17-making-beautiful-maps.Rmd

# Making Beautiful Maps {#making-beautiful-maps}

A map _is not_ the territory it represents, but, if correct, it has a _similar structure_ to the territory, which accounts for its usefulness.
—Alfred Korzybski, _Science and Sanity_, 1933


**Making beautiful maps**, _or making effective maps_, is comparable to being able to tell a good story. Just like a good story, a beautiful map comprises certain elements that, put together, frame the narrative in a way that makes it easy for others to understand and interpret. In this way, a map is a communication device. It conveys information through a visual representation of the spatial relationships of the features for a specific area. To better understand maps as particular form of visual communication, it may be helpful to think about what it is that maps actually do.

Maps represent areas larger than we can see, usually from above, and some depict phenomena that cannot be seen (Manson and Matson 2017), such as growing seasons. They also illustrate spatial relationships, such as this map showing the percentage of the population aged 65 and over by census tract in Moncton, New Brunswick. We can deduce that those aged 65 years and over are concentrated in certain areas of the city, using census tracts as a proxy.

![Maps illustrate how variables are related in space. [Percentage of the population aged 65 and over in 2016 by census tract](https://www12.statcan.gc.ca/census-recensement/2016/geo/map-carte/ref/thematic-thematiques/as/files-fichiers/map-carte-5/2016-92173-002-305-013-02-00-eng.pdf
) (c) [Statistics Canada](https://www-statcan-gc-ca.eu1.proxy.openathens.net/eng/about/about?MM=as), [Open Government Licence - Canada](https://open.canada.ca/en/open-government-licence-canada).](images/01-moncton.jpg){.center}

**Cartography** is the art and science of designing maps and consists of certain principles and rules that help ensure accuracy and clarity. All maps are simplified representations of a place and reflect the particular choices and motivations of the cartographer. Cartography is as much about deciding what to omit from a map as it choosing what to include. Making beautiful maps involves the art, and science, of selecting and modifying data and portraying concepts with clarity and precision. Decisions around colour, font size, graphical hierarchies, classification themes, and legends are all elements of maps that determine how effective, and accurate, they are in conveying information. In this chapter, you will learn about what elements go into making an effective map, the different categories of maps, and the principles of map design. 

:::: {.box-content .learning-objectives-content}

::: {.box-title .learning-objectives-top}
#### Learning Objectives {-}
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1. Identify elements of cartographic design
2. Describe different types of maps
3. Describe different types of symbology and classification and their uses

::::

### Key Terms {-}

Map elements, symbology, classification, cartography, north arrow, scale, thematic, choropleth, dot density, reference, isoline

## Types of Maps

There are many different types of maps. Just try doing a Google search, and you will come across descriptions of thematic, cadastral, topographic, and physiographic maps, to name but a few. There is no standard agreement on how many different categories of maps exist. Although some differentiate among as many as five  types of maps based on the functions they serve (ICSM 2021), all maps can also be categorized into just two types, **reference** and **thematic**. Reference maps represent the human-made or natural features of the landscape and are sometimes thought of as basemaps. They provide information about a particular location, such as in the example below, depicting the longest rivers in the location of Canada and where they overlap with the United States. Topographic maps based on the [National Topographic System (NTS)](https://www.nrcan.gc.ca/earth-sciences/geography/topographic-information/maps/national-topographic-system-maps/9767){target="_blank"} are another example of reference maps.

![One example of a reference map. [Longest Rivers of Canada](https://en.wikipedia.org/wiki/List_of_longest_rivers_of_Canada#/media/File:Longest_Rivers_of_Canada.png
) (c) [Shannon1](https://en.wikipedia.org/wiki/User:Shannon1), [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/).](images/02-longRivers.png){.center}

### Thematic Maps

Thematic maps depict the spatial distribution of particular features, which are symbolized according to the quantitative or qualitative values of their attributes. Thematic maps, as the name implies, emphasize a particular theme, using qualitative or quantitative data. Whereas reference maps show the locations of things, such as the locations of mountains, a thematic map might represent the geology of those mountains. 

