For the Carto lab, I took an exploratory look at locations of art galleries across
the five boroughs, overlaid that with a map of WiFi spots. I was curious to see where
else besides Chelsea, SoHo, and Lower Manhattan had a concentration of galleries.
After seeing the layered map, I wanted to see if there was any link between gallery
presence and WiFi spots? Lastly, which boroughs have the least WiFi spots?
The first visualization (below) http://nycmobility.org/truck/ I found on the NYU
Rudin Center site, but did not ultimately use. It was useful in that it showed me how
to work with Carto, and what it could do. The map Satisfied curiosity about transport
routes as well. The east side has more truck routes, access points, and connections to
other boroughs and Long Island.
The second visualization (below) was closer to the final product, in terms of
subject matter. It was also more useful, as it was more along the lines of what I wanted
to create. Design-wise, not what I was looking for, necessarily, but was a starting
point. However, this map sparked curiosity about WiFi hotspots (I was thinking of the
Link terminals popping up – https://www.link.nyc/find-a-link.html), as I had been
seeing them around town more frequently.
The third visualization I came across was the closest to the final product. This
is the dataset I wound up using (from the NYC Open data site). Once created,
wanted to add something else to make the map more detailed, so I added the art
gallery overlay to the existing map.
To create the map, I used Carto, and data sets (WiFi, and gallery locations)
from the NYC Open data site. Before settling on the final product, I worked with
other data sets (subway maps), but did not use that one because it did not quite work
out the way I had envisioned. The train data populated the map, but did not overlay
well with the WiFi and gallery layers. That could have potentially been resolved by
designating galleries with a different symbol, so as to avoid any confusion over what
was being shown. Also, the hover-over function that would display
addresses/intersections did not work.
To create the final product, I started looking around for appropriate data sets in
advance of the lab. The process began as an exploratory task to see what was out there
– what would work in Carto, as well as what I may want to see in visualization form.
As with the 2nd lab, I found data sets, but when plugging them into Carto, they did not
necessarily turn out as expected, or may not have worked at all. I created several maps
(such as the truck routes, and the train stations) before settling on what seemed to
meet at the intersection of functionality and interest.
After finding appropriate sets, I imported the shapefiles into Carto, and created
the map. It initially began with one layer (WiFi?), then added the other layer on top of
that, and began adjusting colors and other facets to show addresses and names for
galleries, as well as WiFi spots. Throughout the creation process, I made changes in
Carto to enhance visual presentation (placement of names, fonts, colors) to show the
different items being mapped.
The map (interactive) can be found at the link below:
Results-wise, the most gallery-heavy neighborhoods were in Manhattan, as
shown above. Neighborhoods such as Chelsea/West Chelsea, SoHo, Upper East
Side/Madison Avenue, and 5th Avenue, near the famed Museum Mile had the greatest
concentration of galleries. Most galleries were below 96th Street, with comparatively
few galleries east of Lexington Avenue. Above 125th Street, galleries become fewer in
number, with a handful in the upper reaches of Manhattan (Inwood, etc).
Interestingly, WiFi locations did not necessarily overlap with gallery locations.
A stretch of 8th Avenue in Hell’s Kitchen (43 to 46th St) has a multitude of WiFi spots,
but no galleries. 3rd Avenue, below 79th street had a heavy concentration of WiFi, but
fewer galleries than the west side of Manhattan. Uptown, from Central Park North to
135th Street, much more likely to find WiFi than galleries.
In the Bronx, WiFi is much more concentrated, usually near public parks, but
very few galleries (under 6) according to dataset. Brooklyn follows a similar pattern,
with WiFi clustered near parks. Galleries appear in areas such as Williamsburg, but
outside there, they are scattered about the borough.
Queens showed a similar pattern, with WiFi being near parks (and in some
cases, train lines, such as the E/F). Last but not least, Staten Island had its WiFi
clustered on the eastern side of the island, with a total of three galleries, all in the
northern half of the borough.
Future research could include adding a layer to show train stations, to see what
patterns if any, exist around gallery and public transit proximity. Also, one could
include a map indicating population, to see population density in neighborhoods with
galleries – is there any sort of relationship between the two? Shifting from galleries,
one thing to look into could be which WiFi spots are the most heavily-used in a given