The document discusses building a model to predict the hourly rate of certain events for each city in the US based on location, date, time of day, and weather. It uses 500,000 geolocation data points from 2001-2013 to train a Poisson regression model with features like weekday, hour, time since new years, and weather to make predictions. The model was tested on 2013 data and showed improved predictions when augmented with external weather data, even when applying a "rain danger coefficient" calculated from a few cities to fill in weather data gaps nationwide.
2. Goal: Predict the hourly rate of a certain kind of
event for each city in the US.
(Think number of arrests for public intoxication)
f(city, date, time of day, weather) -> #events/hour
3. Data:
● 500,000 geolocation data points across the US (2001-2013).
● < 1000 events/ year in major cities.
● Pulled in external weather data to augment the model
6. Poisson Regression
● GLM perfect for count / rate data that is Poisson distributed.
● Trained with MLE (python, statsmodels,patsy)
● Features: weekday, hour of day, #days from new years, weather, year
7. About me! Discrete Math: Random Graphs
Theoretical CS: (related to P = NP)
9. Adding Rain:
● data: (lat, long, timestamp, weather)
Problem: In periods with no events, don’t know weather,.
Solution:
● Pick a few cities where we can fill in the gaps on weather data
● Calculate multiplicative “rain danger coeffient” and apply nationwide
