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- Welcome to Mapping with R (01_01)
-
Geospatial Data
- Making Maps is Complex (01_02)
- mapview for Quick Maps (01_03)
- sf for Simple Features (01_04)
- Turning Data Frames into sf Objects (01_05)
- Importing Shapefiles (01_06)
- Joining Geospatial Datasets (01_07)
- Disambiguating Country Names (01_08)
- Converting Addresses to Coordinates (01_09)
- U.S.-Specific Datasets (01_10)
- Advice on Finding International Datasets (01_11)
- CRS and Projections: Geographic and Projected CRS (01_12)
- CRS and Projections: How to Choose a CRS (01_13)
- Introducing Raster GIS with raster and stars (01_14)
- Basics of Using the raster Package (01_15)
-
Static Maps
- ggplot2 Essentials (02_01)
- Starting a Map in ggplot2 (02_02)
- Labelling ggplot2 Maps (02_03)
- Compare Locations/Events with Geobubble Charts (02_04)
- Highlight a Region in a Country with ggplot2 (02_05)
- Make a Choropleth Map of Discrete Variables with ggplot2 (02_06)
- Make a Choropleth Map of Continuous Variables with ggplot2 (02_07)
- Faceting Choropleth Maps with ggplot2 (02_08)
- Visualize Raster Data with ggplot2 (02_09)
- Adding Scale Bars and North Arrows with ggplot2 (02_10)
-
Interactive Maps
- What is leaflet? (03_01)
- Starting a Map in leaflet (03_02)
- Necessary HTML for Labelling leaflet Maps (03_03)
- Highlight a Region in a Country with leaflet (03_04)
- Compare Locations/Events with Geobubble Charts in leaflet (03_05)
- Make a Choropleth Map of Discrete Variables with leaflet (03_06)
- Make a Choropleth Map of Continuous Variables with leaflet (03_07)
- Visualize Raster Data with leaflet (03_08)
-
Wrapping Up
- You Did It!
Mapping with R
CRS and Projections: Geographic and Projected CRS (01_12)
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This lesson is called CRS and Projections: Geographic and Projected CRS (01_12), part of the Mapping with R course. This lesson is called CRS and Projections: Geographic and Projected CRS (01_12), part of the Mapping with R course.
Transcript
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Learn More
The biggest thing to take away from this video is:
Don’t use geographic CRS for analysis
If you want to read more about coordinate reference systems, Chapter 7 of Geocomputation with R is a good place to start.
In the video I demonstrate the size of 1 degree of longitude changes dependent on latitude. For a more thorough exploration of this I recommend reading this ThoughtCo article. The mathematics of these calculations is definitely not important for your career as a maker of mapo data visualisations. But, if you’re interested in these things see here for a derivation of the degree length formula
When we used st_buffer() to draw circles around the cities we used this code:
world_cities %>%
top_n(10, pop) %>%
st_buffer(10) The size of the buffer is "10" which equates to 10 degrees in CRS 4326. As we'll see in the next video, CRS 3857 is a good choice for a global projected CRS and if we wanted to draw circles with a radius of 1000km we would use this code:
world_cities %>%
top_n(10, pop) %>%
st_transform(3857) %>%
st_buffer(1000E3) %>%
mapview()
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