doi:10.12202/j.0476-0301.2020224戴一华¹,
刘志锋^{1, 2,,},
王一航^{1, 2},
杨志鹏³
1.北京师范大学地理科学学部，100875，北京
2.北京师范大学地表过程与资源生态国家重点实验室，人与环境系统可持续研究中心，100875，北京
3.国家自然科学基金委员会，100085，北京
基金项目:第二次青藏高原综合科学考察研究资助项目(2019QZKK0405)；国家自然科学基金资助项目（41871185）

详细信息

通讯作者:刘志锋（1986-），男，博士，副教授. 研究方向：景观地理与景观可持续科学. E-mail：Zhifeng.Liu@bnu.edu.cn

中图分类号:K909

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出版历程

收稿日期:2020-06-07
网络出版日期:2021-01-21
刊出日期:2021-06-30

Urban land use classification based on big data: case of Xining

Yihua DAI¹,
Zhifeng LIU^{1, 2,,},
Yihang WANG^{1, 2},
Zhipeng YANG³
1. Faculty of Geographical Science, Beijing Normal University,100875， Beijing , China
2. Center for Human-Environment System Sustainability (CHESS), State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University，100875， Beijing , China
3. National Natural Science Foundation of China，100085，Beijing, China



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摘要
摘要:以西宁市为例，基于宜出行和兴趣点（points of interest，POI）2类常用大数据以及最大似然、支持向量机和神经网络3种常用分类方法，开展了城市土地利用分类研究．通过对比不同数据与方法组合下的城市土地利用分类精度，确定了提取城市土地利用信息的最优数据组合方式和分类方法．并基于分类结果对西宁市的城市土地利用格局进行了分析．结果显示，基于POI和宜出行数据的神经网络分类方法获取的研究区城市土地利用信息精度最高，总体精度为71.25%，Kappa系数为0.62．主要原因在于综合POI和宜出行可以更加充分地反映不同土地利用类型的特征，而神经网络可以有效综合多源大数据的信息．因此，基于多源大数据和神经网络为快速有效地获取城市土地利用信息提供了有效途径，具有较大的应用潜力．
关键词:城市土地利用/
大数据/
兴趣点/
宜出行/
机器学习
Abstract:Urban land use is the result of interactions among social, political, economic, technological and other factors within and without cities．Urban land use classification not only helps to analyze land use pattern, but also has great significance for rational urban zoning and promotion of sustainable development．Urban land use classification in Xining is done based on two types of commonly used big data (Easygo, points of interest or POI) and three common classification methods (Maximum Likelihood, Support Vector Machine, Artificial Neural Networks)．By comparing the accuracy of results under different data and methods, optimal data combination and classification method for extracting urban land use information are determined．The classification results are used to analyze urban land use patterns in Xining．Urban land use information obtained by neural network classification method based on Easygo and POI was found to have the highest accuracy, with overall accuracy at 71.25% and a Kappa coefficient at 0.62. Easygo and POI can reflect more information about characteristics of different land use．Artificial Neural Networks can fully integrate information of multi-source big data．Therefore, it provides a potential way to timely and accurately obtain urban land use information with multi-source big data and Artificial Neural Networks．
Key words:urban land use/
big data/
point of interest/
Easygo/
machine learning