Embark on a whirlwind tour of #geospatial analysis concepts and techniques. John Fay (Nicholas School of the Environment, Duke University) offers a sweeping overview of #vector data, #raster data, #remotesensing data, and a range of techniques for analysis and #visualization of #spatialdata.

Along the way, Fay highlights the applicability of #geospatial analysis to energy challenges like optimizing solar array placement, identifying energy infrastructure to improve energy access, mapping #poweroutages, biogas pipeline planning, and expanding a network of electric vehicle charging stations.

He also identifies tools and resources for further exploration.

1️⃣ 0:00 - 𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚 𝐌𝐨𝐝𝐞𝐥
0:04 - Introduction to spatial data frame and vector data model
3:27 - Geometries and georeferencing
4:58 - Spatial analyses: geometric calculations, spatial joins, spatial selection, geometric
modification, overlay analysis
10:52 - Advanced spatial analyses: linear referencing, spatial interpolation,
spatial statistics, and data exploration
19:28 - Data visualization example: Texas power outages
2️⃣ 21:12 𝐑𝐚𝐬𝐭𝐞𝐫 𝐃𝐚𝐭𝐚 𝐌𝐨𝐝𝐞𝐥
22:04 - Introduction to raster data model
22:35 - Raster data and georeferencing
23:28 - Map algebra (including both local, focal, and zonal operations)
25:20 - Elevation-based analysis of surfaces and hydrology
28:19 - Distance analysis
3️⃣ 30:16 - 𝐑𝐞𝐦𝐨𝐭𝐞 𝐒𝐞𝐧𝐬𝐢𝐧𝐠 𝐃𝐚𝐭𝐚
30:44 - Introduction and structure of remote sensing data
32:02 - Ocular inspection, standard algorithms, continuous change detection,
pixel-based classification, object-based classification
4️⃣ 37:07 - 𝐖𝐫𝐚𝐩-𝐮𝐩, 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞, 𝐚𝐧𝐝 𝐝𝐚𝐭𝐚 𝐬𝐞𝐭𝐬


Part of the Foundations for Energy Data Analytics Series. Some videos in the series introduce (like this one!) data science concepts and techniques that are applicable to energy challenges. Others are designed to provide background on energy systems and policy, elucidating contexts that are rich with opportunities for data scientists. (   • Foundations for Energy Data Analytics  )

This talk was originally presented during a workshop of the Energy Data Analytics Ph.D. Student Fellows Program (https://energy.duke.edu/energy-data-a..., organized by the Energy Data Analytics Lab (https://energy.duke.edu/research/ener...) at Duke University. The Fellows Program is funded by a grant from the Alfred P. Sloan Foundation, Grant-G2020-13922. (https://sloan.org)

(Note: Conclusions reached or positions taken by researchers or other grantees represent the views of the grantees themselves and not those of the Alfred P. Sloan Foundation or its trustees, officers, or staff).

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