1 Introduction

This session, we’ll learn how to modify and stylize geospatial visualizations in Data Studio for insight and clarity.

We address the use of:

  • Data Ink, or visual elements that relate directly to our data
  • Non-Data Ink, or visual elements that provide context and clarity in understanding our data

In particular, we focus on gradient, divergent, and discrete color palettes to convey quantitative data.


1.1 Review

Recall from the previous session that our reports need a data source with the right variable type.

We learned how to:

  1. Create reports, name them, and begin with a blank canvas
  2. Connect data from many different sources, e.g. Google Sheets
  3. Edit connections by naming them, selecting sheets, and limiting cell ranges (e.g. A1:D50)
  4. Edit fields, or variables, to ensure they are the right type (e.g. “Text”, “Date”, or “Geo”)
  5. Create visualizations, like bar charts and tables

You can review the previous session here.


1.2 Practice Data

For this session, we’ll need a variety of variable types.

We will use two open data profiles for Syracuse, New York. One is in CNY Vitals Pro the other is in Data USA:


Downloading Data from CNY Vitals Pro

Download tables from CNY Vitals’ location profile overviews by simply clicking “Download Data”.


Downloading Data from Data USA

Download tables from Data USA’s location profiles by clicking “View Data” and “Download as CSV”.


1.3 Copying Datasets

To make a copy of our practice data, first open each table below:

Once opened in Google Sheets:

  1. Click File in the upper-left menu, then click Make a Copy
  2. Make sure Folder is set to “My Drive” and click “OK”


Step 1: Select “Make a Copy”

To copy a table in Google Sheets, first click “File” and “Make a copy”.


Step 2: Verify Name & Destination

Make sure the file name and destination folder are okay, then click “OK”.


You should now have copies of Demographics in Syracuse and Global Diversity in Syracuse in G Drive.


1.4 Create & Connect

Create a new report on the Google Data Studio homepage. Then:

  1. Edit “Untitled Report” to name your report
  2. Click “Create New Data Source” and select the connector Google Sheets
  3. In the Edit Connector interface, under Spreadsheet, click “Demographics in Syracuse”
  4. Make sure that all Options are checked and that no “Optional Range” is specified
  5. Select “Connect” to visit the Edit Fields interface

You must repeat this process again for the “Global Diversity in Syracuse” G Sheet.


1.5 Editing Variable Types

Recall that when we connect sources to Data Studio reports, we can edit field (variable) types.


Editing Field or Variable Types

Simply select a variable row under the “Type” column to modify its type, e.g. “Text” or “Date”.


Editing Types: We’re using many variable types, so make sure the following are specified correctly:

Global Diversity in Syracuse fields and types:

  • Origin | “Geo” | “Country”
  • Year | “Date & Time” | “Year (YYYY)”
  • Nativity | “Text”
  • Code | “Geo” | “Country Code”
  • Count | “Numeric” | “Number”


Adding Additional Data Sources: To connect another data source or edit one you’re using, simply:

  1. Click Resource in the upper menu
  2. Select on Manage added data sources
  3. Click “Edit” to edit an existing source
  4. Click “Add a Data Source” to add a new one


Adding New or Editing Current Data Sources

You can always add new sources, remove old sources, and edit current sources for your reports.


Demographics in Syracuse fields and types:

  • Year | “Date & Time” | “Year (YYYY)”
  • Race | “Text”
  • Count | “Numeric” | “Number”
  • Percent | “Numeric” | “Percent”
  • Geography | “Geo” | “Region Code”

Finally, click “Add to Report”. You now have two different data sources feeding into your dashboard!


2 Layouts & Themes

When in edit mode (versus view mode), without anything selected, we may modify our layout and theme.


Modifying Your Report Layout

The layout of your report dictates its canvas size, grid resolution, and even menu behavior.


The following layout options correspond to the above image and include the:

  • (A) Header: A high-level menu with typical Google app functions
  • (B) Navigation: A report-level menu for navigating multiple pages
  • (C) Responsivity: An option for report behavior to remain static or resize to fit your screen
  • (D) Margins: The option to define the edges of your report as distinct from your browser
  • (E) Canvas Size: The whitespace in your report where you may place text and visual elements
  • (F) Grid Resolution: The horizontal and veritcal lines for alignment and “snap-to-grid” functionality
  • (G) Report-Level Components: Widgets or other elements that appear on or may affect every page


2.1 Key Layout Options

Headers not only contain your title, but have higher-level report functions, including options to:

  • Transition from view mode to edit mode
  • Download your report for offline use
  • Automate email deliveries for collaborators, executives, etc.
  • Generate sharing links, sharing by email, and setting permissions
  • Refreshing data for real time reports
  • Copying reports


The Report Header

Use the header for high-level functions typical of Google apps.


