Welcome to Lesson 9! Last week, we discussed some of the new technologies that have been influential on current trends in cartography, including interactive and animated maps and 3D visualization. While interactive and dynamic maps present a myriad of opportunities for creating new and exciting designs, they also introduce many new challenges. Studies of interactive maps draw from research not only in cartography and psychology but in other cognate fields such as human-computer interaction (HCI), human factors, and usability engineering. We will discuss various approaches for studying dynamic maps in this lesson.

Dynamic maps change based on interactions (either active or passive) by the map reader. In such cases, we begin to consider the map reader as, instead, a map user. Additionally, as these maps typically appear alongside other media (e.g., supplemental charts, article text, videos), we also consider these map-adjacent elements and how they influence the user experience. In Lab 9, we put this knowledge to use and design an interactive data visualization story with the data storytelling platform StoryMaps.

Lesson 9 is a two-week effort. For this lesson, you will choose a spatially related map topic. This topic can be any spatially-related idea and can be focused on anywhere in the world. Once the topic is selected, you will need to acquire appropriate datasets. You will be asked to create three (3) interactive maps. The maps should reflect datasets that support and explain the spatial distribution of the topic of your choosing. The map designs should be cast along a common theme and be supportive of the overall topic. Here is a breakdown of what you should aim to accomplish during the two weeks.

In the first week, you should...

• Choose a topic of interest for Lab 9
• Collect appropriate data in support of your topic of interest
• Download, clean, and format your data in a spreadsheet
• Design three (3) separate maps using your chosen data using ArcGIS Online
• Select appropriate symbolization methods, color schemes, data classifications, titles, map marginalia, etc., for all maps
• Apply a consistent design theme to all maps for a consistent and coherent appearance

In the second week, you should

• Develop a StoryMaps narrative that "tells" your story in a cohesive presentation
• Integrate your three (3) maps into the StoryMaps as supporting evidence for that story
• Apply a consistent overall design and theme to the StoryMaps environment that complements and is complemented by the three (3) maps
• Include descriptive text throughout your StoryMaps that explains what each map shows and how that information adds to the overall narrative

Learning Outcomes

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

• discuss how the advent of the interactive map has added additional dimensions to the study of map design;
• compare different methods of map evaluation, including experimental and design studies;
• generate insights using (geo)visual analytic tools by exploring maps with linked, coordinated views;
• write supporting text that facilitates effective communication of a map or other visualization’s data and ideas;
• create an engaging interactive data visualization story, integrating design knowledge obtained throughout the course.

Lesson Roadmap

Questions?

If you have questions, please feel free to post them to the Lesson 7 Discussion Forum. While you are there, feel free to post your own responses if you, too, are able to help a classmate.

We often consider how our map readers might interpret or respond to a map we make. Predicting these behaviors and designing our maps to account for them is a complex problem that we have discussed throughout this course. When making maps, we often must choose a suitable projection, symbolize data appropriately, visualize additional elements such as terrain, and so on. We also account for contextual factors: for example, we might expect our map readers to be stressed or working within time constraints. We may also need to design for media-based constraints such as black-and-white newspaper printing, or for challenging viewing scenarios, such as small sizes (e.g., in an academic journal article) or far distances (e.g., in a slideshow presentation).

You might recall the maps in Figure 9.1.1 from Lesson 1 - each was designed with a different type of map reader in mind. 

Figure 9.1.1 (1.4.1): Two similar maps of city water utilities appropriate for different audiences. The map on the left includes additional symbols that are useful only to experts. The map on the right would be more suitable for a busy audience or non-experts.

Credit: BeavertonOregon.gov

Figure 9.1.1 shows how minor alterations to a static map (here, technically sections of a larger map) can make it more suitable for a given map audience or purpose. Last lesson, we discussed interactive maps— maps that change based on some form of user input. This realm of mapping has turned our focus from the map reader to the map user (Roth et al. 2017). We now must consider not just how our map’s audience will interpret the map we design in a single state, but how they will interpret it as they use it, which is to say, as they alter it. An interactive map must work not only in one state, but ideally in every state that might be reasonably encountered by the map user. This is no small task.

