Types of Maps



Types of Maps

• Choropleth Mapping

• Dasymetric Mapping

• Isarithmic Mapping

• Proportional Symbol

• Dot Mapping

Choropleth Mapping

When best used

• Most appropriate for phenomenon that is distributed within enumeration units and changes only at those boundaries

• When there is no significant variation in the size and shape of the enumeration units

• When the data have been standardized to account for variation in the size of the enumeration units

Classed or Unclassed?

• Unclassed – more accurately reflects the data but this is mathematical, not perceptual

• Classed – uses a limited number of categories making the map easier to make and use

• Unclassed – more often than not used by the researcher for data exploration

• Classed- more often than not used to present the data to a general audience

• Classed maps are more effective for the acquisition of specific information

• For the acquisition of general information, there is no real difference between classed and unclassed maps

Classification?

• Select the method appropriate to your purpose and data

o The optimal method

▪ Places similar data values in the same class

▪ Considers in detail how data are distributed and calculates “best fit”

▪ Can assist in determining the appropriate number of classes

▪ Minimizes an objective measure of classification error

• Two algorithms available

o Jenks-Caspall

o Fisher-Jenks

o In ArcGIS, this method is called “natural breaks (Jenks)” as a classification option

• Problems with optimal method

o Difficulty in understanding concept

o May produce gaps in the legend

“Positioning of breaks may be pre-determined by the classification procedure (e.g. Jenks Natural breaks), but these are often manually adjustable for some of the schemes provided — this is particularly useful if specific values or intervals are preferred, such as whole numbers or some convenient values such as 1000s, or if comparisons are to be made across a series of maps. In some instances options are provided for dealing with zero-valued and/or missing data prior to classification, but if not provided the data should be inspected in advance and subsets selected prior to classification where necessary. Some authors recommend that maps with large numbers of zero or missing data values should have such regions identified in a class of their own.”

Color and the Choropleth Map

• Kind of Data

o Plays a role in color selection

▪ Unipolar – no dividing point, does not involve 2 complementary phenomenon (per capita income)

• Use sequential scheme

o Hold hue and saturation constant, vary lightness (light to dark, light for low values and dark for higher values)

▪ Bipolar – has a natural or meaningful dividing point (% population change has a natural point of zero)

• Use diverging schemes

o Two hues diverge from a common light hue or neutral gray

▪ Balanced – two phenomenon coexisting in a complimentary fashion (French & English speakers in Canada)

• Use diverging scheme

o emphasize the mid point of the balanced data

• Color Naming, Color Vision Impairment, and Simultaneous Contrast

o The ability of the map reader to recognize or name the color and interpret the map

o Vischeck website

▪

o Table 14.2 Color Pairs Appropriate for Diverging Color

▪ Naming, Color Vision Impaired, Contrast

• Other Color Issues

o Purpose of map or how you intend it to be used (map use task)

▪ For specific tasks readers prefer unordered hues because these discrimination between colors is easier

▪ For specific tasks readers prefer ordered hues

▪ Hue lightness schemes appear to be most effective

▪ Difficult to anticipate for the map will be used so select a color scheme that will allow for maximum readability and flexibility

o Color Associations

▪ Are cognitive and cultural and change over time

o Color Aesthetics & Age of the intended audience

▪ Preferred colors – blue, green, purple, red, yellow

▪ Young children and young adults prefer color

• Color Specification

o Soft Copy or Screen Display

▪ Color Ramping

• ColorBrewer – allows you to experiment with color schemes and gives you the information to build your desired scheme

• Users select from color palette the endpoints of a desired scheme and the computer interpolates values between the endpoints based on RGB values

o Problem – shades may not appear equally spaced

• Legend Design

o Vertical – high values shown at top preferably

o Horizontal

▪ Follows the orientation of the traditional number line, values increasing from left to right

o Numeric Values for the legend should be placed at the bottom of the legend boxes (horizontal), or to the right of the boxes (vertical)

o Legend boxes should be placed edge to edge

o Depending on the anticipated map reader you could also include a dispersion graph

Dasymetric Mapping

• Uses areal symbols to represent presumed zones of uniformity but the bounds of the zones need not match the enumeration unit boundaries.

