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    수도권과 비수도권의 도시활력 핵심 영향요인 차이분석 : 택지개발지구를 중심으로 = Differences in the Key Determinants of Urban Vitality between Capital and Non-capital Regions : Evidence from Housing Development Districts

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    https://www.riss.kr/link?id=A110467281

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    국문 초록 (Abstract) kakao i 다국어 번역

    본 연구는 동일한 제도적 틀에서 조성된 전국 803개 택지개발지구를 분석 단위로 설정하여, 수도권과 비수도권 간 도시활력 관련 영향요인의 구조적 차이를 비교·분석한다. 다양한 예측모형을 통해 지역별 도시활력 관련 변수 기여도를 해석함으로써, 획일적 개발 기준의 한계를 검증하고 권역 특성을 반영한 차별화된 도시계획 및 택지개발 전략 수립의 필요성을 제시하고자 한다.
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    본 연구는 동일한 제도적 틀에서 조성된 전국 803개 택지개발지구를 분석 단위로 설정하여, 수도권과 비수도권 간 도시활력 관련 영향요인의 구조적 차이를 비교·분석한다. 다양한 예측모형...

    본 연구는 동일한 제도적 틀에서 조성된 전국 803개 택지개발지구를 분석 단위로 설정하여, 수도권과 비수도권 간 도시활력 관련 영향요인의 구조적 차이를 비교·분석한다. 다양한 예측모형을 통해 지역별 도시활력 관련 변수 기여도를 해석함으로써, 획일적 개발 기준의 한계를 검증하고 권역 특성을 반영한 차별화된 도시계획 및 택지개발 전략 수립의 필요성을 제시하고자 한다.

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    This study analyzes structural differences in urban vitality formation between capital and non-capital regions across 803 housing development districts in South Korea, all developed under identical national regulations. Living population density from KT mobile signal data (total, daytime, and nighttime) served as the vitality proxy, with 24 explanatory variables covering land use, physical environment, infrastructure, and urban scale. Seven models were compared (OLS, SEM, Random Forest, XGBoost, LightGBM, MLP, and TabNet), with XGBoost selected as the single interpretive model for SHAP analysis to ensure consistent cross-regional comparison. Capital region districts showed approximately 38% higher vitality than non-capital regions. While non-capital districts exhibited greater relative heterogeneity (CV=0.67 vs. 0.52), absolute intra-regional disparity (IQR) was wider in the capital region. XGBoost and Random Forest showed the most stable predictive performance; the capital region had a higher single hold-out R² (0.61 vs. 0.49), but the non-capital region outperformed on 5-fold CV (R²=0.51 vs. 0.28), implying that capital region vitality is driven by complex factors beyond physical variables. SHAP analysis identified population size and industrial cluster accessibility as common key drivers in both regions. In the capital region, industrial clusters dominated daytime vitality and park accessibility ranked highly, whereas in the non-capital region, commercial cluster accessibility exerted strong influence across all time periods, especially at night. This suggests that capital region vitality is structurally linked to industrial and employment functions, while non-capital vitality is more closely associated with the spatial agglomeration of consumption and service activities. These findings highlight the limitations of uniform development standards and underscore the need for region-specific urban planning and housing development strategies.
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    This study analyzes structural differences in urban vitality formation between capital and non-capital regions across 803 housing development districts in South Korea, all developed under identical national regulations. Living population density from ...

    This study analyzes structural differences in urban vitality formation between capital and non-capital regions across 803 housing development districts in South Korea, all developed under identical national regulations. Living population density from KT mobile signal data (total, daytime, and nighttime) served as the vitality proxy, with 24 explanatory variables covering land use, physical environment, infrastructure, and urban scale. Seven models were compared (OLS, SEM, Random Forest, XGBoost, LightGBM, MLP, and TabNet), with XGBoost selected as the single interpretive model for SHAP analysis to ensure consistent cross-regional comparison. Capital region districts showed approximately 38% higher vitality than non-capital regions. While non-capital districts exhibited greater relative heterogeneity (CV=0.67 vs. 0.52), absolute intra-regional disparity (IQR) was wider in the capital region. XGBoost and Random Forest showed the most stable predictive performance; the capital region had a higher single hold-out R² (0.61 vs. 0.49), but the non-capital region outperformed on 5-fold CV (R²=0.51 vs. 0.28), implying that capital region vitality is driven by complex factors beyond physical variables. SHAP analysis identified population size and industrial cluster accessibility as common key drivers in both regions. In the capital region, industrial clusters dominated daytime vitality and park accessibility ranked highly, whereas in the non-capital region, commercial cluster accessibility exerted strong influence across all time periods, especially at night. This suggests that capital region vitality is structurally linked to industrial and employment functions, while non-capital vitality is more closely associated with the spatial agglomeration of consumption and service activities. These findings highlight the limitations of uniform development standards and underscore the need for region-specific urban planning and housing development strategies.

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