Welcome to Lesson 6! In previous lessons, we discussed broad concepts related to map and map symbol design, including designing for a map’s audience, medium, and purpose. We learned about visual variables and how to designate order and category with map symbols. In the context of text on maps, we discussed these ideas in greater detail; we created symbols with labels and learned how to place them appropriately on maps. We then put everything together in a map layout.

So far, we have only designed maps that use more or less concrete data, such as road networks, lakes, or travel routes. You began to work with more abstract statistical data from the US Census Bureau in Lesson 5. In this lesson, we discuss another type of thematic map symbolization, the proportional symbol and the ways in which we can use maps to effectively visualize spatial statistical data. When deciding how to map, we’ll continue to consider the spatial dimensions and models of geographic phenomena, levels of data measurement, and appropriate methods of visual encoding.

Learning Outcomes

By the end of this lesson, you should be able to:

• identify the visual variables used to display both quantitative and qualitative data in a given map.
• identify the spatial dimension, model, and level of measurement of geographic phenomena.
• select appropriate visual variables for data encoding based on the characteristics of the phenomenon to be mapped.
• use knowledge of data measurement levels and visual variables to thoughtfully critique thematic maps.

Lesson Roadmap

Questions?

If you have questions, please feel free to post them to the Lesson 6 Discussion Forum. While you are there, feel free to post your own responses if you, too, are able to help a classmate.

We first introduced thematic maps in Lesson 1, and described them as maps intended to highlight features, data, or concepts (either quantitative or qualitative). In assignments 1 and 2, we used visual variables to show order and category of typical map features. In assignments 3 and 4, we introduced the use of projections and symbolized methods for terrain visualization.

The maps we’ve created so far have visualized fairly tangible information—we have indeed been creating abstract representations of the real world, with roads, rivers, lakes, county lines, etc., with hues and shapes different from what would be captured by a photograph. We have also visualized the concept of travel routes, be they on foot or by plane. But on balance, our designs have more or less matched a physical phenomenon or object. So, in this lesson, we turn to more abstract depictions of the world, designed using thematic, statistical data. View for example, the map in Figure 6.1.1.

Figure 6.1.1: A county-level map of unemployment.

Credit: Cary Anderson© Penn State is licensed under CC BY-NC-SA 4.0.
Data Sources: Esri, US Census Bureau

This map uses color value—not to show category or hierarchy of map features—but to visually encode county- level quantitative unemployment data. Figure 6.1.1 also simplifies the map of the US (not showing even major highways or mountain ranges, but only state and county boundaries) to emphasize the map’s theme.

Due to thoughtful use of color and a simple layout design, this map successfully communicates geographic trends of unemployment across the United States. Was this the best design and symbolization choice to show this geographic distribution? Is there a better way?

View the map in Figure 6.1.2.

Figure 6.1.2: A proportional symbol map of population.

Credit: Cary Anderson© Penn State is licensed under CC BY-NC-SA 4.0.
Data Source: Esri, The National Map

This map uses a similar color scheme and layout, but encodes its data (this time population rather than unemployment) primarily using proportionally-sized symbols. Color value is used for additional effect, a technique called dual encoding.

Both of these maps (6.1.1; 6.1.2) employ appropriate cartographic conventions (e.g., assigning lighter color values to lower data values and darker color values to higher data values). But there are other conventions that this cartographer could have used with each dataset that would have been equally appropriate (e.g., using a diverging color scheme for Figure 6.1.1 and a single hue fill for Figure 6.1.2). There are also other symbolization methods that they could have used that would have been—arguably—not meeting the map purpose. How do you decide?

Before beginning the how of making a map, we need to take a step back and consider the what—the geographic phenomena we want our map to be about.

Geographic phenomena are elements that exist over geographic space. When we say geographic, we typically mean anything tangible that is associated with Earth. On the other hand, spatial refers to connections that exist in space (broadly defined). So, while still spatial and can be mapped, the connections between the neurons in your brain or the arrangement of atoms in a ceramic material are generally not referred to as “geographic" phenomena. In this lesson, you will learn tools for conceptualizing, visualizing, and communicating the many geographic phenomena that do.

