This is a project of the Data Science Working Group at Code for San Francisco
The original ask, or how this project got started.
Paula Chiu is our contact as SF Gov (she's part of our Slack group too)
These DSWG members are contributors to this project, and how to get in touch with us on slack:
|Earl Dos Santos||@earldossantos|
|Juan Carlos Collins||@juancarlos|
Working Plan/Current Priorities
- Create a data model that can span several quarters, adjusting for the name mismatch (@brgoggin, @jfquinn, @arashaghevli)
- Analyze how long projects take for completion. Determine what relationship between project size and completion time is. (@brgoggin, @juancarlos)
- Come up with detailed UI design (@caressalc27, @alwynalau)—no longer working on this as of 10/13/2017
Questions we want to answer
The main question(s) we want to tackle with an interactive visualization would be the following.
- How long it takes a project to go from start to end.
- Does it depend on neighborhood? Size of project?
- What factors depend on this?
- What status of the projects take longer
- Is there a status of the project where it's common for projects to get cancelled?
- At some point in time in the lifecycle of a project, the # of units are defined? This number changes towards the end (usually decreases).Typically speaking how many units do we lose over the course of a project?
- What factors tend to lead to this?
- How many projects are being built per neighborhood?
Keep in mind we want to look at this at the Neighborhood and Zoning district level not at a individual project level.
Other Questions (From previous meeting):
- How many units are being built per neighborhood per time period?
- how many of those are affordable?
- How many projects are being built per neighbood
- How much space designated as "light industrial" is being gained/lost per neighboorhood?
- Projects approved and filed over time:
- what happens to the planning process per neighborhood
- when were projects filed/approved/started/completed?
- Size of project vs speed of getting on market?
- A way to gauge compliance with Nov 2016's Measure X
How do I access the data?
See data/README.md for information about analyzing the data. The data is checked into the repository under
data/cleaned, and you should not need to download it yourself for most purposes.
See data/README.MD for details about the data
|[2009-Quarter-2]||[Internal to Planning Department]|
|[2009-Quarter-3]||[Internal to Planning Department]|
|[2010-Quarter-1]||[Internal to Planning Department]|
|[2010-Quarter-2]||[Internal to Planning Department]|
|[2010-Quarter-3]||[Internal to Planning Department]|
|[2010-Quarter-4]||[Internal to Planning Department]|
|[2011-Quarter-1]||[Internal to Planning Department]|
|[2011-Quarter-2]||[Internal to Planning Department]|
|[2011-Quarter-3]||[Internal to Planning Department]|
|[2011-Quarter-4]||[Internal to Planning Department]|
|[2012-Quarter-3]||[Internal to Planning Department]|
Annual Housing Inventory Reports
Affordable Housing Reports
Useful Term Dictionary
Entitlement Status: 0 = Under Planning Review, -1 = Approved By Planning
APN: Assessor Parcel Number (blocklot, blklot)
MIPS: Managerial, Information, Professional Services. (Same as Office)
CIE: Cultural, Institutional, Educational
PDR: Production, Distribution, Repair
Setting up Python Environment
First make sure you have python3 and virtualenv installed.
Run this command to make a virtualenv:
virtualenv --python=$(which python3) VE
Run this command to enter the virtualenv:
Then run this command to install the dependencies:
brew install gdal --HEAD pip install -r requirements.txt