Project description:Dense data acquisition for 3-D high-resolution aquifer mapping through heliborne transient electromagnetic (HTEM) survey is continually not possible due to various technical and administrative constraints. Consequently, we apply ground geophysical surveys at possibly closer spacing to collect the sub-surface information in the no-fly area, which provides only a regional aquifer picture. In the area near Patna of Northern India, an extent of 18% was covered under the HTEM survey, and the rest was surveyed by ground geophysical methods. Both data are integrated using the theory of regionalized variables. The parameters of multi-aquifers i.e., top of the first aquifer, top of the separating clay layer, top and the bottom of second aquifer, are estimated together with their respective resistivities. The estimations are made at an interval of 250 m, practically an appropriate distance at which dense data generation was carried out using the HTEM survey. The integrated approach generates the data in the no-fly area with the same spatial density as the flown area. With this, we achieved the goal of completing the 3-D aquifer mapping of the entire area with dense data at high spatial resolution. This is a unique finding to manage the handicapped situation in this HTEM surveys, and an aide to overcome such constraints with cost-effectiveness.
Project description:There is increasing interest to control or eradicate the major neglected tropical diseases. Accurate modelling of the geographic distributions of parasitic infections will be crucial to this endeavour. We used 664 community level infection prevalence data collated from the published literature in conjunction with eight environmental variables, altitude and population density, and a multivariate Bayesian generalized linear spatial model that allows explicit accounting for spatial autocorrelation and incorporation of uncertainty in input data and model parameters, to construct the first spatially-explicit map describing LF prevalence distribution in Africa. We also ran the best-fit model against predictions made by the HADCM3 and CCCMA climate models for 2050 to predict the likely distributions of LF under future climate and population changes. We show that LF prevalence is strongly influenced by spatial autocorrelation between locations but is only weakly associated with environmental covariates. Infection prevalence, however, is found to be related to variations in population density. All associations with key environmental/demographic variables appear to be complex and non-linear. LF prevalence is predicted to be highly heterogenous across Africa, with high prevalences (>20%) estimated to occur primarily along coastal West and East Africa, and lowest prevalences predicted for the central part of the continent. Error maps, however, indicate a need for further surveys to overcome problems with data scarcity in the latter and other regions. Analysis of future changes in prevalence indicates that population growth rather than climate change per se will represent the dominant factor in the predicted increase/decrease and spread of LF on the continent. We indicate that these results could play an important role in aiding the development of strategies that are best able to achieve the goals of parasite elimination locally and globally in a manner that may also account for the effects of future climate change on parasitic infection.
Project description:The world has now facing a health crisis due to outbreak of novel coronavirus 2019 (COVID-19). The numbers of infection and death have been rapidly increasing which result in a serious threat to the social and economic crisis. India as the second most populous nation of the world has also running with a serious health crisis, where more than 8,300,500 people have been infected and 123,500 deaths due to this deadly pandemic. Therefore, it is urgent to highlight the spatial vulnerability to identify the area under risk. Taking India as a study area, a geospatial analysis was conducted to identify the hotspot areas of the COVID-19. In the present study, four factors naming total population, population density, foreign tourist arrivals to India and reported confirmed cases of the COVID-19 were taken as responsible factors for detecting hotspot of the novel coronavirus. The result of spatial autocorrelation showed that all four factors considered for hotspot analysis were clustered and the results were statistically significant (p value < 0.01). The result of Getis-Ord Gi* statistics revealed that the total population and reported COVID-19 cases have got high priority for considering hotspot with greater z-score (> 3 and > 0.7295 respectively). The present analysis reveals that the reported cases of COVID-19 are higher in Maharashtra, followed by Tamil Nadu, Gujarat, Delhi, Uttar Pradesh, and West Bengal. The spatial result and geospatial methodology adopted for detecting COVID-19 hotspot in the Indian subcontinent can help implement strategies both at the macro and micro level. In this regard, social distancing, avoiding social meet, staying at home, avoiding public transport, self-quarantine and isolation are suggested in hotspot zones; together with, the international support is also required in the country to work jointly for mitigating the spread of COVID-19. Electronic supplementary material The online version of this article (10.1007/s41324-020-00375-1) contains supplementary material, which is available to authorized users.
Project description:The Umm er Radhuma (UER) Formation is a major karst aquifer in Saudi Arabia. This study investigated the hydraulic and petrophysical characteristics of the folded UER carbonate aquifer using integrated hydrological and geophysical logging datasets to understand its complex hydraulic setting as well as detect possible water flow. Petrophysical analysis showed that the UER aquifer has three zones with different lithologic and hydraulic properties. The upper zone attains the best properties with average values of 20%, >100 mD, 3.30 × 10-5-1.34 × 10-3 m/s, and 1.49 × 10-3-6.04 × 10-2 m2/s, with respect to effective porosity, permeability, hydraulic conductivity and transmissivity. The gamma-ray logs indicate a good fracture system near the upper zone of the UER Formation. Pumping test measurements of transmissivity, hydraulic conductivity and storage coefficients were matched with those from geophysical logs and found to be within the expected range for confined and leaky aquifers. Hydrogeological properties were mapped to detect possible groundwater flow in relation to the dominant structure. The underground water of the folded UER aquifer was forced along meandering flow patterns from W-E to SW-NE through the anticlinal axes. The integrated approach can be further used to enhance local aquifer models and improve strategies for identifying the most productive zones in similar aquifer systems.
Project description:The characteristics and determinants of health and disease are often organized in space, reflecting our spatially extended nature. Understanding the influence of such factors requires models capable of capturing spatial relations. Drawing on statistical parametric mapping, a framework for topological inference well established in the realm of neuroimaging, we propose and validate an approach to the spatial analysis of diverse clinical data-GeoSPM-based on differential geometry and random field theory. We evaluate GeoSPM across an extensive array of synthetic simulations encompassing diverse spatial relationships, sampling, and corruption by noise, and demonstrate its application on large-scale data from UK Biobank. GeoSPM is readily interpretable, can be implemented with ease by non-specialists, enables flexible modeling of complex spatial relations, exhibits robustness to noise and under-sampling, offers principled criteria of statistical significance, and is through computational efficiency readily scalable to large datasets. We provide a complete, open-source software implementation.