Cartography in internet-based view services : methods to improve cartography when geographic data from several sources are combined

Sammanfattning: We live in an information intensive society and geographic data are part of this information. These geographic data are used by private persons to plan holiday trips or to find the way to business meetings. Companies utilize geographic data in fields such as forestry, construction work, and tourism. Local municipalities require geographic data for planning purposes, and in the academic world much research is based on geographic data. To enable sharing of these data, searching, viewing, and downloading are facilitated by web-based services following common standards. These services are part of, and regulated by Spatial Data Infrastructures (SDI). An important component of an SDI is a geoportal. That is a web site acting as a gateway to web services that enable a user to search, view, and download geographic data. In this master thesis the main focus is on view services. If these view services have limited symbology options they may prevent geoportals from reaching their full potential. It might be so that all data required for a map are found from different sources, but limited symbology options prevent a user from designing a legible map. This situation can be improved by allowing user-defined symbology or with a larger number of symbologies available from a service. Cartography can also be improved by methods that enable a user to control symbology to a larger extent. To facilitate development of methods to improve cartography when data from several sources are combined and viewed in a geoportal a test bed, the Cartographic enhanced geoportal (CEG), is implemented in this study. Two methods are implemented in CEG. The polygon overlay method enables polygon features to be overlaid other data without hiding underlying information; this is achieved by symbolizing polygons with boundary and icons. The colour saturation method enables a user to invoke visual hierarchies in a map by deemphasizing less important information. Another potential problem in view services is maps that are difficult to read due to excessive amount of information. One approach to solve this problem is to identify areas with poor legibility in a map and apply generalization operations on these areas; that is more efficient than applying generalization on the entire map A study was performed to investigate if clustering techniques can be used to identify areas that are difficult to read. The density based DBSCAN clustering algorithm was implemented and tested. The study shows a promising result; however, a more extensive investigation must be performed to draw any conclusions.