ANALISIS GEOGRAPHICALLY WEIGHTED REGRESSION DENGAN PEMBOBOT KERNEL BI-SQUARE UNTUK ANGKA PENGANGGURAN DI KABUPATEN BOJONEGORO

2019 ◽  
Vol 2 (1) ◽  
pp. 51-59
Author(s):  
Alif Yuanita Kartini
Keyword(s):  
Regression Model ◽  
Unemployment Rate ◽  
Geographically Weighted Regression ◽  
Predictor Variable ◽  
Poverty Rate ◽  
Weighted Regression ◽  
Unemployed People ◽  
Global Regression ◽  
The Government ◽  
Gwr Model

The unemployment rate in Bojonegoro Regency has increased every year. Based on data from the Bojonegoro Regency Industry and Manpower Office (Disperinaker), at the end of June 2018 the number of unemployed people increased from the original 23,000 people to 24,000 people. It is far away from the government target to decrease this rate in the same year. The Great amount of the unemployment number is closely related to unequitable of developing program. As a result, the left areas with a great number of unemployment appears. There are several population indicators that are considered to have a great effect on the unemployment rate. Due to of those indicators, it is urgently needed to analyze the correlation between they did the analysis of correlation those factors toward poverty rate in Bojonegoro regency. Unfortunately, unemployment is spatial matter which there is correlation between unemployment rate and used predictor variable is not constant for all districts in Bojonegoro regency. That situation commonly becomes a problem in analyzing especially when applied global regression. Dealing with the problem, the researcher intended to analyze by applying Geographically Weighted Regression (GRW) method by applying Kernel Bi-square. The result shows that global regression model is able to explain variation of data is about 69,8%, but the use of regression model is not able to fulfil residual assumption. It appears heterokedasticity which shows that various residual model regression is not constant. Those problems can be solved by applying GWR which it is chosen by the center of Bojonegoro district. GWR model approved that it has better result than the previous one because it is able to explain variation and it is better in explaining the variation, it is about 72,11%.    Angka pengangguran di Kabupaten Bojonegoro dari tahun ke tahun semakin meningkat. Berdasarkan data dari Dinas Perindustrian dan Tenaga Kerja (Disperinaker) Kabupaten Bojonegoro, pada akhir Juni 2018 jumlah pengangguran semakin meningkat dari semula 23.000 orang menjadi 24.000 orang. Kondisi tersebut masih jauh dari target pemerintah untuk menurunkan angka pengangguran pada tahun 2018. Tingginya angka pengangguran tersebut tidak terlepas dari adanya ketidakmerataan pembangunan, sehingga memunculkan daerah tertinggal dengan angka pengangguran yang tinggi. Ada beberapa indikator kependudukan yang dianggap berpengaruh besar terhadap tingkat pengangguran, oleh karena itu ingin dilakukan analisa hubungan antara indikator kependudukan terhadap angka kemiskinan di Kabupaten Bojonegoro. Namun pengangguran merupakan suatu permasalahan spasial, dimana hubungan antara angka pengangguran dengan variabel prediktor yang digunakan tidak konstan (non-stationer) untuk seluruh Kecamatan di Kabupaten Bojonegoro. Kondisi tersebut seringkali menjadi masalah pada analisa ketika menggunakan regresi global. Oleh karena itu ingin dibandingkan jika dilakukan analisa menggunakan metode Geographically Weighted Regression(GWR) dengan Pembobot Kernel Bi-Square. Hasil model regresi global yang diperoleh mampu menerangkan keragaman data sebesar 69,8%, namun penggunaan regresi global tersebut tidak mampu memenuhi asumsi residual yaitu terjadi heterokedasticity yang menunjukkan bahwa varians dari residual model regresi global masih belum konstan. Permasalahan tersebut dapat diselesaikan dengan menggunakan GWR, dimana model GWR yang dipilih adalah model dengan pusat Kecamatan Bojonegoro. Model GWR yang diperoleh terbukti lebih baik karena mampu menerangkan keragaman dengan lebih baik yaitu sebesar 72,11%.

scholarly journals PEMODELAN REGRESI BERGANDA DAN GEOGRAPHICALLY WEIGHTED REGRESSION PADA TINGKAT PENGANGGURAN TERBUKA DI JAWA TENGAH

2017 ◽  
Vol 9 (2) ◽  
pp. 133
Author(s):  
Tiani Wahyu Utami ◽  
Abdul Rohman ◽  
Alan Prahutama
Keyword(s):  
Labor Force ◽  
Unemployment Rate ◽  
Geographically Weighted Regression ◽  
Poor Performance ◽  
Predictor Variables ◽  
Weighted Regression ◽  
Residual Variance ◽  
Central Java ◽  
Gdp Growth Rate ◽  
Gwr Model

