Spatiotemporal database

A spatiotemporal database is a type of database that is designed to handle data that has both spatial and temporal properties. It encompasses research on models, languages, data structures, query algorithms, architectures, and system implementations that address the challenges of combining space and time. Spatiotemporal databases can manage data that evolves over time and treat spatial and temporal properties of objects uniformly.From: Handbook of Data Structures and Applications [2019], Digital Transformation of Enterprise Architecture [2019]

Mobility

View Chapter

Purchase Book

Published in Vivek Kale, Digital Transformation of Enterprise Architecture, 2019

Vivek Kale

This chapter’s appendix describes aspects related to spatio-temporal databases: It introduces fundamental concepts in temporal data model and temporal database. Last part of appendix introduces spatio-temporal data model and spatio-temporal database system. Spatio-temporal database can deal with the objects’ temporal and spatial properties uniformly, and it can manage data that is evolving with time.

Applying human mobility and water consumption data for short-term water demand forecasting using classical and machine learning models

View Article

Journal Information

Published in Urban Water Journal, 2020

Kamil Smolak, Barbara Kasieczka, Wieslaw Fialkiewicz, Witold Rohm, Katarzyna Siła-Nowicka, Katarzyna Kopańczyk

To ensure maximum efficiency in data processing, a spatiotemporal database was created using the open-source database software PostgreSQL and its extensions: PostGIS for spatial data and TimescaleDB for time-series. All the data have to be transformed and loaded into a database predefined structure. This has to be done manually. Therefore, the model is independent of the structure of provided data. Furthermore, the data are aggregated spatially into DMAs and temporally to one-hour bins. If provided data (either water consumption or mobility data) have lower temporal resolution it can be aggregated to the larger bins. Similarly, if an infrastructure manager provides different spatial segmentation (i.e. DMAs), then the data are aggregated to the provided areas. The datasets used in this study were thoroughly tested for their reliability, representativeness and consistency.

Spatiotemporal database

Explore chapters and articles related to this topic

Mobility

Applying human mobility and water consumption data for short-term water demand forecasting using classical and machine learning models