The need for disciplinarity, interdisciplinarity and scientific societies - the cartographic perspective

Figures & data

Figure 1. Data classification using equidistant (left), quantile (middle) and aChor method (right): With the first two methods, the local extreme value region (outlined; with value 4.14) falls in same class/color as some neighbors, while the aChor method preserves the extreme value information graphically (own figure)

Figure 2. Example of geometric point clutter (topic: pubs in the UK; source: Google maps)

Figure 3. Interplay of cartography with other disciplines, including Geovisualization and Geovisual analytics (source: Schiewe 2013)

Schiewe, J. 2013. “Geovisualization and Geovisual Analytics: The Interdisciplinary Perspective on Cartography.” Kartographische Nachrichten Special Issue 2013: 122–126.

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Data and Method Availability Statement

Data set presented in fig. 1 (deprive index of Hamburg, 2016) is open data: http://suche.transparenz.hamburg.de/dataset/sozialmonitoring-integrierte-stadtteilentwicklung-bericht-2016-anhang

Methods for the project aChor (section 3.2; fig. 1) are available under the GitHub account: https://github.com/Ariel505/aChor