Geographic phenomena are often classified according to the spatial dimension best used to describe their nature. These include points, lines, polygons, and volumes (3D). As you likely remember, we used the spatial dimension of map elements (e.g., line vs. point) in a previous lab to decide how to symbolize and apply feature labels to our maps.

Points exist in a singular location. Points are usually specified using a coordinate pair (x, y or latitude and longitude), though they occasionally include a z-value (height). Points are most appropriate in situations where the specific geometry of a feature is unimportant, or if the scale of the map is too small to usefully or accurately render the geometry of a feature. Points are also useful in cases where you are trying to minimize the amount of visual information being presented in a map. Points are used to map point locations such as weather recording stations, control points, or stream gages.

Figure 6.2.1: This map uses point symbols to show bike share stations in the city of Milwaukee.

Credit: city.milwaukee.gov/

Lines are one-dimensional spatial features defined by a sequence of at least two pairs (x, y) of coordinates. A third dimension, z (height), can also be assigned to lines, but this is uncommon. Lines are used to map geographic phenomena that are best conceived of as linear features, including features that have greater dimensionality in reality (e.g., streams are defined by surface area and volume). There are also linear features that do not visibly exist in the real world (e.g., property lines). Often, someone else has decided for you whether or not a given feature should be encoded as a line rather than a polygon, but if you’re trying to make this determination, you could think in terms of how many dimensions are needed to sufficiently present the geographic phenomenon. For example, Figure 6.2.2 is drawn at a scale such that the width of the Blue Ridge Parkway would be difficult to represent, and road width would be an immaterial variable anyway– the goal of this map isn’t furthered by that data (the map reader doesn't need to be able to accurately measure the road's width). We only need to know the path of the road (where it exists), so a line is the appropriate choice for representation here (the thickness of which is irrelevant).

Figure 6.2.2: A map that uses linear features to encode roads and hiking trails.

Credit: nps.gov/

Polygon features, also called area features, are represented by a sequence of (x, y) points that form a boundary that encloses a space. Areal phenomena can include natural features like lakes and islands, as well as human-defined locations like cities or census blocks.

Figure 6.2.3: A map that uses area features to show buildings, parking lots, so on.

Credit: health.act.gov.au/

2-½ and 3-D features are sometimes grouped together, but the distinction between them is important. 2-½D features define a continuous surface—they have an x, y, and a z at every location. A good example is elevation, which varies continuously across the landscape. Therefore, a topographic map is a common depiction of 2-½D phenomena.

Figure 6.2.4: A Topographic Map, which depicts a 2½ D surface.

Credit: USGS

True 3D maps have an x, y, and z, plus an additional data value, at every location and height. Imagine, as an example, a map of elevation like the one above; but at every point along the terrain surface, there are additional measurements being taken at various depths of that surface. Thus, rather than depicting a continuous 2D surface, true 3D maps depict a continuous volume.

Figure 6.2.5: A 3D map showing sea depth and temperature.

Credit: NOAA

As mentioned earlier, the scale of your map has significant influence on what spatial dimension will best represent the phenomenon you intend to map. Cities, for example, are often drawn as polygons on large-scale maps, but may appear as points on smaller-scale maps. Rivers are usually drawn as lines on small-scale maps but are better represented as areas on large-scale maps. We will discuss this more during discussions of cartographic generalization later in the course.

When conceptualizing the geographic phenomena we want to map, it is important to consider the best way that these phenomena can be modeled. In general, we can categorize the best model for a given phenomenon as existing somewhere along two continuums: (1) from discrete to continuous, and (2) from smooth to abrupt.

You likely learned the difference between discrete (e.g., as shown by a histogram) and continuous (e.g., as shown by the bell curve) variables in an introductory statistics course. The distinction in cartography is similar.

Discrete phenomena have well-defined boundaries: they occur at specific locations, with space in between. Examples include trees, houses, cities, and roads.

Continuous phenomena, conversely, have ill-defined or irrelevant boundaries but exist everywhere. Examples include temperature, air quality, and elevation.

