High Spatial Granularity Research Articles

Routes of importation and spatial dynamics of SARS-CoV-2 variants during localized interventions in Chile.

Human mobility is strongly associated with the spread of SARS-CoV-2 via air travel on an international scale and with population mixing and the number of people moving between locations on a local scale. However, these conclusions are drawn mostly from observations in the context of the global north where international and domestic connectivity is heavily influenced by the air travel network; scenarios where land-based mobility can also dominate viral spread remain understudied. Furthermore, research on the effects of nonpharmaceutical interventions (NPIs) has mostly focused on national- or regional-scale implementations, leaving gaps in our understanding of the potential benefits of implementing NPIs at higher granularity. Here, we use Chile as a model to explore the role of human mobility on disease spread within the global south; the country implemented a systematic genomic surveillance program and NPIs at a very high spatial granularity. We combine viral genomic data, anonymized human mobility data from mobile phones and official records of international travelers entering the country to characterize the routes of importation of different variants, the relative contributions of airport and land border importations, and the real-time impact of the country's mobility network on the diffusion of SARS-CoV-2. The introduction of variants which are dominant in neighboring countries (and not detected through airport genomic surveillance) is predicted by land border crossings and not by air travelers, and the strength of connectivity between comunas (Chile's lowest administrative divisions) predicts the time of arrival of imported lineages to new locations. A higher stringency of local NPIs was also associated with fewer domestic viral importations. Our analysis sheds light on the drivers of emerging respiratory infectious disease spread outside of air travel and on the consequences of disrupting regular movement patterns at lower spatial scales.

Seasonal forecast of two-meter temperature and precipitation in Tanzania: A hybrid cluster and point-by-point machine learning approach

Abstract In seasonal weather forecasting, the exact location within a study area determines the relationship between local predicted variables and global predictors. Both dynamic models and machine-learning approaches that define models for single grid points, can determine these relationships with high spatial granularity, but at high computational cost. To avoid the latter, clustering of predicted variables is often used in machine-learning approaches, which however sacrifices geographical resolution. In this paper, we present a machine-learning approach that is a hybrid between grid-point and cluster-based approaches (finres_S2S). This approach preserves geographical resolution, but at low computational cost, and is tested for monthly two-meter temperature and precipitation in Tanzania, and for a lead time of up to 6 months. The finres_S2S approach has a number of advantages over both the cluster and point approaches, including that of perform well compared to a commonly used forecasting dataset from a dynamic model. We find that the dominant predictors for the application area are associated with the El Nino Southern Oscillation, the Madden-Julian-Oscillation and the Indian Ocean Dipole.

A data-driven approach for estimating travel-related carbon emissions with high spatial and temporal granularity

A data-driven approach for estimating travel-related carbon emissions with high spatial and temporal granularity

Web-based Dashboard on ECMO Utilization in Germany: An Interactive Visualization, Analyses, and Prediction Based on Real-life Data

In Germany, a comprehensive reimbursement policy for extracorporeal membrane oxygenation (ECMO) results in the highest per capita use worldwide, although benefits remain controversial. Public ECMO data is unstructured and poorly accessible to healthcare professionals, researchers, and policymakers. In addition, there are no uniform policies for ECMO allocation which confronts medical personnel with ethical considerations during health crises such as respiratory virus outbreaks.Retrospective information on adult and pediatric ECMO support performed in German hospitals was extracted from publicly available reimbursement data and hospital quality reports and processed to create the web-based ECMO Dashboard built on Open-Source software. Patient-level and hospital-level data were merged resulting in a solid base for ECMO use analysis and ECMO demand forecasting with high spatial granularity at the level of 413 county and city districts in Germany.The ECMO Dashboard (https://www.ecmo-dash.de/), an innovative visual platform, presents the retrospective utilization patterns of ECMO support in Germany. It features interactive maps, comprehensive charts, and tables, providing insights at the hospital, district, and national levels. This tool also highlights the high prevalence of ECMO support in Germany and emphasizes districts with ECMO surplus – where patients from other regions are treated, or deficit – origins from which ECMO patients are transferred to other regions. The dashboard will evolve iteratively to provide stakeholders with vital information for informed and transparent resource allocation and decision-making.Accessible public routine data could support evidence-informed, forward-looking resource management policies, which are urgently needed to increase the quality and prepare the critical care infrastructure for future pandemics.

