As for today, political elections are the key form of people’s participation in the formation of the state in all democratic countries, which is why theoretical works in the field of spatial modeling of voter choice appeared relatively long ago and played a major role in the development of both further theoretical and empirical research in this area. In this survey we firstly give a brief overview of the history of the formation of spatial modeling in relation to election results and political preferences of individuals from the point of view of research methodology, based on the classical theoretical ‘proximity model’ and ‘directional model’, where rational individuals determine their political positions and compare them with the positions of candidates. Secondly, we explain the appearance of the studies of the mutual influence of voters living in neighboring territories on each other as one of the factors that determine the voters’ political positions and, accordingly, the final choice of a candidate. We also point out the authors’ different explanations of the reasons for the appearance of such mutual influence of voters and other factors affecting voters living in neighboring territories (also called as ‘contextual effects’) and emphasize the importance of taking them into account in the studies of electoral preferences. A separate chapter in this paper presents the systematization and description of the main empirical approaches to spatial modeling of electoral choice: at the beginning, we present the basic econometric spatial models (used by the authors regardless of the subject of the study), and then we describe the empirical work in the field of voter choice, depending on the hypotheses, focusing on the research methodology and the data used. In conclusion, we define the main directions for the research development and the vector of further practical work in this area. This paper will help researchers understand existing fundamental works, evaluate current approaches to the modeling of electoral choice, and improve theoretical or empirical spatial analysis