Evaluation of Urban Ecological Environment and Its Relationship with Human Activities with Multi-source Data

CHENG Penggen; YUE Chen

doi:10.13203/j.whugis20200382

Volume 42 Issue 11

Nov. 2022

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Geomatics and Information Science of Wuhan University > 2022 > 47(11): 1927-1937. > DOI: 10.13203/j.whugis20200382

CHENG Penggen, YUE Chen. Evaluation of Urban Ecological Environment and Its Relationship with Human Activities with Multi-source Data[J]. Geomatics and Information Science of Wuhan University, 2022, 47(11): 1927-1937. DOI: 10.13203/j.whugis20200382

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Evaluation of Urban Ecological Environment and Its Relationship with Human Activities with Multi-source Data

CHENG Penggen^{1, 2,},
YUE Chen^1, ,

1.
Faculty of Geomatics, East China University of Technology, Nanchang 330013, China
2.
Key Laboratory of Mine Environmental Monitoring and Improving Around Poyang Lake, Ministry of Natural Resources, Nanchang 330013, China

Funds:

The National Natural Science Foundation of China 41861052

The National Natural Science Foundation of China 41861062

the National Key Research and Development Program of China 2017YFB0503704

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Author Bio:
CHENG Penggen, PhD, professor, specializes in theory and application of geographic information system, urban ecological environment comprehensive evaluation. E-mail: pgcheng1964@163.com
Corresponding author:
YUE Chen, master. E-mail: 872438008@qq.com
Received Date: November 07, 2020
Available Online: November 15, 2022
Published Date: November 04, 2022

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Abstract

Abstract

Objectives 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.
Methods Based on multisource data, we propose to construct human activity indicators (residential area walkability index, street vitality index, urban function mixing index) using point of interest(POI), OpenStreetMap (OSM) and residential area data, and urban ecological environment indicator (remote sensing ecological index)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 dataset, the relationship between the urban ecological environment and human activities is revealed.
Results We demonstrate the application of our method using a case study of Nanchang city. The results show that: (1) The 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. (2) From the analysis of the performance results of each model on the dataset, XGB has the best regression effect, followed by PLR.(3)There 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. (4) In 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.
Conclusions Using multisource data and machine learning models can provide an important reference for exploring the relationship between urban ecological environment and human activities.
- multi-source data,
- ecological environment,
- human activities,
- machine learning

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