Even basic interactions such as panning around a slippy map can introduce challenges. Figure 9.1.2, for example, shows two locations on an OpenStreetMap basemap, both at a 1:5,000 scale.

Figure 9.1.2: Rural Indiana (top), and New York City (bottom).

Credit: OpenStreetMap © OpenStreetMap contributors. The data is available under the Open Database License (CC BY-SA). Screenshot from ArcGIS Pro.

These maps are shown at the same scale but appear vastly different—and this makes sense, given that they are very different places. What this example highlights, however, is the variety between locations that pan-able maps must often be designed to cover. Web maps typically cannot be designed separately for each individual area on Earth (imagine the time that would take!), so cartographers use generalization algorithms and design rules to ensure that their maps will work at locations, rural and urban, near and far, and at scales both small and large.

Panning (i.e., moving the map to another location) is among the most basic functions performed with interactive maps. Additional functions such as filtering and route-planning introduce further complexities to interactive map design. For insight on how to best support such tasks, cartographers have turned to the study of usability.

Usability is defined by the International Organization for Standardization (ISO 9241-11:2018) as “the extent to which a system, product or service can be used by specified users to achieve specified goals with effectiveness, efficiency and satisfaction in a specified context of use.” Designers of websites, mobile apps, and many other technologies consider system usability when building their products. Though it is a subject with a rich history and many facets, Jakob Nielsen’s (1994) usability heuristics provide an excellent foundation for assessing the usability of a system (such as an interactive map).

Figure 9.1.3 demonstrates two of Nielsen's usability heuristics: error prevention and consistency and standards.

Figure 9.1.3: Warning messages from Sublime Text (top), and Microsoft Word (bottom).

Credit: Cary Anderson, Penn State University, is licensed under CC BY-NC-SA 4.0, screenshots from Sublime Text and Microsoft Word.

As suggested by the ISO (2018) definition, an important component of usability, and one that ought to be considered when implementing the usability heuristics, is the idea of context of use. For example, a routing app might be designed specifically so that the interface can be safely manipulated (or not) while the user’s vehicle is in motion.

Despite the importance of context in designing usable systems, a significant amount of scientific research related to usability has focused on developing more generalizable findings, such as whether users can identify changes in animated maps (Fish, Goldsberry, and Battersby 2011). When we consider how to assess maps in terms of their usefulness, it is helpful to distinguish between these two primary approaches: traditional, experimental research intended to elucidate generalizable insights, and design studies that focus on context-specific design. Roth and Harrower (2008) describe these sorts of studies as a continuum from controlled experimentation to usability testing. Despite the helpfulness of conceptualizing cartographic evaluation methods as existing along a continuum, we discuss these methods as falling more generally into one of two categories: (1) experimental studies, and (2) design studies, for simplicity and brevity.

As noted in the previous section, experimental studies seek to identify generalizable findings. These studies draw heavily from work in psychology, a discipline with a rich history of closely-controlled experimental research. Research conducted by Fish, Goldsberry, and Battersby (2011) on change blindness is a helpful example of experimental research in cartography.

Figure 9.2.1: An example of an animated map to demonstrate the difference between animation frames.

Credit: blogs.cdc.gov

Fish, Goldsberry, and Battersby (2011) found that not only did their participants often incorrectly identify which locations had changed from the previous animation frame, but they were consistently overconfident in their reports. A suggestion made by the authors to mitigate this effect was to incorporate tweening, or gradual graphic transitioning between animation frames, into animated map designs. This suggestion is applicable to a wide variety of animated mapping contexts.

Similar studies have been conducted on other aspects of map design. Limpisathian (2017), for example, studied the influence of visual line and color contrast on map reader perceptions of feature hierarchy and aesthetic quality. Unlike Fish et al., who conducted their research with participants in a lab, Limpisathian recruited and tested participants using the crowd-sourcing platform Amazon Mechanical Turk (MTurk). Such platforms have become increasingly popular in recent years as—despite their shortcomings— they enable researchers to run large studies with more diverse sets of participants and at lower costs.

