ИСТИНА

One of the unresolved methodological problems in geographical information science is vague and weakly formalized notion of spatial detail. Traditional concept of scale inherited from the era of manually drawn printed maps no longer acts as a representative measure of detail, since electronic map can be viewed at any scale. Up to the moment a limited number studies reported methods to formalize level of detail (LoD) of the topographic databases and to find LoD inconsistencies based on various distance, size, density and semantic measures. However, still there are no formalizations of LoD that will characterize the detail of a spatial feature, set or database in inambiguous way. One of the problems complicating such development is a spatial heterogeneity of the landscape. Highly complex landscapes, either natural or transformed by human, require more spatial information to represent them adequately in a spatial database. That means that feature density in data covering mountainous and urban landscapes will contain more information than in data covering large water bodies and uninhabited places — at the same level of detail. Therefore feature density alone is not a reliable measure of level of detail. However, there is an obvious correlation of feature density with landscape complexity and economic development of the territory. In the current study we propose a regression-based model which predicts relative feature density (normalized on the mean) in a given spatial extent using the abovementioned factors as independent variable. Middle- and small-scale Russian digital topographic maps are used as input data. Landscape complexity is assessed using information theory indices applied to 100 m resolution Copernicus Global Land Cover raster dataset, while the level of economic development is parameterized based on official regional statistics. The study was supported by the Russian Foundation fo Basic Research grant No. 18-07-01459.