Page 47 of 171

Red light camera ticket data insufficient to find cause for spikes

Spike in tickets issued at 119th/Halsted in May and June 2011. Nodes represent tickets grouped by week.

Spike in tickets issued at 119th/Halsted in May and June 2011. Nodes represent tickets grouped by week.

The Chicago Tribune published a dataset of over 4 million tickets issued to motorists for entering an intersection after the light had turned red. They analyzed the dataset and found unexplained spikes, where the number of tickets, being issued by the handfuls each day, suddenly tripled. (Download the dataset.)

I looked at the tickets issued by two of the 340 cameras. I didn’t find any spikes at Belmont/Sheridan (“400 W BELMONT”) but found a noticeable spike in May and June 2011 at the 119th Street and Halsted Street intersection (“11900 S HALSTED”).

I looked at three violations on one of the days that had an atypical number of tickets issued, May 12, 2011. Each motorist was ticketed, it appears, for turning right on red. It’s not possible, though, without watching the video, to see if the motorist rolled through the turn or indeed stopped before turning right.

The Tribune called tickets issued during these “spikes” “undeserved” but that’s hard to say without see the violations on video. The photos don’t provide enough evidence. The Tribune also reported that appeals during these spike periods were more likely to be overturned than in the period outside the spikes. The reporters discussed the possibility of malfunctions and malicious behavior, calling that an “intervention”.

The Chicago Department of Transportation, which oversees the program formerly operated by Redflex and now operated by Xerox, couldn’t refute either allegation, possibly with “service records, maintenance reports, email traffic, memos or anything else”. David Kidwell and Alex Richards report:

City transportation officials said neither the city nor Redflex made any changes to how violations were enforced. They acknowledged oversight failures and said the explosions of tickets should have been detected and resolved as they occurred. But they said that doesn’t mean the drivers weren’t breaking the law, and they defended the red light camera program overall as a safety success story. The program has generated nearly $500 million in revenue since it began in 2003.

The city was unaware of the spikes until given the evidence by the Tribune in January, said David Zavattero, a deputy director for the Chicago Department of Transportation. In the six months since, city officials have not provided any explanations.

“Trust me when I tell you that we want to know what caused these spikes you have identified as much as you do,” Zavattero said. “So far we can find no smoking gun.”

He acknowledged that faulty camera equipment likely played a role.

“I would say that is likely in some of these cases,” Zavattero said. “I cannot tell you that isn’t possible. It is possible. The old equipment was much more prone to break down than the equipment we are currently installing.”

You can download the data but you will likely produce the same results as the Tribune, but maybe a different conclusion. Their analysis has led people at all levels of the civic sphere to call for an investigation, including citizens, some of whom have filed a lawsuit, Alderman Waguespack and 19 other aldermen, and CDOT commissioner – who operates the red light camera program – Rebekah Scheinfeld.

I think they sufficiently identified a suspicious pattern. By the end of the long story, though, the Tribune didn’t prove its hypothesis that the tickets were “undeserved” or “unfair”.

In violation number 7003374335 you can see the driver of a Hyundai Santa Fe turning right at a red light. The Google Street View for this intersection shows that there is no RTOR restriction, meaning the driver is legally allowed to make a right turn here with a red light after coming to a complete stop and yielding to people in the crosswalk. But we can’t see if they stopped first. The next two violations that day at the same intersection I looked showed the same situation. (Find the violation by going to PhotoNotice and inputting 7003374335, NWD648, and CHI.)

I look forward to the investigation. The Tribune made a great start by analyzing the data and spurring the call for an investigation and it seems there’s not enough information in this dataset to explain why there are more tickets being issued.

One dataset that could help provide context – because these spikes, at least the ones that are sustained, don’t seem random – is knowing the number of vehicles passing through that intersection. The speed camera data has this information and allows one to show how different weekend traffic is from weekday traffic.

The "before" image showing the Santa Fe vehicle approaching the stop bar.

The “before” image showing the Santa Fe vehicle approaching the stop bar.

Violation photo 2

After: The Santa Fe vehicle is seen in a right turn.

Note: The Tribune identified 380 cameras but running a DISTINCT() query on the “camera name” field results in 340 values. Some cameras may have identical names because they’re at the same intersection, but you can’t discern that distinction from the dataset.

Why architects should learn OpenStreetMap

I’m teaching OpenStreetMap 101 at the first MaptimeCHI.

