Environmental Data Analysis and Visualization

Mapping Out the Story

Visualization Critique

mekkographics.com

Visualization Critique

wired.com

Next week’s critiques

  • Ned

  • Mariana

Dataset of the Day

FEMA National Risk Index

https://hazards.fema.gov/nri/

Effectively communicating with visuals

  • What’s the headline?

  • Drawing the eye

  • Conveying the mood

What’s the headline?

What are the most pressing points you want to make with your graph?

statista.com

statista.com

What’s the headline?

Krzywinski, Martin, and Alberto Cairo. 2013. “Storytelling.” Nature Methods 10 (8): 687–687. https://doi.org/10.1038/nmeth.2571.

What’s the headline?

Krzywinski, Martin, and Alberto Cairo. 2013. “Storytelling.” Nature Methods 10 (8): 687–687. https://doi.org/10.1038/nmeth.2571

What’s the headline?

Krzywinski, Martin, and Alberto Cairo. 2013. “Storytelling.” Nature Methods 10 (8): 687–687. https://doi.org/10.1038/nmeth.2571

What’s the headline?

Krzywinski, Martin, and Alberto Cairo. 2013. “Storytelling.” Nature Methods 10 (8): 687–687. https://doi.org/10.1038/nmeth.2571

Catching the eye

It’s easy for the headline to get lost in a data graph. It’s OK to help the audience find it.

https://www.rashdesign.com/blog/2021/9/24/stevewexler

Drawing the eye to the headline

Knaflic, Cole Nussbaumer. 2019. Storytelling with Data. New York: Wiley.

Drawing the eye to the headline

Knaflic, Cole Nussbaumer. 2019. Storytelling with Data. New York: Wiley.

Conveying the mood

buffalo.edu

buffalo.edu

Conveying the mood

Lisa Collier/Leeds College of Art

Lisa Collier/Leeds College of Art

Maps as storytelling devices

Example 1

Example 2

Example 3

Spatial data

Spatial data is data related to a location

Spatial data

Spatial data is data related to a location

Location

  • X position (longitude, easting, etc.)

  • Y position (latitude, northing, etc.)

  • Z position (elevation [optional])

Attributes (literally anything else)

  • Name

  • ID number

  • Species

  • Weight

  • Color

  • Condition

  • Recording method

Spatial data models

Vector

Raster

The sf package

The simple features or sf package gives R functions for handling vector data and interfacing it with the tidyverse.

library(tidyverse)
library(sf)

The sf package

vernalPools<-st_read("data/GISDATA_CVP_PTPOINT.shp",quiet=TRUE)

vernalPools<-st_zm(vernalPools,drop = TRUE, what = "ZM")

vernalPools
Simple feature collection with 7881 features and 3 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 34452.43 ymin: 781370.8 xmax: 329087.3 ymax: 958316.4
Projected CRS: NAD83 / Massachusetts Mainland
First 10 features:
   cvp_num                       criteria  certified                  geometry
1     5098 Obligate Species, Fairy Shrimp 2009-05-31 POINT (101876.8 878024.9)
2     4385               Obligate Species 2007-01-08 POINT (124514.6 895968.7)
3     7707               Obligate Species 2016-12-29 POINT (253914.4 830115.9)
4     7627               Obligate Species 2015-08-06 POINT (119130.7 877295.4)
5     1826               Obligate Species 1999-07-15 POINT (188430.5 937917.1)
6     4622               Obligate Species 2007-10-17   POINT (213979.5 863452)
7     6711               Obligate Species 2012-06-11 POINT (271080.4 935932.4)
8     1306               Obligate Species 1997-09-18   POINT (103664 872042.3)
9     6997               Obligate Species 2013-02-27 POINT (238222.5 927072.7)
10    3441               Obligate Species 2004-06-17 POINT (199376.9 865967.6)

The sf package

ggplot() + 
  geom_sf(data = vernalPools,aes(color=year(certified)),size=0.5) +
  theme_minimal()

This week

Working with vector data in R

Making data spatial

Making maps with ggplot2