Slide #1: Slide #2: Slide #3: Slide #4: Slide #5: Cartographic Basics Slide #6: Cartographic Basics Slide #13: Scale & Generalization

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1 Slide #1: Introduction to Cartography Slide #2: Overview Slide #3: What is a map? (Clarke, 2001) Slide #4: What is a map? (ICA, 1995) o In short, a map is a tool used for sharing and presenting spatial information. That is a tool for communicating. Slide #5: Cartographic Basics Slide #6: Cartographic Basics Slide #13: Scale & Generalization The first is the modification of the data based on changes in map scale Data that may be appropriate for a large scale map, such as 1:24,000, may not be appropriate for a small scale map, such as 1:1,000,000 If we are converting the scale of display from the large-scale to the small-scale map, the data may need to be aggregated Thus, data that was originally displayed according to census tracts may require 1 P a g e

2 aggregating to county values at the smaller scale Neighborhood statistics may be combined to represent city statistics The transformation of the data as a result of a change to a smaller scale are easily done by merging the data into a single value The data transformations include a change in the definition of the data location Data that originally was applied to a point location may now be aggregated and applied to an area location Slide #13: Data aggregation The transformation of data when changing to a larger scale presents different problems Data that are collected for a larger area in order to produce the small-scale map will appear 2 P a g e

3 highly simplified when retained for the depiction at the larger scale A disaggregation of the data requires a greater effort Census data collected at the county level may also be available at the census tract level If the data cannot be easily disaggregated, a return to the original data source may be required in order to acquire more appropriate data for the new map scale o This may require revisiting the original data source for acquisition of more refined data Slide #16: Reference Maps o General purpose documents that serve as base maps o Objective is to show a variety of features o Can be at any scale o Large scale (small area) reference maps have higher positional accuracy. Includes topographic and site maps. 3 P a g e

4 o Small scale maps are more generalized with less positional accuracy. Includes state, country and continental maps. Slide #17: Thematic Maps o Special purpose documents emphasizing single theme o Primary objective is to show the distribution of a single attribute or the relationship among several. o Are typically small scale Slide #21: Measurement Scale It has been customary in recent years for geographers to classify the ways they measure events into categories of data measurement Measurement is an attempt to structure observations about reality Ways of doing this can be grouped into four levels, depending on the mathematical attributes of the observed facts 4 P a g e

5 A given measurement system can be assigned to one of these four levels: o Nominal o Ordinal o Interval o Ratio These are listed in increasing order of sophistication of measurement Methods of cartographic symbolization are chosen specially for representing geographic phenomena or data at these levels of measurement The measurement scale will also play an important role in identifying the thematic map type to be used Nominal Data Nominal scaling is the simplest level of data measurement 5 P a g e

6 Sometimes considered a qualitative measurement to be descriptive Answering the question of what is being mapped An example would be the nominal identification of: o Wheat regions o Corn regions o Soybean regions Each crop region is distinct Arithmetic operations between regions are not possible at this level Political party affiliation (Democrat, Independent, Republican), sex (male, female) and response (yes, no) are other examples At this level of measurement, mathematical operations cannot be performed between classes 6 P a g e

7 Equality/inequality between groups according to their classification or identification or the dominance spatially of one group versus another may be ascertained using this data measurement level Ordinal Data The underlying structure of ordinal measurement is a hierarchy of rank Objects or events are arranged from least to most or vice versa The information obtainable is of the greater than or less than variety Ordinal measurement provides no way of determining how much distance separates the items in the array There are several types of ordinal measurement 7 P a g e

8 In complete ordering, every element in the array has its own position No other element can share this position This kind of ordinal measurement is considered relatively strong because statistical observations about the ranking are possible This is not the case with the second main type, weak ordering, in which elements can share positions (called paired ranks) along the ordinal continuum In the first instance there may be only one major seaport along a coast while in the second instance there may be several minor ports The definition of major and minor may be determined by the number of ships serviced in a year or by the total tonnage of goods that pass through the port annually One interesting feature of the ordinal class is that we can attach any numerical scale to the 8 P a g e

