Prediction of Voting Patterns Based on Census and Demographic Data
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Prediction of Voting Patterns Based on Census and Demographic Data. Analysis Performed by: Mike He ECE 539, Fall 2005. Abstract. Prediction of Voting Patterns in 2004 Presidential Election Multi-Layer Perceptron, Back-Propagation Based on Demographic Data Population Size Gender Composition

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Prediction of Voting Patterns Based on Census and Demographic Data

Analysis Performed by: Mike He

ECE 539, Fall 2005


Abstract
Abstract Demographic Data

  • 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


Voting representations
Voting Representations Demographic Data

  • 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


Experimental procedures
Experimental Procedures Demographic Data

  • 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


Experimental parameters
Experimental Parameters Demographic Data

  • 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


Experiment 1 network structure
Experiment 1 – Network Structure Demographic Data

  • 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


Experiment 2 coefficients
Experiment 2 - Coefficients Demographic Data

  • 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


Classification results
Classification Results Demographic Data

  • 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


Concluding remarks
Concluding Remarks Demographic Data

  • 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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