scholarly journals PEMODELAN SEMIPARAMETRIC GEOGRAPHICALLY WEIGHTED REGRESSION PADA KASUS PNEUMONIA BALITA PROVINSI JAWA TENGAH

2021 ◽  
Vol 10 (2) ◽  
pp. 250-258
Author(s):  
Putri Fajar Utami ◽  
Agus Rusgiyono ◽  
Dwi Ispriyanti
Keyword(s):  
Population Density ◽  
Akaike Information Criterion ◽  
Geographically Weighted Regression ◽  
Information Criterion ◽  
Weighted Regression ◽  
Severe Malnutrition ◽  
Average Temperature ◽  
Central Java ◽  
Non Local ◽  
Local Parameters

Geographical and inter-regional differences have contributed to the diversity of child pneumonia cases in Central Java, so  a spatial regression modelling is formed that is called Geographically Weighted Regression (GWR). GWR is a development of linear regression by involving diverse factors geographical location, so that local parameters are produced.  Sometimes, there are non-local GWR parameters. To overcome some non-local parameters, Semiparametric Geographically Weighted Regression (SGWR) is formed to develop a GWR model with local and global influences simultaneously. SGWR Model is used to estimate the model of percentage of children with pneumonia in Central Java with population density, average temperature, percentage of children with severe malnutrition, percentage of children with under the red line weight, percentage of households behave in clean and healthy lives, and percentage of children who measles immunized. SGWR models on percentage of children with pneumonia in Central Java produce locally significant variables that is population density, average temperature, and percentage of households behave in clean and healthy lives. Variable that globally significant is percentage of children with severe malnutrition. Based on Akaike Information Criterion (AIC), SGWR is a better model to analize percentage of children with pneumonia in Central Java because of smallest AIC. Keywords: Akaike Information Criterion, Geographically Weighted Regression, Semiparametric Geographically Weighted Regression

Second-hand housing batch evaluation model of zhengzhou city based on big data and MGWR model

2021 ◽  
pp. 1-20
Author(s):  
Chaojie Liu ◽  
Jie Lu ◽  
Wenjing Fu ◽  
Zhuoyi Zhou
Keyword(s):  
Real Estate ◽  
Geographically Weighted Regression ◽  
Euclidean Distance ◽  
Evaluation Model ◽  
Housing Prices ◽  
Information Criterion ◽  
Access Point ◽  
Least Square ◽  
Weighted Regression ◽  
Spatial Error

How to better evaluate the value of urban real estate is a major issue in the reform of real estate tax system. So the establishment of an accurate and efficient housing batch evaluation model is crucial in evaluating the value of housing. In this paper the second-hand housing transaction data of Zhengzhou City from 2010 to 2019 was used to model housing prices and explanatory variables by using models of Ordinary Least Square (OLS), Spatial Error Model (SEM), Geographically Weighted Regression (GWR), Geographically and Temporally Weighted Regression (GTWR), and Multiscale Geographically Weighted Regression (MGWR). And a correction method of Barrier Line and Access Point (BLAAP) was constructed, and compared with three correction methods previously studied: Buffer Area (BA), Euclidean Distance (ED), and Non-Euclidean Distance, Travel Distance (ND, TT). The results showed: The fitting degree of GWR, MGWR and GTWR by BLAAP was 0.03–0.07 higher than by ND. The fitting degree of MGWR was the highest (0.883) by BLAAP but the smallest by Akaike Information Criterion (AIC), and 88.3% of second-hand housing data could be well interpreted by the model.

scholarly journals The Akaike information criterion in weighted regression of immittance data

2019 ◽  
Vol 317 ◽  
pp. 648-653 ◽  
Author(s):  
Mats Ingdal ◽  
Roy Johnsen ◽  
David A. Harrington
Keyword(s):  
Akaike Information Criterion ◽  
Information Criterion ◽  
Weighted Regression

scholarly journals Measuring Bandwidth Uncertainty in Multiscale Geographically Weighted Regression Using Akaike Weights

2019 ◽  
Author(s):  
Ziqi Li ◽  
Alexander Stewart Fotheringham ◽  
Taylor M. Oshan ◽  
Levi John Wolf
Keyword(s):  
Geographically Weighted Regression ◽  
Regression Models ◽  
Bandwidth Selection ◽  
Information Criterion ◽  
Weighted Regression ◽  
Parameter Estimates ◽  
Multi Scale ◽  
Single Scale ◽  
Heterogeneous Processes ◽  
Key Parameter

