CategoryGIS

At least 2.5 percent of the land area in Chicago is covered in parking lots and garages

Here’s how I know that at least 2.5 percent of the land area in Chicago is covered in parking lots and garages, as of February 5, 2017.

That’s a lot of polluted water runoff.

I grabbed the land area of 227.3 from the Wikipedia page.

I grabbed all the parking lots from OpenStreetMap via Metro Extracts, which is going to be the most complete map of parking lots and garages.

Volunteer mappers, including me, drew these by tracing satellite imagery.

With the parking lots data in GIS, I can count their area in square feet, which comes out to 160,075,942.42. Convert that to square miles and you get 5.74.

5.74/227.3*100 = 2.5 percent

The last snapshot of parking lot data I have is from February 2016, when only 3.39 square miles of parking lots have been drawn.

There are still many more parking lots to be drawn!

Oh, how Chicago land use is controlled by spot zoning

If you only had a zoning map to try and understand how the different blocks in the City of Chicago relate to their neighborhoods and the city at large, you might have the idea that the city has no neighborhoods, but is actually a collection of tiny, randomly dispersed zones of differing land uses.

And then when you walked those areas you’d find that the zones, which attempt to prescribe a land use, at least nominally, don’t have anything to do with the restaurant, housing, and commercial building mix of uses actually present.

No plan would have been devised to create a map like this.

Over the last five years, and surely over the last 14, the City of Chicago has been divided (really, split) into an increasing number of distinct zoning districts.

The city’s zoning map is updated after each monthly city council meeting, to reflect the numerous changes that the 50 alders have approved individually. (Their collective approval occurs unanimously in an omnibus bill.)

Every few months I ask the Chicago Department of Innovation and Technology (DoIT) for the latest zoning map, in the form of a shapefile (a kind of file that holds geographic information that can be analyzed by many computer programs). While Chicago has one of the country’s best open data offerings, some datasets, like zoning, don’t get updated in the catalog.

There are two ways I can analyze and present the data about the quantity of zoning districts. Both, however, show that the number of distinct zoning districts has increased. This means that the city is divided even more finely than it was just six months ago.

Analysis 1: Period snapshots

I have the zoning shapefile for five periods, snapshots of the city’s zoning map at that time. From August 2012 to now, May 2016, the number of discrete zoning districts (the sum of all B3-5, RS-1, DX-7, etc. zoning classes) has increased 7.8 percent.

Period Zoning districts change

August 2012

11,278

September 2014

11,677

3.42%

June 2015

11,918

2.02%

November 2015

12,015

0.81%

May 2016

12,162

1.21%

I collect the period snapshots to show the history of zoning at a specific address or building in Chicago, which is listed on Chicago Cityscape. For example, the zoning for the site of the new mixed-use development in Bucktown that includes a reconstructed Aldi has changed four times in four years.

aldi zoning history

Analysis 2: Creation date

The zoning shapefiles also have the date at which a zoning district was split or combined to create a new district, either with a different zoning class (RT-4, C1-1, etc.) or a different shape.

With the most recent zoning shapefile I can tell how many new zoning districts were split or combined and a record representing it was added to the list. The records start in 2002, and by the end of the year 7,717 records were created.

The following year, only 14 records were added, and in 2004, only 6. The Chicago City Council adopted a rewritten zoning code in 2004, and I guess that the zoning map was modified prior to adoption. After 2004, the number of new zoning districts picks up:

year zoning districts added by splitting/combining cumulative change

2002

7717

7717

2003

14

7731

0.18%

2004

6

7737

0.08%

2005

267

8004

3.45%

2006

497

8501

6.21%

2007

561

9062

6.60%

2008

592

9654

6.53%

2009

304

9958

3.15%

2010

245

10203

2.46%

2011

271

10474

2.66%

2012

277

10751

2.64%

2013

299

11050

2.78%

2014

397

11447

3.59%

2015

367

11814

3.21%

2016

173

11987

1.46%

none listed

175

12,162

It seems there’s a light relationship between the recession that started in 2008 and the number of zoning changes made. There are more made annually before the recession than after it. It actually seems to track with building permits (sorry, no chart handy).

