No abstract

This article deals with the problem of clustering countries by a specific set of geo-political features, including demographic, geographical, economic, and other data. The K-means method was chosen as the clustering algorithm. This clustering allows us to identify the specifics and important trends in the development of the current geopolitical situation.

No abstract

No abstract

One of the forecasting tools in geopolitics is mathematical models. In this article, we have presented our own spatial model of geopolitical interactions. The model is based on the models of the geopolitical of the geopolitical position of Arzruni-Komlas and the meta-ethnic borderland of P. Turchin. For empirical verification of the proposed modifications, spatial data describing various properties of the planet Earth were collected and processed. These data include information about the relief of the Earth's surface, temperature, soil fertility capacity, degree of urbanization, land and sea surfaces, political affiliation. The results of modeling these geopolitical interactions on the example of a map of Europe with the inclusion of a part of North Africa have a very average nature of approximation to historical reality. Over the course of several hundred iterations, we managed to get very conditionally closer to the outlines of the political borders in this region. Obtaining more accurate results requires a better selection of model parameters and its possible refinement. The spatial model of geopolitical interactions we have built has proved to be quite workable. The model clearly demonstrated the influence of geographical and climatic data on the territorial distribution of the borders of state agencies. Due to the complexity and non-linearity of the considered problem of territorial dynamics of each particular state, it is difficult to assume how the position of borders will change in reality.