Data Visualization and Maps I

HES 505 Fall 2024: Session 25

Carolyn Koehn

Objectives

By the end of today you should be able to:

Describe some basic principles of data visualization
Extend principles of data visualization to the development of maps
Distinguish between several common types of spatial data visualization
Understand the relationship between the Grammar of Graphics and ggplot syntax
Describe the various options for customizing ggplots and their syntactic conventions

But first… Scaling

Assignment 9: Scaling the hazard data

hazard.smooth.scl <- (hazard.smooth - mean(incident.cejst.prep$hazard))/sd(incident.cejst.prep$hazard)
#versus
hazard.smooth.scl.nogood <- scale(hazard.smooth)

Assignment 9: Scaling the hazard data

Assignment 9: Different predictions for different scaling

Introduction to Data Visualization

Principles vs. Rules

Lots of examples of good and bad data visualization
What makes a graphic good (or bad)?
Who decides?

Rule: externally compels you, through force, threat or punishment, to do the things someone else has deemed good or right.
Principle: internally motivating because it is a good practice; a general statement describing a philosophy that good rules should satisfy
Rules contribute to the design process, but do not guarantee a satisfactory outcome

“Graphical excellence is the well-designed presentation of interesting data—a matter of substance, of statistics, and of design … [It] consists of complex ideas communicated with clarity, precision, and efficiency. … [It] is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space … [It] is nearly always multivariate … And graphical excellence requires telling the truth about the data.”
— Edward Tufte

Ugly, Wrong, and Bad

Ugly: graphic is clear and informative, but has aesthetic issues
Bad: graphic is unclear, confusing, or decieving
Wrong: the figure is objectively incorrect

Monstrous Costs’ by Nigel Holmes from Healy 2018

Bad and Wrong

Presentation of the data is (intentionally?) decieving
Presentation is just incorrect

Grammar of Graphics (Wilkinson 2005)

Grammar: A set of structural rules that help establish the components of a language
System and structure of language consist of syntax and semantics
Grammar of Graphics: a framework that allows us to concisely describe the components of any graphic
Follows a layered approach by using defined components to build a visualization
ggplot2 is a formal implementation in R

Aesthetics: Mapping Data to Visual Elements

Define the systematic conversion of data into elements of the visualization
Are either categorical or continuous (exclusively)
Examples include x, y, fill, color, and alpha

Scales

Scales map data values to their aesthetics
Must be a one-to-one relationship; each specific data value should map to only one aesthetic

Principles of Data Visualization

Be Honest
Principle of proportional ink
Avoid unnecessary ‘chart junk’
Use color judiciously
Balance data and context

Extending Data Viz to Maps

Telling stories with maps

Maps organize a lot of information in a coherent way
They invite critique and inspection
They are also aesthetic objects that can engage broader audiences

Key Issues

Thinking about projections
Scale of the map
Errors of Omission

Cartographic Principles

Concept before compilation
Hierarchy with harmony (Important things should look important)
Simplicity from sacrifice
Maximum information at minimum cost
Engage emotion to enhance understanding

Map Elements

Scale

Relates map distance to distance on the ground
Ratio scales (1:24,000 or 1/24,000)
Graphic scales
Large vs. small-scale?

Projection

Distortion makes scale invalid across large areas
Distortion increases with distance from standard line
Five distortions: areas, angles, shapes, distances, and direction

Map Symbols

Graphic code for retrieving information
(De-)emphasize (un)important information
Contrast and the role of colors

Generalization

A good map tells a multitude of little white lies: it supresses truth to help the user see what needs to be seen…
— Mark Monmonier

Geometry

Zhilin et al. 2008

Context

Filter out irrelevant details
Two elements: selection and classification
Reflect interpretations of the relative importance of different features

Data Maps

Point Maps

Dot Maps: quantity represented by amount and concentration of dots
Proportional Symbol Map: Geometric symbols scaled in proportion to a quantity

Ebbinghaus’ illusion

Line Maps

From High Country News

Choropleth

Mapping color to geographies
Common problems

From Healy 2019

Cartogram

Adjusts for differences in area, population, etc
Common Problems

From Healy 2019

{ggplot2} is a system for declaratively creating graphics,
based on “The Grammar of Graphics” (Wilkinson, 2005).

You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.

Advantages of {ggplot2}

consistent underlying “grammar of graphics” (Wilkinson 2005)
very flexible, layered plot specification
theme system for polishing plot appearance
lots of additional functionality thanks to extensions
active and helpful community

The Grammar of {ggplot2}

Component	Function	Explanation
Data	`ggplot(data)`	The raw data that you want to visualise.
Aesthetics	`aes()`	Aesthetic mappings between variables and visual properties.
Geometries	`geom_*()`	The geometric shapes representing the data.

