| Full text | |
| Author(s): |
Ferreira, Leonardo N.
[1, 2, 3]
;
Vega-Oliveros, Didier A.
[4, 5]
;
Cotacallapa, Moshe
[3]
;
Cardoso, Manoel F.
[6]
;
Quiles, Marcos G.
[7]
;
Zhao, Liang
[8]
;
Macau, Elbert E. N.
[7, 3]
Total Authors: 7
|
| Affiliation: | [1] Humboldt Univ, Dept Phys, Berlin - Germany
[2] Potsdam Inst Climate Impact Res, Potsdam - Germany
[3] Natl Inst Space Res, Associated Lab Comp & Appl Math, Sao Jose Dos Campos, SP - Brazil
[4] Univ Estadual Campinas, Inst Comp, Campinas, SP - Brazil
[5] Indiana Univ, Sch Informat Comp & Engn, Bloomington, IN - USA
[6] Natl Inst Space Res, Ctr Earth Syst Sci, Sao Jose Dos Campos, SP - Brazil
[7] Univ Fed Sao Paulo, Inst Sci & Technol, Sao Jose Dos Campos, SP - Brazil
[8] Univ Sao Paulo, Dept Comp & Math, Fac Philosophy Sci & Letters Ribeirao Preto FFCLR, Ribeirao Preto, SP - Brazil
Total Affiliations: 8
|
| Document type: | Journal article |
| Source: | NATURE COMMUNICATIONS; v. 11, n. 1 AUG 12 2020. |
| Web of Science Citations: | 5 |
| 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. Extracting central information from ever-growing data generated in our lives calls for new data mining methods. Ferreira et al. show a simple model, called chronnets, that can capture frequent patterns, spatial changes, outliers, and spatiotemporal clusters. (AU) | |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 17/05831-9 - Analysis of climate indexes influence on wildfires using complex networks and data mining |
| Grantee: | Leonardo Nascimento Ferreira |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 18/24260-5 - Spatiotemporal Data Analytics based on Complex Networks |
| Grantee: | Didier Augusto Vega Oliveros |
| Support Opportunities: | Scholarships abroad - Research Internship - Post-doctor |
| FAPESP's process: | 16/23698-1 - Dynamical Processes in Complex Network based on Machine Learning |
| Grantee: | Didier Augusto Vega Oliveros |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 16/16291-2 - Characterizing time-varying networks: methods and applications |
| Grantee: | Marcos Gonçalves Quiles |
| Support Opportunities: | Scholarships abroad - Research |
| FAPESP's process: | 15/50122-0 - Dynamic phenomena in complex networks: basics and applications |
| Grantee: | Elbert Einstein Nehrer Macau |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 19/00157-3 - Association and causality analyses between climate and wildfires using network science |
| Grantee: | Leonardo Nascimento Ferreira |
| Support Opportunities: | Scholarships abroad - Research Internship - Post-doctor |
| FAPESP's process: | 19/26283-5 - Learning visual clues of the passage of time |
| Grantee: | Didier Augusto Vega Oliveros |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |