The significant growth of spatial and spatiotemporal (SST) data collection as well as the emergence of new technologies has heightened the need for automated discovery of spatiotemporal knowledge. SST data mining techniques are crucial to organizations which make decisions based on large SST datasets. The interdisciplinary nature of SST data mining and the complexity of SST data and relationships pose statistical and computational challenges. This article provides an overview of recent advances in SST data mining and reviews common SST data mining techniques organized by major pattern families, based on an introduction to SST data types and relationships as well as the statistical background. New trends and research needs are also summarized, including SST data mining in network space and SST big data platform development.
|Original language||English (US)|
|Title of host publication||Gis Applications for Socio-Economics and Humanity|
|Number of pages||23|
|State||Published - Jul 21 2017|
- Change footprint
- Data mining
- Prediction model