This lab report examined monuments and statues located throughout the parks of New York City. I decided upon this topic out of general curiosity. With the daily bustle of the city, many of the park’s monuments and statues often go unnoticed. The goals of this lab were to create an interactive map that would demonstrate the number of monuments dispersed throughout the parks of New York City and would also allow for comparisons of the number of monuments within each park to be made.
Before creating my visualization, I utilized example visualizations to help inform and inspire my design. The first example visualization I examined was the NYC Public Art Map and Guide, which similarly used the attribute data applied in my lab project. This map visualization features map markers that indicate the location of every public art and monument displayed within parks throughout New York City. In addition, this interactive map allows you to click on the markers for the name, details and additional information pertaining to that specific monument. The map markers are filtered on top of a Google Map, which is helpful for individuals seeking to find monuments in-person. This map makes is easy to interact with and provides a great visualization of where park monuments and sculptures are specifically located throughout the City.
Another visualization that I found interesting was the NYC LandMark 50, which featured map layers that indicated Historic Districts, Individual Landmarks, Interior Landmarks, and Scenic Landmarks throughout New York City. This map visualization also utilized map markers that geographically indicate specific landmarks and when clicked provide additional information. That I liked about this map visualization was that one could filter multiple layers on top of one another. Specifically, I found it most interesting to utilize the Historic District layer and include one or several of the three landmark filters. This combination made an interesting comparison which showed which types of landmarks were located within historic district areas throughout New York City.
The next visualization I examined was a map visualization of the geotagged tweets made during Hurricane Sandy in 2012. I liked this visualization because clustered similar geolocated data together to demonstrate what areas of New York had more tweets in comparison to others. Also, I liked that this visualization did not layer their point data on top of a google map, but rather used a map with simple visible and invisible boundaries to indicate blocks, bridges, water, parks, etc. What I wish this map included was color range for its tweet count, so that the information is clear and decreeable in areas where tweets clusters are very close together and conjoined.
The materials used to create this lab project included the NYC Parks Monuments dataset and the Parks Properties dataset Shapefile, both acquired from the NYC Open Data website. These datasets were applied to Carto to create the visualization. In addition, in creating this lab project I also utilized the “Mapping and Countermapping” class lecture slides, and referenced the Carto Tutorials.
Once the NYC Parks Monuments dataset and the Parks Properties Shapefile had been downloaded, I input the datasets into Carto as they did not require any cleaning or adjustments. Within Carto, I added a new analysis to my data, “Join Columns from 2nd Layer,” which joined columns from the Park Properties with the Parks Monuments by linking a shared value from both datasets. Here I linked the geolocations from the Park Properties with the coinciding park numbers from the Park Monuments. Once my data was correlated, I ran an “Aggregate intersection” analysis which clustered monuments with the same park locations together and gave them a count measurement. Next I used the “Find centroid of geometries” analysis to calculate the weighted number of monuments within each park location. The visualization produced from these three analyses featured circular points of data geographically located on a map of New Work City – each of these points represented the count of monument(s) located within the corresponding park area.
Within the “Style” feature I was able to change the appearance of the visualization. I adjusted the color range and scale of the data points by utilizing the “fill by value” settings. I was able to make the aggregates with the largest number of park monuments larger than those with the least. This idea to was inspired by the Geotagged Tweets visualization example. In addition, I inverted the color range scale, with smaller counts as the darker hue and larger counts with a lighter hue, so that the smaller points would be more visible against the light gray-scale map. I was able to adjust the style of the Park Properties layer so that the parks’ geolocations were filled in with a light green color, indicating a park area. This layer idea had come from the NYC Landmark 50 visualization example which had data points filled onto layers of areas. While my red any green color choices for this lab are not optimal for color-blindness, I chose green for the park area because it seemed the only appropriate color to symbolize a park on a map, and I chose the red color range for my data points because the red hues stand out. Also, as my data points were scaled and colored according to count, I thought it would be important to include a click-based label that featured the number of monuments represented by the data point.
From my Carto visualization, it is evident that park monuments and statues are diversely spread throughout the five boroughs of New York City. Manhattan clearly is more populated with park monuments than the other boroughs. This is largely due to Central Park’s 208 monument count as well as the large collection within River Side Park and the lower Manhattan historical areas. Both the Bronx and Brooklyn follow closely behind Manhattan, with Queens next and then Staten Island with a few individual monuments spread across the island.
What is most interesting about this visualization is the number of single monuments located throughout New York City. From a zoomed-out position, it appears that these monuments and sculptures are not located within parks. However, by zooming-in to the graph, it is discernable that they are located within park areas, but parks of a very small scale. Therefore, if we look again at this visualization as a whole (zoomed-out), it is remarkable how many park areas exist within the five boroughs of the city. This was something that I was not previously aware of.
We must be mindful that not every park within the city features a park monument. And moreover, keep in mind that there are monuments not featured on this visualization because they do not fall within the dataset’s parameters of residing within a park. Therefore, we can assume that this graph does not clearly show the full scale of the number of park areas or monuments within New York City. But this graph does demonstrate the number and relative scale of park monuments located in New York City.
One aspect of this visualization that I would explore further are the types of monuments features throughout the city. I would do this by reformatting the dataset, which does feature categories of monument types but are not currently organized and use standardized language. I think it would be interesting to visually see how many monuments are of war memorials, historical figures and events, animals, artistic expressions, etc. I am curious if there are more historical and memorial type of monuments located in the older areas of the city, such as lower Manhattan or the old dock/port areas. Also, it would be interesting to see the percentage of these monument categories present within larger parks such as Central Park, Prospect Park, the New York Botanical Garden, Flushing Meadows, and Van Cortlandt Park. This work would provide another dimension to my current visualization.