IRF: Institutional Repository FHNW
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Das IRF ist das digitale Repositorium der FHNW. Es enthält Publikationen, studentische Arbeiten und Projekte.
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FHNW Allgemein Hochschule für Angewandte Psychologie APS FHNWHochschule für Architektur, Bau und Geomatik HABG FHNWHochschule für Gestaltung und Kunst HGK FHNWHochschule für Life Sciences HLS FHNWHochschule für Musik MHS FHNWHochschule für Soziale Arbeit HSA FHNWHochschule für Technik HT FHNWHochschule für Wirtschaft HSW FHNWPädagogische Hochschule PH FHNW
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Publikation
Marketing without moralising. Service orientation and employer relations in the Swiss disability insurance
(Routledge, 2018) Nadai, Eva; Sowa, Frank; Staples, Ronald; Zapfel, Stefan [in: The transformation of work in welfare state organizations: new public management and the institutional diffusion of ideas]
04A - Beitrag Sammelband
Publikation
Real-time generalization of point data in mobile and web mapping using quadtrees
(Taylor & Francis, 29.04.2013) Bereuter, Pia; Weibel, Robert [in: Cartography and Geographic Information Science]
With a focus on mobile and web mapping, we propose several algorithms for on-the-fly generalization of point data, such as points of interest (POIs) or large point collections. In order to achieve real-time performance, we use a quadtree data structure. With their hierarchical subdivision structure and progressive levels of detail, indices of the quadtree family lend themselves as auxiliary data structures to support algorithms for generalization operations, including selection, simplification, aggregation, and displacement of point data. The spatial index can further be used to generate several local and global measures that can then serve to make educated guesses on the density and proximity of points across map scales, and thus enable control of the operation of the generalization algorithms. An implementation of the proposed algorithms has shown that, and thanks to the quadtree index, real-time performance can be achieved even for large point sets. Furthermore, the quadtree data structure can be extended into a caching structure, which can be used to store pre-computed generalizations; thus, a desired level of detail (LOD) can simply be retrieved from cache.
01A - Beitrag in wissenschaftlicher Zeitschrift
Publikation
06 - Präsentation