scholarly journals Examining Multilevel Poverty-Causing Factors in Poor Villages: a Hierarchical Spatial Regression Model

2021 ◽  
Author(s):  
Yanhui Wang ◽  
Yuewen Jiang ◽  
Duoduo Yin ◽  
Chenxia Liang ◽  
Fuzhou Duan
Keyword(s):  
Regression Model ◽  
Linear Model ◽  
Goodness Of Fit ◽  
Spatial Regression ◽  
Hierarchical Linear Model ◽  
Central China ◽  
Spatial Effects ◽  
Spatial Regression Model ◽  
Village Level ◽  
The Village

AbstractThe examination of poverty-causing factors and their mechanisms of action in poverty-stricken villages is an important topic associated with poverty reduction issues. Although the individual or background effects of multilevel influencing factors have been considered in some previous studies, the spatial effects of these factors are rarely involved. By considering nested geographic and administrative features and integrating the detection of individual, background, and spatial effects, a bilevel hierarchical spatial linear model (HSLM) is established in this study to identify the multilevel significant factors that cause poverty in poor villages, as well as the mechanisms through which these factors contribute to poverty at both the village and county levels. An experimental test in the region of the Wuling Mountains in central China revealed the following findings. (1) There were significant background and spatial effects in the study area. Moreover, 48.28% of the overall difference in poverty incidence in poor villages resulted from individual effects at the village level. Additionally, 51.72% of the overall difference resulted from background effects at the county level. (2) Poverty-causing factors were observed at different levels, and these factors featured different action mechanisms. Village-level factors accounted for 14.29% of the overall difference in poverty incidence, and there were five significant village-level factors. (3) The hierarchical spatial regression model was found to be superior to the hierarchical linear model in terms of goodness of fit. This study offers technical support and policy guidance for village-level regional development.

scholarly journals Pemodelan Regresi Spasial Kekar: Studi Kasus Jumlah Kunjungan WIsatawan Mancanegara Asal Eurasia di Indonesia Tahun 2015

2018 ◽  
Vol 2 (1) ◽  
pp. 61-70
Author(s):  
Resti Cahyati ◽  
Anik Djuraidah ◽  
Septian Rahardiantoro
Keyword(s):  
Regression Model ◽  
Spatial Regression ◽  
Estimation Method ◽  
Extreme Value ◽  
Considerable Change ◽  
Spatial Effects ◽  
Regression Parameter ◽  
Spatial Regression Model ◽  
Regression Parameters ◽  
The Relationship

Spatial regression model is a model used to evaluate the relationship between one variable with some other variables considering the spatial effects in each region. One of the causes of imprecise spatial regression model in predicting is the presence of outlier or extreme value. The existence of outlier or extreme value could damage spatial regression parameter estimator. However, discarding the outlier or extreme value in spatial analysis, could change the composition of the spatial effect on the data. Visitor arrivals from Eurasia to Indonesia by nationality in 2015 great diversity caused by the outlier. So in this paper, we need a spatial regression parameter estimation method which is robust where the value of the estimation is not much affected by small changes in the data. The application of the S prediction principle is carried out in the estimation of the coefficient of spatial regression parameters which is robust to the observation of silane. The result of modeling by applying the principle of the S estimator method on the estimation of the stocky spatial regression parameter is able to accommodate the existence of pencilan observation on the spatial regression model quite effectively. This is indicated by a considerable change in the coefficient coefficient estimator parameters of spatial regression is able to decrease the value of MAPE and MAD produced by spatial regression regression modeling.

scholarly journals PEMODELAN PENYEBARAN KASUS DEMAM BERDARAH DENGUE (DBD) DI KOTA DENPASAR DENGAN METODE SPATIAL AUTOREGRESSIVE (SAR)

2017 ◽  
Vol 6 (1) ◽  
pp. 37
Author(s):  
NI MADE SURYA JAYANTI ◽  
I WAYAN SUMARJAYA ◽  
MADE SUSILAWATI
Keyword(s):  
Regression Model ◽  
Spatial Dependence ◽  
Spatial Regression ◽  
Hemorrhagic Fever ◽  
Autoregressive Process ◽  
Spatial Effect ◽  
Spatial Effects ◽  
Spatial Regression Model ◽  
Spatial Autoregressive

One of spatial regression model is Spatial Autoregressive (SAR), which assumes that the autoregressive process only on the dependent variable only by considering the spatial effects. There are two aspects of spatial effects, that is spatial dependence and spatial heterogeneity. One of the problems which considers spatial effect is the spread of Dengue Hemorrhagic Fever (DHF). Denpasar City is an endemic DHF disease because there have been DHF cases in three consecutive years or more. The purpose of this research is to estimate the spread of DHF in  Denpasar City along with the factors that affect it. The results show that the factors that influence the spread of DHF are neighborhood, area and the role of Jumantik at the every village in Denpasar City.