Phenomena can also—independent of their classification as discrete or continuous—be considered either smooth or abrupt.

Smooth phenomena are those that change gradually over geographic space. Examples include precipitation levels and barometric pressure: they vary by location but do not typically change abruptly at geographic bounds.

Abrupt phenomena do change suddenly at geographic boundaries, whether physical or cultural. Examples include state sales tax or municipal water cost.

Often, phenomena are not clearly smooth or abrupt, but fall somewhere in between. The amount of pesticide residue in soil, for example, might vary somewhat continuously over the area of a farm, but change rather abruptly at the boundary of the farm’s fields.

Figure 6.3.1: Discrete vs. Continuous; Abrupt vs. Smooth.

Credit: (MacEachren 1992)

Figure 6.3.1 illustrates various surfaces used to represent geographic phenomena throughout the discrete to continuous and abrupt to smooth continuums. Keep this idea of a continuum in mind—geographic phenomena often cannot be classified into neat categories, and it is typically more fruitful to think of them as “more continuous” or “more discrete” than to try and fit them into a box.

Figure 6.3.2: Map representations that match the phenomena in Figure 6.3.1 above.

Credit: (MacEachren 1992)

Figure 6.3.2: above shows different map representations that are suited to mapping the geographic phenomena located at these relative positions along the continuous-discrete and abrupt-smooth continua. We will discuss the appropriateness of various thematic mapping methods further later in this lesson.

Considering the characteristics of the geographic phenomena you wish to map will inevitably improve the quality of your maps. However, before you design your map, you must understand the distinction between the characteristics of the phenomena and those of your data.

Consider again the map from Figure 6.1.1.

Figure 6.1.1: A county-level map of unemployment.

Credit: Cary Anderson© Penn State is licensed under CC BY-NC-SA 4.0.
Data Sources: Esri, US Census Bureau.

This map illustrates unemployment rates in the United States at the county level. Though it is a well-designed and attractive map, consider the characteristics of unemployment as a geographic phenomenon. The abrupt change in unemployment rates at county boundaries in this map obscures the underlying heterogeneity in unemployment within county bounds. The phenomenon of unemployment varies by person, while the mapped unemployment data varies by county. This doesn’t mean the map is wrong, but it is a reality important to be cognizant of, both while creating your own maps and while critiquing those designed by others. What do you want your map to present to the reader? Different map purposes will help dictate how to present that information.

Relatedly, when creating maps, you will often rely on data that has already been collected by others. Often, this data is collected (as in the example in Figure 6.1.1 above) by enumeration units, such as counties, census tracts, or states. Obviously, containerizing data can create the illusion of a discrete and abrupt phenomenon. Unemployment does vary by person, but it is unlikely that this fine-grained data will be available to you. If you have a coarser level (e.g., state level) data, you cannot create a map that shows variation by person, by county, etc., even if this would be a more accurate depiction of the phenomena's distribution across space. The only way to create a more detailed map is to collect more granular data. Your map design can always be altered to present a simplified depiction of your data—but not the other way around.

Data is typically classified as either qualitative (e.g., land use; political affiliation) or quantitative (e.g., per capita income; temperature)—you likely recall learning about this distinction in earlier courses. The classification of your data as qualitative or quantitative will have significant influence on which visual variables you select to map your data. Color hue, for example, is excellent for qualitative data, while color value suggests order or a sequence and thus is probably a better choice for designing quantitative maps.

Nominal is a common term used to describe qualitative, or categorical data. Land use and land cover maps are popular examples of nominal data. They might show, for example, residential blocks as distinct from parks and green space, but this does not suggest that one is lesser or greater than the other.

Figure 6.4.1: A zoning map using the visual variable color hue.

Credit: hayward-ca.gov

Quantitative data can be further classified as ordinal, interval, or ratio data.
Ordinal data has an order, but cannot be presumed to show differences in magnitude. Sports team rankings, for example, describe which teams are better, but not by how much.