The U.S. DOT should collaborate with existing “National Transit Maps” makers

The U.S. DOT demonstrated one idea for how a National Transit Map might look and work at a conference in February.

The Washington Post reported this month that the United States Department of Transportation is going to develop a “National Transit Map” because, frankly, one doesn’t exist. The U.S. DOT said such a map could reveal “transit deserts” (the screen capture above shows one example from Salt Lake City, discussed below).

Secretary Anthony Foxx wrote in an open letter to say that the department and the nation’s transit agencies “have yet to recognize the full potential” of a data standard called the General Transit Feed Specification that Google promoted in order to integrate transit routing on its maps. Foxx described two problems that arose out of not using “GTFS”.

  1. Transit vehicles have significantly greater capacity than passenger cars, but are often considered just vehicles because we are unable to show where and when the transit vehicles are scheduled to operate. The realistic treatment of transit for planning, performance measures, and resiliency requires real data on transit system operations.
  2. One of the most important social values of transit is that it makes transportation available to people who do not have access to private automobiles, and provides transportation options for those who do. Yet, we cannot describe this value at a national level and in many regions because we do not have a national map of fixed transit routes.

“The solution is straightforward”, Foxx continued, “[is] a national repository of voluntarily provided, public domain GTFS feed data that is compiled into a common format with data from fixed route systems.”

The letter went on to explain exactly how the DOT would compile the GTFS files, and said the first “collection day” will be March 31, this week. As of this writing, the website to which transit agencies must submit their GTFS files is unavailable.

What Foxx is asking for has already been done to some degree. Two national transit maps and one data warehouse already exist and the DOT should engage those producers, and others who would use the map, to determine the best way to build a useful but inexpensive map and database. Each of the two existing maps and databases was created by volunteers and are already-funded projects so it would make sense to maximize the use of existing projects and data.

“Transitland” is a project to host transit maps and timetables for transit systems around the world. It was created by Mapzen, a company funded by Samsung to build open source mapping and geodata tools. Transitland is also built upon GTFS data from agencies all over the world. Its data APIs and public map can help answer the question: How many transit operators serve Bay Area residents, and what areas does each service?

For the United States, Transitland hosts and queries data from transit agencies in 31 states and the District of Columbia. In Washington, D.C., Transitland is aware of four transit agencies. It’s a great tool in that respect: Not all of the four transit agencies are headquartered in D.C. or primarily serve that city. The app is capable of understanding spatial overlaps between municipal and regional geographies and transit agencies.

Transitland has a “GUI” to show you how much transit data it has around the world.

“Transit Explorer” is an interactive map of all rail transit and bus rapid transit lines in the United States, Mexico, and Canada. Yonah Freemark, author of The Transport Politic, created the map using data culled from OpenStreetMap, the National Transit Atlas Database (administered by the DOT and which shows fixed-guideway transit), and his own research. I wrote the custom JavaScript code for the Leaflet-powered map.

No other agency or project has collected this much data about fixed-guideway transit lines in any of the three countries, since the map includes detailed information about line lengths, ridership, and other characteristics that are not included in GTFS data. Transit Explorer, though, does not include local bus service or service frequencies, which the DOT’s map may if it incorporates the full breadth of GTFS data.

Transit Explorer also goes a step further by providing data about under construction and proposed fixed-guideway transit lines, which is information that is very relevant to understanding future neighborhood accessibility to transit, but which is not available through GTFS sources.

Finally, “GTFS Data Exchange” is a website that has been storing snapshots of GTFS feeds from agencies around the world for almost a decade, or about as long as GTFS has been used in Google Maps. The snapshots allow for service comparisons of a single agency across time. For example, there are over 100 versions of the GTFS data for the Chicago Transit Authority, stretching back to November 2009; new versions are added – by “cta-archiver” – twice a month.