scholarly journals PEMODELAN ANGKA HARAPAN HIDUP PROVINSI JAWA TENGAH MENGGUNAKAN ROBUST SPATIAL DURBIN MODEL

2021 ◽  
Vol 10 (1) ◽  
pp. 44-54
Author(s):  
Maghfiroh Hadadiah Mukrom ◽  
Hasbi Yasin ◽  
Arief Rachman Hakim
Keyword(s):  
Life Expectancy ◽  
Regression Model ◽  
Spatial Regression ◽  
Spatial Effects ◽  
Regression Parameter ◽  
Spatial Durbin Model ◽  
Spatial Regression Model ◽  
Central Java ◽  
Regression Parameters ◽  
M Estimator

Spatial regression is a model used to determine relationship between response variables and predictor variables that gets spatial influence. If there are spatial influences on both variables, the model that will be formed is Spatial Durbin Model. One reason for the inaccuracy of the spatial regression model in predicting is the existence of outlier observations. Removing outliers in spatial analysis can change the composition of spatial effects on data. One way to overcome of outliers in the spatial regression model is by using robust spatial regression. The application of M-estimator is carried out in estimating the spatial regression parameter coefficients that are robust against outliers. The aim of this research is obtaining model of number of life expectancy in Central Java Province in 2017 that contain outliers. The results by applying M-estimator to estimating robust spatial durbin model regression parameters can accommodate the existence of outliers in the spatial regression model. This is indicated by the change in the estimating coefficient value of the robust spatial durbin model regression parameter which can increase adjusted R2 value becomes 93,69% and decrease MSE value becomes 0,12551.Keywords: Outliers, M-estimator, Spatial Durbin Model, Number of Life Expectancy.

Field level digital soil mapping of cation exchange capacity using electromagnetic induction and a hierarchical spatial regression model

Soil Research ◽  
2009 ◽  
Vol 47 (7) ◽  
pp. 651 ◽  
Author(s):  
John Triantafilis ◽  
Scott Mitchell Lesch ◽  
Kevin La Lau ◽  
Sam Mostyn Buchanan
Keyword(s):  
Regression Model ◽  
Cation Exchange Capacity ◽  
Spatial Regression ◽  
Soil Management ◽  
Exchange Capacity ◽  
Ancillary Data ◽  
Trend Surface ◽  
Spatial Regression Model ◽  
Field Level ◽  
Mlr Model

At the field level the demand for spatial information of soil properties is rapidly increasing owing to its requirements in precision agriculture and soil management. One of the most important properties is the cation exchange capacity (CEC, cmol(+)/kg) because it is an index of the shrink–swell potential and hence is a measure of soil structural resilience to tillage. However, CEC is time-consuming and expensive to measure. Various ancillary datasets and statistical methods can be used to predict CEC, but there is little scientific literature which implements this approach to map CEC or addresses the issue of the amount of ancillary data required to maximise precision and minimise bias of spatial prediction at the field level. We compare a standard least-squares multiple linear regression (MLR) model which includes 2 proximally sensed (EM38 and EM31), 3 remotely sensed (Red, Green and Blue spectral brightness), and 2 trend surface (Easting and Northing) variables as ancillary data or independent variables, and a stepwise MLR model which only includes the statistically valid EM38 signal data and the Easting trend surface vector. The latter is used as the basis for developing a hierarchical spatial regression model to predict CEC. The reliability of the model is analysed by comparing prediction precision (root mean square error) and bias (mean error) using degraded EM38 transect spacing (i.e. 96, 144, 192, 240, and 288 m) and comparing these with predictions achieved with the 48-m spacing. We conclude that the EM38 data available on the 96- and 144-m spacing are suitable at a reconnaissance level (i.e. broad-scale farming) and 24- or 48-m spacing are suitable at smaller levels where detailed information is necessary for siting the location of water reservoirs. In terms of soil management, CEC predictions determine where suitable subsoil exists for the purpose of soil profile inversion to improve the structural resilience of a topsoil that is susceptible to dispersion and surface crusting.