Architects will learn that OpenStreetMap can be used as a data source when developing projects and as a basis for designing custom maps in project publications (website, anthology, monograph, client presentations).

This meeting is about getting an introduction to OpenStreetMap and learning to make your first edit in the “Wikipedia of maps”.

Thursday, July 17th, from 6-8 PM
Thoughtworks office
200 E Randolph St

RSVP on EventBrite.

Here are two examples of how architects could use OpenStreetMap data.

Example 1 of how to use OpenStreetMap. Instead of publishing a screenshot of Google Maps in your documents or website, create a custom design map like this without having to spend so much time tweaking it in Illustrator. This map was created by Stamen Design using TileMill.

Example 1 of how to use OpenStreetMap. Instead of publishing a screenshot of Google Maps in your documents or website, create a custom design map like this without having to spend so much time tweaking it in Illustrator. This map was created by Stamen Design using TileMill.

And the second.

Willow Creek Church on OpenStreetMap: After

Here’s one example where OpenStreetMap could be useful. Let’s say you’re working on a site plan for Willow Creek Church in South Barrington and you need a general layout of the parking lot. 1. You can get it from OpenStreetMap because it’s already there. 2. You can draw it in OpenStreetMap yourself (to benefit all other OSM users) and then extract it as a shapefile.

Maptime is time for mapmaking and it’s taking the country by storm.

How to ascertain the area of Chicago beach parking lots to find the largest one

This tutorial is a direct response to a question about which Chicago beach has the largest parking lot. Matt Nardella of Moss Design, in a response to a Twitter-based conversation about Alderman Cappleman’s suggestion that perhaps Montrose beach has too much parking, researched on Wikipedia to find the answer. This is where it said that Montrose beach has the largest parking lot of any of Chicago’s 27 beaches.

Now we’re going to try and prove which beach has the largest associated parking lot.

This tutorial will teach how you to (1) display Chicago beaches, (2) download data held in OpenStreetMap, (3) find the parking lots within the OpenStreetMap data, (4) find the parking lots near the beaches, and (5) calculate each parking lot’s area (in square feet). You can use this tutorial to accomplish any one of these three tasks, or the same tasks but on a different part of OpenStreetMap data (like the area of indoor shopping malls).

You’ll need the QGIS software before starting. You’ll also need at least 500 MB of free space. Start a project folder called “Biggest Parking Lots in Chicago” and make two more folders, within this folder, called “origdata” and “data”.

First, let’s get some data about beaches

Since we only want to know about the parking lots near Chicago beaches we need to get a dataset that locates them. This data is presumably within the same OpenStreetMap extract we’re waiting for, but it’s best to go to the most reliable source.

  1. Download the Parks – Facilities & Features shapefile from the City of Chicago open data portal. I’ve already verified that it has all the beaches (as points).
  2. Open the parks shapefile in a new document in QGIS (call it “map01a.qgs”). You might not see the data so right-click the parks layer and select “Zoom to layer extent”.
  3. Filter out all the points that aren’t beaches by using the query builder. Right-click the layer and select “Filter…” and input this filter expression: “FACILITY_N” = ‘BEACH’
  4. Your map will now show 26 points along an invisible lakefront and then the beach at Humboldt Park.
  5. For the rest of this tutorial we’ll reference the beaches layer as ParkFacilities.

Second, let’s get some data from OpenStreetMap

The easiest way to grab data from OpenStreetMap is by using QGIS, a free, open source desktop GIS application that has myriad plugins that match the capabilities of the heavyweight ESRI ArcGIS line of software. We can download OpenStreetMap data straight into QGIS.

  1. Click on the Vector menu and select OpenStreetMap>Download data.
  2. We want as much data as will cover the beaches information so in the Extent section of the dialog box choose “From layer” and select the beaches layer (called ParkFacilities).
  3. Browse to the “origdata” folder you created in the first task and choose the filename “chicago.osm”.
  4. Click OK and watch the progress meter tell you how much data you’ve downloaded from OpenStreetMap.
  5. Once it’s completed downloading, click “Close”. Now we want to add this data to our map.
  6. Drag the chicago.osm file from your file system into the QGIS Layers list. A dialog box will appear asking which layers you want to add.
  7. Select the layer that has the type “MultiPolygon”. This represents areas like buildings and parking lots.

