Here is the video of my talk at the PyData Los Angeles 2019 conference, on the topic of “Tackling Homelessness with Open Data”. link to the presentation in multiple formats.
Here is a link to the presentation in multiple formats.
I’m presenting tomorrow at the PyData Los Angeles 2019 conference, on the topic of “Tackling Homelessness with Open Data”. I hope you can join us there, but if not, here is a link to the presentation in multiple formats.
If you are following along, you may be interested in our many blog posts on homelessness, including the example Colab notebook. You may also be interested in the Homelessness data collection.
Here is a dataset with 4.3M violent crime incidents, from police departments across the country, including San Diego. It isn’t processed well enough to just load into data analysis tools, but it is more detailed than is available anywhere else.
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. ]
After 8 months of hard work form many volunteers and staff, we’re releasing the first version of the Downtown Homelessness Dataset. This dataset has about 41,900 observations of homeless sleepers on 287 blocks in Downtown San Diego, over 62 months.
The dataset is part of our Homelessness Collection. If you’d like to learn how to work on this dataset and the others in the collection, join us for a working session at the next meetup for Data Science for Non Profits, Wednesday 9 October.
The San Diego Data Library is building a detailed data set about Downtown homelessness using 5 years of hand-marked paper maps. The conversion process is human-powered, and we need you help to finish the conversion. When we are finished, we will have a very valuable data for doing detailed analysis of the relationship between unsheltered homeless people and the downtown environment, exploring factors such as restrooms, vacant lots, and the presence of services.
I’ll be presenting at the Data Engineering Meeting tonight, Thursday 5 Sept, on the topic of how the Data Library uses the Metatab data packaging system for processing the data we use in analysis. You can download the presentation.
Use your Computer Vision skills to tackle homelessness.
The Data Library is currently running a data project to convert 5 years of monthly homelessness person locations from scanned paper maps to a geographic dataset. The bulk of the work involves volunteers manually digitizing 350 scanned paper maps in two jobs, but we have enough data to build a computer vision solution.
If you have skills, or motivated interest, in programming OpenCV in Python, you can put your talents to service for humanity, by working on the computer vision portion of this project.
Over the summer we’ve been working on a project to digitize maps of homeless person counts that have been collected over the last 5 years by the Downtown San Diego Partnership. We now have partial results for three neighborhoods, with data positions shown in the map below.
We expect to have the dataset complete in the next few weeks, and after some additional quality assurance, it will be ready for analysis. If you are researcher, part-time analysts, or concerned citizen who wants to use a unique datasets of 5 years of monthly position reports for homeless individuals, let us know.
Over the past few months, the Data Library has been involved in several projects to study homelessness in San Diego. The main projects have been:
Last Spring, we got the first portion of the Downtown homelessness data converted with the help of Kearney High students, and this fall, we’ll be finishing the rest of the data conversion and doing data analysis. If you’d like to participate, you can contact me, or watch for our next project meetup.