GithubHelp home page GithubHelp logo

damienirving / election-analysis Goto Github PK

View Code? Open in Web Editor NEW
1.0 1.0 0.0 6.64 MB

Analysis of AEC data

Home Page: https://public.tableau.com/profile/damien4566#!/

Jupyter Notebook 99.42% Python 0.58%

election-analysis's People

Contributors

damienirving avatar

Stargazers

 avatar

Watchers

 avatar  avatar  avatar

election-analysis's Issues

Full preference data

The AEC makes available the full details of everyone's senate ballot (i.e. how they numbered every single column):

Prior to that group ticket voting was in place and hence the same richness of information was not available.

Processed data files for consistency

The AEC and TEC use a different format for their csv data files. I should therefore process the raw data files into a consistent format that I can use in all subsequent analysis. I'm thinking two different scripts - aec_to_tasgreens.py and tec_to_tasgreens.csv - that produce two different csv files:

ordinary_votes.csv with the following column headers (i.e. for each polling place):
| PollingPlaceNm | PremisesNm | Latitude | Longitude | LegCo | LocalCouncil | DivisionNm | GreensVotes | TotalVotes | GreensPercentage |

vote_types.csv with the following column headers (i.e. for the statewide and electorate totals and Greens totals):
| OrdinaryVotes | OutOfDivisionVotes | MobileVotes | PostalVotes | PrePollVotes | ProvisionalVotes |

Errors in Trevallyn Fed2019 data

It seems that in the 2019 federal election data the two Trevallyn booths have been mixed up.

In 2016 (and before that)

PollingPlaceID = 8338
PollingPlaceNm = Riverside West
PremisesNm = Trevallyn Life Church
Greens senate vote = 8%

PollingPlaceID = 8342
PollingPlaceNm = Trevallyn
PremisesNm = Trevallyn Primary School
Greens senate vote = 24%

2019

PollingPlaceID = 8342
PollingPlaceNm = Trevallyn
PremisesNm = Trevallyn Life Church
Greens senate vote = 10%

PollingPlaceID = 8338
PollingPlaceNm = Trevallyn Central
PremisesNm = Trevallyn Primary School
Greens senate vote = 24%

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.