Qualitative thematic maps illustrate the spatial extent of categorical data (Anderson 2020), such as the 1956 map bedrock geology map below.

![A thematic map using qualitative categorical data. [Bedrock geology](https://www.davidrumsey.com/luna/servlet/detail/RUMSEY~8~1~323176~90092279:-16--Bedrock-geology-#
) (c) Comtois and Nicholson, [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/).](images/03-geology.jpg){.center}

Quantitative thematic maps depict the spatial patterns of numerical data, often expressed as rates or percentages, as in the map below showing the difference in population change between two periods, where the difference is expressed as a percentage.

![A thematic map depicting population data as a percentage. [Difference in population change](https://www150.statcan.gc.ca/n1/pub/92-195-x/2016001/other-autre/theme/theme-eng.htm
) (c) [Statistics Canada](https://www-statcan-gc-ca.eu1.proxy.openathens.net/eng/about/about?MM=as), [Open Government License - Canada](https://open.canada.ca/en/open-government-licence-canada).](images/04-population.jpg){.center}

### Choropleth Maps

There are many different types of thematic types. A commonly used thematic map is a **choropleth** map, which applies different colours or shading to the entire extent of a predefined aereal unit (Weiss et al. 2008). Data used in choropleth maps must be standardized and represented as rates or ratios rather than raw counts or numbers.

One of the reasons choropleth maps are so widely used is because much of the data with which we're concerned, such as demographic data, is tied to aereal units like census tracts or counties.

The map below represents, using percentages instead of absolute numbers, the spatial distribution of the percentage of Cantonese speakers per total population in each census tract in the city of Vancouver, British Columbia. Household population of mother tongue language speakers by census tract is an example of demographic data collected by Statistics Canada and can serve as a proxy for linguistic diversity. This map shows greater concentration of Cantonese speakers in census tracts in the East Vancouver area.

```{r 05-choropleth, fig.cap = fig_cap, out.width="80%", echo = FALSE}
fig_cap <- paste("A choropleth map representing the 
percentage of Cantonese speakers per total population of census tracts in the City of Vancouver [Household Population by Mother Tongue](https://www12.statcan.gc.ca/census-recensement/2016/ref/dict/pop095-eng.cfm) retrieved from [SimplyAnalytics](https://simplyanalytics.com/).")
knitr::include_graphics(here::here("images", "05-choropleth.png"))
```

This map is also a good example of the potential problems with choropleth maps, which has to do with something called the [Modifiable areal unit problem](https://en.wikipedia.org/wiki/Modifiable_areal_unit_problem){target="_blank"}, or MAUP. This results when data such as population, which is the measurement of population per unit area, is aggregated into an areal unit, where the shape and scale of the unit affects the resulting rates, or values, of the data. In this map example, the areal unit is a census tract, which vary by size and population density across the city and are delineated according to factors other than number of mother tongue language speakers. Without knowing anything about census tracts, we may assume based on this map that an entire census tract contains a disproportionate percentage of Cantoneses speakers, when in reality it may be just a few households more than the census tract next to it. This map fails to convey that kind of nuance and illustrates one of the weaknesses of choropleth maps, in spite of their wide use and applicability, particularly when displaying census data.

### Dot Density Maps

In contrast to choropleth maps, raw data/counts (e.g. number of mother tongue language speakers) as well as rates/ratios (e.g. number of mother tongue language speakers per square kilometer) can be used in **dot density** maps. These maps use a one-to-one density, where one dot represents one count, or a one-to-many density, where one dot represents a number of counts to map the spatial distribution of a certain phenomenon. In the map below, one dot represents 20 speakers, and rather than mapping one language community, we can view the spatial distribution of two language communities to better understand different settlement patterns between two groups of Chinese speakers. 

