Tag: open data

Chicagoland’s massive parking footprint – as measured on September 16, 2018

Using the footprints of parking lots and garages drawn into OpenStreetMap as a data source, the area of land in Chicagoland occupied by parking lots and garages is 247,539,968 square feet. (The data was exported using HOT Export Tool; you can replicate my export.)

That converts to:

  • 5,682.71 acres
  • 8.88 mi^2 (square miles)
  • 22.99 km^2 (square kilometers)
  • ≈ 0.26 × area of Manhattan (≈ 87 km^2 )
  • 3.9% area of Chicago is parking (Chicago is ~589.56 km^2 )

(I forgot to measure the portion of this within Chicago, and now the data snapshot is gone. I fixed this in the 2019 report.)

Which Chicago buildings have the worst energy efficiency?

About five years ago (I’m too lazy to look it up right now), the City of Chicago adopted an energy benchmarking law. This means that owners of buildings of a certain size would soon be required to report how much energy (electricity, natural gas, district steam, chilled water, and other fuels) their buildings use. Every few years they have to audit their reports.

The city has posted three years of energy reports for the “covered” buildings (the ones of a certain size) on its data portal. I copied the Chicago Energy Benchmarking dataset into the Chicago Cityscape database (for future features) and then loaded it into QGIS so I could analyze the data and find the least efficient buildings in Chicago.

The dataset has all three years so I started the analysis by filtering only for the latest year, 2016. I first visualized the data using the “ghg_intensity_kg_co2e_sq_ft” column, which is “greenhouse gas intensity, measured in kilograms of carbon dioxide equivalent per square foot”. In other words, how much carbon does the building cause to be emitted based on its energy usage and normalized by its size.

In QGIS, to symbolize this kind of quantitative data, it helps to show them in groups. Here are “small fry” emitters, medium emitters, and bad emitters. I used the “Graduated” option in the Symbology setting and chose the Natural Breaks (Jenks) mode of dividing the greenhouse gas intensity values into four groups.

There are four groups, divided using the Natural Breaks (Jenks) method. There’s only one building in the “worst” energy users group, which is Salem Baptist Church, marked by a large red dot. The darker red the dot, the more energy per square foot that building consumes.

Among the four groups, only one building in Chicago that reported in 2016 was in the “worst emitters” group: Salem Baptist Church of Chicago at 10909 S Cottage Grove Avenue in Pullman.

The Salem Baptist Church building was built in 1960, has a gross floor area of 91,800 square feet, and an Energy Star rating of 1 because it emits 304.6 kilograms of carbon dioxide equivalent per square foot (kgco2esf). (The Energy Star rating scale is from 1 to 100.)

The next “worse” emitter in the same “Worship Facility” category as Salem Baptist Church is several magnitudes of order lower. That’s St. Peter’s Church at 110 W Madison Street in the Loop, built in 1900, which emits 11.7 kilograms of carbon dioxide equivalent per square foot (but which also has an Energy Star rating of 1).

The vast difference is concerning: Did the church report its energy usage correctly, or are they not maintaining their HVAC equipment or the building and it’s leaking so much air?

A different building was in the “worst” emitter category in 2015 but improved something about the building by 2016 to use a lot less energy. Looking deeper at the data for Piper’s Alley, however, something else happened.

In 2015, Piper’s Alley reported a single building with 137,176 gross square feet of floor area. The building’s owner also reported 5,869,902 kBTUs of electricity usage and 1,099,712,681 kBTUs of natural gas usage. Since these are reported in kilo-BTUs that means that you multiply each number by 1,000. Piper’s Alley reported using 1 trillion BTUs of natural gas. Which seems like an insane amount of energy usage, but could be totally reasonable – I’m not familiar with data on how much energy a “typical” large building uses.

Piper’s Alley in Old Town is the building that reported two different floor areas and vastly different energy usage in 2015 and 2016. The building’s owner didn’t report data for 2014 (although it may not have been required to).

There’s another problem with the reporting for Piper’s Alley, however: For 2016, it reported a gross floor area of 217,250 square feet, which is 36 percent larger than the area it reported in 2015. The building reported using significantly more electricity (58 percent more) and significantly less natural gas (137 percent less), for a vastly lowered kgco2esf value.

I think the energy benchmarking data set needs more eyes on it. Discuss in the comments below, or reply to my Twitter thread.

How to download data from ArcGIS MapServers using your computer’s command line

A lot of geospatial data (GIS) is stored on ArcGIS MapServers, which is part of the Esri “stack” of products that municipalities use to manage and publish GIS data. And a lot of people want that data. If you have ArcGIS software on your Windows computer, then it can be pretty easy to plug in the map server URL and manipulate and extract the data.

For the rest of us who don’t have an extremely expensive license to that software, you can use a “command line” tool (written in Python) on any computer to download any layer of GIS data hosted on the ArcGIS MapServer and automatically convert it to GeoJSON.

You’ll need to install the Python package pyesridump, from the OpenAddresses GitHub repository, created by Ian Dees and other contributors.

Installing pyesridump is easy if you have pip installed, using the command pip install esridump.

The next thing you’ll need is the URL to a layer in a MapServer, and these are not easy to find.

Finding data to download

I can guarantee the county where you live has one. Before you continue, check to see if your county (or other jurisdiction) has the “open data portal” add-on to their ArcGIS stack.

