In my project “Trajectory Analytics Toolbox”, I will be creating a general-purpose trajectory analytics toolbox in R. See my first blog post for more background information.
There have been many packages in R developed for handling trajectory data, however each package is limited to a specific domain and does not provide functionality for general purpose trajectory analysis and visualization. This Google Summer of Code 2020 project aims to generalize trajectory analysis and visualization by creating a package named traviz.
With EGU 2020 cancelled, this blog post elaborates on the abstract submitted by the 52°North software project
sos4R development team. See the program entry for the abstract EGU2020-19453 in the official program. Please refer to this work by citing the DOI
Accessing environmental time series data in R from Sensor Observation Services with ease
Time series data of in-situ measurements are the key to many environmental studies. The first challenge in any analysis typically arises when the data needs to be imported into the analysis framework. Standardization is one way to lighten this burden. Unfortunately, relevant interoperability standards might be challenging for non-IT experts as long as they are not dealt with behind the scenes of a client application. One standard to provide access to environmental time series data is the Sensor Observation Service (SOS) specification published by the Open Geospatial Consortium (OGC). SOS instances are currently used in a broad range of applications such as hydrology, air quality monitoring, and ocean sciences. Data sets provided via an SOS interface can be found around the globe from Europe to New Zealand.
The R package sos4R (Nüst et al., 2011) is an extension package for the R environment for statistical computing and visualization, which has been demonstrated as a powerful tool for conducting and communicating geospatial research (cf. Pebesma et al., 2012). The features presented in this article are available in the new release version 0.4 on CRAN.more >
ArcGIS is the central tool to handle and derive geoinformation in many applications. However, the standard kriging capabilities only include a few covariance functions and hide the estimation and fit quality of the semivariogram to a large degree. R is less appealing for working with maps, but features a variety of statistical, i.e. geostatistical, extensions. In this showcase, we exploit the semivariogram modeling and kriging capabilities of the gstat R package. In order to give the user visual control over the estimation procedure and the model selection, we use the Shiny framework to realize an interactive graphical user-interface for the semivariogram fitting step. This tool hides the entire R implementation from the user, but delivers a good deal of the geostatistical power to perform kriging.more >