I also added weather and wave observations from the API of the Finnish Meteorological Institute. Clicking on a track brings up these details, provided the closest weather stations and wave buoys are within 30 nautical miles from the track. They are therefore only approximations, and can vary from the conditions actually experienced on the boat, depending on its location relative to the stations/buoys and nearby islands.

It should also be noted that the tracking accuracy of Moves is lower than that of proper chart plotters, which is only natural considering battery consumption. There are also clear errors when close to shore, as Moves has a tendency to place the location on known roads. Tracks and all values should therefore be taken with a grain of salt.

The source code can be found on GitHub, and the app itself is here.

Using that API, Sampsa Kuronen created Aurat kartalla, which is a beautiful visualization of the real-time data. It allows you to specify a time interval, and shows different activities (snow removal, spreading sand, de-icing with salt, etc) with different colors.

I decided to try my own version with shiny, for a couple of reasons:

1. In addition to identifying different activities, the API also includes a flag specifying “bicycle and pedestrian lanes”. Aurat kartalla always shows them with the same color, therefore not distinguishing between e.g. spreading sand and de-icing with salt. Although I personally don’t really mind that much, for some cyclists this is important information. Many have suffered flat tires because of the sand, and many feel that the salt rusts their bikes.
2. Outside bicycle and pedestrian lanes, Aurat kartalla does show the different activities with different colors. But when there are multiple activities performed on the same route, it can be difficult to tell them apart.
3. I had never created a shiny app that polls an external API and automatically updates its data, so it was simply an interesting experiment.

Here are links to the resulting shiny app and its source code on GitHub.

As my goal was to provide granular control to really check what activities had been performed along a specific route, at first I included a separate setting to distinguish between streets and bicycle/pedestrian lanes. However, after looking at the results on a couple of snowy days, I noticed that this flag wasn’t really that reliable. Exact same routes were plowed both with and without it.

I can think of two possible explanations. The first one is that the flag really just specifies the equipment used, and some plows are marked for bicycle/pedestrian lanes, while others are not. And that in reality, both can also operate outside these target routes. The second one is that the presence of the flag relies on the plow driver explicitly specifying when they are plowing a bicycle/pedestrian lane, and that this is simply often forgotten (as, to be honest, I would expect to happen in reality).

Therefore, I removed the separation between streets and bicycle/pedestrian lanes, and instead show both at the same time. But the main point is still to be able to unambiguously distinguish between the different activities that have been performed. However, this goal suffers a bit from the fact that the API doesn’t actually contain all of the plows in use, so there is no way to tell for sure whether something has not been performed.

Nevertheless, it was a fun experiment. And in any case, I think Aurat kartalla provides a more beautiful overall visualization of the same data, and with better performance.

When I was at the Recurse Center, I needed a little project to work on learning ggplot2 and shiny, and chose to do a remake of this old blog post on the shares of different modes of transportation I use. The end result is packaged into a shiny app, and shows for example that in New York I walked a lot more than I used to while living in Amsterdam or Helsinki, and that last summer I spent a lot of time on boats. A more subtle difference is that in Amsterdam I seemed to use trams only for commuting to work.

I also wanted to try out some mapping with leaflet, and made another app that shows all my movement while I was in New York. If all checkboxes are turned on, four months of data seems to be a bit much to handle performance-wise, and the app becomes slow and sometimes crashes (turns gray and stops responding). Anyways, it was a fun experiment to do.

The source code is available on GitHub.

In addition to being a convenient way to move around, another nice thing about the system is that they release their ride data publicly. I decided do a little experiment in R. I downloaded all their data and used dplyr to count the number of bikes arriving to and leaving from each station. I did that for each hour of the day and separately for weekdays and weekends.

In order to visualize the patterns, I plotted them with ggmap and made a little shiny app.

To identify neighborhoods with similar usage patterns, I used K-means clustering and put the results in another shiny app. It also contains a plot of the variance explained for assessing a suitable value for K.

The source code is available on GitHub.

First, there has been an increasing move back toward the cities, where transit is more readily available. Second, millennials seem especially uninterested in owning their own cars. Third, the trend away from driving actually dates back to 2004, when the economy was still thriving. A government measure called “per capita vehicle miles traveled,” which had gone up steadily for decades began trending down that year and has fallen ever since. After 8 consecutive years of declines, on average we’re driving as much as we did in 1996.

Everybody likes free parking, including me, probably you. But just because the driver doesn’t pay for it doesn’t mean that the cost goes away. If you don’t pay for parking your car, somebody else has to pay for it. And that somebody is everybody. We pay for free parking in the prices of the goods we buy at places where the parking is free. And we pay for parking as residents when we get free parking with our housing. We pay for it as taxpayers. Increasingly, I think we’re paying for it in terms of the environmental harm that it causes.

And:

I did use data to estimate that parking subsidies in the United States are somewhere between 1 and 4 percent of the total GNP, which is about in the range of what we spend for Medicare or national defense. So that’s the cost of parking not paid for by drivers.

Perspective.