Learn about Chicago's most polluting buildings, and what they can do to clean up their act!
Home Page: http://electrifychicago.net
I’m Viktor Köves, a front-end engineer and web accessibility specialist who is passionate about creating user experiences that are intuitive, beautiful, and accessible.
When natural gas and electricty use are zero, the fractions for medians say 1/infinity. It should probably be changed to not show up.
A good hit list would be the top 10 GHG emitters and then the top 10 of each list, focusing on famous buildings that people are likely to be interested in, like museums.
Looking at Marina Towers, it is a bit confusing seeing #4 lowest without it saying what it is in reference to (all buildings noted). We should also note medians among the property type, so that when we say a building is, say, #1 worst in its category, we can see how much worse it is than average.
Note: Surag already has this data pulled into a JSON file
All energy uses are in a standard unit (kBTU) and so we should be able to easily show how much energy a building gets from each source, like so:
It might be worth getting feedback on whether this is useful, but I think it's good to show off if a building has low usage in some areas that's offset by, say, high district heat use.
Some buildings have public data available on their retrofits, like The Rookery here: https://www.chicago.gov/content/dam/city/sites/retrofit-chicago-2/pastparticipants/The%20Rookery.pdf (Electrify Chicago on Rookery)
View all case studies here: https://www.chicago.gov/city/en/sites/retrofit-chicago2/home/participant-achievments/past-participants.html#case-studies
The city data has been updated as of Jan 8th, 2024 with new 2022 data! We're currently on 2021 data, so we should update our data.
Interesting note: It seems IIT buildings no longer reported 🕵🏻♂️
The gas free buildings page is great, but a map would be super helpful!
As the site gets more complex, it's important we validate core behaviors, including:
Provide context on building type for emissions intensity (grocery is usually high for instance). This might look like saying "_x median for this type" or just listing the median of that type.
Loyola is another big Chicago university, so we should find and tag those. Here's a starting list from Rachel at Climate Reality:
Some unflagged IIT buildings:
IIT Research Tower @ 10 West 35th St
Illinois Institute of Technology @ 3300 South Federal Street
Pritzker Science Center @ 3105 S Dearborn St
Some unflagged DePaul buildings:
CDM Center @ 243 S Wabash Ave
Thomas P. Levan Center @ 2322 N Kenmore Ave
McCabe Hall @ 900 W Benden Ave
College of Education Building @ 2247 N Halstead Ave
Peter V. Byrne Hall @ 2219 N Kenmore Ave
Munroe Hall @ 2312 N Clifton Ave
Seton Hall (listed as 2325 N Sheffield on electrifychicago.net, but 2425 in DePaul’s own listing)
Belden-Racine Hall @ 2311 N Racine Ave
Arts & Letters Hall @ 2315 N Kenmore Ave
Arthur J Schmitt Academic Center @ 2320 N Kenmore Ave
Lewis & O’Malley @ 25 E Jackson Blvd
Daley Center @ 14 E Jackson Blvd
990 W Fullerton Ave
John T Richardson Library @ 2350 N Kenmore Ave
1150 W Fullerton
An unflagged Northwestern building:
Chicago Campus 2 @ 251 E Huron St
Some unflagged University of Chicago buildings:
Laird Bell Law Quadrangle @ 1111 E 60th St
Kersten Physics Teaching Center @ 5720 S Ellis Ave
Gleacher Center @ 450 N Cityfront Plz
Social Sciences Research Building @ 1126 E 59th St
Some Loyola University of Chicago buildings, to get us started with a new Loyola large building owner page:
Damen Student Center @ 6511 N Sheridan Rd
Corboy Law Center @ 25 E Pearson St
Norville Center - Gentile Center @ 6526 N Winthrop
Mundelein Center @ 1020 W Sheridan Rd
Halas Recreation Center @ 6458 N Winthrop Ave
Crown Center @ 1001 1025 W Loyola Ave
Blocked by issue #73, which will integrate the full data source.
Buildings like Keating may still be very high emitters, but they also have had emissions (and natural gas use) decrease over time, so to give a full picture it would be good to integrate historical data into a line graph showing the emissions over time.
This would also make it so that if we get, say, 2021 emissions data the old data would still be available and you could see the change YoY.
Since we have lots of fields you might want to see over time, we'd probably want to make the graph interactive, letting the user travel between different visualizations, but defaulting to a really useful one, like GHG intensity.
We should look into how we can use Docker to ensure a consistent development environment for all of our volunteers.
If a building does not report their data, they don't get listed on the site. We should also pull the non reporting buildings (maybe over a certain size?) and list those.
We should output a json file of unique property types here: https://github.com/vkoves/electrify-chicago/blob/main/src/data/dist/property-types.json
Right now you have to manually create the property-types.json by copy pasting some logs so that the FE knows what the property types are. Python should do this automatically.
Since all properties are in Chicago, we could use an average price per therm and do some calculations to show how much we think a property is spending on gas and electric use. This would be useful for activists to be able to pitch to building owners that they are spending way more than other buildings.
We may even be able to make an estimated cost per square foot!
A helpful resource: https://www.bls.gov/regions/midwest/news-release/AverageEnergyPrices_Chicago.htm
Using 2021 data from the source above because it's our latest year data, we could put that in a constant
July 2021 Chicago gas price per Therm: $1.101
July 2021 Chicago electric price per kWh: $0.147
Running a prod build generates a lot of loud errors we should investigate, potentially about generating pages for buildings without emissions.
