Geospatial Clustering Analysis using H3 Library

This project focuses on utilizing clustering techniques on geospatial data, specifically targeting commercial locations and anonymized mobile device data in Córdoba, Argentina. The aim is to uncover patterns and insights that can inform decision-making processes.

Dataset Overview

1. mobile_data_cordoba.csv: An anonymized dataset containing mobile devices along with timestamp information.
2. population_2010.geojson: Official population data from 2010.
3. ICV_argentina.geojson: Data on the Socioeconomic Level Index (ICV) ranging from 1 to 10, where 1 represents the highest socioeconomic level.
4. locations_cordoba.csv: Geospatial data represented by H3 level 9 hexagons, defining the influence zone for intersection with mobile devices.

Analysis Approach

1. Data Preprocessing:

   • Clean and preprocess the datasets.
   • Merge relevant datasets based on spatial and temporal attributes.

2. Feature Engineering:

   • Calculate unique devices per H3 level 9 hexagon.
   • Analyze device movement across hexagons to understand cannibalization.
   • Estimate population density and socioeconomic level around commercial locations.

3. Clustering:

   • Utilize the H3 library for spatial clustering of commercial locations and mobile device data.
   • Explore various clustering algorithms such as K-means, DBSCAN, or hierarchical clustering.
   • Evaluate clustering results using appropriate metrics (e.g., silhouette score, elbow method).

4. Interpretation and Visualization:

   • Visualize clusters on maps to understand spatial distribution.
   • Provide detailed insights into each cluster's characteristics.
   • Explain the rationale behind variable selection and the determination of the number of clusters.

Additional Considerations

• External Data Sources: While external data sources are not expected to be incorporated, potential additional data sources or variables that could enhance the analysis can be explored.
• Documentation: Maintain clear documentation of the codebase, including explanations of functions and methodologies used.
• Presentation: Prepare a comprehensive presentation summarizing the exploratory process, clustering results, and insights derived from the analysis.