| Full text | |
| Author(s): |
Lopes, Gesiel Rios
;
Delbem, Alexandre C. B.
;
da Silva, Roberto Fray
;
Bielenki, Claudio, Jr.
;
Vannucchi, Sergio Henrique
;
de Mattos, Leme
;
Scatolini, Denise
;
Ghiglieno, Filippo
;
Saraiva, Antonio Mauro
Total Authors: 9
|
| Document type: | Journal article |
| Source: | PROCEEDINGS OF THE 3RD ACM SIGSPATIAL INTERNATIONAL WORKSHOP ON SPATIAL COMPUTING FOR EPIDEMIOLOGY, SPATIALEPI 2022; v. N/A, p. 4-pg., 2022-01-01. |
| Abstract | |
The decision-making process for complex problems based on heterogeneous and multiple data sources requires structuring information with adequate representation for the phenomenon under analysis. This is specifically true for temporal and spatial problems, as those dimensions add complexity to data processing, information extraction, and interpretation of results. However, most available methodologies and tools were not developed considering multiple stakeholders and feedback loops. Therefore, a tool is needed to allow managers and researchers to gain deep insight into significant volumes of heterogeneous data to make better decisions. In this context, this paper introduces an open-source tool that consistently integrates data from different sources and contexts, optimizing and facilitating the analysis, management, and representation of space and the phenomena that occur in it. The tool uses techniques from multi-criteria analysis to define the influence of the data and map algebra to combine these data and build thematic maps from multiple heterogeneous sources. This allows users to perform geospatial statistical analysis, outlier detection, and evaluation of regions of interest without the need for specific knowledge in modelling the phenomena, supporting decision-making. The tool is then implemented on a case study for evaluating epidemics and infectious and neglected diseases in an urban environment in Brazil. The insights obtained allow the decision-maker to understand better the data inputs and the different possible results of the multi-criteria decision-making model, considering different weight combinations. It is vital to observe that the tool can be used in different contexts, areas, and data sources with different spatial and temporal aspects. (AU) | |
| FAPESP's process: | 19/07665-4 - Center for Artificial Intelligence |
| Grantee: | Fabio Gagliardi Cozman |
| Support Opportunities: | Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program |
| FAPESP's process: | 20/16578-5 - MultiMaps: geo-referenced Spatio-temporal analyzes of multiple epidemics |
| Grantee: | Gesiel Rios Lopes |
| Support Opportunities: | Scholarships in Brazil - Technical Training Program - Technical Training |