scholarly journals Grounded Design and GIScience - A framework for informing the design of geographical information systems and spatial data infrastructures

2019 ◽  
Author(s):  
Alexander Kmoch ◽  
Evelyn Uuemaa ◽  
Hermann Klug
Keyword(s):  
Information Systems ◽  
Spatial Data ◽  
Geographical Information Systems ◽  
Information Science ◽  
Design Science ◽  
Environmental Data ◽  
Geographical Information ◽  
Data Infrastructure ◽  
Development Framework ◽  
Grounded Design

Geographical Information Science (GIScience), also Geographical Information Science and Systems, is a multi-faceted research discipline and comprises a wide variety of topics. Investigation into data management and interoperability of geographical data and environmental data sets for scientific analysis, visualisation and modelling is an important driver of the Information Science aspect of GIScience, that underpins comprehensive Geographical Information Systems (GIS) and Spatial Data Infrastructure (SDI) research and development. In this article we present the 'Grounded Design' method, a fusion of Design Science Research (DSR) and Grounded Theory (GT), and how they can act as guiding principles to link GIScience, Computer Science and Earth Sciences into a converging GI systems development framework. We explain how this bottom-up research framework can yield holistic and integrated perspectives when designing GIS and SDI systems and software. This would allow GIScience academics, GIS and SDI practitioners alike to reliably draw from interdisciplinary knowledge to consistently design and innovate GI systems.

scholarly journals Grounded Design and GIScience - A framework for informing the design of geographical information systems and spatial data infrastructures

2019 ◽  
Author(s):  
Alexander Kmoch ◽  
Evelyn Uuemaa ◽  
Hermann Klug
Keyword(s):  
Information Systems ◽  
Spatial Data ◽  
Geographical Information Systems ◽  
Information Science ◽  
Design Science ◽  
Environmental Data ◽  
Geographical Information ◽  
Data Infrastructure ◽  
Development Framework ◽  
Grounded Design

Geographical Information Science (GIScience), also Geographical Information Science and Systems, is a multi-faceted research discipline and comprises a wide variety of topics. Investigation into data management and interoperability of geographical data and environmental data sets for scientific analysis, visualisation and modelling is an important driver of the Information Science aspect of GIScience, that underpins comprehensive Geographical Information Systems (GIS) and Spatial Data Infrastructure (SDI) research and development. In this article we present the 'Grounded Design' method, a fusion of Design Science Research (DSR) and Grounded Theory (GT), and how they can act as guiding principles to link GIScience, Computer Science and Earth Sciences into a converging GI systems development framework. We explain how this bottom-up research framework can yield holistic and integrated perspectives when designing GIS and SDI systems and software. This would allow GIScience academics, GIS and SDI practitioners alike to reliably draw from interdisciplinary knowledge to consistently design and innovate GI systems.

Spatial Monitoring and Routing System for the Transportation of Hazardous Materials

2001 ◽  
pp. 227-236 ◽  
Author(s):  
Azedine Boulmakoul ◽  
Robert Laurini ◽  
Karine Zeitouni
Keyword(s):  
Information Systems ◽  
Geographical Information Systems ◽  
Data Access ◽  
Environmental Information ◽  
Environmental Data ◽  
Geographical Information ◽  
Data Interoperability ◽  
Processing Technologies ◽  
Environmental Information Systems ◽  
Effective Use

The concept of Environmental Information Systems (EIS) emerged from the concerns and the efforts carried on by world wide private and official organisations in order to promote an effective use of environmental data. These data are of various natures such as statistics, thematic maps, or documents describing the identification and the quantification of the environmental resources. The Environmental Information Systems became institutional tools providing pragmatic solutions for sustainable development in various fields. The objective of an EIS is to increase the quality and the efficiency in the decision-making process. To achieve this goal, the EIS requires the integration of various information processing technologies: Geographical Information Systems (GIS); Database Management Systems (DBMS); Space Imagery; Decision Support Systems (DSS); etc. However, the implementation of such an integration generates new requirements, namely data interoperability, data description by metadata, reverse engineering from existing applications and remote data access and data processing. This leads to the reconsideration of the analysis and design methodology.

scholarly journals Open computational landscape genetics

2016 ◽  
Author(s):  
Stéphane Joost ◽  
Solange Duruz ◽  
Estelle Rochat ◽  
Ivo Widmer
Keyword(s):  
Population Genetics ◽  
Landscape Genetics ◽  
Evolutionary Biology ◽  
Geographical Information Systems ◽  
Information Science ◽  
Molecular Data ◽  
Geographic Information ◽  
Research Field ◽  
Environmental Data ◽  
Geographical Information

Geographical Information Systems (GIS) are considered to be applications-led technology. Consequently, geographic information scientists commonly find themselves as guest in host disciplines in order to best exploit spatial analysis tools and methods, appropriately guided by experts in the field. An example is population genetics in evolutionary biology. Genetic information being linked to living organisms can be partially characterized by geographic coordinates. A research field named landscape genetics emerged at the intersection of genetics, environmental and geographic information science. Geocomputation and programming efforts carried out with the help of open sources technologies and dedicated to the analysis of genetic data gather together a key scientific community whose goal is to extract new knowledge from the present data tsunami caused by the advent of high throughput molecular data and of new sources of high resolution environmental data. While the level of sophistication of the population genetics functions included in the analytical frameworks developed until now are cutting-edge, advanced geo-competences are also required to reinforce the spatial side of this discipline. They will be particularly useful in conservation programmes for wildlife preservation, but also in farm animal genetic resources conservation.

