High resolution population distribution maps for Southeast Asia in 2010 and 2015

Machine learning based urban sprawl assessment using integrated multi-hazard and environmental-economic impact

The increasing demand for land development due to human activities has fueled urbanization. However, uncontrolled urban development in some regions has resulted in urban environmental problems arising from an imbalance between supply and demand. This study aims to develop an integrated model for evaluating and prioritizing the management of hazardous urban sprawl in the Bandung metropolitan region of Indonesia. The novelty of this study lies in its pioneering application of long-term remote sensing data-based and machine learning techniques to formulate an urban sprawl priority index. This index is unique in its consideration of the impacts stemming from human economic activity, environmental degradation, and multi-disaster levels as integral components. The analysis of hazardous urban sprawl across three distinct time periods (1985-1993, 1993-2008, and 2008-2018) revealed that the 1993-2008 period had the highest increase in human economic activity, reaching 172,776 ha. The 1985-1993 period experienced the highest level of environmental degradation in the study area. Meanwhile, the 1993-2008 period showed the highest concentration of multi-hazard locations. The combined model of hazardous urban sprawl, incorporating the three parameters, indicated that the highest priority for intervention was on the outskirts of urban areas, specifically in West Bandung Regency, Cimahi, Bandung Regency, and East Bandung Regency. Regions with high-priority indices require greater attention from the government to mitigate the negative impacts of hazardous urban sprawl. This model, driven by the urban sprawl priority index, is envisioned to regulate urban movement in a more sustainable manner. Through the efficient monitoring of urban environments, the study seeks to guarantee the preservation of valuable natural resources while promoting sustainable urban development practices.

Spatial epidemiology of human anthrax in Son La province, Vietnam, 2003-2022

AIMS

Anthrax is reported with frequency but poorly understood in Southeast Asian countries including Vietnam. In Vietnam, anthrax surveillance is national. However, case detection, prevention, and control are implemented locally at the provincial level. Here, we describe the epidemiological characteristics, identify spatial clusters of human anthrax, and compare the variation in livestock anthrax vaccine coverage to disease incidence in humans and livestock using historical data in Son La province, Vietnam (2003-2020).

METHODS AND RESULTS

Most human cases occurred between April and September. Most of the patients were male, aged 15-54 years old. The human cases were mainly reported by public district hospitals. There was a delay between disease onset and hospitalization of ~5 days. We identified spatial clusters of high-high incidence communes in the northern communes of the province using the local Moran's I statistic. The vaccine coverage sharply decreased across the study period. The province reported sporadic human anthrax outbreaks, while animal cases were only reported in 2005 and 2022.

CONCLUSIONS

These results suggest underreporting for human and livestock anthrax in the province. Intersectoral information sharing is needed to aid livestock vaccination planning, which currently relies on reported livestock cases. The spatial clusters identify areas for targeted surveillance and livestock vaccination, while the seasonal case data suggest prioritizing vaccination campaigns for February or early March ahead of the April peak. A regional approach for studying the role of livestock trading between Son La and neighbouring provinces in anthrax occurrence is recommended.

Evaluating the relative influence of climate and human activities on recent vegetation dynamics in West Bengal, India

Assessing the relative importance of climate change and human activities is important in developing sustainable management policies for regional land use. In this study, multiple remote sensing datasets, i.e. CHIRPS (Climate Hazard Group InfraRed Precipitation with Station Data) precipitation, MODIS Land Surface Temperature (LST), Enhanced Vegetation Index (EVI), Potential Evapotranspiration (PET), Soil Moisture (SM), WorldPop, and nighttime light have been analyzed to investigate the effect that climate change (CC) and regional human activities (HA) have on vegetation dynamics in eastern India for the period 2000 to 2022. The relative influence of climate and anthropogenic factors is evaluated on the basis of non-parametric statistics i.e., Mann-Kendall and Sen's slope estimator. Significant spatial and elevation-dependent variations in precipitation and LST are evident. Areas at higher elevations exhibit increased mean annual temperatures (0.22 °C/year, p < 0.05) and reduced winter precipitation over the last two decades, while the northern and southwest parts of West Bengal witnessed increased mean annual precipitation (17.3 mm/year, p < 0.05) and a slight cooling trend. Temperature and precipitation trends are shown to collectively impact EVI distribution. While there is a negative spatial correlation between LST and EVI, the relationship between precipitation and EVI is positive and stronger (R2 = 0.83, p < 0.05). Associated hydroclimatic parameters are potent drivers of EVI, whereby PET in the southwestern regions leads to markedly lower SM. The relative importance of CC and HA on EVI also varies spatially. Near the major conurbation of Kolkata, and confirmed by nighttime light and population density data, changes in vegetation cover are very clearly dominated by HA (87%). In contrast, CC emerges as the dominant driver of EVI (70-85%) in the higher elevation northern regions of the state but also in the southeast. Our findings inform policy regarding the future sustainability of vulnerable socio-hydroclimatic systems across the entire state.

Dynamics of urban expansion and form changes impacting carbon emissions in the Guangdong-Hong Kong-Macao Greater Bay Area counties

Cities are the main carriers of social and economic development, and they are also important sources of carbon emissions. Therefore, it is essential to explore the impact of urban expansion and form changes on carbon emissions. Here, we attempted to analyzes the relationship between urban expansion and carbon emissions at the county level in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 1997 to 2017. It further decomposes the driving effects of carbon emissions from multiple factors, and considers the spatial heterogeneity between different urban form changes and driving effects. The results show that: The relationship between urban expansion and carbon emissions in the GBA has gone through three stages from 1997 to 2017, with 2012 as a turning point. Optimization of economic development models and strict protection of the ecological environment can effectively control carbon emissions. After 2012, the economic development effect (GE) and population scale effect (PE) are the driving factors of carbon emissions, while the carbon emission intensity effect (CE) and urban land intensity effect (UE) are the inhibitory factors of carbon emissions. The contribution rate of UE to carbon emission reduction can reach 86 %. The impact of urban form changes on carbon emissions has spatial heterogeneity. The changes in urban form have a significant impact on the carbon emissions of counties in Dongguan and Shenzhen. The increase in fragmentation indirectly promotes carbon emissions. In 2007-2012, the increase in centrality significantly weakened the economic development effect, which is conducive to emission reduction. After 2007, the increase in compactness in counties in the eastern part of the GBA, including Zhongshan and Zhuhai, is not conducive to emission reduction.

HiMIC-Monthly: A 1 km high-resolution atmospheric moisture index collection over China, 2003-2020

Near-surface atmospheric moisture is a key environmental and hydro-climatic variable that has significant implications for the natural and human systems. However, high-resolution moisture data are severely lacking for fine-scale studies. Here, we develop the first 1 km high spatial resolution dataset of monthly moisture index collection in China (HiMIC-Monthly) over a long period of 2003~2020. HiMIC-Monthly is generated by the light gradient boosting machine algorithm (LightGBM) based on observations at 2,419 weather stations and multiple covariates, including land surface temperature, vapor pressure, land cover, impervious surface proportion, population density, and topography. This collection includes six commonly used moisture indices, enabling fine-scale assessment of moisture conditions from different perspectives. Results show that the HiMIC-Monthly dataset has a good performance, with R2 values for all six moisture indices exceeding 0.96 and root mean square error and mean absolute error values within a reasonable range. The dataset exhibits high consistency with in situ observations over various spatial and temporal regimes, demonstrating broad applicability and strong reliability.

Combining environmental DNA and visual surveys can inform conservation planning for coral reefs

Environmental DNA (eDNA) metabarcoding has the potential to revolutionize conservation planning by providing spatially and taxonomically comprehensive data on biodiversity and ecosystem conditions, but its utility to inform the design of protected areas remains untested. Here, we quantify whether and how identifying conservation priority areas within coral reef ecosystems differs when biodiversity information is collected via eDNA analyses or traditional visual census records. We focus on 147 coral reefs in Indonesia's hyper-diverse Wallacea region and show large discrepancies in the allocation and spatial design of conservation priority areas when coral reef species were surveyed with underwater visual techniques (fishes, corals, and algae) or eDNA metabarcoding (eukaryotes and metazoans). Specifically, incidental protection occurred for 55% of eDNA species when targets were set for species detected by visual surveys and 71% vice versa. This finding is supported by generally low overlap in detection between visual census and eDNA methods at species level, with more overlap at higher taxonomic ranks. Incomplete taxonomic reference databases for the highly diverse Wallacea reefs, and the complementary detection of species by the two methods, underscore the current need to combine different biodiversity data sources to maximize species representation in conservation planning.

