Eric Busboom.
A map of the density of On-Sale alcohol permits, the permits required for serving alcohol at a bar or restaurant.
One of the recent datasets in our data repository is for alcohol permit from the California Alcoholic Beverage Control Board. This analysis presents a map of the tracts with the higest density of On-Sale permits, in permits per square kilometer. On-sale permits are for alcohol sales that will be consumed on premises, primarily bars and restaurants
In preparation for the Data Science for Non Profits meeting tomorrow night (Wednesday, 9 Oct), I’ve created a Google Colab notebook demonstrating how to access the homeless sleeper dataset and compare the distribution of homeless sleepers to crime, pedestrians and businesses. The main output is this chart ( click to expand).
[ To run this notebook, click on the “Open In Playground Mode”, or make a copy of it to your account. ]
Eric Busboom.
San Diego’s CityIQ system consists of about 4,400 smart street lamps, which use a variety of sensors to track parking, pedestrians, traffic and a variety of environmental conditions. This analysis reveals pedestrian paterns using time-oriented heatmaps.
This dataset aggregates the number of pedestrians, traveling in either direction, into 15 minute intervals, per walkway, and links the walkways to communities. Then, data is vizualized by community in a heat map with two time dimensions, known as a rhythm map. Since this data is only available for the prior 6 months, we can’t analyze long term patterns, so we only present a few types of maps, including traffic for hour of day versus day of week and for hour of day versus week of year.
I’m in training this afternoon for using the API for San Diego’s CityIQ Smart street lamps. These street lamps have cameras and computers that can detect and report:
I’ll be working on extracting some of this data into an analysis package, so let me know if you’d like to work on a data project using the data.
For its data program for Summer 2018, The San Diego Regional Data Library proposes to build a data repository for water quality data, to make it easier to find water quality data, link it together and to create maps, reports and analyses. The project will be run with a small paid staff and a group of volunteers, and if the project is successful, we will pursue funding for continued maintenance. This project is over, but see theproject website for archived results. Data we expect to collect, process and store will include:
link Disadvantaged Student Performance For Summer of 2017, The San Diego Regional Data Library will be running a data internship program, working on pro-bono data analysis projects for nonprofits, governments and journalists in San Diego County. If your organization has questions that can be answered with data, you can have an undergraduate data analyst work on your project, with professional guidance, for up to 12 weeks. Project sponsors must be able to describe their needs as a set of questions that can be answered with available data, and must be able to meet with the interns at their site for 1 hour per week. Projects must have a social goal or benefit. We’re currently running two projects, and expect to be running three or four more this summer. If your organization needs data analysis this summer, please contact the Data Library Director, Eric Busboom at (619) 363-2607 or eric@sandiegodata.org.
We completed our 2015 Data Contest with final presentations and winners at the awards ceremony on Tuesday. Here are the winners and their presentations:
We also have two Honorable Mentions:
We completed the 2015 SDSU Data Contest on Saturday, with a fantastic collection of excellent submissions. The Judges are reviewing them now, but until you learn the winners at the Awards Ceremony on Tuesday, you can see all of the submissions here. Everyone is welcome at the Awards Ceremony, so follow the link to register.
Probably the most common statistic that people deal with is the average, which can often be a good approximation of the typical or general case. However, there are many cases where the average fails, and the most extreme example I’ve seen in recent data is lawyers’ salary distributions. The NALP has been publishing salary distributions for a few years, and the blog Social Evolution Forum provides a good overview of the distribution. Since 2000 or so, the distribution is extraordinarily bi-modal, making the average, as well as the median, a poor statistic to represent the typical case. 
In these situations, the average is meaningless, and it is better to report the two ( or three ) modes. The Blog Empirical Legal Studies has more of the charts and a discussion about the system that created them.
Along with SDSU and Teradata, the San Diego Regional Data Library is running a data analysis contest for San Diego area High School and College students. The contest starts February 28. Top prize is $1200. For complete details and to register, see the contest announcement page.