scholarly journals THE BEST GLOBAL AND LOCAL VARIABLES OF THE MIXED GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION MODEL

2019 ◽  
Vol 3 (3) ◽  
pp. 320-330
Author(s):  
Nuramaliyah Nuramaliyah ◽  
Asep Saefuddin ◽  
Muhammad Nur Aidi
Keyword(s):  
Information Criterion ◽  
School Enrollment ◽  
Weighted Regression ◽  
Participation Rates ◽  
Literacy Rate ◽  
Temporal Variables ◽  
Result Show ◽  
Enrollment Rates ◽  
Global And Local ◽  
Better Than

Geographically and temporally weighted regression (GTWR) is a method used when there is spatial and temporal diversity in an observation. GTWR model just consider the local influences of spatial-temporal independent variables on dependent variable. In some cases, the model not only about local influences but there are the global influences of spatial-temporal variables too, so that mixed geographically and temporally weighted regression (MGTWR) model more suitable to use. This study aimed to determine the best global and local variables in MGTWR and to determine the model to be used in North Sumatra’s poverty cases in 2010 to 2015. The result show that the Unemployment rate and labor force participation rates are global variables. Whereas the variable literacy rate, school enrollment rates and households buying rice for poor (raskin) are local variables. Furthermore, Based on Root Mean Square Error (RMSE) and Akaike Information Criterion (AIC) showed that MGTWR better than GTWR when it used in North Sumatra’s poverty cases.

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.

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

2020 ◽  
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

Abstract COVID-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 (R2 = 0:973) with smaller Akaike Information Criterion (AICc = -77:93) as compared to the global method (ordinary least square). The GWR method also comes up with lower spatial autocorrelation (Moran’s I = -0.0436 and p < 0:01) in the residuals. It is found that more than 87.5% of local R2 values are larger than 0.60 and almost 60% of R2 values are within the range 0:80 - 0:97. Moreover, some interesting local variations in the relationships are also found.

Identification of Essential Proteins in Yeast Using Mean Weighted Average and Recursive Feature Elimination

2019 ◽  
Vol 12 (1) ◽  
pp. 5-10 ◽  
Author(s):  
Sivagnanam Rajamanickam Mani Sekhar ◽  
Siddesh Gaddadevara Matt ◽  
Sunilkumar S. Manvi ◽  
Srinivasa Krishnarajanagar Gopalalyengar
Keyword(s):  
Drug Design ◽  
Weighted Average ◽  
Living Organism ◽  
Experimental Result ◽  
Recursive Feature Elimination ◽  
Protein Interaction Data ◽  
Essential Proteins ◽  
Protein Protein Interaction ◽  
Result Show ◽  
Better Than

Background: Essential proteins are significant for drug design, cell development, and for living organism survival. A different method has been developed to predict essential proteins by using topological feature, and biological features. Objective: Still it is a challenging task to predict essential proteins effectively and timely, as the availability of protein protein interaction data depends on network correctness. Methods: In the proposed solution, two approaches Mean Weighted Average and Recursive Feature Elimination is been used to predict essential proteins and compared to select the best one. In Mean Weighted Average consecutive slot data to be taken into aggregated count, to get the nearest value which considered as prescription for the best proteins for the slot, where as in Recursive Feature Elimination method whole data is spilt into different slots and essential protein for each slot is determined. Results: The result shows that the accuracy using Recursive Feature Elimination is at-least nine percentages superior when compared to Mean Weighted Average and Betweenness centrality. Conclusion: Essential proteins are made of genes which are essential for living being survival and drug design. Different approaches have been proposed to anticipate essential proteins using either experimental or computation methods. The experimental result show that the proposed work performs better than other approaches.

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 Engagement of Occupational Safety and Health at Mahkamah’s Welding Worker Medan

2018 ◽  
Vol 1 (1) ◽  
pp. 48-53
Author(s):  
Eka Mahyuni ◽  
Kalsum ◽  
Muhammad Makmur Sinaga
Keyword(s):  
Occupational Safety ◽  
Occupational Safety And Health ◽  
The Self ◽  
Safety And Health ◽  
Before And After ◽  
Physical Risk ◽  
Result Show ◽  
Rest Time ◽  
Self Protection ◽  
Better Than