Also unlike choropleth maps, dot density maps do not need to be tied to enumeration units, although they can be, such as in this map example, which uses another method of mapping the same data that was used in the choropleth map above. For their part, dots are distributed randomly, and a potential misleading aspect of dot density maps is the false interpretation that the dots are placed in precise locations in space.

```{r 06-dotDensity, fig.cap = fig_cap, out.width="80%", echo = FALSE}
fig_cap <- paste("Dot density map showing the distribution of Cantonese and Mandarin mother tongue language speakers by census tract [Household Population by Mother Tongue](https://www12.statcan.gc.ca/census-recensement/2016/ref/dict/pop095-eng.cfm) retrieved from [SimplyAnalytics](https://simplyanalytics.com/).")
knitr::include_graphics(here::here("images", "06-dotDensity.png"))
```

### Isoline Maps

```{r 07-isoline, fig.cap = fig_cap, out.width="80%", echo = FALSE}
fig_cap <- paste("An isoline map representing growing seasons from the _Atlas of Canada_. Isolines are lines which connect points with identical values. ('Growing Seasons', @Norman Leon Nicholson & Paul Comtois, CC BY-NC-SA 3.1) retrieved from [David Rumsey Map Collection](https://www.davidrumsey.com/luna/servlet/s/c68r55).")
knitr::include_graphics(here::here("images", "07-isoline.jpg"))
```

 - 07-isoline.jpg from row 23 of [OER Content Sharing](https://docs.google.com/spreadsheets/d/1LqzXn00wMeIjHWstNT3tMImNDZirLGc3g72jFOQc_8I/edit#gid=817407192){target="_blank"}

### Diagrammatic Maps

_Note: I think I would combine diagrammatic and cartogram subsections._

### Cartograms

```{r 08-cartogram, fig.cap = fig_cap, out.width="80%", echo = FALSE}
fig_cap <- paste("A effective illustration of a cartogram representing the population (%) with no access to clean drinking water. ([@World Mapper](https://worldmapper.org/maps/no-water-access-per-capita/#&gid=1&pid=1), CC BY-NC-SA 4.0).")
knitr::include_graphics(here::here("images", "08-cartogram.png"))
```

- 08-cartogram.png from row 42 of [OER Content Sharing](https://docs.google.com/spreadsheets/d/1LqzXn00wMeIjHWstNT3tMImNDZirLGc3g72jFOQc_8I/edit#gid=817407192){target="_blank"}

_Note: Paul, see example interactive quiz at bottom of this [Introduction to Geomatics](https://openpress.usask.ca/introgeomatics/chapter/thematic-maps/){target="_blank"} OER textbook._

#### Additional Resources on Types of Maps

- [Thematic Maps](https://openpress.usask.ca/introgeomatics/chapter/thematic-maps/){target="_blank"}
- [Choropleth Maps](https://www.axismaps.com/guide/choropleth){target="_blank"}
- [Dot Density Maps](https://www.axismaps.com/guide/dot-density){target="_blank"}
- [Dot density map](http://wiki.gis.com/wiki/index.php/Dot_density_map){target="_blank"}
- [Types of Maps](https://www.e-education.psu.edu/geog486/node/641){target="_blank"}


## Map Composition
Most effective maps comprise standard elements that help orient the map viewer in space and provide helpful contextual information. There is no one way to compose a map, but a good cartographer will arrange and design map elements to provide the greatest clarity. Many maps not only include the phenomenon or object being mapped, called the **figure**, but also the spatial context of the figure, representing where it is in relation to the space around it, called the **ground**. Maps often have a **frame**, which bounds the figure and any other map elements within a predetermined length and width. Some frames are visible to the map reader, while others are not and simply act as a bounding box for the cartographer while designing the map.

### Figure

```{r 09-figure, fig.cap = fig_cap, out.width="80%", echo = FALSE}
fig_cap <- paste("The components of effective map composition.('Map composition.', @Steven Manson, CC BY-NC-SA 4.0) [_Mapping, Society, and Technology_](https://open.lib.umn.edu/mapping/chapter/4-design-and-symbolization/#return-footnote-399-1).")
knitr::include_graphics(here::here("images", "09-figure.jpg"))
```

- 09-figure.jpg from row 32 of [OER Content Sharing](https://docs.google.com/spreadsheets/d/1LqzXn00wMeIjHWstNT3tMImNDZirLGc3g72jFOQc_8I/edit#gid=817407192){target="_blank"}

### Ground

- use 09-figure.jpg
 
### Frame

- use 09-figure.jpg

## Elements of Maps
Whereas the figure is the most prominent element of a map, most maps also contain additional elements that provide important supplementary information, without which the purpose of the map would be less clear. Thinking back to the dot density example from the previous section, it would be difficult to understand what the map was about without a title and a **legend**, which contained labeling and symbols explaining the units in the map. 

