This comprehensive volume blends broad coverage of basic methods for symbolizing spatial data with an introduction to cutting-edge data visualization techniques. Offers clear descriptions of various aspects of effective, efficient map design, with an emphasis on the practical application of design theories and appropriate use of map elements. Clearly contrasts different approaches for symbolizing spatial data, in addition to individual mapping techniques. This edition includes updated material on the history of thematic cartography, maps and society, scale and generalization, and cartograms and flow mapping. For those interested in learning more about cartography

This paper investigates as part of the National Historical Geographic Information System project (http://www.nhgis.org/) the creation of a multi-scale database of historical US census units. This database will include, at a minimum, three different scales (1:150,000, 1:400,000 and 1:1,000,000) and boundary data for all documented census since 1790. Besides the commitment to the production need, the main challenge in the generalization of these spatio-temporal data is the maintenance of geometric and topological consistency both within a dataset and between datasets for one target scale. We propose to address this challenge through: (1) a generalization framework based on the constraint based generalization paradigm and the active object approach; and (2) a topological data model linking all datasets, which represent different census years, for one target scale. The framework is implemented in ESRIs ArcGIS environment using ArcGIS 9.0, Oracle, C# and ArcObjects. The implementation of the model generalization process was completed and successfully tested for the three target scales of the final database. Model generalization accomplishes the removal of redundant points and the removal of boundary-change sliver polygons. The implementation of the cartographic generalization process is still on-going and has focused, until recently, on different approaches for the enlargement and elimination of too small census units or detached parts of a census unit as well as on the reduction of the outline granularity of census units boundaries. Results that were automatically generalized with the current version of the prototype exhibit satisfying quality based on a preliminary visual evaluation.

Although considerable developments in automated generalization have taken place over the last thirty years, it is still difficult to solve generalization problems with off-the-shelf software due to the limited capability of the algorithms and complexity of the databases. At the National Historical Geographic Information System (NHGIS) project at the University of Minnesota (http://www.nhgis.org/), work is currently underway to design a multiple scale database at1:150,000, 1:400,000, and 1:1,000,000 through the application of data models and generalization algorithms.The NHGIS is taking a two-fold approach using both specific algorithmic approaches and object-oriented datamodeling. Early results from the application of a mixture of standard and custom-tailored algorithms such as theDouglas and Visvalingham routines has shown promising results, especially along coastal areas. Examples of coastalgeneralization at a variety of scales are provided. The project will continue to develop the needed generalizationalgorithms as specific geographical conditions are encountered. Current work is identifying the specific constraints such as distance between points and/or objects and specific algorithms needed for generalization at the various scales and applying these in a comprehensive generalization framework that goes beyond the tract boundary specific approach.

The National Historical Geographic Information System (NHGIS) is a five-year NSF-funded project designed to create a comprehensive U.S. census database-at the census tract and county level-for both the geographical and attribute data. Technological change presents an unprecedented opportunity to make these data readily available for social science research; thus bringing the complete census within reach of social scientists unlocking the potential of two centuries of data collection, and stimulating research in economics, history, sociology, geography and other fields.