Ukraine is one of the countries where the problem of waste management is particularly acute and deteriorating every year. The Regional Waste Management Plan in Volyn region by 2030 envisages a reduction in the total amount of landfilled waste from 97.68% to 30%, and the number of sites for their disposal – up to 4–8 regional landfills per region. Ecological-economic mechanism of solid non-hazardous waste (SNHW) management is based on a harmonious combination of environmental constraints with the economic attractiveness of regional landfills and involves working with geographically defined objects based on the use of methods of processing geospatial information, one of which is mathematical-cartographic modelling. Thus, the main purpose of this work is to substantiate the possibilities of applying the method of mathematical-cartographic modelling in the design of the system of regional landfills of SNHW in the Volyn region. In order to address the issue of placement of SNHW management facilities, the territory of the region is divided into four management clusters. The division of the territory took into account the composition, properties, methods of solid waste collection, logistics, load on waste processing complexes, volumes of waste generated, spatial planning, etc. Three probable options for the location of regional landfills have been developed for the Lutsk SNHW cluster. The results of the study can be used in the development and adjustment of regional plans, waste management programs, as well as in the work of the executive bodies of the united territorial communities.

One of the most challenging tasks in modelling of house pricing is to take into account the location factors. Geographically Weighted Regression (GWR) as a local regression model is an extremely effective instrument for spatial data analysis. The aim of the study is to model the relationships between a residential real estate price (per sq.m) and both building and location characteristics for Gomel using GWR. The data of the Belarus’ National Cadastral Agency on real estate transactions (apartments) in Gomel in 2019 are used as initial. The global Moran I index has been used to estimate a spatial autocorrelation of the dependent variable (price per square meter of residential real estate). Several factors having the impact on the apartment sale prices have been determined. Independent variables having been used in analysis can be divided into building characteristics and spatial characteristics. The building characteristics section includes the number of rooms within the property, property area (square meters), building age, number of floors in the building, floor of the property. The spatial characteristics group contains proximity to city centre, recreation areas, supermarkets, bus stops, healthcare and educational facilities. A regression model of housing price in Gomel has been developed. Mapping variable regression coefficients allows exploring spatial features of the impact of the different explanatory variables on the property price. Geographically weighted regression modelling has revealed the pricing peculiarities inherent for certain areas of the city.