October 2022
Beginner to intermediate
279 pages
6h 51m
English
Content preview from Python for Geospatial Data Analysis
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Chapter 4. Geospatial Analytics in the Cloud: Google Earth Engine and Other Tools
How do you access geospatial data? Although data professionals with enterprise accounts may not think about the limitations of personal computing and relying on open source data, the rest of us often work within limits. Geospatial analysis in the cloud has narrowed the divide, since that means we no longer need to store large volumes of data locally. Never before has the general public had open source access to geospatial data on such a global scale. This chapter will show you where to find data for exploration and learning.
Space programs in the US and around the world have gathered data from satellites and sensors for decades, but only recently have we had the capacity to manipulate that data in real time for analysis. The USGS hosts EarthExplorer (Landsat), and the Copernicus Open Access Hub provides data from European Space Agency (ESA) Sentinel satellites. Landsat high-resolution satellite images enable us to evaluate and measure environmental change, understand the impact of climate science and agricultural practices, and respond to natural disasters across time and space, to name a few examples. The advent of free satellite images has enabled decision makers from economically challenged areas across the world to bring insights into view and focus on solutions.
Spatial analysis includes methods and tools applied to location data, in which the results vary based on the location or frame analyzing ...
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