The Grammar of {ggplot2}

Component	Function	Explanation
Data	`ggplot(data)`	The raw data that you want to visualise.
Aesthetics	`aes()`	Aesthetic mappings between variables and visual properties.
Geometries	`geom_*()`	The geometric shapes representing the data.
Statistics	`stat_*()`	The statistical transformations applied to the data.
Scales	`scale_*()`	Maps between the data and the aesthetic dimensions.
Coordinate System	`coord_*()`	Maps data into the plane of the data rectangle.
Facets	`facet_*()`	The arrangement of the data into a grid of plots.
Visual Themes	`theme()` and `theme_*()`	The overall visual defaults of a plot.

A Basic ggplot Example

The Data

Bike sharing counts in London, UK, powered by TfL Open Data

covers the years 2015 and 2016
incl. weather data acquired from freemeteo.com
prepared by Hristo Mavrodiev for Kaggle
further modification by myself

Variable	Description	Class
date	Date encoded as `YYYY-MM-DD`	date
day_night	`day` (6:00am–5:59pm) or `night` (6:00pm–5:59am)	character
year	`2015` or `2016`	factor
month	`1` (January) to `12` (December)	factor
season	`winter`, `spring`, `summer`, or `autumn`	factor
count	Sum of reported bikes rented	integer
is_workday	`TRUE` being Monday to Friday and no bank holiday	logical
is_weekend	`TRUE` being Saturday or Sunday	logical
is_holiday	`TRUE` being a bank holiday in the UK	logical
temp	Average air temperature (°C)	double
temp_feel	Average feels like temperature (°C)	double
humidity	Average air humidity (%)	double
wind_speed	Average wind speed (km/h)	double
weather_type	Most common weather type	character

`ggplot2::ggplot()`

Data

ggplot(data = bikes)

Aesthetic Mapping

= link variables to graphical properties

positions (x, y)
colors (color, fill)
shapes (shape, linetype)
size (size)
transparency (alpha)
groupings (group)

Aesthetic Mapping

ggplot(data = bikes) +
  aes(x = temp_feel, y = count)

aesthetics

aes() outside as component

ggplot(data = bikes) +
  aes(x = temp_feel, y = count)

aes() inside, explicit matching

ggplot(data = bikes, mapping = aes(x = temp_feel, y = count))

aes() inside, implicit matching

ggplot(bikes, aes(temp_feel, count))

aes() inside, mixed matching

ggplot(bikes, aes(x = temp_feel, y = count))

Geometries

= interpret aesthetics as graphical representations

points
lines
polygons
text labels
…

Geometries

ggplot(
    bikes,
    aes(x = temp_feel, y = count)
  ) +
  geom_point()

Visual Properties of Layers

ggplot(
    bikes,
    aes(x = temp_feel, y = count)
  ) +
  geom_point(
    color = "#28a87d",
    alpha = .5,
    shape = "X",
    stroke = 1,
    size = 4
  )

Setting vs Mapping of Visual Properties

ggplot(
    bikes,
    aes(x = temp_feel, y = count)
  ) +
  geom_point(
    color = "#28a87d",
    alpha = .5
  )
ggplot(
    bikes,
    aes(x = temp_feel, y = count)
  ) +
  geom_point(
    aes(color = season),
    alpha = .5
  )

Mapping Expressions

ggplot(
    bikes,
    aes(x = temp_feel, y = count)
  ) +
  geom_point(
    aes(color = temp_feel > 20),
    alpha = .5
  )

Mapping Expressions

ggplot(
    bikes,
    aes(x = temp, y = temp_feel)
  ) +
  geom_point(
    aes(color = weather_type == "clear"),
    alpha = .5,
    size = 2
  )

Mapping to Size

ggplot(
    bikes,
    aes(x = temp, y = temp_feel)
  ) +
  geom_point(
    aes(color = weather_type == "clear",
        size = count),
    alpha = .5
  )

Setting a Constant Property

ggplot(
    bikes,
    aes(x = temp, y = temp_feel)
  ) +
  geom_point(
    aes(color = weather_type == "clear",
        size = count),
    shape = 18,
    alpha = .5
  )

Adding More Layers

ggplot(
    bikes,
    aes(x = temp_feel, y = count,
        color = season)
  ) +
  geom_point(
    alpha = .5
  ) +
  geom_smooth(
    method = "lm"
  )

Statistical Layers

`stat_()` and `geom_()`

ggplot(bikes, aes(x = temp_feel, y = count)) +
  stat_smooth(geom = "smooth")

`stat_()` and `geom_()`

ggplot(bikes, aes(x = temp_feel, y = count)) +
  geom_smooth(stat = "smooth")

`stat_()` and `geom_()`

ggplot(bikes, aes(x = season)) +
  stat_count(geom = "bar")
ggplot(bikes, aes(x = season)) +
  geom_bar(stat = "count")

