Geo-Spatial Data Visualization and Critical Metrics Predictions for Canadian Elections

TL;DR

This paper presents a geospatial data visualization and prediction tool for Canadian election data, addressing the challenge of scattered information by providing interactive visualizations and trend analysis to facilitate better understanding and comparison.

Contribution

The paper introduces a novel interactive visualization and prediction tool for Canadian election data, integrating geospatial analysis and open-sourcing the implementation.

Findings

Enhanced data interpretation through geospatial visualization

Effective trend analysis and prediction capabilities

Open-source tool promotes reproducibility and further development

Abstract

Open data published by various organizations is intended to make the data available to the public. All over the world, numerous organizations maintain a considerable number of open databases containing a lot of facts and numbers. However, most of them do not offer a concise and insightful data interpretation or visualization tool, which can help users to process all of the information in a consistently comparable way. Canadian Federal and Provincial Elections is an example of these databases. This information exists in numerous websites, as separate tables so that the user needs to traverse through a tree structure of scattered information on the site, and the user is left with the comparison, without providing proper tools, data-interpretation or visualizations. In this paper, we provide technical details of addressing this problem, by using the Canadian Elections data (since 1867) as a specific case study as it has numerous technical challenges. We hope that the methodology used here can help in developing similar tools to achieve some of the goals of publicly available datasets. The developed tool contains data visualization, trend analysis, and prediction components. The visualization enables the users to interact with the data through various techniques, including Geospatial visualization. To reproduce the results, we have open-sourced the tool.