Prediction of Voting Patterns Based on Census and Demographic Data

Analysis Performed by: Mike He

ECE 539, Fall 2005

AbstractAbstract

Prediction of Voting Patterns in 2004 Prediction of Voting Patterns in 2004 Presidential ElectionPresidential Election

Multi-Layer Perceptron, Back-PropagationMulti-Layer Perceptron, Back-Propagation Based on Demographic DataBased on Demographic Data

Population SizePopulation Size Gender CompositionGender Composition Racial CompositionRacial Composition Age CompositionAge Composition

Voting RepresentationsVoting 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

Experimental ProceduresExperimental Procedures

Data Pre-ProcessingData Pre-Processing Network Structure DeterminationNetwork Structure Determination

# of Hidden Layers, Neurons in Layers# of Hidden Layers, Neurons in Layers

Coefficients DeterminationCoefficients Determination Training, Training Error TestingTraining, Training Error Testing

Error from vote percentages, calling for Error from vote percentages, calling for candidatecandidate

Testing on Testing Data SetTesting on Testing Data Set

Experimental ParametersExperimental Parameters

14 Features, 3 Outputs14 Features, 3 Outputs Hyperbolic Tangent Activation Function for Hyperbolic Tangent Activation Function for

Hidden LayersHidden Layers Sigmoid Activation Function for Output Sigmoid Activation Function for Output

LayerLayer Learning coefficient Learning coefficient αα=0.2=0.2 Momentum coefficient Momentum coefficient μμ=0.5=0.5

Experiment 1 – Network StructureExperiment 1 – Network Structure

Many different structures tested according Many different structures tested according to total square errorto total square error

Best performers isolated for further testingBest performers isolated for further testing Comparison of error across multiple trials Comparison of error across multiple trials

between tested structuresbetween tested structures Winner: 15 neurons in hidden layer, 4 Winner: 15 neurons in hidden layer, 4

hidden layershidden layers

Experiment 2 - CoefficientsExperiment 2 - Coefficients

To determine optimum To determine optimum αα and and μμ Different sets of coefficients tested based Different sets of coefficients tested based

on total square error as well as maximum on total square error as well as maximum square errorsquare error

Chosen configuration:Chosen configuration: αα = 0.2, and = 0.2, and μμ = 0.5 = 0.5

Classification ResultsClassification Results

Application of MLP to attempt to predict Application of MLP to attempt to predict which candidate will win each countywhich candidate will win each county

100 training and prediction trials100 training and prediction trials For Wisconsin (training data), 77% For Wisconsin (training data), 77%

classification rateclassification rate For Minnesota (testing data), 75% For Minnesota (testing data), 75%

classification rateclassification rate Less than 3% standard deviation in Less than 3% standard deviation in

classification rate between trialsclassification rate between trials

Concluding RemarksConcluding Remarks

Impressive overall predictive powerImpressive overall predictive power Retains predictive power for different states:Retains predictive power for different states:

Wisconsin and Minnesota similar demographically, Wisconsin and Minnesota similar demographically, different politicallydifferent politically

Predictions based only on demographics – Predictions based only on demographics – innocuous data leads to powerful resultsinnocuous data leads to powerful results

Demonstrates effectiveness of MLP’s as well as Demonstrates effectiveness of MLP’s as well as element of truth in common generalizations of element of truth in common generalizations of demographic voting tendenciesdemographic voting tendencies