You may be familiar with principles for good data visualization when it comes to ordinary bar plots, scatterplots, and line plots. However, geospatial data visualization has its own set of principles for effective and honest communication.
The answer to what is the “best” style of geospatial data visualization often depends on the type of data at hand and what you hope to communicate as a storyteller. Therefore, it is important to understand the fundamental differences among your thematic mapping options.
Accordingly, this blog compares and contrasts four of the most common types of thematic maps: choropleth, dot density, proportional symbols, and 3D choropleth. To emphasize the strengths of each option, I have visualized the same data set through each thematic mapping style, noting what kinds of stories each has to tell.
Note that I use the term “thematic map” only to distinguish it from a general reference map. Rather than navigation, the purpose of a thematic map is to visualize some kind of data in geographic space. In this context, I am discussing thematic maps that place colors, circles, dots, or polygons on top of a reference map.
Read More: Recently, Atlan has published several resources on geospatial data visualization.
- Vasavi Ayalasomayajula highlighted seven techniques to visualize geospatial data.
- I looked at how the R packages
leafletcould be used to create an interactive choropleth.
- Atlan also published a free Introduction to GIS in R course, which included lengthy lessons on creating static, animated and interactive maps.
About the Data Set
The data set used in this blog covers the three latest decades of Indian census figures on district-level household access to electricity and latrines.
In addition to being easy to understand, I find this data set instructive for three reasons. First, the population density is highly uneven, which is common in geospatial data of human settlements. Second, it has information that we want to communicate in a raw count in some cases, but also as a standardized rate in others. The tension between visualizing a raw count versus a standardized rate highlights the fundamental differences among these thematic mapping options.
Lastly, on top of teaching about data visualization principles, this data set holds stories that are valuable in their own right. At a fine-grain level, and cross-tabulated by key social identities, this data set tracks thirty years of progress (and lack thereof, in some cases) for hundreds of millions of people towards access to two fundamental amenities needed for healthy, productive lives. The data gives rich context for stories of public health, economic growth, competitive federalism, and societal inequality, to name a few.
Read More: For more information on the the data set, please see my earlier Atlan blog.
About the Software
Once again, I’ll use Shiny to quickly compare each type of thematic map using the same data set. Instead of
leaflet however, I’ll use the
mapdeck library from David Cooley. Mapdeck is a relatively new R library under active development, which makes it easy to plot interactive maps using Mapbox GL and Deck.gl.
Note: Shiny is an R package for interactive web apps. See RStudio’s documentation to get started.
The Shiny app in these examples can be found here.
Note: The code for wrangling the data and creating the visualization can be found in this blog’s GitHub repository. See the folder “shiny_thematic_mapping” for the code creating the visualization. See the
dots.R script for how I created the data behind the dot density map. See the
3d_choropleth.R script for how I created the data behind the other maps.
- A choropleth is a well-understood style for spatially mapping a standardized rate or ratio (such as a percentage or a density).
- It is a poor choice to visualize count data because color doesn’t communicate the magnitude of differences well.
The choropleth is likely the most common thematic map. This style colors enumeration units (such as districts in India, in this case) according to the value of some quantity.
In the example below, we can see how the percentage of households with access to electricity sharply varies by district with respect to time, societal cross-section, or demographic cross-section.
Many parts of India, particularly South India and Gujarat, become more yellow as more households have gained access to electricity over time. At the same time, other regions, such as parts of Uttar Pradesh and Bihar, have remained stubbornly blue and purple, representing very low levels of electricity access.
It is important to note that here I am using a choropleth to map a rate or a ratio as opposed to a raw count. A percentage is one example of a standardized rate. Our data must fall in the range of 0 to 100.
Standardized data is very different from raw count data. The number of households with electricity in a district is an example of a raw count. It has not been manipulated or transformed in any way. Converting this raw count to a percentage standardizes it.
R packages (such as
mapdeck) will let you create a choropleth with raw count data, but it is usually a bad idea. Depending on the range of your data, color is often a bad way to communicate the magnitude of differences amongst enumeration units. Percentages are a better way to show these differences and quickly give insight into a number of important questions. (My previous blog post explored some of these questions in greater detail.)