9 ranking without violating the underlying structure If we know the order is: o K L M o we can have either o K = 5, L = 3, M = 1 o or o K = 500, L = 300, M = 1 We would still retain the original representation Remember that in ordinal measurement we do not know how much difference separate the events in the array For example, several geography students spend a summer touring 50 large cities throughout North America After they return, their professor asks them to rank the cities based on their appeal, from best liked to least liked 9 P a g e

10 In this array, a city s position is known in the overall ranking, but it is not possible to discern how much it differs from those ranking above and below it Other examples of ordinal measurement are: o Social class o Social power (more or less) o Agreement (strongly agree, strongly disagree) We may also utilize the ordinal class in refining a nominal description based upon importance of the category Whereas, nominally we identify a linear feature as a road, we use the ordinal measurement to differentiate between a major road or a minor road, an interstate highway or a national highway 10 P a g e

11 Other examples would include: o the distinction between a seaport versus a major seaport or o a forest versus a mixed deciduous forest the characteristic that defines ordinal apart from other approaches is that we cannot discern magnitude difference between the observations, only hierarchical distinction of categories Interval Data At this measurement, we can array the events in order of rank and know the distance between ranks Observations with numerical scores at the interval measurement are important in geographical analysis because data at this level are needed to perform fundamental statistical tests having predictive power 11 P a g e

12 Units on an interval scale are equal throughout That is, one degree on the Fahrenheit scale is assumed to be the same regardless of whether it s at or degrees Magnitude scales at the interval scale have no natural origin Any beginning point may be used The classic example is the Fahrenheit temperature scale There is no absolute values associated with interval measurement They are relative In the interval approach, units are agreed upon by researchers and are assumed to be standard from one set of conditions to another Variables at the interval scale do not have absolute zero as a starting point 12 P a g e

13 Ratio Data Like interval, ratio measurement involves ordering events with known distances separating the events The difference is that ratio magnitudes are absolute, having a known starting point This scale of measurement has a zero (absence of magnitude) as its starting point The Kelvin absolute temperature scale is an example here Other examples include: o Weight (20 lbs is twice as heavy as 10 lbs) o Distance (100 miles is twice as far as 50 miles) o Elevation above sea level is another ratio scale where the average elevation of sea level is set to zero The ratio approach is important to geography because more sophisticated statistical tests can be performed using this level of measurement 13 P a g e

14 The amount of information that can be obtained, statistical confidence, and predictive power, increase as one progresses from nominal to ratio measurement Cartographically. We view interval and ratio as being equal The thematic map generated from interval data will utilize the same design techniques if it were created using ratio data 14 P a g e

15 Slide #22: Effective Symbolization o A map can say more than 1000 words Selection of Thematic Map Symbols Slide #25: Homogeneity The qualitative feature data may give the mapmaker information about the actual degree of homogeneity within each feature. The features may be truly homogeneous uniform in structure or composition throughout. 2 - For example, a stand of trees might prove to be entirely of the same species. The stand is a completely homogeneous qualitative area feature since all of its defining objects (trees) are of the same category (species) within the homogeneous area (stand). The interesting thing about features mapped as though they were homogeneous is that most are actually not. 15 P a g e

16 In our tree example, although the trees within the stand may be the same species, there may be an understory of shrub, brush or grass. At the edges of the stand, the trees may mix with other species creating a mixed species transition zone between two single species stands. Slide #26: Mapmakers deal with this in several ways They could decide to define the boundary between the two single species stands as the middle of the transition zone Now neither of the stands are homogeneous area features, although they are mapped as being so Slide #26: They may add a transition zone to the classification so that the map now includes a mixed class as well as the two single species classes 16 P a g e

17 This solution increases the complexity of the map, but allows the single species stands and the transition zone to be mapped as homogeneous areas that more accurately reflect what s on the ground They may then add a note to the map explaining that they ve included the transition zone as part of one of the stands Slide #27: An example of homogeneity within line features.. For example many roads are probably completely asphalt or totally concrete When these homogeneous features are shown on a map as asphalt or concrete roads, the maps agree with what s on the ground However, other features may have more than one surface type, such as a forest road that s mostly gravel with short sections of asphalt and other sections that are dirt 17 P a g e

