Introduction
When I first move to New York, I was debating on which area to stay. During my research, two of the most important factors are subway stations and parks. I want to live in an area with convenient transportation. I prefer areas with parks because I like to go to parks on my free time. I think having green space for people to relax is especially important for people living in big cities such as New York. Therefore, I created this map based on datasets I found from NYC Open Data website. I hope this map provides some useful information for people who are haunting for a convenient and nice area to live.
This map answers following questions:
- How many parks in this area?
- Where are the parks located?
- How many subway stations in this area? Where are the subway stations?
- What lines pass through this area?
Inspiration
I was drawn to these two maps because of their simplicity. It is easy to read. The colors are well chosen and conventional. The labels are available for readers who want to explore more information, but it does not take attention away from the big picture. For me, I changed the labels into pop-ups to offer more interaction.
Materials
- NYC Open Data— an open dataset resource
- Carto— a platform for building maps or location-based visualizations.
Methods
- Finding useful data sets and combine them to make a meaningful map
I came across these three datasets separately. They are available in the format of GeoJSON. In the beginning, I only focused on parks, however, I found parks alone was a little dull and there was no new arguments to be made based on one single dataset. Therefore, I chose the perspective of someone looking forward to moving to New York. I combined the park datasets with borough dataset and subway station dataset to make the map more meaningful and informative to people who are haunting for a convenient and nice area to live or who are interested in such information in their area.
2. Color & Visuals
I tried to use the most representative colors for different data categories, so people can interpret the map with less effort. When it comes to park, people naturally think of green. Therefore, I chose green for park zones. When I googled the color for subway, most people associate it with the color green or yellow. In order to differentiate it from the parks, I chose the color yellow to represent subway stations.
The borough dataset only acts the role of differentiating and highlighting different areas. Thus I decreased the transparency of the color of borough. I want it to be visible but not so strong to draw attention away from the park and subways.
I changed the base map to a dark one with subway lines. I think compare to the default yellow one, it serves our purpose of being able to locate subway stations better.
3. Pop-ups
For parks, when you click on the map, it shows a pop up box with the park name, location and site name (different zone of the park). For subway stations, when you click on the map, it shows you the name of the station and the lines available.
4. Widget & Layer
I added a widget of borough name, so people can highlight the borough that they are interested in. I also added layer options, therefore people can remove or add layers per their needs and preference.
Reflection:
The overall process is enjoyable and I liked the final result. The biggest struggle for me was to find build meanings or find new perspectives based on the datasets. From this lab, I learned that it is really important to take time to understand your datasets. I enjoyed the visual options and interactive features on Carto. I liked the pop-ups as an additional feature for reader to explore a little more.
If I could do further research, I want to find data about apartment buildings in New York (I tried but I didn’t find useful data sets), so this map can really be an helper in assisting people evaluating their living area during apartment haunting. Or else, I hope that the user could enter their address and the map will be able to locate it and show nearby parks and subway stations.
Even though reader can still find their approximate apartment location in the map by the borough and street (if they zoom in), I still want it to be easier to navigate.
Data Sets Used: