Download presentation Envisioning Development
Map link: http://envisioningdevelopment.net/map
For my map critique, I chose to focus on an interactive map created by the Center for Urban Pedagogy in partnership with the Pratt Center for Community Development, the Fifth Avenue Committee, and other community-based organizations and policy groups across the City.
I chose this map thinking that it would be a good one to review in relation to some of the other maps we’ve been critiquing and reviewing in class. I thought it would be particularly useful to review following Adrian’s excellent critique of the Radical Cartography map -- another census-data driven map based on income levels.
A bit of background on the project: the map is just one project in a larger suite of program materials called Envisioning Development toolkits. In addition to the online map, there is an affordable housing toolkit with an interactive felt chart, a guidebook, and a map to help residents hold workshops where they can learn about policy in New York City.
The project’s key objectives are to help communities understand how their neighborhoods are getting built and take an active role in shaping neighborhoods. Their audience is primarily lower income residents in NYC, with little experience in data visualizations or mapping -- though the applications of the project have significance for a much wider audience of urban planners, developers, educators, etc.
Overall, I think the interactive map does an excellent job at demystifying data related to land use and income demographics in NYC. As with other CUP projects, it employs an innovative design-driven process to create effective tools for community awareness/participation.
The visual and interactive design are user-friendly, intuitive, and inviting -- I found myself clicking on the map for a full 30 minutes when I first came across it a year ago. The introductory text upon landing clearly tells people what the map is showing and why it’s important. The flash interface is easy to navigate, allowing for a sense of discovery and exploration. There is clear, detailed, and thorough labeling throughout the site, and users can visit linked pages to see related materials that provide deeper information. The rent “sliders” in combination with the animated income bar graphs are particularly effective in conveying complex income demographic information. The popup windows that appear upon clicking on a neighborhood provide deeper access more information (and the printable pdf is great). The related materials accessible on the larger site are also great -- especially the accompanying video, which is simultaneously playful and educational.
There are several elements of the map that I think could apply to my project. Following the Envisioning Development model, I’d consider including
A) a clear link to introduce objective of project upon landing
B) an easy to navigate interface
C) clearly labeled data that incorporates color coding, and
D) sliders to filter information.
If there’s time, I’d also like to consider developing a video tutorial to lead you through the project.
I would also want to depart from the Envisioning Development model in some significant ways.
I’d want to include
A) clearer data sources and citations,
B) avoid arbitrary data breakdowns,
C) create opportunities to view more than one data set at a time, and
D) Allow for snapshot visualization as well as drilling deeper into the data
In researching additional inspiration for this project, I also drew heavily from two sources: Healthycity.org and a post on NYC sanitation district maps in John Krygier’s Making Maps: DIY Cartography blog (published in Harper’s Weekly (June 1, 1894) using 1890 U.S. Census data).
The category, sub-category, and layering effects on the Healthycity site were particularly particularly relevant to my project, and gave me a lot of ideas about the possibilities of data overlays and filters. The NYC sanitation district maps – like the Radical Cartography map cited earlier -- showed me how much information can be effectively conveyed through juxtaposing/layering multiple sets of data using different classification systems (ie. texture, pattern, color, size, etc). These are elements that I plan to explore further in my project moving forward.