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Background Onchocerciasis is a disease caused by infection with Onchocerca volvulus , which is transmitted to humans via the bite of several species of black fly, and is responsible for permanent blindness or vision loss, as well as severe skin disease. Predominantly endemic in parts of Africa and Yemen, preventive chemotherapy with mass drug administration of ivermectin is the primary intervention recommended for the elimination of its transmission. Methods A dataset of 18,116 geo-referenced prevalence survey datapoints was used to model annual 2000–2018 infection prevalence in Africa and Yemen. Using Bayesian model-based geostatistics, we generated spatially continuous estimates of all-age 2000–2018 onchocerciasis infection prevalence at the 5 × 5-km resolution as well as aggregations to the national level, along with corresponding estimates of the uncertainty in these predictions. Results As of 2018, the prevalence of onchocerciasis infection continues to be concentrated across central and western Africa, with the highest mean estimates at the national level in Ghana (12.2%, 95% uncertainty interval [UI] 5.0–22.7). Mean estimates exceed 5% infection prevalence at the national level for Cameroon, Central African Republic, Democratic Republic of the Congo (DRC), Guinea-Bissau, Sierra Leone, and South Sudan. Conclusions Our analysis suggests that onchocerciasis infection has declined over the last two decades throughout western and central Africa. Focal areas of Angola, Cameroon, the Democratic Republic of the Congo, Ethiopia, Ghana, Guinea, Mali, Nigeria, South Sudan, and Uganda continue to have mean microfiladermia prevalence estimates exceeding 25%. At and above this level, the continuation or initiation of mass drug administration with ivermectin is supported. If national programs aim to eliminate onchocerciasis infection, additional surveillance or supervision of areas of predicted high prevalence would be warranted to ensure sufficiently high coverage of program interventions.

Inference for predicted exceedance sets is important for various environmental issues such as detecting environmental anomalies and emergencies with high confidence. A critical part is to construct inner and outer predicted exceedance sets using an algorithm that samples from the predictive distribution. The simple currently used sampling procedure can lead to misleading conclusions for some locations due to relatively large standard errors when proportions are estimated from independent observations. Instead we propose an algorithm that calculates probabilities numerically using the Genz–Bretz algorithm, which is based on quasi-random numbers leading to more accurate inner and outer sets, as illustrated on rainfall data in the state of Paraná, Brazil.

Floods are one of the most frequent, dangerous natural disasters globally. During the period from 1990 to 2020, more than 50% of the world's recorded disasters were related to floods. This problem stems largely from the inadequate planning and economic circumstances of human settlements in flood-prone plains. Geospatial modelling can be a powerful tool for large-scale flood modelling. The hydrological and hydraulic models theory, GIS-based multi-criteria evaluation techniques, and machine learning algorithms for flood simulation are presented. The most used techniques and methodologies for the geospatial simulation of floods in the last decade are presented. This paper also shows the input data requirements and the algorithms used for each geospatial technique and a description of the tools and some relevant examples of geospatial flood studies are given. A comprehensive assessment of the characteristics of the flood models is presented based on its modelling approach, either for flood susceptibility, hazard, vulnerability, or risk.

Quantitative assessment of urban vibrancy is crucial to understanding urban development and promoting sustainability, especially for rapidly developing areas and regions that have experienced post-disaster reconstruction. Taking Dujiangyan City, the hardest-hit area of the earthquake, as an example, this paper quantifies the urban economic, social, and cultural vibrancy after reconstruction by the use of multi-source data, and conducts a geographic visualization analysis. The purpose is to establish an evaluation framework for the relationship between the urban built environment elements and vibrancy in different dimensions, to evaluate the benefits of post-disaster restoration and reconstruction. The results show that the urban vibrancy reflected by classified big data can not be completely matched due to the difference in the data generation and collection process. The Criteria Importance Though Inter-criteria Correlation and entropy (CRITIC-entropy) method is used to construct a comprehensive model is a better representation of the urban vibrancy spatial characteristics. On a global scale, comprehensive vibrancy demonstrates high continuity and a bi-center structure. In the old town, the distribution of various urban vibrancies show diffusion characteristics, while those in the new district demonstrated a high degree of aggregation, and the comprehensive vibrancy is less sensitive to land-use mixture and more sensitive to residential land.

The COVID-19 pandemic caused an unprecedented demand for immediate and far-reaching organizational change. In this context, oncological care experienced delays and routine service disruption, significantly impacting patients’ outcomes. Using a unique dataset with two data sources, 366 oncological patients and 68 Breast Unit team leaders in the Italian National Health Service, this study analyzes healthcare organizational characteristics and the role of transferred perceptions of care safety among patients with breast cancer during the Covid 19 pandemic. We used quantitative methods building spatial econometric models. Furthermore, we have employed qualitative data coming from patients’ interviews. Our results reveal that team stability and continuity in care provision affected patients’ perception of safety. Moreover, they highlight the creation of an invisible network among patients who experienced similar conditions and circumstances during the COVID-19 pandemic. Implications for managers and policymakers are discussed.

