Project Overview:

This project involves the analysis of cross-border activities between two neighboring countries, Country A and Country B, with a focus on identifying potential threats and significant patterns. The region is known for its geopolitical tensions, and there has been an increase in movements across the border, raising concerns about national security.

Dataset:

Geospatial Data: Includes GPS coordinates, timestamps, locations, and movement types (entry/exit) for the past 6 months. Satellite Details: Contains satellite data covering key areas along the border.

Tools and Techniques:

Python: For data cleaning, preprocessing, and analysis. Pandas & NumPy: Used for handling and manipulating large datasets. Geopandas & Shapely: Applied for geospatial analysis to map and visualize the movements across the border. Matplotlib & Seaborn: For visualizing the patterns and trends in the data. Jupyter Notebook: To document the analysis process and present findings.

Key Features:

Geospatial Analysis: Mapped the GPS coordinates to identify hotspots of activity along the border. Trend Analysis: Analyzed the temporal patterns of cross-border movements to detect any unusual activity. Risk Identification: Highlighted potential threats by identifying spikes in movement types and correlating them with geopolitical events. Recommendations: Provided actionable insights to the senior intelligence team, including potential areas for increased surveillance and resource allocation.