Data for Housing

Data for Housing topic

Exploring Rental Affordability in the San Francisco Bay Area Neighborhoods with R

November 5, 2024
Taesoo Song
by Taesoo Song. Many American cities continue to face severe rental burdens. However, we rarely examine rental affordability through the lens of quantitative data. In this blog post, I demonstrate how to download and visualize rental affordability data for the San Francisco Bay Area using R packages like `tidycensus` and `sf`. This exercise shows that mapping census data can be a straightforward and powerful way to understand the spatial patterns of housing dynamics and can offer valuable insights for research, policy, and advocacy.

Using Big Data for Development Economics

March 18, 2024
Leïla Njee Bugha
by Leïla Njee Bugha. The proliferation of new sources of data emerging from 20th and 21st century technologies such as social media, internet, and mobile phones offers new opportunities for development economics research. Where such research was limited or impeded by existing data gaps or limited statistical capacity, big data can be used as a stopgap and help accurately quantify economic activity and inform policymaking in many different fields of research. Reduced cost and improved reliability are some key benefits of using big data for development economics, but as with all research designs, it requires thoughtful consideration of potential risks and harms.

Mapping Census Data with tidycensus

November 6, 2023
Alex Ramiller
by Alex Ramiller. The U.S. Census Bureau provides a rich source of publicly available data for a wide variety of research applications. However, the traditional process of downloading these data from the census website is slow, cumbersome, and inefficient. The R package “tidycensus” provides researchers with a tool to overcome these challenges, enabling a streamlined process to quickly downloading numerous datasets directly from the census API (Application Programming Interface). This blog post provides a basic workflow for the use of the tidycensus package, from installing the package and identifying variables to efficiently downloading and mapping census data.

Avery Richards

Senior Data Science Fellow
School of Public Health

Avery is an MPH graduate at the School of Public Health. With a background in literature and behavioral health, his current research focuses on innovations in applied epidemiology, including multidisciplinary approaches to health and social science data. Avery's general interests include public health surveillance, data quality assurance, and geospatial analysis.

Where the Streets Have No Name: Spatial Data in Informal Settlements

February 1, 2022
Marina Blum

In our era, with Google Maps on every smartphone, it may feel like spatial data is easy to come by. However, this is not the case for many communities in the world. In particular, for informal settlements, developed “outside state control over urban design, planning, and construction,” accurate maps can be hard to come by. You may open up Google Maps to find a few streets with no names, or sometimes, nothing at all. Informal settlements are...

Project HOME: Modeling and Mapping Eviction Rates in California

August 18, 2021
Swetha Pola

6 months ago, the D-Lab community made possible a connection between the UC Berkeley School of Information, D-Lab Data Science Fellows, and the Urban Displacement Project (UDP). A summer of brainstorming, collaboration, and multiple Zoom sessions later, the team at Project HOME is excited to present our 5th Year Master of Information and Data...

Handling Missing Data

May 4, 2021
Brooks Jessup, Ph.D.

I recently started working with a set of eviction data for a project on housing precarity at the Urban Displacement Project. As I began exploring the dataset, I was excited to find that it appeared to contain a wealth of historical data we could use to train a robust model for predicting eviction rates in urban neighborhoods. However, my initial excitement soon had to be scaled back when a standard check for missing data revealed that many of the observations lacked values for precisely the variable we aimed to predict. I was now faced with the problem of what to do about this...