The UN Sustainable Development Goals seek universal health coverage and accessibility to quality healthcare services by 2030 for creating a healthier and equitable world. This study highlights the role of geospatial model in assessing the geographic coverage of healthcare facilities in Manipur, India, and the need for scaling-up of the existing health centres in the region. A geodatabase on the existing healthcare facilities has been developed in the study. Mapping of health centre facilities, coverage analysis and scaling-up assessment are carried out using ArcGIS and AccessMod. The model results show that locations of the existing healthcare services are significantly spatially clustered amongst themselves, with an observed mean distance of 2.62 km. Scaling-up analysis considering the projected population of 2020 indicates the requirement of 66 new health facility centres, mostly in the hill districts of Manipur. This study indicates the need for scaling-up healthcare facilities that can cover the entire population in each district of Manipur. It also indirectly addresses one of the fundamental aspects of the healthcare system, i.e. equity in the distribution of healthcare facilities and their accessibility to all sections of the society.

Throughout the 20th century and in the first decades of the 21st century, the geospatial dynamic exhibiting the highest rate of change globally corresponds to urban expansion surrounding metropolitan areas and large cities. Around intermediate-sized cities, there have also been rapid changes in their geographical space, but study in these areas has had less academic attention and development. Considering this context, this article intends to analyze the dynamics in the periurbanization of communes with intermediate-sized cities. In this study, three geographical criteria were defined for the definition of the study area and seven geospatial indicators of sociodemographic, socioeconomic and land occupation categories, with the purpose of determining the composition of the periurbanization process. Finally, the discussion presents a perspective on the dynamics of periurbanization, the interpretation of future projections identifying three geospatial phenomena and a proposal for a geospatial chorematic model with the composition of periurbanization, based on three subregional intermediate-sized cities in the Metropolitan Region of Santiago de Chile. This research contributes new reflections to the debate around spatial planning and periurban research in Latin America and the Global South.

The present paper aims to reduce and detect the environmental impact of radioelement concentrations and waste marble powder (WMP) in the surrounding environment and additionally, improve the characteristics of the expansive soil and investigate the effect of stabilizers on the swelling soil to use a foundation layer. Different geotechnical laboratory tests have been performed on representative clay samples to define the physical and mechanical characteristics. The results indicate that the swelling pressure and swelling potential are reduced from (805.7 to 576 kN/m2) and (15.78 to 7.11%), respectively. The plasticity index decreased from 35.9% (high plasticity) to 19.4% (low plasticity), and the free swell index becomes zero by adding waste marble powder. Geospatial techniques have also been used to produce the distributions layers of different geotechnical characteristics and radioelements’ concentrations. These layers were integrated to produce a geospatial model. This model showed a noticeable improvement in the properties of clayey soils after the use of waste marble powder, which ranged from low expansion to high expansion. The radioelement concentrations of all samples are below an acceptable limit, except that the concentration of 40K is greater than the acceptable limit. This study recommends a 40% replacement of WMP. This amount is suitable and economical for this kind of treatment because of its positive solutions to protect the environment from pollution and reduce the cost of construction.

Aridity is a condition in which there is a moisture deficit in the air and soil that affects large areas of the earth’s surface worldwide. It is a global problem caused mainly by factors related to climatic events and human actions. In the arid regions of Mexico, prolonged periods of drought are very common and water scarcity is the predominant feature. The main objective of this study is to develop a prospective geospatial simulation model for arid zones in the short and medium term (2030 and 2050) for the northwestern region of Mexico. A retrospective analysis of the variables that cause aridity was conducted based on historical data from satellite information obtained from various sources between 1985 and 2020, taking 2020 as the reference year; from this information the rate of change per year was obtained, followed by the simulated rates of change for the years 2030 and 2050. A methodology used to obtain arid zones using multicriteria evaluation techniques, weighted linear combination, and Geographic Information Systems. In order to generate the prospective model for arid zones, the variables were modeled to adjust the rate of change for each of them, with the same methodology subsequently applied to obtain the base year (2020), and aridity suitability maps were obtained for the years 2030 and 2050. The main results indicate that the prospective scenarios point to an increase in arid regions of 0.38% and 0.70%, respectively, which is equivalent to an area of approximately 240,164.63 km2 and 241,760.75 km2, respectively. This will cause a decrease in the subhumid–dry and humid regions of 0.10% and 0.19%, respectively, for the projected years. Statistical and geospatial aridity indicators were also generated at different levels, which helps to better understand the problem of aridity in vulnerable regions.

It is well known that individuals in the same community can be exposed to a highly variable number of mosquito bites. This heterogeneity in bite exposure has consequences for the control of vector-borne diseases because a few people may be contributing significantly to transmission. However, very few studies measure sources of heterogeneity in a way which is relevant to decision-making. We investigate the relationship between two classic measures of heterogeneity, spatial and individual, within the context of lymphatic filariasis, a parasitic mosquito-borne disease. Using infection and mosquito-bite data for five villages in Papua New Guinea, we measure biting characteristics to model what impact bed-nets have had on control of the disease. We combine this analysis with geospatial modelling to understand the spatial relationship between disease indicators and nightly mosquito bites. We found a weak association between biting and infection heterogeneity within villages. The introduction of bed-nets increased biting heterogeneity, but the reduction in mean biting more than compensated for this, by reducing prevalence closer to elimination thresholds. Nightly biting was explained by a spatial heterogeneity model, while parasite load was better explained by an individual heterogeneity model. Spatial and individual heterogeneity are qualitatively different with profoundly different policy implications.