Ifoaa/Abuja-Urban-Growth-Prediction

Abuja Urban Growth Prediction using GIS & Python is a geospatial analytics project designed to understand and anticipate patterns of urban expansion and infrastructure pressure within Abuja, Nigeria. 🌍 Abuja Urban Growth Prediction using GIS & Python 🧭 About This Project Abuja Urban Growth Prediction using GIS & Python is a geospatial analytics project designed to analyze and anticipate patterns of urban expansion and infrastructure pressure in Abuja, Nigeria. The project combines spatial data from road networks and population statistics to evaluate how infrastructure distribution influences urban development patterns. Using GIS techniques and Python-based spatial analysis, it generates indicators that help identify areas under increasing growth and infrastructure stress. Rather than treating the city as static, this project models Abuja as a dynamic system where population pressure, road connectivity, and infrastructure distribution interact to shape future urban growth. The final output supports evidence-based insights for urban planning, transport development, and smart-city decision-making. 🎯 Project Objective The objective is to identify and analyze areas in Abuja that are likely to experience: Rapid urban growth Infrastructure pressure Accessibility imbalance Uneven road connectivity This is achieved by developing spatial indicators based on: Road density Population distribution Connectivity patterns Infrastructure concentration 🗺️ Study Area The analysis focuses on Abuja, Federal Capital Territory (FCT), Nigeria, including key urban districts and surrounding expansion corridors where rapid development is occurring. 📂 Datasets Used 🚧 Road Network Data (OpenStreetMap) Road geometries Road classifications Road lengths Network structure and connectivity 👥 Population & Administrative Data (Nigeria LGAs) Local Government Area boundaries Population estimates Administrative attributes Area statistics 🛠️ Tools & Technologies GIS Tools QGIS Python Libraries Pandas GeoPandas Matplotlib Scikit-learn NetworkX OSMnx 📊 Methodology The analysis follows a structured spatial workflow: Data Collection → Data Cleaning → Road Network Processing → S …