Use this data visualization style guide to create a uniform look and feel to all of Urban’s charts and graphs. This site contains guidelines that are in line with data visualization best practices and proven design principles. It also eliminates the burden of design and color decisions when creating charts.
These guidelines are primarily for publishing charts to the website. Minor sizing and typographic adjustments need to be made to insert charts into documents.
Urban.org uses the font Lato. When possible, this font should also be used to create charts. If Lato is not installed on your computer, contact IT. A suitable replacement is Arial. Good chart typography creates a hierarchy among elements and guides the reader through the visual.
Typeface (Alternative) |
Web Size |
Print Size |
Case |
Color |
Notes |
|
Figure number |
Lato Regular |
11 |
8 |
ALL CAPS |
#1696d2 or rgb(22, 150, 210) |
|
Title |
Lato Bold (Arial Bold) |
20 (24 for maps–see "Maps" section) |
12 |
Title Case |
#000000 or rgb(0,0,0) |
The main point of the chart. Try to keep shorter than two lines and avoid qualifiers. |
Subtitle |
Lato Regular (Arial) |
16 |
9.5 |
Sentence case |
#000000 or rgb(0,0,0) |
Use this to add qualifiers or further clarification to the title. |
X and Y axis titles |
Lato Regular Italic (Arial Italic) |
12 |
8.5 |
Sentence case |
#000000 or rgb(0,0,0) |
Always horizontal, above the top axis label Include units or multipliers in parenthesis (millions), ($2014) |
X and Y axis labels |
Lato Regular (Arial) |
12 |
8.5 |
Sentence case |
#000000 or rgb(0,0,0) |
Always horizontal, avoid units or multipliers. Those should be added to the axis title in parenthesis |
Key labels |
Lato Regular (Arial) |
12 |
9.5 |
Sentence case |
#000000 or rgb(0,0,0) |
Always horizontal. Avoid redundant key labels if possible. |
Direct labels |
Lato Bold (Arial Bold) |
12 |
9.5 |
Sentence case |
#000000 or rgb(0,0,0) |
Use for line or column charts with three or fewer series. |
Data-point label |
Lato Regular (Arial) |
12 |
8.5 |
Sentence case |
#000000 or rgb(0,0,0) |
Always horizontal. No units or multipliers. Only directly label columns if there are fewer than 10 total columns in the chart. |
Source and Notes |
Lato Regular (Arial) |
11 |
8 |
Sentence case |
#000000 or rgb(0,0,0) |
Bold “Source:” and “Notes:” as well as any statistical significance indicators. |
The main content well on the Urban website is 760px wide, which translates to approximately 125.83 characters wide in Excel. Because Excel’s chart title and subtitle fields are limiting in terms of formatting, we use a regular text box for all the text at the top of the graphic, as well as the source and notes text at the bottom. We also include an Urban tag in the lower-right corner of the figure so that when media members copy-paste our charts into their stories, Urban’s brand is on them.
This tagline is built from standard text using Lato Regular with 1pt of expanded character spacing. Urban is colored blue—#1696d2 or rgb(22,150,210)—and Institute is black. The text is in uppercase and should be 9pt for web, 7pt for print.
Urban's main colors are cyan, gray, and black. Yellow and magenta are used as secondary colors throughout the new Urban brand. Tertiary colors for graphics include space gray, green, and red, and should be used infrequently.
When selecting colors for charts and graphs, we must first consider the type of data we are presenting. Usually, data can be grouped into one of two main groups: categorical or sequential.
Categorical palettes are best when you want to distinguish discrete chunks of data that do not have an inherent ordering.
Sequential color mapping is appropriate when data range from relatively low or uninteresting values to relatively high or interesting values. For sequential data, it’s better to use a palette that has a relatively subtle shift in hue accompanied by a large shift in brightness and saturation. This approach naturally draws the eye to the relatively important parts of the data. Sequential colors are used for sequential groups (not necessarily the way the data trends). So age groups young to old and date ranges might be something that I use sequential colors for.
Note: Yellow and Magenta should be used sparingly, as these colors should be considered for highlighting purposes (such as drawing attention to a certain category or indicating a trend line).
For more information about the subtleties of color and choosing colors for your data visualizations, refer to Datawrapper's blog post "A detailed guide to colors in data vis style guides", Nightingale magazine's blog posts on the subject, and this collection of blog posts from Rock Content, formerly Visual.ly.
The following color combinations are intended to take some of the guess work out of the process of assigning colors to charts. They're only examples, and can be mixed-and-matched depending on the story you are trying to tell with your data.
