Introduction

New York owns its rich cultural heritage as the treasures. More than thousands of cultural organizations, including museums, theaters, and galleries, appeals to countless tourists and residents, to engage the artistic community.

More or less, people have their own maps of New York City based on the literature, film, and other kinds of city tales, and districts of their maps usually come with areas like Brooklyn, Manhattan, Bronx, and some other different neighborhoods. Therefore, it is interesting to see how the city distributes its school districts. According to how the distribution of school districts and city cultural organization data, it’s intuitive to see how much accessibility of students in some areas can engage in the artistic community and what kinds of cultural organizations can be discovered there.

Process

I used NYC OpenData to search for museums and galleries related data. Then I was surprised to find a data set contains all the NYC cultural organizations’ information, including names, addresses, disciplines, and phone numbers. Also, I found the school district distribution of NYC at NYC OpenData. It offered data with the geological school distribution.

As for visualization tools, CARTO | Unlock the power of spatial analysis can help carry out the spatial analysis with easy-to-start features. As a web-based application, it was convenient to import the data from NYC OpenData to Carto with several clicks. Here are two map images after importing two different datasets on Carto.

After importing two datasets into one file to make a map, the two datasets were shown as two different layers in the left toolbar. By editing each layer, the map would result in different appearances.

At first, I planned to build a map showing cultural organizations’ density in different school districts. However, I found those datasets overlapped its function when achieving this plan. It was tedious that one was a location-based dataset, and the other only presenting the value of quantity.

As the dataset of the school districts only contained information on how geologically the school districts were distributed. The only adjustments I made to the data was creating the color index to differentiating different areas. By clicking into the school districts’ layer and chose the menu: style, there were several selections to customize the results.

The NYC cultural organization dataset had a category about the discipline. Setting the discipline as the value, the final map I got included both visual information of density and diversity of cultural organizations in different school districts in New York City.

Results & discussion

Plus, it would show more details about its name, address, phone number and discipline when hovering on each organization icon.

This interactive map is playful, and it told the results intuitively, and it could offer some useful detailed information about those cultural organizations. However, the map still looked messy. It did not reflect directly on how a school district was composed of the diverse discipline of museums, galleries, and other cultural organizations.

Reflection

Overall, Carto is a helpful tool to realize data visualization to the novice user. It vividly achieves visual results, as its user inference has relatively straightforward guidelines to follow and tutorial documents. It has function like layers, being similar to Adobe software, which makes designers easy to start.

When I look back at this entire process, I did not face as many difficulties as the projects I did. As for my future steps, I would consider more working on a visualization result with more detailed content, such as refining details on density and diversity inside each school district. Besides, I think I should be prepared and ready to import more datasets and analyze them to get a multi-dimensioned result.

Sources

NYC OpenData opendata.cityofnewyork.us

Carto carto.com