| 集成多源地理大数据感知城市空间分异格局 |
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| 引用本文： | 刘瑜,詹朝晖,朱递,柴彦威,马修军,邬伦.集成多源地理大数据感知城市空间分异格局[J].武汉大学学报(信息科学版),2018,43(3):327-335. |
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| 作者姓名： | 刘瑜 詹朝晖 朱递 柴彦威 马修军 邬伦 |
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| 作者单位： | 1.国土资源部城市监测与仿真重点实验室, 广东 深圳, 518034 |
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| 基金项目： | 国土资源部城市土地资源监测与仿真重点实验室开放基金KF-2016-02-023国家自然科学基金41625003 |
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| 摘 要： | 多源地理大数据为地理现象的分布格局、相互作用及动态演化提供了前所未有的社会感知手段。城市是人类活动最为集中的区域，产生了多种地理大数据，并支持对于城市空间的理解。城市内部的分异格局是城市研究和规划所要面对的重要议题，社会感知数据提供了从"人-地-静-动"4个维度刻画城市分异格局的途径。梳理了不同类型大数据对于表达这4个维度特征的支持，并借鉴"生态位"模型，通过一个实例研究展示了集成多源数据量化城市空间分异特征的应用，最后讨论了相关的理论问题。
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| 关 键 词： | 地理大数据 空间分异 社会感知 城市 |
| 收稿时间： | 2017-11-23 |
Incorporating Multi-source Big Geo-data to Sense Spatial Heterogeneity Patterns in an Urban Space |
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| Affiliation: | 1.Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen 518034, China2.Institute of Remote Sensing and Geographical Information Systems, Peking University, Beijing 100871, China3.College of Urban and Environmental Sciences, Peking University, Beijing 100871, China4.School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China5.Key Laboratory of Machine Perception, Peking University, Beijing 100871, China |
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| Abstract: | Multi-source big geo-data provides us an unprecedented opportunity to investigate geographic phenomena from perspective of their spatial distribution patterns, spatial interactions and dynamic evolution. Cities are the most concentrated areas of human activities and thus massive amount of geographic big data have been produced to improve our understanding of urban spaces. The spatial heterogeneity patterns in cities is an essential topic in geographic research and urban planning. Social sensing offers an analytical framework to characterize urban spatial heterogeneity from four dimensions:human, environment, statics and dynamics. This paper summarizes the contributions of different types of big geo-data in characterizing urban features. Borrowing the concept of "niche model" from ecological studies, a case study is introduced to demonstrate the quantification of spatial heterogeneity patterns in urban space incorporating multi-source big geo-data. Theoretical issues such as unit selection are also discussed to address some related problems. |
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