Spatial data linkage; transportation accessibility analysis (travel time indicator destination indicator; departure indicator); demand and supply of childcare facilities; national and public childcare facilities; temporal childcare facilities; regional clustering
This study establishes its own database combining diverse types of available public data, and analyzes and assesses childcare services and infrastructure in terms of accessibility. This study also predicts the likely changes in accessibility of the childcare service infrastructure resulting from the creation of additional facilities, and explores the policy implications of accessibility-based clustering of those facilities. In particular, this study uses diverse accessibility indicators, analyzes and assesses how an increase in the number of national and public childcare facilities can improve accessibility, simulates changes in accessibility of these services by simulating various locations at which new facilities could be located, and performs clustering analyses on the accessibility of childcare infrastructure for each region or municipality.
The goal of this study is to provide empirical evidences from big data analysis upon which the Ministry of Health and Welfare (MOHW) and local governments can base their plans for expanding the national & public childcare infrastructure. The findings and policy suggestions provided by this study can also be cited as examples of data-driven policymaking on expanding the national & public infrastructure for childcare, emphasized by the recently-elected Moon Jae-in administration.
Finally, this study proposes the development and use of a policymaking support system, consisting of databases supporting data-driven and scientific policymaking. To perform analysis of the distribution of childcare infrastructure in terms of accessibility, this study involved setting up a number of databases and carried out clustering analysis and simulations. By systematically applying data-based analysis and machine learning methods to diverse areas of policymaking, policymakers can effectively make use of big data and find data-driven policy solutions. Such a policymaking support system, designed to support diverse simulations and provide and visualize information necessary for effective policies, should be used in all areas of policymaking over and beyond childcare.
Ⅰ. Research Background & Purpose 1 Ⅱ. Literature Review 7 1. Literature Review 9 2. Departing from the Existing Literature 13 3. The Need for Future Research 17 Ⅲ. Databases & Research Method 21 1. Scope & Database of Accessibility Analysis 24 2. Accessibility Analysis: Method & Indicators 27 3. Clustering Analysis & Indicators Based on Spatial Data 30 Ⅳ. Research Findings 35 1. Analysis of the Distribution of National/Public Childcare Facilities and Assessment of the Accessibility of Childcare Services After Expansion of National & Public Childcare Infrastructure in 2012 38 2. Distribution of Hourly Care Facilities & Forecasts on Changes in Accessibility Based on Simulation of New Hourly Care Facility Locations 45 3. Clustering Analysis on the Supply-Demand Indicators & Characteristicsvyw of All Neighborhoods Nationwide 50 4. Clustering Analysis on Supply-Demand and Accessibility Indicators & Characteristics of Neighborhoods in Seoul 61 Ｖ. Policy Implications and Recommendations 73 Bibliography 81