial dependency test and spatial regression model selection were performed using Moran's I and Langrange Multiplier (LM) tests. From the results of the study, the drop out case for junior high school didn’t have spatial dependencies either in lag or error and based on the classical regression model obtained predictor variable significantly affect the response variable was the number of poor people . As for the case of high school drop out rate, obtained spatial dependency in error so that spatial regression model used was Spatial Error Model (SEM) and weighting matrix was queen contiguity. The weighted matrix represents the measure of proximity between observation areas. The result of spatial analysis indicates that the significant predictor variable influencing the response variable was the number of the poor  and the population density , with  = 89.78% and AIC = 430,604.Keywords: Lagrange Multiplier, Moran's I, School Drop Out, Spatial Regression, Spatial Error Model (SEM).

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