Mapping past human activity using low representative site location datasets and elevation data

Archaeological maps based on the location of sites are strongly biased by the degree of archaeological recognition and inform little about the real pattern of past human activities, especially on areas poorly covered by surveys. Continuous maps and spatial models, independent of the degree of archaeological recognition of the area, can used as a tool for explanation of the patterns of past human activity [1]. There are several methods (see: [2, 3, 4, 1] for details) which couple information about the location of archaeological remnants and variables derived from natural datasets and social and economic variables. These methods use Geographic Information Science technology and statistical algorithms and result in maps of past human activity. Correct models require the user to know the importance of variables what is difficult to proceed on insensibly contrasted areas like temperate lowlands (Jasiewicz, Hildebrandt-Radke 2009). and requires a large amount of representative data so its application is limited only to well recognized areas where data representativeness is not questionable - which does not occur very often. Furthermore, archaeological remnants tend to be clustered also that some areas were examined more thoroughly than others. This leads to the problem of data imbalance. The term refers to any dataset that exhibits an radically unequal distribution between its classes [5, 6].