Introduction
Initially, I was interested in mapping art galleries in NYC to discover how the location of galleries may play a role in attracting people to different areas of NYC, specifically Harlem and the Bronx. However, after mapping the art galleries I saw that there were not many in Harlem or the Bronx. Unsurprisingly, downtown Manhattan still, and will most likely remain the hub of the city. So I decided to add another layer of data— subway entrances and lines—to depict how close art galleries are to the subway and the routes people should take on the subway lines.
Throughout designing the map, I figured that the intended audience would be anyone who is interested in art and culture. In addition, perhaps someone who is interested in opening up an art gallery would find this helpful to learn more about the best and popular areas This map is especially useful for people who are new to NYC and would want to learn more about independent artists in the city.
Inspiration
The inspiration for the map visualization came from a post by Uber’s engineering team. Using Uber movement data and traffic datasets, the map depicted some of the most dangerous traffic locations in Manhattan. Although the topic of the map visualization is quite different from mine, I appreciated the use of color of the streets to visualize speeding behaviors. In addition, the red dots show fatal crashes while the blue dots show vehicle crashes. I think the use of layers and colors added to the insight and analysis in visualizing Manhattan’s most dangerous streets and intersections.
Materials Used
The datasets I used for the map visualization came from NYC Open Data. I used three different datasets—NYC art galleries, NYC subway entrances, and NYC subway lines. I downloaded the datasets, which were conveniently made into shapefiles and then uploaded it to Carto to design the map.
Results
I first created the map using the NYC art galleries dataset and then added the other datasets as layers. I wanted to do an analysis on the nearest subway entrances to art galleries so I created an additional layer by copying the subway entrances one. In this layer, I used the “Find nearest” analysis in Carto with the target layer being art galleries. I set the parameters to 5, which meant that it would produce the five nearest art galleries to each subway entrance.
Since I used a simple basemap, I chose to make the colors of the points bold. The pink points depicts art galleries while the blue points depict subway entrances. The subway lines are in black so that it doesn’t distract from the overall visualization, but it also helps visualize these art galleries in NYC along the train lines.
Reflection
I think Carto was a bit easier to learn how to use than other visualization software. The topic I chose was simple and perhaps I would’ve gained more insight if about the NYC art and culture scene if I had used a different dataset other than subway train lines. Again, researching a topic to do the map visualization on was probably the most challenging part for me.