Spatial Data and GIS - Vector Data

File Formats Exercise

Complete these exercises to practice the skills you learned in the file formats chapters.

last updated: 26 Jun 2020

Spatial Data Formats for Earth Data Science

Two of the major spatial data formats used in earth data science are vector and raster data. Learn about these two common spatial data formats for earth data science workflows in this chapter.

last updated: 26 Jun 2020

Customize your Maps in Python using Matplotlib: GIS in Python

When making maps, you often want to create legends, customize colors, adjust zoom levels, or even make interactive maps. Learn how to customize maps created using vector data in Python with matplotlib, geopandas, and folium.

last updated: 30 Jan 2020

How to Dissolve Polygons Using Geopandas: GIS in Python

When you dissolve polygons, you remove the interior boundaries of a set of polygons with the same attribute value and create one new merged or combined polygon for each attribute value. Learn how to dissolve polygons in Python using GeoPandas.

last updated: 04 Apr 2020

GIS in Python: Reproject Vector Data.

Often when spatial data do not line up properly on a plot, it is because they are in different coordinate reference systems (CRS). Learn how to reproject a vector dataset to a different CRS in Python using the to_crs() function from GeoPandas.

last updated: 04 Apr 2020

GIS in Python: Reproject Vector Data.

Often when spatial data do not line up properly on a plot, it is because they are in different coordinate reference systems (CRS). Learn how to reproject a vector dataset to a different CRS in Python using the to_crs() function from GeoPandas.

last updated: 07 Apr 2020

Customize Map Extents in Python: GIS in Python

When making maps, sometimes you want to zoom in to a specific area in your map. Learn how to adjust the x and y limits of your matplotlib and geopandas map to change the spatial extent that is displayed.

last updated: 30 Jan 2020

Plot Spatial Raster Data in Python.

When plotting rasters, you often want to overlay two rasters, add a legend, or make the raster interactive. Learn how to make a map of raster data that has these attributes using Python.

last updated: 25 Jun 2020

Extract Raster Values Using Vector Boundaries in R

This lesson reviews how to extract pixels from a raster dataset using a vector boundary. You can use the extracted pixels to calculate mean and max tree height for a study area (in this case a field site where tree heights were measured on the ground. Finally you will compare tree heights derived from lidar data compared to tree height measured by humans on the ground.

last updated: 03 Sep 2019

GIS in R: Plot Spatial Data and Create Custom Legends in R

In this lesson you break down the steps required to create a custom legend for spatial data in R. You learn about creating unique symbols per category, customizing colors and placing your legend outside of the plot using the xpd argument combined with x,y placement and margin settings.

last updated: 03 Sep 2019

GIS With R: Projected vs Geographic Coordinate Reference Systems

Geographic coordinate reference systems are often used to make maps of the world. Projected coordinate reference systems are use to optimize spatial analysis for a region. Learn about WGS84 and UTM Coordinate Reference Systems as used in R.

last updated: 13 Mar 2020

Coordinate Reference System and Spatial Projection

Coordinate reference systems are used to convert locations on the earth which is round, to a two dimensional (flat) map. Learn about the differences between coordinate reference systems.

last updated: 03 Sep 2019