scholarly journals Empirical Insights from a Study on Outlier Preserving Value Generalization in Animated Choropleth Maps

2021 ◽  
Vol 10 (4) ◽  
pp. 208
Author(s):  
Christoph Traun ◽  
Manuela Larissa Schreyer ◽  
Gudrun Wallentin
Keyword(s):  
Time Series ◽  
Empirical Research ◽  
Global Trends ◽  
First Order ◽  
Choropleth Maps ◽  
Temporal Generalization ◽  
Perceptual Limits ◽  
Local Outlier

Time series animation of choropleth maps easily exceeds our perceptual limits. In this empirical research, we investigate the effect of local outlier preserving value generalization of animated choropleth maps on the ability to detect general trends and local deviations thereof. Comparing generalization in space, in time, and in a combination of both dimensions, value smoothing based on a first order spatial neighborhood facilitated the detection of local outliers best, followed by the spatiotemporal and temporal generalization variants. We did not find any evidence that value generalization helps in detecting global trends.

scholarly journals Aanalytical Solution for Motion Around Radiated Varying Mass Body

2019 ◽  
Vol 16 ◽  
pp. 8407-8419
Author(s):  
Marwa Abdullah Bin Humaidan ◽  
M. I. El-Saftawy ◽  
H. M. Asiri
Keyword(s):  
Time Series ◽  
Radiation Pressure ◽  
Pressure Effect ◽  
Hamiltonian Function ◽  
Perturbation Techniques ◽  
First Order ◽  
D Operator ◽  
Order Solution ◽  
Delaunay Variables ◽  
Varying Mass

In this work we will add the radiation pressure effect of varying mass body to the model of varying mass Hamiltonian function, including Periastron effect. The problem was formulated in terms of Delaunay variables. The solution of the problem was constructed based on Delava – Hansilmair perturbation techniques. Finally we find the first order solution for the problem as time series by calculating the desired order for the D operator and variables.

scholarly journals RECONSTRUCTION OF UNIDIRECTIONALLY COUPLED TIME-DELAYED SYSTEMS OF FIRST ORDER FROM TIME SERIES OF THE DRIVEN SYSTEM

2017 ◽  
Vol 25 (1) ◽  
pp. 84-93 ◽  
Author(s):  
Mikhail D. Prokhorov ◽  
◽  
Vladimir I. Ponomarenko ◽  
Ilya V. Sysoev ◽  
◽  
...  
Keyword(s):  
Time Series ◽  
Delayed Systems ◽  
First Order ◽  
Driven System

Corner Reflector Network as a Geodetic Reference for Landslide Monitoring via InSAR Time Series: Case Study from Slovakia

2021 ◽  
Author(s):  
Richard Czikhardt ◽  
Juraj Papco ◽  
Peter Ondrejka ◽  
Peter Ondrus ◽  
Pavel Liscak
Keyword(s):  
Time Series ◽  
Horizontal Displacement ◽  
Terrestrial Reference Frame ◽  
Sar Interferometry ◽  
Global Navigation Satellite Systems ◽  
Reference Network ◽  
Landslide Monitoring ◽  
First Order ◽  
Order Estimation ◽  
Better Than

<p>SAR interferometry (InSAR) is inherently a relative geodetic technique requiring one temporal and one spatial reference to obtain the datum-free estimates on millimetre-level displacements within the network of radar scatterers. To correct the systematic errors, such as the varying atmospheric delay, and solve the phase ambiguities, it relies on the first-order estimation network of coherent point scatterers (PS).</p><p>For vegetated and sparsely urbanized areas, commonly affected by landslides in Slovakia, it is often difficult to construct a reliable first-order estimation network, as they lack the PS. Purposedly deploying corner reflectors (CR) at such areas strengthens the estimation network and, if these CR are collocated with a Global Navigation Satellite Systems (GNSS), they provide an absolute geodetic reference to a well-defined terrestrial reference frame (TRF), as well as independent quality control.</p><p>For landslides, line-of-sight (LOS) InSAR displacements can be difficult to interpret. Using double CR, i.e. two reflectors for ascending/descending geometries within a single instrument, enables the assumption-less decomposition of the observed cross-track LOS displacements into the vertical and the horizontal displacement components.</p><p>In this study, we perform InSAR analysis on the one-year of Sentinel-1 time series of five areas in Slovakia, affected by landslides. 24 double back-flipped trihedral CR were carefully deployed at these sites to form a reference network, guaranteeing reliable displacement information over the critical landslide zones. To confirm the measurement quality, we show that the temporal average Signal-to-Clutter Ratio (SCR) of the CR is better than 20 dB. The observed CR motions in vertical and east-west directions vary from several millimetres up to 3 centimetres, with average standard deviation better than 0.5 mm.<br>Repeated GNSS measurements of the CR confirm the displacement observed by the InSAR, improve the positioning precision of the nearby PS, and attain the transformation into the national TRF.</p>

scholarly journals Global trends toward urban street-network sprawl

2020 ◽  
Vol 117 (4) ◽  
pp. 1941-1950 ◽  
Author(s):  
Christopher Barrington-Leigh ◽  
Adam Millard-Ball
Keyword(s):  
Time Series ◽  
Sustainable Urban Development ◽  
Street Network ◽  
High Resolution Data ◽  
Global Trends ◽  
Urban Street ◽  
Graph Theoretic ◽  
Time Period ◽  
Path Dependent ◽  
Changes Over Time

