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CHENG PengGen, YUE Chen, ZHU XinYan. Evaluation of Urban Ecological Environment and Its Relationship with Human Activities Based on Multi-Source Data[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200382
Citation: CHENG PengGen, YUE Chen, ZHU XinYan. Evaluation of Urban Ecological Environment and Its Relationship with Human Activities Based on Multi-Source Data[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200382

Evaluation of Urban Ecological Environment and Its Relationship with Human Activities Based on Multi-Source Data

doi: 10.13203/j.whugis20200382
Funds:

The National Key Research and Development Program of China,No.2017YFB0503704

  • Received Date: 2021-02-15
    Available Online: 2021-05-07
  • 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.
  • [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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Evaluation of Urban Ecological Environment and Its Relationship with Human Activities Based on Multi-Source Data

doi: 10.13203/j.whugis20200382
Funds:

The National Key Research and Development Program of China,No.2017YFB0503704

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.

CHENG PengGen, YUE Chen, ZHU XinYan. Evaluation of Urban Ecological Environment and Its Relationship with Human Activities Based on Multi-Source Data[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200382
Citation: CHENG PengGen, YUE Chen, ZHU XinYan. Evaluation of Urban Ecological Environment and Its Relationship with Human Activities Based on Multi-Source Data[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200382
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