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in the United States on a map, you’d have trouble seeing the base map and each dot would overlay another. So dots are not always a good way to convey information.

       Proportional symbols vary in size in direct relation to numerical values. Thus, circles whose areas are proportional to population may indicate the locations and sizes of cities (Figure 5-4).

      Line symbols

Schematic illustration of the map uses proportional circles to indicate the size of cities.

      (© John Wiley & Sons Inc.)

      FIGURE 5-4: This map uses proportional circles to indicate the size of cities.

       Nominal lines note the locations of particular linear features, such as roads, railways, rivers, and borders. They may appear as solid, dashed, or embellished lines, the standard symbol for railroads being an example of the latter. Colors may also be employed. Blue lines, for example, are commonly used to indicate rivers.

       Ordinal lines vary in thickness or color to indicate relative importance. On many maps, for example, city, state, and country boundaries are progressively thicker so as to indicate the relative importance of the political units they mark. In Figure 5-4, the line that separates the United States and Canada is thicker than the lines that separate the states and provinces. Similarly, lines that symbolize roads often vary in thickness in proportion to the width of the highway or number of lanes.

       Flow lines indicate movement, travel or trade along a given route or between two points. On some maps, the thickness of the lines varies in direct proportion to the quantity or volume of the flow. Thus, on a map of immigration, arrows of varying widths may be used to indicate the volume of movement between sender and receiver regions (as shown in Figure 5-5).

       Isolines connect points of equal value with respect to a certain phenomenon. Didn’t you just hear this recently? Oh yes, the contour lines shown in Figure 5-3b are an example (flick back if you missed that part of this chapter). Similarly, daily weather maps often contain isolines that connect points with identical atmospheric pressure or the day’s projected high temperature or precipitation.

Schematic illustration of the map uses flow lines of different widths to indicate hypothetical migrant flow.

      (© John Wiley & Sons Inc.)

      Area symbols

      Area symbols use gray tones or colors to depict phenomena that characterize areas as opposed to points or lines and are separated into two basic varieties:

       Nominal symbols identify qualitative characteristics or phenomena that pertain to areas or regions. Figure 5-6, for example, uses nominal symbols to identify official languages of South American countries.

       Choropleth maps (from the Greek choros and pleth, meaning place and value respectively) use colors or gray tones to show how the quantity or numerical value of something varies from one area to the next. Figure 5-7 uses gray tones to depict population density in South American countries. Choropleth maps always process data, and do not map raw numbers. These maps always show data per something else. Cows per square mile. Income per capita. Doing so removes the influence that the area size has on the data representation. For example, wouldn’t you expect Texas to have a lot of tornadoes simply because Texas has so much land area? More informative is how many tornadoes that state has per square mile, say to Florida, for direct comparison purposes.

      (© John Wiley & Sons Inc.)

      FIGURE 5-6: This map uses nominal area symbols to identify the distribution of Primary European Languages of South America.

Schematic illustration of the choropleth map uses shades of gray to show population density in South America.

      (© John Wiley & Sons Inc.)

      FIGURE 5-7: This choropleth map uses shades of gray to show population density in South America.

      Map-making technology has come a long way from chiseling on stone, through ink on papyrus, through pen on paper splayed across a drafting table, and into the modern world. In the past three decades we have seen a revolution, a geospatial revolution, in how Earth data is collected, measured, and analyzed. That last part — analysis — is crucial. Data analyzed becomes information. Information can be acted on. This action takes the form of problem solving. And with this geospatial revolution, we are acting faster, more accurately, and hopefully for the betterment of all people than ever before. Three of these technologies — separate but integrated — are geographic information systems (GIS), global positioning systems (GPS), and remote sensing.

      Today nearly all cartography at the professional level is done on a computer. The maps in this book are an example of this. Special kinds of software are available that allow cartographers to make maps with a degree of speed, accuracy, and data management that were unimaginable three decades ago. These qualities have also served to make mapmaking a powerful tool for a variety of businesses and planners. And in that regard, the most significant, cutting-edge field in contemporary cartography is the geographic information system (GIS).

      Giving you the complete lowdown on GIS would involve a lot of techo-babble that you don’t want to read and I don’t want to write. So perhaps the best way for me to describe GIS begins with a description of what it has replaced.

      If you had poked around a city or regional planning office years ago, you’d be sure to find a huge table someplace with a huge base map that showed the streets and roads of the city or region in question. There would also be numerous overlays of different phenomena drafted on individual pieces of transparent film. For example, one transparent overlay might show the location of property boundaries. Others might show land use, sewage pipes, water mains, building characteristics, telephone lines, school districts, voting precincts, contour lines, wooded areas, and anything else that may be deemed useful for planning purposes.

      If all of this sounds a bit tedious, then you get the point.

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