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We report on work in progress on the integration of the GRASS GIS, the R data analysis programming language and environment, and the PostgreSQL database system. All of these components are released under Open Source licenses. This means that application programming interfaces are documented both in source code and in other materials, simplifying in...
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... components of geographical information systems involved in integration with data analysis and statistical programming environments are shown in Fig. 1. It is of course possible to reduce or extend the kinds of GIS activity that could benefit from integration with statistics, from screening input data for extreme outliers to mapping − for example interactive choice of class intervals for thematic maps of continuous or count ...
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We report on work in progress on the integration of the GRASS GIS, the R data analysis programming language and environment, and the PostgreSQL database system. All of these components are released under Open Source licenses. This means that application programming interfaces are documented both in source code and in other materials, simplifying in...
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... While this fraction is likely to grow in the near future, a full integration of all modules is improbable as it would duplicate functionality (though this of course already has happened) and interface functions that are unnecessary within the QGIS environment, such as the GRASS database functions. If the user's intention is to use GRASS's database management system (DBMS), the direct R-GRASS integration via the spgrass6 (Bivand et al., 2013) and rgrass7 packages (Bivand and Neteler, 2000) would be the appropriate path. RQGIS does not provide access to this DBMS since the GRASS plugin of the QGIS processing toolbox only allows restricted access to GRASS's DBMS functionality. ...
Integrating R with Geographic Information Systems (GIS) extends R’s statistical capabilities with numerous geoprocessing and data handling tools available in a GIS. QGIS is one of the most popular open-source GIS, and it furthermore integrates other GIS programs such as the System for Automated Geoscientific Analyses (SAGA) GIS and the Geographic Resources Analysis Support System (GRASS) GIS within a single software environment. This and its QGIS Python API makes it a perfect candidate for console-based geoprocessing. By establishing an interface, the R package RQGIS makes it possible to use QGIS as a geoprocessing workhorse from within R. Compared to other packages building a bridge to GIS (e.g., rgrass7, RSAGA, RPyGeo), RQGIS offers a wider range of geoalgorithms, and is often easier to use due to various convenience functions. Finally, RQGIS supports the seamless integration of Python code using reticulate from within R for improved extendability.
... Para la construcci6n del SIG se lIevaron a cabo los siguientes procedimientos: 1. Recopilacion de informaci6n digital previamente generada, 2, Conversi6n digital de mapas tematicos existentes y 3. Generaci6n de mapas tematicos con bases de datos existentes 4. Adici6n de metadatos a las capas generadas. La proyeccion utilizada para este estudio es Universal Transversa de Mercator (UTM) con datum NAD27, seleccionada por ser una proyecci6n predeterminada en los programas utilizados (ArcView, ArcGIS y AutoCAD) (Bivand y Neteler, 2000). ...
Geographic Information Systems (GIS) have become powerful tools to integrate, visualise and analyse spatial data obtained from different sources. However, the number of statistical methods implemented in most GIS is not comparable to those available in statistical software.RArcInfo is a package for the R programming language to import data from Arc/Info Version 7.x binary coverages and E00 files, which provide a complete structure to describe spatial data.Once the data are imported into R, several packages are available to perform spatial analysis, access databases to integrate new data and other interesting possibilities. The result is an increase in the spectrum of statistical tools available to process the data.
A method for modeling and graphically comparing incidence rates of adverse events that vary over geographical regions is shown and discussed. To achieve our goal, we used a statistical model to compare incidence rates and showed how informative three-dimensional graphs of such incidence rates can be created dynamically using R and interactively controlled using Google Earth with Keyhole Markup Language (KML). These methods are applied to the terrorism events in regions of Southern Thailand that occurred from 2004 to 2009.
Many environmental processes depend on the amount of solar radiation at the ground level. Ground measurements are often available, even for long time series, and are used as input for spatial interpolation models to produce continuous maps of solar radiation. The aim of this work is to evaluate the results of different kriging interpolation approaches. This kind of comparison presents a relevant meaning since the availability of a physical model for the direct evaluation of solar radiation, which has been used as reference to validate the interpolation results. The procedure integrates the use of some GIS-GRASS capabilities, such the r.sun module which implements the solar radiation physical model, and of the statistical analysis tools provided by the R software package. Some interpolations have been performed taking into account also the values of slope and aspect as geomorphological quantities correlated to the solar radiation value. The study has been performed at different spatial scales to evaluate how resolution affects the results. Accuracy maps have been produced and a brief analysis has been also performed to check the occurrence of significant correlation between geomorphological features and the magnitude of errors. The analyzed solar radiation measurements refer to a zone of the Italian Trentino-Alto Adige region located in the South-East part of the alpine arc.
Geocomputation, with its necessary focus on software development and methods innovation, has enjoyed a close relationship with free and open source software communities. These extend from communities providing the numerical infrastructure for computation, such as BLAS (Basic Linear Algebra Subprograms), through language communities around Python, Java and others, to communities supporting spatial data handling, especially the projects of the Open Source Geospatial Foundation. This chapter surveys the stack of software components available for geocomputation from these sources, looking in most detail at the R language and environment, and how OSGeo projects have been interfaced with it. In addition, attention will be paid to open development models and community participation in software development. Since free and open source geospatial software has also achieved a successively greater presence in proprietary software as computational platforms evolve, the chapter will close with some indications of future trends in software component stacks, using Terralib as an example.
This paper reports on recent experience with the development of aspace, an Open Source (OS) library for the geographic visualization and analysis of activity-travel behaviour. The paper begins with an overview of recent progress with respect to the convergence of Open Source technology, spatial analysis, and travel behaviour research. The remainder of the paper focuses on aspace; a collection of functions that, when combined with data describing the geographical location of daily activities, can be used to visualize and describe spatial properties of individual and household activity spaces. These properties include: size, orientation, shape, and the geographical dispersion of activity locations contained within the activity space. Several planar geometries are used to transform measurable spatial properties into intuitive objects for visualizing spatial patterns of activity participation. Experiments are conducted, using data from the first wave of the 2003 Toronto Travel Activity Panel Survey, to demonstrate the potential application of aspace for basic and applied policy-based research into activity-travel behaviour. The toolkit is distributed as a downloadable `package' from the Open Source R Project for Statistical Computing.
Introduction Over the past decade geoinformation field has evolved from a highly specialized niche to a technology with broad impact on society and its interaction with nature. Geographic Information Systems (GIS) applications now range from simple navigation to critical and extremely complex tasks, such as prediction and management of natural disasters. Due to the increased use of GPS, faster access to georeferenced data, expanding field of remote sensing and real-time monitoring, GIS technology is entering many new disciplines and industries and GIS is becoming a part of general computational infrastructure. It is therefore natural that geospatial tools are being developed also within the Open Source and Free Software community [60]. Several important trends can be identified in the current development of geospatial technology: modern, highly automated mapping and monitoring technologies produce massive amounts of spatiotemporal georeferenced data (e.g., LIDAR [39], [53], IFSARE,
