 across the middle of the image.
The set of classes (road, vegetation, sound water, etc.) was determined using ENVI's Principal Component Analysis (PCA) of the near-infrared data in the before image. This set of classes was then applied to the after image to determine which areas changed from one class to another.
Near-Infrared Aerial Image Showing Amount of Road Loss Near Pea Island
The dashed red line shows the remaining road area after Hurricane Irene washed part of Highway 12 away. The reduced road area is superimposed on the image of the Pea Island section before the storm hit. Hurricane Irene destroyed 2,330.4 square meters of road in this section of Hatteras Island. Other areas of the Island suffered additional road loss not pictured here. The land lost due to inlet formation in the Pea Island section of Hatteras Island is 57,629.0 square meters.
Details of the New York Times Kaggle Competition
Image created by Holly Davis using the Wordle software.
Competition goal: Predict the popularity of New York Times blog articles.
Algorithms Used: I tested logistic regression, CART and random forest statistical models, as well using k-means clustering and bag of words algorithms to improve model results.
Data preparation/Model training: I explored imputing missing data and used 10-fold cross-validation for training the models.
Result: Despite using cross-validation, my final model overfit the data causing my final rank in the competition to be 965 out of 2923 competitors (within the top 33% of competitors). Had I not wanted to squeeze out a little more performance (or if I had had less time to devote to the competition), I would have stopped with a simpler, but quite satisfying random forest model which would have placed 383 out of 2923 (within the top 13% of competitors).
Details of the Kaggle Competition
Image credit: Show of Hands. Image shows the type of data that can be collected using the Show of Hands app.
Competition goal: Predict voting outcomes using informal polling questions from the "Show of Hands" app.
Algorithms Used: Linear regression, CART and random forest.
Data preparation/Model training: During data exploration, I determined that some records were invalid due to a key variable being out of bounds. After removing this spurious data, I imputed values that were missing.
Result: My final rank in the competition was 110 out of 2875 competitors (within the top 4% of competitors). I attribute my high rank to my data preparation since the algorithms used and the training were straightforward.