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The data will reside in the following tables (based largely on the fact that the data sources are quite different):

  • community_demographics
  • voter_demographics
  • candidate_demographics
  • elected_body_demographics

Initial thoughts on the columns in these tables are below. If we're going to later extend this beyond county commissions or look at sub-county district data, how does this need to be revised?

community_demographics (based on ACS data)

  • Year
  • Jurisdiction type (e.g., county)
  • Jurisdiction identifier (name)
  • Total population (presumably voting age)
  • Gender counts: 3 columns: male, female, non-binary (please correct if this is wrong)
  • Race counts: [TBD]

voter_demographics (based on voter registration data)

  • Election year (for earlier years, as of election day; for current year, data should be updated regularly)
  • Voting status (did they vote in this year's election?)
  • Jurisdiction type (e.g., county)
  • Jurisdiction identifier (name)
  • Total population (presumably voting age)
  • Gender counts: 3 columns: male, female, non-binary (please correct if this is wrong)
  • Race counts: [TBD]

candidate_demographics (based on BOE candidate lists)

  • Election year
  • Voting status (did they vote in this year's election?)
  • Jurisdiction type (e.g., county)
  • Jurisdiction identifier (name)
  • Elected office
  • Total population (presumably voting age)
  • Gender counts: 3 columns: male, female, non-binary (please correct if this is wrong)
  • Race counts: [TBD]

Note: The demographic data can be drawn from their voter registration, but we probably need to get candidate lists with home address in order to ensure a correct match.

elected_body_demographics

  • Election year (following the election)
  • Jurisdiction type (e.g., county)
  • Jurisdiction identifier (name)
  • Elected office
  • Total population (presumably voting age)
  • Gender counts: 3 columns: male, female, non-binary (please correct if this is wrong)
  • Race counts: [TBD]