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Spatial embedding information


Spatial embedding is one of feature learning techniques used in spatial analysis where points, lines, polygons or other spatial data types.[1] representing geographic locations are mapped to vectors of real numbers. Conceptually it involves a mathematical embedding from a space with many dimensions per geographic object to a continuous vector space with a much lower dimension.

Such embedding methods allow complex spatial data to be used in neural networks and have been shown to improve performance in spatial analysis tasks[2][3]

  1. ^ Schneider, Markus (2009), "Spatial Data Types", in LIU, LING; ÖZSU, M. TAMER (eds.), Encyclopedia of Database Systems, Boston, MA: Springer US, pp. 2698–2702, doi:10.1007/978-0-387-39940-9_354, ISBN 978-0-387-39940-9, retrieved 2021-01-19
  2. ^ Li, Youru; Zhu, Zhenfeng; Kong, Deqiang; Xu, Meixiang; Zhao, Yao (2019-07-17). "Learning Heterogeneous Spatial-Temporal Representation for Bike-Sharing Demand Prediction". Proceedings of the AAAI Conference on Artificial Intelligence. 33: 1004–1011. doi:10.1609/aaai.v33i01.33011004. ISSN 2374-3468.
  3. ^ Cao, Hancheng; Xu, Fengli; Sankaranarayanan, Jagan; Li, Yong; Samet, Hanan (2020-05-01). "Habit2vec: Trajectory Semantic Embedding for Living Pattern Recognition in Population". IEEE Transactions on Mobile Computing. 19 (5): 1096–1108. doi:10.1109/TMC.2019.2902403. ISSN 1536-1233. S2CID 86694179.

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