SPATIAL AUTOREGRESSIVE (SAR) MODEL WITH ENSEMBLE LEARNING-MULTIPLICATIVE NOISE WITH LOGNORMAL DISTRIBUTION (CASE ON POVERTY DATA IN EAST JAVA)

2021 ◽  
Vol 14 (1) ◽  
pp. 89-97
Author(s):  
Dewi Retno Sari Saputro ◽  
Sulistyaningsih Sulistyaningsih ◽  
Purnami Widyaningsih
Keyword(s):  
Regression Model ◽  
Ensemble Learning ◽  
Spatial Data ◽  
Lognormal Distribution ◽  
Multiplicative Noise ◽  
Spatial Regression ◽  
Spatial Error ◽  
Spatial Regression Model ◽  
Sar Model ◽  
Spatial Autoregressive

The regression model that can be used to model spatial data is Spatial Autoregressive (SAR) model. The level of accuracy of the estimated parameters of the SAR model can be improved, especially to provide better results and can reduce the error rate by resampling method. Resampling is done by adding noise (noise) to the data using Ensemble Learning (EL) with multiplicative noise. The research objective is to estimate the parameters of the SAR model using EL with multiplicative noise. In this research was also applied a spatial regression model of the ensemble non-hybrid multiplicative noise which has a lognormal distribution of cases on poverty data in East Java in 2016. The results showed that the estimated value of the non-hybrid spatial ensemble spatial regression model with multiplicative noise with a lognormal distribution was obtained from the average parameter estimation of 10 Spatial Error Model (SEM) resulting from resampling. The multiplicative noise used is generated from lognormal distributions with an average of one and a standard deviation of 0.433. The Root Mean Squared Error (RMSE) value generated by the non-hybrid spatial ensemble regression model with multiplicative noise with a lognormal distribution is 22.99.

The Influence of Climate Factors on Cocoa Productivity in Sulawesi, 2019

2021 ◽  
Vol 1 (1) ◽  
pp. 21-30
Author(s):  
Marta Sundari ◽  
Pardomuan Robinson Sihombing
Keyword(s):  
Regression Model ◽  
Economic Activity ◽  
Oil And Gas ◽  
Climatic Factors ◽  
Average Length ◽  
Spatial Regression ◽  
Model Estimation ◽  
Climate Factors ◽  
Spatial Regression Model ◽  
Average Monthly Rainfall

Cocoa is one of the plantation commodities that has an important role in Indonesia's economic activity and is one of Indonesia's export commodities which is quite important as a source of foreign exchange and oil and gas. Sulawesi Island is one of the cocoa-producing islands in Indonesia. This study aims to determine a spatial regression model between the average cocoa productivity per month with the average drinking temperature per month, the average monthly rainfall and the average length of sunshine per month and the climatic factors that affect cocoa productivity in Sulawesi. The best model estimation uses the AIC value; the best model has the smallest AIC value. In this study, the SARMA spatial regression model is the best model with the specified criteria.

scholarly journals PEMODELAN JUMLAH TINDAK KRIMINALITAS DI PROVINSI JAWA TIMUR DENGAN ANALISIS REGRESI SPATIAL AUTOREGRESSIVE AND MOVING AVERAGE

2018 ◽  
Vol 7 (4) ◽  
pp. 346
Author(s):  
NI MADE LASTI LISPANI ◽  
I WAYAN SUMARJAYA ◽  
I KOMANG GDE SUKARSA
Keyword(s):  
Population Density ◽  
Regression Model ◽  
Average Length ◽  
Moving Average ◽  
Spatial Regression ◽  
Higher Order ◽  
Spatial Effect ◽  
Spatial Regression Model ◽  
Spatial Autoregressive ◽  
Total Average

One of spatial regression model is spatial autoregressive and moving average (SARMA) which assumes that there is a spatial effect on dependent variable and error. SARMA can analyze the spatial effect on the higher order. The purpose of this research is to estimate the model of the total crime in East Java along with factors that affect it. The results show that the model can describe total crime in East Java is SARMA(0,1). The factors that influence the total crime  are population density (), poverty total (), average length of education at every regency/city and error from the neigbors.

scholarly journals Generalized Exponentiated Weibull Linear Model in the Presence of Covariates

2017 ◽  
Vol 6 (3) ◽  
pp. 75
Author(s):  
Tiago V. F. Santana ◽  
Edwin M. M. Ortega ◽  
Gauss M. Cordeiro ◽  
Adriano K. Suzuki
Keyword(s):  
Regression Model ◽  
Linear Model ◽  
Goodness Of Fit ◽  
Likelihood Function ◽  
Confidence Bands ◽  
Model Parameters ◽  
Random Component ◽  
Monte Carlo Simulation Study ◽  
New Model ◽  
Exponentiated Weibull

