The scooter data is comprised of geolocation data points from Jump (both scooters and bikes), Lime, Skip, and Spin. This is a subset of operators in DC, so while it doesn't provide a complete picture, it provides enough of a view of what locations are relatively more favorable than others. It also has the benefit of providing a cross-sectional view of usage, as opposed to being confined to a single company's usage data.
The data covers the period from November 2019 to February 2020. This period was selected to exclude the impact of COVID-19 on usage. However, a potential drawback is that it focuses on colder months with relatively little daylight when usage may be relatively low compared to months with warmer weather and longer daylight.
Furthermore, the data is not uniform across companies with variation in the type and frequency of data. To get something useful from the dataset required assumptions on my part about what activity did and did not consitutue a trip, amongst other factors. Therefore it's my best estimate for the scooter market in Washington DC though it may not 100% line up with each company's internal data.
Data on each square's characteristics is based on information from Washington DC's Open Data portal which incldues data on the location of different lots, as well as information about what is on the lot (i.e. Residential, Commercial, etc...). Tax record data from the same source was also used to supplement the analysis.
A note on Industrial: The two highest-usage squares were categorized as Industrial. Looking on Google Maps, there didn't seem to be much to support this outcome. In addition, all scooters were from the same company in these two squares. Therefore they were excluded from the majority of the analysis under the assumption there was an error in the data.