Other elements are important for spatial orientation, such as the **scale** and **north arrow**, which provide distance and directional information, particularly critical for navigational maps such as a park trail map.

Just as in writing, it is also important to provide information about the data sources used in creating the map. Not only does this credit the authors of the data, it also provides transparency and the ability to assess the reliability of the data.

### Text

```{r 10-text, fig.cap = fig_cap, out.width="80%", echo = FALSE}
fig_cap <- paste("Typography standards for cartography.('Cartographic style guide.', @Eric Gaba, CC BY 2.5) retrieved from [Wikipedia](https://en.wikipedia.org/wiki/Typography_(cartography)#/media/File:Maps_template-en.svg).")
knitr::include_graphics(here::here("images", "10-text.png"))
```

- 10-text.png from row 43 of [OER Content Sharing](https://docs.google.com/spreadsheets/d/1LqzXn00wMeIjHWstNT3tMImNDZirLGc3g72jFOQc_8I/edit#gid=817407192){target="_blank"}

### Legend

- can use 09-figure.jpg

### Scale and North Arrow

```{r 11-scale, fig.cap = fig_cap, out.width="80%", echo = FALSE}
fig_cap <- paste("Six maps of Ottowa at different scales from the _Atlas of Canada_. ('Comparison of scales', @Norman Leon Nicholson, CC BY-NC-SA 3.1) retrieved from [David Rumsey Map Collection](https://www.davidrumsey.com/luna/servlet/detail/RUMSEY~8~1~323156~90092341:-6--Comparison-of-scales-).")
knitr::include_graphics(here::here("images", "11-scale.jpg"))
```

- 11-scale.jpg from row 21 of [OER Content Sharing](https://docs.google.com/spreadsheets/d/1LqzXn00wMeIjHWstNT3tMImNDZirLGc3g72jFOQc_8I/edit#gid=817407192){target="_blank"}
- image is great example of both concept of scale and scale bars, not of north arrow

### Measured Grid

- see Wikipedia entry referenced in row 41 for two possible images

### Citation

- - can use 09-figure.jpg

## Symbolization
**Symbolization** is the process by which cartographers choose how to represent the features on a map. Unless the map comprises an aerial or satellite image of the surface of the earth, all maps are representations of reality, whereby information is conveyed through the use of symbols.

There are many ways to visually alter symbols - represented by points, lines, or polygons -  to convey information and show both qualitative and quantitative differences between features in the map. These include varying symbols by size, shape, and colour, to name just a few. Using points to map the location of Canada's cities, for example, the cartographer may use different shapes to illustrate differences between cities and towns. To show variations in populations of those cities, the cartographer might vary the size of the symbols.

```{r 12-symbolization, fig.cap = fig_cap, out.width="80%", echo = FALSE}
fig_cap <- paste("The components of symbolization ('Symbolization', @Steven Manson, CC BY-NC-SA 4.0) [_Mapping, Society, and Technology_](https://open.lib.umn.edu/mapping/chapter/4-design-and-symbolization/#return-footnote-399-6).")
knitr::include_graphics(here::here("images", "12-symbolization.png"))
```

### Separable

```{r 13-separable, fig.cap = fig_cap, out.width="80%", echo = FALSE}
fig_cap <- paste("Separable and integral visual combinations delineated by value/shape and heighth/width, respectively. (@Martin E. Elmer) [_Symbol Considerations for Bivariate Thematic Mapping_](https://minds.wisconsin.edu/bitstream/handle/1793/67887/Elmer%20Martin%20E%202012.pdf?sequence=1&isAllowed=y).")
knitr::include_graphics(here::here("images", "13-separable.png"))
```

