Description:
The article presents the results of a study on geoinformation monitoring of demographic and migration processes in the South of the European part of Russia, including cities with a population of 100 thousand or more. Nowadays, Russia experiences a demographic crisis that began in the 1990s. In general, this process leads to a reduction in the population, the transformation of the gender and age structure, which provokes an increment of the need of foreign migrants as a possible resource for maintaining the country’s population and increment of the labor force. This process largely determines the priorities of the national state policy, in particular, in 2018, the national project “Demography” was developed, the main aim of which is solving the demographic problems of the country.Geoinformation monitoring of the indicated processes is built on the ethnic composition of the population of regions and cities, so that, from the point of view of demographic processes the ethnic diversity of the South of the country has a high demographic potential, which is demonstrated in recent decades by individual national-territorial entities (the Republic of Chechnya, Ingushetia, Dagestan, Kabardino-Balkaria, etc.). But the demographic potential of these regions does not correspond to their level of socio-economic development, which is extremely low in comparison with the national background with a number of regions in the South of the country. It leads to a migration outflow of the population. On the other hand, there are regions where the level of socio-economic development is higher (for example, the Krasnodar and Stavropol Territories, the Rostov Region), but the demographic situation in these regions is worse (mortality prevails over the birth rate).The article deals with the main stages of planning GIS monitoring of demographic and migration processes, in particular, the collection of base information, GIS modeling, and mapping of processes. As the main software platform for GIS monitoring, we used the tools offered by ESRI’s ArcGIS Spatial Analyst. The study period is defined from 2010 to 2019.