Spatial Analysis and Modeling Tool Version 2 (SAMT2), a spatial modeling tool kit written in Python

Ecological Informatics

Volumn 30, Issue , 2015, Pages 1-5

  Wieland, Ralf ^a   Groth, Karin ^a   Linde, Felix ^a   Mirschel, Wilfried ^a  

^a LEIBNIZ CENTRE FOR AGRICULTURAL LANDSCAPE RESEARCH ZALF   (Germany)

Author keywords

Cython; Fuzzy modeling; Python; QT; Spatial analysis and modeling tool; SVM

Indexed keywords

DATA PROCESSING; FUZZY MATHEMATICS; GRAPHICAL METHOD; INFORMATICS; PARALLEL COMPUTING; SOFTWARE; SPATIAL ANALYSIS; STAKEHOLDER; VISUALIZATION;

EID: 84941270609     PISSN: 15749541     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecoinf.2015.08.002     Document Type: Article

References (23)

1. Albanese D., Visintainer R., Merler St, Riccadonna S., Jurman G., Furlanello C. mlpy: Machine Learning Python 2012, 4. Cornell Universitiy Library, (arXiv.org arXiv:1202.6548v2).
2. Alsberg B.K., Hagen O.J. How octave can replace Matlab in chemometrics. Chemom. Intell. Lab. Syst. 2006, 84(12):195-200.
3. Biddiscombe J., Soumagne J., Oger G., Guibert D., Piccinali J.-G. Parallel Computational Steering and Analysis for HPC Applications using a ParaView Interface and the HDF5 DSM Virtual File Driver. Eurographics Symposium on Parallel Graphics and Visualization 2011, 1-10. T. Kuhlen, R. Pajarola, K. Zhou (Eds.).
4. Boyd D.S., Foody G.M. An overview of recent remote sensing and GIS based research in ecological informatics. Ecol. Inform. 2011, 6(1):25-36.
5. Dalcin L.D., Paz R.R., Kler P.A., Cosimo A. Parallel distributed computing using Python. Adv. Water Resour. 2011, 34(9):1124-1139.
6. Foote K.E., Lynch M. Geographic Information Systems as an Integrating Technology: Context, Concepts, and Definitions http://www.colorado.edu/geography/gcraft/notes/intro/intro.html.
7. Giordano R., Liersch S. A fuzzy GIS-based system to integrate local and technical knowledge in soil salinity monitoring. Environ. Model Softw. 2012, 36:49-63.
8. Lopez-Rubio F.J., Lopez-Rubio E. Features for stochastic approximation based foreground detection. Comput. Vis. Image Underst. 2015, 133:30-50.
9. Ma Ch., Zhang H.H., Wang X. Machine learning for Big Data analytics in plants. Trends Plant Sci. 2014, 19(12):798-808.
10. Merzky A., Weidner O., Jha Sh. SAGA: A standardized access layer to heterogeneous Distributed Computing Infrastructure 2015, SoftwareX, (available online on March 20, 2015). 2352-7110.
11. Oliphant Travis E. Python for scientific computing. Comput. Sci. Eng. 2007, 9:10-20.
12. Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Vanderplas J., Passos A., Cournapeau D., Brucher M., Perrot M., Duchesnay E. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 2011, 12:2825-2830.
13. QGIS Development Team QGIS Geographic Information System 2015, Open Source Geospatial Foundation Project, (http://qgis.osgeo.org).
14. Rossant C. IPython Interactive Computing and Visualization Cookbook 2014, 494. Packt Publishing.
15. Santana F.S., de Siqueira M.F., Saraiva A.M., Correa P.L.P. A reference business process for ecological niche modelling. Ecol. Inform. 2008, 3(1):75-86.
16. Smirnoff A., Paradis S.J., Boivin R. Generalizing surficial geological maps for scale change: ArcGIS tools vs. cellular automata model. Comput. Geosci. 2008, 34(11):1550-1568.
17. Uehara T., Li B., Bo-Mo Kim, Yoon S., Quach Q., Kim H., Chon T.-S. Inferring conflicting behavior of zebrafish (Danio rerio) in response to food and predator based on a self-organizing map (SOM) and intermittency test. Ecol. Inform. 2014, (in press).
18. van der Walt S., Colbert S.C., Varoquaux G. The NumPy Array: a structure for efficient numerical computation. Comput. Sci. Eng. 2011, 13(2):22-30.
19. Werner A., Dreger F., Schwarz J. German: Informationsgeleitete Pflanzenproduktion mit Precision Farming als zentrale inhaltliche und technische Voraussetzung fr eine nach- haltige Entwicklung der landwirtschaftlichen Landnutzung - pre agro II, BMBF, FKz.: 0339740/2 2008, 689.
20. Wieland R., Voss M., Holtmann X., Mirschel W., Ajibefun I. Spatial Analysis and Modeling Tool (SAMT): 1. Structure and possibilities. Ecol. Inform. 2006, 1:67-76.
21. Wieland R., Mirschel W., Zbell B., Groth K., Pechenick A., Fukuda K. A new library to combine artificial neural networks and support vector machines with statistics and a database engine for application in environmental modeling. Environ. Model Softw. 2010, 25(4):412-420.
22. Wieland R., Mirschel W., Groth K., Pechenick A., Fukuda K. A new method for semi-automatic fuzzy training and its application in environmental modeling. Environ. Model Softw. 2011, 26(12):1568-1573.
23. Zasada S.J., Coveney P.V. Virtualizing access to scientific applications with the application hosting environment. Comput. Phys. Commun. 2009, 180(12):2513-2525.