Gary Richard Watmough

Over 200 million of the world’s 273 million out-of-school children live in Central and Southern Asia or Sub-Saharan Africa, where distance to school is a persistent barrier, and walking remains the dominant travel mode. Tanzania presents a critical case: over 300,000 children dropped out before completing primary school in 2023, and only 8.5% complete upper-secondary education. We estimated walking travel time to the nearest primary and secondary school using a 100-metre resolution cost-surface that incorporated land cover, topography, and road networks. Travel time surfaces were linked to Demographic and Health Survey (DHS) data and analysed using multilevel Poisson regression for primary school-age children (aged 7–13) and single-level models for secondary school-age adolescents (aged 14–19). Among primary school-age children (n = 9213), poor geographic access (walking time > 45 min) was associated with a 27.9% higher prevalence of non-participation after socioeconomic adjustment, consistent across wealth quintiles and maternal education groups. Among secondary school-age adolescents (n = 2585), poor access was not significant. Household wealth and maternal education showed strong protective relationships at both levels. Each additional 10 min of walking travel time was associated with a 2.9% higher prevalence of non-participation, equivalent to approximately 18% per additional hour. Geographic access is an independent barrier to primary school-age participation irrespective of socioeconomic position, as there was approximately a 2.9% higher prevalence of non-participation in school for every additional 10 min of travel time, supporting geographically targeted infrastructure investment at primary level and demand-side interventions at secondary level.

Target 3 of the Kunming-Montreal Global Biodiversity Framework aims to increase global protected and conserved area coverage to at least 30% by 2030. The impact on people, whether positive or negative, will depend on the social context of additional areas and how they are governed and managed. Here, we show that Target 3 could affect large and socially diverse populations under different implementation scenarios. Nearly half the human population lives within 10 km of areas included in a scenario maximising biodiversity representation. Four percent live near areas included in an Indigenous and traditional territories-based scenario, including many in areas with low Human Development Index scores (74%) and high participation in wild harvesting (91%). A scenario prioritising nature’s contributions to people is intermediate on all measures. Our results demonstrate that Target 3 is a highly ambitious social as well as ecological target, requiring an equally ambitious commitment to development funding and support for local residents.

Land use cover change (LUCC) is a major driver of global environmental and socio-economic transformations, with implications for carbon emissions, biodiversity, and sustainable development. However, robust historical analyses have often been limited by a lack of high-quality, spatially detailed baseline data. This study addresses this gap by being the first to apply deep learning-based image segmentation techniques to extract a comprehensive set of land use cover (LUC) information from historical topographic maps at a fine spatial resolution and at a cross-country scale. Specifically, we utilized topographic maps (1:50000) created by the U.S. Army Map Service during the Vietnam War (1963–1973) to create detailed historical LUC maps of Laos and Vietnam. We compared multiple model architectures on the manually labeled training data, with the UNet++ achieving the best performance. The resulting maps, produced at 4 m and 30 m resolutions, include 10 LUC classes and achieved high overall accuracies of 98.8% for Laos and 98.6% for Vietnam on separate test sets. Analysis of the maps revealed forest cover losses of 18.2% in Laos and 25.0% in southern Vietnam (below 17° N) by 1990 and a 36.8% reduction of mangrove forests in Vietnam by 1996. Transitions from forest to shrubland dominated in northern parts of Laos and Vietnam while transitions to cropland were most prevalent in Savannakhet province of Laos and the Southeast region of Vietnam. The maps provide a novel baseline for assessing post-war LUCC dynamics in Southeast Asia allowing for spatially explicit analyses of conflict and policy impacts at the time. Furthermore, the study demonstrates a transferable methodology that makes archival map collections accessible for large-scale historical global change research.

Thousands of people are injured every year from explosive remnants of war which include unexploded ordnance (UXO) and abandoned ordnance. UXO has negative long-term impacts on livelihoods and ecosystems in contaminated areas. Exact locations of remaining UXO are often unknown as survey and clearance activities can be dangerous, expensive and time-consuming. In Vietnam, Lao PDR and Cambodia, about 20% of the land remains contaminated by UXO from the Vietnam War. Recently declassified historical KH-9 satellite imagery, taken during and immediately after the Vietnam War, now provides an opportunity to map this remaining contamination. KH-9 imagery was acquired and orthorectified for two study areas in Southeast Asia. Bomb craters were manually labeled in a subset of the imagery to train convolutional neural networks (CNNs) for automated crater detection. The CNNs achieved a F1-Score of 0.61 and identified more than 500,000 bomb craters across the two study areas. The detected craters provided more precise information on the impact locations of bombs than target locations available from declassified U.S. bombing records. This could allow for a more precise localization of suspected hazardous areas during non-technical surveys as well as a more fine-grained determination of residual risk of UXO. The method is directly transferable to other areas in Southeast Asia and is cost-effective due to the low cost of the KH-9 imagery and the use of open-source software. The results also show the potential of integrating crater detection into data-driven decision making in mine action across more recent conflicts.

Physical access to health facilities is an important factor in determining treatment seeking behaviour and has implications for targets within the Sustainable Development Goals, including the right to health. The increased availability of high-resolution land cover and road data from satellite imagery offers opportunities for fine-grained estimations of physical access which can support delivery planning through the provision of more realistic estimates of travel times. The data presented here is of travel time to health facilities in Uganda, Zimbabwe, Tanzania, and Mozambique. Travel times have been calculated for different facility types in each country such as Dispensaries, Health Centres, Clinics and Hospitals. Cost allocation surfaces and travel times are provided for child walking speeds but can be altered easily to account for adult walking speeds and motorised transport. With a focus on Uganda, we describe the data and method and provide the travel maps, software and intermediate datasets for Uganda, Tanzania, Zimbabwe and Mozambique.

Migrants are among the groups most vulnerable to infection with viruses due to the social and economic conditions in which they live. Therefore, spatial modeling of virus transmission among migrants is important for controlling and containing the COVID-19 pandemic. This research focused on modeling spatial associations between COVID-19 incidence rates and migrant workers. The aim was to understand the spatial relationships between COVID-19 infection rates of migrants and the type of workplace at the subnational level in Oman. Using empirical Bayes smoothing as well as local indicators of spatial associations, six work sectors (health, agriculture, retail and business, administrative, manufacturing, and mining) were investigated as risk factors for disease incidence. The results indicated that the six work sectors each had a significant spatial association with cases of COVID-19. High rates of COVID-19 cases in relation to the workplace were clustered in the densely populated areas of Muscat. Similarly, high rates of COVID-19 cases were located in the northern part of the country, along the Al-Batnah plain, where migrants are often employed in the agricultural sector. Further, the rate of COVID-19 in migrants employed in the health sector was higher than that for the other sectors. Therefore, working in the health sector can be considered a hot spot for the spread of COVID-19 infections. Due to a paucity of studies addressing the spatial analysis of COVID-19 associations with workplaces, the findings of this research are useful for decision makers to set the necessary policies and plans to control the outbreak of the virus not only in Oman or the Gulf Cooperation Council but also in other developing societies.