Josh Cohen, writing in Next City, highlighted the significance of Google’s invention of GTFS, saying, “Prior to the adoption of GTFS, creating such a map would’ve been unwieldy and likely produced an out-of-date product by the time it was completed.” The DOT’s own National Transit Atlas Database includes only fixed-guideway (a.k.a. trains) routes, and hasn’t been updated since 2004.

Not all GTFS feeds are created equal, though. Some transit agencies don’t include all of the data, some of which is optional for Google Map’s purpose, that would make the National Transit Map useful for the spatial analysis the DOT intends. Many agencies don’t include the “route shapes”, or the geographic lines between train stations and bus stops. Researchers are able to see where the vehicles stop, but not which streets or routes they take. Foxx’s letter doesn’t acknowledge this. It does, however, mention that transit agencies can use some federal funds to create the GTFS data.

David Levinson, professor at the University of Minnesota, believes the map will bias coverage (geographic reach of transit service) over frequency (how many buses are run each day that someone could ride).

The U.S. DOT’s chief data officer, Dan Morgan, whom I met at Transportation Camp 2015 in Washington, D.C., presented at the FedGIS Conference this year one idea to demonstrate coverage and frequency in Salt Lake City, using the GTFS data from the Utah Transit Authority.

Levinson also tweeted that it will be difficult for a national map to show service because of the struggles individual transit providers have symbolizing their own service patterns.

Foxx’s letter doesn’t describe how planners will be able to download the data in the collection, but whichever app they build or modify will cost money. Before going much further, and before spending any significant funds, Foxx should consult potential users and researchers to avoid duplicating existing projects that may ultimately be superior resources.

Foxx can also take advantage of “18F” a new agency within the General Services Administration to overcome government’s reputation for creating costly and difficult to use apps. The GSA procures all kinds of things the federal government needs, and 18F may be able to help the DOT create the National Transit Map (and database) in a modern, tech and user-friendly way – or write a good RFP for someone else to make it.

Look for the National Transit Map this summer.

Converting a transit agency’s GTFS to shapefile and GeoJSON with QGIS

Many years ago I wrote a tutorial on how to use an ArcGIS plugin to convert a transit agency’s GTFS package – a group of files that describe when and where their buses and trains stop – into files that could easily be manipulated by popular GIS desktop software.

That was so long ago, before I became an expert in using QGIS, a free and open source alternative to ArcGIS.

This tutorial will show you how to convert GTFS to a shapefile and to GeoJSON so you can edit and visualize the transit data in QGIS.

Prerequisites

First you’ll need to have QGIS installed on your computer (it works with Linux, Mac, and Windows). Second you’ll need a GTFS package for the transit agency of your choice (here’s the one for Pace Suburban Bus*, which operates all suburban transit buses in Chicagoland). You can find another transit agency around the world on the GTFS Data Exchange website.

Section 1: Let’s start

  1. Open QGIS.
  2. Load your GTFS data into the QGIS table of contents (also called the Layers Panel). Click Layer>Add Layer>Add Delimited Text Layer. You will be adding one or two files depending on which ones are provided.

    QGIS add delimited text layer

    Add delimited text layer.

  3. Now, here it can get tricky. Not all transit agencies provide a “shapes.txt” file. The shapes.txt file draws out the routes of buses and trains. If it’s not provided, that’s fine, but if you turn them into routes based on the stops.txt data, then you will have funny looking and impossible routes.

    QGIs browse for the stops.txt file

    Browse for the stops.txt file

  4. Click on “Browse…” and find the “stops.txt”. QGIS will read the file very quickly and determine which fields hold the latitude and longitude coordinates. If its determination is wrong, you can choose a different “X field” (longitude) and “Y field” (latitude).
  5. Click “OK”. A new dialog box will appear asking you to choose a coordinate reference system (EPSG). Choose or filter for “WGS 84, EPSG:4326”. Then click “OK”.
  6. The Pace bus stops in the Chicagoland region are now drawn in QGIS!

