RadMaps: A Geospatial Framework for Simultaneously Modelling Capacity and Geographic Constraints on Radiotherapy Access
Abstract
Access to radiotherapy is constrained by two compounding factors: insufficient machine capacity to meet patient demand and geographic distance from treatment facilities. Existing analyses address these factors separately, in turn constraining the insights available to advocates, planners and policymakers. This paper presents RadMaps, an open-source geospatial framework that simultaneously models capacity and geographic constraints on radiotherapy access at any spatial scale. RadMaps operates on Uber's H3 hexagonal grid and integrates population density data with GLOBOCAN 2024 annual cancer incidence estimates and the IAEA DIRAC facility inventory. Radiotherapy demand is estimated using cancer site-specific radiotherapy utilisation rates, and geographic access is modelled via configurable decay functions (step, exponential, Gaussian, or power law) of distance or driving time. A capped three-step floating catchment area (3SFCA) method allocates demand to facilities subject to both capacity and geographic constraints, producing a localised access metric for every H3 hexagon. Globally, RadMaps estimates that installed capacity could meet 68.5% of radiotherapy demand, and 73.0% of demand is within geographic reach of a facility (2 h driving time with Gaussian decay, H3 Resolution-5), both comparable with published estimates. When both constraints apply, only 60.0% is met, leaving 3.7 million patients per year untreated. Countries are also grouped into one of four access profiles: strong access, capacity constrained, geography constrained, and doubly constrained. RadMaps provides a flexible, open-source framework for analysing, visualising and identifying radiotherapy access gaps at regional to global scales, with applications in advocacy, infrastructure planning, and policy prioritisation. Its modular framework is also extensible to other spatial access modelling applications.
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BibTeX
@misc{wroe2026radmaps,
title = {{RadMaps: A Geospatial Framework for Simultaneously Modelling Capacity and Geographic Constraints on Radiotherapy Access}},
author = {Laurence Wroe and Archie Brown and Sophia Martin and Alika Ho and Steinar Stapnes},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2605.23456},
eprint = {2605.23456},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.23456},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - RadMaps: A Geospatial Framework for Simultaneously Modelling Capacity and Geographic Constraints on Radiotherapy Access
AU - Laurence Wroe
AU - Archie Brown
AU - Sophia Martin
AU - Alika Ho
AU - Steinar Stapnes
PY - 2026
DA - 2026/10/28
PB - arXiv
DO - 10.48550/arxiv.2605.23456
UR - https://arxiv.org/abs/2605.23456
AB - Access to radiotherapy is constrained by two compounding factors: insufficient machine capacity to meet patient demand and geographic distance from treatment facilities. Existing analyses address these factors separately, in turn constraining the insights available to advocates, planners and policymakers. This paper presents RadMaps, an open-source geospatial framework that simultaneously models capacity and geographic constraints on radiotherapy access at any spatial scale. RadMaps operates on Uber's H3 hexagonal grid and integrates population density data with GLOBOCAN 2024 annual cancer incidence estimates and the IAEA DIRAC facility inventory. Radiotherapy demand is estimated using cancer site-specific radiotherapy utilisation rates, and geographic access is modelled via configurable decay functions (step, exponential, Gaussian, or power law) of distance or driving time. A capped three-step floating catchment area (3SFCA) method allocates demand to facilities subject to both capacity and geographic constraints, producing a localised access metric for every H3 hexagon. Globally, RadMaps estimates that installed capacity could meet 68.5% of radiotherapy demand, and 73.0% of demand is within geographic reach of a facility (2 h driving time with Gaussian decay, H3 Resolution-5), both comparable with published estimates. When both constraints apply, only 60.0% is met, leaving 3.7 million patients per year untreated. Countries are also grouped into one of four access profiles: strong access, capacity constrained, geography constrained, and doubly constrained. RadMaps provides a flexible, open-source framework for analysing, visualising and identifying radiotherapy access gaps at regional to global scales, with applications in advocacy, infrastructure planning, and policy prioritisation. Its modular framework is also extensible to other spatial access modelling applications.
ER -