Experimental studies often use web surveys, which can measure task (e.g., map data retrieval) accuracy and speed. Some surveys take advantage of new technologies such as eye-tracking, which measures fixations of the human eye. Griffin and Robinson (2015), for example, used eye-tracking to measure the efficiency of color and leader-line approaches when highlighting geovisualizations. Eye-tracking is a popular method for understanding user response to design, and is regularly used by web design practitioners and in marketing research. Figure 9.2.2 shows an example record of eye-tracking from a study performed on the Healthcare.gov website. Similar studies have been conducted with maps and other spatial tools.

Figure 9.2.2: An example of output generated from an eye-tracking study: numbers show the order of fixations by the participant’s eye.

Credit: usabilitygeek.com

While experimental studies focus on producing generalizable findings (e.g., “people suffer from change blindness when viewing animated maps”), design studies focus on more specific use contexts. The goal of these studies is generally to improve a specific map or mapping product. Testing often begins early in the design stage, with preliminary design sketches and/or paper prototypes (Figure 9.3.1). Paper prototypes are generally preferred to more formalized mock-ups early in the design process, as they cost little to create, leaving designers more willing to change their designs in accordance with suggestions by testers. Research has also found that testers of "sketchy" designs and paper prototypes are more likely to elicit big picture design suggestions than more formalized prototypes (Dykes and Lloyd 2011). This is because test users are more able to focus on the overall functions of a tool when they view it as unfinished—they are not distracted by small design details (Dykes and Lloyd 2011).

Figure 9.3.1: Examples of a participant using a paper software prototype.

Credit: Nielsen Norman Group

As design studies focus on a specific use context, it is important to test with target users (i.e., the intended users of the product) whenever possible. A map designed to be used by utility maintenance workers, police officers, for example, will likely require input from these users to be made sufficiently useful in that context. A popular mantra in usability research is this: you are not your users. When designing a map intended for use by the general public (e.g., Figure 9.3.2), it might be enough to test your design with a group of college undergrads for course credit, or through a crowdsourcing platform such as Amazon Mechanical Turk. For more specialized contexts, recruiting those target users is necessary.

Figure 9.3.2: An example of an interactive crime map designed for use by the community/general public.

Credit: NASA FIRMS

Roth, Ross, and MacEachren (2015) emphasize the importance of involving target users throughout the map design process. In their work designing an interactive mapping tool to support the needs of the Harrisburg, PA Bureau of Police, they suggest an iterative approach to system design. They outline three U’s of interactive map design: user (i.e., considerations of target users and use-case scenarios), utility (i.e., whether the map performs the tasks that its users require), and usability (i.e., whether the tool’s functions align with usability design principles/heuristics).

When we talk about interactivity in maps, we must consider not just user interactivity within maps, but interactivity among maps, as well as with other tools and visual graphics. Interactive mapping has played an important role in the field of visual analytics, defined as “the science of analytical reasoning facilitated by interactive visual interfaces” (Thomas and Cook 2005).

Recall the Cartography Cube from Lesson 1 (review this concept in the Communicating with Maps section). Most of the maps we have designed thus far would be considered to be in the communication (public, static, and intended to present known information) corner of the cube. Visual analytic tools typically belong in the opposite corner—these tools are characterized by high human-map interaction and are often designed with private data or data that is otherwise meant for domain experts. They also focus on revealing unknowns (i.e., generating insights), rather than communicating known trends.

Figure 9.4.1: The Cartography Cube.

Credit: (MacEachren and Taylor, 1994)

One domain in which visual analytics has been particularly popular is in public health and epidemiology. An example tool is shown below (Figure 9.4.2). The Pennsylvania Cancer Atlas is an interactive tool designed at the GeoVISTA Center at Penn State, with assistance from the Centers for Disease Control (CDC) (Bhowmick et al. 2008). The atlas includes a choropleth county-level map of Pennsylvania, coordinated charts and tables, and filtering and selection options to compare data across the views. In the view shown below, for example, Bedford County has been selected on the map by the user, and the scatterplot and table have been highlighted to focus on that county as well. This connecting of multiple visual depictions of data is called coordinated views.

Figure 9.4.2: The Pennsylvania Cancer Atlas, developed at the GeoVISTA Center at Penn State.

