Spatiotemporal data analysis with chronological networks

Leonardo N Ferreira,Moshé Cotacallapa,Elbert E N Macau,Didier A Vega-Oliveros,Manoel F Cardoso,Liang Zhao,Marcos G Quiles

doi:10.1038/s41467-020-17634-2

Abstract

The number of spatiotemporal data sets has increased rapidly in the last years, which demands robust and fast methods to extract information from this kind of data. Here, we propose a network-based model, called Chronnet, for spatiotemporal data analysis. The network construction process consists of dividing a geometric space into grid cells represented by nodes connected chronologically. Strong links in the network represent consecutive recurrent events between cells. The chronnet construction process is fast, making the model suitable to process large data sets. Using artificial and real data sets, we show how chronnets can capture data properties beyond simple statistics, like frequent patterns, spatial changes, outliers, and spatiotemporal clusters. Therefore, we conclude that chronnets represent a robust tool for the analysis of spatiotemporal data sets.

Highlights

The number of spatiotemporal data sets has increased rapidly in the last years, which demands robust and fast methods to extract information from this kind of data
Large amounts of spatiotemporal data are collected every day from several domains, including georeferenced climate variables, epidemic outbreaks, crime events, social media, traffic, and transportation dynamics, among many others
The interest is justified by the benefits that the network representation provides, like the ability to describe sub-manifold in dimensional space and to capture dynamic and topological structures—hierarchical structures and global or local patterns, independent of the data distribution[6,7]

Summary

Introduction

The number of spatiotemporal data sets has increased rapidly in the last years, which demands robust and fast methods to extract information from this kind of data. Functional or correlation networks[9] have been widely applied to map spatiotemporal data into networks Such a method connects nodes, i.e., time-series from spatial grid-cells, according to their correlation. Functional networks have been employed in a diversity of areas like: in earth sciences[4,5,8,10,11], bioinformatics[12], finance[13], among others One drawback of this kind of networks appears when short-length time series are considered, making the statistical significance of correlations questionable and may result in spurious links in the network. This method produces single and static representations of the time-series, discarding the spatial location Another way to construct networks considers the chronological order in the spatiotemporal events to define the links. The previous works have identified valuable information in a couple of domains, the proposed methods and results are restricted to the specific problem and were not generalized to a variety of domains with spatiotemporal events

Methods

Results

Conclusion