COVID-19 Flow-Maps an open geographic information system on COVID-19 and human mobility for Spain

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus, which has spread all over the world leading to a global pandemic. The fast progression of COVID-19 has been mainly related to the high contagion rate of the virus and the worldwide mobility of humans. In the absence of pharmacological therapies, governments from different countries have introduced several non-pharmaceutical interventions to reduce human mobility and social contact. Several studies based on Anonymized Mobile Phone Data have been published analysing the relationship between human mobility and the spread of coronavirus. However, to our knowledge, none of these data-sets integrates cross-referenced geo-localised data on human mobility and COVID-19 cases into one all-inclusive open resource. Herein we present COVID-19 Flow-Maps, a cross-referenced Geographic Information System that integrates regularly updated time-series accounting for population mobility and daily reports of COVID-19 cases in Spain at different scales of time spatial resolution. This integrated and up-to-date data-set can be used to analyse the human dynamics to guide and support the design of more effective non-pharmaceutical interventions.

Modeling of hydrocarbon systems and quantitative assessment of the hydrocarbon potential of Eastern Arctic seas

Background. Three prospective sedimentary complexes — Aptian-Upper Cretaceous, Paleogene and Neogene — are predicted in the waters of the Eastern Arctic seas. Here, the search for oil and gas is associated with harsh Arctic conditions at sea, as well as with high geological risks and significant expenditures under the conditions of poor knowledge of the region. In this regard, the localisation of prospecting drilling objects and the assessment of the geological risks of deposit discovery should be carried out.Aim. To assess geological risks and to determine the probability of discovering oil and gas fields, as well as to identify prospective areas for licensing and exploration in the water areas of the Eastern Arctic.Materials and methods. Structural and heat flow maps along with the results of geochemical analysis, as well as typical terrestrial sections were used as initial materials. Using the method of basin analysis, the modelling of generation-accumulation hydrocarbon systems (GAHS) and the quantitative assessment of its hydrocarbon potential in the Eastern Arctic water area was carried out. The assessment of geological risks and the probability of field discovery was performed using the conventional methodology widely applied by oil companies.Results. The GAHS modelling using a variation approach showed that, regardless of the kerogen type, with average values of Сorg in sediments, potential oil-and-gas source strata (OGSS) were capable of saturating the prospective objects with hydrocarbons. The “OGSS assessment” factor was determined as “encouraging” (0.7). Active geodynamic regime and the manifestation of several folding phases within the study area provided favourable conditions for the formation of anticlinaltraps in sedimentary basins. However, the cap rock quality rating was assessed as “neutral” (0.5). The overall risk for the “Trap assessment” factor was estimated based on the minimum criterion of 0.5.Conclusion. The most prospective areas recommended for licensing were selected, and the recommendations for further geological exploration work in these areas were given in order to clarify their hydrocarbon potential and reduce geological risks.

Review of the Heat Flow Mapping in Polish Sedimentary Basin across Different Tectonic Terrains

Heat flow patterns variability related to the age of the consolidated, and differences in, sedimentary thickness of the sedimentary succession are important constraints upon the thermal state of the sedimentary fill and its geothermal energy potential. Heat flow in the Permian basin of central Europe varies from a low of 40 mWm−2 in the Precambrian Platform to 80 mWm−2 in the Paleozoic basement platform influencing temperature for geothermal potential drilling depth. Continuity of thermal patterns and compatibility of heat flow Q across the Permian basin across the Polish–German basin was known from heat flow data ever since the first heat flow map of Europe in 1979. Both Polish and German heat flow determinations used lab-measured thermal conductivity on cores. This is not the case for the recent heat flow map of Poland published in 2009 widely referenced in Polish geological literature. Significant differences in heat flow magnitude exist between many historical heat flow maps of Poland over the 1970s–1990s and recent 21st century patterns. We find that the differences in heat flow values of some 20–30 mWm−2 in Western Poland exist between heat flow maps using thermal conductivity models using well log interpreted mineral and porosity content and assigned world averages of rock and fluid thermal conductivity versus those measured on cores. These differences in heat flow are discussed in the context of resulting mantle heat flow and the Lithosphere-Asthenosphere Boundary depth modelled differences and possible overestimates of deep thermal conditions for enhanced geothermal energy prospects in Poland.

ONE ARABESQUE IN THE SMALL WORLD OF OD WEBMAPS

Arabesque is an application for the exploration and geovisualisation of origin-destination flows (or spatial networks), developed within the framework of the Univ. Gustave Eiffel (ex. IFSTTAR)-funded research project geographic flow visualisation (gflowiz) geoflowiz, in collaboration with the CNRS. It allows both the exploration and the filtering of OD data and their representation, with a strong emphasis on geographic information layering and features' semiology. The key-objective is to propose an easy way to produce a modern cartography (a geovisualisation) of thematic flows (e.g. bilateral flow volume), at several geographic scales, even from your own datasets. The objective of this article is to position Arabesque in the range of geoweb applications for producing flow maps, by comparing its functionalities with those of similar web applications – Magrit, Kepler.gl, flowmap.blue – pointing out their respective advantages and limitations. The analysis of its functionalities is compared on the same flow dataset – MOBSCO, i.e. a dataset describing the school mobility of French pupils and students on a given year – for a practical and empirical “validation” of its contributions. We demonstrate that the configurations and appearances of these tools’ visual output depend largely on the culture of their developers, and on the use and audiences for which they have been developed.