- 13-separable.png from row 33, page 7 of [OER Content Sharing](https://docs.google.com/spreadsheets/d/1LqzXn00wMeIjHWstNT3tMImNDZirLGc3g72jFOQc_8I/edit#gid=817407192){target="_blank"}

### Integral

- use 13-separable.png for this too

### Graduated

```{r 14-graduated, fig.cap = fig_cap, out.width="80%", echo = FALSE}
fig_cap <- paste("Graduated symbols vary symbol size representing a category of values ('Proportional & Graduated Symbols', @Anthony Robinson, CC BY-NC-SA 4.1) [_Maps and the Geospatial Revolution_](https://www.e-education.psu.edu/maps/l5_p3.html).")
knitr::include_graphics(here::here("images", "14-graduated.png"))
```

- 14-graduated.png from row 45 of [OER Content Sharing](https://docs.google.com/spreadsheets/d/1LqzXn00wMeIjHWstNT3tMImNDZirLGc3g72jFOQc_8I/edit#gid=817407192){target="_blank"}

### Configurable

```{r 15-configurable, fig.cap = fig_cap, out.width="80%", echo = FALSE}
fig_cap <- paste("Separable and integral visual combinations delineated by value/shape and heighth/width, respectively. (@Martin E. Elmer) [_Symbol Considerations for Bivariate Thematic Mapping_](https://minds.wisconsin.edu/bitstream/handle/1793/67887/Elmer%20Martin%20E%202012.pdf?sequence=1&isAllowed=y).")
knitr::include_graphics(here::here("images", "15-configurable.png"))
```

- 15-configurable.png from row 33 of [OER Content Sharing](https://docs.google.com/spreadsheets/d/1LqzXn00wMeIjHWstNT3tMImNDZirLGc3g72jFOQc_8I/edit#gid=817407192){target="_blank"}


### Proportional

```{r 16-proportional, fig.cap = fig_cap, out.width="80%", echo = FALSE}
fig_cap <- paste("Proportional symbols vary size of symbols in relation to their attribute value. ('Proportional & Graduated Symbols', @Anthony Robinson, CC BY-NC-SA 4.1) [_Maps and the Geospatial Revolution_](https://www.e-education.psu.edu/maps/l5_p3.html).")
knitr::include_graphics(here::here("images", "16-proportional.png"))
```

- 16-proportional.png from row 45 of [OER Content Sharing](https://docs.google.com/spreadsheets/d/1LqzXn00wMeIjHWstNT3tMImNDZirLGc3g72jFOQc_8I/edit#gid=817407192){target="_blank"}

### Line Weight

```{r 17-lineWeight, fig.cap = fig_cap, out.width="80%", echo = FALSE}
fig_cap <- paste("Varying the thickness of different lines in the map can help focus attention and differentiate between map features. (['Map Elements and Design Principles'](https://alg.manifoldapp.org/read/introduction-to-cartography/section/b9662d0a-4926-4947-922d-a67a2cae0eec#2), @Uli Ingram, CC-BY-SA-4.0).")
knitr::include_graphics(here::here("images", "17-lineWeight.png"))
```

#### Additional Resources

- [The Graduated Colour Map: A Minefield for Armchair Cartographers](https://gis.blog.ryerson.ca/2020/03/26/the-graduated-colour-map-a-minefield-for-armchair-cartographers/)

## Colour
Cartographers use **colour** in maps to communicate information, to differentiate among features, and to illustrate spatial patterns. Maps are interpreted visually, so cartographic decisions about colour are particularly important.

Colour can also influence the feeling of a map. Some cartographers caution against using red for symbols, for example, because it can convey a sense of alarm. In general, it is a good idea to pay attention to how colours convey intensity.

Choice of colours should also take into account that some people are colourblind, especially when choosing a colour scheme. [Colorbrewer 2.0](https://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3) is an online tool to help cartographers evaluate how easy it is to differentiate among colours in a colour scheme.

Colour is actually comprised of three elements: hue, chroma, and lightness, or value.