Credit: (Bhowmick et al. 2008), available at ResearchGate. (CC BY 2.0)

A more recent example is FluView, a visual analytic dashboard designed by the CDC for analyzing data related to the incidence of the flu in the United States. FluView is shown in Figure 9.4.3 below—you can try it out by selecting the link here: FluView.

Figure 9.4.3: FluView, developed by the CDC.

Credit: CDC.gov

A demo of a more complex geovisualization built around visual storytelling, Detecting Disease Spread from Microblogs, is shown in the video in Figure 9.4.4. below:

Though health and public safety applications are popular uses for (geo)visual analytic tools, they have been used in many domains. Figure 9.4.5 below shows the geovisualization tool MapSieve, designed for analyzing spatial patterns of student engagement in online courses taken by students all over the world.

Figure 9.4.5: A Screenshot of the Geovisual Analytic tool MapSieve.

Credit: Robinson, Anthony C., and Sterling D. Quinn. 2018. "A Brute Force Method for Spatially-Enhanced Multivariate Facet Analysis." Computers, Environment and Urban Systems 69 (June 2017). Elsevier: 28-38. Reproduced with permission from Dr. Anthony Robinson, Penn State University.

While the tools above focus on fairly complex data that often require domain knowledge for effective interpretation, similar visualization tools are also often used in more fun, less serious contexts that are more geared towards a general audience. Figure 9.4.6, for example, shows a Tableau (data visualization software) dashboard that visualizes Airbnb data in Portland, Oregon. We will take a closer look at dashboards like this later in this lesson.

Figure 9.4.6: A Tableau dashboard visualizing Airbnb data from Portland, OR.

Credit: The City of Portland, Oregon (click the link to explore the full Tableau Story).

Similar interactive tools are often designed for mapping election results or other data of public interest. Graphics are often accompanied by a significant amount of text, both within the main view as explanatory text, or adjacent, to tell a story supported by the data. We discuss this more in the next section: Data Journalism.

As demonstrated by the Portland Airbnb example, interactive maps designed for public consumption often do not stand alone. Except in the case of very simple data visualizations, these maps and graphics tend to be accompanied by additional text, both within the visualization interface and outside. Such maps are often included—in either static or interactive form—in the type of articles and other media described as data journalism.

Data journalism is a general term that refers to the increasing influence of numerical data in news reporting; data are often reported and/or visualized alongside articles and reports disseminated to the public. Though data journalism does not necessarily have to include visual depictions of data, it often does, and for good reason. Visual graphics tend to captivate readers, and charts, maps, and graphics can be much better at communicating data at a glance than data tables and spreadsheets alone. The article in Figure 9.5.1 is an example of this. The article includes a large map, as well as a set of small multiple maps, to visualize the geographic distribution of ammonia. The article text gives the reader additional information about the ammonia gas.

Figure 9.5.1: An example of data journalism: from NASA's Earth Observatory blog.

Click to read the text contained in Figure 9.5.1

If you have ever inhaled hazy, acrid air on a "code purple" or "code red" air quality day, you may have wondered what triggered the public health warning. Often the culprit is fine particulate matter (PM_2.5), a harmful mixture of airborne particles with diameters smaller than 2.5 micrometers, or 30 times thinner than a human hair.

Some of the most abundant PM_2.5 particles—ammonium sulfate and ammonium nitrate—have ammonia (NH₃) as a key ingredient. A colorless gas with a pungent smell, ammonia reacts with other common substances in the atmosphere (sulfuric acid and nitric acid) to form these two classes of particles. In some cases, ammonium sulfates and ammonium nitrates make up as much as 80 percent of the particles in PM_2.5.

Ammonia has a long history on Earth. In fact, scientists think it was one of the key gases present when life emerged some four billion years ago. These days, it is a common ingredient in cleaning products, fertilizers, and refrigerants.

The gas has some natural sources; it leaks into the atmosphere when bacteria break down organic matter and when fires burn. However, most ammonia in the atmosphere got there because of human activity, most notably food production. In the United States and Europe, about 80 percent of ammonia emissions come from agriculture. Concentrated livestock operations are a particularly potent source of the gas because it seeps from animal wastes. Large farms that use ammonia-based fertilizers to grow grains or other crops are another major source of emissions.

