Mapping urban obesity: Spatial patterns of adiposity and green space accessibility in Malta
Abstract
Background: Obesity is a major public health challenge in high-density urban settings. Built-environment characteristics, including access to open and green spaces, may influence weight-related outcomes. This study examined whether obesity and abdominal adiposity cluster geographically in relation to proximity to open and green spaces within Malta’s Principal Urban Areas (PUAs).
Methods: Individual-level data were obtained from the most recent National Health Examination Survey. Residential addresses were geocoded at street level and linked to proximity indicators of open and green spaces. Obesity was defined using BMI classification and waist circumference as a marker of visceral adiposity. Spatial patterns were assessed using choropleth mapping and Kernel Density Estimation (KDE) (800 m radius). Global spatial autocorrelation (Moran’s I) was evaluated at 300m, 800m and 1000m thresholds. Spatial accessibility was assessed using Euclidean distance within PUAs.
Results: Town-level mapping showed higher obesity prevalence in central and harbour-adjacent localities, while overweight was more evenly distributed. KDE revealed neighbourhood-level obesity hotspots, particularly in southern and south-eastern corridors. Waist circumference showed clearer spatial differentiation than BMI, with repeated high-risk hotspots in large high-density towns and strong female clustering in the south-east. Moran’s I indicated weak and non-significant global spatial autocorrelation for BMI and waist categories. Obesity p-values were 0.25(300 m) and 0.14(800 m); overweight were 0.51 (300 m) and 0.57 (800 m). Waist circumference categories showed p-values >0.05.
Conclusions: Obesity and visceral adiposity show neighbourhood heterogeneity in dense urban environments but limited global clustering relative to open-space proximity. Environmental exposure likely interacts with socio-demographic and behavioural factors. Fine-scale spatial analyses may support targeted urban health interventions.
Key messages: • Obesity shows neighbourhood-level hotspots in Malta’s dense urban areas.
• BMI alone may underestimate spatial metabolic risk; waist circumference reveals additional vulnerability patterns.
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BibTeX
@article{cuschieri2026mapping,
title = {{Mapping urban obesity: Spatial patterns of adiposity and green space accessibility in Malta}},
author = {S Cuschieri and N Camilleri Ballucci and T Bajada and S Scheiber},
journal = {The European Journal of Public Health},
year = {2026},
month = oct,
doi = {10.1093/eurpub/ckag169.1247},
url = {https://doi.org/10.1093/eurpub/ckag169.1247},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Mapping urban obesity: Spatial patterns of adiposity and green space accessibility in Malta
AU - S Cuschieri
AU - N Camilleri Ballucci
AU - T Bajada
AU - S Scheiber
PY - 2026
DA - 2026/10/01
JO - The European Journal of Public Health
DO - 10.1093/eurpub/ckag169.1247
UR - https://doi.org/10.1093/eurpub/ckag169.1247
AB - Background: Obesity is a major public health challenge in high-density urban settings. Built-environment characteristics, including access to open and green spaces, may influence weight-related outcomes. This study examined whether obesity and abdominal adiposity cluster geographically in relation to proximity to open and green spaces within Malta’s Principal Urban Areas (PUAs). Methods: Individual-level data were obtained from the most recent National Health Examination Survey. Residential addresses were geocoded at street level and linked to proximity indicators of open and green spaces. Obesity was defined using BMI classification and waist circumference as a marker of visceral adiposity. Spatial patterns were assessed using choropleth mapping and Kernel Density Estimation (KDE) (800 m radius). Global spatial autocorrelation (Moran’s I) was evaluated at 300m, 800m and 1000m thresholds. Spatial accessibility was assessed using Euclidean distance within PUAs. Results: Town-level mapping showed higher obesity prevalence in central and harbour-adjacent localities, while overweight was more evenly distributed. KDE revealed neighbourhood-level obesity hotspots, particularly in southern and south-eastern corridors. Waist circumference showed clearer spatial differentiation than BMI, with repeated high-risk hotspots in large high-density towns and strong female clustering in the south-east. Moran’s I indicated weak and non-significant global spatial autocorrelation for BMI and waist categories. Obesity p-values were 0.25(300 m) and 0.14(800 m); overweight were 0.51 (300 m) and 0.57 (800 m). Waist circumference categories showed p-values >0.05. Conclusions: Obesity and visceral adiposity show neighbourhood heterogeneity in dense urban environments but limited global clustering relative to open-space proximity. Environmental exposure likely interacts with socio-demographic and behavioural factors. Fine-scale spatial analyses may support targeted urban health interventions. Key messages: • Obesity shows neighbourhood-level hotspots in Malta’s dense urban areas. • BMI alone may underestimate spatial metabolic risk; waist circumference reveals additional vulnerability patterns.
SN - 1101-1262
SN - 1464-360X
ER -