Informing Urban Flood Risk Adaptation by Integrating Human Mobility Big Data During Heavy Precipitation.

Understanding the impact of heavy precipitation on human mobility is critical for finer-scale urban flood risk assessment and achieving sustainable development goals #11 to build resilient and safe cities. Using ∼2.6 million mobile phone signal data collected during the summer of 2018 in Jiangsu, China, this study proposes a novel framework to assess human mobility changes during rainfall events at a high spatial granularity (500 m grid cell). The fine-scale mobility map identifies spatial hotspots with abnormal clustering or reduced human activities. When aggregating to the prefecture-city level, results show that human mobility changes range between -3.6 and 8.9%, revealing varied intracity movement across cities. Piecewise structural equation modeling analysis further suggests that city size, transport system, and crowding level directly affect mobility responses, whereas economic conditions influence mobility through multiple indirect pathways. When overlaying a historical urban flood map, we find such human mobility changes help 23 cities reduce 2.6% flood risks covering 0.45 million people but increase a mean of 1.64% flood risks in 12 cities covering 0.21 million people. The findings help deepen our understanding of the mobility pattern of urban dwellers after heavy precipitation events and foster urban adaptation by supporting more efficient small-scale hazard management.

A CMOS-MEMS Pixel Sensor for Thermal Neutron Imaging.

A monolithic pixel sensor with high spatial granularity (35 × 40 μm2) is presented, aiming at thermal neutron detection and imaging. The device is made using the CMOS SOIPIX technology, with Deep Reactive-Ion Etching post-processing on the backside to obtain high aspect-ratio cavities that will be filled with neutron converters. This is the first monolithic 3D sensor ever reported. Owing to the microstructured backside, a neutron detection efficiency up to 30% can be achieved with a 10B converter, as estimated by the Geant4 simulations. Each pixel includes circuitry that allows a large dynamic range and energy discrimination and charge-sharing information between neighboring pixels, with a power dissipation of 10 µW per pixel at 1.8 V power supply. The initial results from the experimental characterization of a first test-chip prototype (array of 25 × 25 pixels) in the laboratory are also reported, dealing with functional tests using alpha particles with energy compatible with the reaction products of neutrons with the converter materials, which validate the device design.

MOO-GAPS: A multi-objective optimization model for global animal production and sustainability

Agricultural and livestock production involves significant trade-offs between multiple sustainable development goals, including reducing hunger and poverty, and reducing emissions of greenhouse gasses. Here we describe a multi-objective optimization tool for livestock production to evaluate trade-offs among environmental and economic objectives with high spatial granularity and the capability to be aggregated to national or global scales. We illustrate the use of this tool analysing how and where to produce beef to optimize the weighted sum of environmental and economic objectives, in this case minimizing greenhouse gas emissions and minimizing costs of production. We present how the outputs of this model can inform a sustainable transition of the industry and policy decisions, with a focus on inherent trade-offs. By comparing optimal production with current production, we highlight the potential environmental and economic efficiencies that can be made by changing the location or methods of production. As such, this tool provides a platform to identify trade-offs and synergies among multiple diverse sustainability goals, critical in the livestock and agricultural sectors.

High spatial granularity residential heating load forecast based on Dendrite net model

With the application of smart meters, more information is available from residential buildings for support heat load forecast. Yet, there is still a lack of an effective method to exploit the value of the high spatial granularity information, particularly for residential communities with high randomness in human behaviors. To fill this gap, this paper proposes a data-driven heat load forecast method based on the field measurements of smart meters A white-box machine learning algorithm, namely Dendritic Network, is employed to aggregate and analyze data obtained from different locations of district heating systems. An online correction mechanism is then proposed to regulate the forecast horizon and ensure the adaptiveness of the machine learning model under different scenarios. The results demonstrate that the dendritic network employed in the proposed machine learning method shows higher forecast accuracy, the root-mean-square error is 0.0029, in some research cases the coefficient of determination can reach 0.99–1, and wide scope of application in the area of heat load forecast.

