StillSpace

StillSpace is a Flask + OSMnx web app for intelligent urban walking routes.

Instead of showing only the shortest path, it can compare:

• Fastest
• Calm
• Safe
• Accessibility
• Dog Walk
• Weather Smart

The app uses OpenStreetMap graph data (city.graphml), computes routes with NetworkX, and renders them in Leaflet.

Features

• Multi-mode route planning over a real street graph
• Geometry-accurate route rendering (curves, not straight node jumps)
• Marker-to-route connector segments for seamless visual start/end alignment
• Coverage guardrails (rejects clicks too far from mapped streets)
• Weather-aware weighting with mock/live weather support
• Safety-zone overlay API
• Search history persistence using SQLite
• Responsive UI with simple and advanced route mode flows

Tech Stack

• Backend: Python, Flask, NetworkX, OSMnx, Shapely
• Frontend: HTML, CSS, JavaScript, Leaflet
• Data: OpenStreetMap GraphML
• Storage: SQLite (search_history.db)

Project Structure

stillspace/
  app.py
  wsgi.py
  city.graphml
  database.py
  routing_utils.py
  route_modes.py
  weather_service.py
  safety_zones.py
  accessibility.py
  dog_mode.py
  qa_smoke.py
  templates/
    index.html
  static/
    css/app.css
    js/app.js
  docs/screenshots/
  .env.example
  requirements.txt

API Endpoints

POST /smart_route

Request body:

{
  "start_lat": 30.7333,
  "start_lon": 76.7794,
  "end_lat": 30.7392,
  "end_lon": 76.7739,
  "route_mode": "calm",
  "dog_sub_mode": "relax",
  "preference_bias": 50
}

Main response fields:

• fastest_route
• smart_route
• shortest_stats
• smart_stats
• comparison
• connectors (visual bridge segments)
• snapped_nodes
• snap_distances_m

GET /api/safety_zones

Returns GeoJSON polygons for safety overlays.

GET /api/history

Returns recent searches from SQLite.

DELETE /api/history/<id>

Deletes one search history row.

GET /health

Health metadata including graph bounds and max snap distance.

Local Setup

1. Create and activate a virtual environment.

Windows:

python -m venv .venv
.venv\Scripts\activate

macOS/Linux:

python -m venv .venv
source .venv/bin/activate

2. Install dependencies:

pip install -r requirements.txt

3. Create .env from template:

cp .env.example .env

4. Run:

python app.py

Open http://127.0.0.1:5000.

Environment Variables

Defined in .env.example:

• FLASK_DEBUG
• FLASK_HOST
• FLASK_PORT
• LOG_LEVEL
• GRAPH_PATH
• MAX_SNAP_DISTANCE_M
• DB_PATH
• USE_MOCK_WEATHER
• OPENWEATHER_API_KEY
• WEATHER_CACHE_TTL_SEC
• WEATHER_REQUEST_TIMEOUT_SEC

Smoke Test

python qa_smoke.py

Production Run

gunicorn wsgi:app --bind 0.0.0.0:5000 --workers 2 --threads 4

Notes

• city.graphml is loaded at startup.
• Very large graphs require more RAM and slower cold starts.
• For best UX, keep selected points within the graph coverage bounds.