Estimating the Parameters of a Robust Geographically Weighted Regression Model in Gross Regional Domestics Product in East Java (original) (raw)
Related papers
2024
The success of development in a region in Indonesia can be measured by economic growth, especially in the financial sector using the Gross Regional Domestic Product (GRDP) growth rate. The GRDP figure at the Regency and City level in West Java is one of Indonesia's highest and most diverse. This is due to various factors, including the geographical location of West Java, which is directly adjacent to DKI Jakarta, which is the center of the national economy. Although classified as high, the diversity of GRDP values between regions in West Java needs attention to equalize economic growth. The diversity of GRDP values can be modeled by the Improved Geographically and Temporally Weighted Regression (I-GTWR) method by taking samples in 2018-2022. The I-GTWR modeling method considers the influence of spatial heterogeneity and spatialtemporal interaction, which has been proven to produce better results than the GTWR method in modeling GRDP in Central Java in 2011-2015.
2019
Geographically weighted regression (GWR) is a model that can be used for data with spatial varying. Geographically and Temporally Weighted Regression (GTWR) is a development of the GWR model for data spatial and temporal varying. Parameter estimation in GTWR model uses weighted least square method which is very sensitive to outliers data. The outlier caused bias in parameter estimation, so it must be handled by robust GTWR (RGTWR). In this research, S-estimator was used to handle outliers and estimate an RGTWR. Both GTWR and RGTWR is used to build model crime rate in East Java 2011-2015. The Crime rate is used as a response variable and the percentage of poor people, population density, and human development index are used as explanatory variables. The best model in this research is RGTWR using S-estimator. RGTWR using S-estimator has a coefficient of determination equal to 98,2 meanwhile RMSE equal to 33.941 and MAD equal to 4.994.
Proceedings of the 2nd International Conference Postgraduate School, 2018
Underdevelopment problem of a region can be seen from the dimensions of the economy, human resources, financial capability, infrastructure, accessibility, and regional characteristics. One method to see a region is underdeveloped or not is by looking the percentage of people living in poverty in a region in the publication data of underdeveloped regional indicators issued by the Central Bureau of Statistics (BPS). The results showed that the percentage of people in East Java Province who are living in poverty using linear regression is not yet appropriate. The percentage of people living in poverty spread spatially because there is heterogeneity between the observation sites which means that the observation of a location depends on the observation in another location with adjacent distance so that the spatial regression modeling was done with Adaptive Bisquare Kernel function. The grouping results with GWR resulted in nine groups based on significant variables. Each group eas characterized by life expectancy, mean years of schooling, expenditure and literacy rate.
International Journal of Scientific Research in Science, Engineering and Technology, 2019
Infrastructure development is government focus in 2015-2019. One indicator used to measure economic activities construction sector in one area is GRDP of the construction sector. The Robust Geographically and Temporally Weighted Regression (RGTWR) model is the development of GTWR model to overcome the outliers. This study used GRDP the construction sector as a response variable with population, local revenue, area, and the number of construction establishments as explanatory variables. The RGTWR model is more effective in describing the value of data GRDP the construction sector of the regencies/municipalities in Java Island in 2010-2016. This is indicated by a decrease in the value of RMSE, MAD, and MAPE. The RGTWR model with M-estimator has been able to reduce the measure goodness of fit model, even though the decrease is not too large. Some residuals the RGTWR model still detected as outlier so it is recommended to use another estimator.
The Model of Mixed Geographically Weighted Regression (MGWR) for Poverty Rate in Central Java
This research aims to model the level of poverty in Central Java with spatial effect. The method of analysis that used in this study is Mixed Geographically Weighted Regression (MGWR).MGWR model parameter estimation is obtained by using Weighted Least Square (WLS) with gaussian kernel weighting function. Based on the result of the research, therewere five significant variables predictor that estimated to affect the level of poverty in the district/cityin Central Java, namely a minimum wage (X1), the percentage of the number of families who work in the agricultural sector (X2), the percentage of the number of families that use national health insurance program " Jamkesmas " (X4), the percentage of the number of families that have the facility to defecate(X6), and inflation (X8).Classfication accuracy (R2) of MGWR model is 73,95%, it means that MGWR model is able to explain the variation of the poverty level in 73,95%, the remaining of 26,05% is affected by another factor in the outside of the models with the AIC is180,49.There is no spatial effect or geographical factors influence the rate of poverty in Central Java. These variables predictors had asimiliar effect in each district/city.