Interval data describes orders of magnitude but has an arbitrary zero point. Credit scores, exam grades, and the hours on a clock are all examples of interval data: the intervals between points in all three of these ranges is equal, and none of them have an absolute zero point. Additionally, you can add or subtract interval values, but you can’t multiply them— 2 o’clock plus 3 hours = 5 o’clock, but you can’t multiply 2 o’clock by 3 hours. The classic example is temperature: 0º Fahrenheit and 0º degrees Celsius both serve as the zero point on their respective scales, but refer to different temperatures and therefore arbitrary.

Ratio data, conversely, has a non-arbitrary zero point. Examples of ratio data include counts of forest fire incidence, and yearly household income (e.g., $50,000 is twice as much as $25,000). Interval and ratio data are often grouped together and classified as numerical data.

Understanding your data’s spatial dimensions, geographic model, and levels of measurement will help you select which visual variables to use in your map. Recall the table of visual variables we first encountered in Lesson 1 (Figure 6.5.1). This is a good time to check your knowledge and consider which of the following seven visual variables are best for visualizing data category, and which are best for visualizing order.

Figure 6.5.1: Bertin's visual variables.

Credit: Adapted from Visual Variables, Axis Maps. Available under the Open Database License CC BY-NC-SA 4.0

Some visual variables are also better than others for encoding data with different levels of measurement. Bertin (1967) only considered size (other than position on the map) to be a truly quantitative variable, its visual representation able to be matched precisely to a numerical value (although this is arguably true for orientation and position as well). This makes it a good choice for mapping ratio-level data, as making mathematical calculations with such data can be useful. Visual variables that can typically encode only category, not order (e.g., color hue; shape) are best for qualitative data.

Note that the visual variables presented in Figure 6.5.1 are those originally proposed by Bertin, and though they are likely the most common in use, this is not a comprehensive list. The graphic also does not demonstrate the many ways in which these variables might be altered and/or combined to create new designs. At the end of this lesson, we will assess a variety of maps, many of which use multiple visual variables. We will also discuss multivariate mapping further in Lesson 7 (Multivariate and Uncertainty Visualization).

The figures above focus on geometric visual variables (e.g., color; pattern; size), though another common mapping technique is to use pictographic or iconic symbols (Figure 6.5.2).

Figure 6.5.2: Symbol iconicity.

Credit: Cary Anderson, Penn State University, after (MacEachren et al. 2012).

Iconic symbols are those that provide a closer visual match to their referent, or the real-world element meant to be depicted by the map symbol (Maceachren et al. 2012). The map in Figure 6.5.3 below uses flower symbols that are drawn similarly to how they appear in reality to create an engaging and useful map. It is important to balance usability and realism when using iconic symbols on maps - ensure that they do not become overcrowded, or distract from the map's purpose.

Figure 6.5.3: A map of the National Cherry Blossom Festival.

Credit: National Parks Service

Another important consideration that should be weighed when considering the use of iconic symbols is the cultural context of those symbols. Some iconic symbols may be meaningful only to a specific group of people. For example, in the United Kingdom, the symbol used for speed cameras is a 19th century-style bellows camera (Figure 6.5.4). Especially for young people who may have never seen this type of camera, its symbolic rendering may be completely meaningless. Iconic symbols, therefore, are very culturally contextualized and that context should be weighed before icon symbols are chosen to be used on a map. This article [11] further explores the idea of symbols and icons and their meaning in cartography.

Figure 6.5.3: UK road sign indicating the presence of a speed camera.

United Kingdom - Department for Transport

Like other continuums we have discussed (e.g., discrete to continuous; abrupt to smooth), map symbols cannot always be classified as simply abstract or iconic, and instead, exist somewhere in the middle. National Geographic's Atlas of Happiness for example, uses smiling face graphics to encode data about happiness. Thus, it is less abstract than if this data had been encoded only with color value or size, but less iconic than if more realistic graphic images of people were used.

Visual variables are used in many mapping techniques: in addition to selecting which visual variables you use for your maps, you will also need to choose what type of thematic map you will create. While the four most popular thematic map types are choropleth, isarithmic, proportional symbol, and dot maps, other more sophisticated symbolization methods have been developed.