Spatial clusters of human and livestock anthrax define high-risk areas requiring intervention in Lao Cai Province, Vietnam 1991-2022

Anthrax, a widespread zoonosis in low and middle-income countries with low disease awareness and insufficient livestock vaccination coverage, has been known in Lao Cai Province in northern Vietnam for years before its apparent absence in 2009, which requires investigation as this infection is frequently reported from neighbouring provinces and countries. We aimed to describe the seasonal patterns of anthrax (1991-2008), compare livestock anthrax vaccine coverage to disease occurrence (1991- 2022), and delineate the high-risk areas to inform local disease surveillance in the province. We illustrated the seasonal pattern of anthrax and provided a comparison between livestock vaccine coverage and disease occurrence by purely spatial SaTScan (Poisson model, 25% population at risk) to detect spatial clusters of human and livestock anthrax using population derived from zonal statistics routines. The number of cases, crude cumulative incidence, and spatial clusters of human and livestock anthrax were mapped in QGIS. Results indicate peak anthrax incidence from May to October. Buffalo, domestic cattle, and horses accounted for 75% of total animal cases. Horse anthrax was more common in Lao Cai than in its neighbours and often occurred in years with human mortality. Vaccination covered less than 30% of the livestock population. We found an apparent pattern where anthrax was controlled from 1998-2003 with higher vaccine coverage (>20%) and identified spatial clusters of human and livestock anthrax in Muong Khuong, Bao Thang, and Bac Ha districts of Lao Cai. The local public health and veterinary agencies are recommended to revisit the high-risk areas and communicate with neighbouring provinces for a regional approach to anthrax surveillance and control.

A global dataset of carbon pumping by the world's largest tropical rivers

The eco-morphodynamic activity of large tropical rivers interacts with riparian vegetation causing implications for the carbon cycle within inland waters. Through a multi-temporal analysis of satellite data spanning the years 2000-2019, we analyzed rivers exceeding 200 m in width across the tropical regions, revealing a Carbon Pump mechanism driving an annual mobilization of 12.45 million tons of organic carbon. The study identifies fluvial eco-morphological signatures as proxies for carbon mobilization, emphasizing the link between river migration and carbon dynamics. To enhance accessibility, our results are encapsulated in a visually compelling WebGIS application, offering a comprehensive understanding of the eco-geomorphological influences on the global carbon cycle within large tropical rivers. Our findings are instrumental in determining the carbon intensity of future hydropower dams, thereby contributing to informed decision-making in the realm of sustainable energy infrastructure. This study elucidates the intricate relationships that govern the nexus of tropical river dynamics, riparian ecosystems, and the global carbon cycle.

Spatial analysis of human and livestock anthrax in Lai Chau province, Vietnam (2004-2021)

Anthrax is reported globally with varying disease intensity and seasonality among countries. In Vietnam, anthrax epidemiology and ecology remain understudied. We used historical data of human and livestock anthrax from 2004 to 2021 in Lai Chau province, to identify spatial clusters of human and livestock anthrax, describe epidemiological characteristics, and compare livestock anthrax vaccine coverage to human and livestock disease incidence. Local Moran's I (LISA) using spatial Bayes smoothed commune-level cumulative incidence (per 10,000) for the study period, epidemiological descriptive statistics, livestock vaccine coverage data, and annual incidence rates (per 10,000) at provincial level were used. LISA identified a human anthrax hotspot (high-high) in the southeast which did not overlap spatially with livestock anthrax hotspots in southeastern and northeastern communes. Most human cases were male, aged 15-59 years, handled sick animals, and/or consumed contaminated meat. Almost all cases were reported by grassroot health facilities with a delay of 6.3 days between exposure and case notification to the national surveillance system. 80 % of human cases were reported from June-October. The increase in disease incidence occurred shortly after livestock anthrax vaccine coverage decreased. This study informs vaccination strategy and targeted surveillance and control measures in newly identified high-risk areas and seasons of anthrax.

Updating estimates of Plasmodium knowlesi malaria risk in response to changing land use patterns across Southeast Asia

BACKGROUND

Plasmodium knowlesi is a zoonotic parasite that causes malaria in humans. The pathogen has a natural host reservoir in certain macaque species and is transmitted to humans via mosquitoes of the Anopheles Leucosphyrus Group. The risk of human P. knowlesi infection varies across Southeast Asia and is dependent upon environmental factors. Understanding this geographic variation in risk is important both for enabling appropriate diagnosis and treatment of the disease and for improving the planning and evaluation of malaria elimination. However, the data available on P. knowlesi occurrence are biased towards regions with greater surveillance and sampling effort. Predicting the spatial variation in risk of P. knowlesi malaria requires methods that can both incorporate environmental risk factors and account for spatial bias in detection.

METHODS & RESULTS

We extend and apply an environmental niche modelling framework as implemented by a previous mapping study of P. knowlesi transmission risk which included data up to 2015. We reviewed the literature from October 2015 through to March 2020 and identified 264 new records of P. knowlesi, with a total of 524 occurrences included in the current study following consolidation with the 2015 study. The modelling framework used in the 2015 study was extended, with changes including the addition of new covariates to capture the effect of deforestation and urbanisation on P. knowlesi transmission.

DISCUSSION

Our map of P. knowlesi relative transmission suitability estimates that the risk posed by the pathogen is highest in Malaysia and Indonesia, with localised areas of high risk also predicted in the Greater Mekong Subregion, The Philippines and Northeast India. These results highlight areas of priority for P. knowlesi surveillance and prospective sampling to address the challenge the disease poses to malaria elimination planning.

Geospatial mapping of 2-hour access to timely essential surgery in the Philippines

OBJECTIVES

Timely access to safe and affordable surgery is essential for universal health coverage. To date, there are no studies evaluating 2-hour access to Bellwether procedures (caesarean section, laparotomy, open fracture management) in the Philippines. The objectives of this study were to measure the proportion of the population able to reach a Bellwether hospital within 2 hours in the Philippines and to identify areas in the country with the most surgically underserved populations.

METHODS

All public hospitals with Bellwether capacities were identified from the Philippines Ministry of Health website. The service area tool in ArcGIS Pro was used to determine the population within a 2-hour drive time of a Bellwether facility. Finally, suitability modelling was conducted to identify potential future sites for a surgical facility that targets the most underserved regions in the Philippines.

RESULTS

428 Bellwether capable hospitals were identified. 85.1% of the population lived within 2 hours of one of these facilities. However, 6 regions had less than 80% of its population living within 2 hours of a Bellwether capable facility: Bicol, Eastern Visayas, Zamboanga, Autonomous region of Muslim Mindanao, Caraga and Mimaropa. Suitability analysis identified four regions-Caraga, Mimaropa, Calabarzon and Zamboanga-as ideal locations to build a new hospital with surgical capacity to improve access rates.

CONCLUSION

85.1% of the population of the Philippines are able to reach Bellwether capable hospitals within 2 hours, with regional disparities in terms of access rates. However, other factors such as weather, traffic conditions, financial access, availability of 24-hour surgical services and access to motorised vehicles should also be taken into consideration, as they also affect actual access rates.

A daily high-resolution (1 km) human thermal index collection over the North China Plain from 2003 to 2020

Human-perceived temperature (HPT) describes the joint effects of multiple climatic factors such as temperature and humidity. Extreme HPT events may reduce labor capacity and cause thermal discomfort and even mortality. These events are becoming more frequent and more intense under global warming, posing severe threats to human and natural systems worldwide, particularly in populated areas with intensive human activities, e.g., the North China Plain (NCP). Therefore, a fine-scale HPT dataset in both spatial and temporal dimensions is urgently needed. Here we construct a daily high-resolution (~1 km) human thermal index collection over NCP from 2003 to 2020 (HiTIC-NCP). This dataset contains 12 HPT indices and has high accuracy with averaged determination coefficient, mean absolute error, and root mean squared error of 0.987, 0.970 °C, and 1.292 °C, respectively. Moreover, it exhibits high spatiotemporal consistency with ground-level observations. The dataset provides a reference for human thermal environment and could facilitate studies such as natural hazards, regional climate change, and urban planning.

