taxis-vis-data-backend
📊 Django Backend for analysing and viz. filtered taxi trip data from any city, ready for ML integration 👀
Science Score: 62.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
✓Academic publication links
Links to: ieee.org -
○Committers with academic emails
-
✓Institutional organization owner
Organization vida-nyu has institutional domain (vida.engineering.nyu.edu) -
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (13.7%) to scientific vocabulary
Keywords
Repository
📊 Django Backend for analysing and viz. filtered taxi trip data from any city, ready for ML integration 👀
Basic Info
Statistics
- Stars: 0
- Watchers: 7
- Forks: 0
- Open Issues: 0
- Releases: 0
Topics
Metadata Files
README.md
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
uvorpip) - (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 → The user-facing interface that triggers these analysis requests.
- Original Taxis Vis Paper (IEEE) → Research behind the system.
Happy Analysing!
The Taxis Vis Team 🚀
Owner
- Name: VIDA-NYU
- Login: VIDA-NYU
- Kind: organization
- Location: New York, NY
- Website: https://vida.engineering.nyu.edu/
- Twitter: nyuvida
- Repositories: 92
- Profile: https://github.com/VIDA-NYU
Visualization, Imaging, and Data Analysis Center at New York University
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
title: "Taxis Vis: Data Analysis Backend"
authors:
- name: Simon Provost
orcid: https://orcid.org/0000-0001-8402-5464
- name: Prof. Juliana Freire
orcid: https://orcid.org/0000-0003-3915-7075
- name: Prof. Claudio Silva
orcid: https://orcid.org/0000-0003-2452-2295
- name: João Rulff
orcid: https://orcid.org/0000-0003-3341-7059
date-released: 2025-01-27
version: 0.1.0-alpha
url: https://github.com/VIDA-NYU/Taxis-Vis-Data-Backend
abstract: >
The Taxis Vis Data Analysis Backend handles filtered taxi trip data, delivering visual-ready
analytics and enabling future machine learning integration.
GitHub Events
Total
- Push event: 1
Last Year
- Push event: 1
Committers
Last synced: 8 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Provost Simon | s****t@e****u | 14 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 8 months ago
All Time
- Total issues: 0
- Total pull requests: 2
- Average time to close issues: N/A
- Average time to close pull requests: less than a minute
- Total issue authors: 0
- Total pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 2
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 2
- Average time to close issues: N/A
- Average time to close pull requests: less than a minute
- Issue authors: 0
- Pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 2
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
Pull Request Authors
- simonprovost (2)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- cookiecutter >=2.6.0
- django >=4.2.18
- django-cors-headers >=4.6.0
- django-filter >=24.3
- djangorestframework >=3.15.2
- pandas >=2.2.3
- python-decouple >=3.8
- shapely >=2.0.6
- arrow 1.3.0
- asgiref 3.8.1
- binaryornot 0.4.4
- certifi 2024.12.14
- chardet 5.2.0
- charset-normalizer 3.4.1
- click 8.1.8
- colorama 0.4.6
- cookiecutter 2.6.0
- django 4.2.18
- django 5.1.5
- django-cors-headers 4.6.0
- django-filter 24.3
- djangorestframework 3.15.2
- idna 3.10
- jinja2 3.1.5
- markdown-it-py 3.0.0
- markupsafe 3.0.2
- mdurl 0.1.2
- numpy 2.0.2
- numpy 2.2.2
- pandas 2.2.3
- pygments 2.19.1
- python-dateutil 2.9.0.post0
- python-decouple 3.8
- python-slugify 8.0.4
- pytz 2024.2
- pyyaml 6.0.2
- requests 2.32.3
- rich 13.9.4
- shapely 2.0.6
- six 1.17.0
- sqlparse 0.5.3
- taxis-vis-data-backend 0.2.0
- text-unidecode 1.3
- types-python-dateutil 2.9.0.20241206
- typing-extensions 4.12.2
- tzdata 2025.1
- urllib3 2.3.0