The problems in employment was the growing number of Open Unemployment Rate (OUR). The open unemployment rate is a number that indicates the number of unemployed to the 100 residents are included in the labor force. The purpose of this study is mapping the data OUR in Central Java and the suspect and identify linkages between factors that cause OUR in the District / City of Central Java in 2014. Factors that allegedly include population density (X1), Inflation (X2), the GDP value (X3), UMR Value (X4), the percentage of GDP growth rate (X5), Hope of the old school (X6), the percentage of the labor force by age (X7) and the percentage of employment (X8). Geographically Weighted Regression (GWR) is a method for modeling the response of the predictor variables, by including elements of the area (spatial) into the point-based model. This research resulted in the conclusion that the OLS regression models have poor performance because the residual variance is not homogeneous. There were no significant differences between GWR models with OLS model or in other words generally predictor variables did not affect the response variable (rate of unemployment in Central Java) spatially. However, GWR model could captured modelling in each region. Keywords: multiple linear regression, geographiically weighted regression, open unemployement rate in Central Java.

scholarly journals Spatial interaction of groundwater and surface topographic using geographically weighted regression in built-up area

2020 ◽  
Author(s):  
Asep Mulyadi ◽  
Moh. Dede ◽  
Millary Agung Widiawaty
Keyword(s):  
Regression Model ◽  
Geographically Weighted Regression ◽  
Flat Surface ◽  
Spatial Interaction ◽  
Weighted Regression ◽  
P Value ◽  
Groundwater Levels ◽  
Geographically Weighted Regression Model ◽  
Primary Water ◽  
Global Regression

Groundwater is a primary water resource for human living. In Indonesia, excessive exploitation of groundwater generally occurs in the built-up area due to over-discharge processes characterized by a cone of depression. This research revealed the spatial interaction between groundwater levels and surface topographic using geographically weighted regression in built-up area. Groundwater levels data are obtained from 72 wells in Cikembang, Bandung Regency, whereas surface topographic based on BIG's DEMNas data which has 8 meters spatial resolution. This study showed significant spatial interaction between groundwater levels and surface topographic in the built-up area. The interaction has a clustered pattern with p-value less than 0.01. It indicated in the area with flat surface topographic has lower groundwater levels than others. There are several points who indicated the cone of depression in the built-up area with flat topographic. The geographically weighted regression model has high spatial variability and better results than the global regression model to assess groundwater level interaction with surface topographic.

scholarly journals PEMODELAN DEFORESTASI HUTAN LINDUNG DI INDONESIA MENGGUNAKAN MODEL GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION (GTWR)

2018 ◽  
Vol 7 (3) ◽  
pp. 314-325
Author(s):  
Thea Zulfa Adiningrumh ◽  
Alan Prahutama ◽  
Rukun Santoso
Keyword(s):  
Regression Model ◽  
Spatial Data ◽  
Information Criterion ◽  
Gaussian Kernel ◽  
Weighted Regression ◽  
Statistical Analysis Method ◽  
Global Regression ◽  
Gwr Model ◽  
Parameter Estimators ◽  
The Relationship

Regression analysis is a statistical analysis method that is used to modeling the relationship between dependent variables and independent variables. In the linear regression model only produced parameter estimators are globally, so it’s often called global regression. While to analyze spatial data can be used Geographically Weighted Regression (GWR) method. Geographically and Temporally Weighted Regression (GTWR) is the development of  GWR model to handle the instability of a data both from the spatial and temporal sides simultaneously. In this GWR modeling the weight function used is a Gaussian  Kernel, which requires the bandwidth value as a distance parameter. Optimum bandwidth can be obtained by minimizing the CV (cross validation) coefficient value. By comparing the R-square, Mean Square Error (MSE) and Akaike Information Criterion (AIC) values in both methods, it is known that modeling the level of deforestation in protected forest areas in Indonesia in 2013 through 2016 uses the GTWR method better than global regression. With the R-square value the GTWR model is 25.1%, the MSE value is 0.7833 and AIC value is 349,6917. While the global regression model has R-square value of 15.8%, MSE value of 0.861 and AIC value of 361,3328. Keywords : GWR, GTWR, Bandwidth, Kernel Gaussian

scholarly journals Penerapan Model Geographically Weighted Regression(GWR) Pada Produksi Ubi Jalar

2018 ◽  
Vol 1 (1) ◽  
pp. 52
Author(s):  
Yuliana Susanti
Keyword(s):  
Regression Model ◽  
Sweet Potato ◽  
Geographically Weighted Regression ◽  
Potato Production ◽  
Weighted Regression ◽  
Soil Conditions ◽  
Sweet Potatoes ◽  
Irian Jaya ◽  
Geographically Weighted Regression Model ◽  
Gwr Model

