Mapping demographic trends is essential for understanding the socio-economic landscape of a region. One such demographic trend is the distribution of the old age population, which has significant implications for healthcare, infrastructure, and social services. In this blog, we’ll explore how we used Google Earth Engine (GEE) to map the old age population in India.

Dataset and Methodology

For this analysis, we utilized the “WorldPop Global Project Population Data: Constrained Estimated Age and Sex Structures of Residential Population per 100x100m Grid Square” dataset. This dataset provides detailed population estimates segmented by age and sex at a fine spatial resolution of 100×100 meters.

Using GEE, we performed the following steps to calculate the old age population:

1. Data Preparation: We loaded the WorldPop dataset into GEE and selected the bands representing the population aged 60 years and above. Specifically, we summed the bands M_60, M_65, F_60, F_65, M_70, M_75, M_80, F_70, F_75, and F_80 to get the total old age population.
2. Calculation: We divided the total old age population by the total population to get the percentage of the old age population. This percentage was then multiplied by 100 to convert it to a percentage format.
3. Visualization: We visualized the resulting data using a color gradient from blue (low percentage) to red (high percentage) to create a clear and intuitive map.

Results

The resulting map revealed interesting regional patterns in the old age population distribution across India:

• Western States: Rajasthan and Haryana show higher percentages of the old age population, reaching up to 20%.
• Southern States: Kerala and Tamil Nadu also exhibit high percentages of old age residents.
• Eastern India: States like West Bengal and Bihar have a medium share of the old age population, around 10%.

These patterns highlight regions that may require more focused attention regarding elder care and associated services.

Conclusion

The use of Google Earth Engine in this analysis demonstrates the platform’s power in processing large geospatial datasets efficiently. GEE allows researchers and policymakers to create simple yet informative maps that can drive data-driven decision-making. With its robust computational capabilities and extensive dataset library, GEE is an invaluable tool for geospatial analysis and visualization.

By mapping the old age population in India, we gain critical insights into regional demographic trends, enabling better planning and resource allocation for the aging population. This example underscores how GEE can be leveraged to turn complex data into actionable insights, making it an essential tool in the realm of geographic information systems (GIS).