The paper presents a methodology for assessing city areas on the basis of residents’ opinions. In contrast to preceding approaches, this methodology combines the opinions on the various features of living in certain micro-districts as well as the way these districts are perceived. Therefore, this technique includes the rational and emotional assessments. Moreover, we assess both the district residents and the inhabitants living outside the area. Thus, the research combines the assessments from inside and outside. The evaluation is based on the representative survey of the residents of Ekaterinburg. The research hypothesis is that city districts will differ significantly in the attractiveness as well as in a set of determinants for the assessments given by residents. The authors’ methodology for rating micro-districts includes three groups of indicators. First, there is the assessment of the residents’ satisfaction level in various aspects of their lives. Second, we assess the residents’ emotional appraisal of the areas, in which they live. Last, there is a choice of the city’s best micro-district to live in. The empirical research found a high degree of differentiation in the obtained ratings and highlighted their socio-economic determinants such as the assessment of personal financial position, housing type, assessment of personal living conditions. The findings of this research provide insights for managerial decisionmaking in strategic regional planning. In particular, these results can be used to address the polarization of the development of urban areas revealed in this research. Further research can be conducted in three directions. Firstly, a deeper analysis of the availability and attractiveness of different types of infrastructure for residents (medical, educational, relaxation infrastructures, etc.) would be of interest. Secondly, micro-district could be ranked by different groups of population (retirees, persons with disabilities, etc.). Last, further research needs an assessment of the different micro-districts attractiveness for work and leisure activities.

The research is aimed at developing and testing a methodology for analyzing demographic potential of Russian regions. The initial data are the regional official Russian statistics indicators. We proposed an approach for assessing the demographic potential based on a differentiated analysis of its quantitative and qualitative components. The paper presents the developed methodology for estimating the demographic potential, combining multidimensional data classification (fuzzy clustering) and expert assessments. Application of the proposed methodology revealed five specific models in the demographic space of Russia. The first model combines a low level of quantitative components of the demographic potential with a high level of its quality. The second model is characterized by average levels of both components. In the third model, an average level of the quantitative component is accompanied by a rather low level of the demographic potential’s quality. The fourth model combines a high level of quantitative component of the demographic potential with an imbalance of its quality indicators, and the fifth — a high level of both components. We have obtained estimates for the quantitative and qualitative components of the demographic potential for each region and rated them. This has allowed identifying “anchor”-regions and “driver-regions, as well as regions with the most and least balanced assessments of the two components. The paper shows the potential application of the developed methodology. In particular, this methodology allows identifying groups of regions, which need the implementation of specific measures for increasing the quantity and improving the quality of the demographic potential. The most significant limitation of the developed methodology is the lack of a complete set of indicators in the official Russian statistics for assessing the demographic potential. Future research will be aimed at applying fuzzy clustering methods to various demographic phenomena, since this approach takes into account the natural uncertainty, which is typical for such processes and, therefore, makes the results of demographic analysis more formalized and valid.