GeoRiskAccess is a comprehensive project that leverages Geographic Information Systems (GIS) to analyze pollution and risk factors in urban environments, with a focus on Pune, India, particularly the Katraj area.
GeoRiskAccess integrates real-time sensor data from various environmental monitoring devices to provide insights into urban environmental conditions and associated risks. The project aims to create a web-based platform for informed decision-making in urban planning, disaster management, and public health interventions.
- Real-time data collection from multiple sensor types
- GIS-based analysis of environmental and urban factors
- Web-based platform for data visualization and risk assessment
- Focus on Pune, India, with emphasis on the Katraj area
The project incorporates data from various sources and sensors, including:
- DHT Sensor: Measure temperature and humidity
- RTC Module: Record real-time data with a timestamp
- MQ135 Sensors: Monitor gases like CO, NO2, and SO2
- Sharp Optical Dust Sensor (GP2Y1014AU0F): Detect fine particulate matter (PM2.5)
- Sentinel-5P (S5P):
- NO2: 7 km x 3.5 km resolution, Daily
- SO2: 7x3.5 km² (along-track x across-track) at nadir, Daily
- CO: 7 km x 3.5 km resolution, Daily
- MODIS:
- NDVI: 250 meters resolution, 8-days interval
- AOD (Aerosol Optical Depth): 1 km resolution, 8 days interval
- CAAQMS: PM2.5 point data, Hourly
GeoRiskAccess analyzes several factors to assess environmental conditions and risks:
- Population density
- Locations of fire stations, police stations, and parks
- Air quality (PM2.5 and other pollutants)
- Traffic density
- Land use patterns
- Weather conditions
- Topography
- Nighttime Lights (NTL) for economic activity
- Normalized Difference Vegetation Index (NDVI) for vegetation cover
git clone https://github.com/ashishjha1034/GeoRiskAccess.gitOR
git clone https://github.com/Harsh-1807/GeoRiskAccess.gitcd GeoRiskAccesspip install -r requirements.txtUsers can:
- Select specific locations within Pune (focusing on Katraj area)
- Access visualizations of environmental data
- Obtain risk factor assessments based on a trained model
- Provide valuable insights into urban environmental conditions
- Facilitate informed decision-making for urban planning and management
- Contribute to sustainable development and urban resilience
- Enable effective addressing of pollution and risk factors in urban areas
├── .idea
├── Lib/site-packages
├── Scripts
├── static
├── templates
├── .gitattributes
├── NTL_Katraj (1).csv
├── RISKK.csv
├── main_indices.csv
├── mainapp.py
├── ndvi_katraj_time.csv
├── new.py
├── practise.csv
├── practise.xlsm
├── practisef.csv
├── pyvenv.cfg