Illuminating Inequality: Spatial Patterns of Light Pollution and Socioeconomic Disparity Across England

Artificial light pollution at night (ALAN) poses significant environmental and social challenges, impacting biodiversity, human health, and energy efficiency. This study examines the spatial distribution of ALAN across England and its relationship with population density, income, and Index of Multiple Deprivation (IMD). Using 2022 VIIRS data, Moran’s I, Ordinary Least Squares (OLS), and Geographically Weighted Regression (GWR) were applied to assess these relationships. GWR outperformed OLS, capturing spatial heterogeneity and revealing localized associations. Population density emerged as the strongest predictor, particularly in urban areas, while income was a key factor in affluent southern regions. Elevated ALAN in deprived areas suggest the influence of additional factors like land use and infrastructure. These findings underscore the need for localized lighting strategies to address environmental and social disparities and promote sustainable urban planning.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-04
DOI
https://doi.org/10.5281/zenodo.15335919
Primary Topic
Impact of Light on Environment and Health
Type
article
Field-Weighted Citation Impact
0.00
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article

Illuminating Inequality: Spatial Patterns of Light Pollution and Socioeconomic Disparity Across England

Zenodo (CERN European Organization for Nuclear Research)
Impact of Light on Environment and Health
article

Illuminating Inequality: Spatial Patterns of Light Pollution and Socioeconomic Disparity Across England

article en

Abstract

Artificial light pollution at night (ALAN) poses significant environmental and social challenges, impacting biodiversity, human health, and energy efficiency. This study examines the spatial distribution of ALAN across England and its relationship with population density, income, and Index of Multiple Deprivation (IMD). Using 2022 VIIRS data, Moran’s I, Ordinary Least Squares (OLS), and Geographically Weighted Regression (GWR) were applied to assess these relationships. GWR outperformed OLS, capturing spatial heterogeneity and revealing localized associations. Population density emerged as the strongest predictor, particularly in urban areas, while income was a key factor in affluent southern regions. Elevated ALAN in deprived areas suggest the influence of additional factors like land use and infrastructure. These findings underscore the need for localized lighting strategies to address environmental and social disparities and promote sustainable urban planning.

Zenodo (CERN European Organization for Nuclear Research)
Sustainable cities and communities
Openalex Percentile: Top 61%
Impact of Light on Environment and Health
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