Third, display the OpenStreetMap data and eliminate everything but the parking lots

We only want to compare parking lots in this dataset with beaches in the previous dataset so we need to eliminate everything from the OpenStreetMap data that’s not a parking lot. Since OSM data depends on tags we can easily select and show all the objects where “amenity” = “parking”.

  1. Filter out all the polygons that aren’t parking lots by using the query builder. Right-click the layer and select “Filter…” and input this filter expression: “amenity” = ‘parking’. Hopefully all the parking lots have been drawn so we can analyze a complete dataset!
  2. Your map will now show little squares, rectangles, and myriad odd shapes that represent parking lots around Chicagoland. (Most of these have been drawn by hand.) It should look like Image XXX.
  3. Since this data is stored in a projection with the codename of EPSG:3435 and the OpenStreetMap data is stored with codename of EPSG:4326 we need to convert the beaches to match the beaches (because we’re going to be using feet as a  measuring distance instead of degrees).
  4. Right-click the layer and select “Save As…” and choose the format “ESRI Shapefile”. Then click the top Browse button and select a location on your hard drive for the converted file.
  5. For “CRS” choose “Selected CRS”. Then click the bottom Browse button and search for the EPSG with the codename 3435. Select the checkbox named “Add saved file to map” so the new layer will be immediately added to our map.

Fourth, select all the parking lots near a beach

This task will select all the parking lots near the beaches. I chose 2,000 feet but you could easily choose a different distance. You might want to measure on Google Earth some minimum and maximum distances between beaches and their respective, associated parking lots.

(This task is easier using PostGIS which has a ST_DWithin function to find objects within a certain distance because we can avoid having to create the buffer in QGIS.)

  1. Create a 2,000 feet buffer. Select Vector>Geoprocessing tools>Buffer.
  2. In the Buffer(s) dialog box, select ParkFacilities (which has your beaches) as the “Input vector layer”. Choose a distance of 2000 (the units are pre-chosen by the projection and since we’re using a projection that’s in feet, the distance unit will be feet).
  3. Browse to your project folder’s “data” folder and save the “Output shapefile” as “beaches buffer 2000ft.shp”.
  4. Click “Add results to canvas” and then click OK.
  5. Double check that 2,000 feet was enough to select the parking lots. In my case, I see that the point representing Montrose beach was further than 2,000 feet away from a parking lot.
  6. Let’s do it again but with 3,000 feet this time, and saving the “Output shapefile” as “beaches buffer 3000ft.shp”.
  7. This time it worked and the nearest parking lots are now in the 3,000 feet radius buffer. You can see in Image XXX how the two concentric circles stretch out from the beach point towards the parking lots.

We’re not done. We’re next going to use our newly created 3,000 feet buffers to tell us which parking lots are in them. These will be presumed to be our beach parking lots.

  1. Use the “Select by location” tool to find the beaches that intersect our 3,000 feet buffers. Select Vector>Research Tools>Select by location.
  2. Follow me: we want to select features in parking 3435 [our parking lots] that intersect features in beaches buffer 3000ft [our beaches]. We’ll modify the current selection by creating a new selection so that we don’t accidentally include any features previously selected.
  3. You’ll now see a bunch of parking lots turn yellow meaning they are actively selected.
  4. Let’s save our selected parking lots as a new file so it will be easier to analyze just them. Right-click “parking 3435” and select “Save Selection As…” (it’s important to choose “Save Selection As” instead of “Save As” because the former will save just the parking lots we’ve selected).
  5. Save it as “selected parking 3435.shp” in your “data” folder. The CRS should be EPSG:3435 (NAD83 Illinois StatePlane East Feet). Check off “Add saved file to map” and click OK.
  6. Turn off all other layers except ParkFacilities to see what we’re left with and you’ll see what I show in Image XXX.

Fifth, let’s calculate

Calculating the area is probably the easiest part of this tutorial.

  1. Close all attribute tables you may have opened.
  2. Select Vector>Geometry Tools>Export/Add geometry columns and choose “selected parking 3435” as your input vector layer.
  3. Leave all other options as-is and press OK. When told about how QGIS can’t access something simultaneously, choose “Yes”.
  4. QGIS should have told you that “selected parking 3435” has been updated. Right-click the layer and choose “Open Attribute Table”.
  5. Scroll to the far right and you’ll see a new column called AREA. This represents the parking lot’s area in square feet.
  6. Click on the AREA column heading to sort it from smallest to largest. Scroll to the bottom of the list and you’ll find the parking lot with the largest area. Double check – is it near a beach?

