[논문]도시공간적 요인을 고려한 로지스틱 회귀분석 기반 체감더위 예측 모형 개발

성욱제; 박형민; 임재연; 서유진; 손정민; 민진규; 엄정희

doi:10.11108/kagis.2024.27.1.081

[국내논문] 도시공간적 요인을 고려한 로지스틱 회귀분석 기반 체감더위 예측 모형 개발
Development of a Prediction Model for Personal Thermal Sensation on Logistic Regression Considering Urban Spatial Factors

한국지리정보학회지 = Journal of the Korean Association of Geographic Information Studies, v.27 no.1, 2024년, pp.81 - 98

성욱제 (경북대학교 조경학과) , 박형민 (경북대학교 통계학과) , 임재연 (경북대학교 조경학과) , 서유진 (경북대학교 통계학과) , 손정민 (경북대학교 조경학과) , 민진규 (국립산림과학원 도시숲연구과) , 엄정희 (경북대학교 산림과학.조경학부 조경학전공)

초록
AI-Helper

본 연구는 시민들이 직접 체감하는 더위인 체감더위를 열환경 단위로 설정하여 공간적 요인과의 상관관계를 분석하고 체감더위 예측 모형을 개발하였다. 체감더위를 수집하기 위해 리빙랩이라는 시민 참여형 연구방법론을 적용하여 시민들이 직접 체감더위를 기록하고 주변의 온도를 측정하도록 하였다. 수집한 체감더위 정보의 입력지점에 기반하여 주변 도시공간적 요소를 수집하여 통계 분석을 위한 데이터셋을 구축하였다. 구축한 데이터를 활용하여 로지스틱 회귀분석을 수행하였다. 분석 결과, 기온은 주변 공간환경에 영향을 받으며, 건물 높이, 녹지율, 도로율과 음의 상관관계를 가지고, 기온과 천공률과 양의 상관관계를 가진다고 분석되었다. 또한 도로율, 천공률, 녹지율 순으로 체감더위에 강한 영향을 미치는 것으로 나타났다. 본 연구의 결과는 기후변화 대응을 위한 국지적인 열환경 대책을 마련하기 위한 열환경 평가의 기초자료로 활용될 수 있을 것으로 기대된다.

Abstract ▼ AI-Helper

This study analyzed the impact of urban spatial factors on the thermal environment. The personal thermal sensation was set as the unit of thermal environment to analyze its correlation with environmental factors. To collect data on personal thermal sensation, Living Lab was applied, allowing citizens to record their thermal sensation and measure the temperature. Based on the input points of the collected personal thermal sensation, nearby urban spatial elements were collected to build a dataset for statistical analysis. Logistic regression analysis was conducted to analyze the impact of each factor on personal thermal sensation. The analysis results indicate that the temperature is influenced by the surrounding spatial environment, showing a negative correlation with building height, greenery rate, and road rate, and a positive correlation with sky view factor. Furthermore, the road rate, sky view factor, and greenery rate, in that order, had a strong impact on perceived heat. The results of this study are expected to be utilized as basic data for assessing the thermal environment to prepare local thermal environment measures in response to climate change.

주제어

표/그림 (19)

그림 FIGURE 1. Site study
그림 FIGURE 2. Analysis schemes for study
그림 FIGURE 3. Instruments for operating Living Labs
표 TABLE 1. Methods of operating Living Labs
그림 FIGURE 4. Spatial distribution of basic data in Jung-gu
표 TABLE 2. Basic data utilized in analysis
그림 FIGURE 5. Analysis of sky view factor
그림 FIGURE 6. Distribution of personal thermal sensation
그림 FIGURE 7. Analysis of urban spatial factors
표 TABLE 3. Descriptive statistics of each variable
그림 FIGURE 8. Box plots for the analysis variables
그림 FIGURE 9. Histograms of the analysis variables
그림 FIGURE 10. Correlation coefficients between variables
그림 FIGURE 11. ROC Curve
표 TABLE 4. Regression coefficients and statistics of the prediction model
표 TABLE 5. Classification table of personal thermal sensation based on the prediction model
그림 FIGURE 12. Distribution of personal thermal sensation types by temperature
표 TABLE 6. Evaluation of AUC in Machine Learning
표 TABLE 7. Prediction results using Machine Learning(Elastic net)

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표제어: PCR

동의어: Packet Collision Rate

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