Data Visualization

Addison Pickrell

IUSE Undergraduate Advisory Board
Mathematics
Sociology

Addison is an aspiring mathematician and social scientist (Class of '27). He loves collecting books he'll never read, is an open-source and open-access advocate, and an aspiring community organizer and systems disrupter. Ask me about community-based participatory action research (CBPAR), critical pedagogy, applied mathematics, and social science.

Tracking Urban Expansion Through Satellite Imagery

December 12, 2023
Leïla Njee Bugha
by Leïla Njee Bugha. Among its many uses, remote sensing can prove especially useful to document changes and trends from eras or settings, where traditional sources are either inexistent or infrequently collected. This is the case when one wants to study urban expansion in sub-Saharan countries over the past 20 years. To further remedy the lack of data on land cover uses from earlier time periods, classification methods can be used as well. Using easily accessible satellite imagery from Google Earth Engine, I provide here an example combining remote sensing with classification to detect changes in the land cover in Nigeria since 2000 due to urban expansion.

Exploratory Data Analysis in Social Science Research

November 14, 2023
Kamya Yadav
by Kamya Yadav. Causal inference has become the dominant endeavor for many political scientists, often at the expense of good research questions and theory building. Returning to descriptive inference – the process of describing the world as it exists – can help formulate research questions worth asking and theory that is grounded in reality. Exploratory data analysis is one method of conducting descriptive inference. It can help social science researchers find empirical patterns and puzzles that motivate their research questions, test correlations between variables, and engage with the existing literature on a topic. In this blog post, I walk through results from exploratory data analysis I conducted for my dissertation project on political ambition of women.

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.

Introduction to Item Response Theory

October 24, 2023
Mingfeng Xue
by Mingfeng Xue. Measurements (e.g., tests, surveys, questionnaires) are inevitably involved with various sources of errors. Among many psychometric theories, item response theory stands out for its capability of detailed analyses at the item level and its potential to reduce some of the measurement errors. This post first discussed the limitations of conventional summation and average, which give rise to the IRT models, and then introduced a basic form of the Rasch model, including expressions of the model, the assumptions underlying it, some of its advantages, and software packages. Some codes are also provided.

María Martín López

Data Science Fellow
Psychology

María Martín López is a PhD student in the Cognition area within the Department of Psychology. Her research relates to cognitive computational and quantitative models of individual differences in behaviors, thoughts, and emotions. She is particularly interested in how we can create and leverage novel algorithms to understand, measure, and predict processes relating to externalizing psychopathology (e.g. impulsivity, aggression, substance use). She answers these questions using a range of computational and quantitive models including AI, NLP, SEM, time series analysis, multi-level...

Using Forest Plots to Report Regression Estimates: A Useful Data Visualization Technique

October 17, 2023
Sharon Green
by Sharon Green. Regression models help us understand relationships between two or more variables. In many cases, results are summarized in tables that present coefficients, standard errors, and p-values. Reading these can be a slog. Figures such as forest plots can help us communicate results more effectively and may lead to a better understanding of the data. This blog post is a tutorial on two different approaches to creating high-quality and reproducible forest plots, one using ggplot2 and one using the forestplot package.

Suraj Nair

Data Science Fellow
School of Information

I am a PhD Student at the School of Information. My research interests lie at the intersection of development economics and machine learning, with a focus on the use of large scale digital data and new computational tools to study pressing issues in global development.

Emma Turtelboom

Data Science Fellow
Astronomy

I am a PhD student in the Astronomy department, and I study planets outside our own solar system. I'm interested in learning how the properties of host stars affect planetary systems. In my free time, I love swimming, hiking, reading, and baking.

Melike Sümertaş

Data Science Fellow
History

I hold a PhD in History from Boğaziçi University, Istanbul and B.A and M.A degrees from Middle East Technical University in Ankara, Department of Architecture, and Program in Architectural History. My research focuses on the urban/architectural/visual culture of the late Ottoman Empire and its capital city Istanbul, with a particular interest in the Greek-Orthodox community. My current project in the History Department of UC Berkeley under the umbrella of the Istanpolis collaboration led by Prof. Christine Philliou, focuses on utilizing digital humanities tools for urban/...