Updating estimates of Plasmodium knowlesi malaria risk in response to changing land use patterns across Southeast Asia

Background

Plasmodium knowlesi is a zoonotic parasite that causes malaria in humans. The pathogen has a natural host reservoir in certain macaque species and is transmitted to humans via mosquitoes of the Anopheles Leucosphyrus Group. The risk of human P. knowlesi infection varies across Southeast Asia and is dependent upon environmental factors. Understanding this geographic variation in risk is important both for enabling appropriate diagnosis and treatment of the disease and for improving the planning and evaluation of malaria elimination. However, the data available on P. knowlesi occurrence are biased towards regions with greater surveillance and sampling effort. Predicting the spatial variation in risk of P. knowlesi malaria requires methods that can both incorporate environmental risk factors and account for spatial bias in detection.

Methods & Results

We extend and apply an environmental niche modelling framework as implemented by a previous mapping study of P. knowlesi transmission risk which included data up to 2015. We reviewed the literature from October 2015 through to March 2020 and identified 264 new records of P. knowlesi, with a total of 524 occurrences included in the current study following consolidation with the 2015 study. The modelling framework used in the 2015 study was extended, with changes including the addition of new covariates to capture the effect of deforestation and urbanisation on P. knowlesi transmission.

Discussion

Our map of P. knowlesi relative transmission suitability estimates that the risk posed by the pathogen is highest in Malaysia and Indonesia, with localised areas of high risk also predicted in the Greater Mekong Subregion, The Philippines and Northeast India. These results highlight areas of priority for P. knowlesi surveillance and prospective sampling to address the challenge the disease poses to malaria elimination planning.

High-resolution gridded population datasets for Latin America and the Caribbean using official statistics

"Leaving no one behind" is the fundamental objective of the 2030 Agenda for Sustainable Development. Latin America and the Caribbean is marked by social inequalities, whilst its total population is projected to increase to almost 760 million by 2050. In this context, contemporary and spatially detailed datasets that accurately capture the distribution of residential population are critical to appropriately inform and support environmental, health, and developmental applications at subnational levels. Existing datasets are under-utilised by governments due to the non-alignment with their own statistics. Therefore, official statistics at the finest level of administrative units available have been implemented to construct an open-access repository of high-resolution gridded population datasets for 40 countries in Latin American and the Caribbean. These datasets are detailed here, alongside the 'top-down' approach and methods to generate and validate them. Population distribution datasets for each country were created at a resolution of 3 arc-seconds (approximately 100 m at the equator), and are all available from the WorldPop Data Repository.

Informing One Health Anthrax Surveillance and Vaccination Strategy from Spatial Analysis of Anthrax in Humans and Livestock in Ha Giang Province, Vietnam (1999-2020)

Anthrax, caused by Bacillus anthracis, has a nearly global distribution but is understudied in Southeast Asia, including Vietnam. Here, we used historical data from 1999 to 2020 in Ha Giang, a province in northern Vietnam. The objectives were to describe the spatiotemporal patterns and epidemiology of human and livestock anthrax in the province and compare livestock vaccine coverage with human and livestock anthrax incidence. Annual incidence rates (per 10,000) for humans, buffalo/cattle, and goats were used to explore anthrax patterns and for comparison with livestock annual vaccine variations. A data subset describes anthrax epidemiology in humans by gender, age, source of infection, type of anthrax, admission site, and season. Zonal statistics and SaTScan were used to identify spatial and space-time clusters of human anthrax. SaTScan revealed space-time clusters in 1999, 2004, and 2007-2008 in the province, including in the northeastern, eastern, and western areas. Most human anthrax was reported between July and October. Most patients were male, aged 15-59 years, who had handled sick animals and/or consumed contaminated meat. High case-fatality rates were reported with gastrointestinal or respiratory cases. Our data suggest that vaccination in buffalo and cattle reduces the disease burden in humans and vaccinated animals but does not reduce the incidence in unvaccinated animals (goats). This study identified spatial areas of high risk for anthrax and can inform One Health surveillance and livestock vaccination planning in contextual settings similar to Ha Giang province.

Characterizing mobility patterns of forest goers in southern Lao PDR using GPS loggers

BACKGROUND

In the Greater Mekong Subregion (GMS), forest-going populations are considered high-risk populations for malaria and are increasingly targeted by national control programmes' elimination efforts. A better understanding of forest-going populations' mobility patterns and risk associated with specific types of forest-going trips is necessary for countries in the GMS to achieve their objective of eliminating malaria by 2030.

METHODS

Between March and November 2018, as part of a focal test and treat intervention (FTAT), 2,904 forest-goers were recruited in southern Lao PDR. A subset of forest-goers carried an "i-Got-U" GPS logger for roughly 2 months, configured to collect GPS coordinates every 15 to 30 min. The utilization distribution (UD) surface around each GPS trajectory was used to extract trips to the forest and forest-fringes. Trips with shared mobility characteristics in terms of duration, timing and forest penetration were identified by a hierarchical clustering algorithm. Then, clusters of trips with increased exposure to dominant malaria vectors in the region were further classified as high-risk. Finally, gradient boosting trees were used to assess which of the forest-goers' socio-demographic and behavioural characteristics best predicted their likelihood to engage in such high-risk trips.

RESULTS

A total of 122 forest-goers accepted carrying a GPS logger resulting in the collection of 803 trips to the forest or forest-fringes. Six clusters of trips emerged, helping to classify 385 (48%) trips with increased exposure to malaria vectors based on high forest penetration and whether the trip happened overnight. Age, outdoor sleeping structures and number of children were the best predictors of forest-goers' probability of engaging in high-risk trips. The probability of engaging in high-risk trips was high (~ 33%) in all strata of the forest-going population.

CONCLUSION

This study characterized the heterogeneity within the mobility patterns of forest-goers and attempted to further segment their role in malaria transmission in southern Lao People's Democratic Republic (PDR). National control programmes across the region can leverage these results to tailor their interventions and messaging to high-risk populations and accelerate malaria elimination.

The island biogeography of human population size

For decades, biogeographers have sought a better understanding of how organisms are distributed among islands. However, the island biogeography of humans remains largely unknown. Here, we investigate how human population size varies among 486 islands at two spatial scales. At a global scale, we tested whether population size increases with island area and declines with island elevation and nearest mainland, as is common in non-human species, or whether humans escape such biogeographic constraints. At a regional scale, we tested whether population sizes vary among islands within archipelagos according to the positioning of different cultural source pools. Results illustrate that on a global scale, human populations increased in size with island area, similar to non-human species, yet they did not decline in size with elevation and distance to nearest mainland. At a regional scale, human population size often varied among islands within archipelagos relative to the location of different cultural source pools. Despite broad-scale similarities in the geographical distribution of human and non-human species among islands, results from this study indicate that the island biogeography of humans may also be influenced by archipelago-specific social, political and historical circumstances.

Spatial integration framework of solar, wind, and hydropower energy potential in Southeast Asia

Amid its massive increase in energy demand, Southeast Asia has pledged to increase its use of renewable energy by up to 23% by 2025. Geospatial technology approaches that integrate statistical data, spatial models, earth observation satellite data, and climate modeling can be used to conduct strategic analyses for understanding the potential and efficiency of renewable energy development. This study aims to create the first spatial model of its kind in Southeast Asia to develop multi-renewable energy from solar, wind, and hydropower, further broken down into residential and agricultural areas. The novelty of this study is the development of a new priority model for renewable energy development resulting from the integration of area suitability analysis and the estimation of the amount of potential energy. Areas with high potential power estimations for the combination of the three types of energy are mostly located in northern Southeast Asia. Areas close to the equator, have a lower potential than the northern countries, except for southern regions. Solar photovoltaic (PV) plant construction is the most area-intensive type of energy generation among the considered energy sources, requiring 143,901,600 ha (61.71%), followed by wind (39,618,300 ha; 16.98%); a combination of solar PV and wind (37,302,500 ha; 16%); hydro (7,665,200 ha; 3.28%); a combination of hydro and solar PV (3,792,500 ha; 1.62%); and a combination of hydro and wind (582,700 ha; 0.25%). This study is timely and important because it will inform policies and regional strategies for transitioning to renewable energy, with consideration of the different characteristics present in Southeast Asia.

