Impacts of Human Development Index and Percentage of Total Population on Poverty using OLS and GWR models in Central Java, Indonesia

Mahara, Duhania Oktasya; Fauzan, Achmad

doi:10.20885/eksakta.vol2.iss2.art8

Cited by 5 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The goodness of fit test of the GWR model with the fixed kernel tricube weighting function for modeling life expectancy in East Java Province shows that there is a statistical difference between the GWR model with the fixed kernel tricube weighting function and the OLS regression model. The results of this study are not in line with research in Central Java Province which explains that there is no difference between the OLS regression model and the GWR model using fixed kernel tricube weighting function in modeling poverty [5]. The significant difference between the OLS model and the GWR model is caused by the presence of spatial weights with a range from 0 to close to 1 which are different at each location and cause the F statistic to get bigger and the p-value to get smaller.…”

Section: Discussioncontrasting

confidence: 92%

Geographically weighted regression with fixed kernel tricube on life expectancy in East Java

Hanin Ruliyani,

Kuntoro

2023

World J. Adv. Res. Rev.

View full text Add to dashboard Cite

Violation of the homoscedasticity assumption will cause biased and inefficient parameter estimation. Geographically Weighted Regression (GWR) is a spatial regression analysis that will produce local parameter estimates. Data on life expectancy for East Java Province in 2021 shows that there is a violation of the homoscedasticity assumption. The aim of the study is to analyze Geographically Weighted Regression with fixed kernel tricube for life expectancy in East Java Province in 2021. This study use a non-reactive research design using secondary data. The results show that the GWR model is different from the OLS model and produces four regional groups which are divided based on significant predictor variables. Three significant predictor variables are the percentage of households that have access to proper sanitation (X1), the coverage of exclusive breastfeeding for infants aged less than 6 months (X2), and the percentage of monthly expenditure per capita for food (X5). Based on this study, group mapping is obtained showing that areas in the same group tend to be close together and parameter estimates that are local to the observation locations.

show abstract

Section: Discussioncontrasting

confidence: 92%

Geographically weighted regression with fixed kernel tricube on life expectancy in East Java

Hanin Ruliyani,

Kuntoro

2023

World J. Adv. Res. Rev.

View full text Add to dashboard Cite

show abstract

“…Several previous studies, such as those conducted by Mahara et al, have contributed to analyzing the impact of the Human Development Index (HDI) and the total population percentage on poverty in Central Java Province. The utilization of Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models in this research highlighted the increase in the number of impoverished people in Central Java Province in 2020, concluding that the GWR model, particularly with the Adaptive Kernel Bisquare weighting function, yielded superior results compared to the OLS model [22]. In line with this, Xindong's study in Sichuan Province, China, explored the relationship between poverty and geographic factors using OLS and GWR.…”

Section: Related Workmentioning

confidence: 62%

Gwrpca Algorithmic Framework: Analyzing Spatial Dynamics of Poverty in East Java Province

Al Azies

2024

JITET

View full text Add to dashboard Cite

This study employs Regression Principal Component Analysis (RPCA) and Geographically Weighted Regression Principal Component Analysis (GWRPCA) algorithms to analyze poverty in East Java Province, using data from Statistics Indonesia (BPS). The research investigates regency/city-level poverty percentages and identifies influential factors such as education, literacy rates, housing conditions, and economic indicators. The results reveal that GWRPCA, with an 85.10% R2 value, outperforms RPCA, highlighting its effectiveness in capturing spatial diversity and providing a nuanced portrayal of poverty characteristics across regencies/cities in East Java. In conclusion, GWRPCA emerges as a powerful algorithmic tool for informing targeted poverty alleviation policies, offering insights into spatial variations. The study suggests future research directions to explore evolving spatial patterns and consider additional variables for a more comprehensive analysis. The findings emphasize the significance of spatial considerations in devising effective, context-specific strategies for each regency/city in East Java

show abstract

“…The GWR and OLS were employed in central java, Indonesia, to evaluate the impact of the human development index and population size on poverty. This study’s outcomes reported good performance of these models (Mahara and Fauzan 2021 ). The OLS and GWR methods are used to assess the influences of geographical parameters on forming land surface temperature (Kashki et al 2021 ).…”

Section: Introductionmentioning

confidence: 57%

Integration of Moran’s I, geographically weighted regression (GWR), and ordinary least square (OLS) models in spatiotemporal modeling of COVID-19 outbreak in Qom and Mazandaran Provinces, Iran

Isazade¹,

Qasimi²,

Dong

et al. 2023

Model. Earth Syst. Environ.

View full text Add to dashboard Cite

Globally, the COVID-19 pandemic is a top-level public health concern. This paper attempts to identify the COVID-19 pandemic in Qom and Mazandaran provinces, Iran using spatial analysis approaches. This study was based on secondary data of confirmed cases and deaths from February 3, 2020, to late October 2021, in two Qom and Mazandaran provinces from hospitals and the website of the National Institute of Health. In this paper, three geographical models in ArcGIS 10.8.1 were utilized to analyze and evaluate COVID-19, including geographic weight regression (GWR), ordinary least squares (OLS), and spatial autocorrelation (Moran I). The results from this study indicate that the rate of scattering of confirmed cases for Qom province for the period was 44.25%, while the rate of dispersal of the deaths was 4.34%. Based on the GWR and OLS model, Moran’s statistics demonstrated that confirmed cases, deaths, and recovered followed a clustering pattern during the study period. Moran’s Z-score for all three indicators of confirmed cases, deaths, and recovered was confirmed to be greater than 2.5 (95% confidence level) for both GWR and OLS models. The spatial distribution of indicators of confirmed cases, deaths, and recovered based on the GWR model has been more scattered in the northwestern and southwestern cities of Qom province. Whereas the spatial distribution of the recoveries of the COVID-19 pandemic in Qom province was 61.7%, the central regions of this province had the highest spread of recoveries. The spatial spread of the COVID-19 pandemic from February 3, 2020, to October 2021 in Mazandaran province was 35.57%, of which 2.61% died, according to information published by the COVID-19 pandemic headquarters. Most confirmed cases and deaths are scattered in the north of this province. The ordinary least squares model results showed that the spatial dispersion of recovered people from the COVID-19 pandemic is more significant in the central and southern regions of Mazandaran province. The Z-score for the deaths Index is more significant than 14.314. The results obtained from this study and the information published by the National Headquarters for the fight against the COVID-19 pandemic showed that tourism and pilgrimages are possible factors for the spatial distribution of the COVID-19 pandemic in Qom and Mazandaran provinces. The spatial information obtained from these modeling approaches could provide general insights to authorities and researchers for further targeted investigations and policies in similar circumcises.

show abstract

Impacts of Human Development Index and Percentage of Total Population on Poverty using OLS and GWR models in Central Java, Indonesia

Cited by 5 publications

References 0 publications

Geographically weighted regression with fixed kernel tricube on life expectancy in East Java

Geographically weighted regression with fixed kernel tricube on life expectancy in East Java

Gwrpca Algorithmic Framework: Analyzing Spatial Dynamics of Poverty in East Java Province

Integration of Moran’s I, geographically weighted regression (GWR), and ordinary least square (OLS) models in spatiotemporal modeling of COVID-19 outbreak in Qom and Mazandaran Provinces, Iran

Contact Info

Product

Resources

About