    Pace bus stops are shown

    Pace bus stops are shown

  7. If the GTFS package you downloaded includes a “shapes.txt” file (that represents the physical routes and paths that the buses or trains take), import that file also by repeating steps 4 and 5.

Section 2: Converting the stops

It’s really easy now to convert the bus or train stops into a shapefile or GeoJSON representing all of those points.

  1. Right-click the layer “stops” in the table of contents (Layers Panel) and click “Save As…”.
  2. In the “Save vector layer as…” dialog box, choose the format you want, either “ESRI Shapefile” or “GeoJSON”. **
  3. Then click “Browse” to tell QGIS where in your computer’s file browser you want to save the file. Leave the “CRS” as-is (EPSG:4326).

    Convert the bus stops to a shapefile or GeoJSON.

    Convert the Pace bus stops to a shapefile or GeoJSON.

  4. Then click “OK” and QGIS will quickly report that the file has been converted and saved where you specified in step 3.

Section 3: Converting the bus or train routes

The “shapes.txt” file is a collection of points that when grouped by their route number, show the physical routes and paths that buses and trains take. You’ll need a plugin to make the lines from this data.

  1. Install the plugin “Points to Paths”. Click on Plugins>Manage and Install Plugins… Then click “All” and search for “points”. Click the “Points to Paths” plugin and then click the “Install plugin” button. Then click “Close”.

    Install the Points to Paths plugin.

    Install the Points to Paths plugin.

  2. Pace bus doesn’t provide the “shapes.txt” file so we’ll need to find a new GTFS package. Download the GTFS package provided by the Chicago Transit Authority, which has bus and rail service in Chicago and the surrounding municipalities.
  3. Load the CTA’s “shapes.txt” file into the table of contents (Layers Panel) by following steps 4 and 5 in the first section of this tutorial.  Note that this data includes both the bus routes and the train routes.

    QGIS load CTA bus and train stops

    Import CTA bus and train stops into QGIS

  4. Now let’s start the conversion process. Click on Plugins>Points to Paths. In the next dialog box choose the “shapes” layer as your “Input point layer”.
  5. Select “shape_id” as the field with which you want to “Point group field”. This tells the plugin how to distinguish one bus route from the next.
  6. Select “shape_pt_sequence” as the field with which you want to “Point order field”. This tells the plugin in what order the points should be connected to form the route’s line.
  7. Click “Browse” to give the converted output shapefile a name and a location with your computer’s file browser.
  8. Make sure all  of the options look like the one in this screenshot and then click “OK”. QGIS and the plugin will start working to piece together the points into lines and create a new shapefile from this work.

    These are the options you need to set to convert the CTA points (stops) to paths (routes).

    These are the options you need to set to convert the CTA points to paths (routes).

  9. You’ll know it’s finished when the hourglass or “waiting” cursor returns to a pointer, and when you see a question asking if you would like the resulting shapefile added to your table of contents (Layers Panel). Go ahead and choose “Yes”.

    QGIS: CTA bus and train points are converted to paths (routes)

    The CTA bus and train points, provided in a GTFS package, have been converted to paths (routes/lines).

  10. Now follow steps 1-4 from Section 3 to convert the routes/lines data to a shapefile or GeoJSON file.**

Notes

* As of this writing, the schedules in Pace’s GTFS package are accurate as of January 18, 2016. It appears their download link always points to the latest version. Transit schedules typically change several times each year. Pace says, “Only one package is posted at any given time, typically representing Pace service from now until a couple of months in the future. Use the Calendar table to see on which days and dates service in the Trips table are effective.”

** Choose GeoJSON if you want to show this data on a web map (like in Leaflet or the Google Maps API), or if you want to share the data on GitHub.

How to make a map of places of worship in Cook County using OpenStreetMap data

The screenshot shows the configuration you need to find and download places of worship in Cook County, Illinois, using the Overpass Turbo website.

If you’re looking to make a map of churches, mosques, synagogues and other places of worship, you’ll need data. The Yellow Pages won’t help because you can’t download that. And Google Maps doesn’t let you have a slice of their database, either. That’s where OpenStreetMap comes in. It’s a virtual planet that anyone can edit and anyone can have for free.