Population disruption: observational study of changes in the population distribution of the UK during the COVID-19 pandemic

Background: Mobility data have demonstrated major changes in human movement patterns in response to COVID-19 and associated interventions in many countries. This involves sub-national redistribution, short-term relocations, and international migration. Aggregated mobile phone location data combined with small-area census population data allow changes in the population distribution of the UK to be quantified with high spatial and temporal granularity. Methods: In this paper, we combine detailed data from Facebook, measuring the location of approximately 6 million daily active Facebook users in 5km2 tiles in the UK with census-derived population estimates to measure population mobility and redistribution. We provide time-varying population estimates and assess spatial population changes with respect to population density and four key reference dates in 2020 (first UK lockdown, end of term, beginning of term, Christmas). Results: We show how population estimates derived from Facebook data vary compared to mid-2020 small area population estimates by UK national statistics agencies. We also estimate that between March 2020 and March 2021, the total population of the UK declined and we identify important spatial variations in this population change, showing that low-density areas have experienced lower population decreases than urban areas. We estimate that, for the top 10% highest population tiles, the population has decreased by 6.6%. Finally, we provide evidence that geographic redistributions of population within the UK coincide with dates of non-pharmaceutical interventions including lockdowns and movement restrictions, as well as seasonal patterns of migration around holiday dates. Conclusions: The methods used in this study reveal significant changes in population distribution at high spatial and temporal resolutions that have not previously been quantified by available demographic surveys in the UK. We found early indicators of potential longer-term changes in the population distribution of the UK although it is not clear if these changes will persist after the COVID-19 pandemic.

Development and characterization of a novel alpha particle SOI pixel sensor for neutron detection

Semiconductor sensors for neutron detection have been initially proposed as direct replacement of 3He detectors but have recently raised interest in imaging systems. We have developed a monolithic sensor with high spatial granularity in SOIPIX technology for the detection of alpha particles of energy compatible with the reaction products of neutrons with typical converter materials. The chipset is composed by the main pixel matrix and the test structures needed for a better understanding of the chip functionality and for studying the electronic tuning strategies. The fabricated prototypes have been recently delivered and the first tests under alpha source have been performed. In this paper we will present the proposed devices and the results of the experimental characterization.

Measuring sustainable tourism with online platform data

Sustainability in tourism is a topic of global relevance, finding multiple mentions in the United Nations Sustainable Development Goals. The complex task of balancing tourism’s economic, environmental, and social effects requires detailed and up-to-date data. This paper investigates whether online platform data can be employed as an alternative data source in sustainable tourism statistics. Using a web-scraped dataset from a large online tourism platform, a sustainability label for accommodations can be predicted reasonably well with machine learning techniques. The algorithmic prediction of accommodations’ sustainability using online data can provide a cost-effective and accurate measure that allows to track developments of tourism sustainability across the globe with high spatial and temporal granularity.

The Missing Millions in Maps: Exploring Causes of Uncertainties in Global Gridded Population Datasets

Gridded population datasets model the population at a relatively high spatial and temporal granularity by reallocating official population data from irregular administrative units to regular grids (e.g., 1 km grid cells). Such population data are vital for understanding human–environmental relationships and responding to many socioeconomic and environmental problems. We analyzed one very broadly used gridded population layer (GHS-POP) to assess its capacity to capture the distribution of population counts in several urban areas, spread across the major world regions. This analysis was performed to assess its suitability for global population modelling. We acquired the most detailed local population data available for several cities and compared this with the GHS-POP layer. Results showed diverse error rates and degrees depending on the geographic context. In general, cities in High-Income (HIC) and Upper-Middle-Income Countries (UMIC) had fewer model errors as compared to cities in Low- and Middle-Income Countries (LMIC). On a global average, 75% of all urban spaces were wrongly estimated. Generally, in central mixed or non-residential areas, the population was overestimated, while in high-density residential areas (e.g., informal areas and high-rise areas), the population was underestimated. Moreover, high model uncertainties were found in low-density or sparsely populated outskirts of cities. These geographic patterns of errors should be well understood when using population models as an input for urban growth models, as they introduce geographic biases.

Real-time, spatial decision support to optimize malaria vector control: The case of indoor residual spraying on Bioko Island, Equatorial Guinea.

