Visualizing Urban Futures - A Framework for Visualizing Multidimensional Geospatial Data

  • In urban planning, sophisticated simulation models are key tools to estimate future population growth for measuring the impact of planning decisions on urban developments and the environment. Simulated population projections usually result in large, macro-scale, multivariate geospatial data sets. Millions of records have to be processed, stored, and visualized to help planners explore and analyze complex population patterns. We introduce a database driven framework for visualizing geospatial multidimensional simulation data based on the output from UrbanSim, a software for the analysis and planning of urban developments. The designed framework is extendable and aims at integrating empirical-stochastic methods and urban simulation models with techniques developed for information visualization and cartography. First, we develop an empirical model for the estimation of residential building types based on demographic household characteristics. The predicted dwelling type information is important for the analysis of future material use, carbon footprint calculations, and for visualizing simultaneously the results of land usage, density, and other significant parameters in 3D space. Our model uses multinomial logistic regression to derive building types at different scales. The estimated regression coefficients are applied to UrbanSim output in order to predict residential building types. The simulation results and the estimated building types are managed in an object-relational geodatabase. From the database, density, building types, and significant demographic variables are visually encoded as scalable, georeferenced 3D geometries and displayed on top of aerial photographs in a Google Earth visual synthesis. The geodatabase can be accessed and the visualization parameters can be chosen through a web-based user interface. The geometries are encoded in KML, Google's markup language, as ready-to-visualize data sets. The goal is to enhance human cognition by displaying abstract representations of multidimensional data sets in a realistic context and thus to support decision making in planning processes.

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Metadaten
Verfasserangaben:Ariane Christine Middel
URN (Permalink):urn:nbn:de:hbz:386-kluedo-22643
ISBN:978-3-939432-75-3
ISSN:1610-2673
Schriftenreihe (Bandnummer):Schriftenreihe / Fachbereich Informatik (26)
Betreuer:Hans Hagen
Dokumentart:Dissertation
Sprache der Veröffentlichung:Englisch
Jahr der Fertigstellung:2007
Jahr der Veröffentlichung:2007
Veröffentlichende Institution:Technische Universität Kaiserslautern
Titel verleihende Institution:Technische Universität Kaiserslautern
Datum der Annahme der Abschlussarbeit:11.04.2008
Datum der Publikation (Server):11.09.2008
Freies Schlagwort / Tag:UrbanSim; multidimensional datasets; multinomial regression; urban planning
GND-Schlagwort:Datenbank; Geoinformationssystem ; Google Earth ; Visualisierung
Fachbereiche / Organisatorische Einheiten:Fachbereich Informatik
DDC-Sachgruppen:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Lizenz (Deutsch):Standard gemäß KLUEDO-Leitlinien vor dem 27.05.2011

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