Variation in colon cancer survival for patients living and receiving care in London, 2006–2013: does where you live matter?

BackgroundMarked geographical disparities in survival from colon cancer have been consistently described in England. Similar patterns have been observed within London, almost mimicking a microcosm of the country’s survival patterns. This evidence has suggested that the area of residence plays an important role in the survival from cancer.MethodsWe analysed the survival from colon cancer of patients diagnosed in 2006–2013, in a pre-pandemic period, living in London at their diagnosis and received care in a London hospital. We examined the patterns of patient pathways between the area of residence and the hospital of care using flow maps, and we investigated whether geographical variations in survival from colon cancer are associated with the hospital of care. To estimate survival, we applied a Bayesian excess hazard model which accounts for the hierarchical structure of the data.ResultsGeographical disparities in colon cancer survival disappeared once controlled for hospitals, and the disparities seemed to be augmented between hospitals. However, close examination of patient pathways revealed that the poorer survival observed in some hospitals was mostly associated with higher proportions of emergency diagnosis, while their performance was generally as expected for patients diagnosed through non-emergency routes.DiscussionThis study highlights the need to better coordinate primary and secondary care sectors in some areas of London to improve timely access to specialised clinicians and diagnostic tests. This challenge remains crucially relevant after the recent successive regroupings of Clinical Commissioning Groups (which grouped struggling areas together) and the observed exacerbation of disparities during the COVID-19 pandemic.

Investigating Optimal Training and Uncertainty Quantification for CNN-based Optical Flow

Optical Flow (OF) techniques provide “dense estimation” flow maps (i.e. pixel-level resolution) of timecorrelated images and thus are appealing to applications requiring high spatial resolutions. OF methods revolve around mathematical descriptions of the image as a collection of features, in which the pixel-level light intensity is the primary variable (Horn and Schunck, 1981). Feature tracking often involves the notion of scale invariance. Traditional OF approaches, merely based on mathematical formulations, have suffered from many challenges, especially when directly applied to images of fluid flows textured with tracer particles (hereafter PIV-like images). Due to the limited number of computationally manageable features and suboptimal regularization methods, successful implementation of past approaches has been limited to highly textured images and small displacement dynamic ranges.

A spatiotemporal approach for social media sentiment analysis

The rapid growth of user-generated unstructured data through social media has raised several challenges and research opportunities. These data constitute a rich source of information for sentiment analysis and help the understanding of spontaneously expressed opinions. In the past few years, many scientific proposals have addressed sentiment analysis issues. However, most of them do not take into account both spatial and temporal dimensions, which would enable a more accurate analysis. To the best of our knowledge, this approach has not received much attention in the literature. In this article, we formalized a spatiotemporal sentiment analysis technique and applied this technique to a case study of tweets about the FIFA 2014 World Cup. Our approach exploits the summarization of sentiment analysis using the spatial and temporal dimensions and automatically generates opinion change flow maps through both dimensions. The results enable the tracking of opinion change flow maps through spatial and temporal analysis.

Automated Generation of Cerebral Blood Flow Maps Using Deep Learning and Multiple Delay Arterial Spin-Labelled MRI

The purpose of this work was to develop and evaluate a deep learning approach for estimation of cerebral blood flow (CBF) and arterial transit time (ATT) from multiple post-label delay (PLD) arterial spin-labelled (ASL) MRI. Six-PLD ASL MRI was acquired on a 1.5T or 3T system among 99 older males and females with and without cognitive impairment. We trained and compared two network architectures: standard feed-forward convolutional neural network (CNN) and U-Net. Mean absolute error (MAE) was evaluated between model estimates and ground truth obtained through conventional processing. The best-performing model was re-trained on inputs with missing PLDs to investigate generalizability to different PLD schedules. Relative to the CNN, the U-Net yielded lower MAE on training data. On test data, the U-Net MAE was 8.4±1.4 ml/100g/min for CBF and 0.22±0.09 s for ATT. Model uncertainty, estimated with Monte Carlo dropout, was associated with model error. Network estimates remained stable when tested on inputs with up to three missing PLD images. Mean processing times were: U-Net pipeline = 10.77s; ground truth pipeline = 10min 41s. These results suggest hemodynamic parameter estimation from 1.5T and 3T multi-PLD ASL MRI is feasible and fast with a deep learning image-generation approach.