Data Viz | lubov mckone

Adjusting Course Toward Equity Lessons Learned in Boston's COVID Housing Recovery Plan

Mon, 12 Apr 2021 15:15:00 +0000

A presentation I prepared for the Spring 2021 National Human Services Data Consortium, detailing how the City of Boston’s Supportive Housing Division identified and corrected inequities in its housing matching algorithm.

You can listen to the recording here and find the slides here

TidyTuesday

Tue, 15 Dec 2020 00:00:00 +0000

An archive of my #TidyTuesday visualizations and code. #TidyTuesday is a weekly data challenge founded in 2018 by Thomas Mock and organized by the R4DS (“R for Data Science”) online learning community. Each week, a new dataset is provided at TidyTuesday for members of the community to practice cleaning, reshaping, and visualizing via the tidyverse collection of R packages.

12.15.2020: American Ninja Warrior (code)

11.24.2020: Washington Hiking Trails (code)

Boston CoC System Exploration App

Tue, 01 Jan 2019 00:00:00 +0000

Open app (Credentials required)

Purpose / Audience

This repo houses a web application that displays core metrics about the Boston Homelessness Continuum of Care that can be filtered across a variety of demographics and subpopulations.

The goal of this web app is to allow and empower the Supportive Housing Division to explore and ask questions of its HMIS data. The app provides answers to broad system performance questions like “is the average length of stay decreasing?” but also allows users to dig deeper and uncover insights from the data, like “White veterans are older and have shorter lengths of stay than veterans of color” (no clue if that’s true - just an example). The baseline measures displayed in the app are an adaption/expansion of HUD’s System Performance Measures. The Supportive Housing Division collaborated to workshop and tailor these measures to be more relevant to the goals of our CoC.

Data

The data used to power this app was pulled from the City of Boston’s HMIS. R scripts cleaned, reshaped, and deidentified HMIS data into key reporting tables, which were then written to a Postgres backend.

The app operates off the following key reporting tables:

Census
Number of individuals in Emergency Shelter / Street Outreach on a given night, by Gender, Race, Ethnicity, Household Status, and Veteran Status

Inflow
Number of individuals experiencing their first-ever stay in an Emergency Shelter / Street Outreach in a given year, by Gender, Race, Ethnicity, Household Status, and Veteran Status

Exits to Permanent Housing
Number of individuals exiting to a permanent housing destination in a given year, by Gender, Race, Ethnicity, Household Status, and Veteran Status

Returns to Shelter
Number of individuals returning to Emergency Shelter or Street Outreach from a prior exit to a permanent housing destination in a given year, by Gender, Race, Ethnicity, Household Status, and Veteran Status

The tables above all have the following structure, with ‘count’ corresponding to the appropriate metric in each table:

year	race	ethnicity	gender	veteranstatus	householdtype	count

Length of Stay
Average length of stay in Emergency Shelter / Street Outreach in a given year, by Gender, Race, Ethnicity, Household Status, and Veteran Status. This table has a slightly different structure so that we can take a weighted average length of stay depending which demographic filters are selected:

year	race	ethnicity	gender	veteranstatus	householdtype	numclients	avglos

Screenshots

The app is secured to protect government information. Screenshots of the app with fictitious data can be found below to see a sample of the app’s appearance:

Built With

Python Flask
Plotly Dash
Hosted on Heroku

Boston Evictions Shiny App

Tue, 01 May 2018 00:00:00 +0000

Purpose and Background

The purpose of this project was to help the Office of Housing Stability gain insight from their eviction data. The main objectives were to help the office understand geographic variation in evictions, the property managers who were evicting the most tenants, and to evaluate the City’s progress on reducing evictions.

Data

The data underlying this application was intially collected by volunteers who transcribed physical records from Boston’s Housing Court into an excel spreadsheet. The data was cleaned and reshaped for visualization in the app. The app includes data on evictions from 2014-2016.

App and Screenshots

Open app (Credentials required)

The app is secured to protect individual-level data. The screenshots below show the appearance and capabilities of the application.

Data Viz | lubov mckone

Adjusting Course Toward Equity Lessons Learned in Boston's COVID Housing Recovery Plan

TidyTuesday

12.15.2020: American Ninja Warrior (code)

11.24.2020: Washington Hiking Trails (code)

Boston CoC System Exploration App

Purpose / Audience

Data

Screenshots

Built With

Boston Evictions Shiny App

Purpose and Background

Data

App and Screenshots

Filters and appearance

Levels of Geographic Visualization

Street-Level Insight

Export the underlying data

Built with