Fast Mapping onto Census Blocks
Name
2005.03156.pdf
Description
Accepted version
Size
1.81 MB
Format
Adobe PDF
Checksum (MD5)
bb36c4234912db76d7b1a7f2dffc7052
Author(s)
Kepner, Jeremy
Kipf, Andreas
Engwirda, Darren
Vembar, Navin
Jones, Michael
Milechin, Lauren
Gadepally, Vijay
Hill, Chris
Kraska, Tim
Arcand, William
Date Issued
2020
Journal
2020 IEEE High Performance Extreme Computing Conference, HPEC 2020
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Kepner, Jeremy, Kipf, Andreas, Engwirda, Darren, Vembar, Navin, Jones, Michael et al. 2020. "Fast Mapping onto Census Blocks." 2020 IEEE High Performance Extreme Computing Conference, HPEC 2020.
Version
Author's final manuscript
Abstract
© 2020 IEEE. Pandemic measures such as social distancing and contact tracing can be enhanced by rapidly integrating dynamic location data and demographic data. Projecting billions of longitude and latitude locations onto hundreds of thousands of highly irregular demographic census block polygons is computationally challenging in both research and deployment contexts. This paper describes two approaches labeled 'simple' and 'fast'. The simple approach can be implemented in any scripting language (Matlab/Octave, Python, Julia, R) and is easily integrated and customized to a variety of research goals. This simple approach uses a novel combination of hierarchy, sparse bounding boxes, polygon crossing-number, vectorization, and parallel processing to achieve 100,000,000+ projections per second on 100 servers. The simple approach is compact, does not increase data storage requirements, and is applicable to any country or region. The fast approach exploits the thread, vector, and memory optimizations that are possible using a low-level language (C++) and achieves similar performance on a single server. This paper details these approaches with the goal of enabling the broader community to quickly integrate location and demographic data.
MIT Department
Lincoln Laboratory
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
MIT-IBM Watson AI Lab
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/HPEC43674.2020.9286157