Credit: Earthobservatory.nasa.gov

As demonstrated by the links above, media outlets frequently report on important and emotionally engaging information. Journalists take on the challenging job of reporting this information to the public. Often, pairing interactive maps and graphics with carefully curated text is the most effective way to do so.

Given recent trends, including the proliferation of interactive maps and visual analytics, cartographers have begun to focus more on maintaining a balance of text, graphics, and other elements in their work. Think back to our discussion in Lesson 2 of frame lines and neat lines for map layouts—such simple guidelines seem almost irrelevant in the context of data journalism and interactive map making. While cartographers still face traditional design constraints when creating maps for print (e.g., magazine spreads, print advertisements), they must now also work with more complex design formats such as infinite scrolling web pages and interactive dashboards.

In previous lessons, we discussed the importance of design thinking that reaches beyond the map—configuring page layouts and explanatory text in a neat, usable, aesthetically pleasing fashion. Given our current focus on the map user, note that ideally, this design thinking ought to be implemented at all stages of map interaction. For example, see Figure 9.5.2. Shown in this view is the map “on-hover,” which means that the user has hovered their cursor over the point that is highlighted. The tooltip, which appears (Figure 9.5.3), must present an amount of information that is adequate but not overwhelming for map users. It could be argued that this is not successfully accomplished here—the coordinate location is likely unnecessary information, and the addition of a short description of the property would assist the map user.

Figure 9.5.2: An interactive map dashboard shown on hover.

Credit: The City of Portland, Oregon

Figure 9.5.3: A closer view of the tooltip in Figure 9.5.2.

Credit: The City of Portland, Oregon

The “visual information-seeking mantra”, first proposed by computer scientist Ben Shneiderman, is frequently referenced by information designers: “Overview first, zoom and filter, and details-on-demand” (Shneiderman 1996). We will use the Portland Airbnb Tableau dashboard to explore this mantra in practice. First, in Figure 9.5.4, the starting view of the dashboard, which shows all of the listings in Portland: overview first.

Figure 9.5.4: A starting map view which shows all mapped data (“overview first”).

Credit: The City of Portland, Oregon

From the starting view, the user can zoom in and/or pan around the map, and filter the map data by selecting a category of interest. The tool state in Figure 9.5.5 shows the view after the user has zoomed into the map and selected the "private room," room type. This data could be further filtered by selecting a property type, such as "hotel-like property." This is the second stage of the mantra: zoom and filter.

Figure 9.5.5: The map view upon map zoom and user selection of the category private room. (“zoom and filter”).

Credit: The City of Portland, Oregon

Figure 9.5.6 shows the view in 9.5.5 upon mouse hover of this hotel-like property near the river. ID numbers for the host and listing, as well as lat/long coordinates, are given in the tooltip. This is the final element of Shneiderman's mantra: details-on-demand.

Figure 9.5.6: Additional details provided for an individual property upon user click in a tooltip (“details on demand”).

Credit: The City of Portland, Oregon

Small snippets of text, such as tooltips, titles, weblinks, and error messages associated with your maps, will often be designed by you, the cartographer. Such text is often called microcontent, and despite its minimal nature, it can have a large impact on user interpretation of your visualizations. The Nielsen Norman Group provides a helpful reference on how to write such content here: Microcontent: A Few Small Words Have a Mega Impact on Business. Their site is also an informative reference for many aspects of usability and user experience design.

The visual analytic tools we have explored thus far include both maps and graphs, and these different data visualization elements have been connected via coordinated views, permitting user filtering, zooming, and more. Given the limited space available in these dashboards—particularly if they are intended for viewing on small, mobile screens—an important question surfaces: do I need a map at all?

When designing data visualizations, maps often provide an invaluable source for insight generation. However, they are not necessarily always the best choice for your data—even if the data contain spatial information.

View the dashboard below in Figure 9.6.1.

Figure 9.6.1: A dashboard of drug overdose data developed by the city of Philadelphia, PA.

Credit: City of Philadelphia.

