## map design techniques

##### CV-12 - Bivariate and Multivariate Maps
• Differentiate the interpretation of a series of three maps and a single multivariate map, each representing the same three related variables
• Design a single map symbol that can be used to symbolize a set of related variables
• Create a map that displays related variables using different mapping methods (e.g., choropleth
• and proportional symbol, choropleth and cartogram) Create a map that displays related variables using the same mapping method (e.g., bivariate choropleth map, bivariate dot map)
• Design a map series to show the change in a geographic pattern over time
• Detect a multivariate outlier using a combination of maps and graphs
• Explain the relationship among several variables in a parallel coordinate plot
##### CV-11 - Common Thematic Map Types
• Describe the design considerations for each of the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map
• Evaluate the strengths and limitations of each of the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map
• Explain why choropleth maps should (almost) never be used for mapping count data and suggest alternative methods for mapping count data
• Choose suitable mapping methods for each attribute of a given type of feature in a GIS (e.g., roads with various attributes such as surface type, traffic flow, number of lanes, direction such as one-way)
• Select base information suited to providing a frame of reference for thematic map symbols (e.g., network of major roads and state boundaries underlying national population map)
• Create maps using each of the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow
• Create well-designed legends using the appropriate conventions for the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow
##### CV-32 - Cartograms

Cartograms are used for thematic mapping. They are a particular class of map type where some aspect of the geometry of the map is modified to accommodate the problem caused by perceptually different geographies. Standard thematic maps, such as the choropleth, have inherent biases simply due to the fact that areas will likely be very different in size from one another. The tendency to see larger areas as more important, regardless of the variable being mapped, can cause confusion. Cartograms tackle this by modifying the geography, effectively normalizing it to create a map where each area takes on a new shape and/or size based on the variable being mapped. Cartograms therefore depict geographical space diagrammatically as they lose their relationship with true coordinate system geometry. There are four main types of cartogram which each represent the mapped variable differently – non-contiguous, contiguous, graphical and gridded.

##### CV-18 - Representing Uncertainty

Using geospatial data involves numerous uncertainties stemming from various sources such as inaccurate or erroneous measurements, inherent ambiguity of the described phenomena, or subjectivity of human interpretation. If the uncertain nature of the data is not represented, ill-informed interpretations and decisions can be the consequence. Accordingly, there has been significant research activity describing and visualizing uncertainty in data rather than ignoring it. Multiple typologies have been proposed to identify and quantify relevant types of uncertainty and a multitude of techniques to visualize uncertainty have been developed. However, the use of such techniques in practice is still rare because standardized methods and guidelines are few and largely untested. This contribution provides an introduction to the conceptualization and representation of uncertainty in geospatial data, focusing on strategies for the selection of suitable representation and visualization techniques.

##### CV-17 - Spatiotemporal Representation

Space and time are integral components of geographic information. There are many ways in which to conceptualize space and time in the geographic realm that stem from time geography research in the 1960s. Cartographers and geovisualization experts alike have grappled with how to represent spatiotemporal data visually. Four broad types of mapping techniques allow for a variety of representations of spatiotemporal data: (1) single static maps, (2) multiple static maps, (3) single dynamic maps, and (4) multiple dynamic maps. The advantages and limitations of these static and dynamic methods are discussed in this entry. For cartographers, identifying the audience and purpose, medium, available data, and available time to design the map are vital aspects to deciding between the different spatiotemporal mapping techniques. However, each of these different mapping techniques offers its own advantages and disadvantages to the cartographer and the map reader. This entry focuses on the mapping of time and spatiotemporal data, the types of time, current methods of mapping, and the advantages and limitations of representing spatiotemporal data.

##### CV-14 - Terrain Representation

Terrain representation is the manner by which elevation data are visualized. Data are typically stored as 2.5D grid representations, including digital elevation models (DEMs) in raster format and triangulated irregular networks (TINs). These models facilitate terrain representations such as contours, shaded relief, spot heights, and hypsometric tints, as well as automate calculations of surface derivatives such as slope, aspect, and curvature. 3D effects have viewing directions perpendicular (plan), parallel (profile), or panoramic (oblique view) to the elevation’s vertical datum plane. Recent research has focused on automating, stylizing, and enhancing terrain representations. From the user’s perspective, representations of elevation are measurable or provide a 3D visual effect, with much overlap between the two. The ones a user can measure or derive include contours, hypsometric tinting, slope, aspect, and curvature. Other representations focus on 3D effect and may include aesthetic considerations, such as hachures, relief shading, physiographic maps, block diagrams, rock drawings, and scree patterns. Relief shading creates the 3D effect using the surface normal and illumination vectors with the Lambertian assumption. Non-plan profile or panoramic views are often enhanced by vertical exaggeration. Cartographers combine techniques to mimic or create mapping styles, such as the Swiss-style.

##### CV-12 - Bivariate and Multivariate Maps
• Differentiate the interpretation of a series of three maps and a single multivariate map, each representing the same three related variables
• Design a single map symbol that can be used to symbolize a set of related variables
• Create a map that displays related variables using different mapping methods (e.g., choropleth
• and proportional symbol, choropleth and cartogram) Create a map that displays related variables using the same mapping method (e.g., bivariate choropleth map, bivariate dot map)
• Design a map series to show the change in a geographic pattern over time
• Detect a multivariate outlier using a combination of maps and graphs
• Explain the relationship among several variables in a parallel coordinate plot
##### CV-11 - Common Thematic Map Types
• Describe the design considerations for each of the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map
• Evaluate the strengths and limitations of each of the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map
• Explain why choropleth maps should (almost) never be used for mapping count data and suggest alternative methods for mapping count data
• Choose suitable mapping methods for each attribute of a given type of feature in a GIS (e.g., roads with various attributes such as surface type, traffic flow, number of lanes, direction such as one-way)
• Select base information suited to providing a frame of reference for thematic map symbols (e.g., network of major roads and state boundaries underlying national population map)
• Create maps using each of the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow
• Create well-designed legends using the appropriate conventions for the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow
##### CV-32 - Cartograms

Cartograms are used for thematic mapping. They are a particular class of map type where some aspect of the geometry of the map is modified to accommodate the problem caused by perceptually different geographies. Standard thematic maps, such as the choropleth, have inherent biases simply due to the fact that areas will likely be very different in size from one another. The tendency to see larger areas as more important, regardless of the variable being mapped, can cause confusion. Cartograms tackle this by modifying the geography, effectively normalizing it to create a map where each area takes on a new shape and/or size based on the variable being mapped. Cartograms therefore depict geographical space diagrammatically as they lose their relationship with true coordinate system geometry. There are four main types of cartogram which each represent the mapped variable differently – non-contiguous, contiguous, graphical and gridded.

##### CV-18 - Representing Uncertainty

Using geospatial data involves numerous uncertainties stemming from various sources such as inaccurate or erroneous measurements, inherent ambiguity of the described phenomena, or subjectivity of human interpretation. If the uncertain nature of the data is not represented, ill-informed interpretations and decisions can be the consequence. Accordingly, there has been significant research activity describing and visualizing uncertainty in data rather than ignoring it. Multiple typologies have been proposed to identify and quantify relevant types of uncertainty and a multitude of techniques to visualize uncertainty have been developed. However, the use of such techniques in practice is still rare because standardized methods and guidelines are few and largely untested. This contribution provides an introduction to the conceptualization and representation of uncertainty in geospatial data, focusing on strategies for the selection of suitable representation and visualization techniques.