scholarly journals Activity Space Maps: a novel human mobility data set for quantifying time spent at risk

2021 ◽  
Author(s):  
Daniel T Citron ◽  
Shankar Iyer ◽  
Robert C Reiner ◽  
David L Smith
Keyword(s):  
Human Mobility ◽  
Epidemiological Data ◽  
Global Scale ◽  
Activity Space ◽  
Data Sets ◽  
Data Set ◽  
Epidemiological Modeling ◽  
Vector Borne Diseases ◽  
Geographic Space ◽  
Mobility Data

Activity Space Maps are a novel global-scale movement and mobility data set which describes how people distribute their time through geographic space. The maps are intended for use by researchers for the purposes of epidemiological modeling. Activity Space Maps are designed to complement existing digitally-collected mobility data sets by quantifying the amount of time that people spend in different locations. This information is important for estimating the duration of contact with the environment and the potential risk of exposure to disease. More concretely, the type of information contained in Activity Space Maps will make it easier to model the spatial transmission patterns of vector-borne diseases like malaria and Dengue fever. We will discuss the motivation for designing Activity Space Maps, how the maps are generated from mobile phone user app location history data, and discuss an example use case demonstrating how such data may be used together with spatial epidemiological data to advance our understanding of spatial disease patterns and the relationship between travel behaviors and infection risk.

scholarly journals Human mobility data from the BBC Pandemic project

2021 ◽  
Author(s):  
Andrew JK Conlan ◽  
Petra Klepac ◽  
Adam J Kucharski ◽  
Stephen Kissler ◽  
Maria L Tang ◽  
...  
Keyword(s):  
Human Mobility ◽  
Data Sets ◽  
Mobility Patterns ◽  
Administrative District ◽  
Data Set ◽  
Public Science ◽  
Mobility Data ◽  
The United Kingdom ◽  
Television Documentary ◽  
Gps Trajectories

AbstractWe present human mobility data for the United Kingdom collected from the “BBC Pandemic”, a public science project linked to the BBC Four television documentary of the same name. Mobile phone GPS trajectories submitted by users and collected over a 24 hour period were aggregated to construct anonymised origin-destination flux matrices at the local administrative district (LAD). We use these data to explore how mobility patterns change with age and employment status - unique stratifications that are not available from other publicly and privately held mobility data sets. We validate the consistency of the aggregated BBC mobility data set against census workflow data and illustrate how the systematic differences in mobility rates with age affect the risk and pattern of transmission between regions with 18-30 year old’s contributing the greatest risk of transmission to adjacent regions, but older 60-100 years playing the most important role in more remote low-density locations.

Unsupervised Learning for Human Mobility Behaviors

2021 ◽  
Author(s):  
Siyuan Liu ◽  
Shaojie Tang ◽  
Jiangchuan Zheng ◽  
Lionel M. Ni
Keyword(s):  
Unsupervised Learning ◽  
Human Mobility ◽  
Data Sets ◽  
Individual Data ◽  
Context Reasoning ◽  
Mobility Data ◽  
Temporal And Spatial ◽  
Mobile Context ◽  
Location Sensing ◽  
Spatial Sparsity

Learning human mobility behaviors from location-sensing data are crucial to mobility data mining because of its potential to address a range of analytical purposes in mobile context reasoning, including exploration, inference, and prediction. However, existing approaches suffer from two practical problems: temporal and spatial sparsity. To address these shortcomings, we present two unsupervised learning methods to model the mobility behaviors of multiple users (i.e., a population), considering efficiency and accuracy. These methods intelligently overcome the sparsity in individual data by seeking temporal commonality among users’ heterogeneous location behaviors. The advantages of our models are highlighted through experiments on several real-world mobility data sets, which also show how our methods can realize the three analytical purposes in a unified manner.

scholarly journals Global validation of improved SCIAMACHY scientific ozone limb data using ozonesonde measurements

2015 ◽  
Vol 8 (5) ◽  
pp. 4817-4858
Author(s):  
J. Jia ◽  
A. Rozanov ◽  
A. Ladstätter-Weißenmayer ◽  
J. P. Burrows
Keyword(s):  
Global Scale ◽  
Data Sets ◽  
Vertical Profiles ◽  
Data Set ◽  
Latitude Range ◽  
Ozone Profile ◽  
The Tropics ◽  
Relative Differences ◽  
Significant Underestimation ◽  
Good Agreement

