  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.
