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How to Build an Interactive Geographic Data Visualization Dashboard with Python and React

Develop a web-based dashboard that visualizes geographic data across multiple counties and states, allowing users to interact with the map, filter data, and analyze opportunities based on various attributes.

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Simple Summary

This coding plan outlines the development of an interactive data visualization dashboard using Python, React, and Mapbox to display and analyze geographic data across multiple counties and states.

Product Requirements Document (PRD)

Goals:

  • Create an interactive map-based dashboard to visualize data across 20+ counties in different states
  • Enable users to filter and analyze data to identify opportunities
  • Provide ability to select areas and view detailed data in tables and charts
  • Implement data manipulation capabilities using Python

Target Audience:

  • Internal team members analyzing geographic data and opportunities

Key Features:

  1. Interactive map with state/county outlines and data points
  2. Ability to click on counties or use dropdowns to select areas
  3. Custom region selection (rectangular) on the map
  4. Data filtering and sorting functionality
  5. Detailed data view in tables and charts for selected areas
  6. Python-based data manipulation capabilities
  7. Integration of multiple data sources

User Requirements:

  • Simple state/county outline map with data points
  • Ability to outline an area and see detailed data for that region
  • Charts and graphs to represent data ($ value, time since x, etc.)
  • Handling of thousands of data points per county across 20+ counties
  • Monthly data updates, with some annual data
  • Dark mode (nice-to-have)

User Flows

  1. Data Exploration Flow:

    • User opens the dashboard
    • Views the initial map with all data points displayed
    • Uses filters to highlight potential opportunities
    • Clicks on specific counties or uses dropdowns to select areas of interest
    • Views detailed data in tables and charts for the selected region

  2. Custom Region Analysis Flow:

    • User navigates to the map view
    • Draws a custom rectangular region on the map
    • System displays data specific to the selected region
    • User analyzes the data through tables and charts

  3. Data Manipulation Flow:

    • User accesses the Python-based data manipulation interface
    • Performs custom data operations or analyses
    • Views updated results on the dashboard

Technical Specifications

Frontend:

  • React for building the user interface
  • Mapbox for map visualization (free tier)

Backend:

  • Python with pandas for data processing and manipulation
  • Flask or FastAPI for creating a backend API

Data Storage:

  • Supabase for authentication and storage

Integration:

  • Python-based backend API to connect data processing with React frontend

Future Considerations:

  • Potential integration of Claude API
  • Exploration of Dash by Plotly for interactive web applications

Development Approach:

  • Agile methodology with short sprints
  • AI-assisted coding using Cursor, Gemini, GPT, and Claude

API Endpoints

N/A

Database Schema

N/A

File Structure

project-root/ ├── frontend/ │ ├── public/ │ └── src/ │ ├── components/ │ │ ├── Map.js │ │ ├── DataTable.js │ │ ├── Charts.js │ │ └── Filters.js │ ├── pages/ │ ├── utils/ │ ├── App.js │ └── index.js ├── backend/ │ ├── api/ │ ├── data_processing/ │ ├── models/ │ └── main.py ├── data/ ├── tests/ └── README.md

Implementation Plan

  1. Set up project structure and version control
  2. Implement basic React frontend with Mapbox integration
  3. Develop Python backend with pandas for data processing
  4. Create API endpoints for data retrieval and manipulation
  5. Implement map interaction features (clicking, dropdowns)
  6. Add custom region selection functionality
  7. Develop data filtering and sorting capabilities
  8. Create detailed data view with tables and charts
  9. Integrate multiple data sources
  10. Implement user authentication with Supabase
  11. Add dark mode feature
  12. Conduct thorough testing and bug fixing
  13. Deploy MVP for initial user feedback
  14. Iterate and add additional features based on feedback

Deployment Strategy

Information not available in conversation.

Design Rationale

The design decisions were made based on the following factors:

  1. Need for a cost-effective and easy-to-implement mapping solution led to choosing Mapbox (free tier).
  2. Requirement for data manipulation capabilities and familiarity with pandas led to selecting Python for backend processing.
  3. Desire for a modern, interactive frontend led to choosing React.
  4. Need for authentication and storage led to incorporating Supabase.
  5. Agile development approach with short sprints was chosen to quickly develop an MVP and iterate based on feedback.
  6. The core feature of matching overlapping attributes and visualizing opportunities based on outlying attributes drove the initial focus of the implementation plan.