scholarly journals Social Mobility Patterns in the World's Populated Cities Through COVID-19

2021 ◽  
Vol 16 (3) ◽  
pp. 1-15
Author(s):  
Ana Lorena Jiménez-Preciado ◽  
Nora Gavira-Durón
Keyword(s):  
Social Mobility ◽  
Kernel Density ◽  
Gaussian Kernel ◽  
Density Estimator ◽  
Mobility Patterns ◽  
Informal Market ◽  
Ternary Diagrams ◽  
Simultaneous Movement ◽  
Mobility Reduction ◽  
Principal Contribution

Objective: identify social mobility patterns in the world's most populated cities from the ravaging pandemic of COVID-19 and the confinement and social distancing measures. Method: ternary diagrams to examine the simultaneous movement to different places (grocery, services, parks, workplaces, residence, and transit). Specifically, we use crosshair ternary plots and a Gaussian Kernel Density Estimator (KDE) for ternary density diagrams. Results: for the most part, the mobility reduction was between 40% and 60% in the selected cities. Nevertheless, there were more significant transit cases, but not workplaces or residences, suggesting that the informal market may absorb part of the labor work. Limitations and implications: the main limitation of this analysis is in scaling the data since the mobility statistics represent negative percentages. Main contribution: the work's principal contribution and originality lie in using ternary diagrams, allowing the identification of social mobility patterns in the largest cities and understanding how displacement of populations has changed since COVID-19.

scholarly journals An evaluation of different measures of dynamically recrystallized grain size for paleopiezometry or paleowattometry studies

Solid Earth ◽  
2015 ◽  
Vol 6 (2) ◽  
pp. 475-495 ◽  
Author(s):  
M. A. Lopez-Sanchez ◽  
S. Llana-Fúnez
Keyword(s):  
Grain Size ◽  
Kernel Density ◽  
Kernel Density Estimator ◽  
Gaussian Kernel ◽  
Robust Estimator ◽  
Density Estimator ◽  
Lithospheric Deformation ◽  
Single Measure ◽  
Frequency Peak ◽  
Recrystallized Grain Size

Abstract. Paleopiezometry and paleowattometry studies are essential to validate models of lithospheric deformation and therefore increasingly common in structural geology. These studies require a single measure of dynamically recrystallized grain size in natural mylonites to estimate the magnitude of differential paleostress (or the rate of mechanical work). This contribution tests the various measures of grain size used in the literature and proposes the frequency peak of a grain size distribution as the most robust estimator for paleopiezometry or paleowattometry studies. The novelty of the approach resides in the use of the Gaussian kernel density estimator as an alternative to the classical histograms, which improves reproducibility. A free, open-source, easy-to-handle script named GrainSizeTools ( http://www.TEOS-10.org) was developed with the aim of facilitating the adoption of this measure of grain size in paleopiezometry or paleowattometry studies. The major advantage of the script over other programs is that by using the Gaussian kernel density estimator and by avoiding manual steps in the estimation of the frequency peak, the reproducibility of results is improved.

scholarly journals Gaussian Kernel Density Estimator for Voxel Size Selection in Atom Probe Tomography

2013 ◽  
Vol 19 (S2) ◽  
pp. 992-993 ◽  
Author(s):  
K. Kaluskar ◽  
K. Rajan
Keyword(s):  
Atom Probe Tomography ◽  
Kernel Density ◽  
Atom Probe ◽  
Kernel Density Estimator ◽  
Gaussian Kernel ◽  
Density Estimator ◽  
Voxel Size ◽  
Size Selection

Extended abstract of a paper presented at Microscopy and Microanalysis 2013 in Indianapolis, Indiana, USA, August 4 – August 8, 2013.

Online clutter estimation using a Gaussian kernel density estimator for multitarget tracking

2015 ◽  
Vol 9 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Xin Chen ◽  
Ratnasingham Tharmarasa ◽  
Thia Kirubarajan ◽  
Mike McDonald
Keyword(s):  
Kernel Density ◽  
Kernel Density Estimator ◽  
Gaussian Kernel ◽  
Density Estimator ◽  
Multitarget Tracking

scholarly journals Fourier Ring Correlation and Anisotropic Kernel Density Estimation Improve Deep Learning Based SMLM Reconstruction of Microtubules

2021 ◽  
Vol 1 ◽  
Author(s):  
Andreas Berberich ◽  
Andreas Kurz ◽  
Sebastian Reinhard ◽  
Torsten Johann Paul ◽  
Paul Ray Burd ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Similarity Index ◽  
Kernel Density ◽  
Ground Truth ◽  
Quality Measure ◽  
Gaussian Kernel ◽  
Limiting Factor ◽  
Density Estimator

Single-molecule super-resolution microscopy (SMLM) techniques like dSTORM can reveal biological structures down to the nanometer scale. The achievable resolution is not only defined by the localization precision of individual fluorescent molecules, but also by their density, which becomes a limiting factor e.g., in expansion microscopy. Artificial deep neural networks can learn to reconstruct dense super-resolved structures such as microtubules from a sparse, noisy set of data points. This approach requires a robust method to assess the quality of a predicted density image and to quantitatively compare it to a ground truth image. Such a quality measure needs to be differentiable to be applied as loss function in deep learning. We developed a new trainable quality measure based on Fourier Ring Correlation (FRC) and used it to train deep neural networks to map a small number of sampling points to an underlying density. Smooth ground truth images of microtubules were generated from localization coordinates using an anisotropic Gaussian kernel density estimator. We show that the FRC criterion ideally complements the existing state-of-the-art multiscale structural similarity index, since both are interpretable and there is no trade-off between them during optimization. The TensorFlow implementation of our FRC metric can easily be integrated into existing deep learning workflows.

