Authors: Jiman Park*, Korea Land and Geospatial Informatics Education Institute, Seungju Mun, Korea Land and Geospatial Informatics Education Institute
Topics: Geographic Information Science and Systems, Geographic Information Science and Systems, Urban Geography
Keywords: Elderly welfare vulnerable district, Machine learning, Artificial neural network, Geographically weighted regression, Spatial autocorrelation
Session Type: Paper
Presentation File: No File Uploaded
Korea is rapidly undergoing an aging society. So the social impact of South Korea with the elderly population will accelerate. The purpose of this study is to establish a methodology for extracting vulnerable districts of the welfare of the aged through machine learning(ML), artificial neural network(ANN) and geospatial analysis. The study area was set as Yongin-city, Gyeonggi-do, Korea. Yongin city is a satellite city in Seoul and urbanization is proceeding due to the increase of population and traffic access. In order to establish the direction of analysis, this progressed after an interview with volunteers who over 65-year old people, public officer and the manager of the aged welfare facility. The indicators are the geographic distance capacity, elderly welfare enjoyment, officially assessed land price and mobile communication based on old people activities where 500 m vector areal unit within 15 minutes in Yongin-city, Gyeonggi-do. As a result, the prediction accuracy of 83.2% in the support vector machine(SVM) of ML using the RBF kernel algorithm was obtained in simulation. Furthermore, the correlation result(0.63) was derived from ANN using backpropagation algorithm. A geographically weighted regression(GWR) was also performed to analyze spatial autocorrelation within variables. As a result of this analysis, the coefficient of determination was 70.1%, which showed good explanatory power. Moran’s I and Getis-Ord Gi coefficients are analyzed to investigate spatially outlier as well as distribution patterns. This study can be used to solve the welfare imbalance of the aged considering the local conditions of the government recently.
To access contact information login