Category: Data

BikeSpike has major potential impact for data collection

March 19, 2013 / / 8 Comments

This is a pretty hilarious video showing the main reason one would get a BikeSpike: to catch a thief. 

Bill Fienup emailed me in December or January asking to meet up to talk about their bike theft tracking device (that does a whole lot of other stuff) but I couldn’t meet until February as the transition from Grid Chicago to Streetsblog Chicago was occupying my brain time. Bill’s part of Team BikeSpike.

The BikeSpike in hand. It’s very small and weighs 3.1 ounces.

It was convenient that they were at 1871 on a Tuesday night; I was there for Hack Night, they for one of the other myriad events that occurs on the 12th floor of the Merchandise Mart. They showed me a 3D-printed mockup of the BikeSpike, and told me what it was capable of doing. They seem to have a good programmer on their team in Josh Billions – yes, that’s his real last name.

I came to their lab near Union Park to talk in depth about BikeSpike with Bill, Josh, Harvey Moon, and Clay Neigher, garner more information, and provide them with some more insight into how the product can be useful to the transportation planning work I do as a Streetsblogger, advocate, and programmer. I brought my friend Brandon Gobel and he became interested to hear about how it could help him manage the future fleet of Bullitt cargo bikes he’s now selling and renting at Ciclo Urbano.

Beside the fact that BikeSpike can show law enforcement workers the EXACT LOCATION OF STOLEN PROPERTY, I like its data collection aspects. Like many apps for iOS (including Moves and Google Latitude), BikeSpike can report its location constantly, creating opportunities for individuals to track training rides and urban planners to see where people ride bikes.

Cities should know where people are riding so they can build infrastructure in those places! Many cities don’t know this until they either count them frequently and in diverse locations, or when they ask. But neither of these methods are as accurate as hundreds, or thousands of people reporting (anonymously) where they ride. I’ve got three examples below.

I imagine the BikeSpike will produce a map like this, which was created by Google Latitude constantly tracking me. 

Team BikeSpike: Clay, Bill, Ben Turner (I didn’t meet him), Josh, and Harvey.

1. Want to see if that new buffered bike lane on South Chicago Avenue actually encourages people to ride there and wasn’t just an extra-space-opportunity? Look at BikeSpike data.

2. The number of people biking on Kinzie Street shot up after a protected bike lane was installed. How many of these new Kinzie riders switched from parallel streets and how many were new to biking? Look at BikeSpike data.

3. Given relatively proximal origins and relatively proximal destinations, will people bike on a buffered bike lane (say Franklin Street or Wabash Avenue) over a protected bike lane (say Dearborn Street)? Look at BikeSpike data.

There are many other things BikeSpike can do with its GPS and accelerometer, including detect if your bike was wiggled or you’ve crashed. I want a BikeSpike but you’ll have to back the project on Kickstarter before I can get one! They need $135,000 more pledged by April 9.

Get out of Googleville: my presentation on web mapping

February 18, 2013 / / 0 Comments

Alternate headlines: Google Maps versus OpenStreetMap; why OpenStreetMap is better than Google Maps

I presented to the Chicago GIS Network Meetup group on February 5,2013, about alternatives to Google when it comes to mapping on the web. I created the presentation and outline a couple hours before giving it and came up with this slideshow with three frames.

Googleville 1 of 3

Google Maps and its data is a one-way street (or many one-way streets). Google will take data but won’t give it back.

Googleville 2 of 3

Google Maps has all of these features, but they’re easier to manipulate when you use an alternative. Alternatives like: MapBox, TileMill, OpenLayers, OpenStreetMap (made easy with JOSM), GeoCommons – I’m sure there are plenty more.

Googleville 3 of 3

OpenStreetMap is the Wikipedia of online mapping and geographic data. Considering switching to OSM.

Mapping guns in your town: is that okay?

December 25, 2012 / / 6 Comments

This screenshot shows the pistol permit holders in Westchester County, New York. The highest density of permit holders appears to be at the border with Bronx County, also known as the northern edge of New York City. 

An ABC News story I read through the Yahoo! News website tells about The Journal News, covering Westchester (Yonkers, New Rochelle) and Rockland (New City, Pomona) counties in New York, posting the names and addresses, on a map, of gun permit owners. The map contains:

…the addresses of all pistol permit holders in Westchester and Rockland counties. Each dot represents an individual permit holder licensed to own a handgun — a pistol or revolver. The data does not include owners of long guns — rifles or shotguns — which can be purchased without a permit. Being included in this map does not mean the individual at a specific location owns a weapon, just that they are licensed to do so. [Notice that some dots are outside the county.]

This article is interesting to me for two reasons:

1. The article has hyperlinks to the (alleged?) Facebook profiles of two people who commented on The Journal News’s website. I predict this will only become more common. I don’t have a Facebook profile to link to.

2. The rationale to make a map seems reasonable: so people know where there are potentially guns in their neighborhood. It seems reasonable that people want to know where there are potential sources of danger and harm near them.

