Master thesis of Anup Deshmukh, Geographisches Institut der Universität Bonn, 2007

The OGC Web Coverage Server is the current standard for publication of gridded data on theweb. It represents a first cut at the problem of publishing gridded data. However, there is ample evidence that people who wish to publish multidimensional gridded data attempt to add to it. The thesis gives the details about the design and architecture of our Web Service refactoring of OGC specifications for the Web Coverage Service, determines how it falls short of meeting the needs of querying spatio-temporal data, and compares with similar facilities in other, more mature specifications. The pros and cons in adopting the conventions that are legal (if perhaps not “recommended”) according to the current specification will be assessed. (thesis)

Master thesis of Emilia Suomalainen, Department of Engineering Physics and Mathematics der Universität Helsinki, 2006

In a location-allocation problem (LAP), an optimal number of facilities has to be placed in an area of interest in order to satisfy the customer demand. The optimization objective can be, e.g., to minimize costs or to maximize profit and the situation can be characterized by various constraints. The total location area may also be divided into regions which have each their special characteristics. There are often several plausible scenarios for an LAP representing, for example, different budgets limitations, cost functions, or estimates for market growth. In this Thesis, the emphasis is on retail location-allocation problems and it is illustrated with a case study in retailing.Existing tools of GIS software are not sufficient for the visualization of dozens of multi-dimensional location-allocation solutions. This problem can be circumvented either by concentrating on the statistical properties of the solution set instead of examining and comparing the options one by one, or by trying to classify the solutions into groups whose properties can then be more easily compared. The methods of cluster analysis and subgroup discovery could prove useful in grouping the solutions. Also, principal component analysis can be used to reduce the dimensions of the problem and to facilitate visualization. (pdf)