When comparing data by gender do not use blue to represent men and pink to represent women. Instead, choose a color combination like yellow and cyan, or another combination of our main graphic colors.
var colors=["#CFE8F3","#A2D4EC","#73BFE2","#46ABDB","#1696D2","#12719E","#0A4C6A","#062635"];
var colors=["#F5F5F5","#ECECEC","#E3E3E3","#DCDBDB","#D2D2D2","#9D9D9D","#696969","#353535"];
var colors=["#FFF2CF","#FCE39E","#FDD870","#FCCB41","#FDBF11","#E88E2D","#CA5800","#843215"];
var colors=["#F5CBDF","#EB99C2","#E46AA7","#E54096","#EC008B","#AF1F6B","#761548","#351123"];
var colors=["#DCEDD9","#BCDEB4","#98CF90","#78C26D","#55B748","#408941","#2C5C2D","#1A2E19"];
var colors=["#D5D5D4","#ADABAC","#848081","#5C5859","#332D2F","#262223","#1A1717","#0E0C0D"];
var colors=["#F8D5D4","#F1AAA9","#E9807D","#E25552","#DB2B27","#A4201D","#6E1614","#370B0A"];
The following examples use the styles and colors to illustrate common chart types. To deconstruct any example, download the master Excel file.
Urban has created an add-in for Excel that automatically applies Urban colors, chart formatting, font styling, and font sizes to Excel figures. Download the Excel Macro here.
Often, when you want to show composition across several dimensions (usually time), the default choice is a stacked column chart. Though this is not a bad chart type, often a story can be better told by using a different chart.
The debate concerning the effectiveness of pie charts is among the most contentious in the field of data visualization. Many people love pie charts—they are familiar, easily understood, and present “part-to-whole” relationships in an obvious way. But others argue that because pie charts force readers to make comparisons using the areas of the slices or the angles formed by the slices—something that our visual perception does not accurately support—they are not an effective way to communicate information.
Pie chart slices that form 90-degree right angles—that is, slices that form one-quarter increments—are the most familiar to our eyes. Other amounts are more difficult to discern. For that reason, a column or bar chart is preferable in most cases.
The current thinking is pretty much summed up by this tweet from data visualization pioneer Edward Tufte: "Pie chart users deserve same suspicion+skepticism as those who mix up its/it's, there/their. To compare,use little table, sentence, not pies." - Edward Tufte (@EdwardTufte)
That’s not to say that pie charts don’t have a place when communicating research. They're OK to use when the number of slices are fewer than four or five— and when you’re trying to tell a clear ‘part-to-whole’ story about a single slice.
Just because you've got geographic data, doesn't mean that you have to make a map. Many times, there are more efficient storyforms that will get your point across more clearly. If your data shows a very clear geographic trend or if the absolute location of a place or event matters, maps might be the best approach, but sometimes the reflexive impulse to map the data can make you forget that showing the data in another form might answer other—and sometimes more important—questions. Consider using other graphic types when the interesting patterns are not geographic patterns, or when the geographic data is more effective for analysis than for presentation. Many times, a simple bar chart, column chart, scatterplot or table are more effective at translating your research to a reader.
In some cases, maps might actually be misleading. State level US maps, for example, give disproportionate weight to states with large land areas, and tend to obscure smaller states in the Northeast. Maps with more granularity can still be misleading. Many times, what appear to be geographic trends are actually directly correlated to population density (wealth, power consumption, etc.), or in some cases directly correlated to some other hidden variable such as racial composition, age distribution, or even weather.
Depending on the purpose and content, maps should use different projections. US maps for print publication, or use in reports should use the Albers Equal Area projection. Simple interactive county- or state-level maps also use the Albers projection. Zoomable tile-based interactive maps use the Mercator projection.
Maps should follow the same basic rules as other graphics when it comes to colors. Choropleth maps, or maps that show the magnitude of a variable, should normally use the blue sequential ramp.
Sometimes, a map needs to display change in a positive and negative way. In those cases, a diverging scheme works best.
Need to show data across multiple points in time? Consider using small map multiples to show the progression or spread.
The problem with normal state-by-state maps is that the western US has very large states and the eastern US has small states. This leads to the western states being over represented with more visual weight than the states in the northeast.
Recently, many organizations have adopted use of grid or tile maps. The advantage of these maps is that each state has equal visual weight.
Even better, these maps can be created with a simple excel file.
Have a question or looking for more help? Contact Ben Chartoff or Jon Schwabish.
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