Bandwidth, a key parameter in geographically weighted regression models, is closely related to the spatial scale at which the underlying spatially heterogeneous processes being examined take place. Generally, a single optimal bandwidth (geographically weighted regression) or a set of covariate-specific optimal bandwidths (multiscale geographically weighted regression) is chosen based on some criterion such as the Akaike Information Criterion (AIC) and then parameter estimation and inference are conditional on the choice of this bandwidth. In this paper, we find that bandwidth selection is subject to uncertainty in both single-scale and multi-scale geographically weighted regression models and demonstrate that this uncertainty can be measured and accounted for. Based on simulation studies and an empirical example of obesity rates in Phoenix, we show that bandwidth uncertainties can be quantitatively measured by Akaike weights, and confidence intervals for bandwidths can be obtained. Understanding bandwidth uncertainty offers important insights about the scales over which different processes operate, especially when comparing covariate-specific bandwidths. Additionally, unconditional parameter estimates can be computed based on Akaike weights accounts for bandwidth selection uncertainty.

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 Geographically varying relationships of COVID-19 mortality with different factors in India

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Asif Iqbal Middya ◽  
Sarbani Roy
Keyword(s):  
Geographically Weighted Regression ◽  
Information Criterion ◽  
Driving Factors ◽  
Least Square ◽  
Weighted Regression ◽  
Related Factors ◽  
Local Method ◽  
Ordinary Least Square ◽  
Local Methods ◽  
Global And Local

AbstractCOVID-19 is a global crisis where India is going to be one of the most heavily affected countries. The variability in the distribution of COVID-19-related health outcomes might be related to many underlying variables, including demographic, socioeconomic, or environmental pollution related factors. The global and local models can be utilized to explore such relations. In this study, ordinary least square (global) and geographically weighted regression (local) methods are employed to explore the geographical relationships between COVID-19 deaths and different driving factors. It is also investigated whether geographical heterogeneity exists in the relationships. More specifically, in this paper, the geographical pattern of COVID-19 deaths and its relationships with different potential driving factors in India are investigated and analysed. Here, better knowledge and insights into geographical targeting of intervention against the COVID-19 pandemic can be generated by investigating the heterogeneity of spatial relationships. The results show that the local method (geographically weighted regression) generates better performance ($$R^{2}=0.97$$ R 2 = 0.97 ) with smaller Akaike Information Criterion (AICc $$=-66.42$$ = - 66.42 ) as compared to the global method (ordinary least square). The GWR method also comes up with lower spatial autocorrelation (Moran’s $$I=-0.0395$$ I = - 0.0395 and $$p < 0.01$$ p < 0.01 ) in the residuals. It is found that more than 86% of local $$R^{2}$$ R 2 values are larger than 0.60 and almost 68% of $$R^{2}$$ R 2 values are within the range 0.80–0.97. Moreover, some interesting local variations in the relationships are also found.

A geographically weighted regression approach to examine the dynamics of fertility differentials across Africa

2020 ◽  
Vol 36 ◽  
pp. 87-102
Author(s):  
Aniefiok Henry Ekong ◽  
Olaniyi Mathew Olayiwola
Keyword(s):  
Geographically Weighted Regression ◽  
Information Criterion ◽  
Ordinary Least Squares ◽  
Fertility Rate ◽  
Weighted Regression ◽  
Negative Consequences ◽  
African Countries ◽  
Factors Affecting ◽  
Western Africa ◽  
Republic Of The Congo

Studies have shown that fertility rate in Africa is still among the highest in the world. However, there are few spatial investigations into the variation of fertility rate and its determinant in Africa. This study aimed to examine the spatial distribution of fertility rate as well as highlight its significant determinants. Ordinary Least Squares (OLS) regression was carried out on dataset for 53 African countries on Total Fertility Rate (TFR) and eleven determinant factors to obtain a best model, which was then used for Geographically Weighted Regression (GWR). The study showed that TFR was significantly influenced by adolescent fertility rates, contraceptive prevalence rates and gross domestic product per capita. GWR model diagnostics of Akaike Information Criterion and adjusted R-squared showed that GWR fitted TFR in Africa better than OLS model. Also, countries around Middle to Western Africa comprising Burundi, Democratic Republic of the Congo, Central African Republic, Chad, Nigeria, Niger, Benin, Burkina Faso and Mali, were regions with high TFRs that impacted Africa’s positive TFR spatial autocorrelation. More intense works could therefore be carried out in these countries to manage the identified significant factors affecting TFR to address the negative consequences of high TFR in Africa.

