Spatiotemporal Data Mining: A Computational Perspective

Abstract

Explosive growth in geospatial and temporal data as well as the emergence of new technologies emphasize the need for automated discovery of spatiotemporal knowledge. Spatiotemporal data mining studies the process of discovering interesting and previously unknown, but potentially useful patterns from large spatiotemporal databases. It has broad application domains including ecology and environmental management, public safety, transportation, earth science, epidemiology, and climatology. The complexity of spatiotemporal data and intrinsic relationships limits the usefulness of conventional data science techniques for extracting spatiotemporal patterns. In this survey, we review recent computational techniques and tools in spatiotemporal data mining, focusing on several major pattern families: spatiotemporal outlier, spatiotemporal coupling and tele-coupling, spatiotemporal prediction, spatiotemporal partitioning and summarization, spatiotemporal hotspots, and change detection. Compared with other surveys in the literature, this paper emphasizes the statistical foundations of spatiotemporal data mining and provides comprehensive coverage of computational approaches for various pattern families.ISPRS Int. J. Geo-Inf. 2015, 4 2307We also list popular software tools for spatiotemporal data analysis. The survey concludes with a look at future research needs.

Transdisciplinary Foundations of Geospatial Data Science

“…to identify different land cover types using remote sensing imagery, to detect changes in time as well as project future climate events. Spatial prediction is challenging due to specific properties of spatial datasets such as autocorrelation and heterogeneity [47]. Autocorrelation is the term that is used to describe the dependency between nearby samples.…”

“…Autocorrelation is the term that is used to describe the dependency between nearby samples. Heterogeneity describes the variation across different spatial locations [47].…”

“…Given a collection of spatial time series at different locations, teleconnection discovery aims to identify pairs of significant positively or negatively correlated spatial time series at large distances [47]. Teleconnection patterns are important in understanding the variability in climate through the discovery of pressure dipoles which are known to impact precipitation and temperature anomalies in the world [76].…”

Usos de información georreferenciada para prestación de servicios a la población: una revisión de literatura

“…Entendiendo el atípico espacio temporal como aquel elemento cuyo atributo temático difiere significativamente de otros objetos en su vecindario espacio temporal (Shekhar et al, 2015). Para la predicción de variables, se utilizan modelos espacio temporales los cuales tienen como objetivo predecir el valor de una variable dependiente en función de un conjunto de variables explicativas.…”

“…Si se analizan datos de accidentes de tráfico es factible agregarlos en las principales rutas para identificar las áreas con mayor accidentalidad. Los algoritmos para la integración y partición espacio temporal corresponden a técnicas de estimación de densidad de puntos y correlación temporal para series espacio temporales (Shekhar et al, 2015).…”