Developed Real-Time Election Nowcasting Model

• Day: 2025-10-24
• Time: 15:50 to 17:00
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Election Modeling, Data Analysis, Google Sheets, Python, Data Management

Description

Session Goal

The session aimed to develop a real-time election nowcasting model using mesa-level swing estimation and to create a structured spreadsheet for election estimation.

Key Activities

• Model Overview: An overview of the real-time election nowcasting model was discussed, focusing on mesa-level swing estimation and implementation strategies using Google Sheets and Python.
• Methodology Development: Developed a mesa-specific ratio/scale estimator for election forecasting, with practical implementations in Google Sheets and Python.
• Spreadsheet Design: Outlined a structured approach to creating a scalable spreadsheet for election estimation, including tabs, schemas, and operational protections.
• Automation Techniques: Explored methods for displaying ‘last modified’ timestamps in Google Sheets using manual formulas, built-in metadata, and Apps Script solutions.
• Command Line Tools: Discussed methods to display timestamps for files using the tree command and alternatives with find and ls.
• Development Log and Refactor Plan: Conducted a development log and ecosystem audit from 2021 to 2025, proposing a refactor plan for data management.
• Data Lake Management: Planned the organization of data into a data lake, including directory structures and conversion processes using DuckDB.

Achievements

• Established a framework for real-time election nowcasting.
• Developed a comprehensive spreadsheet design for election estimation.
• Implemented timestamp automation in Google Sheets.
• Conducted a thorough ecosystem audit and proposed a refactor plan.

Pending Tasks

• Finalize the implementation of the real-time election nowcasting model.
• Complete the data lake move and ensure all data is organized as planned.

Evidence

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• event_ids: []