Adventures in Big Data Analysis
Using Astronomical Methods to Understand Urban Housing Complexities in Baltimore
Advances in open data, technology, and geospatial analysis have opened up new possibilities for cross-disciplinary research that blends mathematics and social science. One examples involves applying methods developed for analyzing extremely large astronomical data sets to urban datasets to develop better understanding of housing patterns and building abandonment, with the goal of crafting better policies to prevent urban decay and encourage productive utilization of existing housing stock.
As it happens, remarkably little is known about the dynamics of housing divestment and reinvestment. Fundamental questions have not been answered, such as: When and why are particular properties abandoned? Are there early markers of abandonment that can be identified - before a property begins to deteriorate? If a property is renovated, stabilized, or demolished, how will it effect quality of life and property values in the affected area?
We applied methods originally developed for astronomy to build a unique database that can "ingest" spatial and temporal urban data sets, and effortlessly fuse disparate records on the fly, so that can easily be interrogated collectively for exploratory and hypothesis testing studies. Building on a growing collection of datasets and tools, we are analyzing the spatial distribution of vacant buildings in Baltimore, and we are applying machine learning methods to predict abandonments. We are preparing for a massive combinatorial optimization to find city-wide global strategies that can be translated into practical design scenarios to prevent abandonment and promote productive utilization of urban building stocks. Our project is just one example of how big data tools developed in one discipline often find applications in a diversity of other fields.
About the Speaker
Tamas Budavari is an assistant professor in the Department of Applied Mathematics & Statistics of the Whiting School of Engineering at the Johns Hopkins University. He focuses on the mathematical and computational challenges of big data.
Tamas is the Builder of the Sloan Digital Sky Survey and of its data solution, "SkyServer". He also created Hubble Space Catalog, a searchable collection of all Hubble Space Telescope observations.
Among other awards Tamas, is the recipient of Statistics and Applied Mathematical Sciences Institute Gordon and Betty Moore Fellowship.
He earned an MS in Physics and a PhD in Astrophysics from Eotvos Lorand University, Budapest, Hungary.