Abstract. In this manuscript, the latest SCIAMACHY limb ozone scientific vertical profiles, namely the current V2.9 and the upcoming V3.0, are extensively compared with ozone sonde data from the WOUDC database. The comparisons are made on a global scale from 2003 to 2011, involving 61 sonde stations. The retrieval processors used to generate V2.9 and V3.0 data sets are briefly introduced. The comparisons are discussed in terms of vertical profiles and stratospheric partial columns. Our results indicate that the V2.9 ozone profile data between 20–30 km is in good agreement with ground based measurements with less than 5% relative differences in the latitude range of 90° S–40° N (with exception of the tropical Pacific region where an overestimation of more than 10% is observed), which corresponds to less than 5 DU partial column differences. In the tropics the differences are within 3%. However, this data set shows a significant underestimation northwards of 40° N (up to ~15%). The newly developed V3.0 data set reduces this bias to below 10% while maintaining a good agreement southwards of 40° N with slightly increased relative differences of up to 5% in the tropics.

scholarly journals Near real-time adjusted reanalysis forcing data for hydrology

2017 ◽  
Author(s):  
Peter Berg ◽  
Chantal Donnelly ◽  
David Gustafsson
Keyword(s):  
Real Time ◽  
Arctic Region ◽  
Analysis Data ◽  
Global Scale ◽  
Current Data ◽  
The Arctic ◽  
Data Sets ◽  
Initial State ◽  
Time Step ◽  
Data Set

Abstract. Updating climatological forcing data to near current data are compelling for impact modelling, e.g. to update model simulations or to simulate recent extreme events. Hydrological simulations are generally sensitive to bias in the meteorological forcing data, especially relative to the data used for the calibration of the model. The lack of daily resolution data at a global scale has previously been solved by adjusting re-analysis data global gridded observations. However, existing data sets of this type have been produced for a fixed past time period, determined by the main global observational data sets. Long delays between updates of these data sets leaves a data gap between present and the end of the data set. Further, hydrological forecasts require initialisations of the current state of the snow, soil, lake (and sometimes river) storage. This is normally conceived by forcing the model with observed meteorological conditions for an extended spin-up period, typically at a daily time step, to calculate the initial state. Here, we present a method named GFD (Global Forcing Data) to combine different data sets in order to produce near real-time updated hydrological forcing data that are compatible with the products covering the climatological period. GFD resembles the already established WFDEI method (Weedon et al., 2014) closely, but uses updated climatological observations, and for the near real-time it uses interim products that apply similar methods. This allows GFD to produce updated forcing data including the previous calendar month around the 10th of each month. We present the GFD method and different produced data sets, which are evaluated against the WFDEI data set, as well as with hydrological simulations with the HYPE model over Europe and the Arctic region. We show that GFD performs similarly to WFDEI and that the updated period significantly reduces the bias of the reanalysis data, although less well for the last two months of the updating cycle. For real-time updates until the current day, extending GFD with operational meteorological forecasts, a large drift is present in the hydrological simulations due to the bias of the meteorological forecasting model.

scholarly journals Sporadic <i>E</i> signatures revealed from multi-satellite radio occultation measurements

2010 ◽  
Vol 8 ◽  
pp. 225-230 ◽  
Author(s):  
C. Arras ◽  
C. Jacobi ◽  
J. Wickert ◽  
S. Heise ◽  
T. Schmidt
Keyword(s):  
Radio Occultation ◽  
Signal Amplitude ◽  
Global Scale ◽  
Data Sets ◽  
Data Set ◽  
Huge Data ◽  
Large Correlation ◽  
Sporadic E Layers ◽  
E Region ◽  
Sporadic E

Abstract. Radio occultation measurements performed by the satellites CHAMP, GRACE and FORMOSAT-3/COSMIC provide a huge data set for atmospheric and ionospheric investigations on a global scale. The data sets are used to extract information on sporadic E layers appearing in the lower ionospheric E region. This is done by analyzing signal amplitude variations of the GPS L1 signal. Sporadic E altitudes in northern midlatitudes found from radio occultation measurements are compared with ground-based ionosonde soundings. A large correlation of sporadic E altitudes from these two techniques is found.

scholarly journals Analysis of Collective Response Reveals that COVID-19-Related Activities Start From the End of 2019 in Mainland China

2020 ◽  
Author(s):  
Ji Liu ◽  
Tongtong Huang ◽  
Haoyi Xiong ◽  
Jizhou Huang ◽  
Jingbo Zhou ◽  
...  
Keyword(s):  
Early Stage ◽  
Human Mobility ◽  
Mainland China ◽  
Global Scale ◽  
Time Warping ◽  
Mobility Data ◽  
Collective Response ◽  
Cross Correlation Analysis ◽  
The Usa ◽  
Dynamic Time

