Evaluation of Aviation Emissions and Environmental Costs in Europe Using OpenSky and OpenAP

In this paper, we propose a data-driven approach that estimates cruise-level flight emissions over Europe using OpenSky ADS-B data and OpenAP emission models. Flight information, including position, altitude, speed, and the vertical rate are obtained from the OpenSky historical database, gathered at a sample rate of 15 s. Emissions from each flight are estimated at a 30-s time interval. This study makes use of the first four months of flights in 2020 over the major part of Europe. The dataset covers the period before and at the start of the COVID-19 pandemic. The aggregated results show cruise-level flight emissions by different airlines, geographic regions, altitudes, and timeframe (e.g., weeks). We also estimate environmental costs associated with aviation in Europe by using marginal cost values from the literature. Overall, we have demonstrated how open flight data from OpenSky can be employed to rapidly assess aviation emissions at varying spatio-temporal resolutions on a continental scale.

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Predictive mapping for copper–gold magmatic-hydrothermal systems in NW Argentina: Use of a regional-scale GIS, application of an expert-guided data-driven approach, and comparison with results from a continental-scale GIS

Ore Geology Reviews ◽

10.1016/j.oregeorev.2005.10.002 ◽

2006 ◽

Vol 29 (3-4) ◽

pp. 260-286 ◽

Cited By ~ 27

Author(s):

R. Roy ◽

D. Cassard ◽

P.R. Cobbold ◽

E.A. Rossello ◽

M. Billa ◽

...

Keyword(s):

Regional Scale ◽

Hydrothermal Systems ◽

Data Driven ◽

Predictive Mapping ◽

Gis Application ◽

Continental Scale ◽

Copper Gold ◽

Data Driven Approach

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A triple-network organization for the mouse brain

Molecular Psychiatry ◽

10.1038/s41380-021-01298-5 ◽

2021 ◽

Author(s):

Francesca Mandino ◽

Roël M. Vrooman ◽

Heidi E. Foo ◽

Ling Yun Yeow ◽

Thomas A. W. Bolton ◽

...

Keyword(s):

Mouse Brain ◽

Data Driven ◽

Network Organization ◽

Brain Disorder ◽

Default Mode ◽

Temporal Properties ◽

Disease Condition ◽

Data Driven Approach ◽

Species Comparisons ◽

Spatio Temporal

AbstractThe triple-network model of psychopathology is a framework to explain the functional and structural neuroimaging phenotypes of psychiatric and neurological disorders. It describes the interactions within and between three distributed networks: the salience, default-mode, and central executive networks. These have been associated with brain disorder traits in patients. Homologous networks have been proposed in animal models, but their integration into a triple-network organization has not yet been determined. Using resting-state datasets, we demonstrate conserved spatio-temporal properties between triple-network elements in human, macaque, and mouse. The model predictions were also shown to apply in a mouse model for depression. To validate spatial homologies, we developed a data-driven approach to convert mouse brain maps into human standard coordinates. Finally, using high-resolution viral tracers in the mouse, we refined an anatomical model for these networks and validated this using optogenetics in mice and tractography in humans. Unexpectedly, we find serotonin involvement within the salience rather than the default-mode network. Our results support the existence of a triple-network system in the mouse that shares properties with that of humans along several dimensions, including a disease condition. Finally, we demonstrate a method to humanize mouse brain networks that opens doors to fully data-driven trans-species comparisons.

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Quantifying the Spatio-Temporal Process of Township Urbanization: A Large-Scale Data-Driven Approach

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8090389 ◽

2019 ◽

Vol 8 (9) ◽

pp. 389

Author(s):

Xinliang Liu ◽

Yi Wang ◽

Yong Li ◽

Jinshui Wu

Keyword(s):

Large Scale ◽

Driving Forces ◽

Data Driven ◽

Population Migration ◽

Urbanization Process ◽

Large Scale Data ◽

Temporal Process ◽

Data Driven Approach ◽

Spatio Temporal ◽

Scale Data

The integrated recognition of spatio-temporal characteristics (e.g., speed, interaction with surrounding areas, and driving forces) of urbanization facilitates regional comprehensive development. In this study, a large-scale data-driven approach was formed for exploring the township urbanization process. The approach integrated logistic models to quantify urbanization speed, partial triadic analysis to reveal dynamic relationships between rural population migration and urbanization, and random forest analysis to identify the response of urbanization to spatial driving forces. A typical subtropical town was chosen to verify the approach by quantifying the spatio-temporal process of township urbanization from 1933 to 2012. The results showed that (i) urbanization speed was well reflected by the changes of time-course areas of urban cores fitted by a four-parameter logistic equation (R2 = 0.95–1.00, p < 0.001), and the relatively fast and steady developing periods were also successfully predicted, respectively; (ii) the spatio-temporal sprawl of urban cores and their interactions with the surrounding rural residential areas were well revealed and implied that the town experienced different historically aggregating and splitting trajectories; and (iii) the key drivers (township merger, elevation and distance to roads, as well as population migration) were identified in the spatial sprawl of urban cores. Our findings proved that a comprehensive approach is powerful for quantifying the spatio-temporal characteristics of the urbanization process at the township level and emphasized the importance of applying long-term historical data when researching the urbanization process.

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Risk Assessment of the Overseas Imported COVID-19 of Ocean-Going Ships Based on AIS and Infection Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9060351 ◽

2020 ◽

Vol 9 (6) ◽

pp. 351 ◽

Cited By ~ 2

Author(s):

Zhihuan Wang ◽

Mengyuan Yao ◽

Chenguang Meng ◽

Christophe Claramunt

Keyword(s):

Spatial Clustering ◽

Infection Risk ◽

Data Driven ◽

Automatic Identification ◽

Quick Response ◽

Support Mechanism ◽

Risk Levels ◽

Data Driven Approach ◽

Spatio Temporal ◽

And Control

Preventing and controlling the risk of importing the coronavirus disease (COVID-19) has rapidly become a major concern. In addition to air freight, ocean-going ships play a non-negligible role in spreading COVID-19 due to frequent visits to countries with infected populations. This research introduces a method to dynamically assess the infection risk of ships based on a data-driven approach. It automatically identifies the ports and countries these ships approach based on their Automatic Identification Systems (AIS) data and a spatio-temporal density-based spatial clustering of applications with noise (ST_DBSCAN) algorithm. We derive daily and 14 day cumulative ship exposure indexes based on a series of country-based indices, such as population density, cumulative confirmed cases, and increased rate of confirmed cases. These indexes are classified into high-, middle-, and low-risk levels that are then coded as red, yellow, and green according to the health Quick Response (QR) code based on the reference exposure index of Wuhan on April 8, 2020. This method was applied to a real container ship deployed along a Eurasian route. The results showed that the proposed method can trace ship infection risk and provide a decision support mechanism to prevent and control overseas imported COVID-19 cases from international shipping.

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