google_solution_challenge

Urban Livability Prediction Project

future features

• feedback form : asking for new features to be added from the user and priority list of features according to them for improving our formula of calculating livabilty index.
• user profile
• dynamic ranking of top 10 lists

Overview

Creating a model that predicts the livability of a city based on environmental changes, including land use, deforestation, urban expansion, and pollution indices. This project leverages real-time satellite data and machine learning techniques to provide valuable insights for urban planning and public awareness.

what we have to do

• search for data and apis for all kind of pollution indexes
• data of past years for training the machine learning model
• apis for showing the real time info of indexes
• a dynamic list of top ten cities (first we will work on top 10 cities of india) which would change acc. to a livability index considering all the other pollution indexes.

Project Outline

1. Data Collection

• Obtain real-time satellite data from publicly available sources (e.g., Landsat, Copernicus).
• Access pollution indices and air quality data from relevant sources (ground-based monitoring stations, environmental agencies).

2. Data Preprocessing

• Preprocess the data values, dependant and independant environmental factors.
• Integrate pollution indices with corresponding spatial and temporal information.

3. Change Detection

• Use machine learning algorithms (e.g., CNNs) for change detection in satellite images.
• Train the model on pairs of images from different time periods with labeled information on changes (deforestation, urban expansion, etc.).

4. Feature Extraction

• Extract relevant features from satellite data and pollution indices indicative of environmental changes and air quality.
• Features include temperature, pollutant concentrations, etc.

5. Data Analysis

• Analyze the relationship between environmental changes, pollution levels, purchasing power parity and perceived livability.
• Use statistical methods to identify significant patterns and correlations in the data.

6. Livability Index Prediction

• Build a predictive model (e.g., regression model or machine learning model) to estimate a livability index for a city.
• Consider factors such as ppp, air quality, and various other physical and emotional factors in the model.

7. Validation and Testing

• Validate the model using a separate set of data not used during training.
• Test the model's performance against historical data to assess its accuracy in predicting past livability.

8. Visualization

• Develop a user-friendly interface or a dashboard for visualizing predicted livability indices for different cities.
• Include maps, charts, and other visual elements to make the information accessible to the general public and government officials.

9. Future Prospects Prediction

• Use the trained model to predict future environmental changes and livability indices based on ongoing trends.
• Provide insights into potential challenges and areas that require attention for sustainable development.

10. Implementation

• Implement the model as a mobile application accessible to the public.
• Consider collaboration with local governments for more informed urban planning decisions.

11. Ethical Considerations

• Address ethical considerations, including data privacy, bias in the model, and transparency in predictions.

Collaboration

Collaborate with experts in remote sensing, machine learning, and environmental science for accurate and reliable results.