Gary Watmough
Biographic Data
| ID | 2064461 |
|---|---|
| NAME | Gary Watmough |
| GIVEN NAMES | Gary |
| FAMILY NAME | Watmough |
| SIGNATURE | WATMOUGH G |
| AFFILIATIONS | University of Edinburgh |
| ORCID | 0000-0002-0657-2208 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 15 |
| AUTHOR COUNT | 5 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 2 |
Large-scale historical land use mapping in Vietnam and Laos using military topographic maps
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 …
Strata: Mapping climate, environmental and security vulnerability hotspots
Climate and environmental changes, as well as conflict events and violence, can have compounding impacts on livelihoods and the safety and security of population groups, particularly when multiple events are interrelated, coincide or occur in succession. How people are impacted depends on where they are located, how vulnerable they are, and the magnitude of the hazard. Although a significant amount of geospatial data is freely available, there ha…
Spatial Associations between Covid-19 Incidence Rates and Work Sectors: Geospatial Modeling of Infection Patterns among Migrants in Oman
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 infect…
Collective influence of household and community capitals on agricultural employment as a measure of rural poverty in the Mahanadi Delta, India
The main determinants of agricultural employment are related to households' access to private assets and the influence of inherited social-economic stratification and power relationships. However, despite the recommendations of rural studies which have shown the importance of multilevel approaches to rural poverty, very few studies have explored quantitatively the effects of common-pool resources and household livelihood capitals on agricultural …
Understanding the Evidence Base for Poverty–Environment Relationships using Remotely Sensed Satellite Data: An Example from Assam, India
Understanding the Evidence Base for Poverty–Environment Relationships using Remotely Sensed Satellite Data: An Example from Assam, India
Collective influence of household and community capitals on agricultural employment as a measure of rural poverty in the Mahanadi Delta, India
The main determinants of agricultural employment are related to households' access to private assets and the influence of inherited social-economic stratification and power relationships. However, despite the recommendations of rural studies which have shown the importance of multilevel approaches to rural poverty, very few studies have explored quantitatively the effects of common-pool resources and household livelihood capitals on agricultural …
Spatial Associations between Covid-19 Incidence Rates and Work Sectors: Geospatial Modeling of Infection Patterns among Migrants in Oman
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 infect…
Understanding the Evidence Base for Poverty–Environment Relationships using Remotely Sensed Satellite Data: An Example from Assam, India
Collective influence of household and community capitals on agricultural employment as a measure of rural poverty in the Mahanadi Delta, India
The main determinants of agricultural employment are related to households' access to private assets and the influence of inherited social-economic stratification and power relationships. However, despite the recommendations of rural studies which have shown the importance of multilevel approaches to rural poverty, very few studies have explored quantitatively the effects of common-pool resources and household livelihood capitals on agricultural …
Spatial Associations between Covid-19 Incidence Rates and Work Sectors: Geospatial Modeling of Infection Patterns among Migrants in Oman
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 infect…
Strata: Mapping climate, environmental and security vulnerability hotspots
Climate and environmental changes, as well as conflict events and violence, can have compounding impacts on livelihoods and the safety and security of population groups, particularly when multiple events are interrelated, coincide or occur in succession. How people are impacted depends on where they are located, how vulnerable they are, and the magnitude of the hazard. Although a significant amount of geospatial data is freely available, there ha…
Large-scale historical land use mapping in Vietnam and Laos using military topographic maps
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 …
Geography (4 works) · Economics (3 works) · Socioeconomics (3 works) · Agriculture (2 works) · Cartography (2 works) · Ecology (2 works) · Economic growth (2 works) · Engineering (2 works) · Geospatial analysis (2 works) · Income, Poverty, and Inequality (2 works)