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    발생 핫스팟 분석 기반 공시지가의 시공간적 변화 분석에 관한 연구 : 전북 혁신도시 주변지역을 대상으로 = Spatiotemporal Characteristics Analysis of Officially Assessed Land Price based on the Emerging Hotspot Analysis : Focusing on the Around Jeonbuk Innovation City

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    https://www.riss.kr/link?id=T17177183

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    The innovation city project was designed to alleviate the concentration of population in the capital region and enhance regional competitiveness. It aims to activate the local economy and promote balanced development through the relocation of public institutions. In other words, the construction of innovation cities is intended to improve the living environment in rural areas and develop them into innovation-driven economic hubs. Since such large-scale development projects have a significant impact on the real estate prices in the region, it is essential to evaluate these effects objectively.
    This study focuses on the spatial analysis of changes in real estate prices around Jeonbuk Innovation City using GIS-based spatial analysis methods. By using spatial pattern analysis and hotspot analysis with GIS, I examined the rate of change of real estate prices. We evaluated the temporal changes of real estate prices in Jeonbuk Innovative City, with special emphasis on the spatial distribution patterns of real estate price changes.
    Over a 20-year period, from 2005 to 2024, we analyzed property price fluctuations in 19 intervals to determine the quantitative relationship between the development of the innovative city and changes in property prices. The analysis revealed a strong increase in real estate prices during the project confirmation phase, but the subsequent phases showed a trend that did not reflect the expected values. Overall, while there were positive effects initially, the rate of change slowed or even declined later, indicating the impact of innovative city development on surrounding property price changes.
    Next, we used kernel density analysis to examine the distribution and clustering of real estate prices. This analysis showed that we could assess the data regionally rather than relying solely on administrative statistics. In particular, the rate of increase in property prices showed clustering in certain areas, while the rate of decrease showed regional characteristics influenced by government policies on property prices, resulting in concentrated patterns in certain years.
    Moreover, the spatial autocorrelation analysis of property price changes confirmed that both the rate of increase and the rate of decrease were clustered. Statistically significant characteristics were found, with a stronger tendency for clustering in the increase rates compared to the decrease rates.
    The hotspot analysis of real estate price changes allowed us to derive the types of hotspots and cold spots over time. Mean values showed patterns that closely followed the median, while maximum values provided a more comprehensive understanding of property price changes in the surrounding areas of the innovative city.
    Finally, this study established a long-term and localized methodology for monitoring real estate price changes that is applicable to large-scale development projects such as the Innovative City. Therefore, it can serve as a valuable resource for decision-making in real estate price policies and the formulation of regional development strategies. This understanding of the complexity of real estate price changes is expected to make a practical contribution to regional economics and policy-making.
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    The innovation city project was designed to alleviate the concentration of population in the capital region and enhance regional competitiveness. It aims to activate the local economy and promote balanced development through the relocation of public i...

    The innovation city project was designed to alleviate the concentration of population in the capital region and enhance regional competitiveness. It aims to activate the local economy and promote balanced development through the relocation of public institutions. In other words, the construction of innovation cities is intended to improve the living environment in rural areas and develop them into innovation-driven economic hubs. Since such large-scale development projects have a significant impact on the real estate prices in the region, it is essential to evaluate these effects objectively.
    This study focuses on the spatial analysis of changes in real estate prices around Jeonbuk Innovation City using GIS-based spatial analysis methods. By using spatial pattern analysis and hotspot analysis with GIS, I examined the rate of change of real estate prices. We evaluated the temporal changes of real estate prices in Jeonbuk Innovative City, with special emphasis on the spatial distribution patterns of real estate price changes.
    Over a 20-year period, from 2005 to 2024, we analyzed property price fluctuations in 19 intervals to determine the quantitative relationship between the development of the innovative city and changes in property prices. The analysis revealed a strong increase in real estate prices during the project confirmation phase, but the subsequent phases showed a trend that did not reflect the expected values. Overall, while there were positive effects initially, the rate of change slowed or even declined later, indicating the impact of innovative city development on surrounding property price changes.
    Next, we used kernel density analysis to examine the distribution and clustering of real estate prices. This analysis showed that we could assess the data regionally rather than relying solely on administrative statistics. In particular, the rate of increase in property prices showed clustering in certain areas, while the rate of decrease showed regional characteristics influenced by government policies on property prices, resulting in concentrated patterns in certain years.
    Moreover, the spatial autocorrelation analysis of property price changes confirmed that both the rate of increase and the rate of decrease were clustered. Statistically significant characteristics were found, with a stronger tendency for clustering in the increase rates compared to the decrease rates.
    The hotspot analysis of real estate price changes allowed us to derive the types of hotspots and cold spots over time. Mean values showed patterns that closely followed the median, while maximum values provided a more comprehensive understanding of property price changes in the surrounding areas of the innovative city.
    Finally, this study established a long-term and localized methodology for monitoring real estate price changes that is applicable to large-scale development projects such as the Innovative City. Therefore, it can serve as a valuable resource for decision-making in real estate price policies and the formulation of regional development strategies. This understanding of the complexity of real estate price changes is expected to make a practical contribution to regional economics and policy-making.

