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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Mesquitela, J., Elvas, L. B., Ferreira, J. & Nunes, L. (2022). Data analytics process over road accidents data—A case study of Lisbon city. ISPRS International Journal of Geo-Information. 11 (2)
Exportar Referência (IEEE) 
J. Mesquitela et al.,  "Data analytics process over road accidents data—A case study of Lisbon city", in ISPRS Int. Journal of Geo-Information, vol. 11, no. 2, 2022
Exportar BibTeX 
@article{mesquitela2022_1727844831318,
	author = "Mesquitela, J. and Elvas, L. B. and Ferreira, J. and Nunes, L.",
	title = "Data analytics process over road accidents data—A case study of Lisbon city",
	journal = "ISPRS International Journal of Geo-Information",
	year = "2022",
	volume = "11",
	number = "2",
	doi = "10.3390/ijgi11020143",
	url = "https://www.mdpi.com/journal/ijgi"
}
Exportar RIS 
TY  - JOUR
TI  - Data analytics process over road accidents data—A case study of Lisbon city
T2  - ISPRS International Journal of Geo-Information
VL  - 11
IS  - 2
AU  - Mesquitela, J.
AU  - Elvas, L. B.
AU  - Ferreira, J.
AU  - Nunes, L.
PY  - 2022
SN  - 2220-9964
DO  - 10.3390/ijgi11020143
UR  - https://www.mdpi.com/journal/ijgi
AB  - Traffic accidents in urban areas lead to reduced quality of life and added pressure in the cities’ infra-structures. In the context of smart city data is becoming available that allows a deeper analysis of the phenomenon. We propose a data fusion process from different information sources like road accidents, weather conditions, local authority reports tools, traffic, fire brigade. These big data analytics allow the creation of knowledge for local municipalities using local data. Data visualizations allow big picture overview. This paper presents an approach to the geo-referenced accident-hotspots identification. Using ArcGIS Pro, we apply Kernel Density and Hot Spot Analysis (Getis-Ord Gi*) tools, identifying the existence of black spots in terms of location and context conditions, and evaluate the possible human, environmental and circumstantial factors that may influence the severity of accidents. The results were validated by an expert committee. This approach can be applied to other cites wherever this data is available.
ER  -