Structuring geospatial information in big data for decision support

• Grant number: 18/21279-7
• Support type: Scholarships in Brazil - Master
• Effective date (Start): November 01, 2018
• Effective date (End): August 31, 2020
• Field of knowledge: Engineering - Transportation Engineering
• Principal researcher: José Alberto Quintanilha
• Grantee: Thaís Passos Correia
• Home Institution: Escola Politécnica (EP). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 17/50343-2 - Institutional development plan in the area of digital transformation: advanced manufacturing and smart and sustainable cities (PDIp), AP.PDIP

Abstract

The increase in population due to the increase in urbanization favored low-income citizens to seek housing in areas devalued by the real estate market, increasing their vulnerability. Deforestation and unsuitable use of natural resources also contribute to increasing the risk of natural disasters. Slips and floods are among the most common disasters in Brazil, which are responsible for social, environmental and economic damage, resulting in loss of life. Geotechnologies have become an option to assist in mapping these events and, consequently, to help in predicting risks, assessing vulnerabilities and quantifying losses. Big Data has also been notable for its ability to integrate a large number of databases, from a variety of sources and formats; and collaborating to improve decision making, useful for the prevention of environmental disasters. The study will focus on the surroundings of the station Caieiras of Line 7 Rubi of CPTM, near the Abreus Basin, in the municipality of Caieiras, where these events are frequent and may affect the circulation of trains. This research will map the areas subject to landslides and floods through available software and propose possible means of structuring a geospatial database in order to facilitate the decision making of a public manager through a better prediction of these disasters.