Set of applications to visualize SF building energy consumption and greenhouse gas emissions.
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README.md

SF-Environment-Benchmark

Set of applications to visualize SF building energy consumption and greenhouse gas emissions.

This project is part of Data Science Working Group at Code for San Francisco

Project Description

The goal of this project is to visualize building energy consumption and greenhouse gas emissions to encourage owners and building managers to make proper changes. From the SF Environment website:

The Existing Commercial Buildings Ordinance requires commercial building owners to track how much energy their building uses each year, and every 5 years to have a professional identify opportunities to save money by saving energy.

More about the Existing Commercial Buildings Ordinance

The dashboard is a way to show building owners and property managers their building's performance in comparison to other buildings in San Francisco of similar use case and size.

Dataset

DataSF

Live links

Contributing DSWG Members / Authors

Slack Channel: #datasci-energyenv

Name Slack Handle Contribution
Anna Kiefer @askiefer Planning/Development
Eric Youngson @eayoungs Planning/Development
Juliana Vislova @juliana Design
Tyler Field @tyler Development
Peter W @peterw Development
Sanat Moningi @sanat Visualization
Geoffrey Pay @gpay Visualization
Baolin Liu Modeling

Built With

Getting Started

Here's how to get started contributing:

Fork this repo, then clone your repo locally

$ git clone <your-repo>
$ cd <this-repo's-name>
$ git remote add upstream <this-repo>

Create a feature branch:

$ git checkout -b <feature-branch>

Do some work:

$ vim <some-files>

When you're ready, commit, merge any upstream changes, deal with conflicts, and push your branch (aka, forking workflow)

$ git commit -am 'my awesome feature'
$ git pull upstream master
  # solve conflicts
$ git push

Create a Pull Request from your pushed branch (compare branch) to this repo (base branch)

Working on the dashboard component:

Install dependencies:

$ cd dashboard
dashboard$ npm install

Develop

Use Webpack to launch a server and watch files for changes:

dashboard$ npm run start

Use Webpack to watch files, but not run a server:

dashboard$ npm run watch

Deploy

Use Webpack to bundle files for production site:

dashboard$ npm run build

Now the files in dashboard/dist/ are all you need to copy to a production server.

Notes

The script pulls live data from DataSF using the function Dashboard.startQuery().

Buildings are identified using Assessor Parcel Numbers (APNs). The requested APN is read from url params with the function helpers.getUrlVars(). The AJAX request to DataSF is made using apiCalls.propertyQuery(), which is a wrapper around the soda-js library.

If the APN doesn't exist, the page gives the error message "The record for the chosen building was not found".

If a requested property is of an unsupported use type (supported building types are "Office", "Hotel", or "Retail Store"), the page displays the message "The chosen building type is not supported by this dashboard interface". Supported building types are defined as keys in the Dashboard.groups object.

If a building doesn't have data for the latest year, the page will show the data for the latest year available.

If a building has never complied, the page will display a message saying "{BUILDING NAME} could not be ranked against other {BUILDING TYPE}s using the latest benchmark data."

The code in src/js/ is roughly split into modules by function:

  • dashboard.js: form query, make request, handle data, update page.
  • apiCalls.js: wrapper around the soda-js library and helper functions for constructing query strings
  • dataManipulation.js: functions to manipulate and parse the query response from DataSF
  • helpers.js: miscellaneous helper functions

javascript files for individual pages (estar, ghg, eui) in src set options and functions that are unique to each page