open_food_map

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An open-source project for building interactive, data-driven restaurant maps for any city. This repo contains the code of the London Food Map plus a modular pipeline for adding new cities. Contributions welcome: add your favourite city, improve the models, or extend the visualizations.

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# Open Food Map

This project provides tools to collect, analyze, and visualize restaurant data. It is designed to be modular and extensible, allowing you to easily add support for new cities.

## Features

*   **Data Collection**: Scrape restaurant data from Google Maps API using a grid-based search.
*   **Analysis**: Identify "underrated" restaurants using a machine learning model that compares ratings to review counts and other factors.
*   **Visualization**: Generate interactive HTML maps to explore the culinary landscape.

## Project Status & Limitations

This project is a work in progress.

- Coverage reflects what the Google Maps API surfaces in generic searches; it is not a census of all restaurants.
- Rating models capture platform-typical outcomes, not intrinsic food quality.
- Restaurant age, pricing, review text, and reviewer composition are not yet fully incorporated.
- Results should be interpreted as exploratory and comparative, not definitive rankings.

Contributions, critiques, and extensions are very welcome.

## Project Structure

*   `src/`: Source code.
    *   `data_collection/`: Scripts for gathering data.
    *   `analysis/`: Scripts for processing and analyzing data.
    *   `visualization/`: Scripts for creating maps and plots.
*   `data/`: Data storage.
    *   `raw/`: Raw collected data.
    *   `processed/`: Analyzed and cleaned data.
    *   `sample/`: Small sample datasets for testing.
*   `tests/`: Unit tests.

## Setup

#### Google API requirements

The data collection scripts require the **Google Places API**.

Enabling only the **Maps JavaScript API** or **Maps Embed API** is not sufficient and will result in data collection completing with no restaurants found.

If the Places API is not enabled, the collector may run to completion but produce no output files.

1.  **Clone the repository**:
    ```bash
    git clone https://github.com/laurencleek/open_food_map.git
    cd open_food_map
    ```

2.  **Install dependencies**:
    ```bash
    pip install -r requirements.txt
    ```

3.  **Environment Variables**:
    Create a `.env` file in the root directory and add your Google Maps API key:
    ```
    GOOGLE_MAPS_API_KEY=your_api_key_here
    ```

## Usage

### 1. Data Collection

To collect data for a specific city (e.g., London), run:

```bash
python src/data_collection/collect_data.py --city-name london --lat-min 51.28 --lat-max 51.69 --lon-min -0.51 --lon-max 0.33
```

Arguments:
*   `--city-name`: Name of the city (used for file naming).
*   `--lat-min`, `--lat-max`, `--lon-min`, `--lon-max`: Bounding box coordinates.
*   `--grid-step`: Grid step size in km (default: 1.5).
*   `--radius`: Search radius in meters (default: 1500).

### 2. Analysis

To analyze the collected data and find underrated restaurants:

```bash
python src/analysis/analyze_underrated.py --city-name london
```

This will read from `data/raw/london_restaurant_details.csv` (or `data/sample/london_restaurant_details.csv` if not found) and write:

- `data/processed/london_hype_adjusted_ratings.csv`

### 3. Visualization

To generate the interactive dashboard with cuisine filtering and borough analysis:

```bash
python src/visualization/interactive_cuisine_map.py --city-name london
```

This requires the analysis step to be completed first (it reads `data/processed/{city}_hype_adjusted_ratings.csv`). It will generate:

- `output/london_restaurants_interactive.html`

The repository also includes a pre-generated `output/london_restaurants_interactive.html` so you can open it immediately.

## How to Contribute & Create New Maps

We'd love to see maps for more cities! Here is how you (or others) can contribute:

### 1. Pick a City
Decide on a city you want to map. You will need its bounding box coordinates (Latitude Min/Max, Longitude Min/Max). You can find these easily on websites like [bboxfinder.com](http://bboxfinder.com).

### 2. Run the Collector
Run the collection script with your city's coordinates. For example, for **Paris**:

```bash
python src/data_collection/collect_data.py --city-name paris --lat-min 48.81 --lat-max 48.90 --lon-min 2.22 --lon-max 2.46
```

This will generate `data/raw/paris_restaurant_details.csv`.

### 3. Share Your Data
If you want to combine data with others:
1.  **Locate your CSV**: Find the `[city]_restaurant_details.csv` file in `data/raw`.
2.  **Merge**: Since the file structure is identical for every city, you can simply combine CSV files (e.g., using Pandas `pd.concat` or Excel) to create a mega-dataset.
3.  **Visualize**: Run the visualization script on your combined file to see a multi-city map!

### 4. Submit a Pull Request
If you've improved the code or want to share a sample dataset for a new city, please submit a Pull Request on GitHub.

## License

[MIT License](LICENSE)

## Contact

Created by **Lauren leek**.

If you have any questions or suggestions, please feel free to contact me at [laurencaroline.leek@eui.eu](mailto:laurencaroline.leek@eui.eu).

## Support

If you enjoyed this project or found it helpful, consider supporting my work!

*   [☕ Buy me a coffee](https://buymeacoffee.com/laurenleek)
*   [📝 Subscribe to my Substack](https://laurenleek.substack.com)

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