Veranstaltungsprogramm

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Sitzungsübersicht
Session
B19: Spatial Computing
Zeit:
Donnerstag, 04.07.2019:
14:00 - 15:30

Chair der Sitzung: Jochen Albrecht
Ort: B - Blauer Hörsaal
HS402

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Präsentationen

Replication of the Question-based Spatial Computing Approach – Experiences and Suggestions for Further Developments

Selina Studer, Barbara Hofer

Interfaculty Department of Geoinformatics - Z_GIS, University of Salzburg, Salzburg, Austria

Geographic Information Systems (GIS) have developed into complex toolboxes and require analysts to formulate spatial questions according to the requirements of the data formats and tools provided by their specific GIS-application. The recently proposed language for spatial computing aims to provide a question-based and thus more comprehensible approach for spatial analyses that especially supports scientists and experts from other disciplines to conduct spatial analyses in their fields. In this contribution, we apply the question-based spatial computing approach to a case study in the humanitarian field and compare the resulting script to one written using a conventional GIS tool. The comparison of the two versions of the script is based on six criteria covering qualitative and quantitative aspects of the analysis. We also discuss the implementation concept behind the new language. Our results show that the new approach requires fewer computational steps than the conventional script. In addition, the declarative approach allows users to focus on the content of the spatial question, and the query-like character of the language makes it easier to understand for non-GIS specialists. In addition, we share observations on challenges of the further development of the language as an outcome of this study.



MovingPandas: Efficient Structures for Movement Data in Python

Anita Graser

AIT Austrian Institute of Technology, Österreich

Movement data analysis is a high-interest topic in many different scientific domains. Even though Python is the scripting language of choice in the GIS world, there is no Python library available so far that would enable researchers and practitioners to interact with and analyze movement data efficiently. To close this gap, we present MovingPandas, a new Python library for dealing with movement data. Its development is based on an analysis of state-of-the-art conceptual frameworks and existing implementations (in PostGIS, Hermes, and the R package trajectories). We describe how MovingPandas avoids limitations of SimpleFeature based movement data models commonly used in the GIS world and demonstrate its use.



Parallel and Distributed Computing for Large-Size Spatial Multicriteria Decision Analysis Problems: A Computational Performance Comparison.

Christoph Erlacher1,2, Angelika Desch1, Karl-Heinrich Anders1, Piotr Jankowski3, Gernot Paulus1

1Carinthia University of Applied Sciences, Department of Geoinformation and Environmental Technologies, Villach 9524, Austria; 2University of Salzburg, Department of Geoinformatics, Salzburg 5020, Austria; 3San Diego State University, Department of Geography

The article focuses on a cluster-based parallel and distributed approach for large raster datasets in the context of Spatial Multicriteria Decision Analysis (S-MCDA). Specifically, the research reported herein addresses a land prioritization model in respect to conservation practices and includes the top-level indicators “Wildlife”, “Water-Quality”, “Soil-Erosion”, “Enduring-Benefits” and “Air-Quality”. The reliability of model results is examined with a variance-based Spatially-Explicit Uncertainty and Sensitivity (SEUSA) framework. The case study employing the model is located in Southwest Michigan, USA and incorporates millions of mapping units (pixels). As part of model sensitivity analysis, several thousand intermediate raster datasets representing suitability surfaces are generated by means of a Monte Carlo Simulation (MCS), which is an integral part of the SEUSA framework. The creation of the suitability surfaces represents the most time-consuming and memory-intensive step within the SEUSA framework. Sequential computational approaches to implementing SEUSA often have to accept a compromise with respect to problem size and the number of simulations, resulting in low quality of model sensitivity measures. This article presents the concept and implementation of a distributed and parallel Python-Dask solution in order to improve the quality of SEUSA results for computationally intensive spatial models.



 
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