Spatiotemporal data analysis with chronological networks
- URL: http://arxiv.org/abs/2004.11483v2
- Date: Wed, 12 Aug 2020 12:20:45 GMT
- Title: Spatiotemporal data analysis with chronological networks
- Authors: Leonardo N. Ferreira, Didier A. Vega-Oliveros, Moshe Cotacallapa,
Manoel F. Cardoso, Marcos G. Quiles, Liang Zhao, Elbert E. N. Macau
- Abstract summary: We propose a network-based model fortemporal data analysis called It chronnet.
The main goal of this model is to represent consecutive recurrent events between cells with strong links in the network.
In this paper, we describe how to use our model considering artificial and real data sets.
- Score: 4.7842701621852655
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: 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.
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