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Gerrymandering is a method dedicated to establishing a political advantage for a particular party by manipulating district borders. The resulting district is known as a gerrymander. Two basic tactics are used in gerrymandering: “cracking” (reducing the voting power of the opposite party’s followers across many districts) and “packing” (concentrating the contrary party’s voting power in one district to decrease their voting power in other neighborhoods).
In addition to its use of reaching wanted electoral results for a particular party, gerrymandering may be used to help or prevent a specific demographic, such as a political, racial, ethnic, religious, linguistic, or class groups, such as in the United States federal voting district borders that produce a majority of constituents representative of African-American or other racial minorities, known as “majority-minority districts.” Gerrymandering can also be used to protect incumbents.
Below is gerrymandered Texas map.
Another example of gerrymandering; Maryland’s 3rd Congressional District.
The Gerrymandered States of America
Reddit user Putin-is-listening created gerrymandered maps of the 2020 U.S. Presidential Election. In the first map, Joe Biden has 506 electoral votes, while Trump has 8. In the second map, Donald Trump has 446, while Biden has 89. The popular vote is the same.
Fighting Gerrymandering
Several mathematical tests can be used to determine if districts have been gerrymandered. For example, districts with a disproportional representation of given demographics expected to vote for one party or evidence of demographics overly diluted across many district borders can be used to discover bias in district boundaries.
Additional tests have also been developed that have studied the range of votes across districts to see if given districts have a more significant bias for one party.
The over test called efficiency gap can be applied as well, when the votes that are ‘wasted,’ or votes for the defeated party, are tallied. This method measures if districts are too packed, too many voters for one party in one area, or cracked, or voters spread too thin for one party. If a high rate of wasted votes would have had little effect on such districts’ outcome, then the result is a bias for the district in how it represents the population.