While the COVID-19 outbreak is making an impact at a global scale, the collective response to the pandemic becomes the key to analyzing past situations, evaluating current measures, and formulating future predictions. In this paper, we analyze the public reactions to the pandemic using search engine data and mobility data from Baidu Search and Baidu Maps respectively, where we particularly pay attentions to the early stage of pandemics and find early signals from the collective response to COVID-19. First, we correlate the number of confirmed cases per day to daily search queries of a large number of keywords through Dynamic Time Warping (DTW) and Detrended Cross-Correlation Analysis (DCCA), where the keywords top in the most critical days are believed the most relevant to the pandemic. We then categorize the ranking lists of keywords according to the specific regions of the search, such as Wuhan, Mainland China, the USA, and the whole world. Through the analysis on search, we succeed in identifying COVID-19 related collective response would not be earlier than the end of 2019 in Mainland China. Finally, we confirm this observation again using human mobility data, where we specifically compare the massive mobility traces, including the real-time population densities inside key hospitals and inter-city travels departing from/arriving in Wuhan, from 2018 to 2020. No significant changes have been witnessed before December, 2019.

On the influence and limitations of hyper-resolution hydrological modelling – application of the 1 km PCR-GLOBWB model over Europe

2021 ◽  
Author(s):  
Jannis Hoch ◽  
Edwin Sutanudjaja ◽  
Rens van Beek ◽  
Marc Bierkens
Keyword(s):  
Global Scale ◽  
Data Sets ◽  
The Novel ◽  
Data Set ◽  
Meteorological Forcing ◽  
Recent Developments ◽  
Terrestrial Water ◽  
Multiple Challenges ◽  
Global Data ◽  
Development Application

&lt;p&gt;Developing and applying hyper-resolution models over larger extents has long been a quest in hydrological sciences. With the recent developments of global-scale yet fine data sets and advances in computational power, achieving this goal becomes increasingly feasible.&lt;/p&gt;&lt;p&gt;We here present the development, application, and results of the novel 1 km version of PCR-GLOBWB for the period 1981 until 2020. Even though employing global data sets only, we developed, ran, and evaluated the 1 km model for the continent Europe only. In comparison to past versions of PCR-GLOBWB, input data was replaced with sufficiently fine data, for example the recent SoilGrids and MERIT-DEM data. Preliminary results indicate an improvement of model outcome when evaluating simulated discharge, evaporation, and terrestrial water storage.&lt;/p&gt;&lt;p&gt;Additionally, we aim to answer the question to what extent developing hyper-resolution models is actually needed of whether the run times could be saved by using hyper-resolution state-of-the-art meteorological forcing. Therefore, the relative importance of model resolution and forcing resolution was cross-compared. To that end, the ERA5-Land data set was employed at different resolutions, matching the model resolutions at 1 km, 10 km, and 50 km.&lt;/p&gt;&lt;p&gt;Despite multiple challenges still lying ahead before achieve true hyper-resolution, this application of a 1 km model across an entire continent can form the basis for the next steps to be taken.&lt;/p&gt;

scholarly journals Near-real-time adjusted reanalysis forcing data for hydrology

2018 ◽  
Vol 22 (2) ◽  
pp. 989-1000 ◽  
Author(s):  
Peter Berg ◽  
Chantal Donnelly ◽  
David Gustafsson
Keyword(s):  
Real Time ◽  
Global Scale ◽  
Reanalysis Data ◽  
The Arctic ◽  
Data Sets ◽  
Initial State ◽  
Time Step ◽  
Data Set ◽  
Global Data Sets ◽  
Global Data

Abstract. Extending climatological forcing data to current and real-time forcing is a necessary task for hydrological forecasting. While such data are often readily available nationally, it is harder to find fit-for-purpose global data sets that span long climatological periods through to near-real time. Hydrological simulations are generally sensitive to bias in the meteorological forcing data, especially relative to the data used for the calibration of the model. The lack of high-quality daily resolution data on a global scale has previously been solved by adjusting reanalysis data with global gridded observations. However, existing data sets of this type have been produced for a fixed past time period determined by the main global observational data sets. Long delays between updates of these data sets leaves a data gap between the present day and the end of the data set. Further, hydrological forecasts require initializations of the current state of the snow, soil and lake (and sometimes river) storage. This is normally conceived by forcing the model with observed meteorological conditions for an extended spin-up period, typically at a daily time step, to calculate the initial state. Here, we present and evaluate a method named HydroGFD (Hydrological Global Forcing Data) to combine different data sets in order to produce near-real-time updated hydrological forcing data of temperature and precipitation that are compatible with the products covering the climatological period. HydroGFD resembles the already established WFDEI (WATCH Forcing Data–ERA-Interim) method (Weedon et al., 2014) closely but uses updated climatological observations, and for the near-real time it uses interim products that apply similar methods. This allows HydroGFD to produce updated forcing data including the previous calendar month around the 10th of each month. We present the HydroGFD method and therewith produced data sets, which are evaluated against global data sets, as well as with hydrological simulations with the HYPE (Hydrological Predictions for the Environment) model over Europe and the Arctic regions. We show that HydroGFD performs similarly to WFDEI and that the updated period significantly reduces the bias of the reanalysis data. For real-time updates until the current day, extending HydroGFD with operational meteorological forecasts, a large drift is present in the hydrological simulations due to the bias of the meteorological forecasting model.

