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The objective of this research project is to elaborate a method for the definition of Functional Urban Regions (FURs, term used as a general reference to metropolitan regions, urban agglomerations, etc.) based on the Statistical Grid, combining population and transport infrastructure supply data. The Statistical Grid was recently made available by the Brazilian Institute of Geography and Statistics (IBGE) and includes the main information of the 2010 Census in a very detailed manner in spatial terms. Population data are organized in 200 x 200-meter cells in urban areas and 1 x 1 km in rural areas. The use of population indicators is an alternative to the definition of FURs, since the census is usually carried out regularly in most countries and its results are quite reliable. On the other hand, it is known that the process of metropolization also depends on the existence of infrastructure networks, especially transport networks, which play an important role in the integration and organization of space, allowing the flow of goods, services and opportunities to supply the demands of the population. Thus, the supply of road infrastructure will be incorporated into the grid by means of an indicator of spatial coverage that includes weighted influence bands around the roads. Once organized, these data will first be processed and analyzed with spatial analysis techniques, especially the ESDA (Exploratory Spatial Data Analysis) technique, allowing the characterization of spatial dependence and indicating how the values are correlated in space. Data mining techniques, namely: decision trees and artificial neural networks, will be used for the combination and analysis of these data in order to obtain decision rules, which will be employed to elaborate the method of defining FURs. With this proposal, it is expected to contribute to the question of the definition of FURs by using a more disaggregated spatial resolution (i.e., the Statistical Grid) than the municipal subdivision normally used. In this way, it is intended to mitigate the occurrence of a problem known as "ecological fallacy", i.e., when analyzing the results derived from the aggregation of values per unit area, inferring that these values correspond to the individual level. (AU)