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.

Irrigation Water Valuation Using Spatial Hedonic Models in GIS Environment

2010 ◽  
Vol 1 (4) ◽  
pp. 1-13 ◽  
Author(s):  
Zisis Mallios
Keyword(s):  
Irrigation Water ◽  
Regression Models ◽  
Spatial Regression ◽  
Software Tool ◽  
Hedonic Pricing ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Spatial Weights Matrix ◽  
Lag Model ◽  
Spatial Regression Models

Hedonic pricing is an indirect valuation method that applies to heterogeneous goods investigating the relationship between the prices of tradable goods and their attributes. It can be used to measure the value of irrigation water through the estimation of the model that describes the relation between the market value of the land parcels and its characteristics. Because many of the land parcels included in a hedonic pricing model are spatial in nature, the conventional regression analysis fails to incorporate all the available information. Spatial regression models can achieve more efficient estimates because they are designed to deal with the spatial dependence of the data. In this paper, the authors present the results of an application of the hedonic pricing method on irrigation water valuation obtained using a software tool that is developed for the ArcGIS environment. This tool incorporates, in the GIS application, the estimation of two different spatial regression models, the spatial lag model and the spatial error model. It also has the option for different specifications of the spatial weights matrix, giving the researcher the opportunity to examine how it affects the overall performance of the model.

scholarly journals Spatial Regression Models: A Systematic Comparison of Different Model Specifications Using Monte Carlo Experiments

2019 ◽  
pp. 004912411988246 ◽  
Author(s):  
Tobias Rüttenauer
Keyword(s):  
Monte Carlo ◽  
Spatial Data ◽  
Regression Models ◽  
Spatial Regression ◽  
Spillover Effects ◽  
Spatial Error ◽  
Monte Carlo Experiments ◽  
Spatial Autoregressive ◽  
Spatial Regression Models ◽  
Variable Bias

Spatial regression models provide the opportunity to analyze spatial data and spatial processes. Yet, several model specifications can be used, all assuming different types of spatial dependence. This study summarizes the most commonly used spatial regression models and offers a comparison of their performance by using Monte Carlo experiments. In contrast to previous simulations, this study evaluates the bias of the impacts rather than the regression coefficients and additionally provides results for situations with a nonspatial omitted variable bias. Results reveal that the most commonly used spatial autoregressive and spatial error specifications yield severe drawbacks. In contrast, spatial Durbin specifications (SDM and SDEM) and the simple spatial lag of X (SLX) provide accurate estimates of direct impacts even in the case of misspecification. Regarding the indirect “spillover” effects, several—quite realistic—situations exist in which the SLX outperforms the more complex SDM and SDEM specifications.

USE OF SPATIAL AUTOCORRELATION TO BUILD REGRESSION MODELS OF TRANSACTION PRICES

2013 ◽  
Vol 21 (4) ◽  
pp. 65-74 ◽  
Author(s):  
Radosław Cellmer
Keyword(s):  
Spatial Autocorrelation ◽  
Regression Models ◽  
Spatial Regression ◽  
Research Work ◽  
Property Market ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Spatial Weights Matrix ◽  
Lag Model ◽  
Spatial Regression Models

Abstract This paper presents the principles of studying global spatial autocorrelation in the land property market, as well as the possibilities of using these regularities for the construction of spatial regression models. Research work consisted primarily of testing the structure of the spatial weights matrix using different criteria and conducting diagnostic tests of two types of models: the spatial error model and the spatial lag model. The paper formulates the hypothesis that the application of spatial regression models greatly increases the accuracy of transaction price prediction while forming the basis for the creation of cartographic documents including, among others, maps of land value.

scholarly journals Examining Spatial Association of Air Pollution and Suicide Rate Using Spatial Regression Models

2020 ◽  
Vol 12 (18) ◽  
pp. 7444
Author(s):  
Yeran Sun ◽  
Ting On Chan ◽  
Jing Xie ◽  
Xuan Sun ◽  
Ying Huang
Keyword(s):  
Air Pollution ◽  
Suicide Rate ◽  
Suicidal Behaviour ◽  
Regression Models ◽  
Spatial Regression ◽  
Spatial Association ◽  
Positive Association ◽  
Pollution Levels ◽  
Suicide Rates ◽  
Spatial Regression Models

Air pollution can have adverse impacts on both the physical health and mental health of people. Increasing air pollution levels are likely to increase suicide rates, although the causal mechanisms underlying the relationship between pollution exposure and suicidal behaviour are not well understood. In this study, we aimed to further examine the spatial association of air pollution and suicidal behaviour. Specifically, we investigated whether or how PM2.5 levels are spatially associated with the adult suicide rates at the district level across London. As the data used are geospatial data, we used two newly developed specifications of spatial regression models to investigate the spatial association of PM2.5 levels and suicide. The empirical results show that PM2.5 levels are spatially associated with the suicide rates across London. The two models show that PM2.5 levels have a positive association with adult suicide rates over space. An area with a high percentage of White people or a low median household income is likely to suffer from a high suicide rate.

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.

Spatial regression modeling of tree height–diameter relationships

2009 ◽  
Vol 39 (12) ◽  
pp. 2283-2293 ◽  
Author(s):  
Qingmin Meng ◽  
Chris J. Cieszewski ◽  
Mike R. Strub ◽  
Bruce E. Borders
Keyword(s):  
Regression Models ◽  
Spatial Regression ◽  
Tree Height ◽  
Height Growth ◽  
Endogenous Variable ◽  
Exogenous Variables ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Lag Model ◽  
Spatial Regression Models

Tree height–diameter relationships are usually studied using linear or nonlinear models, but exogenous variables, especially spatially autocorrelated and dependent variables of tree diameter or height, are not often considered in height–diameter modeling. Three types of spatial regression models — spatial lag model, spatial error model, and spatial Durbin process model — are explored in this study. The height–diameter relationships are modeled using the spatial regression models to investigate the effects of spatial dependence and spatial autocorrelation and the roles of the exogenous variables generated by neighboring trees. Case study 1 shows that the spatial lag model should be used to analyze height–diameter relationships, in which heights of neighboring trees, which are exogenous variables, and the endogenous variable DBH significantly affect height growth. Case study 2 shows that the spatial error model performs better than other models, and that height growth is not only affected by its endogenous variable diameter but also by unobserved variables that vary spatially and result in residual spatial autocorrelation. Spatial regression models are an approach to height–diameter modeling that provide insight into how the endogenous variable diameter, the exogenous variables height and (or) diameter of neighboring trees, and locally varied but unobserved environmental or ecological variables contribute to height growth.

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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