We present a global time series of street-network sprawl—that is, sprawl as measured through the local connectivity of the street network. Using high-resolution data from OpenStreetMap and a satellite-derived time series of urbanization, we compute and validate changes over time in multidimensional street connectivity measures based on graph-theoretic and geographic concepts. We report on global, national, and city-level trends since 1975 in the street-network disconnectedness index (SNDi), based on every mapped node and edge in the world. Streets in new developments in 90% of the 134 most populous countries have become less connected since 1975, while just 29% show an improving trend since 2000. The same period saw a near doubling in the relative frequency of a street-network type characterized by high circuity, typical of gated communities. We identify persistence in street-network sprawl, indicative of path-dependent processes. Specifically, cities and countries with low connectivity in recent years also had relatively low preexisting connectivity in our earliest time period. We discuss implications for policy intervention in road building in new and expanding cities as a top priority for sustainable urban development.

On fitting the k-C* first order model to batch loaded sub-surface treatment wetlands

2007 ◽  
Vol 56 (3) ◽  
pp. 93-99 ◽  
Author(s):  
O.R. Stein ◽  
B.W. Towler ◽  
P.B. Hook ◽  
J.A. Biederman
Keyword(s):  
Time Series ◽  
Plug Flow ◽  
Typha Latifolia ◽  
Statistical Technique ◽  
Species Variation ◽  
Order Model ◽  
Residual Concentration ◽  
Mixed Effect ◽  
First Order ◽  
Nonlinear Mixed Effect

The k-C* first order model was fit to time-series COD data collected from batch-loaded model wetlands. Four replicates of four plant species treatments; Carex utriculata (sedge), Schoenoplectus acutus (bulrush), Typha latifolia (cattail) and unplanted controls were compared. Temperature was varied by 4 °C from 24 °C to 4 °C to 24 °C over a year-long period. One mathematical fit was made for each wetland replicate at each temperature setting (192 fits). Temperature effects on both parameters were subsequently estimated by fitting the Arrhenius relationship to the estimated coefficients. Inherent interactions between k and C* make values dependent on sample timing and statistical technique for either time series (batch load) or distance profile (plug flow) data. Coefficients calibrated using the Levenberg–Marquardt method are compared to values previously reported using a nonlinear mixed effect regression technique. Overall conclusions are similar across approaches: (a) the magnitude of the coefficients varies strongly by species; (b) the rate constant k decreases with increasing temperature; and (c) temperature and species variation in the residual concentration C* is greater than the variation in k, such that variation in k alone is a poor predictor of performance. However, the magnitudes of the coefficients, especially the rate parameter k, vary between the statistical techniques, highlighting the need to better document the statistical routines used to calibrate the k-C* model.

Correlation analysis of continuous emotional response to music: Correcting for the effects of serial correlation

2001 ◽  
Vol 5 (1_suppl) ◽  
pp. 213-236 ◽  
Author(s):  
Emery Schubert
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Emotional Response ◽  
Serial Correlation ◽  
Time Series Data ◽  
Second Order ◽  
Series Data ◽  
First Order ◽  
The Relationship ◽  
Pearson Correlations

Publications of research concerning continuous emotional responses to music are increasing. The developing interest brings with it a need to understand the problems associated with the analysis of time series data. This article investigates growing concern in the use of conventional Pearson correlations for comparing time series data. Using continuous data collected in response to selected pieces of music, with two emotional dimensions for each piece, two falsification studies were conducted. The first study consisted of a factor analysis of the individual responses using the original data set and its first-order differenced transformation. The differenced data aligned according to theoretical constraints better than the untransformed data, supporting the use of first-order difference transformations. Using a similar method, the second study specifically investigated the relationship between Pearson correlations, difference transformations and the critical correlation coefficient above which the conventional correlation analysis remains internally valid. A falsification table was formulated and quantified through a hypothesis index function. The study revealed that correlations of undifferenced data must be greater than 0.75 for a valid interpretation of the relationship between bivariate emotional response time series data. First and second-order transformations were also investigated and found to be valid for correlation coefficients as low as 0.24. Of the three version of the data (untransformed, first-order differenced, and second-order differenced), first-order differenced data produced the fewest problems with serial correlation, whilst remaining a simple and meaningful transformation.

Estimating Linear Trends: Simple Linear Regression versus Epoch Differences

2015 ◽  
Vol 28 (24) ◽  
pp. 9969-9976 ◽  
Author(s):  
Elizabeth A. Barnes ◽  
Randal J. Barnes
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Linear Trend ◽  
Simple Linear Regression ◽  
Difference Estimator ◽  
First Order ◽  
Epoch Length ◽  
Average Value ◽  
The Difference ◽  
Linear Regression Estimator

Abstract Two common approaches for estimating a linear trend are 1) simple linear regression and 2) the epoch difference with possibly unequal epoch lengths. The epoch difference estimator for epochs of length M is defined as the difference between the average value over the last M time steps and the average value over the first M time steps divided by N − M, where N is the length of the time series. Both simple linear regression and the epoch difference are unbiased estimators for the trend; however, it is demonstrated that the variance of the linear regression estimator is always smaller than the variance of the epoch difference estimator for first-order autoregressive [AR(1)] time series with lag-1 autocorrelations less than about 0.85. It is further shown that under most circumstances if the epoch difference estimator is applied, the optimal epoch lengths are equal and approximately one-third the length of the time series. Additional results are given for the optimal epoch length at one end when the epoch length at the other end is constrained.

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