An interactive webbased geovisual analytics platform for coclustering spatiotemporal data Abstract   Clustering methods are useful in analyzing patterns from big spatiotemporal data. However, previous studies typically rely on traditional clustering methods to explore spatial or temporal patterns. Coclustering methods allow the concurrent analysis of spatial and temporal patterns by identifying location and timestampclusters at the same time. By combining coclustering with coordinated multiple views (CMV) in an interactive geovisual analytics platform, we facilitate the exploratory coclustering analysis of spatiotemporal data and the results. Further enhanced by Web 2.0 standards, our geovisual analytics platform ease the access to coclustering analysis from any web browser. More specifically, our platform allows users to upload data and to visually explore it using interactive CMV to help the selection of coclustering parameters. Our platform also allows users to run coclustering and to visually and interactively explore the results. To illustrate the use of our platform, we analyze Dutch annual average temperature for 28 stations from 1992 to 2011. Results show that our platform not only helps to get a better understanding of the dataset but also to choose the coclustering parameters. Our platform helps to interpret the coclustering results too, and it supports the extraction and exploration of complex patterns buried in the data. In the era of big data, our webbased platform enables the exploration of concurrent spatiotemporal patterns from large datasets by combing both computer power and human interpretative capabilities.

SpaceTime Hierarchical Clustering for Identifying Clusters in Spatiotemporal Point Data Finding clusters of events is an important task in many spatial analyses. Both confirmatory and exploratory methods exist to accomplish this. Traditional statistical techniques are viewed as confirmatory, or observational, in that researchers are confirming an a priori hypothesis. These methods often fail when applied to newer types of data like moving object data and big data. Moving object data incorporates at least three parts: location, time, and attributes. This paper proposes an improved spacetime clustering approach that relies on agglomerative hierarchical clustering to identify groupings in movement data. The approach, i.e., spacexe2x80x93time hierarchical clustering, incorporates location, time, and attribute information to identify the groups across a nested structure reflective of a hierarchical interpretation of scale. Simulations are used to understand the effects of different parameters, and to compare against existing clustering methodologies. The approach successfully improves on traditional approaches by allowing flexibility to understand both the spatial and temporal components when applied to data. The method is applied to animal tracking data to identify clusters, or hotspots, of activity within the animalxe2x80x99s home range.