Map, Map Literacy, Quantitative Literacy, Quantitative Map Literacy, Thematic Maps, Reference Maps
We define quantitative map literacy (QML), a cross between map literacy and quantitative literacy (QL), as the concepts and skills required to accurately read, use, interpret, and understand the quantitative information embedded in a geospatial representation of data on a geographic background. Long used as tools in technical geographic fields, maps are now a common vehicle for communicating quantitative information to the public. As such, QML has potential to stand alongside health numeracy and financial literacy as an identifiable subdomain of transdisciplinary QL.
What concepts and skills are crucial for QML? The obvious answer is, “It depends on the type of map.” Therefore, our first task, and the subject of this paper, is to develop a framework to think and talk about the panoply of maps in a way that permits us to consider the range and distribution of QML content. We use an equilateral triangular plot to conceptualize maps in terms of locational information (L), thematic information (T), and generalization-distortion (G-D), and parameterize the plot with an L/T ratio (horizontal; reflecting the historical practice of cartographers to distinguish locational-reference maps from thematic maps) and G-D levels increasing from base to apex. We show positions for a wide variety of maps (e.g., topographic maps, weather maps, engineering-survey plots, subway maps, maps of air routes, a cartoon map of Orlando for tourists, driving-time maps, county-wide population maps, county-wide multivariable population and income maps, world political map, land use maps, and cartograms). The analysis of how these maps vary across the triangle allows us to proceed with an examination of how QML varies across the panoply of maps.
Xie, Ming, H. L. Vacher, Steven Reader, and Elizabeth Walton.
"Quantitative Map Literacy: A Cross between Map Literacy and Quantitative Literacy."
Numeracy 11, Iss. 1 (2018): Article 4.
Available at: http://scholarcommons.usf.edu/numeracy/vol11/iss1/art4
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