However, a choropleth with percentage data conceals the vastly different populations within individual enumeration units. Geographically large enumeration units draw more attention than smaller units that may in fact hold more people. This is a big problem for Indian districts, where geographically-small metropolitan districts (such as Bangalore) have very large populations compared to larger but more sparsely-populated, primarily rural districts (such as Leh).
Read More: This is the same dilemma encountered in the common red-and-blue maps of U.S. election results. This article from Issie Lapowsky in Wired dives into a wide number of mapping techniques such as dasymetric dot density maps, value-by-alpha maps, and 3D prisms for interpreting the 2016 US presidential election.
When we look at the sea of colors in the choropleth above, we have to remember that India’s population density, like elsewhere in the world, is hardly uniform. Although the choropleth effectively shows relative differences in a standardized rate, it can’t clearly represent the magnitude of differences in raw counts. How might we address this challenge?
Dot Density Maps
- Unlike a choropleth, a dot density map can spatially visualize clusters of raw counts.
- It sacrifices the ability to retrieve numeric data, and the final outcome is highly subject to the choice of dot value and dot size.
A dot density map is one option that can address this problem. Unlike a choropleth, a dot density map is often an excellent choice to spatially visualize raw count data, because it randomly assigns a certain number of dots within each enumeration unit according to a value. This makes it very easy to ascertain where values cluster geographically.
Typically, dot density maps are more complicated to create in R than choropleths. The
dots.R script I wrote draws heavily on functions from a blog by Andreas Beger. His functions are modifications of the
sf::st_sample() function. However, the recent addition of an “exact” argument to the
sf::st_sample() function should make this process much simpler in the future.
Read More: In addition to Beger’s blog cited above, Paul Campbell has an excellent post on creating dot density maps in R, as does Tarak. However, the newly-added “exact” argument to
sf::st_sample() should simplify the code in these examples.
Our Shiny app includes an example of a “one-to-many” dot density map, where each dot represents 25,000 households. (Note that thus far, we always manipulated the data in some way. In the choropleth, I converted raw counts to a percentage. In the dot density map, I divided raw counts by a chosen dot value, in this case 25,000.)
Unlike a choropleth, a dot density map let me depict raw population growth and where it clusters over time. Compared to the choropleth, the dot density map more accurately represents populations in dense, urban centers compared to more sparsely-populated rural areas.
The map below compares households in India with access to both electricity and latrines versus those with neither of the two amenities. The first case shows a large concentration of dots in India’s largest urban centers – places like Delhi, Bangalore, Mumbai, Kolkata — and the state of Kerala. Toggling to depict households with access to neither electricity nor a latrine, however, shows a major shift to Uttar Pradesh and Bihar.
Examining the same data through the choropleth highlights the stark changes in colors, but the dot density map can better represent the raw differences in these populations.
Dot density maps also have the unique ability to map multivariate data. For example, we can plot both urban and rural populations at the same time using different colored dots. This may be their most valuable advantage compared to other geospatial visualizations.
With each dot representing 25,000 households, the map below shows that the overwhelming majority of 2011 households in India without access to electricity or a latrine were rural.
Dot Density Map Weaknesses
Of course the dot density map has its own failings. Perhaps most importantly, we can’t get numeric data from the map. Although it is possible to examine clusters of populations for any given parameter, calculating exactly how many people are in any given category is usually not possible.
By choosing to map population counts, we lose insight into the percentages. Do most households in a certain district have access to electricity? Using the choropleth, we can easily match a district’s color to the percentage given in the legend. By contrast, the dot density map is a poor choice for answering this kind of question.
Another downside is that the final appearance of the dot density map can depends on two subjective factors: dot value and dot size. What value should a single dot represent? 25,000 households or 50,000 households? We may have some methods for deciding this, but no definitive answer. Secondly, how large (either in pixels or a unit like meters, depending on your software) should each dot be? Both questions can have a large impact on the map’s appearance.