18 In this case, the composition of the majority of the road could be used as the category for the entire road segment Slide #28: An alternative is to segment the features by breaking them at locations where the road surface type changes The feasibility of this approach relates to the map scale For example, you would not analyze, navigate, or manage road segments based on what would be minute variations at a small map scale However, roads that have different surface types but were classified into one category on a larger-scale map may cause travel and other problems for the unwary map reader Without the appropriate surface composition information, map users may find themselves unable to plan for associated changes in access, traffic or speed 18 P a g e

19 Single Theme Maps The simplest and most common qualitative thematic maps are those that emphasize a single theme Slide #29: To read these single theme maps, you must first determine what features have been shown with point, line or area symbols Cartographers use point, line and area graphic marks on the maps to represent geographic features Slide #29: The graphic marks are symbolized using what mapmakers call visual variables Certain visual variables work well for qualitative data because they do not impart a magnitude message; others work well for quantitative data because they do Symbols on maps are easiest to read if the mapmaker has assigned the correct visual 19 P a g e

20 variables to the points, lines and areas for the features shown The visual variables that evoke in our mind qualitative differences among features are shape, orientation and color hue, or what we think of as the color of a symbol, such as red, green or blue Slide #30: See figure 7.2, p. 130, The visual variables that. A shape can also be repeated along a line or across an area to create a pattern we sometimes call this line or area pattern Likewise, orientation can be used to create patterns within the graphic marks this is called pattern orientation 20 P a g e

21 Slide #31: For example, a set of point symbols might be designed as circles with lines in them that are oriented in different directions Each of these visual variables gives the impression that features are different in type or kind but not more or less in magnitude Understanding the visual variables and how they are used in creating the graphic marks on maps helps you to correctly interpret the symbols on a map Keep in mind, however, that maps are not always designed as well as they might be Slide #32: Thus, some other visual variable (size, texture, color value or color intensity) may be used that is really most appropriate for showing quantitative differences When maps are incorrectly made using the wrong visual variables, your first reaction will be 21 P a g e

22 that quantitative rather than qualitative information is being shown The only way to keep from being misled is to read the legend to see what the symbols actually represent For example, on the map in figure 7.3, the mapmaker quite properly used color hue to differentiate the habitat classes Slide #33: Show figure 7.3, p. 131, Nevertheless, for some categories color values was varied, which could easily give you the impression that the categories differed in some quantitative way For this map, the legend clarifies any ambiguities in the symbols 22 P a g e

23 Point Feature Maps Point feature maps have symbols that show the existence of something at a specific location The point has a rather loose interpretation here Slide #34: It isn t being used in the strict mathematical sense of a one-dimensional figure but, rather, as the center of a circle, square or some other map symbol representing a point feature found at this location or as we saw earlier, representing a feature conceived of as a point for mapping purposes Qualitative point features are usually represented with point symbols somewhere on a continuum between geometric and pictographic Slide #35: Geometric symbols are simple shapes, such as squares, circles and triangles to represent features 23 P a g e

24 Slide #36: See Figure 7.4A, p. 132 Because they are so simple, they usually require a legend to be interpreted correctly Slide #37: See Figure 7.5, p. 132 At the other end of the continuum are pictographic symbols, which are designed to look like miniature versions of the features they represent Pictographic symbols are often used on landmark maps that require a reader to identify specific buildings or other landmarks at first sight On these maps, small sketches of the buildings can sometimes be used to allow the reader to distinguish individual buildings immediately 24 P a g e

25 Slide #38: These are more like example D in figure 7.4 Somewhere in the middle of the continuum is a type of symbol called a mimetic symbol one that mimes the thing it represents Slide #39: Mimetic symbols are often created as a combination of geometric shapes such as a square with a triangle on top to represent a house, or they can be more complex, like a small cartoon of a particular type of building like a ranger station or a museum Because these symbols are intuitive, they are popular for mapping point features or labeling area or line features 25 P a g e

