Urban Poverty Risk Map Using Night-Time Light (NTL) Data: A Geospatial Analysis of Colombo District, Sri Lanka.
1. Madhumali.U.H.G.H. , 2. Rajanayake R.M.A.B.

GeoEDGE (Pvt) Limited

Abstract

Urban poverty is an acute issue in the fast-growing cities of the Global South. Slums and economic inequalities are often unequally distributed. Traditional methods of measuring poverty in Sri Lanka, and particularly the Colombo District, rely on outdated and seldom conducted household surveys. This study investigates whether data from satellite imagery based on Night-Time Light (NTL) can be an inexpensive yet timely means to identify urban poverty risk at high spatial resolution.

We utilized data from VIIRS-DNB and DMSP-OLS sensors to examine the correlation between socio-economic conditions like income, population density, and accessibility to infrastructure and nighttime light intensity in the GNDs of Colombo. We used machine learning models to classify and map the risk of urban poverty by combining NTL data and other data, such as census data and land use. The resulting maps show considerable variation throughout the city, highlighting underserved regions with low light intensity and little infrastructure.

According to these findings, the study highlights the practical relevance of integrating satellite-based Night-Time Light data with geospatial analysis to support data-informed decision-making in urban development. In cities like Colombo, where ground-level socioeconomic data may not be up to date or even nonexistent, NTL data provide a cost-effective and current solution for spatial imbalance in development identification. By identifying economically disadvantaged areas frequently characterized by low-light zones, this approach makes possible more precise targeting of high-risk areas.This information is important for policymakers, planners, and humanitarian agencies in cities to prepare inclusive urban development strategies, disburse infrastructure investment priorities, and monitor success toward achieving sustainable urban development.

Keywords: Night-Time Satellite Imagery, Urban Poverty, Sustainable Development Goals (SDGs), Spatial Analysis, Remote Sensing

Topic: Topic E: Sustainable Development Goals