• Choropleth maps portray data uniformly throughout the enumeration unit, but the real distribution often does not conform to those units.

• Goal in dasymetric mapping is to create zones of uniform statistical value that may not necessarily follow enumeration unit boundaries but may more accurately portray the data

• Uses standardized data

• Uses ancillary information

o Ancillary information is additional data that is used to more accurately map data associated with enumeration units (when making a map of wheat production with dots for example we want to avoid placing dots in bodies of water)

o The zones used in dasymetric mapping are based on data layers such as land use, land use imagery or other information.

o Using this information zones are created in which the data are mapped in a manner similar to Choropleth maps.

o Most commonly used is land cover/land use

o Data may be divided via the following:

▪ Binary method – land use/land cover divided into 2 groups, habitable vs. uninhabitable

▪ Limiting variable method – create multiple categories of habitability

▪ LandScan global population database uses a broad range of ancillary information: roads, slope, land cover, etc.

Isopleth Mapping

Isopleth maps differ from choropleth maps in that the data is not aggregated to a pre-defined unit like a political area or watershed basin. These maps can take two forms:

• Lines of equal attribute value are drawn such that all values on one side are higher than the "isoline" value and all values on the other side are lower, or

• Ranges of similar attribute value are filled with similar colors or patterns.

This type of map is used to represent continuous area data that varies smoothly over space. Temperature, for example, is a phenomenon that should be mapped using isoplething, since temperature exists at every point (is continuous), yet does not change abruptly at any point (like tax rates do as you cross into another political zone). Elevation maps should always be in isopleth form for this reason.

Key Points, isopleth maps are

• Used to depict smooth continuous phenomena such as rainfall, barometric pressure, depth to bedrock etc.

• Second to the Choropleth map in frequency of use

• Isometric maps - data not standardized to areal unit

• Isopleth maps – data standardized to account for the area over which the conceptual point are collected

Selecting Appropriate Data

• Phenomenon is assumed to be continuous and smooth and exist throughout the region of interest changing gradually between specific point locations.

• Data is sampled as point locations

o Control points

▪ True point data

• Values are measured at point locations

▪ Conceptual point data

• Values are collected over an area but are presumed to be located at point locations

o Represent data value as existing at the centroid of an areal unit

Data Interpolation

• Problem – data are available at irregularly spaced intervals but the phenomenon is assumed to be continuous

• Determine the vale of points that lie between the control points through interpolation

• Manual Interpolation

o Connect neighboring control points with straight lines, interpolate line of equal value.

• Triangulation

o Control points are connected through a series of triangles

Thiessen polygons

• Perpendicular bisector between each point is determined to form edges of triangles

DeLaunay Triangulation

• Connect control points of Thiessen polygons are connected to form triangles

o Contour lines are interpolated as they would be in manual interpolation

Other Methods…Gridding

• No analogy to this method in manual contouring

• Grids are overlaid on the control points

• Values at each grid point are estimated as a function of distance from the control points

• Interpolation between grid points produces a contour line

Inverse distance weighting and Kriging

• Inverse Distance Weighting

o Control points are weighted as an inverse function of their distance from grid points – those closer to the control point are weighted more heavily

o Fast

o Exact, unless smoothing factor specified

o Tends to generate bull’s eye patterns. Simple and effective with dense data. No extrapolation. All interpolated values between data points lie within the range of the data point values and hence may not approximate valleys and peaks well

o Large data sets can be efficiently interpolated

• Kriging

o Considers the spatial autocorrelation in the data both between the grid point and surrounding control points and among the control points themselves.

o Medium to slow

o Exact if no nugget (assumed measurement error)

o Very flexible range of methods based on modeling variograms. Can provide extrapolation and prediction error estimates. Some controversy over aspects of the statistical modeling and inference. Speed not substantially affected by increasing number of data points. Good results may be achieved with ................
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