Week 5: Non-Visual Data Representation

This week our assignment was to make a representation of a public dataset that is experienced with a sense other than sight.

I had previously experimented with weaving data (thanks to Ashley Jane Lewis who taught me how) and made a woven version of Tauba Auerbach’s alphabet using the binary system:

I also lived in Peru for a year and was influenced by Incan history and especially the quipu - a record keeping device that uses string and knot systems.

I knew I wanted to expand on this work. I have been working on the subject of rain gardens and flooding recently. I have also been using 311 (partially related to the flood work) and have been thinking about local data and situated knowledge.

I have also been inspired by Nathalie Meibach’s woven sculptures. 

I liked the small side by side aspect of the alphabet pieces as well and started thinking how smaller bits could be stitched together.

I started to think about ways to differentiate woven textures

• tighter/looser

• warmer/colder

• smooth/rough

• yarn/mixed materials (beads?)

Then I started asking myself, what kind of data would I want to feel? Or what could be important to encode?

I was thinking about the discussion we had around Lon/Lat in class and it made me wonder what it would be like to weave the previous visualization I made of flooding events. I had helped with a dinner project with Marina Zurkow where she made tablecloths out of three portions of the NYC map - what would a woven tablecloth look like? Could I do one for each borough? What would they look like woven together? In my first semester at ITP I created a data visualization sculpture called Data Structures which was a data representation similar to this idea made with projections and cubes. Recently I’ve been thinking about Shannon Mattern’s article Post-it Note City in which she advocates for mapping evictions. I like the idea of weaving a visualization of evictions and leaving a hole instead of doing a stitch where people have been evicted - if it were a blanket you would feel the cold in those spots.

She asks: “Beware the prioritization of flashy maps over the quality of data they present or the value of insights they generate.” What kind of insights would or could this blanket generate?

“I asked Baykurt, Riano, Canteli de Castro, and other designers to identify ways communities can use maps to assert agency and disrupt the neoliberal, algorithmic forces shaping the built environment. One theme that came up repeatedly: visualizing data on housing evictions and gentrification.”

In thinking about weaving approaches, I have also been inspired by my friend Rachel Snack who weaves landscapes and then photographs them in the landscapes. 

What does a landscape woven out of data look and feel like? Could you “wear” a map of NYC?

I initially imagined being able to map this on the individual level, but I found an online tool that maps evictions in NYC and realize there are way too many evictions to be able to do this on an individual level. The tool also has evictions by zip code and community district. Even by zip code, New York may have too many to weave (over 200) They have the past 3 years, so I could make three weavings. I decided to start with one year (2018).

I began by translating the map into a weaving pattern. Then I started weaving this into my mini loom. I was working with yarn that I already had and immediately wished I had a bigger variation in texture and size, which would accentuate the holes more. I think that it may need to be more granular than the representation I chose of one stitch per zip code in order to have a more clear effect.

The result isn’t how I had hoped, although you can see some holes in the pattern. I have the colors going from dark to light signifying housing disappearing, but the aren’t close enough for this to be apparent and it’s also too hard to tell a difference from feeling their textures. At first I didn’t like how messy it looked, but then I started to like this accidental aspect because that’s often how data is. It’s not large enough that you can really feel the heat or cold while holding it, but in a future iteration I can imagine how this could be successfully presented.

Week 3 + 4: Digital Data Visualization with a Public Dataset

I have recently been interested in water infrastructure in NYC through a project last semester observing and creating interfaces and tools for the raingardens around NYC which are part of the city’s green infrastructure plan as well as the FloodSense project at IDM measuring the urban microbiome.

For my data visualization, I decided to map street flooding events as recorded by 311 calls/requests to the city. I used a subset of the the 311 dataset on the open data portal that includes only street flooding reports.

To see quickly what the map of this data looked like, I put it into Kepler, which makes it really fast to map csv files:

I then tried just getting the data into the map example that we worked on in class, but I quickly realized I would need to do some data cleaning because of missing cells. I used R to take out all rows with missing lon/lat information instead of making up a location. I also split up the date/time field into month, day and year so that I could control these variables separately in my map interaction.

Once I got the events to show up I decided to add interaction that would allow the viewer to look through flooding events at different points in time. I decided to turn the canvas into a big “matrix” with month on the horizontal axis and year on the vertical so that the user could compare across both of these dimensions.

I thought what was interesting about the map was that the flooding events draw a very clear map of NYC. I felt I didn’t need to add a basemap for this reason. My goal was also to allow the viewer to see general patterns and impressions rather than get exact flood event details.

Right now the map only shows three years 2017-2019 because the dataset for all 10 years of 311 data is so big, but in the future I’d be interested in making a version that has all of the available years.

You can view the visualization here: https://datapublics-floodmap-class5.glitch.me/

And you can see the code on Glitch here: https://glitch.com/edit/#!/datapublics-floodmap-class5?path=README.md:1:0

Week 2: Data Post Card

This week our assignment was to “create a data visualization on a postcard that shows a 'snapshot' of a particular slice of a public data set.”

I live by Prospect Park and run there a lot and since reading How To Do Nothing by Jenny Odell I have become more curious about what kinds of plants and animals I can see at different times of years. I was also inspired by Tega Brain’s piece “Keeping Time” in which she visualized phenological patterns of Orchids over time. I was curious about what kinds of patterns I might see in Prospect Park on my runs. I used the iNaturalist dataset and downloaded all observations made in Prospect Park. I took the top 10 species observed by number of total observations and used these as my “data snapshot” for my post card. In the resulting visualization you can definitely see when springtime is in the park! I am planning to send it to either my friend who loves finding plants and animals in the city or the Prospect Park Alliance!

Week 1: Data Zine

Our assignment this week was to make a zine about a public dataset. Emily Lin and I made the below about Open Data NYC and specifically highlighting the 311 dataset.