Valencio, Carlos Roberto [UNESP]El Hetti Laurenti, Carlos Henrique [UNESP]Baida, Luiz Carlos [UNESP]Ferrari, Fernando [UNESP]Kawabata, Thatiane [UNESP]Colombini, Angelo CesarIEEE2019-10-042019-10-042014-01-012014 15th International Conference On Parallel And Distributed Computing, Applications And Technologies (pdcat 2014). New York: Ieee, p. 124-130, 2014.http://hdl.handle.net/11449/186402Spatiotemporal data stored in geographic databases provide an evolutionary panorama about the characteristics of a specific region. With integration of prediction concepts and statistical functions to that data, it is possible to make inferences of obtained information, to support in many areas such as management of occupational health, environmental resources, quality of life and, others. In this article is proposed a strategy to calculate the locality of predicted spatial points with the temporal and statistic function series, which will be able to find regions with critical levels. In the concentrations of more dense occurrences, this strategy supports to choose prevention methods and offers a prediction analysis based on georeferenced resources. This work contributes towards to prediction, analysis and visualization of georeferenced data to reduce costs and improve the life quality.124-130engprediction of spatial dataanalysis of geographic dataWeb-based Geographic Information System (WebGIS)occupational healthTrabalho apresentado em evento10.1109/PDCAT.2014.29WOS:000374910000019Acesso aberto