Evaluation of Urban Ecological Environment and Its Relationship with Human Activities Based on Multi-Source Data
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摘要: 探究城市生态环境与人类活动的关系，是目前城市化进程中重要的研究内容。结合多源数据，提出采用POI（Point of Interest）点、OSM（Open Street Map）道路网、住宅区数据构建人类活动指标（住宅区可步行测度指数、街道活力指数、城市功能混合度指数）和利用遥感影像构建城市生态环境指标（遥感生态指数）的方法。并结合多项式回归（Polynomial Regression，PLR）、随机森林回归（Random Forest Regression，RFR）、eXtreme Gradient Boosting Regression（XGB）、支持向量回归机（Support Vector Regression Machine，SVR）等机器学习模型，对城市生态环境与人类活动指标进行回归分析。以南昌市为例开展实例研究，研究表明：①人类活动的三项指标均呈现中心高，向四周逐渐递减的趋势，而城市生态环境指标则表现出相反的态势。②在探究城市生态环境与人类活动关系研究中，XGB的效果最好。③街道活力指数、城市功能混合度指数与城市生态环境的相关性更强，住宅区可步行测度指数与城市生态环境的相关性更差。④在人类活动影响较小的区域城市生态环境会受到其他因素的干扰导致预测结果精度不高，而在人类活动强烈的区域预测精度较高。Abstract: To explore the relationship between urban ecological environment and human activities is an important research content in the current urbanization process. With the in-depth development of the era of big data, multi-source data ubiquitous in the Internet has been fully excavated, which has played an important role in promoting the research of urban ecological environment.Based on multi-source data, the paper proposed toconstruct human activity indicators (residential area walkability index, street vitality index, urban function mixing index) by using Point of Interests (POIs), Open Street Map (OSM) and residential area data, and urban ecological environment indicator (remote sensing ecological index) by using remote sensing images. Combing machine learning models such as polynomial regression(PLR), random forest regression(RFR), eXtreme Gradient Boosting Regression(XGB) and support vector regression machine(SVR), it is effective to make regression analysis on urban ecological environment and human activity indicators.By comparing the performance of different models in this data set, the relationship between the urban ecological environment and human activities is revealed.We demonstrate theapplication of our method using a case study of Nanchang city. The results show that:①The three indexes of human activities all present a central high and gradually decrease to the surroundings, while the urban ecological environment indicators show an opposite trend.②From the analysis of the performance results of each model on the data set, XGB has the best regression effect, followed by PLR.③There is a strong negative correlation between the urban ecological environment and human activities, and the street vitality index, the urban function mixing index are more relevant to the urban ecological environment, and the walkability index of the residential area is less relevant to the urban ecological environment.④In areas where human activities have less impact, the urban ecological environment will be disturbed by other factors, resulting in the low prediction accuracy, while the prediction accuracy in areas with strong human activities is high.
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Key words:
- Multi-Source Data /
- Index /
- Urban /
- Ecological Environment /
- Human Activities /
- Machine Learning
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[1] (徐慧敏,胡守庚.夜光遥感支持下的中国城市规模时空演变分析[J/OL].武汉大学学报·信息科学版:1-13[2020-10-01].https://doi-org-s.webvpn1.ecit.cn/10.13203/j.whugis20190330.) XU Huimin, HU Shougeng. Analysis of Chinese City Size Evolution Using Night-time Light Remote Sensing[J/OL]. Geomatics and Information Science of Wuhan University:1-13[2020-10-01]. https://doi-org-s.webvpn1.ecit.cn/10.13203/j.whugis20190330. [2] (陈逸敏,黎夏.机器学习在城市空间演化模拟中的应用与新趋势[J/OL].武汉大学学报·信息科学版:1-8[2020-10-01].https://doi-org-s.webvpn1.ecit.cn/10.13203/j.whugis20200423.) CHEN Yimin, LI Xia. Applications and New Trends of Machine Learning in Urban Simulation Research[J/OL]. Geomatics and Information Science of Wuhan University:1-8[2020-10-01]. https://doi-org-s.webvpn1.ecit.cn/10.13203/j.whugis20200423. [3] Breiman L. Random Forests[J]. Machine Learning, 2001, 45(1):5-32. [4] Pan B. Application of XGBoost Algorithm in hourly PM2. 5 concentration prediction[C]//IOP Conference Series:Earth and Environmental Science. 2018, 113:012127. [5] HU L, HE S, HAN Z, et al. Monitoring Housing Rental Prices Based on Social Media:An Integrated Approach of Machine Learning Algorithms and Hedonic Modeling to Inform Equitable Housing Policies[J]. Land Use Policy, 2019, 82:657-673. -
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