APPROACH TO ELABORATION OF INTEGRAL INDICATORS FOR ASSESMENT OF SPATIAL OBJECTS CHARACTERISTICS AND ANALYSES OF TYPICAL ERRORS (ON THE BASE OF SUSTAINABLE DEVELOPMENT INDICATORS AND RUSSIAN REGIONS AS AN EXAMPLE)

Abstract

Integral indicators (further – II) in geography is a measurement composed of many simple indicators to characterize complex properties of spatial phenomenon. II is subjective, since a sci-entist should make personal decision to choose approach, the way of mathematic processing and integration of initial data to compos II. Composing II consists of a few stages: definition of the object and the focus of the research; definition of territorial units – their scale and boundaries; selection of particular indicators and their initial structuring (gathering in subgroups); normalizing indicators using threshes; defining weights of particular indicators and algorithm of their in-tegration for II calculation. Verification of results could be done using comparison of calculated and real values in well-known field plots. If it is impossible then cartographic visualization is used – chaotic mix of areas with different II values which resemble patchwork quilt is a witness of mistakes in methodology and selected approach. Typical errors while composing II are: the object and the focus of the research are not defined; dimensionality of indicators is wrong; irrel-evant indicators is used; failed approach to definition the scale or type of territorial units; irrele-vant approach to definition of threshes or inaccurate expert definition of threshes, in particular incorrect application of minimal and maximum values in dataset as low/high threshes. When as-pects of sustainable development are investigated and in many other cases pressure, state, reac-tion and management indicators incorrectly mixed and used together. When territory of Russia is in focus typical errors are: using regions as basic territorial unit (higher administrative division level) instead of natural areas or lower level division, incorrect statistic data deriving for multi-unit regions, mixing divergent socio-economic indicators and also – using unreliable (for some regions) statistical indicators.