scholarly journals OzFlux data: network integration from collection to curation

2017 ◽  
Vol 14 (12) ◽  
pp. 2903-2928 ◽  
Author(s):  
Peter Isaac ◽  
James Cleverly ◽  
Ian McHugh ◽  
Eva van Gorsel ◽  
Cacilia Ewenz ◽  
...  
Keyword(s):  
Quality Control ◽  
Eddy Covariance ◽  
Global Scale ◽  
Data Sets ◽  
Post Processing ◽  
Data Set ◽  
Web Based ◽  
Standard File Format ◽  
Standard Set ◽  
Field Campaigns

Abstract. Measurement of the exchange of energy and mass between the surface and the atmospheric boundary-layer by the eddy covariance technique has undergone great change in the last 2 decades. Early studies of these exchanges were confined to brief field campaigns in carefully controlled conditions followed by months of data analysis. Current practice is to run tower-based eddy covariance systems continuously over several years due to the need for continuous monitoring as part of a global effort to develop local-, regional-, continental- and global-scale budgets of carbon, water and energy. Efficient methods of processing the increased quantities of data are needed to maximise the time available for analysis and interpretation. Standardised methods are needed to remove differences in data processing as possible contributors to observed spatial variability. Furthermore, public availability of these data sets assists with undertaking global research efforts. The OzFlux data path has been developed (i) to provide a standard set of quality control and post-processing tools across the network, thereby facilitating inter-site integration and spatial comparisons; (ii) to increase the time available to researchers for analysis and interpretation by reducing the time spent collecting and processing data; (iii) to propagate both data and metadata to the final product; and (iv) to facilitate the use of the OzFlux data by adopting a standard file format and making the data available from web-based portals. Discovery of the OzFlux data set is facilitated through incorporation in FLUXNET data syntheses and the publication of collection metadata via the RIF-CS format. This paper serves two purposes. The first is to describe the data sets, along with their quality control and post-processing, for the other papers of this Special Issue. The second is to provide an example of one solution to the data collection and curation challenges that are encountered by similar flux tower networks worldwide.

scholarly journals An A-train and MERRA view of cloud, thermodynamic, and dynamic variability within the subtropical marine boundary layer

2017 ◽  
Vol 17 (15) ◽  
pp. 9451-9468 ◽  
Author(s):  
Brian H. Kahn ◽  
Georgios Matheou ◽  
Qing Yue ◽  
Thomas Fauchez ◽  
Eric J. Fetzer ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Marine Boundary Layer ◽  
Global Scale ◽  
Data Sets ◽  
Data Set ◽  
Global Context ◽  
Wind Speeds ◽  
Static Energy ◽  
Thermodynamic Phase ◽  
Modern Era

Abstract. The global-scale patterns and covariances of subtropical marine boundary layer (MBL) cloud fraction and spatial variability with atmospheric thermodynamic and dynamic fields remain poorly understood. We describe an approach that leverages coincident NASA A-train and the Modern Era Retrospective-Analysis for Research and Applications (MERRA) data to quantify the relationships in the subtropical MBL derived at the native pixel and grid resolution. A new method for observing four subtropical oceanic regions that capture transitions from stratocumulus to trade cumulus is demonstrated, where stratocumulus and cumulus regimes are determined from infrared-based thermodynamic phase. Visible radiances are normally distributed within stratocumulus and are increasingly skewed away from the coast, where trade cumulus dominates. Increases in MBL depth, wind speed, and effective radius (re), and reductions in 700–1000 hPa moist static energy differences and 700 and 850 hPa vertical velocity correspond with increases in visible radiance skewness. We posit that a more robust representation of the cloudy MBL is obtained using visible radiance rather than retrievals of optical thickness that are limited to a smaller subset of cumulus. The method using the combined A-train and MERRA data set has demonstrated that an increase in re within shallow cumulus is strongly related to higher MBL wind speeds that further correspond to increased precipitation occurrence according to CloudSat, previously demonstrated with surface observations. Hence, the combined data sets have the potential of adding global context to process-level understanding of the MBL.

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