scholarly journals Open computational landscape genetics

2016 ◽  
Author(s):  
Stéphane Joost ◽  
Solange Duruz ◽  
Estelle Rochat ◽  
Ivo Widmer
Keyword(s):  
Population Genetics ◽  
Landscape Genetics ◽  
Evolutionary Biology ◽  
Geographical Information Systems ◽  
Information Science ◽  
Molecular Data ◽  
Geographic Information ◽  
Research Field ◽  
Environmental Data ◽  
Geographical Information

Geographical Information Systems (GIS) are considered to be applications-led technology. Consequently, geographic information scientists commonly find themselves as guest in host disciplines in order to best exploit spatial analysis tools and methods, appropriately guided by experts in the field. An example is population genetics in evolutionary biology. Genetic information being linked to living organisms can be partially characterized by geographic coordinates. A research field named landscape genetics emerged at the intersection of genetics, environmental and geographic information science. Geocomputation and programming efforts carried out with the help of open sources technologies and dedicated to the analysis of genetic data gather together a key scientific community whose goal is to extract new knowledge from the present data tsunami caused by the advent of high throughput molecular data and of new sources of high resolution environmental data. While the level of sophistication of the population genetics functions included in the analytical frameworks developed until now are cutting-edge, advanced geo-competences are also required to reinforce the spatial side of this discipline. They will be particularly useful in conservation programmes for wildlife preservation, but also in farm animal genetic resources conservation.

scholarly journals Digital spatial data as support for river basin management: The case of Sotla river basin

Spatium ◽  
2013 ◽  
pp. 59-67
Author(s):  
Klemen Prah ◽  
Andrej Lisec ◽  
Anka Lisec
Keyword(s):  
Information Systems ◽  
River Basin ◽  
Spatial Data ◽  
Geographical Information Systems ◽  
River Basin Management ◽  
Geographical Information ◽  
Basin Management ◽  
Alternative Evaluation ◽  
Policy Alternatives ◽  
Social Environmental

Many real-world spatially related problems, including river-basin planning and management, give rise to geographical information system based decision making, since the performance of spatial policy alternatives were traditionally and are still often represented by thematic maps. Advanced technologies and approaches, such as geographical information systems (GIS), offer a unique opportunity to tackle spatial problems traditionally associated with more efficient and effective data collection, analysis, and alternative evaluation. This paper discusses the advantages and challenges of the use of digital spatial data and geographical information systems in river basis management. Spatial data on social, environmental and other spatial conditions for the study area of 451.77 km2, the Slovenian part of the Sotla river basin, are used to study the GIS capabilities of supporting spatial decisions in the framework of river basin management.

scholarly journals COMPLEMENTARITY OF HISTORIC BUILDING INFORMATION MODELLING AND GEOGRAPHIC INFORMATION SYSTEMS

2016 ◽  
Vol XLI-B5 ◽  
pp. 437-443 ◽  
Author(s):  
X. Yang ◽  
M. Koehl ◽  
P. Grussenmeyer ◽  
H. Macher
Keyword(s):  
Information Systems ◽  
Spatial Data ◽  
Geographical Information Systems ◽  
Point Clouds ◽  
3D Models ◽  
Geographical Information ◽  
Building Information Modelling ◽  
Historic Building ◽  
Information Modelling ◽  
Building Information

In this paper, we discuss the potential of integrating both semantically rich models from Building Information Modelling (BIM) and Geographical Information Systems (GIS) to build the detailed 3D historic model. BIM contributes to the creation of a digital representation having all physical and functional building characteristics in several dimensions, as e.g. XYZ (3D), time and non-architectural information that are necessary for construction and management of buildings. GIS has potential in handling and managing spatial data especially exploring spatial relationships and is widely used in urban modelling. However, when considering heritage modelling, the specificity of irregular historical components makes it problematic to create the enriched model according to its complex architectural elements obtained from point clouds. Therefore, some open issues limiting the historic building 3D modelling will be discussed in this paper: how to deal with the complex elements composing historic buildings in BIM and GIS environment, how to build the enriched historic model, and why to construct different levels of details? By solving these problems, conceptualization, documentation and analysis of enriched Historic Building Information Modelling are developed and compared to traditional 3D models aimed primarily for visualization.

Including the spatial dimension: using geographical information systems in hydrology

1996 ◽  
Vol 20 (2) ◽  
pp. 159-177 ◽  
Author(s):  
R.A. McDonnell
Keyword(s):  
Information Systems ◽  
Spatial Data ◽  
Geographical Information Systems ◽  
Spatial Dimension ◽  
Geographical Information ◽  
Gis Technology ◽  
Gis Data ◽  
Spatial Data Models ◽  
The Many ◽  
Hydrological Applications

Developments in geographical information systems (GIS) technology have coincided with moves within hydrology to a more explicit accounting of space through distributed rather than lumped or topological representations. GIS support these spatial data models and provide integrating, measuring and analytical capabilities which have been used in many hydrological applications ranging from inventory and assessment studies through to process modelling. The many examples in the article illustrate how the technology has supported moves away from averaged value representations for catchments towards a greater inclusion of spatial variations in hydrological studies. While the potential of these systems is gradually being realized, there are still various issues, both technical and methodological, which at present limit their use. As new data sources become available, GIS data structures become more flexible and open, and, as the understanding of scale variations in processes improves, the possibilities for using the technology in hydrological research will expand.

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