All code for this project is hosted at github.com/yogat3ch/canvassML
This algorithm builds from previous work with spatial data and canvassing from 2018 See 'My City in Time and Space'. The project was inspired by the desire to assist the Andrew Yang Campaign with optimization of canvassing efforts.
Massachusetts and New Hampshire use a threshold-based delegate allocation system. Fifteen percent of the vote of a congressional district (CD) must be acquired to win a portion of that CDs delegates, split proportionally between other threshold crossing candidates percentages. State delegates are similar, but the threshold is applied at the state level, ie fifteen percent of the state votes must be acquired to receive a proportional amount of the state delegates.
The model uses two types of predictive variables: specific and demographic.
The specific predictors are comprised of aggregate numbers of supporters who have already affiliated themselves as supporters of the campaign. These are typically registered volunteers or others who've expressed their commitment to vote. These numbers are counted towards the vote thresholds needed for a given congressional district/state. A campaign might also use mailing list subscribers aggregated by zip code.
The demographic predictors are comprised of census data. These data sources are used to characterize the demographic make-up of zip codes, and act as predictors for canvassing success per zip code. Together, these data can be used as predictors in a predictive algorithm to be trained on areas where a reliable outcome variable is available, and tested on areas yet to be targeted for canvassing.
In this example, Massachusetts zip codes will be used as the target and data from New Hampshire zip codes will be used as the training set. Adjacent states within the same "political region" are likely to yield more accurate cross-state comparisons than those with large geographic separations.
New Hampshire 2020 primary election data are originally used as the response variable in the training data. This is possible because the primary has not yet taken place in an adjacent state, MA in this case. If voter data is not available, aggregate success rates from existing canvass efforts for an area might also serve as an outcome variable if a vote has not taken place. Polling results, if available aggregated by the required geographies, could also serve as the outcome variable.