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Small-area population forecasting of shrinking cities in south Korea: using SHAP (shapley additive explanations) machine learning

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2023
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AESOP
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The purpose of this study is to identify the utility of machine learning model in projecting the population of small areas. This study was conducted between 2020 and 2040 in the local districts of Korea and compared the research results of cohort component model and machine learning model. As a result of projecting population through the cohort component method and machine learning, it was identified that the accuracy of the machine learning model was much higher. The cohort component model is expected to have a high forecasting error because it only explains population change by three component: birth, death, and migration, and it is confirmed that it is almost unpredictable, especially when there are frequent population changes due to new development. On the other hand, the machine learning model reflects various variables such as socioeconomic factors in the population projecting model, which greatly reduces the prediction error. The machine learning model projected that the population would be evenly distributed across the country, especially on the central part of Busan Metropolitan City, while the cohort component model projected that the population would be concentrated in some areas such as Gijang gun and Gangseo gu. The SHAP value interpreted as the machine learning model relying most heavily on the pre population and fertile women variables to project population. Keywords : Small area Population Projection, Cohort Component Method, Machine Learning, SHAP
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Book of proceedings: 35th AESOP Annual Congress Integrated planning in a world of turbulence, Łódź, 11-15th July, 2023
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CC BY 4.0
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