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Geospatial Data Carpentry Capstone: Glossary

Key Points

Introduction
  • First key point. Brief Answer to questions. (FIXME)

Day 1: Introduction to Geospatial Concepts
  • Raster data is pixelated data where each pixel is associated with a specific location.

  • Raster data always has an extent and a resolution.

  • The extent is the geographical area covered by a raster.

  • The resolution is the area covered by each pixel of a raster.

  • Vector data structures represent specific features on the Earth’s surface along with attributes of those features.

  • Vector objects are either points, lines, or polygons.

Day 2: Introduction to Raster Data
  • Always explore your raster first using the summary() and crs() functions.

  • To plot your raster with ggplot(), first convert your raster to a data frame using the as.data.frame() function and specify the argument xy=TRUE.

  • You may need to reproject your raster, for which you can use the projectRaster() function.

  • There are many plotting and aesthetics options in the ggplot2 package for you to explore.

Day 3: Introduction to Raster Data
  • Always explore your data and note initial geospatial insights.

  • The overlay() function can be used to calculate different summary statistics for your raster.

  • With the overlay() function, you can begin to develop your skills in writing functions.

Day 4: Exploring Vector Data
    • Options for exploring vector files include displaying metadata, and visually examining plots
    • R offers design options for the visual display of vector data, including choosing a color palette, and reversing the order of colors assigned to categories
Day 5: Manipulating Raster Data
  • Convert a data frame to an sf object using the st_as_sf() function.

  • Use the crop() and extract() functions to get raster data of spatial subsets.

  • Remember to check if your data align spatially by looking at the projection, extent, and units

Wrap-up
  • First key point. Brief Answer to questions. (FIXME)

Glossary

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