Information Visualization – Lab 4
Carto – “Rainfall in NYC Green Spots”
The data I compiled to make this visual` work well together. The final visual doesn’t necessarily tell a political or social story like the cool graphs from Radical Cartography did; but my visualization, which is ever-evolving and always updating, is more practical and for everyday use. Like some of the graphs in Radical Cartography, there is an environmental tone. I remember a hand-drawn graph with qualitative data represented in cartoon-y sketches. Even though this assignment seems geared to mirror some kind of social change or trend, nature seems embedded in that category as well.
This visualization is wide-spanning in who it can help as well. It cross-maps precipitation data with a shape file that plotted all of the Greenspaces in the 5 Boroughs. Greenspaces, under the New York Restoration Project, are places (whether community gardens or public parks) that are designated safe zones for diverse plant life in a city that is lacking. People who want to visit a Greenspace that also happens to be a public park can consult the map. Gardeners within a community could also benefit from looking at it. People can plan trips to parks around it. There are many domestic and recreational uses for this visualization. And people who are super into the fine-grain micro details of gardening or soil, can use it to inform them on those things. Rainfall, what is being measured in the graph (deep blue means heavy rain, while light blue means light),came from the National Weather Service. Synched data shows the last half-day rainfall.
Inspiration for Visual
My initial visualization and data for the Carto lab wasn’t about weather. It was a limited graph about the migration of three birds across Europe. I toyed around with making this data into an interesting visual, but simply could not find the right data to pair it with. Something like temperature would have made sense with the graph, but this might have provoked the following reaction: “okay, so this graph says that birds migrate when the temperature changes…so what?” So needless to say, I scrapped the bird map.
The trial-and-error process did bolster my confidence in Carto. I created some cool visuals and explored the program in ways that made me feel more confident as creator of mapped data. It also made me realize that I, at least, had an expectation when it comes to this mapped kind of data. And I think that expectation is that it must be useful in a more significant way than just a bird migration route.
Here’s what I came up with the boring bird data.
Bird Migration Data:
Methods and Materials
My visual ended up feeling weightier than mapping the path of three birds. Precipitation in New York garden spaces felt like it had a lot more ramifications and seemed to affect more people.
The imagine below is from was a video that inspired me to make this kind of graph (link below). It’s a how-to on how to find the weather data from the national weather service and use it in Carto. The video was instrumental in helping me find my way in this lab.
Carto made it easy to sync up the Green Spaces shapefile with the weather data. I think the simplest visual usually tells the story accurately, so I really kept the colors the way they were. Representing precipitation in a color that isn’t blue or a shape that isn’t circular didn’t make sense. Having loud colors or contrasting ones when I’m representing something as placid as precipitation felt wrong. Here the soft, muted colors felt appropriate.
In preparation for this lab, but also in hindsight, I looked at other weather visuals. They were limited in the way that I felt my bird data was. I liked introducing this lab with Radical Cartography because it created an expectation for these maps to actually be useful—to represent weightier information than just a few points of data. The examples that came up on Google were mostly just a bunch of scatter and bar graphs. They felt uninspired in the same way that my initial representation of three carrier pigeon migration patterns. Carto, thus, feels like a very apt way of representing weather data that is stretched over a map. In fact, the other ways of visualizing mapped data just feel clunky to a novice like me.
I like the aesthetics with the final map, as I stated above. I like that the colors aren’t forced, that they literally represent rainfall and put me in the mind of the peaceful dreariness that I associate with rain. Most importantly, I see why the map is useful. Another thing I like about this map is that it is not static. The weather data changes every half-day, updating itself in order to reflect recent rainfall patterns. I really like this about my graph as well—that it can continually tell you new things.
I don’t like that this shape-file only represents New York but is of the whole world. I don’t understand why. The map would be more intuitively if it were just about New York. I don’t like Carto because it doesn’t exclude all the other map area outside of your shape file. I hate having to constantly zoom in if the colors representing the mapped data aren’t prominent enough to see in a full screen. Also, I just hate having to do this every single time. It makes you look unprepared if you have an audience and constantly have to zoom in.
Carto Visual: https://wdennis.carto.com/builder/6a839146-5efb-4fba-9358-63645939ab9d/embed?state=%7B”map”%3A%7B”ne”%3A%5B40.371658891506094,-74.39117431640626%5D,”sw”%3A%5B41.06175061261111,-73.20327758789064%5D,”center”%3A%5B40.71759894
Data Sync How-To: https://carto.com/learn/guides/data-and-sql/sync-real-time-data
Precipitation Data: http://www.srh.noaa.gov/ridge2/RFC_Precip/
NYC Weather Data Example