First we need to figure out what tag people use to identify these places. Sometimes on OSM there are multiple tags that identify the same kind of place. You should prefer the one that’s either more accurate (and mentioned as such in the wiki) or widespread.

The OSM tag info website says that editors have added over 1.2 million places of worship to the planet using “amenity=place_of_worship”.

Now that we know which tag to look for, we need an app that will help us get those places, but only within our desired boundary. Open up Overpass Turbo, which is a website that helps construct calls to the Overpass API, which is one way to find and download data from OSM.

In the default Overpass Turbo query, there’s probably a tag in brackets that says “[amenity=drinking_fountain]”. Change that to say “[amenity=place_of_worship]” (without the quotes). Now change the viewport of the map to show only the area in which you want Overpass Turbo to look for these places of worship. In the query this argument is listed as “({{bbox}})”.

The map has a search bar to find boundaries (cities, counties, principalities, neighborhoods, etc.) so type in “Cook County” and press Enter. The Cook County in Illinois, United States of America, will probably appear first. Select that one and the map will zoom to show the whole county in the viewport.

Now that we’ve set the tag to [amenity=place_of_worship] and moved the map to show Cook County we can click “Run”. In a few seconds you’ll see a circle over each place of worship.

It’s now simple to download: Click on the “Export” button and click “KML” to be able to load the data into Google Earth, “GeoJSON” to load it into a GIS app like QGIS, or “save GeoJSON to gist” to create an instant map within GitHub.

Use Turf to perform GIS functions in a web browser

Turf's merge function joins invisible buffers around each Divvy station into a single, super buffer.

Turf’s merge function joins invisible buffers around each Divvy station into a single, super buffer –all client-side, in your web browser.

I’m leading the development of a website for Slow Roll Chicago that shows the distribution of bike lane infrastructure in Chicago relative to key and specific demographics to demonstrate if the investment has been equitable.

We’re using GitHub to store code, publish meeting notes, and host discussions with the issues tracker. Communication is done almost entirely in GitHub issues. I chose GitHub over Slack and Google Groups because:

  1. All of our research and code should be public and open source so it’s clear how we made our assumptions and came to our conclusions (“show your work”).
  2. Using git, GitHub, and version control is a desirable skill and more people should learn it; this project will help people apply that skill.
  3. There are no emails involved. I deplore using email for group communication.*

The website focuses on using empirical research, maps, geographic analysis to tell the story of bike lane distribution and requires processing this data using GIS functions. Normally the data would be transformed in a desktop GIS software like QGIS and then converted to a format that can be used in Leaflet, an open source web mapping library.

Relying on desktop software, though, slows down development of new ways to slice and dice geographic data, which, in our map, includes bike lanes, wards, Census tracts, Divvy stations, and grocery stores (so far). One would have to generate a new dataset if our goals or needs changed .

I’ve built maps for images and the web that way enough in the past and I wanted to move away from that method for this project and we’re using Turf.js to replicate many GIS functions – but in the browser.

Yep, Turf makes it possible to merge, buffer, contain, calculate distance, transform, dissolve, and perform dozens of other functions all within the browser, “on the fly”, without any software.

After dilly-dallying in Turf for several weeks, our group started making progress this month. We have now pushed to our in-progress website a map with three features made possible by Turf:

