Study on the relationship between PM2.5 concentration and intensive land use in Hebei Province based on a spatial regression model

This paper presents the application of econometric techniques to examine the labour market in Lower Silesia. First the analysis was performed on a data set for variables connected with labour market recorded in poviats (NUTS 4). In order to determine the existence of spatial autocorrelation Moran’s statistics I was calculated. Then the spatial regression model was used to describe the relationship between the rate of unemployment and other variables. Next, LISA cluster maps were generated for units at NUTS 5 level. The results indicate the spatial dimension of the unemployment and its tendency to creating concentrations.

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

Xplore Journal of Statistics ◽

10.29244/xplore.v2i1.85 ◽

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.

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

Soil Research ◽

10.1071/sr08240 ◽

2009 ◽

Vol 47 (7) ◽

pp. 651 ◽

Cited By ~ 45

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.

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

MEDIA STATISTIKA ◽

10.14710/medstat.14.1.89-97 ◽

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.

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

Parameter: Journal of Statistics ◽

10.22487/27765660.2021.v1.i1.15444 ◽

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.

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

Applied Spatial Analysis and Policy ◽

10.1007/s12061-021-09388-1 ◽

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.

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Study on Production and Environmental Factors Based on GMM of Dynamic Panel System

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.693.1922 ◽

2016 ◽

Vol 693 ◽

pp. 1922-1934

Author(s):

B. Li ◽

R. Yi ◽

T. Li

Keyword(s):

Land Use ◽

Industrial Structure ◽

Structure Evolution ◽

Dynamic Panel ◽

Urbanization Level ◽

Industrial Structures ◽

Intensive Land Use ◽

The Relationship ◽

Stimulation Of ◽

New Urbanization

This paper measured the new urbanization level and the degree of intensive land use in 28 provinces of China from 2006 to 2012 through building index system, and built the economic model by utilizing GMM system to demonstrate the relationship between new urbanization, industrial structure evolution and intensive land use based on panel data. The result indicates that the industrial structure evolution is conducive to intensive land use. The development of new urbanization and the intensive land use generate structural contradictions, but it will promote intensive land use indirectly through stimulation of the industrial structure evolution. At the regional level, new urbanization inhibit the intensive land use in the eastern regions and promote it in the western regions, while the effect in the central region is not significant. Industrial structures in some regions stimulate the intensive land use directly or indirectly.

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

E-Jurnal Matematika ◽

10.24843/mtk.2018.v07.i04.p224 ◽

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.

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

Statistical Methods in Medical Research ◽

10.1177/0962280218797362 ◽

2018 ◽

Vol 28 (10-11) ◽

pp. 3226-3241 ◽

Cited By ~ 7

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.

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