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    목차 (Table of Contents)

    • 제1장 서론 1
    • 제1절 연구의 배경 및 목적 1
    • 제2절 연구동향과 연구 차별성 2
    • 1. 개발 정책과 관련된 계량적 지가 연구 3
    • 2. GIS 기반 시공간적 분석 연구 6
    • 제1장 서론 1
    • 제1절 연구의 배경 및 목적 1
    • 제2절 연구동향과 연구 차별성 2
    • 1. 개발 정책과 관련된 계량적 지가 연구 3
    • 2. GIS 기반 시공간적 분석 연구 6
    • 3. 연구의 차별성 9
    • 제3절 연구 내용 및 방법 10
    • 제2장 이론적 고찰 13
    • 제1절 혁신도시 정책 13
    • 1. 혁신도시의 추진 배경과 정의 13
    • 2. 혁신도시정책의 추진경과 14
    • 3. '혁신도시 시즌 2'와 혁신도시 종합발전계획 수립 16
    • 4. 전북혁신도시 개요 17
    • 제2절 공시지가 제도의 현황 및 현실화 21
    • 1. 표준지공시지가 제도 21
    • 2. 개별공시지가 제도 26
    • 3. 개별토지 공시가격의 현실화 32
    • 제3장 공시지가의 시공간 특성 분석 방법론 37
    • 제1절 공시지가의 공간정보 DB 구축 방법 37
    • 제2절 공간패턴 분석 방법 39
    • 제3절 공간 자기상관 분석 방법 41
    • 1. 공간 자기상관의 개념 41
    • 2. 전역적 공간 자기상관 분석 방법 42
    • 3. 국지적 공간 자기상관 분석 방법 43
    • 제4절 시공간 핫스팟 및 발생 핫스팟 분석 방법 46
    • 1. 시공간 핫스팟 분석 방법 46
    • 2. 발생 핫스팟 분석 방법 48
    • 제5절 소결 52
    • 제4장 적용 및 결과 분석 53
    • 제1절 연구대상지 선정 및 데이터베이스 구축 53
    • 1. 연구대상지 선정 53
    • 2. 공시지가의 공간정보 DB 구축 54
    • 3. 공시지가의 공간정보 DB 수정 56
    • 제2절 개발단계별 공시지가 변동률 추이 분석 63
    • 1. 개발대상지 확정 단계의 변동률 분석 63
    • 2. 개발 단계의 변동률 분석 64
    • 3. 이전 및 입주시작 이후 단계의 변동률 분석 65
    • 4. 개발완료 이후 단계의 변동률 분석 66
    • 제3절 공시지가의 시공간 변화 분석 결과 67
    • 1. 커널 밀도 분석을 통한 구역 설정 67
    • 2. 연도별 공간패턴 분석 결과 70
    • 3. 공간 자기상관 분석 결과 75
    • 제4절 공시지가 변동에 대한 발생 핫스팟 분석 결과 77
    • 1. 발생 핫스팟 분석을 위한 3차원 시공간 큐브 생성 77
    • 2. 발생 핫스팟 분석 결과 80
    • 3. 상승필지에 대한 발생 핫스팟 분석 결과 83
    • 4. 하락필지에 대한 발생 핫스팟 분석 결과 94
    • 5. 발생 핫스팟 유형별 발생 원인 분석 105
    • 제5절 소결 124
    • 제5장 결론 126
    • 제1절 결과의 요약 126
    • 제2절 연구의 한계와 후속 연구 127
    • 참고문헌 129
    • 부록 137
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