A novel approach for mapping exposure to land cover at the small statistical geography level

TL;DR

This paper introduces a new method to more accurately estimate environmental exposure at small geographic levels, reducing misclassification in health studies.

Contribution

A novel method combining LSOA and postcode data to improve exposure estimation at small statistical geography units.

Findings

The proposed method showed lower exposure to non-built-up areas compared to traditional averaging methods.

Exposure to built-up areas was higher by 8–10% using the new method.

Results varied by region, urban/rural status, land cover type, and LSOA size.

Abstract

Many studies linking spatial environmental exposures to health outcomes rely on small statistical geography units, such as Lower-layer Super Output Areas (LSOAs), to estimate exposure. However, these units commonly vary in size, particularly between urban and rural areas, leading to potential exposure misclassification. This study proposes a new method for better capturing environmental exposure at the small statistical geography unit level. Using the Living England Habitat Map as an example, we combined LSOA and postcode-level data to account for varying area sizes and mitigate edge effects. We compared our method with the typical approach, which calculates an average at the small geography unit level. Overall, our proposed method resulted in lower exposure to non-built-up areas compared to averaging across entire LSOAs, whereas exposure to built-up areas was higher by 8–10%. However, these patterns varied based on region, urban/rural classification, land cover type, and LSOA size class. We suggest that this proposed method offers a more consistent approach to estimating neighbourhood exposure to nature.

The online version contains supplementary material available at 10.1186/s12942-025-00425-7.

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