arXiv subjects

Manoel F. Cardoso

Publications and source records attributed to Manoel F. Cardoso.

3 recordsLinked to original sources

arXiv2020

Spatiotemporal data analysis with chronological networks

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.

cs.SI↗

arXiv2019

Global Fire Season Severity Analysis and Forecasting

In this paper, we divide the globe into a hexagonal grid and we extracted time series of daily fire counts from each cell to estimate and analyze worldwide fire season severity (FSS), here defined as the accumulated fire detections in a season. The central question here is evaluating the accuracy of time series forecasting methods to estimate short-term (months) and medium-term (seasons) using only historical data of active fire detections. This approach is simple, fast, and use globally available data, making it easier for large scale prediction. Our results comprehend descriptive and predictive analyses of the worldwide seasonal fire activity. We verified that in 99% of the cells, the fire seasons have lengths shorter than seven months and that 57% have their lengths decrease. We also observed a declining tendency in the number of active fire counts during the seasons in 61% cells. However, some regions like the Northeast Brazil and the West Coast of the USA present an increasing trend. We verified that the forecasting error is lower than the mean FSS in 95% of the cells, indicating clear predictability in the FSS.

stat.AP↗

arXiv2018

From spatio-temporal data to chronological networks: An application to wildfire analysis

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.

physics.soc-ph↗