  1. Buffer and dissolving buffers to show the Divvy station walk shed, the distance a reasonable person would walk from their home or office to check out a Divvy station. A buffer of 0.25 miles (two Chicago blocks) is created around each of the 300 Divvy stations, hidden from display, and then merged (dissolved in traditional GIS parlance) into a single buffer. The single buffer –called a “super buffer” in our source code – is used for another feature. Currently the projection is messed up and you see ellipsoid shapes instead of circles.
  2. Counting grocery stores in the Divvy station walk shed. We use the “feature collection” function to convert the super buffer into an object that the “within” function can use to compare to a GeoJSON object of grocery stores. This process is similar to the “select by location” function in GIS software. Right now this number is printed only to the console as we look for the best way to display stats like this to the user. A future version of the map could allow the user to change the 0.25 miles distance to an arbitrary distance they prefer.
  3. Find the nearest Divvy station from any place on the map. Using Turf’s “nearest” function and the Context Menu plugin for Leaflet, the user can right-click anywhere on the map and choose “Find nearby Divvy stations”. The “nearest” function compares the place where the user clicked against the GeoJSON object of Divvy stations to select the nearest one. The problem of locating 2+ nearby Divvy stations remains. The original issue asked to find the number of Divvy stations near the point; we’ll likely accomplish this by drawing an invisible, temporary buffer around the point and then using “within” to count the number of stations inside that buffer and then destroy the buffer.
Right-click the map and select "Find nearby Divvy stations" and Turf will locate the nearest Divvy station.

Right-click the map and select “Find nearby Divvy stations” and Turf will locate the nearest Divvy station.

* I send one email to new people who join us at Open Gov Hack Night on Tuesdays at the Mart to send them a link to our GitHub repository, and to invite them to a Dropbox folder to share large files for those who don’t learn to use git for file management.

Mapping a campground that doesn’t exist: a before and after view of OpenStreetMap

Pretty soon there will be a campground shown in OpenStreetMap, and added to its geocoding database, when I’m done adding it.

I temporarily become addicted to mapping places on OpenStreetMap. In my quest to find and map all campgrounds in Chicagoland – in order to publish them in the Chicago Bike Guide – I came across a campground that was constructed this year and opened in August 2013. This is the story of figuring out how to map the Big Rock Forest Preserve campground in Big Rock, Illinois.

I found on the Kane County Forest Preserve District website that the organization operated a campground at Big Rock Forest Preserve. I couldn’t locate the campground in Google Maps by the address the website gave. I couldn’t find it in OpenStreetMap, either, because no one had mapped it, but it’s there now.

When I searched for the park by name, Google Maps zoomed me to the main entrance of the park, but I still couldn’t see a campground. I downloaded the forest preserve district’s park map (always as a PDF) and followed the roads in Google Maps until I came across the campgrounds approximate position. There was a new road here so I followed that to find a campground under construction.

Google Maps shows the campground and artificial lake under construction.

Google’s imagery of the under-construction campground was taken on May 23, 2013 (get the date from Google Earth). This was great because now I could open JOSM, a powerful desktop OpenStreetMap editor, and locate the site, load Bing’s imagery and start tracing the campground to upload to OSM.

Bing’s imagery in JOSM, the OpenStreetMap-editing app, doesn’t show the campground.

The problem was that Bing’s imagery – and this is typical – was outdated. I could easily compare the imagery side-by-side and based on other landscape features (like the forest edge) guess where to trace the campground, but OSM needs better quality data. Enter MapWarper.

Read the rest of this post on Web Map Academy.

How to convert bike-share JSON data to CSV and then to shapefile

Update January 4, 2013: The easiest way to do this is to use Ian Dees’s Divvy API as it outputs straight to GeoJSON (which QGIS likes). See below.

For Michael Carney’s Divvy bike-share stations + Census tract + unbanked Chicagoans analysis and map he needed the Divvy station locations as a shapefile. I copied the JSON-formatted text of the Divvy real-time station API, converted it to CSV with OpenRefine, and then created a shapefile with QGIS.

Here’s how to create a shapefile of any bike-share system that uses hardware from Public Bike System Co based on Montréal and is operated by Alta Bicycle Share (this includes New York City, Chattanooga, Bay Area, Melbourne, and Chicago): Continue reading

Results of my personal #editathon this weekend

I added a lot of parking lots to OpenStreetMap this weekend, but I also added the Willow Creek Community Church (South Barrington, Illinois) parking lots, driveways, buildings, and retention ponds. I’ll let my before and after screenshots show you what I did. Ian Dees, local organizer for Chicagoland OSM data – he has other roles, too – said there’s an application that can generate these images automatically.