Depending on your audience, you may want your header to behave differently.

  • Always Show is important for audiences who may not use Data Studio often or at all
  • Auto Hide headers only appears when you hover your cursor near the top of the report; less busy
  • Initially Hidden allows the “Wow!” moment that makes your data pop, but then reveals itself


Hidden Headers

Hiding your header can make your report all the crisper, but users may not know how to access it.


Navigation options are important when your report covers different topics that are separable by pages.

  • Left Position provides a leftward navigation menu that users can collapse and expand
  • Top Position provides a top navigation bar that is less intrusive but less intuitive


Left Position “Nav Bars”

Left position navigation, or “Side Navs”, are intuitive and show full page names right off the bat.


Top Position “Nav Bars”

Top position navigation in Data Studio is more compact and less intrusive, but is less intuitive overall.


Key Takeaway: Consider your audience when deciding how you want the layout to appear and behave.

  • Not used to Data Studio? Make navigation bars and headers more apparent
  • Not used to computers? Expand your canvas and put your report on a single page
  • Using Data Studio on the go? Modify your layout accordingly


Pro Tip: Sometimes, page names may be used to guide users like a conversion funnel.


2.2 Key Theme Options

Themes involve “global” style options and default settings are usually fine. Why modify them?

  • Set default color palettes for all data ink, e.g. colorblind- or black and white-friendly tones
  • Set default non-data ink like borders and fill, so you don’t have to afterwards
  • Set default color palletes for organization logos, but only if the data still pops
  • Create daytime modes and nighttime modes for user experience
  • Use an image as a background, like so (this is not recommended)
  • Make export options clearly visible or disabled entirely


Setting “Simple” Themes for Daytime Modes

Daytime modes are less susceptible to glare and make the data pop by eliminating visual competition.


Setting “Simple Dark” Themes for Nighttime Modes

Nighttime modes are critical for both anxiety-ridden insomniacs and troglodytes, such as myself.

Try muting or limiting the use of blue light if you like to fall asleep while perusing data.


Key Takeaway: There’s a decent amount to cover here, potentially, but best practices in data visualization will be peppered throughout these sessions and are very much applicable in themes.


3 Geospatial Graphics

There are several kinds of visualizations you can use in your reports.

  • For certain types, some visualizations are preferable to communicate ideas
  • For other types, certain visualizations are necessary to communicate ideas

Remember: Data visualization is all about communicating a (usually big) idea.


3.1 Geo Maps

Geo Maps represent a family of geospatial visualizations that change according to data type.

  • If variables are coordinates (longitude, latitude) or city-specific, you get a dot map or bubble map
  • If variables are shapes or areas (e.g. states, countries, continents), you get a choropleth map

Note: Data Studio uses “Regions” and “Region Codes” as the type for U.S. states.

Read More:** You can read more about geo maps using on Google’s “Geo Map Reference” guide.


3.1.1 Creating Choropleths

Choropleth Maps are geospatial visualizations that use shapefiles, or polygons of geographic areas.

  • Choropleth maps allow each area to represent quantitative data using fill saturation
  • In other words, typically, the higher the quantity, the darker the area


Choropleth Map of ProLiteracy Membership by State, 2017

Chropleth maps have darker, or more saturated, areas that indicate larger quantities. What’s the story here?


How do I make a choropleth map? The following steps summarize how to make a map, or use the images below.

  1. Click Insert in the upper-left menu and select “Geo Map”
  2. Place the geo map on your canvas
  3. Ensure the correct data source is selected (there are two!)
  4. Select a variable for Dimension, either coordinates, places, or geographic areas
  5. Select a variable for Metric, the quantitative data that affects color saturation
  6. (Optional) Select a variable for Date Range Dimension, which allows us to filter by “Year”
  7. Resize your geo map appropriately


Step 1: Select Chart Type “Geo Map”

Select “Insert” in the menu and “Geo map” in the dropdown.


Step 2: Place Your Geo Map

Simply click-and-drag to place your visualization.


Step 3: Ensure the Correct Data Source

Since we now have two data sources, make sure you’re using the correct one.


Step 4: Select a Location-Related Variable for “Dimension”

Variables may include coordinates, places (e.g. cities), regions, states, countries, and continents.


Step 5: Select a Quantitative Variable for “Metric”

The variable in the “Metric” field is contains the quantitative data to visualize.


Step 6: Select a Date & Time Variable for “Date Range Dimension”

By including a time dimension, we’re able to filter the data visualized for better insights.


Step 7: Resize the Map Appropriately

The corners and sides for every visualization are adjustable via click-and-drag.


3.1.2 More Data Ink Options

Data Ink options may be considered any options in the Data tab when we’ve selected our geo map.