This dashboard does not contain a map, and though it’s possible that adding one might provide additional information or interest, its current form gets across the core message: drug overdoses are increasing in Philadelphia, and this is being driven by opioids in general, and fentanyl in particular.

Given the increasing ubiquity of GPS and other location-based technologies, data that contains a geographic component (e.g., state, county, coordinates) is fairly easy to come by. Still, this does not mean that creating a map is always the answer. Imagine, for example, if you had collected data on the rate of Alzheimer's disease by state. Were you to map this data, popular retirement states such as Florida and Arizona would likely jump out—not because there is anything inherently unhealthy about those locations, but because their populations are significantly older. To eliminate the effect of this confounding variable, you could map age-adjusted Alzheimer's rates instead. It's important to consider, however, whether this would be the most informative way to visualize your data. If you were simply hoping to educate people about Alzheimer's, a graph or chart comparing Alzheimer's rates by gender, age, race, or socioeconomic status might serve your purposes just as well.

Conversely, there are many data visualizations that, unfortunately, treat space (i.e., geography) as just another variable. For example, view the dashboard in Figure 9.6.2 below. The designer of this dashboard chose to visualize states as a list of values, rather than to create a map. Though this is not inherently incorrect, it is a missed opportunity to provide the user with an at-a-glance understanding of spatial patterns in occupational data. Sure, the user could still pick out individual data values or compare average annual earnings state-to-state. But data visualization (cartography included) is about making complex data clear; if your visualization is no more useful than its source data table, then why design it at all?

Figure 9.6.2: An interactive graphic of state-by-state average occupational earnings that would have benefited from a map.

Credit: Oklahoma Works

Critique #5 will be your fourth critique involving a peer review of a map created by someone in this class. In this activity, you will be assigned a colleague's map from this class to critique from Lab 8: Interactive Mapping.

Your peer review assignment includes writing up a 300+ word critique of one of your colleague’s Lesson 8 Lab.

In your written critique, please describe:

• Three (3) things about the map design that you think work well and why.
• Three (3) suggestions you have for improvement of the map design, and why these improvements would be helpful.

According to the two prompts above, a map critique is not just about finding problems, but about reflecting on a map in an overall context. Your critique should focus on the map design that works well as much as it does on suggestions for design improvements. In your discussion, you should connect your ideas back to what we learned in the previous lessons.

Remember, your critique should be as much about reflecting upon design ideas well done as it is about suggesting improvements to the design. In your discussion, connect your ideas to concepts from previous lessons where relevant.

You may find these two resources helpful as you write your critiques:

• Daniel Huffman’s 2020 blog post on how to “Critique with Empathy"
• Ordnance Survey’s (Wesson, Glynn, and Naylor, 2013) list of effective cartographic design principles

Grading Criteria

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

Peer Review Canvas Help

• How do I know if I have a peer review assignment to complete?
• How do I submit a peer review to an assignment?

Narrating Your Map Idea with StoryMaps

Your final lab assignment in this course is to design an interactive story about a chosen dataset using ArcGIS StoryMaps. While this lab draws heavily on concepts discussed in Lesson 9, you will be incorporating knowledge from throughout the course in your design.

Lab Objectives

• Use data from a source of your own choosing to create a geospatial narrative in StoryMaps form. The specific topic is up to your own preference.
• Integrate knowledge gained throughout the course to create engaging interactive maps and graphics.
• Examples for reference here (these are much more complex than is required):
  • What's that bug?
  • Mushroom Finder
  • Have Money, Will Travel

Overall Lab Requirements

• Submit the URL link to your StoryMaps as a text comment in Canvas. There is no PDF deliverable for this lab.

