As a New Yorker, I spend a lot of time traveling on the subway every day, and I believe many people do as well. When we travel underground, our laptops are not able to connect the network. Once we have an emergency which requires a network connection on your laptop(like your client is requesting a file immediately), you need to get out and find the nearest Wifi hotspot to connect your device. Therefore, I think it is necessary for people to know where the nearest wifi hotspot is. So I made this interactive guide map. The goal of this guild map is to help people to find the nearest hotspot around the subway stations.
I found an inspiring map online which has a similar topic. This is a map showing all the wifi hotspots in Milan. I love this because the layout is clean and organized. It is easy to read and grab the information. In this map, all the wifi spots are highlighted in blue while the background information is faded in a warm grey. In addition, each district has a call-out for more detailed information. Overall, I think the visualization of this map is good and clear. I could use the same strategies in my project.
Kaggle – an open data resource
Carto – a data visualized tool for making maps
METHOD AND RESULTS
There were three datasets used in this project. NYC wifi hotspot from Kaggle recorded all the public wifi hotspots in New York City. NYC transit subway stations recorded the location of every subway station. NYC subway routes created by MTA shows every subway routes in NYC. I checked the datasets after I downloaded. They were clean and tidy. So I imported them into Carto directly.
I imported three datasets one by one and edited them together later on. After all three datasets were visualized on the map, the first thought jumped into my mind was “this is too much”. There was so much information on my map and it even made me confused. The subway lines overlapped with the roads. And there were so many dots on the map which I couldn’t tell what are they. Since the map included so much information, there were many decisions involved to make the map looks clearer and more readable.
To make all the analyses stand out, I changed the base map from the default white one to a darker one so that the roads and the blocks were faded into the black background. Therefore, unnecessary information was eliminated.
Then I edited the style of the subway stations and the wifi-hotspots. For the transit stations, I made all the stations in red and added a pop-up on every station with its name. I highlighted them because my goal was to make an interactive map as a reference when people need the wifi hotspots nearby. Mark the stations and their names helped people to locate themselves, and let the travelers know where they are.
For the wifi hotspots, I added the Geocode analysis so that every hotspot showed on the map. Then I changed the dot color into cyan to render a technological feeling.
In addition, I customized the subway lines colors. If using the default color palette, the colors were limited to show 22 lines. Therefore, I changed their colors in CSS to match the New York Metro colors that everyone is familiar with. It is more user-friendly because this color system helped people to recognize the lines easily and quickly.
After finished all the editings, I got a map which showed all the wifi hotspots in NYC. The next step was to find the nearest wifi hotspots around every subway station. I followed the Find nearest instruction on Carto.com and finished my “find nearest”analysis. After that, I got two layers both showing the wifi-hotspots. One showed all of them and the other one only had the nearest wifi around stations. I didn’t delete the layer which had all the hotspots because I wanted to keep it as a reference. In order to make the nearest wifis more distinguishable, I made the nearest dots in a bigger size and the color was more saturated. The others were in smaller dots and had a lower saturated color.
In general, I think Carto is a great tool for analyzing maps. It is very intuitive to use.
There are some things I want to change if I got a chance to revise the project. I think it would be better if the wifi dataset recorded the wifi’s location, not only the latitude or longitude. I mean the name of the place where has the wifi. For example, a shop’s name or a lobby’s name. Then when people click on the wifi dot on the map, the place’s name will pop-up. It could be more reasonable for people who are searching wifi because longitude and latitude don’t mean anything to them.