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Prediction of Voting Patterns Based on Census and Demographic Data

This study analyzes voting patterns in the 2004 Presidential Election based on demographic data, including population size, gender, racial and age composition. Using a Multi-Layer Perceptron and Back-Propagation methods, predictions were made regarding candidate win probabilities in various counties. The research adopted different map visualizations, revealing urban areas' tendency to vote Democratic, while exploring effective network structures and activation functions in a machine learning context. With a 77% classification rate in Wisconsin and 75% in Minnesota, the findings affirm the predictive power of demographic data in electoral outcomes.

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Prediction of Voting Patterns Based on Census and Demographic Data

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  1. Prediction of Voting Patterns Based on Census and Demographic Data Analysis Performed by: Mike He ECE 539, Fall 2005

  2. Abstract • Prediction of Voting Patterns in 2004 Presidential Election • Multi-Layer Perceptron, Back-Propagation • Based on Demographic Data • Population Size • Gender Composition • Racial Composition • Age Composition

  3. Voting Representations • Area-Based Winner- Takes-All Map • Strict Red/Blue binary color coding • Can misrepresent actual popular opinion • Population-Based Winner-Takes-All Cartogram • Counties resized to reflect actual population • More accurately reflects popular opinion • Illustrates high density of urban areas and tendency to vote Democratic • Linearly Shaded Vote-Percentage Map • Colors shaded according to vote percentages • Accurately portrays closeness of most races and political homogeneity throughout country

  4. Experimental Procedures • Data Pre-Processing • Network Structure Determination • # of Hidden Layers, Neurons in Layers • Coefficients Determination • Training, Training Error Testing • Error from vote percentages, calling for candidate • Testing on Testing Data Set

  5. Experimental Parameters • 14 Features, 3 Outputs • Hyperbolic Tangent Activation Function for Hidden Layers • Sigmoid Activation Function for Output Layer • Learning coefficient α=0.2 • Momentum coefficient μ=0.5

  6. Experiment 1 – Network Structure • Many different structures tested according to total square error • Best performers isolated for further testing • Comparison of error across multiple trials between tested structures • Winner: 15 neurons in hidden layer, 4 hidden layers

  7. Experiment 2 - Coefficients • To determine optimum α and μ • Different sets of coefficients tested based on total square error as well as maximum square error • Chosen configuration: • α = 0.2, and μ = 0.5

  8. Classification Results • Application of MLP to attempt to predict which candidate will win each county • 100 training and prediction trials • For Wisconsin (training data), 77% classification rate • For Minnesota (testing data), 75% classification rate • Less than 3% standard deviation in classification rate between trials

  9. Concluding Remarks • Impressive overall predictive power • Retains predictive power for different states: • Wisconsin and Minnesota similar demographically, different politically • Predictions based only on demographics – innocuous data leads to powerful results • Demonstrates effectiveness of MLP’s as well as element of truth in common generalizations of demographic voting tendencies

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