Conclusion

With my analysis, and with the data available from OpenStreetMap when I created this tutorial, there are three abnormally large parking lots:

  1. A linear lot near the Lincoln Park Zoo and North Avenue beach (6.8 acres)
  2. A curving lot near Montrose Beach (4.75 acres)
  3. An irregularly shaped lot near Montrose Beach (4.5 acres)

There’s one major caveat in this analysis and that’s the missing parking lots on beaches south of Navy Pier. This means that no one has drawn them into OpenStreetMap so it’s time to start editing!

Morgan CTA station is fueling new West Loop development

First day of CTA Morgan Station serving the Green and Pink Lines

The Morgan Station was designed by Ross Barney Architects.

DNA Info Chicago discusses an informal study the Chicago Transit Authority conducted that showed, “since the [Morgan Green/Pink Line] station opened in May 2012, residential and business development in the surrounding neighborhood has continued at a faster pace than nearly all other markets within the city during the post-recession period.”

Morgan connects two major restaurant rows, one on Randolph Street that stretches much further east at Jefferson all the way west to Racine, and another on Fulton Market that starts at Carnavale (next to the Kennedy Expressway) and stretches to Morgan. Google’s huge new headquarters (under construction) and Coyne College – an HVAC institute – are within walking distance.

Using ridership information and commercial rents data, as well as information from the Chicago open data portal, CTA spokesperson Catherine Hosinski listed the following findings in the study:

  1. “A more than 20 percent increase in new business licenses.”
  2. “New construction and demolition permits spiked from one to 15. (The new construction/demolition permits issued after the opening of the station were compared to a 21-month period during the construction from August 2010 through May 2012)”
  3. “Average weekday ridership increased 30 percent between May 2013 and May 2014, according to information reflecting a 12-month rolling average over a 36-month period.”
  4. “Average weekend ridership increased more than 20 percent.”

Item #2 is data that we can see on Licensed Chicago Contractors. To help CTA planners and residents see this information more readily I’ve added all 145 CTA stations as places you can explore from the Places page.

How can you use this new place in connection with CTA’s study? With it you’ll be able to easily see building permits issued within half a mile from the CTA station and download this data for the last 9 years. You can also sort by projects’ estimated costs to find the biggest investments near the station.

Hosinski admits an important part of the study when she told DNA Info, “the West Loop was showing signs of becoming a booming neighborhood before the station was built…its presence has contributed to the migration of commuters and residents to the area.” Hosinski also said that you’ll find CTA stations at the heart of “most” Chicago neighborhoods and now CTA stations are part of the heart of tracking building permits.

Here’s how to track building permits (including demolitions and new construction) around any CTA station:

  1. Open the Places page on Chicago Cityscape
  2. Search for a station name (like Morgan), route name, or click “CTA Rail Station” in the right sidebar.
  3. Done! The list is sorted by the permit’s issue date, but click on any of the table headers to sort differently.
Morgan station on Licensed Chicago Contractors

We’re tracking over 1,700 building permits within a half mile of the Morgan station since July 2005.

Chicago wards with the most landmarked places

Montgomery Ward Complex

People float by the Montgomery Ward Complex on Kayaks. Photo by Michelle Anderson.

Last week I met with the passionate staff at Landmarks Illinois to talk about Licensed Chicago Contractors. I wanted to understand the legality for historic preservation and determine ways to highlight landmarked structures on the website and track any modifications or demolitions to them.

I incorporated two new geographies over the weekend: Chicago landmark districts, and properties and areas on the National Register of Historic Places (both available on the City of Chicago open data portal).

I used pgShapeLoader to import them to my DigitalOcean-hosted PostgreSQL database and modified some existing code to start looking at these two new datasets. Voila, you can now track what’s going on in the Montgomery Ward Company Complex – currently occupied by “600 W” (at 600 W Chicago Avenue) hosting Groupon among other businesses and restaurants.

Today I was messing around with some queries after I saw that the ward containing this place on the National Register – the 27th – also had a bunch of other listed spots.

I wrote a query to see which wards have the most places on the National Register. The table below lists the top three wards, with links to their page on Licensed Chicago Contractors. You’ll find that many have no building permits associated with them. This is because of two reasons: the listing’s small geography to look within for permits may not include the geography of issued permits (they’re a few feet off); we don’t have a copy of all permits yet.

[table id=15 /]

4 wards don’t have any listings on the National Register of Historic Places and nine wards have one listing.