Public health interventions require evidence-based decision-making to maximize impact. Spatial decision support systems (SDSS) are designed to collect, store, process and analyze data to generate knowledge and inform decisions. This paper discusses how the use of a SDSS, the Campaign Information Management System (CIMS), to support malaria control operations on Bioko Island has impacted key process indicators of indoor residual spraying (IRS): coverage, operational efficiency and productivity. We used data from the last five annual IRS rounds (2017 to 2021) to estimate these indicators. IRS coverage was calculated as the percentage of houses sprayed per unit area, represented by 100x100 m map-sectors. Optimal coverage was defined as between 80% and 85%, and under and overspraying as coverage below 80% and above 85%, respectively. Operational efficiency was defined as the fraction of map-sectors that achieved optimal coverage. Daily productivity was expressed as the number of houses sprayed per sprayer per day (h/s/d). These indicators were compared across the five rounds. Overall IRS coverage (i.e. percent of total houses sprayed against the overall denominator by round) was highest in 2017 (80.2%), yet this round showed the largest proportion of oversprayed map-sectors (36.0%). Conversely, despite producing a lower overall coverage (77.5%), the 2021 round showed the highest operational efficiency (37.7%) and the lowest proportion of oversprayed map-sectors (18.7%). In 2021, higher operational efficiency was also accompanied by marginally higher productivity. Productivity ranged from 3.3 h/s/d in 2020 to 3.9 h/s/d in 2021 (median 3.6 h/s/d). Our findings showed that the novel approach to data collection and processing proposed by the CIMS has significantly improved the operational efficiency of IRS on Bioko. High spatial granularity during planning and deployment together with closer follow-up of field teams using real-time data supported more homogeneous delivery of optimal coverage while sustaining high productivity.

Where Opioid Overdose Patients Live Far From Treatment: Geospatial Analysis of Underserved Populations in New York State.

BackgroundOpioid addiction and overdose have a large burden of disease and mortality in New York State (NYS). The medication naloxone can reverse an overdose, and buprenorphine can treat opioid use disorder. Efforts to increase the accessibility of both medications include a naloxone standing order and a waiver program for prescribing buprenorphine outside a licensed drug treatment program. However, only a slim majority of NYS pharmacies are listed as participating in the naloxone standing order, and less than 7% of prescribers in NYS have a buprenorphine waiver. Therefore, there is a significant opportunity to increase access.ObjectiveIdentifying the geographic regions of NYS that are farthest from resources can help target interventions to improve access to naloxone and buprenorphine. To maximize the efficiency of such efforts, we also sought to determine where these underserved regions overlap with the largest numbers of actual patients who have experienced opioid overdose.MethodsWe used address data to assess the spatial distribution of naloxone pharmacies and buprenorphine prescribers. Using the home addresses of patients who had an opioid overdose, we identified geographic locations of resource deficits. We report findings at the high spatial granularity of census tracts, with some neighboring census tracts merged to preserve privacy.ResultsWe identified several hot spots, where many patients live far from the nearest resource of each type. The highest density of patients in areas far from naloxone pharmacies was found in eastern Broome county. For areas far from buprenorphine prescribers, we identified subregions of Oswego county and Wayne county as having a high number of potentially underserved patients.ConclusionsAlthough NYS is home to thousands of naloxone pharmacies and potential buprenorphine prescribers, access is not uniform. Spatial analysis revealed census tract areas that are far from resources, yet contain the residences of many patients who have experienced opioid overdose. Our findings have implications for public health decision support in NYS. Our methods for privacy can also be applied to other spatial supply-demand problems involving sensitive data.

Estimating Next Day’s Forest Fire Risk via a Complete Machine Learning Methodology