Welding worker was not the easy task because it has a very high physical risk and the process requires special skills and equipment to prevent accident exposed. This devotional activity is carried out in the welding industry at Jl. Mahkamah with two partners, namely CV. M. Nauli and CV. Cahaya. The aim of training activity made the worker able to analyze the hazards in the workplace so that it will be more careful in their work. The result show that the training could develop the worker to be aware about safety and health work patterns. In order to support the work in accordance with occupational safety and health standards, workers are also given pocket books that contain safety and health working methods and also given the self-protection of welding like welding clothes, welding gloves, welding mask, welding glasses and masks. Based on the evaluation of activities, it show that the worker has develop and always using the self protector in their work evenly. It build the good collaboration between them and they are could arrage the rest time with ergonomics relaxation in 5-10 minutes. The workshop station looks better than before and the workshop doing good house keeping before and after their work.

scholarly journals Work Value among Married and Unmarried Person’: A Comparative Study

2014 ◽  
Vol 1 (2) ◽  
Author(s):  
Ramesh O. Prajapati
Keyword(s):  
Data Collection ◽  
Comparative Study ◽  
T Test ◽  
Nuclear Families ◽  
Significant Difference ◽  
Low Incomes ◽  
Result Show ◽  
Work Value ◽  
Test Result ◽  
Better Than

Aim of the research is to find out the Work value among married and unmarried person’s. So investigator selected two groups one is married and other is unmarried persons, both groups have 200 persons. In one group has 113 married and other one groups has 87 unmarried persons. The all subjects were randomly selected. Data were collected from Ahmadabad district. Scale was use for data collection is personal datasheet and Work value scale developed by super (1970) and this scale was translated into Gujarati by Jalawadiya (2002), and data were analysis by ‘t’ test. Result show, There is no significant mean difference of Work value between married and unmarried persons. There is no significant difference of the Work value of joint and nuclear families. The high income persons work value is better than the low incomes.

scholarly journals Model Partial Least Square Regression (PLSR) Pengaruh Bidang Pendidikan dan Ekonomi Terhadap Tingkat Kemiskinan di Indonesia

2017 ◽  
Vol 7 (1) ◽  
pp. 67
Author(s):  
I Gusti Ayu Made Srinadi
Keyword(s):  
School Enrollment ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Poor People ◽  
Model Estimation ◽  
Poverty Level ◽  
Model Estimate ◽  
Literacy Rate ◽  
Least Square Regression

Partial Least Square Regression (PLSR)  is one of the methods applied in the estimation of multiple linear regression models when the ordinary least square method  (OLS) can not be used. OLS generates an invalid model estimate when multicollinearity occurs or when the number of independent variables is greater than the number of data observations. In conditions that OLS can be applied in obtaining model estimation, want to know the performance of PLSR method. This study aims to determine the model of PLSR the influence of literacy rate, the average of school duration,  school enrollment rate, Income per capita, and open unemployment rate to the level of poverty seen from the percentage of poor people in Indonesia by 2015. Estimated model with OLS , Only variable of literacy rate  are included in the model with the coefficient of determination R2 = 32.52%. PLSR model estimation of cross-validation, leave-one-out method with one selected component has R2 of 33,23%. Both models shows  a negative relationship between poverty and literacy rate. The higher literacy rate will reduce the poverty level, indicating that the success of the Indonesian government in the development of education will support the government's success in reducing poverty level.

scholarly journals Still Influential: The Protestant Emphasis on Schooling

2018 ◽  
Vol 17 (5) ◽  
pp. 641-678 ◽  
Author(s):  
Horst Feldmann
Keyword(s):  
Regression Analysis ◽  
Secondary School ◽  
Cultural Heritage ◽  
School Enrollment ◽  
Historical Legacy ◽  
Enrollment Rates ◽  
Using Data

Abstract From its beginning 500 years ago, Protestantism has been advocating and actively pursuing the expansion of schooling, including the schooling of girls. In many countries, it has thus helped to create a cultural heritage that puts a high value on education and schooling. This paper provides evidence that Protestantism’s historical legacy has an enduring effect. Using data on 147 countries, it finds that countries with larger Protestant population shares in 1900 had higher secondary school enrollment rates over 1975-2010, including among girls. The magnitude of the effect is small though. Using Protestant population shares over 1975-2010, the paper also shows that Protestantism’s influence on schooling has diminished and that contemporary Protestantism, in contrast to historical Protestantism, does not affect schooling. The regression analysis accounts for numerous other determinants of schooling.

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
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