### Hue

```{r 18-hue, fig.cap = fig_cap, out.width="80%", echo = FALSE}
fig_cap <- paste("Hues are what we commonly think of as the names of colours and distinguish between qualitative data.('Hue', @Adrienne Fruver, CC BY-NC-SA 3.0) [_Mapping, Society, and Technology_](https://open.lib.umn.edu/mapping/chapter/4-design-and-symbolization/#footnote-399-13).")
knitr::include_graphics(here::here("images", "18-hue.png"))
```

### Chroma

```{r 19-chroma, fig.cap = fig_cap, out.width="80%", echo = FALSE}
fig_cap <- paste("Higher values represent lighter colorus while lower values refer to dark colours.('Chroma', @Steven Manson, CC BY-NC-SA 4.0) [_Mapping, Society, and Technology_](https://www.e-education.psu.edu/geog486/node/606).")
knitr::include_graphics(here::here("images", "19-chroma.png"))
```

### Lightness


```{r 20-lightness, fig.cap = fig_cap, out.width="80%", echo = FALSE}
fig_cap <- paste("Higher values represent lighter colours while lower values refer to darker colours.('Value', @Cary Anderson, CC BY-NC-SA 4.0) [_Geog 486: Cartography and Visualization_](https://www.e-education.psu.edu/geog486/node/606).")
knitr::include_graphics(here::here("images", "20-lightness.png"))
```

### Bivariate Colour Schemes

```{r 21-bivariate, fig.cap = fig_cap, out.width="80%", echo = FALSE}
fig_cap <- paste("Different examples of bivariate legends.('Bivariate', @Axis Maps, CC BY-NC-SA 4.0) [_Cartography Guide_](https://www.axismaps.com/guide/bivariate-choropleth).")
knitr::include_graphics(here::here("images", "21-bivariate.png"))
```

### Colour Pickers

 - maybe just reference ColorBrewer?

#### Additional Resources

- [Colour Theory and Cartography](https://openpress.usask.ca/introgeomatics/part/colour-theory-and-cartography/)

- [Mapping COVID-19: How maps make us feel](https://www.canadiangeographic.ca/article/mapping-covid-19-how-maps-make-us-feel)

- [ColorBrewer 2.0](https://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3)

## Classification Schemes
Data **classification** is the process of aggregating large numbers of observations into categories of values or data ranges. Classification is typically used for choropleth or graduated symbol maps. The purpose of classifying data is to simplify it for easier interpretation by the map reader. Rather than differentiating each value by modifying the symbol hue or size, values are grouped together into discrete classes.

There are many classification schemes which determine how breaks in the data are defined, and it can sometimes be difficult to understand which classification method is best to use or whether to use classification for your data at all. Choosing the best classification scheme depends on your data, what you are trying to communicate, and who is your intended map audience.

### Qualitative

### Sequential

### Intervals

### Quantiles

### Natural Breaks (Jenna)

### Standard Deviation

#### Additional Resources

- [Better Breaks Define Your Map's Purpose](https://www.esri.com/arcgis-blog/products/arcgis-online/mapping/better-breaks-define-your-maps-purpose/)

- [Simplification](https://open.lib.umn.edu/mapping/chapter/5-simplification/)

## Generalization
One of the cartographer's biggest challenges in making a map is deciding what information to exclude. Because maps are a representation of reality that must fit within certain dimensions and communicate clearly to the map audience, the amount of detailed information included must be limited. This process of simplifying detailed information is known as **generalization**.

Cartographers have developed many techniques for eliminating, highlighting, or subduing visual information in maps, some of which have been incorporated into geoprocessing tools commonly used in GIS software. For example, merging two or more polygons together can be performed by running a tool. 

### Select

### Amalgamate

### Exaggerate

### Displace

### Refine

### Simplify

### Aggregate

### Typify

### Smooth

### Enhance

### Collapse

### Merge

#### Additional Resources

-[Cartographic generalization](http://wiki.gis.com/wiki/index.php/Cartographic_generalization)

## Map Design
**Map design** is the process of incorporating cartographic design principles to produce an interesting and effective map. It involves arranging appropriate map elements, such as a title and legend, in a way that is easy for the map audience to interpret, as well as visually foregrounding more important information so that it stands out against less relevant details. Of course good map design is also about making effective choices for symbol colours and situating the map in such a way that the map audience understands its orientation in space.