A new regression model based on the exponentiated Weibull with the structure distribution and the structure of the generalized linear model, called the generalized exponentiated Weibull linear model (GEWLM), is proposed. The GEWLM is composed by three important structural parts: the random component, characterized by the distribution of the response variable; the systematic component, which includes the explanatory variables in the model by means of a linear structure; and a link function, which connects the systematic and random parts of the model. Explicit expressions for the logarithm of the likelihood function, score vector and observed and expected information matrices are presented. The method of maximum likelihood and a Bayesian procedure are adopted for estimating the model parameters. To detect influential observations in the new model, we use diagnostic measures based on the local influence and Bayesian case influence diagnostics. Also, we show that the estimates of the GEWLM are  robust to deal with the presence of outliers in the data. Additionally, to check whether the model supports its assumptions, to detect atypical observations and to verify the goodness-of-fit of the regression model, we define residuals based on the quantile function, and perform a Monte Carlo simulation study to construct confidence bands from the generated envelopes. We apply the new model to a dataset from the insurance area.

scholarly journals A spatial regression model for the disaggregation of areal unit based data to high-resolution grids with application to vaccination coverage mapping

2018 ◽  
Vol 28 (10-11) ◽  
pp. 3226-3241 ◽  
Author(s):  
CE Utazi ◽  
J Thorley ◽  
VA Alegana ◽  
MJ Ferrari ◽  
K Nilsen ◽  
...  
Keyword(s):  
High Resolution ◽  
Regression Model ◽  
Random Effects ◽  
Vaccination Coverage ◽  
Spatial Regression ◽  
Demographic And Health Surveys ◽  
Linear Predictor ◽  
Areal Data ◽  
Spatial Regression Model ◽  
Health And Development

The growing demand for spatially detailed data to advance the Sustainable Development Goals agenda of ‘leaving no one behind’ has resulted in a shift in focus from aggregate national and province-based metrics to small areas and high-resolution grids in the health and development arena. Vaccination coverage is customarily measured through aggregate-level statistics, which mask fine-scale heterogeneities and ‘coldspots’ of low coverage. This paper develops a methodology for high-resolution mapping of vaccination coverage using areal data in settings where point-referenced survey data are inaccessible. The proposed methodology is a binomial spatial regression model with a logit link and a combination of covariate data and random effects modelling two levels of spatial autocorrelation in the linear predictor. The principal aspect of the model is the melding of the misaligned areal data and the prediction grid points using the regression component and each of the conditional autoregressive and the Gaussian spatial process random effects. The Bayesian model is fitted using the INLA-SPDE approach. We demonstrate the predictive ability of the model using simulated data sets. The results obtained indicate a good predictive performance by the model, with correlations of between 0.66 and 0.98 obtained at the grid level between true and predicted values. The methodology is applied to predicting the coverage of measles and diphtheria-tetanus-pertussis vaccinations at 5 × 5 km2in Afghanistan and Pakistan using subnational Demographic and Health Surveys data. The predicted maps are used to highlight vaccination coldspots and assess progress towards coverage targets to facilitate the implementation of more geographically precise interventions. The proposed methodology can be readily applied to wider disaggregation problems in related contexts, including mapping other health and development indicators.

Geogenic radon potential mapping using geospatial analysis of multiple radon-related variables: a case study from Southeastern Ireland

2020 ◽  
Author(s):  
Mirsina Mousavi ◽  
Quentin Crowley
Keyword(s):  
Regression Model ◽  
Radon Concentration ◽  
Indoor Radon ◽  
Spatial Regression ◽  
Geostatistical Analysis ◽  
Soil Gas ◽  
Natural Radiation ◽  
Spatial Regression Model ◽  
Soil Gas Radon ◽  
Radon Potential

<p>A detailed investigation of geogenic radon potential (GRP) was carried out using geostatistical analysis on multiple radon-related variables to evaluate natural radiation in an area of Southeast Ireland. The geological setting of the study area includes basal Devonian sandstones and conglomerates overlying an offshoot of the Caledonian Leinster Granite, which intrudes Ordovician sediments. The Ordovician sediments contain traces of autunite (Ca(UO<sub>2</sub>)2(PO<sub>4</sub>)<sub>2</sub>·10–12H<sub>2</sub>O), which is a uranium-bearing mineral and a source of radon. To model radon release potential at different locations, a spatial regression model was developed in which soil gas radon concentration measured in-situ using a Radon RM-2 detector was considered as a response value. Proxy variables such as local geology, soil types, terrestrial gamma dose rates, radionuclide concentrations from airborne radiometric surveys, soil gas permeability, distance from major faults and a Digital Terrain Model were used as the main predictors. Furthermore, the distribution of indoor radon concentration was simulated using a soil-indoor transfer factor. Finally, the workability of the proposed GRP model was tested by evaluating the correlation between previously measured indoor radon concentrations and the estimated values by the GRP model at the same measurement locations. This model can also be used to estimate the GRPs of other areas where radon-related proxy values are available.        </p><p><strong>Keywords:</strong> Natural radiation, geogenic radon potential, geostatistical analysis, spatial regression model, indoor radon simulation</p>

Sign in / Sign up

Export Citation Format

Share Document