This page last changed on Nov 23, 2010 by smlind.
Geospatial analysis has a long history in geography and cartography. Geospatial analysis aims to answer the question of where something happens and with what impact on neighboring areas.
Geospatial analysis requires spatial attribute values or geolocations for authors and their papers, extracted from affiliation data or spatial positions of nodes, generated from layout algorithms. Geospatial data can be continuous (i.e., each record has a specific position) or discrete (i.e., each set of keywords has a position or area-shape file – e.g., number of papers per country). Spatial aggregations (e.g., merging via ZIP codes, counties, states, countries, and continents) are common.
Cartographic generalization refers to the process of abstraction such as (1) graphic generalization: the simplification, enlargement, displacement, merging, or selection of entities without enhancing their symbology; and (2) conceptual symbolization: the merging, selection, and symbolization of entities, including enhancement – such as representing high-density areas with a new (city) symbol.
Geometric generalization aims to solve the conflict between the number of visualized features, the size of symbols, and the size of the display surface. Cartographers dealt with this conflict intuitively in part until researchers like Friedrich Töpfer attempted to solve them with quantifiable expressions.
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