Good map design effectively integrates map elements with design principles and symbolization to produce a persuasive map (Deluca and Bonsal 2017).

### Subject

### Projection and Orientation

### Hierarchy

- hierarchy.png from row 44 of [OER Content Sharing](https://docs.google.com/spreadsheets/d/1LqzXn00wMeIjHWstNT3tMImNDZirLGc3g72jFOQc_8I/edit#gid=817407192){target="_blank"} for labeling hierarchy
- see [Visual Hierarchy](https://www.axismaps.com/guide/visual-hierarchy) for discussion of and examples of visual hierachy instead

### Balance



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<iframe width=100% height=800px src='https://paulpickell.ca/scenejs-test/' frameBorder="0" >
  <p>Your browser does not support iframes</p>
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#### Case Study {-}
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#### Case study title max of forty characters {#box-text -}

<p id="box-text">Lorem ipsum dolor sit amet, consectetur adipiscing elit. Praesent non magna non nunc auctor eleifend. Etiam fringilla fermentum nisl at volutpat. Duis sodales lorem interdum, posuere neque sollicitudin, malesuada nisl. Sed laoreet commodo dui et cursus. Curabitur vel porta mauris. In lacinia ac ex ac varius. Nullam at vulputate ligula. Nunc sed faucibus urna, eget placerat sapien. Fusce ut lorem a ante congue consequat et sed tortor. Integer neque urna, vehicula at aliquam non, luctus non mi. Pellentesque et massa vehicula, cursus erat id, aliquet sapien. Sed rhoncus vehicula risus, vel aliquam eros porta eget. Etiam maximus, massa in pretium semper, est libero feugiat ipsum, quis tempor nunc libero in nibh. Nunc sollicitudin mattis metus ut rutrum. Donec urna purus, sodales id nibh in, ultricies tincidunt nibh. Proin porta accumsan aliquet.</p>

<p id="box-text">Cras convallis erat ante, ut tristique est tempor elementum. Nam mollis, ipsum at vehicula vestibulum, est magna finibus nisl, et laoreet metus massa et ipsum. Proin eget eros ac odio euismod volutpat et ac diam. Cras viverra ut libero vel pulvinar. Duis nisi magna, sagittis id ligula ac, efficitur commodo eros. Nulla tincidunt id nulla in lobortis. Sed non mi eu mi fermentum cursus. In elit velit, semper sed gravida at, imperdiet sed nibh. Aliquam quis massa malesuada, venenatis nunc sed, malesuada nulla. Vestibulum malesuada purus ut ex ullamcorper, ut blandit lacus lobortis. Curabitur scelerisque velit justo, quis porttitor purus efficitur ut. Phasellus nec arcu vestibulum, consequat ante id, elementum velit. Proin arcu tortor, cursus vitae sem id, congue semper urna. Integer tempus in est eu consequat. Donec sodales, quam vel finibus faucibus, leo ante dictum quam, id tempus ligula dolor quis erat. Curabitur non elementum sem. Mauris placerat fermentum orci non lacinia. Ut imperdiet dui lectus, ac malesuada felis euismod sed. Nulla non volutpat dui, in suscipit turpis.</p>

<p id="box-text">Case studies should have at least one image or map (no more than 2 total) and the written length should be around 300 words (shown above). Any references to external literature should by hyperlinked with the Digitial Object Identifier (DOI) permanent URL and [entered into the bibliography](https://bookdown.org/yihui/bookdown/citations.html){target="_blank"}. Avoid linking to external resources without a DOI and permanent URL. Contact Paul or try using the Leaflet package in R if you want to add an interactive web map.</p>

::::

## Third Section Header

[This is how we link to something. {target="_blank"} ensures that this link opens in a new window rather than navigating away from the textbook.](https://google.com){target="_blank"}