`stat_()` and `geom_()`

ggplot(bikes, aes(x = date, y = temp_feel)) +
  stat_identity(geom = "point")
ggplot(bikes, aes(x = date, y = temp_feel)) +
  geom_point(stat = "identity")

Wrapped Facets

g <-
  ggplot(
    bikes,
    aes(x = temp_feel, y = count,
        color = season)
  ) +
  geom_point(
    alpha = .3,
    guide = "none"
  )
g +
  facet_wrap(
    vars(day_night)
  )

Scales

= translate between variable ranges and property ranges

feels-like temperature ⇄ x
reported bike shares ⇄ y
season ⇄ color
year ⇄ shape
…

Scales

The scale_*() components control the properties of all the aesthetic dimensions mapped to the data.

Consequently, there are scale_*() functions for all aesthetics such as:

positions via scale_x_*() and scale_y_*()
colors via scale_color_*() and scale_fill_*()
sizes via scale_size_*() and scale_radius_*()
shapes via scale_shape_*() and scale_linetype_*()
transparency via scale_alpha_*()

Scales

The scale_*() components control the properties of all the aesthetic dimensions mapped to the data.

The extensions (*) can be filled by e.g.:

continuous(), discrete(), reverse(), log10(), sqrt(), date() for positions
continuous(), discrete(), manual(), gradient(), gradient2(), brewer() for colors
continuous(), discrete(), manual(), ordinal(), area(), date() for sizes
continuous(), discrete(), manual(), ordinal() for shapes
continuous(), discrete(), manual(), ordinal(), date() for transparency

Continuous vs. Discrete in {ggplot2}

Continuous:
quantitative or numerical data

height
weight
age
counts

Discrete:
qualitative or categorical data

species
sex
study sites
age group

Continuous vs. Discrete in {ggplot2}

Continuous:
quantitative or numerical data

height (continuous)
weight (continuous)
age (continuous or discrete)
counts (discrete)

Discrete:
qualitative or categorical data

species (nominal)
sex (nominal)
study site (nominal or ordinal)
age group (ordinal)

Aesthetics + Scales

ggplot(
    bikes,
    aes(x = date, y = count,
        color = season)
  ) +
  geom_point()

Aesthetics + Scales

ggplot(
    bikes,
    aes(x = date, y = count,
        color = season)
  ) +
  geom_point() +
  scale_x_date() +
  scale_y_continuous() +
  scale_color_discrete()

Scales

ggplot(
    bikes,
    aes(x = date, y = count,
        color = season)
  ) +
  geom_point() +
  scale_x_continuous() +
  scale_y_continuous() +
  scale_color_discrete()

Coordinate Systems

= interpret the position aesthetics

linear coordinate systems: preserve the geometrical shapes
- coord_cartesian()
- coord_fixed()
- coord_flip()
non-linear coordinate systems: likely change the geometrical shapes
- coord_polar()
- coord_map() and coord_sf()
- coord_trans()

Data Visualization and Maps I

Objectives

But first… Scaling

Assignment 9: Scaling the hazard data

Assignment 9: Scaling the hazard data

Assignment 9: Different predictions for different scaling

Introduction to Data Visualization

Principles vs. Rules

Ugly, Wrong, and Bad

Bad and Wrong

Grammar of Graphics (Wilkinson 2005)

Aesthetics: Mapping Data to Visual Elements

Scales

Principles of Data Visualization

Extending Data Viz to Maps

Telling stories with maps

Key Issues

Cartographic Principles

Map Elements

Scale

Projection

Map Symbols

Generalization

Geometry

Context

Data Maps

Point Maps

Ebbinghaus’ illusion

Line Maps

Choropleth

Cartogram

Advantages of {ggplot2}

The Grammar of {ggplot2}

The Grammar of {ggplot2}

A Basic ggplot Example

The Data

ggplot2::ggplot()

Data

Aesthetic Mapping

Aesthetic Mapping

aesthetics

Geometries

Geometries

Visual Properties of Layers

Setting vs Mapping of Visual Properties

Mapping Expressions

Mapping Expressions

Mapping to Size

Setting a Constant Property

Adding More Layers

Statistical Layers

`stat_*()` and `geom_*()`

`stat_*()` and `geom_*()`

`stat_*()` and `geom_*()`

`stat_*()` and `geom_*()`

Facets

Facets

Wrapped Facets

Wrapped Facets

Scales

Scales

Scales

Scales

Continuous vs. Discrete in {ggplot2}

Continuous vs. Discrete in {ggplot2}

Aesthetics + Scales

Aesthetics + Scales

Scales

Coordinate Systems

`ggplot2::ggplot()`

`stat_()` and `geom_()`

`stat_()` and `geom_()`

`stat_()` and `geom_()`

`stat_()` and `geom_()`