↞ My work

Whose land do you inhabit?

View the tool here
A tool for learning about the native history of land in North America. Data from API provided by Native-Land.ca

The question

The land we now call Canada and America was once home to hundreds of nations. Native-Land.ca is a historical map of hundreds of the native lands of Canadian and American indigenous peoples.

The map brings that history to life, allowing us to search by zip code to find which tribes have inhabited our towns and cities.

But the author himself expresses disdainment with the act of visualizing native lands. He views them as inherently colonial

“…they delegate power according to imposed borders that don’t really exist in many nations throughout history. They were rarely created in good faith, and are often used in wrong ways.”

In a sense, a map is a simplification. Furthermore, mapping native lands could in effect be a simplification of indigenous place and identity.

Maps, like other visualization tools, often transform the fuzzy reality in to discrete data.

Data has been (and still is) welded to categorize and allocate power to groups, sometimes to the detriment of disenfranchised groups. For example, white supremacists point to iq distributions of different racial groups as evidence of white superiority.  However, both racial categories and iq are metrics derived based on constructed methodologies. Furthermore, the metrics and the resulting data are susceptible to the cultural context of their Western European creators and those who eventually communicate the data to others.

Is all data visualization, in a sense, colonial? Conversely, is there a way to re-imagine data visualization to convey reality without harmful divisions and assumptions sometimes packaged within them. In this sense, mapping native lands could be a simplification of indigenous place and identity; a reference to their previous misuse. Furthermore, maps, like other visualization tools, often transform fuzzy realities into discrete data.

Along with other design improvements, I set out to visualize indigenous stewardship of lands in a way that de-emphasizes discrete borders, which are associated with the colonial aspect of maps.

The data

I initially focused on visualizing their territory dataset using the API that Native-Land.ca provides to access their data. When sent a longitude and latitude pair, the API will return a list of the names of indigenous nations and a link to their current website.


I implemented two use cases: searching for tribes by zip code and by map click. I envisioned an average American using the tool to explore indigenous nations.

I used Leaflet.js to find the latitude and longitude of a map click and the Native-Land.ca API to find and display the indigenous nations

Then, I implemented an AJAX call to Google’s map API in order to find latitude and longitude coordinates (and the indigenous nations) of a searched zip code.

Territory colors

I improved the coloring of the territories by using the CIE L*a*b* color space to generate maximally distinct colors. The original design assigns colors to territories using random colors in the RGB color space, which is typically a good working solution. However, given the large numbers of territories to be colored, selecting random colors in the CIE L*a*b* color space will ensure maximally distinct colors. Similar to my redesign of Energy Justice Network’s mapping tool, I generated perceptually distinct colors using the CIE L*a*b* color space and IWantHue (as opposed to a palette such as ColorBrewer).

Visualizing fuzzy borders

I de-emphasized the borders of the territories by both removing their dark-colored borders and by blurring their edges with a CSS filter. The overall effect removes the distinct edges and contours of the borders, with some territories merging and blending into one another.


I am dissatisfied with the blurriness of the borders; they seem to be blurry in error. Better methods to convey “fuzziness” are needed. Furhtermore, it is vital ton continue exploring how data visualization methods can be improved to portray nuanced data and history.