Next day wildfire prediction is an open research problem with significant environmental, social, and economic impact since it can produce methods and tools directly exploitable by fire services, assisting, thus, in the prevention of fire occurrences or the mitigation of their effects. It consists in accurately predicting which areas of a territory are at higher risk of fire occurrence each next day, exploiting solely information obtained up until the previous day. The task’s requirements in spatial granularity and scale of predictions, as well as the extreme imbalance of the data distribution render it a rather demanding and difficult to accurately solve the problem. This is reflected in the current literature, where most existing works handle a simplified or limited version of the problem. Taking into account the above problem specificities, in this paper, we present a machine learning methodology that effectively (sensitivity > 90%, specificity > 65%) and efficiently performs next day fire prediction, in rather high spatial granularity and in the scale of a country. The key points of the proposed approach are summarized in: (a) the utilization of an extended set of fire driving factors (features), including topography-related, meteorology-related and Earth Observation (EO)-related features, as well as historical information of areas’ proneness to fire occurrence; (b) the deployment of a set of state-of-the-art classification algorithms that are properly tuned/optimized on the setting; (c) two alternative cross-validation schemes along with custom validation measures that allow the optimal and sound training of classification models, as well as the selection of different models, in relation to the desired trade-off between sensitivity (ratio of correctly identified fire areas) and specificity (ratio of correctly identified non-fire areas). In parallel, we discuss pitfalls, intuitions, best practices, and directions for further investigation derived from our analysis and experimental evaluation.

Dropped in the Ocean – 87Sr/86Sr as a provenance tool for ice-rafted Arctic driftwood

Provenance and age determinations of driftwood provide insights into Holocene Arctic Ocean surface currents and sea ice dynamics, with detailed reconstructions requiring a provenance methodology with fine temporal and spatial resolution. Determination of wood geographical provenance by genus data is limited by its spatially coarse resolution, while provenance by dendrochronological crossdating is reliant on available boreal forest reference chronologies which are abundant for recent centuries but more sparse for older periods of the Holocene. We present the development of novel techniques to refine the provenance of driftwood through radiogenic isotopic analysis ( 87 Sr/ 86 Sr). The use of geochemical techniques addresses limitations of current methods and opens the possibility of defining the role of atmospheric and oceanic circulation in sea ice and climatic changes throughout the Holocene at a finer spatial resolution than currently possible. This study investigates and develops geochemical 87 Sr/ 86 Sr fingerprinting of Arctic driftwood. To this end, it analyses driftwood samples from northern Svalbard and compares this technique with provenance regions obtained through dendrochronology, and with modelled and measured global 87 Sr/ 86 Sr reference databases. We conclude that the utilisation of 87 Sr/ 86 Sr ratios to establish provenance for Arctic driftwood has some potential, but identify important limitations in the method, concerning both the signal in samples and reference values required for provenance. Increased sample populations and source samples for calibration, as well as methodological improvements that address the likely overprinting linked to driftwood transport, are recommended to build upon this work. We conclude that at present dendro-provenancing continues to be the most powerful method to study sea ice dynamics from Arctic driftwood and suggest that given the high spatial granularity of 87 Sr/ 86 Sr across the boreal forest, this technique might only be usable in combination with previously developed provenance tools and reconstructions. This study provides a first step in the use of radiogenic isotopic analysis in a multi-proxy reconstruction of Holocene driftwood incursion onto high Arctic shorelines. • Driftwood is a novel tool for insights into Holocene Arctic sea ice dynamics. • Fine spatial and temporal resolution of driftwood provenance is needed. • Geochemical 87 Sr/ 86 Sr fingerprinting of Arctic driftwood is explored in this study. • 87 Sr/ 86 Sr shows some promise, with limitations in reference data and sample signals. • We conclude that a combined approach to driftwood provenance shows the most promise.

Predicting traffic noise using land-use regression\u2014a scalable approach

BackgroundIn modern societies, noise is ubiquitous. It is an annoyance and can have a negative impact on human health as well as on the environment. Despite increasing evidence of its negative impacts, spatial knowledge about noise distribution remains limited. Up to now, noise mapping is frequently inhibited by the necessary resources and therefore limited to selected areas.ObjectiveBased on the assumption, that prevalent noise is determined by the arrangement of sources and the surrounding environment in which the sound propagates, we build a geostatistical model representing these parameters. Aiming for a large-scale noise mapping approach, we utilize publicly available data, context-aware feature engineering and a linear land-use regression (LUR) model.MethodsCompliant to the European Noise Directive 2002/49/EG, we work at a high spatial granularity of 10 × 10-m resolution. As reference, we use the day–evening–night noise level indicator Lden. Therewith, we carry out 2000 virtual field campaigns simulating different sampling schemes and introduce spatial cross-validation concepts to test the transferability to new areas.ResultsThe experimental results suggest the necessity for more than 500 samples stratified over the different noise levels to produce a representative model. Eventually, using 21 selected variables, our model was able to explain large proportions of the yearly averaged road noise (Lden) variability (R2 = 0.702) with a mean absolute error of 4.24 dB(A), 3.84 dB(A) for build-up areas, respectively. In applying this best performing model for an area-wide prediction, we spatially close the blank spots in existing noise maps with continuous noise levels for the entire range from 24 to 106 dB(A).SignificanceThis data is new, particular for small communities that have not been mapped sufficiently in Europe so far. In conjunction, our findings also supplement conventionally sampled studies using physical microphones and spatially blocked cross-validations.