  • Metrics allows us to rename, transform, and change display formats
  • “Drill Down” in Dimension allows users to highlight a geographic area
  • Filter applies custom filters to data using logical operators (e.g. >, <) and regular expressions


Rename, Transform, and Reformat Variables in “Metric” Field

“Sum” provides the quantitative total for each country, whereas “Count” provides total appearances, e.g.


Customize “Default Date Range”

Show users a particular year, date, time, or range over time by default.


Highlight Select Areas by Enabling “Drill Down”

By enabling “Drill Down”, users can click on an area to highlight it and mute all other areas.


Create Custom Filters to Control Data for a Single Graphic

Select “Add a Filter” to select a premade filter or make a new one with “Create a Filter”.


Customize Filters with Multple Conditions

A single filter contain multiple clauses, or conditions, using logical operators and “regex”.


Custom Filters Only Affect the Visualizations to Which They Are Applied

Filtering the data down to a single year, for example, greatly reduces the range of quantities.


3.2 Mapping with Color

Color Palettes require some consideration and should not be used haphazardly. Consider the kinds of palettes:

3.2.1 Gradiant Palettes

Gradient Color Palettes take a single color palette and use varying levels of saturation to convey quantity.

  • The idea is simple: The less color, the less quantity.
  • This relies on a well know convention in visualization and conventions help audiences in interpretation.


Selecting Your Gradient Color

*Simply click on the “Style” tab and adjust the “Max Color Palette” color to your heart’s content.


Blue Gradient Color Palettes

Blues are all around nice, sharp color palettes and actually create a sense of alertness.


Green Gradiant Color Palettes

Shades of green allow the greatest differentiation in saturation, hence why green is used for night vision.

However, green gradient palettes are difficult to discern for colorblind audiences.


Color Choice Literally Means Nothing - You’ve Been Had!

Seriously. We’re talking about saturation, here, and convey insight perfectly in a printer-friendly black and white.


Key Takeaway: For gradiant color palettes, we’re relying on a convention.

  • Most viewers will understand that more saturation equals higher quantities
  • We even see such conventions in nature - have you ever flown over a beach? Where is the water darkest?


3.2.2 Divergent Color Palettes

Divergent Color Palettes do not rely on saturation, but the transition from one color to another.

  • Divergent Palettes typically aren’t used for quantities from zero to x
  • Instead, divergence is more typical for a gradation with a “middle ground”
  • Temperature, for example, may be -100 to 100 degrees Fahrenheit, with a middle at zero degrees
  • In terms of public and social sector application, this could be profit and loss by geography

For the sake of demonstration, we’ll use the same data to show divergence. Let’s pretend.


A Colorblind-Friendly Divergent Scale

Divergence from yellow to purple is inspired by the Viridis color palette and universally applicable for divergence.


The “Viridis” Gradiants Excel in Consumer-Friendly Divergence Palettes

“Viridis” palettes emerged in the Python language and quickly entered currency in other graphics packages.

Source: “The Viridis Palette for R” (Thinking on Data, 2018)


Green & Red: Divergent Palettes that Leverage Conventions

Leveraging conventions, such as “Green” for profit and “Red” for loss, helps audiences interpret your ideas.


Making a Custom Divergent Palette

Open the “Style” tab to choose “Max”, “Mid”, and “Min” color values using premade colors.


Color Customization with “Hex” (Hexadecimal) Codes

When expanding the color options, select “Custom” to customize a “hex” code.


3.2.3 Discrete Color Palettes

Descrete Palettes are precisely that - discretized or categorical color values that ar not gradiant.

  • This are typically used when geographic areas have a set limit of categories to which they may belong
  • For example, discrete “Red” and “Blue” colors are often used for choropleth maps in U.S. politics


Red v. Blue States, 2018 Midterms

We just used a well-known color convention to learn about discrete color palettes.

Source: Silver, Nate (2018). “The 2018 Map Looked a Lot Like 2012”. FiveThirtyEight.


3.2.4 Color Tips & Tricks

Extract Colors from Images: There are some nifty tricks to picking a color from an image, like a company logo.


Extract Colors Directly from Images for a Perfect Match

This is useful not only for logos, but color psychology and matching centerpiece images, too.


Generate Gradient & Discrete Color Palettes automatically using a site like Coolers.

  • Coolors allows you to use a hex code to find gradient and discrete color palettes


With Coolers, you can lock-in a single color and generate gradients or cycle through, ehem, palatable schemes.


Ensure Accessible Colors using sites like WebAIM to ensure font and background colors, e.g., are accessible.

  • WebAIM can take the contrast between two hex codes and ensure they meet accessibility standards


Color contrast checkers ensure that people can easily read your font.