Sweet potatoes are a major source of carbohydrate, after rice, corn, and cassava. Sweet potato is consumed as an additional or side meal, except in Irian Jaya and Maluku, sweet potato is used as staple food. The main problem faced in increasing sweet potato production is still relies on certain areas, namely Java Island, as the main producer of sweet potato. Differences in production is what often causes the needs of sweet potato in various regions can not be fulfilled and there is a difference price of sweet potato. To fulfill the needs of sweet potato in Java, mapping areas of sweet potato production need to be made so that areas with potential for producing sweet potato can be developed while areas with insufficient quantities of sweet potato production may be given special attention. Due to differences in production in some areas of Java which depend on soil conditions, altitude, rainfall and temperatures, a model of sweet potato production will be developed using the GWR model. Based on the Geographically weighted regression model for each regencies / cities in Java Island, it can be concluded that the largest sweet potato production coming from Kuningan with R2 equal 99.86%.<br />Keywords : Geographically weighted regression, model, sweet potato

scholarly journals PENGUKURAN PENGARUH BELANJA DESA TERHADAP KINERJA PEMBANGUNAN DESA DENGAN MENGGUNAKAN GEOGRAPHICALLY WEIGHTED REGRESSION

2018 ◽  
Vol 20 (1) ◽  
pp. 23
Author(s):  
Eka Purna Yudha ◽  
Bambang Juanda ◽  
Lala M Kolopaking ◽  
Rilus A Kinseng
Keyword(s):  
Rural Communities ◽  
Asset Management ◽  
Rural Areas ◽  
Geographically Weighted Regression ◽  
Financial Management ◽  
Economic Conditions ◽  
Weighted Regression ◽  
The Government ◽  
Gwr Model ◽  
The Village

In 2014, the Government enacted Law No. 6/2014 on Villages with a view to reconstructing village financial and asset management arrangements to accelerate the inclusive and sustainable development of rural areas. The purpose of this study is to analyze the influence of village financial management on the performance of rural development. The study was conducted on 326 Villages in Pandeglang District. The analytical tool of the study using Geographically Weighted Regression (GWR) modeling will look at how the village expenditure is included in the Village Revenue and Expenditure Budget (APBDes). Expenditure of development (infrastructure) of the village has the greatest impact on the performance of village development with the value of elasticity of 0.637. The influence of village expenditure on the GWR model is strongly influenced by the geographical, demographic, and socio-economic conditions of rural communities, resulting in varying outcomes in each village.

scholarly journals Faktor-Faktor yang Menyebabkan Kemiskinan di Provinsi Papua: Analisis Spatial Heterogeneity

2014 ◽  
Vol 14 (2) ◽  
pp. 128-144 ◽  
Author(s):  
Ribut Nurul Tri Wahyuni ◽  
Arie Damayanti
Keyword(s):  
Spatial Heterogeneity ◽  
Geographically Weighted Regression ◽  
Poverty Alleviation ◽  
Complete Information ◽  
Weighted Regression ◽  
Cultivated Land ◽  
Land Area ◽  
Poor Growth ◽  
The Government ◽  
Gwr Model

AbstractPro-poor growth program has not been effective reducing poverty in Papua because the government does not have complete information about the spatial variation of poverty-causing factors (spatial heterogeneity). Therefore, this study will analyze poverty-causing factors using Geographically Weighted Regression (GWR) model. This study finds that the influence of the cultivated land area, use of technical irrigation, source of drinking water, and the electrical infrastructure vary spatially. In additions, multivariate K-means clusteringshows that subdistricts are spatially clustered by geographical conditions. These results imply that poverty alleviation interventions should be dierent for different areas.Keywords: Geographically Weighted Regression, Poverty, Multivariate K-means Clustering, Spatial Heterogeneity AbstrakProgram pro-poor growth (program pembangunan ekonomi yang berpihak kepada penduduk miskin) belum efektif mengurangi kemiskinan di Papua karena pemerintah tidak memiliki informasi lengkap mengenai faktor-faktor yang menyebabkan kemiskinan menurut variasi wilayah (spatial heterogeneity). Oleh karena itu, studi ini akan menganalisis faktor-faktor tersebut dengan menggunakan model Geographically Weighted Regression (GWR). Studi ini menemukan pengaruh luas lahan yang diusahakan, penggunaan irigasi teknis, sumber air minum, dan listrik terhadap kemiskinan bervariasi secara spasial. Sementara itu, multivariate K-means clustering menunjukkan kecamatan mengelompok menurut kondisi geografis. Ini menyiratkan bahwa intervensi pengentasan kemiskinan seharusnya berbeda untuk wilayah berbeda.Kata kunci: Geographically Weighted Regression, Kemiskinan, Multivariate K-means Clustering, Variasi Wilayah Spatial Heterogeneity

scholarly journals Spatiotemporal Changes in Vegetation Cover and Its Influencing Factors in the Loess Plateau of China Based on the Geographically Weighted Regression Model

