Maps are not objective, but a version of reality. When creating them, lots of choices are made: What to map, how to map and whether or not to use a map in the first place.
How are mapping projects changing the way that we understand history and culture?
A number of digital projects are now using GIS (geographic information systems) in order to highlight demographic issues and power dynamics using the familiar interface of the map.
StoryMap requires a Google sign-on, free to use.
(For advanced Users JSON syntax)
Sample StoryMap Projects:
Arya’s Journey (English, Modern Languages, History, Performing Arts)
DIGITAL MAPPING TYPES
Thick mapping is the processes of collecting, aggregating, and visualizing ever more layers of geographic or place-specific data to embody temporal and historical dynamics through a multiplicity of layered narratives, sources, and even representational practices. It is a method of doing ethnography – extensive description about cultural context and meaning – a path to understanding. The Hypercities project (see http://www.hypercities.com and http://hypercities.ats.ucla.edu) is seen as one of the most extensive examples of thick mapping.
Choropleth maps are great to show clear regional pattern in the data, or for local data. For example, regional patterns could be an unusually high unemployment rate in neighboring counties, or the contrast between cities and rural areas.
Choropleth Mapping Guidelines
Choropleth maps work best when showing just one variable. This variable could be the difference between two variables (e.g. the change of the unemployment rate from last year to this year). But if you want to show the correlation between values, choropleth maps might be not your best choice. Consider a dotplot or scatterplot instead.
Make sure to use the right color scheme for your data. There are three different kinds of color schemes for maps: Sequential (e.g. from bright blue to dark blue), diverging (e.g. from red via white to blue) and qualitative/categorical (e.g. one green color, one blue color). Consider a sequential color scheme if you want to drive the attention to the high values, e.g. for unemployment rates. Consider a diverging scheme if you want to drive the attention to both extremes of the scale, e.g. too show the difference in votes between two competing parties. With any color scheme, do use colorblind-friendly colors.
Choropleth maps are great to see the big picture, but not for subtle differences. Readers will have a hard time perceiving the small differences between colors on your map. In addition, the intervals between the colors are not necessarily the same intervals between the values in your data.
Make sure there is a lightness difference in your sequential/diverging color schemes. A gradient from a light color to a dark color enables your readers to quickly spot the regions with low and high values. Using different colors can increase the contrast, but shouldn’t be overdone. In the case of a diverging color scheme, the color in the middle should be the lightest one and the extremes should be the darkest ones. If you’re unsure, use the Datawrapper defaults or use the ColorBrewer palettes.
Choropleth Mapping Best Practices
Examples of Choropleth Maps
The Guardian: EU referendum: full results and analysis Shows the use of a cartogram (a big yellow London) and scatterplots for geographical data.
Berliner Morgenpost: Where the population of Europe is growing – and where it’s declining Demonstrates the importance of small units to see regional patterns.
Berliner Morgenpost: Alle Stimmen der 1779 Wahllokale A election results map that shows that it can be ok to have more than three color hues if readers know the color encoding.
Dot Density Mapping
Dot density maps are a simple yet highly effective way to show density differences in geographic distributions across a landscape. Dot density maps show us intuitively where things clump or cluster. There are two basic types: one-to-one dot density maps (one dot represents one object or count) and one-to-many dot density maps in which one dot stands for a number of things or a value (e.g., 1 dot = 1,000 acres of wheat production).
All dot density maps must be drawn on an equal area map projection. This is critical — using a map projection which does not preserve the size of areas will distort the perceived density of the dots.
Advantages of dot density maps
you can map raw data / simple counts (e.g., number of farms) or rates and ratios (e.g., number of farms per sq kilometer)
your data does not need to be tied to enumeration units and hence some of the concerns inherent in choropleth maps can be side-stepped with dot density maps
dot density maps work fine in black and white, when color is not an option
Limitations of dot density maps
users cannot retrieve rates or numbers from the map directly (you need to add numbers directly on the map or provide a table to accompany the map for your readers)
although most dot density maps distribute dots randomly, map readers may potentially infer dot locations as precise locations of the phenomenon being mapped (e.g., the actual exact location of a person)
Proportional symbol maps
Proportional symbol maps scale the size of simple symbols (usually a circle or square) proportionally to the data value found at that location. The larger the symbol, the “more” of something exists at a location. The most basic method is to scale the circles directly proportionate to the data so that if, for example, Toronto has twice the population of Vancouver, the population symbol for Toronto will have twice the area. However, you can also group your observations into categories or numerical ranges and created graduated symbol maps that may, for example, only have three symbol sizes corresponding to three categories of city size (e.g., cities of <1 million, 1-4 million, and over 4 million people).
Digital Humanities Digital Map Projects
Two Centuries of U.S. Immigration Map (animated)
Hyper Cities (thick mapping)
Flight Patterns (animated map, currently being updated on Google Earth)
Wind Map (animated map)
Open Street Map (ongoing “open access” map project)
Community Geography (communities can request support on how to frame research questions, create, collect, manage, analyze, and interpret geographic data, and use geographic information to create positive community change.
World Map (Harvard’s open-source mapping portal)
Spatial Data Explorer (University of Arizona GIS)
Unfolding (tile-based map library that requires use of code)
Online Software and Data
National Geographic MapMaker Interactive (ideal for Middle School projects)
Teaching Data Visualization to Kids (Lower School - Middle School)
Old Maps Online (search engine for historical maps)
NYPL Map Warper (Over 20,000 downloadable maps. Includes option to overlay historical maps on current maps)
NYC Open Data (Over 1,000 free datasets for NYC: park locations, restaurant inspection results, MTA data, etc...)
Additional Digital Storytelling Online Software
Juxtapose helps storytellers compare two pieces of similar media, including photos, and GIFs. It’s ideal for highlighting then/now stories that explain slow changes over time (growth of a city skyline, regrowth of a forest, etc.) or before/after stories that show the impact of single dramatic events (natural disasters, protests, wars, etc.).
Audio is a powerful device that can add emotion or context to a story. Unfortunately audio clips force uncomfortable choices: read or listen, but not both. Until now. SoundCite is a simple-to-use tool that lets you add inline audio to your story. The audio is not isolated; it plays right under the text you choose.
while keeping TimelineJS's core functionality.installations, is an open-source tool that enables anyone to build visually rich, interactive timelines. Beginners can create a timeline using nothing more than a Google spreadsheet. Experts can use their JSON skills to create custom TimelineJS