Privacy on the Internet is such a big issue currently. The privacy is not only dependent on social media and browsing websites. It also depends on the Internet service providers you use. A public WiFi network this big also brings a new set of security risks. Therefore in this week I decided to do Mapping of New York City’s Public WiFi. Are major public spots are a risk for your data ?
This particular image is partially my source of inspiration. The dot mapping was used to show the positioning of the WiFi Location. In this visualization one more factor inspired me is that the location of the free or limited WiFi to tied with the income density of the boroughs. This helped me to think about various factors that can be linked with WiFi locations.
Process & Result
I got the data for this subject from NYC Open Data. The data found was sorted already which has location of the wifi kiosk and provider and type of wifi. This data I imported on Carto. Then I used shape file of NYC borough with i got from NYC open data as well. First of all, import the NYC wifi data to put the location of the wifi on map. These locations were aggregated by points. These point colors are given as per the providers of the service. This helps me shows that LinkNYC has major number of wifi kiosks.
The placement of the kiosks made me wonder about the reason must be behind the location. Hence imported subway line data. As we all know the amount of population travelling through subway is huge. That might be one of the reasons for the placement. Unfortunately the data about tourist is not available so could not precisely say that tourist population can be one of the reasons for the placement.
Every WiFi kiosk has a connectivity range. Hence for that matter I gave a buffer of 45 meters to the points. Due to this analysis, I realized that so many of the connectivity buffers intersect each other. Does that show that the population in that area is more and hence they need more kiosks to connect more people to WiFi.
Personally, the whole process of using Carto was really interesting. After showing once and experimenting with different features I could get the hang of it. I think if you understand your data and know what you want to portray then starting from importing your data to using analysis tools is instinctive. In future I would really like to portray some data which uses different aggregation styles combined with time. It would be fascinating exercise to create a breathing map.