:::: {.box-content .your-turn-content}

::: {.box-title .your-turn-top}
#### Your turn! {-}
:::

<p id="box-text">Ask the reader to undertake some activity or exercise. Have them explore data, an interactive tool, a web map. Avoid referencing, relying on, or using external URLs. If it can be built, coded, or hosted by us, then it should be.</p>

<iframe width=100% height=800px src='http://paulpickell.ca/raster-calculator/low-pass-filter.html' frameBorder="0" >
  <p>Your browser does not support iframes</p>
</iframe>

::::

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut in dolor nibh. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Praesent et augue scelerisque, consectetur lorem eu, auctor lacus. Fusce metus leo, aliquet at velit eu, aliquam vehicula lacus. Donec libero mauris, pharetra sed tristique eu, gravida ac ex. Phasellus quis lectus lacus. Vivamus gravida eu nibh ac malesuada. Integer in libero pellentesque, tincidunt urna sed, feugiat risus. Sed at viverra magna. Sed sed neque sed purus malesuada auctor quis quis massa.

:::: {.box-content .call-out-content}

::: {.box-title .call-out-top}
#### Call out {-}
:::

<p id="box-text">
This is a call out. Put some important concept or fact in here.
</p>

::::

## Summary

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut in dolor nibh. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Praesent et augue scelerisque, consectetur lorem eu, auctor lacus. Fusce metus leo, aliquet at velit eu, aliquam vehicula lacus. Donec libero mauris, pharetra sed tristique eu, gravida ac ex. Phasellus quis lectus lacus. Vivamus gravida eu nibh ac malesuada. Integer in libero pellentesque, tincidunt urna sed, feugiat risus. Sed at viverra magna. Sed sed neque sed purus malesuada auctor quis quis massa.

### Reflection Questions {-}

1. Explain ipsum lorem.
2. Define ipsum lorem.
3. What is the role of ispum lorem?
4. How does ipsum lorem work?

### Practice Questions {-}

2. Given ipsum, solve for lorem.
3. Draw ipsum lorem.

`r if (knitr::is_html_output()) '
## Recommended Readings {-}
'`
Anderson, C. (2020). Types of Maps. Retrieved June 11, 2021, from _GEOG 486: Cartography and Visualization_ website: https://www.e-education.psu.edu/geog486/node/641

Cote, Paul. (2021). GIS Manual: Elements of Cartographic Style. _PbcGIS_. https://www.pbcgis.com/style/.

Deluca, E., & Bonsal, D. (2017). Design and Symbolization. In S. Manson (Ed.), _Mapping, Society, and Technology_. Retrieved from https://open.lib.umn.edu/mapping/chapter/4-design-and-symbolization/

Intergovernmental Committee on Surveying and Mapping (ICSM). 2021. “Types of Maps.” _Overview to the Fundamentals of Mapping_. https://www.icsm.gov.au/education/fundamentals-mapping/types-maps.

Korzybski, A. (1933). _Science and sanity: an introduction to non-Aristotelian systems and general semantics_ (1st ed.). Retrieved from https://openlibrary.org/books/OL24876034M/Science_and_sanity

Manson, S., & Matson, L. (2017). Maps, Society, and Technology. In S. Manson (Ed.), _Mapping, Society, and Technology_. Retrieved from https://open.lib.umn.edu/mapping/chapter/1-maps-society-and-technology/

Thomas, I. (2001). Thematic cartography today: recalls and perspectives. _Cybergeo: European Journal of Geography_, 189. https://doi.org/10.4000/cybergeo.34958

Weiss, C., Cillis, P., & Rothwell, N. (2009). Population Ecumene of Canada: Exploring the Past and Present. _Geography Working Paper Series_, 2008(003).

Ensure all inline citations are properly referenced here.

```{r include=FALSE}
knitr::write_bib(c(
  .packages(), 'bookdown', 'knitr', 'rmarkdown', 'htmlwidgets', 'webshot', 'DT',
  'miniUI', 'tufte', 'servr', 'citr', 'rticles'
), 'packages.bib')
```