Mapping Big Data by Jared Lander
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Abstract: Maps are one of the best forms of data visualization that readily understood while conveying a considerable amount of information. With the modern web, interactive, pannable, zoomable maps---known as slippy maps---have become the norm. Thanks to packages like {leaflet} it has never been easier to generate these maps. However, they don't scale well out of the box. We'll look at different methods for dealing with large data to make high performance maps.
Bio: Jared P. Lander is Chief Data Scientist of Lander Analytics, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and a bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. Jared oversees the long-term direction of the company and acts as Lead Data Scientist, researching the best strategy, models and algorithms for modern data needs. This is in addition to his client-facing consulting and training. He specializes in data management, multilevel models, machine learning, generalized linear models, data management, visualization and statistical computing. He is the author of R for Everyone, the best-selling book about R Programming geared toward Data Scientists and Non-Statisticians alike. The book is available from Amazon, Barnes & Noble and InformIT. The material is drawn from the classes he teaches at Columbia and is incorporated into his corporate training. Very active in the data community, Jared is a frequent speaker at conferences, universities and meetups around the world. His writings on statistics can be found at jaredlander.com.
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Presented at the 2023 Government & Public Sector R Conference (October 19, 2023)