View in OpenStreetMap now.

I’m also writing a draft tutorial on how to convert GIS data stored as a shapefile to a format you can import into AutoCAD. GRASS will take a .shp and convert it to .dxf (a geo-aware CAD file).

How many miles of roads are in your ward?

Screenshot 1: Showing how some streets are not being counted. There should be a yellow section of road between the two existing yellow road sections. 

A friend recently asked me how many blocks of road are in his ward. He wanted to know so that he could measure how many blocks of streets would have an older style of street lighting after X number of blocks receive the new style of street lighting. For this project, I used two datasets from Chicago’s open data portal: street center lines and wards. The output data is not very accurate as there may be some overlap and some uncounted street segments; this is likely due to a shortcoming in my process. I will show you how to find the number of blocks per ward using QGIS (download Quantum GIS, a free program for all OSes).

Here’s how I did it

  1. Load in the two datasets. Wards and street center lines (zipped shapefiles). They are projected in EPSG:3435.
  2. Exclude several road classifications in the street center lines by querying only for "CLASS" > '1' AND "CLASS" < '5'. The data dictionary for the road classifications is at the end. We don’t want the river, sidewalks, expressways, and any ramps to be included in the blocks per ward analysis.
  3. Intersect. In QGIS, select Vector>Geoprocessing Tools>Intersect. The input vector layer is “Transportation” (the name of the street center lines dataset) and the intersect layer is “Wards”. Save the resulting shapefile as “streets intersect wards”. Click OK. This will take a while.
  4. Add the “streets intersect wards” shapefile to the table of contents.
  5. You’ll notice some of the issues with the resulting shapefile: missing street segments (see screenshot 1). What should QGIS do if a street is a ward boundary?
  6. Obtain street length information, part 1. Remove all the columns in the “streets intersect wards” shapefile that have something to do with geometry. These are now outdated and will confuse you when you add a geometry column generated by QGIS.
  7. Obtain street length information, part 2. With the “streets intersect wards” shapefile selected in the table of contents, select Vector>Geometry Tools>Export/Add geometry columns. Select “streets intersect wards” shapefile as your input layer, leave CRS as “Layer CRS” and save as new shapefile “streets intersect wards geom”.
  8. Add the “streets intersect wards geom” shapefile to the table of contents.
  9. You will see a new column at the end of the attribute table called LENGTH. Since the data is projected in EPSG:3435 (Illinois StatePlane NAD83 East Feet), the unit is feet.
  10. Simply export “streets intersect wards geom” to a CSV file and open the CSV file in a spreadsheet application. From there you can group the data by Ward number and add the street lengths together. (I thought it would be faster to do this in a database so I imported it into a localhost MySQL database and ran a simple query, SELECT wardNum, sum(`chistreets_classes234`.`LENGTH`) as sum FROM chistreets_classes234 WHERE ward > 0 group by wardNum. I then exported this to a spreadsheet to convert feet to miles.)

Because of the errors described in step 5, you shouldn’t use this analysis for any application where accuracy is important. There are road lengths missing in the output dataset (table with street lengths summed by ward) and I cannot tell if the inaccuracy is equally distributed.

[table id=7 /]

Wards 19 (south side) and 41 (Norwood Park, including O’Hare airport) have the highest portion of street length in the city.

Screenshot 2: Ward 41 is seen. 

Street data dictionary

Column is “CLASS”. The value is a string. This dataset lacks alleys. Adapted from the City’s data dictionary.

1. Expressway

2. Arterials (1 mile grid, no diagonals)

3. Collectors (includes diagonals)

4. Other streets (side streets, neighborhood streets)

5. Named alleys (mostly downtown, like Couch Place and Garland Place)

7. Tiered (lower level streets, including LaSalle, Michigan, Columbus, and Wacker)

9. Ramps (goes along with expressway)

E. Extent ( not sure how to describe these; includes riverwalk and lake walk segments, and Navy Pier, also includes some streets, like Mies van der Rohe Way)

RIV. River

S. Sidewalk

99. Unclassified

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