Processing: A Python Framework for the Seamless Integration of Geoprocessing Tools in QGIS

Gräser, Axel; Olaya, Víctor

doi:10.3390/ijgi4042219

Cited by 54 publications

(50 citation statements)

References 12 publications

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“…The framework processing for QGIS [19] addresses the seamless integration of tools from GRASS GIS, R, and other libraries in workflows, which is a distinctive feature in the above mentioned workflow tools. None of the mentioned tools provides an interface for the discovery of operations with tool-independent annotations of the geoprocessing operations or a machine-accessible formalization of constraints of operations.…”

Section: Improved Workflow Developmentmentioning

confidence: 99%

Coupling Knowledge with GIS Operations: The Benefits of Extended Operation Descriptions

Hofer

Papadakis

Mäs

2017

IJGI

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Abstract:The automated development of spatial analysis workflows is one of the envisioned benefits of Web services that provide geoprocessing functionality. Automated workflow development requires the means to translate a user objective into a series of geographic information system (GIS) operations and to evaluate the match between data and operations. Even though full automation is yet out of reach, users benefit from formalized knowledge about operations that is available during workflow development. This article presents user support during workflow development based on a recent approach to extended operation descriptions. User support thereby focuses on the discovery of operations across GIS tools and the validation of chains of spatial analysis operations. The required knowledge about operations is stored in a knowledge base, which builds on an approach called geooperators and extends the geooperator approach with a data-type ontology for describing the interfaces of geooperators and for expressing constraints of geooperator inputs. The advantages of the knowledge base are demonstrated for the construction of a multi-criteria decision making workflow. This workflow contains a set of pre-processing tasks for the input datasets and eventually the calculation of a cost distance raster. A critical discussion of the complexity of the knowledge base and a comparison with existing approaches complement this contribution.

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Section: Improved Workflow Developmentmentioning

confidence: 99%

Coupling Knowledge with GIS Operations: The Benefits of Extended Operation Descriptions

Hofer

Papadakis

Mäs

2017

IJGI

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“…This toolbox provides the ability to extend the capabilities of existing algorithms [96]. The PYQGIS bindings allow comfortable integration of Python code with Qtlibraries and eventually with QGIS.…”

Section: Methodology: Extending the Processing Toolboxmentioning

confidence: 99%

Assessing OpenStreetMap Data Using Intrinsic Quality Indicators: An Extension to the QGIS Processing Toolbox

Sehra

Singh

2017

Future Internet

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OpenStreetMap (OSM) is a recent emerging area in computational science. There are several unexplored issues in the quality assessment of OSM. Firstly, researchers are using various established assessment methods by comparing OSM with authoritative dataset. However, these methods are unsuitable to assess OSM data quality in the case of the non-availability of authoritative data. In such a scenario, the intrinsic quality indicators can be used to assess the quality. Secondly, a framework for data assessment specific to different geographic information system (GIS) domains is not available. In this light, the current study presents an extension of the Quantum GIS (QGIS) processing toolbox by using existing functionalities and writing new scripts to handle spatial data. This would enable researchers to assess the completeness of spatial data using intrinsic indicators. The study also proposed a heuristic approach to test the road navigability of OSM data. The developed models are applied on Punjab (India) OSM data. The results suggest that the OSM project in Punjab (India) is progressing at a slow peace, and contributors' motivation is required to enhance the fitness of data. It is concluded that the scripts developed to provide an intuitive method to assess the OSM data based on quality indicators can be easily utilized for evaluating the fitness-of-use of the data of any region.

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“…Since the cells in our approach, which is focused on use cases such as bicycle-sharing system planning, only encompass a small area around individual intersections, network connectivity does not come into play. Figure 3 shows the tessellation process implemented as a QGIS Processing model (Graser & Olaya, 2015) using GRASS GIS algorithms. The model input parameters include the planning area, tessellation seeds and raster cell size (provided as the length of the side of a square raster cell).…”

Section: Methodsmentioning

confidence: 99%

Tessellating Urban Space based on Street Intersections and Barriers to Movement

Gräser¹

2017

giforum

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This paper presents a novel method to partition urban space with respect to its mobility network rather than abstract administrative boundaries or simple grids. The main goal of our method is to support mobility infrastructure planning and optimization in an urban environment. To this end, our tessellation, which is based on constrained Voronoi tessellation, generates cells that are compact and centred around street intersections. Furthermore, it takes into account unsuitable areas and barriers that obstruct movement through urban space. The resulting cells are fine-grained planning units that reflect the characteristics of the underlying mobility network. Potential applications include demand modelling, location planning and optimization for bicycle-and car-sharing systems, or public transport stops.

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Processing: A Python Framework for the Seamless Integration of Geoprocessing Tools in QGIS

Cited by 54 publications

References 12 publications

Coupling Knowledge with GIS Operations: The Benefits of Extended Operation Descriptions

Coupling Knowledge with GIS Operations: The Benefits of Extended Operation Descriptions

Assessing OpenStreetMap Data Using Intrinsic Quality Indicators: An Extension to the QGIS Processing Toolbox

Tessellating Urban Space based on Street Intersections and Barriers to Movement

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