Predicting Relations Between Factors Involved In Gerrymandering
Proposed research study for data mining subject where we evaluate relations between the various factors involved in gerrymandering for the area of Maryland to be specific and in turn check the validity of the hypothesis through data & attributes ranging from 111th through 115th congressional district election.
With factors like population, votes, party affiliation, type of location, the income of living, we wanted to see the association between these factors and many other factors with each other. With certain running assumptions about population and party-race affiliations, we wanted to analyse information throughout eight congressional elections to determine how they link and affect the decision of gerrymandering. We believe the above-mentioned factors can have major correlations between each other and this analysis tries to delve into this in a much more deeper sense.
Gerrymandering is known as the practice intended to establish a political advantage for a particular party or group by manipulating district boundaries. The resulting district is known as a gerrymander; however, that word is also a verb for the process. The term gerrymandering has negative connotations among various groups in the society.