For certain fields the data type is being interpreted as something undesirable before being converted, changing the data institutionally. For example, this happens with 'Latitude', 'Longitude', which gets truncated as a float type before it gets converted to a string type. This could be happening with other columns as well, but only those two columns are confirmed. We should give the pandas read_csv method fixed data types (at least for the columns we care about and have fixed type information for) to read the data.
The DecarbMyState power plant page does something like this:
That might help people see how big a problem large buildings are, like how Keating is emitting an amount equal to 5,000 cars (24,537 tons of emissions / 4.7 tons per average car).
The original data had ward numbers but from some quick checks it was wrong. We should figure out a way to bring in ward data and make it so:
This would be helpful so alderpeople can contact offending buildings in their ward and use that in things like zoning conversations.
Relates to issue #30
Vien found a few more buildings we can tag, see the attached CSV
Node is on version 22, and before our Docker work, many folks had issues because we use Node 18. Although the dependency isn't an issue anymore, we should make sure we're up to date. Let's see how we can do it, will likely require upgrading Gridsome
Now especially that we are loading in tens of thousands of buildings, we should add pagination to the search page, otherwise you can't even view all buildings of a certain type, like so:
The issue is that Gridsome has a Pager component, but it's mostly intended for static use, while the search page is client-side.
We may also want to add more filtering or order tools, but that can be a separate issue.
See here: https://newbuildings.org/nbi-releases-zero-energy-performance-targets-for-new-construction-projects/
We'll need to map their categories to the city ones and then use the appropriate climate zone (looking at their map, I believe 5A)
This would be useful so folks can explore areas around their home and work and click around to properties. To start this could just be a map with interactive points to view basic details or jump to the details page, but ideally we should color rank each dot to indicate good and bad buildings by GHG intensity.
See PR #52 or other PRs for how to do this, but UIC (unlike others) seems to have a clear building list!
https://fimweb.fim.uic.edu/BuildingsData.aspx
You can then filter by Chicago, but we may want to exclude commercial buildings not in use by UIC right now
Add Google Analytics so we can monitor traffic
The X times the median building line is missing from all buildings (see Crown Hall) making it harder to see how many times worse a building is:
The multiplier (e.g. 90x the median building) should still be in the code but it isn't rendering for some reason.
Let's look up city of Chicago buildings, including police, fire, public housing, etc. Then we can add the property owners into the buildings-custom-info.constant.vue, where you have to:
This PR may be a helpful guide: PR #8
The zipcode has a decimal point instead of being a normal zip code
Calculate the total amount of potential fines based on the number of non-compliant buildings in the data according to this ordinance: https://www.chicago.gov/content/dam/city/progs/env/EnergyBenchmark/SO2017-7060.pdf
Buildings that are non-compliant will show "Not Submitted" in the Reporting Status column of the data. The data in the link shown above should already be filtered.
MSI has no image!
https://electrifychicago.net/building/museum-of-science-and-industry/
https://electrifychicago.net/building/stuart-building
Building ID 256424
For climate folks looking to compare properties, it's really useful to filter by property type, which you can currently search by, but it'll often show other matches.
It would also be good to show more than 50 results (perhaps with pagination) or be able to sort by lowest GHG emissions. Either way, it's currently unclear from the bottom of the search page that results are being truncated after 50.
We want the ability to see the page a building would have had pre-release
The homepage has a poor CLS score due to the main image, we should be able to fix it by adding an explicit width on the img.
When ranking buildings by type, the rankings aren't useful:
This is because different categories are vastly different sizes, like data centers (3 buildings) or K12 (89 buildings). So we should likely do something like:
See PR #68, let's make a GitHub action that runs those tests
For some reason all IIT buildings are mapped to this location by the Dearborn Homes:
We should create a new overrides.json (or corrections.json) that can be used to overwrite fields for buildings in the data pipeline, and fix the IIT building locations manually.
On mobile, the header is getting increasingly cluttered:
We should add a hamburger style mobile menu and make it so that you don't see all those options by default.
This would help us be more present on search engines for buildings that aren't very popular otherwise.
There seems to be a Gridsome plugin that can help: https://gridsome.org/plugins/@gridsome/plugin-sitemap
But we could also do it ourselves
Exciting news, our data source got updated!
We should re-pull the main CSV and update the site with the 2021 data. This is a good opportunity for some data pipeline tuning, since I did a lot of manual work originally, and we should expect annual updates. Some things to do may be:
For the search bar in the header on the main page (where it says "Search property name/address"), it would be nice if it would show suggestions of buildings as you type, similar to how on the Map page you can filter by location and it suggests places/addresses from Google Maps.
Caught by Lighthouse, here's a screenshot from it:
Should be pretty simple, but it might also be worth splitting out the Map page, since it's getting a bit complex. Here's a demo of why this matters:
See sustainability goal to reduce Scope 1 and 2 emissions 50% by 2030, and other resources:
Depends on #89.
Currently we are only feeding the "latest year" (the last year of data that we have, not necessarily the current year) to the frontend of the website. We should track the below fields over time so that we can build further data visualizations (e.g. issue #4).
Fields to track over time:
The main script for this change should be src/data/scripts/clean_and_pare_down_data_current_year.py. The current thinking is we either modify that component to do what we want or, more ideally, we create a new script that outputs to a different file to maintain the stability of current processes.
This should output to a separate CSV file in the dist (e.g. buildings_across_years.csv) so that building list pages and search pages don't need to do a bunch of complex filtering. Also the current ranking UI might break. We'd basically have a latest data set (what we have right now) and the historical data we can query building by building.
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