Looking for someone to make application designed to process CSV files containing UUIDs, latitude/longitude coordinates, and timestamps to accurately identify and extract home addresses.

This application streamlines the process of uploading, processing, and refining large datasets to ensure precise and reliable identification of home addresses from the given data.

Filtering Random Locations: Clustering algorithms, such as DBSCAN, are used to filter out random locations like roads and public places.

Home Location Identification: The application analyzes temporal patterns to identify recurring nighttime locations, which are likely to be home addresses and then converts lat/long to address.

Output Generation

The refined results, including identified home addresses, are generated in a downloadable CSV file.

Implementation Details, but open to suggestions:
Frontend: HTML, CSS, JavaScript (React.js or Vue.js).
Backend: Python (Django or Flask), Node.js.
Database: PostgreSQL (with PostGIS extension for spatial data handling).
Data Processing: Pandas, NumPy, Scikit-learn for clustering, Geopandas for spatial data handling.

Upload CSV File: Users upload a CSV file via the web interface.
Data Cleaning: The application removes invalid entries and formats data correctly.
Clustering: The application uses DBSCAN to filter out random locations.
Home Identification: Temporal analysis identifies home locations based on recurring nighttime visits.
Result Generation: The refined data is available for download and visual inspection on a map.

The Home Address Identification Application ensures an efficient and reliable process for determining home addresses from large datasets, providing users with a powerful tool for data analysis and visualization.

Posted On: July 25, 2024 19:23 UTC
Category: Full Stack Development
Skills:GIS, Python

Country: United States

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