https://github.com/vida-nyu/taxis-vis-data-backend
📊 Django Backend for analysing and viz. filtered taxi trip data from any city, ready for ML integration 👀
https://github.com/vida-nyu/taxis-vis-data-backend
django python taxis taxisvis urban
Last synced: 9 days ago
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📊 Django Backend for analysing and viz. filtered taxi trip data from any city, ready for ML integration 👀
- Host: GitHub
- URL: https://github.com/vida-nyu/taxis-vis-data-backend
- Owner: VIDA-NYU
- Created: 2025-01-27T20:46:46.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-02-12T23:16:39.000Z (8 months ago)
- Last Synced: 2025-04-12T13:20:44.836Z (6 months ago)
- Topics: django, python, taxis, taxisvis, urban
- Language: Python
- Homepage:
- Size: 159 KB
- Stars: 0
- Watchers: 7
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Citation: CITATION.cff
Awesome Lists containing this project
README
Taxis Vis
📊 Data Analysis Backend (Django + Pandas)
---
> [!WARNING]
> **🚨 Important Notice**:
> This current repository and the Taxis-Vis-Frontend are put on hold.
> The goal was to see what is possible to do with today tools on the Javascript end side coupled with Python backend for
> reproducing Taxis-VIS. Now it touches enough yet is not deleted because could be (re-)used.
> Cheers! @Simon.
## 🚀 **Overview**
The **Data Analysis Backend** is a **Django + Pandas** service that performs **analytics** on taxi trip data.
Once the **Taxis Vis Frontend** filters taxi trips, it sends a subset of trips here for **statistical** and **graphical
** analysis,
including **histograms, box plots, scatter plots, and time-series visualizations**, _to name a few_.
> [!NOTE]
> The **Geospatial backend is no longer needed** since **DuckDB-WASM** handles spatial queries directly in the
> frontend.
> This backend is **strictly** for **data analysis & visualization**—not spatial filtering.
---
## 📦 **Installation & Setup**
### **🔧 Prerequisites**
- **Python** (>=3.8)
- **Django** (installed via `uv` or `pip`)
- *(Recommended)* **UV** for seamless virtual environment management
- **Pandas** (for handling data operations)
### **🛠️ Steps to Set Up**
1️⃣ **Clone this repository**:
```bash
git clone https://github.com/VIDA-NYU/Taxis-Vis-Data-Backend.git
cd Taxis-Vis-Data-Backend
```
2️⃣ **Install dependencies** using **UV**:
```bash
uv lock
uv sync
```
3️⃣ **Run the Django server**:
```bash
# With UV (recommended)
uv run python manage.py runserver
# Or manually if using pip/venv (though make sure to be in the correct environment)
python manage.py runserver
```
💡 By default, the backend runs on **http://127.0.0.1:8000**.
---
## 📊 **How It Works: Data Flow**
1️⃣ **User applies filters in the Frontend (Taxis Vis UI).**
2️⃣ **Frontend sends a filtered subset of trips (CSV) to this Django backend.**
3️⃣ **Django processes the CSV using Pandas** and generates **Plotly-compatible JSON** for visualization.
4️⃣ **Frontend receives the JSON** and renders the requested charts dynamically.
---
## 📖 **Further Reading & Resources**
- **[Frontend (React) README](https://github.com/VIDA-NYU/Taxis-Vis-Frontend)** → The user-facing interface that
triggers these analysis requests.
- **[Original Taxis Vis Paper (IEEE)](https://ieeexplore.ieee.org/abstract/document/6634127/)** → Research behind the
system.
---
**Happy Analysing!**
_The Taxis Vis Team_ 🚀