scholarly journals PEMODELAN INCIDENT RATE DEMAM BERDARAH DENGUE DI INDONESIA YANG BERKAITAN DENGAN FAKTOR LINGKUNGAN MENGGUNAKAN METODE GEOGRAPHICALLY WEIGHTED REGRESSION (GWR)

EKOLOGIA ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 64-73
Author(s):  
Kiki Amelia ◽  
Latifa Oktafiani Asril ◽  
Lasmi Febrianti
Keyword(s):  
Population Density ◽  
Dengue Hemorrhagic Fever ◽  
Geographically Weighted Regression ◽  
Physical Environment ◽  
Secondary Data ◽  
Hemorrhagic Fever ◽  
Least Square ◽  
Gaussian Kernel ◽  
Weighted Regression ◽  
Incident Rate

Dengue hemorrhagic fever cases in Indonesia often occur in cities and villages. Every year hundreds to thousands of people must be hospitalized due to this disease. There are several factors of the physical environment that directly or indirectly influence the transmission of this disease. Such as rainfall, air temperature, and humidity. In addition to the physical environment there are several other factors that can increase the occurrence of dengue cases, namely population density and the level of larvae free in an area. For this reason, we conducted a study of the above factors and their contribution in the addition of dengue cases that occurred in Indonesia in 2015 using secondary data. The purpose of this study is to identify and make a BDB iricident rate model related to environmental factors such as temperature, humidity, population density, and the amount of rainfall on the number of cases of dengue hemorrhagic fever in Indonesia in 2015. The method used is the Geographically Weighted Regression method. (GWR). In the GWR model the parameter estimation uses Weighted Least Square (WLS) by weighting the gaussian kernel function. The results of the study concluded that modeling with GWR was better than linear regression and the variables were significantly different in each region.

scholarly journals Regression model analysis for prediction of body weight from body measurements in female Batur sheep of Banjarnegara District, Indonesia

2021 ◽  
Vol 22 (7) ◽  
Author(s):  
Alek Ibrahim ◽  
Wayan Tunas Artama ◽  
I Gede Suparta Budisatria ◽  
Ridwan Yuniawan ◽  
Bayu Andri Atmoko ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Model ◽  
Akaike Information Criterion ◽  
Computer Software ◽  
Correlation Coefficients ◽  
Information Criterion ◽  
Model Analysis ◽  
The Body ◽  
Body Measurements ◽  
Central Java

Abstract. Ibrahim A, Artama WT, Budisatria IGS, Yuniawan R, Atmoko BA, Widayanti R. 2021. Regression model analysis for prediction of body weight from body measurements in female Batur sheep of Banjarnegara District, Indonesia. Biodiversitas 22: 2723-2730. Bodyweight is an important aspect of livestock management. The present study was undertaken to estimate correlation coefficients between biometric traits and identify best predictor of body weight in female Batur sheep from body measurements. Data on body weight and body measurements (body length: BL, chest girth: CG and withers height: WH) were collected from 73 female Batur sheep in Batur Village, Banjarnegara District, Central Java Province, Indonesia. Batur sheep were grouped into 3 categories based on their age, namely groups <1.5 years, 1.5-2.5 years and >2.5 years. The data were analyzed using simple, multiple, and automatic linear regression methods using the SPSS computer software version 25 platform. The correlation coefficient, coefficient determination, adjusted coefficient determination, residual standard error, Akaike information criterion, Bayesian information criterion, and Akaike information criterion corrected were used to determine the best regression formula for the prediction of BW. The average BW (kg), BL (cm), CG (cm), and WH (cm) of 49.27, 63.11, 91.41, and 56.82, respectively was observed in the present study. The correlation coefficients of 0.433, 0.866, and 0.369 for BW with BL, CG, and WH were observed in the present study. The best prediction of BW using two predictors (BL and GC) was BW =-56.522 + 0.509BL + 0.843CG, followed by using three predictors (BL, CG, and WH) was BW =-57.897+ 0.505BL + 0.839CG + 0.034WH, and using the only one predictor (CG) was BW =-28.443 + 0.905CG. The study revealed that CG and its combination with other linear body measurements can effectively define the body weight in Batur sheep. However, the highest R2 of 0.782 was observed when CG and BL were used as predictors.

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