26 Mimetic symbols can be found on tourist maps, children s maps and maps on the web A good example is the push pin you see on Google maps Slide #40: A special type of mimetic symbol is the standard symbol, which is a symbol that is used as a standard in some mapping practice or for some mapping product Sets of standard symbols have been created to show different categories of: o transportation, o recreation and 26 P a g e

27 o other activities The US National Park System uses them to depict visitor facilities in the national parks Slide #41: See Figure 7.6, p. 133 Slide #42: Standard symbols have a more professional look but they still suffer problems associated with more pictographic symbols in general They have to be relatively large for details to be apparent in crowded spaces In addition, only a limited number of environmental features can be successfully symbolized since the reader will become confused if there are too many mimetic symbols, and they will have to refer constantly to the legend to decipher the map 27 P a g e

28 Slide #43: What, for example, is an obvious icon for a vista or an overlook? And what if there are 10 such cryptic symbols on a map? Although these symbols are intended to be intuitive at a glance, you ll often have to check the map legend to determine what is being symbolized Slide #44: See Figure 7.7, p. 133 Mapmakers sometimes cleverly change the orientation or hue of a mimetic symbol to show two or more attributes of the point feature Slide #45: Notice in figure 7.2 that the orientation or hue of the mimetic symbol for a coniferous tree can be changed to show the tree is alive (vertical or green) or dead (horizontal or brown) 28 P a g e

29 Slide #46: Figure 7.2 Mapmakers sometimes use both orientation and hue differences to make sure that you see the attributes of each feature The drawbacks of mimetic symbols are overcome by using geometric shapes such as circles, squares, triangles and so forth Slide #47: Although these symbols may look very abstract, they can be read correctly even when very small They can also be used for larger categories having hundreds of individual features This lets mapmakers pack more information into the map that they can with larger mimetic or pictographic symbols Furthermore, since the correspondence between real world features and their 29 P a g e

30 geometric symbols is strictly arbitrary, any point feature can be represented this way The greater level of abstraction embodied in geometric symbols increases their flexibility as feature display tools It also requires close study of the legend to determine what is being symbolized Slide #48: Line Feature Maps Mapmakers make line feature maps with qualitative line symbols showing different categories of linear features, such as roads, streams or boundaries As with point symbols, the word line isn t used here in the strict mathematical sense of a one-dimensional figure Although line features may be truly onedimensional (such as boundary lines), line feature maps can also be used to show features that are conceived of as lines for mapping 30 P a g e

31 purposes (such as a river, which has a width in reality but is shown with a line on a map) Slide #49: The lines used to symbolize linear features on maps have width as well as length, and the symbol width rarely corresponds directly with the feature width on the ground For example, if you measured the width of a thin blue line that represents a stream on the map in figure 7.8, it will usually be much wider that the width of the corresponding feature on the earth when its map width is converted to ground distance Slide #50: Show Fig 7.8, p P a g e

32 Lines on the map are drawn wider to show the category using the appropriate visual variables color hue, line shape (or pattern), and either shape or orientation of the pattern within the line In order for these visual variables to be seen, the mapmaker must widen the lines on the map You have probably seen lines of different hues on maps Slide #51: Show Fig 7.9, p. 135, Different hues are used to. Slide #52: Some hues have been standardized for certain features For example, water features are usually shown with blue lines Boundaries are depicted with red lines Roads are drawn in black and 32 P a g e

33 contour lines are drawn in brown Other times hue carries a categorical message, as on the map of railroad ownership in figure 7.9 To read these maps correctly, you must refer to the legend Shape repetition (line pattern) is also commonly used to distinguish different categories of line features Slide #53: See Fig 7.2, p. 130, the visual variables that Again, there are some standards For example, administrative boundaries are often shown using a variety of dashed line symbols, and railroads are commonly shown 33 P a g e

34 using a solid line with cross hatches that mimic the railroad ties Slide #54: The individual marks repeated along the line usually are geometric, but they can also be mimetic Mimetic shapes may be easier to discern than abstract geometric shapes, but they re more difficult to miniaturize and repeat along a line Therefore, most pattern line symbols use repetitions of geometric shapes On many maps, color hue and line pattern are used together to allow the map to show a greater variety of lines When you think about the number of line symbols on a map, this can be very helpful Slide #55: For example, the lines on a map can be used to represent features that are linear (fences or walls), as well as networks (roads and railroads), the edges of area features 34 P a g e