The required reading gives more detailed descriptions, but below we give a general overview of the four most popular types of thematic maps.

Figure 6.5.4: A choropleth Map.

Credit: Census.gov

Choropleth Maps are maps in which color or shading is applied to distinct enumeration units, usually statistical or administrative areas. Color hue, saturation, and value are the most frequently used visual variables in choropleth mapping, though pattern is sometimes used as well. As discussed in Lesson 5, choropleth mapping should almost never be used to encode exact counts (e.g., number of people living in each state), as the visual encoding of color by enumeration units makes this confusing (i.e., due to the varying sizes of the enumeration units). For example, consider that more people live in California than in any other state. You could create a state-by-state choropleth map showing counts of, say, universities or gas stations, and California would likely lead in both simply due to its geographic expanse. But a map showing this would not provide much useful information—California has more people and things because it is a bigger enumeration unit. The map would tell us nothing interesting about California's system of education, or its residents' consumption of gas However, if you were to map universities per capita, then we would be able to meaningfully compare rates between states, and a choropleth map would be an appropriate method.

Figure 6.5.5: An isarithmic map of a low temperature forecast in the US.

Credit: The New York Times

Isarithmic Maps are like choropleth maps in that they typically use color value to encode data values, but unlike choropleth maps, they do not visualize the enumeration units from which they are built. Isarithmic maps are preferred for mapping phenomena that vary continuously over space (like temperatures), as they better represent the distributions of these phenomena than choropleth maps. The primary disadvantage of isarithmic maps is that they require quite a bit of data to design them accurately. They should also not be used to map data that change abruptly at administrative boundaries (e.g., percent sales tax). Choropleth mapping is a simpler and more appropriate method for mapping such data.

Figure 6.5.6: A proportional symbol map of vehicle miles driven per person in major cities.

Credit: U.S. Department of Transportation, Bureau of Transportation Statistics; Federal Highway Administration, Highway Statistics Series 2022,  Table HM-71.

Proportional Symbol Maps are best suited for mapping abrupt, discrete data; they visualize data using the size of a symbol (most often a circle) placed inside an enumeration unit. Size is the visual variable used in proportional symbol mapping - as the symbols are scaled in proportion to the data values that the symbol represents. As the symbols are scaled only based on the data value—irrespective of the size of the enumeration unit—this permits the reader to not only view the variation between symbols, but also perform a visual comparison of the size of the symbol and the size of the enumeration unit over which it is placed. Note that the map in 6.5.6, unlike the previous two maps (6.5.4 and 6.5.5) displays count data (population) rather than a rate (percent in poverty; people per sq. mile). This is an appropriate choice for a proportional symbol map.

When mapping count data such as population counts, you should use a proportional symbol map, or you should standardize your data before using it to make a choropleth or isarithmic map. You explored standardizing data in Lesson 4. However, proportional symbols can also be used to map standardized data such as rates  (e.g., cancer rate per 100,000) or densities (e.g., people per sq. mile).

Figure 6.5.7: A dot map showing acres of wetlands in the US in 1992.

Credit: nrcs.usda.gov

Dot Maps are like proportional symbol maps in that they are most appropriate for visualizing discrete data. Rather than displaying a different-sized symbol per enumeration unit, however, dot maps are constructed by filling enumeration units with a count of symbols (usually dots) based on the count of the variable of interest within the unit. Thus, this technique is preferred over proportional symbols for mapping data which vary more continuously over geographic space. It also ensures that your symbols will not overlap one another, which is sometimes the case with proportional/graduated symbols.

It's important to think carefully when creating and reading dot maps. Often, dot maps made with a computer mapping application are made by scattering the appropriate number of dots randomly throughout each enumeration unit. To a novice viewer, they give the illusion of high precision— you might assume that if every dot represents one thing, that the dots are placed on the map exactly where those things exist! However, this is very rarely the case.

Ultimately, which symbolization method you choose for your mapping purpose depends not only on what phenomenon you are mapping, but also on the scale at which you map it and the intended information you wish to present to the map reader.