Enthusiastic, 2022
Unemployment is one of the problems that hinder employment development programs. Based on East Java BPS data, the Open Unemployment Rate in East Java in 2019 is about 3.92 percent. In 2020, unemployment increased by 466.02 thousand people. On the other hand, OUR increased by 2.02 percent to 5.84 percent in August 2020. In addition to the indicators affecting OUR, each observation location has different characteristics, so multiple linear regression modeling is inappropriate. Geographically Weighted Regression is one of the spatial analyses developed from multiple linear regression for data containing spatial heterogeneity effects. The weighting functions used for this GWR model are Kernel Fixed and Adaptive functions (Gaussian, Bi-Square, Tricube, and Exponential). The analytical step carried out in estimating the parameters is to use WLS. The best weighting was obtained in the test, namely the Adaptive Tricube. Based on the study results, the GWR model with Adaptive Tricube weighted resulted in the value of R-Squared = 87.02%. However, the best model is obtained from the GWR model with exponential weighting with the smallest Akaike Information Criterion (AIC) value compared to the others, AIC = 95.77804 with R-Squared = 77.41.
Jurnal Ekonomi Pembangunan: Kajian Masalah Ekonomi dan Pembangunan
The important role of a region's transportation infrastructure strongly affects the economic growth of the region and tends to affect the surrounding areas. The effect is called spillover effect. The aim of the research was to recognize the direct effect and spillover effect (indirect) of transportation infrastructure on the economic growth in Central Java. To identify the spillover effects, it is necessary to recognize the different characteristics of each region which have the implications on the various transportation infrastructures at each region in Central Java. Therefore, the spatial modeling was conducted. In this study, the spatial modeling employed was Spatial Durbin Error Model (SDEM). The SDEM is another form of Spatial Error Model (SEM). It does not allow for lag effects of endogenous variables, but it allows for spatial error and spatial lag on exogenous variables in which it simplifies the interpretations on direct effects and spillover effect. According to SDEM e...
2017
GWR analysis is an expansion of a global regression analysis that generates parameter estimators to predict each point or location where the data is observed and collected. This analysis can accommodate spatial influence in an estimation of the regression model. One of the important issues that arise in GWR modeling is the non-constant variety between observations. Bayesian GWR analysis (BGWR) is considered as one of the best solutions to address the problems that arise in GWR modeling. Through the Bayesian approach, observations that potentially generate a non-constant variety can be detected and weighted directly so as to reduce their effect on model parameter estimation. In this study, the weights used are the adaptive Gaussian Kernel function, where the resulting bandwidth varies for each location of observation. This weighting is applied to compare the estimation results of GWR and BGWR model parameters. The results of the analysis show that the BGWR model is better than the GW...
In regular regression equation, a response variable is connected with some predictor variables in one main output, which is parameter measurement. This parameter explains relationships of every predictor variable with response variable. However, when it is applied to spatial data, this model is not always valid because the location difference can result in different model estimation. One of the analyses that recommend spatial condition is locally linear regression called Geographically Weighted Regression (GWR). The basic idea from this GWR model is the consideration of geographical aspect or location as weight in estimating the model parameter. Model parameter estimation of GWR is obtained using Weight Least Square (WLS) by giving different weights to every location where the data is obtained. In many analyses of GWR, also in this research, the weight used is Gauss Kernel, which needs bandwidth value as distance parameter that still affects each location. Bandwidth optimum can be obtained by minimalizing cross validation value. In this research, the researcher aims to compare the results of global regression model with GWR model in predicting poverty percentage. The data used as a case study are data from 33 cities and regencies in North Sumatera province.