35 (administrative boundaries), and the land surface form (contours) In addition, there may be lines to show the graticule, the edges of the mapped area, and other bounded areas on the page such as the legend or title Having a variety of line symbols to choose from helps the cartographer create a map that you can more easily decipher Area Feature Maps Slide #56: Qualitative area feature maps use area symbols to portray features that are homogeneous within regions On such thematic maps, area features are best symbolized using the visual variables that give the impression of differences in type or kind 35 P a g e

36 Slide #57: These include: o Color hue o Pattern shape o Pattern orientation For example, a mapmaker may use two hues to show the states carried by the Democrat (blue) and Republican (red) party presidential candidate Slide #58: See Fig 7.10, p. 135 Alternatively, she could also use a pattern of donkeys as mimetic symbols to fill the Democratic states and a pattern of elephants for the Republican ones Slide #59: Or she could use a combination of pattern shape and hue (red elephants and blue donkeys) to make sure you correctly see the category for each region 36 P a g e

37 Slide #60: If mapmakers instead use visual variables that give a magnitude impression, such as: o color lightness, o color intensity, o pattern texture or o size, you ll likely see quantitative rather than qualitative differences in the data You may think that one symbol depicts more of something than another symbol, when this wasn t the mapmaker s intent Slide #61: Show Fig 7.11, p. 136, Color hue is used to 37 P a g e

38 This maps shows different Level III ecoregions Level IV ecoregions are shown using the same hue but varied in lightness and intensity Without regarding the legend, your first impression might be that the map is showing quantitative information as well as qualitative Slide #62/63: Figures 7.10 and 7.11 exemplify two basic types of qualitative thematic maps that differ in the kind of data collection areas being mapped, though both use color hue as the primary visual variable The presidential election map in figure 7.10 is based upon legislatively define data collection areas (states) On the map, states are given one of two hues, depend on which candidate received the most votes The areas are rightfully portrayed as homogeneous, since the candidate with the 38 P a g e

39 most votes receives all the state s electoral college votes Slide #64: This type of map is often called a categorical map a map that has polygons enclosing areas assumed to be uniform or areas to which a single description can apply Slide #65: The ecoregion map in figure 7.11 is an example of mapping area features that are inherently homogeneous in some way, such as having the same bedrock geology, soils or vegetation Though this map is compiled by letting the environmental data determine the boundary between classes (which differs markedly from one compiled using already defined administrative or other boundaries bearing no 39 P a g e

40 natural relation to the data) many mapmakers still call it a categorical map Notice that the different categories may be purely of one feature ( High Desert Wetlands ) or of two or more intermixed features found in certain regions ( Owyhee Uplands and Canyons )you can see that the mapmaker can define inherently homogeneous areas in many ways, so you should carefully read the map legend to understand what each category means Slide #66: Multivariate Maps Most qualitative thematic maps use a separate symbol to represent each category 40 P a g e

41 But sometimes mapmakers show several attributes of the feature within the same symbol on a multivariate map They accomplish this feat in two ways One method is to use a different visual variable to show each attribute Slide #67: In theory, mapmakers could show four different qualitative attributes at once by varying the symbol s shape, hue, pattern and orientation In practice, symbols showing even two or three feature attributes can be very difficult to read if they are not created carefully You ll usually have little trouble telling when multivariate information has been mapped, because the symbols will appear more complex than those showing a single attribute Slide #68: With the second method of symbolizing multivariate information, 41 P a g e

42 mapmakers show a concept defined by a composite of attributes rather than raw data for a single theme At first glance, many qualitative thematic maps seem to show a single theme when they may actually show multivariate information Slide #69: This is the case with the ecoregion map in figure 7.11 Slide #70: The concept of an ecoregion is defined by: o Temperature o Precipitation o Vegetation o Soils geology o Human influences on the landcape 42 P a g e

