Using Artificial Intelligence to Analyze Population Density in Iraq with Google Earth Engine and QGIS

This study uses a machine learning model to predict population density from satellite data, and geographical analysis in Iraq. Then used to identify population growth or decline areas using of Google Earth Engine and QGIS to analyze areas based on the combination of Land User Regression (LUR) and Nocturnal Light methodologies as well as the zonal statistics algorithm. Estimates of each ward’s total population and young population are also obtained. Modeling a specific issue or problem connected with population density, LUR methods can be used in new areas of population density by referencing statistics from existing situations. Finally, the result of this paper can be used to give the best guide to policies and programming in the country of Iraq, including planning, urban areas, economic, and disaster relief. The results showed that there is population growth in urban areas in Iraq, while there is a decline in rural areas. These trends are expected to continue in the future. These trends have important implications for Iraq. An increase in population in urban areas is likely to increase demand for public services, such as water, sanitation, education, and healthcare. Population decline in rural areas will likely lead to reduced agricultural production and increased poverty. This study provides important evidence for policymakers in Iraq. Overall this data can be used to improve planning and development in the country and identify areas with increasing or decreasing population density. They can also be used to identify areas that require more investment in public services.