Abstract
The geographic data models are mainly based on geographical entities nowadays. There still exist some problems about these models: separation of the concept and storage for a complete geographic entity (river, railway, etc.), delayed updates in some real-time applications, lack of relationship between geographical entities, difficult management and linkage update for multi-source heterogeneous data, and so on. A unified data model for geographic entities and geographic events is designed to respond to the problems. In this model, data can be organized based on the basic granularity of the conceptually complete geographic entity. Geographical events is fully described using geometry, attribute and relationship information, and interaction between geographical entities and geographical events is achieved. Based on GeoJSON, GeoEntityJSON and GeoEventJSON are designed to implement the physical model of the data. Taking a residential area in a certain city in China as an example, a real estate management model is established. The data model is used to realize the storage management and visualization of real estate.
Supported in part by the National Natural Science Foundation of China under Grant No. 41471321. and No. U19A2058.
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Xiong, W., Chen, H., Guo, N., Gong, Q., Luo, W. (2021). ENSTDM: An ENtity-Based Spatio-Temporal Data Model and Case Study in Real Estate Management. In: Pan, G., et al. Spatial Data and Intelligence. SpatialDI 2021. Lecture Notes in Computer Science(), vol 12753. Springer, Cham. https://doi.org/10.1007/978-3-030-85462-1_3
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