  As a case study into an algorithmic approach to congressional districting,
North Carolina provides a lot to explore. Statistical modeling has called into
question whether recent North Carolina district plans are unbiased. In
particular, the literature suggests that the district plan used in the 2016
U.S. House of Representatives election yields outlier results that are
statistically unlikely to be obtained without the application of bias.
Therefore, methods for creating strong and fair district plans are needed.
Informed by previous districting models and algorithms, we build a model and
algorithm to produce an ensemble of viable Congressional district plans. Our
work contributes a ``Rocks-Pebbles-Sand'' concept and procedure facilitating
reasonable population equity with a small overall number of county splits among
districts. Additionally, our methodology minimizes the initial need for
granular, precinct-level data, thereby reducing the risk of covert
gerrymandering. This case study indicates plausibility of an approach built
upon an easy-to-understand intuition.
