From spatio-temporal data to chronological networks

Abstract

Network theory has established itself as an appropriate tool for complex systems analysis and pattern recognition. In the context of spatiotemporal data analysis, correlation networks are used in the vast majority of works. However, the Pearson correlation coefficient captures only linear relationships and does not correctly capture recurrent events. This missed information is essential for temporal pattern recognition. In this work, we propose a chronological network construction process that is capable of capturing various events. Similar to the previous methods, we divide the area of study into grid cells and represent them by nodes. In our approach, links are established if two consecutive events occur in two different nodes. Our method is computationally efficient, adaptable to different time windows and can be applied to any spatiotemporal data set. As a proof-of-concept, we evaluated the proposed approach by constructing chronological networks from the MODIS dataset for fire events in the Amazon basin. We explore two data analytic approaches: one static and another temporal. The results show some activity patterns on the fire events and a displacement phenomenon over the year. The validity of the analyses in this application indicates that our data modeling approach is very promising for spatio-temporal data mining.

Spatiotemporal data analysis with chronological networks

“…Therefore, the links represent events in a chronological occurrence, which we call, for the sake of simplicity, as chronnet. In this way, we generalize the previously reported mechanism of connections 3,15,16 transforming the spatiotemporal data set into a network, as explored in other domains like text-mining 17,18 and social media 19,20 .…”

Temporal Network Pattern Identification by Community Modelling

“…We test our method with a public available real-world temporal network dataset related to fires events 5 . In a global scale, the fire event activity is collected mainly through satellite instruments like the Moderate Resolution Imaging Spectroradiometer (MODIS), and the dataset is specifically from the Global Daily Fire Location Product (MCD14ML) 40 .…”

“…Since the Amazon basin is mostly located at the south-hemisphere, the period from July to December corresponds to the dry season with a high tendency of fires and the period from January to June corresponds to the wet season, with a low frequency of fires 5,42 . Figure 11(b) shows the two communities detected in the target network, one represents the season with high wildfire activity and the other represents the one with the low wildfire activity.…”

“…Figure 11(c) shows the node-community disposition over time, where the low and high wildfire states are identified by our method. Note that the points outside of the temporal state patterns are explained by the fraction of cells where happen wild-fires and are located in the north-hemisphere, i.e., in a different temporal phase 5,42 .…”

“…In 26 , the authors discussed the interpretation of temporal network theory in the context of the dynamic functional brain connectome. Many other studies have been conducted to characterize temporal networks by revealing the community evolution in such networks 5,15,20,[27][28][29][30][31] . In multilayer networks, including temporal networks, inter-layer links can potentially change the modular structure of each layer 28 .…”