Spatiotemporal event detection: a review

Abstract

The advancements of sensing technologies, including remote sensing, in situ sensing, social sensing, and health sensing, have tremendously improved our capability to observe and record natural and social phenomena, such as natural disasters, presidential elections, and infectious diseases. The observations have provided an unprecedented opportunity to better understand and respond to the spatiotemporal dynamics of the environment, urban settings, health and disease propagation, business decisions, and crisis and crime. Spatiotemporal event detection serves as a gateway to enable a better understanding by detecting events that represent the abnormal status of relevant phenomena. This paper reviews the literature for different sensing capabilities, spatiotemporal event extraction methods, and categories of applications for the detected events. The novelty of this review is to revisit the definition and requirements of event detection and to layout the overall workflow (from sensing and event extraction methods to the operations and decision-supporting processes based on the extracted events) as an agenda for future event detection research. Guidance is presented on the current challenges to this research agenda, and future directions are discussed for conducting spatiotemporal event detection in the era of big data, advanced sensing, and artificial intelligence.

Taking the pulse of COVID-19: a spatiotemporal perspective

“…Take one finding, the relative humidity (RH) was largely in the range of 45-85% in affected areas, for example, relative humidity can be set into this range in the ABM for simulating COVID-19 outbreak. We are conducting quantitative analytics to extract the relationships between COVID-19 outbreak and its socioeconomic impacts and a variety of relevant factors as appropriate in a big spatiotemporal data event extraction fashion (Figure 17) (Yu et al 2020), such as economic conditions, temperature and humidity, air quality and UV index, etc., using machine learning, clustering and regression, feature selection, anomaly detection, as well as nonlinear extensions via techniques such as generalized additive models and deep-learning methods.…”

PreciPatch: A Dictionary-based Precipitation Downscaling Method

“…Results from case studies have shown positive feedback on the proposed downscaling method, demonstrating good quality for simulating spatially distributed precipitation fields and yielding the best performance among all other tested methods through visual inspection and quantitative analyses. It is hard for traditional and machine learning super-resolution based methods to exclude the interpolation process and include the non-linear characteristics of precipitation event into the method design [41]. Meanwhile, many downscaling methods like bicubic interpolation and DeepSD do not have extra constraints to enforce the final outputs to hold rainfall consistency with the initial inputs.…”

An On-Demand Service for Managing and Analyzing Arctic Sea Ice High Spatial Resolution Imagery

“…HSR sea ice images captured by satellites or airplanes provide detailed observational data for extracting geophysical attributes of sea ice features, such as floe or melt pond shape, distribution, and coverage. HSR images, however, pose a serious challenge for discovering spatiotemporal patterns of sea ice from this heterogeneous big data in a timely manner [39]. We design and build the ArcCI system based on cloud computing to handle this big data challenge.…”