Optimization approaches for generalization and data abstraction Export

@article{citeulike:3874494, abstract = {The availability of methods for abstracting and generalizing spatial data is vital for understanding and communicating spatial information. Spatial analysis using maps at different scales is a good example of this. Such methods are needed not only for analogue spatial data sets but even more so for digital data. In order to automate the process of generating different levels of detail of a spatial data set, generalization operations are used. The paper first gives an overview on current approaches for the automation of generalization and data abstraction, and then presents solutions for three generalization problems based on optimization techniques. Least-Squares Adjustment is used for displacement and shape simplification (here, building groundplans), and Self-Organizing Maps, a Neural Network technique, is applied for typification, i.e. a density preserving reduction of objects. The methods are validated with several examples and evaluated according to their advantages and disadvantages. Finally, a scenario describes how these methods can be combined to automatically yield a satisfying result for integrating two data sets of different scales.}, author = {Sester, M.}, citeulike-article-id = {3874494}, citeulike-linkout-0 = {http://dx.doi.org/10.1080/13658810500161179}, doi = {10.1080/13658810500161179}, editor = {Fisher, P. and Gahegan, M. and Lees, B.}, journal = {International Journal of Geographical Information Science}, keywords = {ann, deformation, generalisation, integration, least-square, typification}, number = {8}, pages = {871--897}, posted-at = {2009-01-11 16:06:17}, priority = {3}, publisher = {Taylor \& Francis}, title = {Optimization approaches for generalization and data abstraction}, url = {http://dx.doi.org/10.1080/13658810500161179}, volume = {19}, year = {2005} }

TY - JOUR ID - citeulike:3874494 L3 - citeulike-article-id:3874494 N2 - The availability of methods for abstracting and generalizing spatial data is vital for understanding and communicating spatial information. Spatial analysis using maps at different scales is a good example of this. Such methods are needed not only for analogue spatial data sets but even more so for digital data. In order to automate the process of generating different levels of detail of a spatial data set, generalization operations are used. The paper first gives an overview on current approaches for the automation of generalization and data abstraction, and then presents solutions for three generalization problems based on optimization techniques. Least-Squares Adjustment is used for displacement and shape simplification (here, building groundplans), and Self-Organizing Maps, a Neural Network technique, is applied for typification, i.e. a density preserving reduction of objects. The methods are validated with several examples and evaluated according to their advantages and disadvantages. Finally, a scenario describes how these methods can be combined to automatically yield a satisfying result for integrating two data sets of different scales. IS - 8 JF - International Journal of Geographical Information Science EP - 897 TI - Optimization approaches for generalization and data abstraction PB - Taylor & Francis VL - 19 SP - 871 KW - ann KW - deformation KW - generalisation KW - integration KW - least-square KW - typification AU - Sester, M A2 - Fisher, P A2 - Gahegan, M A2 - Lees, B PY - 2005/// UR - http://dx.doi.org/10.1080/13658810500161179 ER -