Method for Automatic Collocation Extraction from Ukrainian Corpora

The article deals with the methods for automatic collocation extraction from Ukrainian corpora. The task of collocation extraction is considered in terms of a corpus-oriented approach [1], based on statistical measures. The term «collocation» is defined as a non-random combination of two words that go together regularly

The future is distributed: a vision of sustainable economies

“The Future is distributed: a vision of sustainable economies” is a collection of case studies on distributed economies, a concept describing sustainable alternatives to the existing business models. The authors of this publication are international Masters students of the Environmental Sciences, Policy and Management Programme at the International Institute for Industrial Environmental Economics at Lund University in Sweden. The aim of their work is to demonstrate that local, small-scale, community-based economies are not just part of the theory, but have already been implemented in various sectors and geographical settings

GRASS GIS Vector Processing: towards GRASS 7

The upcoming GRASS GIS 7 release improves not only raster processing and general design but the vector processing in the first place. GRASS GIS, as a topological GIS, recognizes that the topology plays the key role in the vector processing and analysis. Topology ensures that adjacent geographic components in a single vector map are related. In contrast to non-topological GIS, a border common to two areas exists only once and is shared between the two areas. Topological representation of vector data helps to produce and maintain vector maps with clean geometry as well as enables the user to perform certain analyses that can not be conducted with non-topological or spaghetti data. Non-topological vector data are automatically converted to a topological representation upon import. Further more, various cleaning tools exist to remove non-trivial topological errors. In the upcoming GRASS GIS 7 release the vector library was particularly improved to make it faster and more efficient with an improved internal vector file format. This new topological format reduces memory and disk space requirements, leading to a generally faster processing. Opening an existing vector requires less memory providing additionally support for large files. The new spatial index performs queries faster (compared to GRASS GIS 6 more than 10 times for large vectors). As a new option the user can select a file-based version of the spatial index for large vector data. All topological cleaning tools have been optimized with regard to processing speed, robustness, and system requirements. The topological engine comes with a new prototype for direct read/write support of Simple Features API/OGR. Additionally vector data can be directly exchanged with topological PostGIS 2 databases. Considering the wide spread usage of ESRI Shapefile, a non-topological format for vector data exchange, it is particularly advantageous that GRASS GIS 7 offers advanced cleaning tools. For power users and programmers, the new Python interface allows to directly access functions provided by the underlying C library; this combines the ease of writing Python scripts with the power of optimized C functionality in the library backend

Analyzing rasters, vectors and time series using new Python interfaces in GRASS GIS 7

GRASS GIS 7 is a free and open source GIS software developed and used by many scientists (Neteler et al., 2012). While some users of GRASS GIS prefer its graphical user interface, significant part of the scientific community takes advantage of various scripting and programing interfaces offered by GRASS GIS to develop new models and algorithms. Here we will present different interfaces added to GRASS GIS 7 and available in Python, a popular programming language and environment in geosciences. These Python interfaces are designed to satisfy the needs of scientists and programmers under various circumstances. PyGRASS (Zambelli et al., 2013) is a new object-oriented interface to GRASS GIS modules and libraries. The GRASS GIS libraries are implemented in C to ensure maximum performance and the PyGRASS interface provides an intuitive, pythonic access to their functionality. GRASS GIS Python scripting library is another way of accessing GRASS GIS modules. It combines the simplicity of Bash and the efficiency of the Python syntax. When full access to all low-level and advanced functions and structures from GRASS GIS library is required, Python programmers can use an interface based on the Python ctypes package. Ctypes interface provides complete, direct access to all functionality as it would be available to C programmers. GRASS GIS provides specialized Python library for managing and analyzing spatio-temporal data (Gebbert and Pebesma, 2014). The temporal library introduces space time datasets representing time series of raster, 3D raster or vector maps and allows users to combine various spatio-temporal operations including queries, aggregation, sampling or the analysis of spatio-temporal topology. We will also discuss the advantages of implementing scientific algorithm as a GRASS GIS module and we will show how to write such module in Python. To facilitate the development of the module, GRASS GIS provides a Python library for testing (Petras and Gebbert, 2014) which helps researchers to ensure the robustness of the algorithm, correctness of the results in edge cases as well as the detection of changes in results due to new development. For all modules GRASS GIS automatically creates standardized command line and graphical user interfaces and documentation. Finally, we will show how GRASS GIS can be used together with powerful Python tools such as the NumPy package and the IPython Noteboo