Here are links to the open data portals enabled by Esri for Lake County, Illinois, and Broomfield County, Colorado). This is much easier to browse and find data to download (in shapefile and other formats) and you can skip this tutorial.

I don’t have a good recommendation to find the MapServer URL, though. A reader suggested looking for MapServers for jurisdictions around the world by looking through Esri’s portal of open data called ArcGIS Hub. Once you locate a dataset you want, you can find the MapServer URL under About>Data Source on the right side of the page.

I normally find them by looking at the HTML source code of a MapServer I already know about.

For this example I’ll use one of the GIS layers in the Cook County, Illinois, election service MapServer – here’s the layer for the Cook County commissioners districts.

Fetch the data

Once you have the URL the command is simple:

esri2geojson http://cookviewer1.cookcountyil.gov/ArcGIS/rest/services/cookElectnSrvc/MapServer/11 cookcounty_commissioners.geojson

  • The first term, esri2geojson tells your computer which program to load.
  • The second term is the URL of the MapServer URL.
  • The third term is the filename and location where you want to store the file. I prefer running the command “inside” the folder where I want the file to be stored. You can also specify a full path of the file. On a Mac this would look like ~/Users/username/Documents/GIS/projectname/cookcounty_commissioners.geojson

After you enter the command into your computer’s terminal, press enter. esri2geojson will report back once, after it finds and understands the MapServer URL you gave it. When it’s done, the command will “close” and your computer’s terminal will wait for the next command.

Do you have questions, or need some help? Leave a comment below.

Fun with stats: Building permits by street name and number edition

John Hancock Center

The John Hancock Center. Photo by Kevin Dickert.

 

On which street are the most building permits issued?

Michigan Avenue!

But where on Michigan Avenue are the most building permits issued?

Take a guess!

First, can you answer: Are most building permits issued to North Michigan Avenue (between Madison Street, 0 north/south, and Oak Street, 1000 north), or South Michigan Avenue (between Madison Street, 0 north/south, and um, somewhere south of 130th Street, 13000 south)?

Here’s the answer…

Even though South Michigan Avenue is at least 13x longer than North Michigan Avenue, South Michigan Avenue has 39 percent fewer building permits!

From 2006 to yesterday (Saturday), there were 7,828 building permits issued to projects on North Michigan Avenue and 4,714 building permits issued on South Michigan Avenue.

The most common address on North Michigan Avenue to receive building permits was 875 N Michigan Avenue. It’s also the most common address to receive building permits on all Chicago streets.

What’s there? The John Hancock Center (tower)!

The average building address number on North Michigan Avenue is 540.6. That means that building permits on North Michigan Avenue concentrate around Grand Avenue, which is near the city’s biggest Marriott hotel, and is where the Under Armor flagship store is.

The next most common street – after South Michigan Avenue – is North Clark Street, which extends from Madison Street (0 north/south) to the northern edge of the city at Howard Street, which is 7600 north, about 7.6 times longer than North Michigan Avenue.

S. Clark Street Signs

Businesses in the 400 block of South Clark Street, as of when the photo was taken in November 2008. I believe the hotel is still there. This is the busiest block of South Clark Street, for building permits. Photo by Bruce Laker.

South Clark Street doesn’t register in the top 10 or even the top 100. It comes it at number 162, with 772 building permits. This is surprising to me because South Clark Street runs from Madison Street (0 north/south) in downtown and goes to 2200 south, and has a lot of downtown office buildings.

South LaSalle Street (3,613 building permits), South Wabash Avenue (2,916), and South Dearborn (1,611) are all in the top 50. The data could be wrong somehow.

A map of maps

The map of maps.

Over on my website Chicago Cityscape I’ve assembled a map of maps: There are 20,432 maps in 36 layers. You might say there are 36 maps, and each of those maps has an arbitrary number of boundaries within. I say there are 20,000+ maps because there’s a unique webpage for each of them that can tell you even more information about that map.

This post is to throw out some analysis of these maps, in addition to the simple counts above.

The data comes from the City of Chicago, Cook County, and the U.S. Census Bureau. Some layers have come from bespoke sources, including the entrances of CTA and Metra stations drawn by Yonah Freemark and me for Transit Explorer. The sections of the Chicago River were divided and sliced by the Metropolitan Planning Council. The neighborhood and business organizations layers were drawn by me, by interpreting textual descriptions of the organizations’ boundaries, or by visually copying an organization’s own map.

There are 6,879 unique words longer than 2 characters, in the metadata of this map of maps. The most common word is “annexation”, which makes sense, given that the layer with the most maps shows the 10,668 Cook County annexation actions since 1830 – the first known plat was incorporated in the City of Chicago.

The GeoJSON file, an open source, human readable GIS format, comes out to 30 MB, and it make break your browser when you try to display this layer.

The next group of words are also generic, like “planned” and “development”, related to the Planned Development kind of zoning process in Chicago – called Planned Unit Development in other jurisdictions.

After that, some names of municipalities that traded back and forth between unincorporated Cook County and incorporated municipalities are on the list.

Working down the list, however, it gets really boring and I’m going to stop. I bet if you’re a smarter data science person you can find more interesting patterns in the words, but I’ve also increased the number of generic words (like planned development) by adding these as keywords to each map’s “full text search” index, to ensure that they would respond to a variety of search phrases from users.