This dataset contains over 200,000 observations of electric consumption and cost by building in New York City, organized by borough.
The cost of electricity can be measured in two ways:
We see that the average cost of electricity is highest in Manhattan and is the lowest in Staten Island and FHA buildings when measured by electricity consumption, by the relationship seems to invert when measured by demand.
I’m under the impression that FHA sponsored buildings have their electricity costs subisized by the government.
We can also organize the data based on the building’s source of funding.
When measured by cost per kilowatt hour, we see that Mixed Finance/LLC1 buildings have the highest rates, with Federal Coops and LLC2 financed buildings enjoying the lowest electricity rates.
However, when measured by electricity demand, Federal Coops pay by far the highest electricity rates.
There are only a few electricity vendors in the New York City metro area, but we find each vendor tends to charge dramatically different rates to their clients depending on how we measure the cost.
When measured by kilowatt hour, ConEdison seems to charge extremely high rates when compared to its peers, and the New York Power Authority seems to offer the lowest rates. When measured by electricity demand in kilowatts, however, the New York Power Authority charges much higher rates than its two peers who charge by electricity demand.
Finally, there are roughly 75000 instances where buildings in our dataset have been billed by both kilowatt hour and by kilowatt. We explore the relationship between these two rates by plotting the charges to each building by both measurements.
If you analyze the plot by looking for dots on the top and bottom of the plot, you can tell that buildings in the Bronx and Staten Island have a high variability in their cost of electricity when measured by demand, with the buildings being charged the highest kilowatt rates residing in the Bronx or Staten Island. Queens, on the other hand, enjoys consistently lower rates when measured by demand than other boroughs.
If you now analyze the plot by looking for dots on the left and right sides of the chart, we can see that the Brooklyn and Staten Island buildings have much lower kilowatt hour rates than their peers in other boroughs. Queens is all over the place when measured by electricity consumption, and their is a clear division in the Manhattan dataset between points, which might represent different types of buildings.
This dataset contains information about the locations and type of WiFi hotspots scattered throughout New York City. Over 1600 WiFi hotspots are observed.
We can see that the vast majority of WiFi hotspots in New York City are free, with a few limited and partner hotspots scattered throughout Manhattan.
Limited Free WiFi hotspots are much more common in Brooklyn, but downtown Brooklyn has a large cluster of free WiFi hotspots.
When analyzed by location, we can see that outdoor WiFi hotspots are much more popular in downtown Brooklyn, Chelsea, Harlem, and just below Central Park. Subway WiFi hasn’t quite made it into Brooklyn yet, and Outdoor WiFi kiosks line both sides of Manhattan.
When looking at the top 6 WiFi providers in the city, we can see a clear concentration in locations such Harlem, Downtown Brooklyn, and subway stations. Link NYC is the real standout player here, with their commercial WiFi hotspots clearly dominating the east and west sides of Manhattan.
This dataset contains information about the locations of subway entrances throughout the city, as well as the specific subway lines available at each location.
We can aggregate the data by calculating the number of lines available at each location. This shows the center of the city near Port Authority is clearly a transit hub, and the further you get from Port Authority, the lower the concentration of subway lines.
We can also examine individual subway lines at will, although the dataset does not contain sufficient information to plot the subway’s path through the stations.
This has been a short data analysis on several open data sets by Dakota Wixom for Hodges Ward Elliot.