Interactive Map of Survey Respondents

Overview

This project generates an interactive heatmap and circle marker map to visualize the geographical distribution of survey participants based on their Canadian postal codes, specifically focusing on Brampton, Ontario.

Use Case

The map provides insights into where survey respondents are concentrated within Brampton. This helps businesses, organizations, or analysts identify high-density areas, supporting better outreach strategies, resource allocation, and targeted decision-making.

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How It Works

1. Data Cleaning

• The script cleans the postal codes from the survey data.
• It removes invalid characters, standardizes the format (e.g., A1A1A1 → A1A 1A1), and excludes invalid entries.

2. Mapping Postal Codes

• Cleaned postal codes are mapped to their latitude and longitude using a reference file (CanadianPostalCodes202403.csv).
• The script groups data by postal codes and calculates the count of respondents for each location.

3. Interactive Map Generation

• Using Folium, the script generates an interactive map:
  • Circle Markers: Each marker represents a postal code.
    • The size of the circle is proportional to the number of respondents at that postal code.
    • Markers display detailed information on hover, such as postal code and count.

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Prerequisites

Before running the script, ensure you have the following installed:

• Python 3.x
• Required libraries:

  pip install pandas folium

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How to Run

1. Clone the repository:

   git clone https://github.com/MasegoM94/interactive_map_python.git
   cd interactive_map_python

2. Prepare the Input Data:

   • Place the following files in the same directory:
     • fake_postal_codes.csv: Input file containing survey data with a column named postal_code.
     • CanadianPostalCodes202403.csv: A reference file containing postal codes, latitude, and longitude. (This file was obtained from the following site (https://www.serviceobjects.com/blog/free-zip-code-and-postal-code-database-with-geocoordinates/) and will require you to disclose what the data will be used for)

3. Run the Script: Execute the Python script:

   python interactive_map.py

4. View the Map:

   • The generated interactive map will be saved as an HTML file (e.g., brampton_map.html).
   • Open the file in a web browser to explore the map.

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File Structure

interactive_map_python/
│-- fake_postal_codes.csv       # Input survey data
│-- CanadianPostalCodes202403.csv # Postal code reference file
│-- interactive_map.py          # Main Python script
│-- cleaned_postal_data.csv     # Cleaned postal code output (generated)
│-- brampton_map.html           # Interactive map output (generated)

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Example Output

• Postal Code: M5V 1A1
• Count: 10
• Circle markers on the map will grow in size based on the respondent count.

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Tools and Libraries Used

• Python: Data processing and cleaning.
• Pandas: For data manipulation and cleaning.
• Folium: To create interactive maps and visualize data.

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Acknowledgments

This project leverages open-source tools and libraries. Data is provided in CSV format and can be adjusted to suit other locations or use cases.

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Future Enhancements

• Add heatmap visualization for higher-level insights.
• Enable dynamic input file selection.
• Allow filtering based on custom regions.