43 You may think of ecoregions as a single theme, but each component of ecoregions plays a crucial part Slide #71: Similarly, the concept of a soil class is defined by a set of soil attributes, including: o Slope o Depth o Drainage o Color o Texture Slide #72: See Fig 7.12, p. 137 o Reading these kinds of maps can be tricky because the symbols on these multivariate thematic maps look exactly like those on single theme maps o Only the nature of the information symbolized, not the form of the symbols, has been changed 43 P a g e

44 o Therefore, it is essential to check the legend as your initial step in map reading Slide #73: Multivariate Point Symbols Mapmakers like to use point symbols to show multivariate qualitative information because they can pack information into each symbol by combining several visual variables The resulting symbol is sometimes referred to as a glyph Slide #74: Some multivariate point symbols are pictographic, but most are geometric For example, the symbols for landfills and dumps shown in figure 7.13 are circles and squares Slide #75: See Fig 7.13, p P a g e

45 Arrows in two opposite directions indicates the status of operation, and different hues represent ownership Mapmakers sometimes use the same visual variable more than once to create multivariate symbols Slide #76: For instance, they may use two shapes, such as a star within a circle, which is a common way to show capitol cities More often they will show two different attributes of a feature with two different qualitative visual variables as in figure 7.13 Although they should use visual variables with qualitative connotations to construct these symbols, they don t always do so 45 P a g e

46 Multivariate Line Symbols You probably won t find many examples of maps showing multivariate qualitative linear data One reason is that their options for visual variables are limited to use primary color hue and pattern shape (a shape repeated along a line) But occasionally you ll find such maps Slide #77: For example, road maps may be enhanced with multivariate data It s common to see highway maps with red lines for freeways, black lines for state highways and gray lines for US or state highways Sometimes these single variable line symbols are augmented to show a second variable For instance, dot patterns may be added alongside each line to show the scenic routes that can occur along any of the various road types 46 P a g e

47 You may also see multivariate line feature maps with line symbols wide enough to have different hues and line patterns within the two bounding lines Slide #78: These are called cased line symbols as the interior line is bounded by a casing that is shown in a different color You will find these symbols on many kinds of maps Slide #79: For example, road categories might be shown by different hues, and the casing lines could be patterned as solid, dashed, or dotted lines to show where the roads are in tunnels or on bridges These cased line symbols tend to cover a larger amount of map space, as we saw earlier with the stream lines on maps, so they are often used only for major roads 47 P a g e

48 Multivariate Area Symbols Mapmakers can make qualitative multivariate maps for area features in a number of ways One common method is to overlap two types of area symbols that are appropriate for nominal data The most common visual variables they use are color hue for one attribute and pattern shape (repetition of shape within the polygon that represents the area) for the other attribute For example, the map in figure 7.14 Slide #80: Show Fig 7.14, p. 138 Shows the type of ocean bottom off the Oregon coast by using light gray for sand, dark brown for bedrock and light brown hue for silt and mud 48 P a g e

49 Vertical and horizontal dashed line patterns are overlaid to show where crab or shrimp are harvested The area patterns for shellfish type overlap to form + signs where both crab and shrimp are harvested This map lets you see if there is any geographical relationship between the type of ocean bottom and the kind of shellfish harvested there Slide #81: On other maps, two different hues, such as yellow and blue are used for two different categories so that their area of overlap is seen as the hue combination in this case green However, the overlap area may go unrecognized, because green is normally seen as a separate hue, not as a mixture of yellow and blue 49 P a g e

50 Another problem with this approach occurs if two hues whose combination is not easily recognized are used Slide #82: Could you predict what hue should be used to show the overlap between an area symbolized with purple and another shown in green? In such cases, it s necessary to refer to the map legend to see what each hue represents An alternative is to use alternating bands of the two hues within the overlap area in this example, stripes of purple and green These symbols are more easily seen as areas in which both attributes are found Slide #98: An example of poor map design: o Some typical map symbolization errors with a GIS: Map title too large 50 P a g e

51 Map elements unbalanced within the neat line Figure too small Too many classes (3-7 is best) Class shades should not vary in hue Class shades should follow light to dark in sequence Class breaks should not overlap Compass rose adds nothing Scale bar is too long Worst of all: it is impossible to see the spatial pattern of the data! 51 P a g e