I’m a D-Lab GSR and a graduate student in The Goldman School’s Master of Public Policy/The I School’s Graduate Certificate in Applied Data Science. I have 5 years of experience working on data problems in government and nonprofits. I’m interested in social policy, program evaluation, and computational methods. Python is my principal language, but I’ve developed experience using and teaching a variety of other tools, including R, Excel, Tableau, and JavaScript. I deeply enjoy teaching data science methods and am excited to be a part of the D-Lab.
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.
Ella is a PhD student in the Energy and Resources Group. Her research examines water governance in a changing climate, drawing on geo-spatial techniques. Her past work includes applications of topic modelling in climate change adaptation research, and inductive coding of semi-structured interviews.
When working with spatial networks, both ArcGIS and Python packages such as NetworkX and iGraph are very useful tools. In the past, I have used both tools to help me better understand spatial network topology and network flow. In this blog post, I hope to share with you some cool features that these tools have...
The first time that I mapped out poverty statistics at a municipal scale, I was completely mind blown (figure 1). Looking at the spatial inequities from a bird’s-eye view drove my desire to find more granular data of social indicators to better understand intra-urban socioeconomic inequities. Spatial data techniques help us to find patterns and anomalies across data that improves our understanding of people’s lives in cities, raising new questions about urban infrastructure in terms of public goods provision, land-use, and access. However, finding granular socioeconomic...
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...
Hi everyone! I'm one of the D-Lab Data Science Fellows that joined the D-Lab this year. I'm in my second semester of the MCP/MS (City Planning / Transportation Engineering) program. My academic background is actually in Physics, and I've been doing research on radiation detection in urban areas before deciding to come back to school. I hope to bring my physics background and computational skills to the field of urban planning, to better understand and model urban/regional systems using complex systems and computational methods, and to bridge the divide between data science and...
I'm a dual-degree MCP City Planning / MS Transportation Engineering student. My background is in physics, and I'm interested in understanding and modelling urban and regional systems, including their morphology/form, interactions, and fundamental dynamics, using complex systems and computational methods. I'm also interested in the emergence and evolution of social complexity, urbanism, and regional networks of cities.
I’m an interdisciplinary data scientist, with a background in Middle Eastern languages (Hebrew, Arabic, and historical languages like Sumerian, Akkadian, Assyrian and Babylonian). I’ve worked in Syria, Lebanon, Israel, and Turkey with archaeological sites and museums. My technical skills include: translation and data storytelling, data forensics (3D imaging, mapping, modeling), computational linguistics (CTA, NLP, OCR), and network analysis (SNA). My roles on campus include: Research Training Manager of the Computational Social Science Training Program; Postdoc Lecturer...
Proximity analysis is one of the cornerstones of spatial analysis. It refers to the ways in which we use spatial methods to ask "what is happening near here". It is at the heart of the Tobler's first law of geography which I paraphrase as "Everything is related but near things are more related." In practice, proximity analysis...
