scholarly journals A Comparison of ArcGIS and QGIS for Animation

2017 ◽  
pp. 23-32
Author(s):  
Owen Stuckey
Keyword(s):  
Time Series ◽  
Data Processing ◽  
Open Source ◽  
Time Series Data ◽  
Series Data ◽  
Data Input ◽  
Input Processing ◽  
Similarities And Differences ◽  
Baseline Cost ◽  
Criteria For Evaluation

I compare two GIS programs which can be used to create cartographic animations—the commercial Esri ArcGIS and the free and open-source QGIS. ArcGIS implements animation through the “Time Slider” while QGIS uses a plugin called “TimeManager.” There are some key similarities and differences as well as functions unique to each plugin. This analysis examines each program’s capabilities in mapping time series data. Criteria for evaluation include the number of steps, the number of output formats, input of data, processing, output of a finished animation, and cost. The comparison indicates that ArcGIS has more control in input, processing, and output of animations than QGIS, but has a baseline cost of $100 per year for a personal license. In contrast, QGIS is free, uses fewer steps, and enables more output formats. The QGIS interface can make data input, processing, and output of an animation slower.

Theoretical Consideration on the Application of Continuous Wavelet Transform to Time-Series Processing for Magnetotelluric Data

2020 ◽  
Author(s):  
Hiroki Ogawa ◽  
Yuki Hama ◽  
Koichi Asamori ◽  
Takumi Ueda
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Data Processing ◽  
Time Series Data ◽  
Series Data ◽  
Short Time Fourier Transform ◽  
Numerical Errors ◽  
Spectral Transform ◽  
Frequency Components ◽  
Short Time

Abstract In the magnetotelluric (MT) method, the responses of the natural electromagnetic fields are evaluated by transforming time-series data into spectral data and calculating the apparent resistivity and phase. The continuous wavelet transform (CWT) can be an alternative to the short-time Fourier transform, and the applicability of CWT to MT data has been reported. There are, however, few cases of considering the effect of numerical errors derived from spectral transform on MT data processing. In general, it is desirable to adopt a window function narrow in the time domain for higher-frequency components and one in the frequency domain for lower-frequency components. In conducting the short-time Fourier transform, because the size of the window function is fixed unless the time-series data are decimated, there might be difference between the calculated MT responses and the true ones due to the numerical errors. Meanwhile, CWT can strike a balance between the resolution of the time and frequency domains by magnifying or reducing the wavelet, according to the value of frequency. Although the types of wavelet functions and their parameters influence the resolution of time and frequency, those calculation settings of CWT are often determined empirically. In this study, focusing on the frequency band between 0.001 Hz and 10 Hz, we demonstrated the superiority of utilizing CWT in MT data processing and determined its proper calculation settings in terms of restraining the numerical errors caused by the spectral transform of time-series data. The results obtained with the short-time Fourier transform accompanied with gradual decimation of the time-series data, called cascade decimation, were compared with those of CWT. The shape of the wavelet was changed by using different types of wavelet functions or their parameters, and the respective results of data processing were compared. Through these experiments, this study indicates that CWT with the complex Morlet function with its wavelet parameter k set to 6 ≤ k < 10 will be effective in restraining the numerical errors caused by the spectral transform.

scholarly journals TRENTOOL: A Matlab open source toolbox to analyse information flow in time series data with transfer entropy

2011 ◽  
Vol 12 (1) ◽  
pp. 119 ◽  
Author(s):  
Michael Lindner ◽  
Raul Vicente ◽  
Viola Priesemann ◽  
Michael Wibral
Keyword(s):  
Time Series ◽  
Open Source ◽  
Information Flow ◽  
Time Series Data ◽  
Transfer Entropy ◽  
Series Data

scholarly journals rhizoTrak: A flexible open source Fiji plugin for user-friendly manual annotation of time-series images from minirhizotrons

2019 ◽  
Author(s):  
Birgit Möller ◽  
Hongmei Chen ◽  
Tino Schmidt ◽  
Axel Zieschank ◽  
Roman Patzak ◽  
...  
Keyword(s):  
Time Series ◽  
Open Source ◽  
Data Exchange ◽  
Time Series Data ◽  
Full Range ◽  
Open Data ◽  
Series Data ◽  
Root Turnover ◽  
User Friendly ◽  
Time Series Images

AbstractBackground and aimsMinirhizotrons are commonly used to study root turnover which is essential for understanding ecosystem carbon and nutrient cycling. Yet, extracting data from minirhizotron images requires intensive annotation effort. Existing annotation tools often lack flexibility and provide only a subset of the required functionality. To facilitate efficient root annotation in minirhizotrons, we present the user-friendly open source tool rhizoTrak.Methods and resultsrhizoTrak builds on TrakEM2 and is publically available as Fiji plugin. It uses treelines to represent branching structures in roots and assigns customizable status labels per root segment. rhizoTrak offers configuration options for visualization and various functions for root annotation mostly accessible via keyboard shortcuts. rhizoTrak allows time-series data import and particularly supports easy handling and annotation of time series images. This is facilitated via explicit temporal links (connectors) between roots which are automatically generated when copying annotations from one image to the next. rhizoTrak includes automatic consistency checks and guided procedures for resolving conflicts. It facilitates easy data exchange with other software by supporting open data formats.ConclusionsrhizoTrak covers the full range of functions required for user-friendly and efficient annotation of time-series images. Its flexibility and open source nature will foster efficient data acquisition procedures in root studies using minirhizotrons.