The names and addresses were obtained through “routine” (their words, not mine, but it is pretty routine and normal) Freedom of Information Act (FOIA) requests. The quantity and types of guns are not considered to be public record, although this may not be true, according to the ABC News article.

Finding a new way to measure cities’ bike friendliness in the United States, part 2

November 5, 2012 / / 0 Comments

One of the reasons I developed my own bike friendly city ranking system was to provide a better measurement when comparing cities. Since the League of American Bicyclists (LAB) uses a nominal ranking (Platinum, Silver, Gold, Bronze), the difference in bike friendliness between cities of the same rank may be small or great. A numerical scoring system on a predictable and familiar scale will better highlight the distance of one city to another on achieve that city’s level of bike friendliness.

I created a method that would compare my ranking to LAB’s ranking and that was to find the variance (which isn’t the same as range) in scores in my ranking for each nominal level in LAB’s ranking. Platinum cities had a very high variance and Bronze cities had the lowest variance. Gold and Silver had swapped positions: Gold cities had a lower variance than Silver cities.

The beauty with creating your own bike friendly measurement system is that you can make the outcome order whatever you want.

In the days since, I’ve developed another bike friendliness measurement system, one that’s easier to understand, whose rankings are still relative to other cities, and that can be weighted. (I’m emphasizing the bike commute mode share.) It uses percentile scoring so all scores are positive but still based on the distribution of values. I’ve listed the scores for Method 1 (which uses a normalizing function based on mean and standard deviation) and Method 2 below.

[table id=5 /]

Finding a new way to measure cities’ bike friendliness in the United States

November 3, 2012 / / 8 Comments

A really smart person could come up with a way to measure day-to-day bike friendliness based on how well cities adhere to standards that keep roads clear of obstructions that further frustrate the commute, like construction projects that squeeze bikes and cars together. 

I work at home. There are some days when I only leave my house to get milk from the Mexican grocery store at the end of my block (which makes awesome burritos). That means I ride my bike half as much as people who commute to work. on their bikes. Today I had a bunch of errands to run: drop off stuff, buy stuff, take pictures of stuff for my blog, Grid Chicago.

It was a very frustrating experience. I don’t need to go into details about how I was harassed by people who the state so graciously awarded a license to drive. But it happened. And it happens a hundred times a day to people cycle commuting in Chicago. I got to thinking about “bike friendly” cities. Is there a way to incorporate driver attitudes in there? I tweeted:

[tweet_embed id=264575958374305792]

Later I had the idea to use some very simple but objective measurements to create a new bike friendliness metric. It would help ensure that “Silver” (a ranking the League of American Bicyclists [LAB] uses) in one city means the same as “Silver”. It can expand from here but basically it works like this:

  • The share of people going to work who go by bike is a proxy for how “friendly” a city is to biking.
  • If a city has a lot of people biking to work, it must be friendly.
  • If a city has a few people biking to work, it must be non-friendly.
  • Cities are compared to each other to determine friendly and non-friendly.
  • The metric uses standard deviation to score cities.

Stop me if this has already been done.

I created a spreadsheet that lists the top 10 populous cities in the United States. I then added 10 more cities: Austin, Boston, Davis, Madison, Minneapolis, Portland, San Francisco, Seattle, and Washington, D.C. In the next column I listed their bike commute share from the American Community Survey 2006-2010 5-year estimates. I calculated the standard deviation and mean of these shares and then in another column used Apple Numbers’s STANDARDIZE function:

The STANDARDIZE function returns a normalized value from a distribution characterized by a given mean and standard deviation.

I think that’s what I want. And the output is close to what I expected. I then found the LAB ranking for each city and found the variance of each ranking to see how far apart each city within one ranking was from another city in the same ranking. The results were interesting: the higher the ranking, the more variance there was.

Hurricane Sandy prompted a lot of New Yorkers to bike. It made headlines, even. Photo by Doug Gordon. 

I wanted to add another metric of bike friendliness, and that’s density. To me, a higher density of people would mean a higher density of places to go (shop, eat, learn, enjoy) and friends and family would be closer, too. Or the possibility of meeting new people nearby would be higher. Yeah, I’m making a lot of assumptions here. So I applied the STANDARDIZE function there as well. I added this number to the previous STANDARDIZE result and that became the city’s score.

So, in this new, weird ranking system, the most bicycle friendly cities are…drum roll please…

  1. Davis, California (Platinum)
  2. New York City (Silver) *
  3. San Francisco (Gold) *
  4. Boulder (Platinum)
  5. Boston (Silver)
  6. Philadelphia (Silver)
  7. Tie: Chicago*, Washington, D.C. (Silver)
  8. Tie: Portland* (Platinum), Minneapolis* (Gold)

Remember, I said above that any author of a list should spend at least a day cycling in each city. I’ve starred the cities where I’ve done that – I’ve cycled in 5 cities for at least a day.

I only calculated 20 cities. Ideally I’d calculate it for the top 50 most populous cities AND for every city that’s been ranked by LAB.

LAB cities list (PDF). My spreadsheet (XLS).