GRASS GIS: a peer-reviewed scientific platform and future research repository

Geographical Information System (GIS) is known for its capacity to spatially enhance the management of natural resources. While being often used as an analytical tool, it also represents a collaborative scientific platform to develop new algorithms. Thus, it is critical that GIS software as well as the algorithms are open and accessible to anybody [18]. We present how GRASS GIS, a free and open source GIS, is used by many scientists to implement and perform geoprocessing tasks. We will show how integrating scientific algorithms into GRASS GIS helps to preserve reproducibility of scientific results over time [15]. Moreover, subsequent improvements are tracked in the source code version control system and are immediately available to the public. GRASS GIS therefore acts as a repository of scientific peer-reviewed code, algorithm library, and knowledge hub for future generation of scientists. In the field of hydrology, with the various types of actual evapotranspiration (ET) models being developed in the last 20 years, it becomes necessary to inter-compare methods. Most of already published ETa models comparisons address few number of models, and small to medium areas [3, 6, 7, 22, 23]. With the large amount of remote sensing data covering the Earth, and the daily information available for the past ten years (i.e. Aqua/Terra-MODIS) for each pixel location, it becomes paramount to have a more complete comparison, in space and time. To address this new experimental requirement, a distributed computing framework was designed, and created [3, 4]. The design architecture was built from original satellite datasets to various levels of processing until reaching the requirement of various ETa models input dataset. Each input product is computed once and reused in all ETa models requiring such input. This permits standardization of inputs as much as possible to zero-in variations of models to the models internals/specificities. All of the ET models are available in the new GRASS GIS version 7 as imagery modules and replicability is complete for future research. A set of modules for multiscale analysis of landscape structure was added in 1992 by [1], who developed the r.le model similar to FRAGSTATS ([10]). The modules were gradually improved to become r.li in 2006. Further development continued, with a significant speed up [9] and new interactive user interface. The development of spatial interpolation module v.surf.rst started in 1988 [11] and continued by introduction of new interpolation methods and finally full integration into GRASS GIS version 4 [13]. Since then it was improved several times [8]. The module is an important part of GRASS GIS and is taught at geospatial modeling courses, for example at North Carolina State University [14]. GRASS GIS entails several modules that constitute the result of active research on natural hazard. The r.sim.water simulation model [12] for overland flow under rainfall excess conditions was integrated into the Emergency Routing Decision Planning system as a WPS [17]. It was also utilized by [16] and is now part of Tangible Landscape, a tangible GIS system, which also incorporated the r.damflood, a dam break inundation simulation [2]. The wildfire simulation toolset, originally developed by [24], implementing Rothermel’s model [21], available through the GRASS GIS modules r.ros and r.spread, is object of active research. It has been extensively tested and recently adapted to European fuel types ([5, 19, 20]

Spatio-temporal data handling and visualization in GRASS GIS

Drowning in too many maps? Have some fun exploring fascinating geometries of changing landscapes in Space Time Cube and creating 2D and 3D animations from time series of geospatial data. Learn about the new capabilities for spatio-temporal data handling in GRASS GIS 7 (http://grass.osgeo.org/grass7/) and explore various techniques for dynamic visualizations. First, we will introduce you to GRASS GIS 7, including its spatio-temporal capabilities and you will learn how to manage and analyze geospatial data time series. Then, we will explore new tools for visualization of spatio-temporal data. You will create both 2D and 3D dynamic visualizations directly in GRASS GIS 7. Additionally, we will explain the Space Time Cube concept using various applications based on raster and vector data time series. You will learn to manage and visualize data in space time cubes (voxel models). No prior knowledge of GRASS GIS is necessary, we will cover the basics needed for the workshop. All relevant material including an overview of the tools and hands-on practical instructions along with the sample data sets will be available on-line. And, by the way, GRASS GIS is a free and open source geographic information system (GIS) used for geospatial data management, analysis, modeling, image processing, and visualization which runs on Linux, MS Windows, Mac OS X and other system

How to write a Python GRASS GIS 7 addon

GRASS GIS is a leading software in analysis of geodata, it offers more than 400 modules in its core version plus many addons (i.e., user contributed modules). But what if the tool you are looking for is not present in GRASS GIS? So, simply create your own, we will show you how to do that in this workshop. In GRASS GIS 7, Python is the default language for creating addons. There are two main Python libraries included in GRASS GIS. Python Scripting Library allows you to perform analysis and compute new data by chaining existing modules to create your own workflow. With PyGRASS library wrapping the C functions, you can create new data sets (vector and raster) directly through Python calls, increasing considerably the power and performance of your scripts. You can conveniently mix both GRASS Python libraries with other Python libraries like NumPy, or SciPy. In this workshop, we will guide you through the basic steps of writing your own Python scripts, starting with calling and chaining GRASS GIS modules, followed by a more pythonic experience when using PyGRASS to access and modify your data directly. You will then upgrade your script into an addon by defining a simple interface to enable automatically generated GUI. The next part of workshop will look into more advanced usage of GRASS GIS 7 capabilities, including Python spatio-temporal API to handle time series in your addons, creating your own toolbox with your newly developed addons and finally, introducing the new testing framework you should use as a responsible person to make sure your addons are in great shape

Sensitivity analysis of time of induction heating process on charge parameters

A model of determining the time necessary for induction heating of a ferromagnetic cylindrical charge to the Curie temperature is presented, together with finding its sensitivity to variable input parameters. While the geometry of the inductor and charge are known together with the frequency of the field current, the input variables are represented by electromagnetic and thermal properties of the charge (nonlinear saturation curve, temperature dependence of its magnetic permeability and temperature dependencies of electric and thermal conductivities and specific heat capacity). The methodology is illustrated with a typical example

GRASS GIS 7.4: What's new in a nutshell?

Even after more than 30 years since the first release, GRASS GIS keeps offering a lot of modern features. The new stable GRASS GIS 7.4 version series is available since February 2018. It comes with several new features, enhancements and fixes. The international development team has further improved the usability, migrated selected add-ons into the core package and revised the orthorectification of aerial images. Raster data storage has also been further optimized for processing of massive data sets in the cloud and new algorithms for vector data analysis have been integrated. GRASS GIS is also available on the Docker Hub. The presentation shows the most important innovations in GRASS GIS 7.4 in an easy to understand way with many screenshots and examples