Spatial analysis of human and livestock anthrax in Dien Bien province, Vietnam (2010-2019) and the significance of anthrax vaccination in livestock

Anthrax is a serious zoonosis caused by Bacillus anthracis, which primarily affects wild herbivorous animals with spillover into humans. The disease occurs nearly worldwide but is poorly reported in Southeast Asian countries. In Vietnam, anthrax is underreported, and little is known about its temporal and spatial distributions. This paper examines the spatio-temporal distribution and epidemiological characteristics of human and livestock anthrax from Dien Bien province, Vietnam from 2010 to 2019. We also aim to define the role of livestock vaccination in reducing human cases. Historical anthrax data were collected by local human and animal health sectors in the province. Spatial rate smoothing and spatial clustering analysis, using Local Moran's I in GeoDa and space-time scan statistic in SaTScan, were employed to address these objectives. We found temporal and spatial overlap of anthrax incidence in humans and livestock with hotspots of human anthrax in the east. We identified three significant space-time clusters of human anthrax persisting from 2010 to 2014 in the east and southeast, each with high relative risk. Most of the human cases were male (69%), aged 15-59 years (80%), involved in processing, slaughtering, or eating meat of sick or dead livestock (96.9%) but environmental and unknown exposure were reported. Animal reports were limited compared to humans and at coarser spatial scale, but in areas with human case clusters. In years when livestock vaccination was high (>~25%), human incidence was reduced, with the opposite effect when vaccine rates dropped. This indicates livestock vaccination campaigns reduce anthrax burden in both humans and livestock in Vietnam, though livestock surveillance needs immediate improvement. These findings suggest further investigation and measures to strengthen the surveillance of human and animal anthrax for other provinces of Vietnam, as well as in other countries with similar disease context.

Urbanization Impact on Regional Sustainable Development: Through the Lens of Urban-Rural Resilience

The urban-rural system is an economically, socially, and environmentally interlinked space, which requires the integration of industry, space, and population. To achieve sustainable and coordinated development between urban and rural systems, dynamic land use change within the urban-rural system and the ecological and social consequences need to be clarified. This study uses system resilience to evaluate such an impact and explores the impact of land use change, especially land conversion induced by urbanization on regional development through the lens of urban-rural resilience. The empirical case is based on the Beijing-Tianjin-Hebei Urban Agglomeration (BTHUA) in China from 2000 to 2020 when there was rapid urbanization in this region. The results show that along with urbanization in the BTHUA, urban-rural resilience is high in urban core areas and low in peripheral areas. From the urban core to the rural outskirts, there is a general trend that comprehensive resilience decreases with decreased social resilience and increased ecological resilience in this region. Specifically, at the city level, comprehensive resilience decreases sharply from the urban center to its 3-5 km buffer zone and then remains relatively stable in the rural regions. A similar trend goes for social resilience at the city level, while ecological resilience increases sharply from the urban center to its 1-3 km buffer zone, and then remains relatively stable in the rural regions in this region, except for cities in the west and south of Hebei. This study contributes to the conceptualization and measurement of urban-rural resilience in the urban-rural system with empirical findings revealing the impact of rapid urbanization on urban-rural resilience over the last twenty years in the BTHUA in China. In addition, the spatial heterogeneity results could be used for policy reference to make targeted resilience strategies in the study region.

Variabilities of δ13C and carbonaceous components in ambient PM2.5 in Northeast India: Insights into sources and atmospheric processes

A year-long sampling campaign of ambient PM_2.5 (particulate matter with aerodynamic diameter ≤2.5 mm) at a regional station in the North-Eastern Region (NER) of India was performed to understand the sources and formation of carbonaceous aerosols. Mass concentration, carbon fractions (organic and elemental carbon), and stable carbon isotope ratio (δ¹³C) of PM_2.5 were measured and studied along with cluster analysis and Potential Source Contribution Function (PSCF) modelling. PM_2.5 mass concentration was observed to be highest during winter and post-monsoon seasons when the meteorological conditions were relatively stable compared to other seasons. Organic carbon (OC) concentration was more than two times higher in the post-monsoon and winter seasons than in the pre-monsoon and monsoon seasons. Air mass back trajectory cluster analysis showed the dominance of local and regional air masses during winter and post-monsoon periods. In contrast, long-range transported air masses influenced the background site in pre-monsoon and monsoon. Air mass data and PSCF analysis indicated that aerosols during winter and post-monsoon are dominated by freshly generated emissions from local sources along with the influence from regional transport of polluted aerosols. On the contrary, the long-range transported air masses containing aged aerosols were dominant during pre-monsoon. No significant variability was observed in the range of δ¹³C values (-28.2‰ to -26.4‰) during the sampled seasons. The δ¹³C of aerosols indicates major sources to be combustion of biomass/biofuels (C3 plant origin), biogenic aerosols, and secondary aerosols. The δ¹³C variability and cluster/PSCF modelling suggest that aged aerosols (along with enhanced photo-oxidation derived secondary aerosols) influenced the final δ¹³C during the pre-monsoon. On the other hand, lower δ¹³C in winter and post-monsoon is attributed to the freshly emitted aerosols from biomass/biofuels.

Spatiotemporal Patterns of Anthrax, Vietnam, 1990–2015

Anthrax is a priority zoonosis for control in Vietnam. The geographic distribution of anthrax remains to be defined, challenging our ability to target areas for control. We analyzed human anthrax cases in Vietnam to obtain anthrax incidence at the national and provincial level. Nationally, the trendline for cases remained at ≈61 cases/year throughout the 26 years of available data, indicating control efforts are not effectively reducing disease burden over time. Most anthrax cases occurred in the Northern Midlands and Mountainous regions, and the provinces of Lai Chau, Dien Bien, Lao Cai, Ha Giang, Cao Bang, and Son La experienced some of the highest incidence rates. Based on spatial Bayes smoothed maps, every region of Vietnam experienced human anthrax cases during the study period. Clarifying the distribution of anthrax in Vietnam will enable us to better identify risk areas for improved surveillance, rapid clinical care, and livestock vaccination campaigns.

Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways

Spatially explicit population grid can play an important role in climate change, resource management, sustainable development and other fields. Several gridded datasets already exist, but global data, especially high-resolution data on future populations are largely lacking. Based on the WorldPop dataset, we present a global gridded population dataset covering 248 countries or areas at 30 arc-seconds (approximately 1 km) spatial resolution with 5-year intervals for the period 2020-2100 by implementing Random Forest (RF) algorithm. Our dataset is quantitatively consistent with the Shared Socioeconomic Pathways' (SSPs) national population. The spatially explicit population dataset we predicted in this research is validated by comparing it with the WorldPop dataset both at the sub-national and grid level. 3569 provinces (almost all provinces on the globe) and more than 480 thousand grids are taken into verification, and the results show that our dataset can serve as an input for predictive research in various fields.

How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia

Disaggregated population counts are needed to calculate health, economic, and development indicators in Low- and Middle-Income Countries (LMICs), especially in settings of rapid urbanisation. Censuses are often outdated and inaccurate in LMIC settings, and rarely disaggregated at fine geographic scale. Modelled gridded population datasets derived from census data have become widely used by development researchers and practitioners; however, accuracy in these datasets are evaluated at the spatial scale of model input data which is generally courser than the neighbourhood or cell-level scale of many applications. We simulate a realistic synthetic 2016 population in Khomas, Namibia, a majority urban region, and introduce several realistic levels of outdatedness (over 15 years) and inaccuracy in slum, non-slum, and rural areas. We aggregate the synthetic populations by census and administrative boundaries (to mimic census data), resulting in 32 gridded population datasets that are typical of LMIC settings using the WorldPop-Global-Unconstrained gridded population approach. We evaluate the cell-level accuracy of these gridded population datasets using the original synthetic population as a reference. In our simulation, we found large cell-level errors, particularly in slum cells. These were driven by the averaging of population densities in large areal units before model training. Age, accuracy, and aggregation of the input data also played a role in these errors. We suggest incorporating finer-scale training data into gridded population models generally, and WorldPop-Global-Unconstrained in particular (e.g., from routine household surveys or slum community population counts), and use of new building footprint datasets as a covariate to improve cell-level accuracy (as done in some new WorldPop-Global-Constrained datasets). It is important to measure accuracy of gridded population datasets at spatial scales more consistent with how the data are being applied, especially if they are to be used for monitoring key development indicators at neighbourhood scales within cities.