Proportional Symbolization

In Lab 6, we will explore two symbolization methods for data that are considered discrete and abrupt. Proportional and range-graded symbols are two approaches to represent discrete and abrupt data using symbols that are scaled according to the individual data values. Proportional symbolization scales each symbol size in direct relation to each data value. For example, if you had unique data related to all 88 Ohio counties, there would be 88 symbols of different sizes on your map. A commonly used symbol is the circle. With range-graded symbolization, the data are classed into finite classes. This approach mirrors what you experienced in Lab 5 with the data classification for data for choropleth mapping. This method is also known as graduated symbol (which is Esri-speak). Both range-graded and graduated are a bit ambiguous. A better term would simply be classed proportional circles, but that is probably too long.

In addition to the proportional and range-graded symbolization methods, we will also examine a symbolization method mapping qualitative data via the choropleth method. This method, known as chorochromatic symbolization, is useful when you wish to map qualitative data using the choropleth method.

As a cartographer, you will often have to choose between which approach is better for your data. Essentially, consider the use of proportional symbols when the recovery of the original data values is important. Proportional symbolization method is appropriate for a dataset whose range is not excessive. In such cases where the range is great, extremely large or small symbols could result. Range graded symbolization addresses datasets with large ranges by setting a fixed number of symbol sizes according to a classification method applied to the data. As with other options in the map-making process, the choice between using proportional symbols and range-graded symbols depends on the map's purpose and data characteristics.

In Lab 5, we used data from the American Community Survey, provided by the US Census - a commonly used source of geospatial data for statistical maps. In this lab, we use the same data source, but you also will have the opportunity to choose your own data for this assignment.

The first part of Lab 6 will focus on searching for and downloading data from the US Census Bureau’s data explorer website. This website offers access to all census data collected since 1990 – both at the decennial census and the one- and five-year estimates from the American Community Survey. The second part of the lab allows you to explore using proportional symbols to map your chosen census data. The third part of the lab allows you to explore using range-graded symbols to map your chosen census data. The second and third parts will take place in a new mapping application – Tableau.

This lab, which you will submit at the end of Lesson 6, will be reviewed/critiqued by one of your classmates in Lesson 7 (critique #4).

Lab Objectives

• Create three (3) maps of county-level data from a state of your choosing, sourced from the US Census Bureau. The state must have at least 30 counties.
  • One map must use proportional symbolization.
  • One map must use range-graded symbolization.
  • One map must use chorochromatic symbolization (using a qualitative variable extracted from the census data).
• Learn how Tableau can be used to create interactive maps.
• Calculate class breaks for the range graded symbolization using either quantiles, equal intervals, natural breaks, or mean-standard deviation.
• Understand the impact of different symbolization approaches on the information illustrated on each map and be able to reflect upon and write about these decisions.

Overall Lab Requirements

For Lab 6, you will create three (3) maps, each of which should be created as its own sheet in Tableau. In total, you will have three separate Tableau sheets. You will also write a short reflection statement about the map creation process in Tableau.

• Prepare visually balanced layouts for each map with all required elements suitably sized and balanced negative space.
• Create an effective design for the visual hierarchy: overall title, subtitle(s), legend title(s), legend class labels, metadata (data source/year, your name, and date of completion). Use thoughtful and efficient wording when labeling map elements.

Map Requirements

Map One: Proportional Symbols

• Choose a census variable of interest to map from the provided American Community Survey (ACS) data. As a hint, choose a variable from the 5-year estimate to download.
• Use Tableau to complete all cartographic work.
• Using Tableau,
  • choose an appropriate symbol (e.g., circles, squares, etc.) for this map
  • attend to the design aesthetic of the basemap, county outline fill, symbol fill, and symbol outline colors
  • create a descriptive map title, subtitle, and legend title
  • add metadata to the map that reports on the data source and year, your name and date of map completion
  • include a legend and legend title (note that Tableau is not very flexible in altering a legend for proportional symbols with a large number of mapped features)

