Towards the intelligent era of spatial analysis and modeling
Towards the intelligent era of spatial analysis and modeling
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DOI：
10.1145/3557918.3565863
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发表时间：
2022-11
期刊：
影响因子：
--
通讯作者：
Di Zhu;Song Gao;Guofeng Cao
中科院分区：
文献类型：
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作者：
Di Zhu;Song Gao;Guofeng Cao
地理现象被认为是复杂的，由于空间依赖性的异质性。不可能用统计学或物理学的语言来描述一个普遍的规律，它可以完美地描述现实世界的地理过程，并解释它如何形成某些观察到的模式。传统的空间分析基于严格的统计原则，强假设或经典的计算工作流程，在拥抱地理空间数据的爆炸性增长和最新的技术创新时，面临着巨大的挑战和机遇。在这里，我们强调了智能空间分析（伊萨）的前景，这是一套新的空间分析方法，基于空间显式深度神经网络，具有更灵活的数据表示，复杂空间依赖性模块，较弱的模型先验假设，因此预测/解释未知的能力增强。空间分析中的三个基本主题，即，地理统计学，空间计量经济学和流量分析阐述了伊萨的视野中的例子。我们还讨论了伊萨的挑战性问题，以探索机器/深度学习与地理空间人工智能前沿空间分析之间的更深层次联系。
Geographic phenomena are considered complex due to the heterogeneous nature of spatial dependencies. It is impossible to specify a universal law described in statistical or physical languages that can perfectly characterize a real-world geographic process and explain how it forms certain observed patterns. Traditional spatial analytics based on strict statistical principles, strong assumptions, or classic computation workflows are facing great challenges and opportunities when embracing the explosive growth of geospatial data and recent technical innovations. Here, we highlight the promises of Intelligent Spatial Analytics (ISA), a new set of spatial analytical approaches based on spatially explicit deep neural networks with more flexible data representation, modules for complex spatial dependence, weaker model prior assumptions, and hence the enhanced ability to predict/explain unknowns. Three essential topics in spatial analysis, i.e., geostatistics, spatial econometrics, and flow analytics are elaborated as examples in the vision of ISA. We also discuss challenging issues of ISA as an invitation to explore deeper linkages between machine/deep learning and spatial analysis at the frontier of Geospatial Artificial Intelligence.