Specific Requirements

StoryMaps (Overall)

• Select a topic or idea that you wish to map (e.g., the influenza season in the U.S., changing enrollment trends in U.S. schools, trade imbalances in African countries, wildfire occurrences, etc.). Almost any "spatially-related topic" of interest will work for this assignment. You can focus anywhere in the world. However, as an fyi, datasets from the U.S. are more readily available than other locations.
• Using data from your chosen topic, you will use ArcGIS Online to create three maps. The maps should ideally reflect different datasets related to the topic of choice. These maps should explore different symbolization methods, color choices, data classification methods, and so forth, demonstrating the different elements learned throughout the term. Ultimately, the design decisions should reflect the data that you are mapping, what you wish to communicate about the data, and the stated purpose.
• These three maps will be integrated into a singular StoryMaps that will help tie together the maps and descriptive text in a unifying narrative.
• Create a StoryMaps which includes:
  • At least three (3) interactive maps designed using ArcGIS Online. These maps should be embedded directly into your StoryMaps and will allow a level of user interactivity.
    • Your 3 main maps cannot be Express Maps.
    • Incorporate appropriate cartographic concepts and techniques that we have learned over the course of the semester.
  • At least two (2) StoryMaps tools/functions (e.g., Swipe/Sideshow/Sidecar/Map Tour/etc.). These could incorporate images related to your narrative or additional maps (e.g., for the “Swipe” option), but the maps will not count towards the primary 3-map requirement.
  • At least 300 words of explanatory text—including a brief introduction—that provides a useful and compelling narrative for your data.
  • Use consistent look and feel throughout the StoryMaps; employ consistent/complementary colors and fonts in your StoryMaps narrative that align with the designs of your maps.
  • You are encouraged—but not required—to include non-map supporting elements (images, videos, data visualizations, etc.) in addition to the required elements listed here.
• Your overall layout should not use a StoryMaps template – the overall design must be of your own creativity.

Lab Instructions

1. Choose a spatially-related topic for Lab 9. There are no restrictions on the geographic area to be mapped. Once a suitable topic is selected and appropriate data is collected, download the required data.
2. You should reach out to the instructor if you wish to discuss your topic, data appropriateness, and the topic/data suitability for this project before getting too far into the assignment.
3. Log in to ArcGIS Online with your PSU email (you should have an active account– if not, contact your instructor).
4. Complete the design of the three (3) maps using ArcGIS Online
5. These pages will walk you through the process of creating a simple map in ArcGIS Online. You should complete this map-making process in AGOL before designing a StoryMap.
   1. Get started with ArcGIS Online
   2. Build interactive maps with ArcGIS Online
6. Design and build a "story" of your chosen topic using the StoryMaps environment, including the three (3) maps you created with AGOL.
7. Your StoryMaps must include descriptive text presenting the overall topic idea. Additionally, there should be descriptive text that discusses each map, its pattern, and how that information adds evidence to your overall topic.
8. For assistance in creating StoryMaps, explore the Lab 9 Visual Guide and utilize these online tutorials and training materials, such as those listed below:
   • Get started with ArcGIS StoryMaps
   • ArcGIS StoryMaps, Storytelling that resonates
   • Various StoryMaps resources

Grading Criteria

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

Ready to Begin?

Further instructions are available in the Lesson 9 Lab Visual Guide.

Summary

Congrats, you've made it to the end of the course! In this lesson, we discussed how the recent re-visioning of the map reader as a map user has changed the cartographic design process, as well as how we evaluate maps. We discussed many elements that may be integrated with maps, such as graphs, charts, and explanatory text, and explored the different mediums (e.g., interactive dashboards, data journalism) in which these elements are combined.

At the end of the lesson, we discussed when not to map, encouraging a practical approach to data visualization that views maps as a valuable tool but not a panacea. Relatedly, we note that much of cartographic design theory is widely applicable, and can be applied when designing other data visualizations or writing graphic-adjacent text—from microcontent to full articles.

In Lab 9, we designed an interactive map-based story using the visual analytics platform ArcGIS Online and StoryMaps. Though this lab focused heavily on concepts from Lessons 8 and 9, we also drew from concepts throughout the course—refining layouts, symbolizing data, color choices, and thinking critically about map audience and purpose. Your StoryMaps' narrative is now available on the web for you to share with others as a demonstration of your skills in map design and data visualization. You're now ready and able to create, analyze, critique, and share high-quality maps!

Reminder - Complete all of the Lesson 9 tasks!

You have reached the end of Lesson 9! Double-check the to-do list on the Lesson 9 Overview page to make sure you have completed all of the activities listed there. After that, you'll have finished the course!