Detecting Election Fraud through Data Analysis (DEFDA)
This project aims to promote improved methods of detecting election fraud through data analysis and promote data-based decision making within civil society.
By analyzing data from previous elections and reporting on the trends, CRRC-Georgia reports on election irregularities in the 2016 parliamentary elections using a quantitative data analysis method. This will add another option for election monitoring and fraud detection. By engaging with civil society and the general public through presentations and media, the project aims to promote a more informed understanding of the electoral process.
The methodology is based on the principles widely used in detecting election fraud outlined in the specific literature. The methods include but are not limited to:
- Preparation of district and precinct-level maps of election results;
- Analysis of voter turnout including preparing maps of concentration of unusually high turnout;
- Correlation analysis of precinct-level election outcomes and turnout values;
- Analysis of election-day turnout patterns (per-minute rate of passing voters);
- Analysis of election patterns in particularly vulnerable regions (e.g. in minority-dominated areas of Georgia).