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SELECTION OF INDICATORS AND METHODS OF ESTIMATION OF THE DEGREE OF INTER-TERRITORIAL DIFFERENCES IN HOUSING AND MUNICIPAL DEVELOPMENT OF URBAN AGGLOMERATION
Abstract
The relevance of the study is determined by the need to select indicators that assess the level of housing and communal development of municipalities in the metropolitan area and methods for assessing the degree of interterritorial differences in the housing and municipal development of the urban agglomeration in order to identify these differences and, subsequently, to carry out a set of measures for their equalization for harmonious development agglomeration as a whole. Based on the main alignment objectives and these factors affecting interterritorial differences in the housing and municipal development of the urban agglomeration, the following indicators have been selected to assess the level of housing and municipal development of municipalities in the metropolitan area: the level of housing provision, the cost of housing, the ratio of the provision of housing and municipal services, the quality index of provision of housing and municipal services and the degree of waste processing. The chosen system of indicators is adapted to the capacity of the existing statistical base for agglomeration and provides the maximum informative results of differentiation management in the housing and municipal complex for making management decisions. The article proposes a methodology consisting of statistical methods (range of variation (R), mean square deviation (?), coefficient of variation (V?)) and taking into consideration all factors, conditions and peculiarities of housing and municipal development of municipalities in the urban agglomeration. The calculation of indicators assessing the level of housing and municipal development for each municipal entity in the Krasnoyarsk agglomeration has been carried out. The degree of interterritorial differences in the housing and municipal development of the Krasnoyarsk agglomeration was calculated based on the values of indicators using the proposed set of statistical methods.
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References7
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