Spatiotemporal data analysis with chronological networks
This provides a fast method for researchers and practitioners handling large spatiotemporal data, though it appears incremental as it builds on existing network science approaches.
The authors tackled the challenge of analyzing large spatiotemporal datasets by proposing a network-based model called chronnet, which divides space into grid cells connected chronologically to represent recurrent events, enabling the use of network science tools for pattern extraction, and demonstrated its effectiveness on artificial and real-world fire detection data.
The amount and size of spatiotemporal data sets from different domains have been rapidly increasing in the last years, which demands the development of robust and fast methods to analyze and extract information from them. In this paper, we propose a network-based model for spatiotemporal data analysis called chronnet. It consists of dividing a geometrical space into grid cells represented by nodes connected chronologically. The main goal of this model is to represent consecutive recurrent events between cells with strong links in the network. This representation permits the use of network science and graphing mining tools to extract information from spatiotemporal data. The chronnet construction process is fast, which makes it suitable for large data sets. In this paper, we describe how to use our model considering artificial and real data. For this purpose, we propose an artificial spatiotemporal data set generator to show how chronnets capture not just simple statistics, but also frequent patterns, spatial changes, outliers, and spatiotemporal clusters. Additionally, we analyze a real-world data set composed of global fire detections, in which we describe the frequency of fire events, outlier fire detections, and the seasonal activity, using a single chronnet.