Multi-Criteria Assessment for City-Wide Rooftop Solar PV Deployment: A Case Study of Bandung, Indonesia

The world faces the threat of an energy crisis that is exacerbated by the dominance of fossil energy sources that negatively impact the sustainability of the earth’s ecosystem. Currently, efforts to increase the supply of renewable energy have become a global agenda, including using solar energy which is one of the rapidly developing clean energies. However, studies in solar photovoltaic (PV) modelling that integrates geospatial information of urban morphological building characters, solar radiation, and multiple meteorological parameters in low-cost scope have not been explored fully. Therefore, this research aims to model the urban rooftop solar PV development in the Global South using Bandung, Indonesia, as a case study. This research also has several specific purposes: developing a building height model as well as determining the energy potential of rooftop solar PV, the energy needs of each building, and the residential property index. This study is among the first to develop the national digital surface model (DSM) of buildings. In addition, the analysis of meteorological effects integrated with the hillshade parameter was used to obtain the solar PV potential value of the roof in more detail. The process of integrating building parameters in the form of rooftop solar PV development potential, energy requirements, and residential property index of a building was expected to increase the accuracy of determining priority buildings for rooftop solar PV deployment in Bandung. This study shows that the estimated results of effective solar PV in Bandung ranges from 351.833 to 493.813 W/m2, with a total of 1316 and 36,372 buildings in scenarios 1 and 2 being at a high level of priority for solar PV development. This study is expected to be a reference for the Indonesian government in planning the construction of large-scale rooftop solar PV in urban areas to encourage the rapid use of clean energy. Furthermore, this study has general potential for other jurisdictions for the governments focusing on clean energy using geospatial information in relation with buildings and their energy consumption.

Agents on a Landscape: Simulating Spatial and Temporal Interactions in Economic and Ecological Systems

Modeling how communities benefit from common-property, depletable ecosystem services, such as non-timber forest product (NTFP) extraction, is challenging because it depends on agent proximity to resources and competition among agents. This challenge is greater when agents face complex economic decisions that depend on the state of the landscape and the actions of other agents. We address this complexity by developing an agent-based model, founded on standard economic theory, that defines household production and utility functions for millions of spatially-explicit economic agents. Inter-agent competition is directly modeled by defining how NTFP extraction of one agent changes the extraction efficiency and travel-time of nearby agents, thereby modifying agents’ profit functions and utility maximization. We demonstrate our simulation using Tanzania as a case study. Our application relies on estimates of NTFP stocks, local wages, and traversal times across a landscape network of grid-cells, which we derive using geospatial and household data. The results of our simulation provide spatially explicit and aggregate estimates of NTFP extraction and household profit. Our model provides a methodological advance for studies that require understanding the impacts of conservation policies on households that rely on natural capital from forests. More broadly, our model shows that agent-based approaches to spatial activity can incorporate valuable insights on decision-making from economics without simplifying the underlying theory, making strong assumptions on agent homogeneity, or ignoring spatial heterogeneity.

Measuring the effects of COVID-19-related disruption on dengue transmission in southeast Asia and Latin America: a statistical modelling study

BACKGROUND

The COVID-19 pandemic has resulted in unprecedented disruption to society, which indirectly affects infectious disease dynamics. We aimed to assess the effects of COVID-19-related disruption on dengue, a major expanding acute public health threat, in southeast Asia and Latin America.

METHODS

We assembled data on monthly dengue incidence from WHO weekly reports, climatic data from ERA5, and population variables from WorldPop for 23 countries between January, 2014 and December, 2019 and fit a Bayesian regression model to explain and predict seasonal and multi-year dengue cycles. We compared model predictions with reported dengue data January to December, 2020, and assessed if deviations from projected incidence since March, 2020 are associated with specific public health and social measures (from the Oxford Coronavirus Government Response Tracer database) or human movement behaviours (as measured by Google mobility reports).

FINDINGS

We found a consistent, prolonged decline in dengue incidence across many dengue-endemic regions that began in March, 2020 (2·28 million cases in 2020 vs 4·08 million cases in 2019; a 44·1% decrease). We found a strong association between COVID-19-related disruption (as measured independently by public health and social measures and human movement behaviours) and reduced dengue risk, even after taking into account other drivers of dengue cycles including climatic and host immunity (relative risk 0·01-0·17, p<0·01). Measures related to the closure of schools and reduced time spent in non-residential areas had the strongest evidence of association with reduced dengue risk, but high collinearity between covariates made specific attribution challenging. Overall, we estimate that 0·72 million (95% CI 0·12-1·47) fewer dengue cases occurred in 2020 potentially attributable to COVID-19-related disruption.

INTERPRETATION

In most countries, COVID-19-related disruption led to historically low dengue incidence in 2020. Continuous monitoring of dengue incidence as COVID-19-related restrictions are relaxed will be important and could give new insights into transmission processes and intervention options.

FUNDING

National Key Research and Development Program of China and the Medical Research Council.

Development of Spatial Model for Food Security Prediction Using Remote Sensing Data in West Java, Indonesia

The food crisis is a problem that the world will face. The availability of growing areas that continues to decrease with the increase in food demand will result in a food crisis in the future. Good planning is needed to deal with future food crises. The absence of studies on the development of spatial models in estimating an area’s future food status has made planning for handling the food crisis suboptimal. This study aims to predict food security by integrating the availability of paddy fields with environmental factors to determine the food status in West Java Province. Food status modeling is done by integrating land cover, population, paddy fields productivity, and identifying the influence of environmental factors. The land cover prediction will be developed using the CA-Markov model. Meanwhile, to identify the influence of environmental factors, multivariable linear regression (MLR) was used with environmental factors from remote sensing observations. The data used are in the form of the NDDI (Normalized Difference Drought Index), NDVI (Normalized Difference Vegetation Index), land surface temperature (LST), soil moisture, precipitation, altitude, and slopes. The land cover prediction has an overall accuracy of up to 93%. From the food status in 2005, the flow of food energy in West Java was still able to cover the food needs and obtain an energy surplus of 6.103 Mcal. On the other hand, the prediction of the food energy flow from the food status in 2030 will not cover food needs and obtain an energy deficit of up to 13,996,292.42 Mcal. From the MLR results, seven environmental factors affect the productivity of paddy fields, with the determination coefficient reaching 50.6%. Thus, predicting the availability of paddy production will be more specific if it integrates environmental factors. With this study, it is hoped that it can be used as planning material for mitigating food crises in the future.

A Population Spatialization Model at the Building Scale Using Random Forest

Population spatialization reveals the distribution and quantity of the population in geographic space with gridded population maps. Fine-scale population spatialization is essential for urbanization and disaster prevention. Previous approaches have used remotely sensed imagery to disaggregate census data, but this approach has limitations. For example, large-scale population censuses cannot be conducted in underdeveloped countries or regions, and remote sensing data lack semantic information indicating the different human activities occurring in a precise geographic location. Geospatial big data and machine learning provide new fine-scale population distribution mapping methods. In this paper, 30 features are extracted using easily accessible multisource geographic data. Then, a building-scale population estimation model is trained by a random forest (RF) regression algorithm. The results show that 91% of the buildings in Lin’an District have absolute error values of less than six compared with the actual population data. In a comparison with a multiple linear (ML) regression model, the mean absolute errors of the RF and ML models are 2.52 and 3.21, respectively, the root mean squared errors are 8.2 and 9.8, and the R2 values are 0.44 and 0.18. The RF model performs better at building-scale population estimation using easily accessible multisource geographic data. Future work will improve the model accuracy in densely populated areas.

Spatial Distribution and Population Estimation of Dogs in Thailand: Implications for Rabies Prevention and Control

Poor management of dog populations causes many problems in different countries, including rabies. To strategically design a dog population management, certain sets of data are required, such as the population size and spatial distribution of dogs. However, these data are rarely available or incomplete. Hence, this study aimed to describe the characteristics of dog populations in Thailand, explore their spatial distribution and relevant factors, and estimate the number of dogs in the whole country. First, four districts were selected as representatives of each region. Each district was partitioned into grids with a 300-m resolution. The selected grids were then surveyed, and the number of dogs and related data were collected. Random forest models with a two-part approach were used to quantify the association between the surveyed dog population and predictor variables. The spatial distribution of dog populations was then predicted. A total of 1,750 grids were surveyed (945 grids with dog presence and 805 grids with dog absence). Among the surveyed dogs, 86.6% (12,027/13,895) were owned. Of these, 51% were classified as independent, followed by confined (25%), semi-independent (21%), and unidentified dogs (3%). Seventy-two percent (1,348/1,868) of the ownerless dogs were feral, and the rest were community dogs. The spatial pattern of the dog populations was highly distributed in big cities such as Bangkok and its suburbs. In owned dogs, it was linked to household demographics, whereas it was related to community factors in ownerless dogs. The number of estimated dogs in the entire country was 12.8 million heads including 11.2 million owned dogs (21.7 heads/km²) and 1.6 million ownerless dogs (3.2 heads/km²). The methods developed here are extrapolatable to a larger area and use much less budget and manpower compared to the present practices. Our results are helpful for canine rabies prevention and control programs, such as dog population management and control and rabies vaccine allocation.

