LSOA Area Ranking Project This project calculates and ranks different Lower Layer Super Output Areas (LSOAs) based on various factors such as rent, commute times, green spaces, schools, and safety. It allows users to assign weights to these factors and obtain a ranking based on the final weighted score for each LSOA.
Features Weighted Ranking: Customize the weightings of different factors to compute a final score for each LSOA. Data Normalization: Automatically normalizes key metrics (e.g., rent per square meter) to a 0-1 scale. Data Merging: Merges multiple data sources (e.g., commute, rent, schools) for each LSOA. Postcode and Language Filtering: Support for filtering data by frequent postcodes and language demographics. Prerequisites Ensure you have the following installed:
Python 3.x Pandas (pip install pandas) Project Structure bash Copy code . ├── languages/ │ └── langcensus.csv # Language census data ├── data/ │ └── lsoa_stats.csv # LSOA statistics data ├── lsoa_lat_long.py # Contains functions for data handling and normalization ├── README.md # Project documentation └── main.py # Main script to run the ranking process Installation Clone the repository:
bash Copy code git clone https://github.com/your-repo/lsoa-ranking-project.git cd lsoa-ranking-project Install the required dependencies:
bash Copy code pip install -r requirements.txt Place your data files in the appropriate directories:
langcensus.csv should be in the languages/ folder. lsoa_stats.csv should be in the data/ folder. Usage Calculating LSOA Rankings Define the weightings in the AreaWeightings dataclass:
python Copy code from lsoa_lat_long import AreaWeightings
weights = AreaWeightings( commute_weighting=0.1, rent_per_m2=0.3, culture_weight=0.2, green_space=0.15, crime=0.1, schools=0.15 ) Use the get_ranking_from_weights function to calculate the ranking:
python Copy code from lsoa_lat_long import get_ranking_from_weights
df_ranked = get_ranking_from_weights(freq_post_code="E01000405", language="English", weights=weights) print(df_ranked) Save the output:
python Copy code df_ranked.to_csv('ranked_lsoa.csv', index=False) Running the Project Run the main script to generate the ranking: bash Copy code python main.py Example Output The output will be a CSV file (ranked_lsoa.csv) containing LSOAs sorted by their weighted final score:
Copy code lsoa,final_score E01000406,0.85 E01000405,0.78 E01000407,0.73 Customization You can modify the weights in AreaWeightings to adjust the importance of each factor in the final ranking. Adjust the weights based on your criteria (e.g., higher weight for rent or green space).
File Path Configuration To access the langcensus.csv file correctly, the file path is constructed dynamically using os:
python Copy code import os
working_dir = os.getcwd() file_path = os.path.join(working_dir, 'languages', 'langcensus.csv') License This project is licensed under the MIT License.
This README.md file provides a concise overview of the project, installation steps, usage examples, and customization options. You can further expand or adjust the details based on specific project needs.