Map Two: Range Graded Symbols

• Using the same census data that you did for map one, use range graded symbolization.
• Choose a classification method to determine the class breaks. The available methods include equal interval, quantile, natural breaks, and mean-standard deviation. The choice of the method is up to you but make sure the method is appropriate for the data distribution that you are mapping.
• Use Excel to assist in the following:
  • create a dot plot as you did in Lab 5 to see the distribution of the data for this map
  • include a screenshot of your dot plot with lines manually drawn to demonstrate the breaks you identified
  • identify the data classification you selected and why you thought it appropriate.
• Using Tableau,
  • choose an appropriate symbol (e.g., circles, squares, etc.) for this map
  • apply the data classification limits to the data
  • attend to the design aesthetic of the basemap, county outline fill, symbol fill, and symbol outline colors
  • create a descriptive map title, subtitle, and legend title
  • add metadata to the map that reports on the data source and year, your name and date of map completion
  • include a legend and legend title (the legend for this map will be better designed since you are mapping classes rather than the number of mapped features)

Map Three: Chorochromatic Map

• Using the same census data that you did for map one, derive a single qualitative variable of interest related to the data chosen for maps one and two.
• Using Tableau,
  • choose an appropriate qualitative color scheme for this map
  • attend to the design aesthetic of the basemap, symbol fill, and symbol outline colors
  • create a descriptive map title, subtitle, and legend title.
  • add metadata to the map that reports on the data source and year, your name and date of map completion,
  • include a legend and legend title (note that the legend for this map will be better designed since you are mapping qualitative data with a limited number of categories)

For additional assistance, explore the Lab 6 Visual Guide and utilize online tutorials and training materials such as those listed below:

• Tableau 10 Essential Training. Your access to LinkedIn Learning (aka Lynda.com) is provided through your Penn State login
• These two links are tutorials provided by Tableau
  • Build a Simple Map
  • Customize how your Map Looks

Reflection Statement

Include a short write-up (< 250 words) that includes the following commentary:

• State the variables you used to create the proportional/range-graded symbol map and the chorochromatic map
• For the range graded map, comment on the overall distribution of the data as shown on the dot plot (normal, positively skewed, negatively skewed)
• State the classification method you selected and why (relate this discussion back to the data distribution and what information you intend for the map to portray – remember the concepts from Lesson 5)
• Explain why you chose and how you derived the qualitative variable of interest for your chorochromatic map
• Comment on two (2) positive aspects of working with Tableau
• Comment on two (2) aspects of working with Tableau that were challenging

Lab Instructions

The data for this lab will be self-selected from the US Census Bureau’s data explorer website. Details on how to access this site, how to search for data, and format the data for download will be presented in the Lesson 6 Lab Visual Guide.

Grading Criteria

Registered students can view a rubric for this assignment in Canvas.

Ready to Begin?

More instructions are available in the Lesson 6 Lab Visual Guide.

You've reached the end of Lesson 6!

In this lesson, we learned a lot about thematic maps - what they are, why we design them, and how to choose the best thematic mapping technique based on characteristics of your data and of the geographic phenomena you wish to map. We discussed challenges you might encounter when making thematic maps, such as when the level of measurement of the data available to you doesn't match the level of measurement of the phenomena.

In Lab 6, we created proportional and range-graded (graduated) symbol maps - exploring the differences between map types and their appropriateness. Though our focus this lesson was on using these two symbolization methods, you'll notice that concepts we learned earlier - such as visual variables, map labels, and layout design - have remained of high importance. The tasks in this course are intended to build upon each other. I look forward to watching you thoughtfully integrate concepts from throughout the course into your maps each week. In addition, in this lab, you explored a new mapping application, Tableau. It is important to explore other mapping applications outside of ArcGIS Pro as each application offers something unique. Using Tableau, you witnessed the ability to quickly create and implement a common design across different maps. Exploring Tableau functionality continues into Lab 7 where you will work on multivariate symbolization and experience the powerful linking and brushing interactivity that this application brings to the map making process.

Reminder - Complete all of the Lesson 6 tasks!

You have reached the end of Lesson 6! Double-check the to-do list on the Lesson 6 Overview page to make sure you have completed all of the activities listed there before you begin Lesson 7.