scholarly journals PhilDB - The time series database with built-in change logging

2015 ◽  
Author(s):  
Andrew MacDonald
Keyword(s):  
Time Series ◽  
Big Data ◽  
Open Source ◽  
High Performance ◽  
Time Series Data ◽  
Handling Time ◽  
Series Data ◽  
Meta Data ◽  
Static Data ◽  
Data Tracking

PhilDB is an open-source time series database. It supports storage of time series datasets that are dynamic, that is recording updates to existing values in a log as they occur. Recent open-source systems, such as InfluxDB and OpenTSDB, have been developed to indefinitely store long-period, high-resolution time series data. Unfortunately they require a large initial installation investment before use because they are designed to operate over a cluster of servers to achieve high-performance writing of static data in real time. In essence, they have a ‘big data’ approach to storage and access. Other open-source projects for handling time series data that don’t take the ‘big data’ approach are also relatively new and are complex or incomplete. None of these systems gracefully handle revision of existing data while tracking values that changed. Unlike ‘big data’ solutions, PhilDB has been designed for single machine deployment on commodity hardware, reducing the barrier to deployment. PhilDB eases loading of data for the user by utilising an intelligent data write method. It preserves existing values during updates and abstracts the update complexity required to achieve logging of data value changes. PhilDB improves accessing datasets by two methods. Firstly, it uses fast reads which make it practical to select data for analysis. Secondly, it uses simple read methods to minimise effort required to extract data. PhilDB takes a unique approach to meta-data tracking; optional attribute attachment. This facilitates scaling the complexities of storing a wide variety of data. That is, it allows time series data to be loaded as time series instances with minimal initial meta-data, yet additional attributes can be created and attached to differentiate the time series instances as a wider variety of data is needed. PhilDB was written in Python, leveraging existing libraries. This paper describes the general approach, architecture, and philosophy of the PhilDB software.

Time Series Data Processing for Classifying Wandering Patterns in People with Dementia

2021 ◽  
pp. 1-1
Author(s):  
Arnoldo Diaz-Ramirez ◽  
Jesus E. Miranda-Vega ◽  
Daniel Ramos-Rivera ◽  
Dalia A. Rodriguez ◽  
Wendy Flores-Fuentes ◽  
...  
Keyword(s):  
Time Series ◽  
Data Processing ◽  
Time Series Data ◽  
Series Data ◽  
People With Dementia

Automatic offset detection using R open source libraries

2021 ◽  
Author(s):  
Shambo Bhattacharjee ◽  
Alvaro Santamaría-Gómez
Keyword(s):  
Time Series ◽  
Cluster Analysis ◽  
Open Source ◽  
Change Point ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data ◽  
Position Time Series ◽  
Gnss Time Series ◽  
Gnss Position Time Series

&lt;p&gt;Long GNSS position time series contain offsets typically at rates between 1 and 3 offsets per decade. We may classify the offsets whether their epoch is precisely known, from GNSS station log files or Earthquake databases, or unknown. Very often, GNSS position time series contain offsets for which the epoch is not known a priori and, therefore, an offset detection/removal operation needs to be done in order to produce continuous position time series needed for many applications in geodesy and geophysics. A further classification of the offsets corresponds to those having a physical origin related to the instantaneous displacement of the GNSS antenna phase center (from Earthquakes, antenna changes or even changes of the environment of the antenna) and those spurious originated from the offset detection method being used (manual/supervised or automatic/unsupervised). Offsets due to changes of the antenna and its environment must be avoided by the station operators as much as possible. Spurious offsets due to the detection method must be avoided by the time series analyst and are the focus of this work.&lt;/p&gt;&lt;p&gt;&lt;br&gt;Even if manual offset detection by expert analysis is likely to perform better, automatic offset detection algorithms are extremely useful when using massive (thousands) GNSS time series sets. Change point detection and cluster analysis algorithms can be used for detecting offsets in a GNSS time series data and R offers a number of libraries related to performing these two. For example, the &amp;#8220;Bayesian Analysis of Change Point Problems&amp;#8221; or the &amp;#8220;bcp&amp;#8221; helps to detect change points in a time series data. Similarly, the &amp;#8220;dtwclust&amp;#8221; (Dynamic Time Warping algorithm) is used for the time series cluster analysis. Our objective is to assess various open-source R libraries for the automatic offset detection.&lt;/p&gt;

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