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 673
Author(s):  
Chen Yang ◽  
Meichen Fu ◽  
Dingrao Feng ◽  
Yiyu Sun ◽  
Guohui Zhai
Keyword(s):  
Regression Model ◽  
Influencing Factors ◽  
Loess Plateau ◽  
Geographically Weighted Regression ◽  
Vegetation Cover ◽  
Weighted Regression ◽  
Vegetation Coverage ◽  
The Loess Plateau ◽  
Geographically Weighted Regression Model ◽  
Spatiotemporal Changes

Vegetation plays a key role in ecosystem regulation and influences our capacity for sustainable development. Global vegetation cover has changed dramatically over the past decades in response to both natural and anthropogenic factors; therefore, it is necessary to analyze the spatiotemporal changes in vegetation cover and its influencing factors. Moreover, ecological engineering projects, such as the “Grain for Green” project implemented in 1999, have been introduced to improve the ecological environment by enhancing forest coverage. In our study, we analyzed the changes in vegetation cover across the Loess Plateau of China and the impacts of influencing factors. First, we analyzed the latitudinal and longitudinal changes in vegetation coverage. Second, we displayed the spatiotemporal changes in vegetation cover based on Theil-Sen slope analysis and the Mann-Kendall test. Third, the Hurst exponent was used to predict future changes in vegetation coverage. Fourth, we assessed the relationship between vegetation cover and the influence of individual factors. Finally, ordinary least squares regression and the geographically weighted regression model were used to investigate the influence of various factors on vegetation cover. We found that the Loess Plateau showed large-scale greening from 2000 to 2015, though some regions showed decreasing vegetation cover. Latitudinal and longitudinal changes in vegetation coverage presented a net increase. Moreover, some areas of the Loess Plateau are at risk of degradation in the future, but most areas showed a sustainable increase in vegetation cover. Temperature, precipitation, gross domestic product (GDP), slope, cropland percentage, forest percentage, and built-up land percentage displayed different relationships with vegetation cover. Geographically weighted regression model revealed that GDP, temperature, precipitation, forest percentage, cropland percentage, built-up land percentage, and slope significantly influenced (p < 0.05) vegetation cover in 2000. In comparison, precipitation, forest percentage, cropland percentage, and built-up land percentage significantly affected (p < 0.05) vegetation cover in 2015. Our results enhance our understanding of the ecological and environmental changes in the Loess Plateau.

scholarly journals Remote Sensing Estimation of Bamboo Forest Aboveground Biomass Based on Geographically Weighted Regression

2021 ◽  
Vol 13 (15) ◽  
pp. 2962
Author(s):  
Jingyi Wang ◽  
Huaqiang Du ◽  
Xuejian Li ◽  
Fangjie Mao ◽  
Meng Zhang ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Geographically Weighted Regression ◽  
Local Variation ◽  
Zhejiang Province ◽  
Weighted Regression ◽  
Landsat 8 ◽  
Bamboo Forest ◽  
Prediction Ability ◽  
Gwr Model ◽  
Forest Aboveground Biomass

Bamboo forests are widespread in subtropical areas and are well known for their rapid growth and great carbon sequestration ability. To recognize the potential roles and functions of bamboo forests in regional ecosystems, forest aboveground biomass (AGB)—which is closely related to forest productivity, the forest carbon cycle, and, in particular, carbon sinks in forest ecosystems—is calculated and applied as an indicator. Among the existing studies considering AGB estimation, linear or nonlinear regression models are the most frequently used; however, these methods do not take the influence of spatial heterogeneity into consideration. A geographically weighted regression (GWR) model, as a spatial local model, can solve this problem to a certain extent. Based on Landsat 8 OLI images, we use the Random Forest (RF) method to screen six variables, including TM457, TM543, B7, NDWI, NDVI, and W7B6VAR. Then, we build the GWR model to estimate the bamboo forest AGB, and the results are compared with those of the cokriging (COK) and orthogonal least squares (OLS) models. The results show the following: (1) The GWR model had high precision and strong prediction ability. The prediction accuracy (R2) of the GWR model was 0.74, 9%, and 16% higher than the COK and OLS models, respectively, while the error (RMSE) was 7% and 12% lower than the errors of the COK and OLS models, respectively. (2) The bamboo forest AGB estimated by the GWR model in Zhejiang Province had a relatively dense spatial distribution in the northwestern, southwestern, and northeastern areas. This is in line with the actual bamboo forest AGB distribution in Zhejiang Province, indicating the potential practical value of our study. (3) The optimal bandwidth of the GWR model was 156 m. By calculating the variable parameters at different positions in the bandwidth, close attention is given to the local variation law in the estimation of the results in order to reduce the model error.

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