School Location Analysis by Integrating the Accessibility, Natural and Biological Hazards to Support Equal Access to Education

This study proposes a new model for land suitability for educational facilities based on spatial product development to determine the optimal locations for achieving education targets in West Java, Indonesia. Single-aspect approaches, such as accessibility and spatial hazard analyses, have not been widely applied in suitability assessments on the location of educational facilities. Model development was performed based on analyses of the economic value of the land and on the integration of various parameters across three main aspects: accessibility, comfort, and a multi-natural/biohazard (disaster) risk index. Based on the maps of disaster hazards, higher flood-prone areas are found to be in gentle slopes and located in large cities. Higher risks of landslides are spread throughout the study area, while higher levels of earthquake risk are predominantly in the south, close to the active faults and megathrusts present. Presently, many schools are located in very high vulnerability zones (2057 elementary, 572 junior high, 157 senior high, and 313 vocational high schools). The comfort-level map revealed 13,459 schools located in areas with very low and low comfort levels, whereas only 2377 schools are in locations of high or very high comfort levels. Based on the school accessibility map, higher levels are located in the larger cities of West Java, whereas schools with lower accessibility are documented far from these urban areas. In particular, senior high school accessibility is predominant in areas of lower accessibility levels, as there are comparatively fewer facilities available in West Java. Overall, higher levels of suitability are spread throughout West Java. These distribution results revealed an expansion of the availability of schools by area: senior high schools, 303,973.1 ha; vocational high schools, 94,170.51 ha; and junior high schools, 12,981.78 ha. Changes in elementary schools (3936.69 ha) were insignificant, as the current number of elementary schools is relatively much higher. This study represents the first to attempt to integrate these four parameters—accessibility, multi natural hazard, biohazard, comfort index, and land value—to determine potential areas for new schools to achieve educational equity targets.

Using Daily Nighttime Lights to Monitor Spatiotemporal Patterns of Human Lifestyle under COVID-19: The Case of Saudi Arabia

A novel coronavirus, COVID-19, appeared at the beginning of 2020 and within a few months spread worldwide. The COVID-19 pandemic had some of its greatest impacts on social, economic and religious activities. This study focused on the application of daily nighttime light (NTL) data (VNP46A2) to measure the spatiotemporal impact of the COVID-19 pandemic on the human lifestyle in Saudi Arabia at the national, province and governorate levels as well as on selected cities and sites. The results show that NTL brightness was reduced in all the pandemic periods in 2020 compared with a pre-pandemic period in 2019, and this was consistent with the socioeconomic results. An early pandemic period showed the greatest effects on the human lifestyle due to the closure of mosques and the implementation of a curfew. A slight improvement in the NTL intensity was observed in later pandemic periods, which represented Ramadan and Eid Alfiter days when Muslims usually increase the light of their houses. Closures of the two holy mosques in Makkah and Madinah affected the human lifestyle in these holy cities as well as that of Umrah pilgrims inside Saudi Arabia and abroad. The findings of this study confirm that the social and cultural context of each country must be taken into account when interpreting COVID-19 impacts, and that analysis of difference in nighttime lights is sensitive to these factors. In Saudi Arabia, the origin of Islam and one of the main sources of global energy, the preventive measures taken not only affected Saudi society; impacts spread further and reached the entire Islamic society and other societies, too.

The current burden of Japanese encephalitis and the estimated impacts of vaccination: Combining estimates of the spatial distribution and transmission intensity of a zoonotic pathogen

Japanese encephalitis virus (JEV) is a major cause of neurological disability in Asia and causes thousands of severe encephalitis cases and deaths each year. Although Japanese encephalitis (JE) is a WHO reportable disease, cases and deaths are significantly underreported and the true burden of the disease is not well understood in most endemic countries. Here, we first conducted a spatial analysis of the risk factors associated with JE to identify the areas suitable for sustained JEV transmission and the size of the population living in at-risk areas. We then estimated the force of infection (FOI) for JE-endemic countries from age-specific incidence data. Estimates of the susceptible population size and the current FOI were then used to estimate the JE burden from 2010 to 2019, as well as the impact of vaccination. Overall, 1,543.1 million (range: 1,292.6-2,019.9 million) people were estimated to live in areas suitable for endemic JEV transmission, which represents only 37.7% (range: 31.6-53.5%) of the over four billion people living in countries with endemic JEV transmission. Based on the baseline number of people at risk of infection, there were an estimated 56,847 (95% CI: 18,003-184,525) JE cases and 20,642 (95% CI: 2,252-77,204) deaths in 2019. Estimated incidence declined from 81,258 (95% CI: 25,437-273,640) cases and 29,520 (95% CI: 3,334-112,498) deaths in 2010, largely due to increases in vaccination coverage which have prevented an estimated 314,793 (95% CI: 94,566-1,049,645) cases and 114,946 (95% CI: 11,421-431,224) deaths over the past decade. India had the largest estimated JE burden in 2019, followed by Bangladesh and China. From 2010-2019, we estimate that vaccination had the largest absolute impact in China, with 204,734 (95% CI: 74,419-664,871) cases and 74,893 (95% CI: 8,989-286,239) deaths prevented, while Taiwan (91.2%) and Malaysia (80.1%) had the largest percent reductions in JE burden due to vaccination. Our estimates of the size of at-risk populations and current JE incidence highlight countries where increasing vaccination coverage could have the largest impact on reducing their JE burden.

Japanese encephalitis is a vector-transmitted, zoonotic disease that is endemic throughout a large portion of Asia. Vaccination has significantly reduced the JE burden in several formerly high-burden countries, but vaccination coverage remains limited in several other countries with high JE burdens. A better understanding of both the spatial distribution and the magnitude of the burden in endemic countries is critical for future disease prevention efforts. To estimate the number of people living in areas within Asia suitable for JEV transmission we conducted a spatial analysis of the risk factors associated with JE. We estimate that over one billion people live in areas suitable for local JEV transmission. We then combined these population-at-risk estimates with estimates of the force of infection (FOI) to model the national-level burden of JE (annual cases and deaths) over the past decade. Increases in vaccination coverage have reduced JE incidence from over 80,000 cases in 2010 to fewer than 57,000 cases in 2019. We estimate that vaccination has prevented almost 315,000 cases and 115,000 deaths in the past decade. Our results also call attention to the countries, and high-risk areas within countries, where increases in vaccination coverage are most needed.

Which Gridded Population Data Product Is Better? Evidences from Mainland Southeast Asia (MSEA)

The release of global gridded population datasets, including the Gridded Population of the World (GPW), Global Human Settlement Population Grid (GHS-POP), WorldPop, and LandScan, have greatly facilitated cross-comparison for ongoing research related to anthropogenic impacts. However, little attention is paid to the consistency and discrepancy of these gridded products in the regions with rapid changes in local population, e.g., Mainland Southeast Asia (MSEA), where the countries have experienced fast population growth since the 1950s. This awkward situation is unsurprisingly aggravated because of national scarce demographics and incomplete census counts, which further limits their appropriate usage. Thus, comparative analyses of them become the priority of their better application. Here, the consistency and discrepancy of the four common global gridded population datasets were cross-compared by combing the 2015 provincial population statistics (census and yearbooks) via error-comparison based statistical methods. The results showed that: (1) the LandScan performs the best both in spatial accuracy and estimated errors, then followed by the WorldPop, GHS-POP, and GPW in MSEA. (2) Provincial differences in estimated errors indicated that the LandScan better reveals the spatial pattern of population density in Thailand and Vietnam, while the WorldPop performs slightly better in Myanmar and Laos, and both fit well in Cambodia. (3) Substantial errors among the four gridded datasets normally occur in the provincial units with larger population density (over 610 persons/km2) and a rapid population growth rate (greater than 1.54%), respectively. The new findings in MSEA indicated that future usage of these datasets should pay attention to the estimated population in the areas characterized by high population density and rapid population growth.

