A Framework for Voting Behavior Prediction Using Spatial Data (International Journal of Innovative Research in Computer Science & Technology, 2022)
The greatest method to anticipate the future  is to look at what has happened in the past. We shall present  important election behavioral predictions in this paper. This  study article will focus on the data offered by Present age wise voting statistics, voter demographics, votes cast, and  spatial correlation among surrounding states in order to  validate that a place's exit poll data. The major goals of our  paper are to first encourage voting among different age  groups based on projected circumstances, and then to  understand the influence of a state's neighbours.  Conclusively studying the entire voting scenario of  previous years, which will aid in the forecast of citizens'  voting behavior in the approaching years, as well as  recognizing the root cause of the weaker portions and  improving upon the flaws for a better future. Our main goal  is to use some current voting data from a region to train and  determine the major voting population in the various states  of the United States based on their geographical influence.  This will aid in the analysis of the current situation as well  as assisting the government in creating awareness in places  where it is missing.
Keywords: Spatial Analysis; Voter Turnout; Data Analytics; ARIMA Model; Recurrent Neural Network (RNN); Time Series Forecasting