scholarly journals A Fast-Converging Kernel Density Estimator for Dispersion in Horizontally Homogeneous Meteorological Conditions

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1343
Author(s):  
Gunther Bijloos ◽  
Johan Meyers
Keyword(s):  
Convergence Rate ◽  
Path Integral ◽  
Concentration Field ◽  
Kernel Density ◽  
Particle Dispersion ◽  
Meteorological Conditions ◽  
Kernel Density Estimator ◽  
Gaussian Kernel ◽  
Density Estimator ◽  
Data Set

Kernel smoothers are often used in Lagrangian particle dispersion simulations to estimate the concentration distribution of tracer gasses, pollutants etc. Their main disadvantage is that they suffer from the curse of dimensionality, i.e., they converge at a rate of 4/(d+4) with d the number of dimensions. Under the assumption of horizontally homogeneous meteorological conditions, we present a kernel density estimator that estimates a 3D concentration field with the faster convergence rate of a 1D kernel smoother, i.e., 4/5. This density estimator has been derived from the Langevin equation using path integral theory and simply consists of the product between a Gaussian kernel and a 1D kernel smoother. Its numerical convergence rate and efficiency are compared with that of a 3D kernel smoother. The convergence study shows that the path integral-based estimator has a superior convergence rate with efficiency, in mean integrated squared error sense, comparable with the one of the optimal 3D Epanechnikov kernel. Horizontally homogeneous meteorological conditions are often assumed in near-field range dispersion studies. Therefore, we illustrate the performance of our method by simulating experiments from the Project Prairie Grass data set.

scholarly journals Estimating the Gait Speed of Older Adults in Smart Home Environments

2022 ◽  
Vol 3 (2) ◽  
Author(s):  
Björn Friedrich ◽  
Enno-Edzard Steen ◽  
Sandra Hellmers ◽  
Jürgen M. Bauer ◽  
Andreas Hein
Keyword(s):  
Older Adults ◽  
Gait Speed ◽  
Smart Home ◽  
Kernel Density ◽  
Kernel Density Estimator ◽  
Gaussian Kernel ◽  
Measurement Unit ◽  
Density Estimator ◽  
Home Environments ◽  
Smart Home Environments

AbstractMobility is one of the key performance indicators of the health condition of older adults. One important parameter is the gait speed. The mobility is usually assessed under the supervision of a professional by standardised geriatric assessments. Using sensors in smart home environments for continuous monitoring of the gait speed enables physicians to detect early stages of functional decline and to initiate appropriate interventions. This in combination with a floor plan smart home sensors were used to calculate the distance that a person walked in the apartment and the inertial measurement unit data for estimating the actual walking time. A Gaussian kernel density estimator was applied to the computed values and the maximum of the kernel density estimator was considered as the gait speed. The proposed method was evaluated on a real-world dataset and the estimations of the gait speed had a deviation smaller than $$0.10 \, \frac{\mathrm{m}}{\mathrm{s}}$$ 0.10 m s , which is smaller than the minimal clinically important difference, compared to a baseline from a standardised geriatrics assessment.

scholarly journals Intra-urban differentials of congenital and acquired syphilis and syphilis in pregnant women in an urban area in northeastern Brazil

2021 ◽  
Author(s):  
Talita Araujo de Souza ◽  
Karen Kaline Teixeira ◽  
Reginaldo Lopes Santana ◽  
Cinthia Barros Penha ◽  
Arthur de Almeida Medeiros ◽  
...  
Keyword(s):  
Relative Risk ◽  
Pregnant Women ◽  
Congenital Syphilis ◽  
Kernel Density ◽  
Poisson Model ◽  
Northeastern Brazil ◽  
Smoothing Function ◽  
Density Estimator ◽  
Epidemic Disease ◽  
Peripheral Areas

Abstract Background Currently syphilis is considered an epidemic disease worldwide. The objective of this study was to identify intra-urban differentials in the occurrence of congenital and acquired syphilis and syphilis in pregnant women in the city of Natal, in northeast Brazil. Methods Cases of syphilis recorded by the municipal surveillance system from 1 January 2011 to 30 December 2018 were analysed. Spatial statistical analyses were performed using the kernel density estimator of the quadratic smoothing function (weighted). SaTScan software was applied for the calculation of risk based on a discrete Poisson model. Results There were 2163 cases of acquired syphilis, 738 cases of syphilis in pregnant women and 1279 cases of congenital syphilis. Kernel density maps showed that the occurrence of cases is more prevalent in peripheral areas and in areas with more precarious urban infrastructure. In 2011–2014 and 2015–2018, seven statistically significant clusters of acquired syphilis were identified. From 2011 to 2014, the most likely cluster had a relative risk of 3.54 (log likelihood ratio [LLR] 38 895; p<0.001) and from 2015 to 2018 the relative risk was 0.54 (LLR 69 955; p<0.001). Conclusions In the municipality of Natal, there was a clustered pattern of spatial distribution of syphilis, with some areas presenting greater risk for the occurrence of new cases.

Sign in / Sign up

Export Citation Format

Share Document