Population's Potential Accessibility to Specialized Palliative Care Services: A Comparative Study in Three European Countries

BACKGROUND

Palliative care is a priority for health systems worldwide, yet equity in access remains unknown. To shed light on this issue, this study compares populations' driving time to specialized palliative care services in three countries: Ireland, Spain, and Switzerland.

METHODS

Network analysis of the population's driving time to services according to geolocated palliative care services using Geographical Information System (GIS). Percentage of the population living within a 30-min driving time, between 30 and 60 minutes, and over 60 min were calculated.

RESULTS

The percentage of the population living less than thirty minutes away from the nearest palliative care provider varies among Ireland (84%), Spain (79%), and Switzerland (95%). Percentages of the population over an hour away from services were 1.87% in Spain, 0.58% in Ireland, and 0.51% in Switzerland.

CONCLUSION

Inequities in access to specialized palliative care are noticeable amongst countries, with implications also at the sub-national level.

Estimating malaria chemoprevention and vector control coverage using program and campaign data: A scoping review of current practices and opportunities

Background

Accurate estimation of intervention coverage is a vital component of malaria program monitoring and evaluation, both for process evaluation (how well program targets are achieved), and impact evaluation (whether intervention coverage had an impact on malaria burden). There is growing interest in maximizing the utility of program data to generate interim estimates of intervention coverage in the periods between large-scale cross-sectional surveys (the gold standard). As such, this study aimed to identify relevant concepts and themes that may guide future optimization of intervention coverage estimation using routinely collected data, or data collected during and following intervention campaigns, with a particular focus on strategies to define the denominator.

Methods

We conducted a scoping review of current practices to estimate malaria intervention coverage for insecticide-treated nets (ITNs); indoor residual spray (IRS); intermittent preventive treatment in pregnancy (IPTp); mass drug administration (MDA); and seasonal malaria chemoprevention (SMC) interventions; case management was excluded. Multiple databases were searched for relevant articles published from January 1, 2015 to June 1, 2018. Additionally, we identified and included other guidance relevant to estimating population denominators, with a focus on innovative techniques.

Results

While program data have the potential to provide intervention coverage data, there are still substantial challenges in selecting appropriate denominators. The review identified a lack of consistency in how coverage was defined and reported for each intervention type, with denominator estimation methods not clearly or consistently reported, and denominator estimates rarely triangulated with other data sources to present the feasible range of denominator values and consequently the range of likely coverage estimates.

Conclusions

Though household survey-based estimates of intervention coverage remain the gold standard, efforts should be made to further standardize practices for generating interim measurements of intervention coverage from program data, and for estimating and reporting population denominators. This includes fully describing any projections or adjustments made to existing census or population data, exploring opportunities to validate available data by comparing with other sources, and explaining how the denominator has been restricted (or not) to reflect exclusion criteria.

Spatial distribution of rural population using mixed geographically weighted regression: Evidence from Jiangxi Province in China

On the basis of the spatial panel data of 2000, 2005, 2010, and 2015, this study uses a mixed geographically weighted regression model to explore the spatial distribution characteristics and influencing factors of the rural (permanent) population in Jiangxi Province, China. Results show that residents in the county area have a significant spatial positive autocorrelation, especially in the lake and mountain areas and the global Moran’ I index is more than 0.05. The influence of social and economic factors presents spatial homogeneity. The effect of urbanization and per capita disposable income is negative, whereas that of agricultural output value and rural electricity consumption is positive. The influence of climate factors presents spatial heterogeneity. The influence coefficient of rainfall in 2015 ranges from [-0.061, 0.133], which has a negative effect on the southwest mountain areas and a positive effect on the northeast lake areas., The influence coefficient of temperature in 2015 ranges from [-0.110, 0.094], which has a positive effect on the southwest mountain areas and a negative effect on the northeast lake areas. The influence coefficients of wind speed and relative humidity range from [-0.090, 0.153] and [-0.069, 0.130] in 2015 respectively, which further reinforce this effect. Therefore, scholars should pay attention to the universal adaptability of economic and social factors. Moreover, they should consider the spatial difference of climatic factors to promote urbanization following the local conditions. Finally, policymakers and concerned non-governmental institutions should fully understand the sensitivity of the rural population in underdeveloped mountain areas to climate factors to promote their rational distribution.

An Improved Index for Urban Population Distribution Mapping Based on Nighttime Lights (DMSP-OLS) Data: An Experiment in Riyadh Province, Saudi Arabia

Knowledge of the spatial pattern of the population is important. Census population data provide insufficient spatial information because they are released only for large geographic areas. Nighttime light (NTL) data have been utilized widely as an effective proxy for population mapping. However, the well-reported challenges of pixel overglow and saturation influence the applicability of the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) for accurate population mapping. This paper integrates three remotely sensed information sources, DMSP-OLS, vegetation, and bare land areas, to develop a novel index called the Vegetation-Bare Adjusted NTL Index (VBANTLI) to overcome the uncertainties in the DMSP-OLS data. The VBANTLI was applied to Riyadh province to downscale governorate-level census population for 2004 and 2010 to a gridded surface of 1 km resolution. The experimental results confirmed that the VBANTLI significantly reduced the overglow and saturation effects compared to widely applied indices such as the Human Settlement Index (HSI), Vegetation Adjusted Normalized Urban Index (VANUI), and radiance-calibrated NTL (RCNTL). The correlation coefficient between the census population and the RCNTL (R = 0.99) and VBANTLI (R = 0.98) was larger than for the HSI (R = 0.14) and VANUI (R = 0.81) products. In addition, Model 5 (VBANTLI) was the most accurate model with R2 and mean relative error (MRE) values of 0.95% and 37%, respectively.

Understanding dynamics of population flood exposure in Canada with multiple high-resolution population datasets

In recent years, geospatial data (e.g. remote sensing imagery), and other relevant ancillary datasets (e.g. land use land cover, climate conditions) have been utilized through sophisticated algorithms to produce global population datasets. With a handful of such datasets, their performances and skill in flood exposure assessment have not been explored. This study proposes a comprehensive framework to understand the dynamics and differences in population flood exposure over Canada by employing four global population datasets alongside the census data from Statistics Canada as the reference. The flood exposure is quantified based on a set of floodplain maps (for 2015, 1 in 100-yr and 1 in 200-yr event) for Canada derived from the CaMa-Flood global flood model. To obtain further insights at the regional level, the methodology is implemented over six flood-prone River Basins in Canada. We find that about 9% (3.31 million) and 11% (3.90 million) of the Canadian population resides within 1 in 100-yr and 1 in 200-yr floodplains. We notice an excellent performance of WorldPop, and LandScan in most of the cases, which is unaffected by the representation of flood hazard, while Global Human Settlement and Gridded Population of the World showed large deviations. At last, we determined the long-term dynamics of population flood exposure and vulnerability from 2006 to 2019. Through this analysis, we also identify the regions that contain a significantly larger population exposed to floods. The relevant conclusions derived from the study highlight the need for careful selection of population datasets for preventing further amplification of uncertainties in flood risk. We recommend a detailed assessment of the severely exposed regions by including precise ground-level information. The results derived from this study may be useful not only for flood risk management but also contribute to understanding other disaster impacts on human-environment interrelationships.

Spatial distribution of rural population from a climate perspective: Evidence from Jiangxi Province in China

The research on rural population distribution from a climate perspective is rare. Therefore, this study adopts this perspective and uses the ordinary least squares and spatial econometric models to explore the spatial distribution characteristics of the rural population in the Poyang Lake ecological economic zone. Results show that (1) a significant spatial autocorrelation is present in the distribution of rural population, and a spatial correlation exists between the population distribution and climatic factors, (2) the influence of climatic factors on the distribution of rural population in the Poyang Lake ecological economic zone is greater than that of economic factors, and (3) the annual average sunshine and annual average rainfall have a significant negative effect on the distribution of the regional rural population, which is contrary to the expectations., so we then analyze this negative effect on the regional rural population distribution. It is found that (1) the influence of climate factors on the distribution of rural population in lake area is far more than that of economic factors, and more consideration should be given to the influence of climate factors on the population distribution in the lake area, (2) different geographical capital and natural resource endowment, the influence of climate on micro-regional population distribution may be different from the general law, (3) the spatial measurement model which takes spatial dependence into account can reveal the influence of climate on rural population distribution more accurately.