A nested drone-satellite approach to monitoring the ecological conditions of wetlands

Monitoring wetlands is necessary in order to understand and protect their ecohydrological balance. In Ireland, traditionally wetland-monitoring is carried out by manual field visits which can be very time-consuming. To automate the process, this study extends the ability of remote sensing-based monitoring of wetlands by combining RGB image processing, machine learning algorithms, and satellite data analysis to create seasonal maps of vegetation communities within the wetlands. The methodology matches multispectral and broad coverage of open-source Sentinel-2 (S2) imagery with the high spatial granularity of Unmanned Aerial Vehicles (UAV) or drone images. Single sensor drone imagery was captured, colour corrected and classified using random forest (RF) classifier for a subset of the wetland. The classified imagery was upsampled to satellite imagery scale to create training data for vegetation-segmentation in the entire wetland. The process was repeated for multiple seasons, and an annual map was created utilising the majority voting. The proposed framework has been evaluated on various wetlands across Ireland, with results presented herein for an ombrotrophic peatland complex, Clara Bog. The accuracy of the maps was checked utilising a set of area-based performance metric. The application of this method thereby reduces the number of field surveys typically required to assess the long-term ecological change of such wetland habitats. The performance of the proposed method demonstrates that the technique is a robust, quick, and cost-effective way to map wetland habitats seasonally and to explore their ecohydrological synergies.

The contribution of telco data to fight the COVID-19 pandemic: Experience of Telefonica throughout its footprint

AbstractThe COVID-19 pandemic is a global challenge for humanity, in which a large number of resources are invested to develop effective vaccines and treatments. At the same time, governments try to manage the spread of the disease while alleviating the strong impact derived from the slowdown in economic activity. Governments were forced to impose strict lockdown measures to tackle the pandemic. This significantly changed people’s mobility and habits, subsequently impacting the economy. In this context, the availability of tools to effectively monitor and quantify mobility was key for public institutions to decide which policies to implement and for how long. Telefonica has promoted different initiatives to offer governments mobility insights throughout many of the countries where it operates in Europe and Latin America. Mobility indicators with high spatial granularity and frequency of updates were successfully deployed in different formats. However, Telefonica faced many challenges (not only technical) to put these tools into service in a short timing: from reducing latency in insights to ensuring the security and privacy of information. In this article, we provide details on how Telefonica engaged with governments and other stakeholders in different countries as a response to the pandemic. We also cover the challenges faced and the shared learnings from Telefonica’s experience in those countries.

Participatory Air Quality and Urban Heat Islands Monitoring System

The widespread use of low-cost environmental monitoring systems, together with recent developments in the design of Internet of Things architectures and protocols, has given new impetus to smart city applications. Such progress should, in particular, considerably improve the fine characterization of a wide range of physical quantities within our cities. Indeed, the cost-effectiveness of these emerging sensors combined with their reduced size allows for high-density deployments resulting in higher spatial granularity. In this article, we briefly present the 3M’Air project that aims to explore the potential of participatory citizen measures using low-cost sensors in order to improve the local knowledge of air quality and temperature and then bridge the gap between individual exposure and regional measurements. We then present the design, implementation, and evaluation of our low-cost, small-size wireless sensor network (WSN)-based participatory monitoring system. This system is composed of mobile sensing nodes measuring temperature, humidity, and a number of pollutants (NO2, PM1, PM2.5, and PM10). The collected data are sent to a server for analysis and building temperature and air quality maps. To validate our platform, we have carried out multiple tests to compare our sensor nodes to reference stations and to each other. We have also evaluated the energy consumption of our nodes under different configurations. The results are satisfactory and show that our nodes can be used in environmental participatory monitoring.