Outlook from the soil perspective of urban expansion and food security

The use of soil as support for built-up areas represents only one of its several functions. Farmlands at the fringe of conurbations have more chance of being converted into built-up areas due to the favourable topography and the accessibility to existing infrastructure, being in the vicinity of urban areas. We analysed the global land-take during the period 2000-2014. The data are based on a global dataset describing the spatial evolution of human settlements using the Global Human Settlement Layer, which was derived from Landsat images collected in 1975, 1990, 2000 and 2014. Although the global land-take represents roughly 0.1% of the global terrestrial Earth, it affects 1% of the naturally fertile soils, according to the proposed Soil Productivity Indexes (SPI), based upon the potential soil productivity, calculated on the basis of the Harmonized World Soil Database. We have found that, few large conurbations develop on potentially high productive soil, while scarcely productive soils sustain the expansion of several megalopolises. On a global scale and through the centuries, considered comparatively as individual overall age of settlements, a trend between the intrinsic quality of the soils and its use for settlement purposes as major competitor, was not observed.

Incorporating human mobility data improves forecasts of Dengue fever in Thailand

Over 390 million people worldwide are infected with dengue fever each year. In the absence of an effective vaccine for general use, national control programs must rely on hospital readiness and targeted vector control to prepare for epidemics, so accurate forecasting remains an important goal. Many dengue forecasting approaches have used environmental data linked to mosquito ecology to predict when epidemics will occur, but these have had mixed results. Conversely, human mobility, an important driver in the spatial spread of infection, is often ignored. Here we compare time-series forecasts of dengue fever in Thailand, integrating epidemiological data with mobility models generated from mobile phone data. We show that geographically-distant provinces strongly connected by human travel have more highly correlated dengue incidence than weakly connected provinces of the same distance, and that incorporating mobility data improves traditional time-series forecasting approaches. Notably, no single model or class of model always outperformed others. We propose an adaptive, mosaic forecasting approach for early warning systems.

Improved Transferability of Data-Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data-driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a "sample selection bias." In this article, we enhance data-driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.

Poultry farm distribution models developed along a gradient of intensification

Efficient planning of measures limiting epidemic spread requires information on farm locations and sizes (number of animals per farm). However, such data are rarely available. The intensification process which is operating in most low- and middle-income countries (LMICs), comes together with a spatial clustering of farms, a characteristic epidemiological models are sensitive to. We developed farm distribution models predicting both the location and the number of animals per farm, while accounting for the spatial clustering of farms in data-poor countries, using poultry production as an example. We selected four countries, Nigeria, Thailand, Argentina and Belgium, along a gradient of intensification expressed by the per capita Gross Domestic Product (GDP). First, we investigated the distribution of chicken farms along the spectrum of intensification. Second, we built farm distribution models (FDM) based on censuses of commercial farms of each of the four countries, using point pattern and random forest models. As an external validation, we predicted farm locations and sizes in Bangladesh. The number of chicken per farm increased gradually in line with the gradient of GDP per capita in the following order: Nigeria, Thailand, Argentina and Belgium. Interestingly, we did not find such a gradient for farm clustering. Our modelling procedure could only partly reproduce the observed datasets in each of the four sample countries in internal validation. However, in the external validation, the clustering of farms could not be reproduced and the spatial predictors poorly explained the number and location of farms and farm sizes in Bangladesh. Further improvements of the methodology should explore other covariates of the intensity of farms and farm sizes, as well as improvements of the methodology. Structural transformation, economic development and environmental conditions are essential characteristics to consider for an extrapolation of our FDM procedure, as generalisation appeared challenging. We believe the FDM procedure could ultimately be used as a predictive tool in data-poor countries.

Assessment of Population Exposure to Urban Flood at the Building Scale

The assessment of populations affected by urban flooding is crucial for flood prevention and mitigation but is highly influenced by the accuracy of population datasets. The population distribution is related to buildings during the urban floods, so assessing the population at the building scale is more rational for the urban floods, which is possible due to the abundance of multi-source data and advances in GIS technology. Therefore, this study assesses the populations affected by urban floods through population mapping at the building scale using highly correlated point of interest (POI) data. The population distribution is first mapped by downscaling the grid-based WorldPop population data to the building scale. Then, the population affected by urban floods is estimated by superimposing the population data sets onto flood areas, with flooding simulated by the LISFLOOD-FP hydrodynamic model. Finally, the proposed method is applied to Lishui City in southeast China. The results show that the population affected by urban floods is significantly reduced for different rainstorm scenarios when using the building-scale population instead of WorldPop. In certain areas, populations not captured by WorldPop can be identified using the building-scale population. This study provides a new method for estimating populations affected by urban flooding.

Tropical peatlands and their conservation are important in the context of COVID-19 and potential future (zoonotic) disease pandemics

The COVID-19 pandemic has caused global disruption, with the emergence of this and other pandemics having been linked to habitat encroachment and/or wildlife exploitation. High impacts of COVID-19 are apparent in some countries with large tropical peatland areas, some of which are relatively poorly resourced to tackle disease pandemics. Despite this, no previous investigation has considered tropical peatlands in the context of emerging infectious diseases (EIDs). Here, we review: (i) the potential for future EIDs arising from tropical peatlands; (ii) potential threats to tropical peatland conservation and local communities from COVID-19; and (iii) potential steps to help mitigate these risks. We find that high biodiversity in tropical peat-swamp forests, including presence of many potential vertebrate and invertebrate vectors, combined, in places, with high levels of habitat disruption and wildlife harvesting represent suitable conditions for potential zoonotic EID (re-)emergence. Although impossible to predict precisely, we identify numerous potential threats to tropical peatland conservation and local communities from the COVID-19 pandemic. This includes impacts on public health, with the potential for haze pollution from peatland fires to increase COVID-19 susceptibility a noted concern; and on local economies, livelihoods and food security, where impacts will likely be greater in remote communities with limited/no medical facilities that depend heavily on external trade. Research, training, education, conservation and restoration activities are also being affected, particularly those involving physical groupings and international travel, some of which may result in increased habitat encroachment, wildlife harvesting or fire, and may therefore precipitate longer-term negative impacts, including those relating to disease pandemics. We conclude that sustainable management of tropical peatlands and their wildlife is important for mitigating impacts of the COVID-19 pandemic, and reducing the potential for future zoonotic EID emergence and severity, thus strengthening arguments for their conservation and restoration. To support this, we list seven specific recommendations relating to sustainable management of tropical peatlands in the context of COVID-19/disease pandemics, plus mitigating the current impacts of COVID-19 and reducing potential future zoonotic EID risk in these localities. Our discussion and many of the issues raised should also be relevant for non-tropical peatland areas and in relation to other (pandemic-related) sudden socio-economic shocks that may occur in future.

Gridded population survey sampling: a systematic scoping review of the field and strategic research agenda

INTRODUCTION

In low- and middle-income countries (LMICs), household survey data are a main source of information for planning, evaluation, and decision-making. Standard surveys are based on censuses, however, for many LMICs it has been more than 10 years since their last census and they face high urban growth rates. Over the last decade, survey designers have begun to use modelled gridded population estimates as sample frames. We summarize the state of the emerging field of gridded population survey sampling, focussing on LMICs.

METHODS

We performed a systematic scoping review in Scopus of specific gridded population datasets and "population" or "household" "survey" reports, and solicited additional published and unpublished sources from colleagues.

RESULTS

We identified 43 national and sub-national gridded population-based household surveys implemented across 29 LMICs. Gridded population surveys used automated and manual approaches to derive clusters from WorldPop and LandScan gridded population estimates. After sampling, some survey teams interviewed all households in each cluster or segment, and others sampled households from larger clusters. Tools to select gridded population survey clusters include the GridSample R package, Geo-sampling tool, and GridSample.org. In the field, gridded population surveys generally relied on geographically accurate maps based on satellite imagery or OpenStreetMap, and a tablet or GPS technology for navigation.

CONCLUSIONS

For gridded population survey sampling to be adopted more widely, several strategic questions need answering regarding cell-level accuracy and uncertainty of gridded population estimates, the methods used to group/split cells into sample frame units, design effects of new sample designs, and feasibility of tools and methods to implement surveys across diverse settings.