Also, there are other more technical considerations. For instance, how do you handle populations falling just below the dot value threshold? If a single dot represents 25,000 households and a district has 24,999 households in the given category, should the map assign a dot? Techniques like stochastic rounding that account for probability are useful in this situation. Compared to a choropleth, the dot density map may be more difficult for an average viewer to interpret.
With all of these concerns in mind, is there a way to map both percentage and raw count?
Proportional Symbols Maps
- Proportional symbols maps give the rare ability to spatially visualize both a standardized rate and a raw count.
- When there are many observations, the symbols often become too congested.
One method for mapping both the percentage and the raw total of a given variable is through a proportional symbols map, informally known as a bubble map. In this type of thematic map, we draw a symbol (usually a circle) from the center of the enumeration unit. We can assign the radius of the circle to reflect the raw total and the color to represent the percentage.
Compared to a choropleth, some of the geographic information of the map has been lost – district shapes are covered by circles. Nevertheless, the proportional symbols map shows enough geospatial information for many use cases. Without needing the exact geographic shapes, the circles can still reveal regional trends.
Read More: Several packages make it easy to create proportional symbols maps in R. See the
tm_bubbles() function of the
tmap package or the
propSymbolsChoroLayer() function of the
In our Shiny app, the colors of the circles communicate the percentage of households having access to electricity or a latrine – a fact I was unable to show in the dot density map. At the same time, the size of the circles reflects the raw count, solving the choropleth’s problem of concealing population totals.
The example below traces India’s household access to latrines from 1991 to 2011 on a proportional symbols map. It tells two stories simultaneously. First, through the increasing size of the circles, we see that, in raw terms, India’s population with access to a latrine has grown considerably since 1991. Second, the colors of the circles communicate a typical value for access to latrines in a particular district.
Note that the dot density map shows the first story, but not the second, whereas the choropleth tells the second story, but not the first.
To see this story for just one district, let’s look at Bangalore. Each decade, the size of the circle representing Bangalore has grown substantially, suggesting its population with access to a latrine has increased, in raw terms. At the same time, the color changes from the 70-80% band, to 80-90%, to 90-100%, suggesting that mean improvements have accompanied the increase in population.
Proportional Symbols Map Weaknesses
The proportional symbols map can flexibly handle many types of data, but a common problem is congestion when the number of enumeration units is high. With 640 districts in 2011, this is certainly a problem for this particular data set. If instead we were perhaps dealing with Indian states or large cities, this option might have been more effective.
When facing congestion, we often need to scale or transform the circles by some factor. Like choosing a dot value in the dot density map, this can also be somewhat arbitrary.
add_scatterplot() requires circle radius to be in meters, so I divided the raw household counts by a factor of 10. This outcome works better for some parameter selections than others.
Another problem is in the interpretation of circle sizes. A two-dimensional quantity such as area can be difficult to interpret accurately compared to a one-dimensional quantity such as length. Sometimes you will find a “graduated” symbols map where symbol size is binned to a few categories to make it easier to match a circle to the size that it represents. In this case, however, a legend for circle size is regrettably absent.
Do we have any other options to depict both a raw count and a percentage geographically?
- A 3D choropleth is a visually-striking option that can communicate a standardized rate, raw count, and geographic shape at the same time.
- It can only be viewed in an interactive setting, and even then, it can be difficult to interpret.
One last option is a 3D choropleth, made possible by the elevation argument of
This visually-striking option manages to map three important quantities. It maps the percentage to color and the raw count to height, while maintaining the geographic shape of each enumeration unit. The choropleth could achieve only the first and third items; the proportional symbols map only the first two.
Below, we can see how the raw population of households with access to a latrine has grown (shown by the rising height of the district shapes), particularly from 2001 to 2011. The traditional choropleth fails to capture this population growth because it cannot communicate raw counts. At the same time, the colors of the district shapes communicate typical values in a way that the dot density map failed to do.
The example belows explores the distribution of India’s 2011 population with access to neither electricity nor a latrine. The low purple areas represent districts with small populations where most households have both electricity and a latrine.
Compared to the dot density map, the third dimension allows us to vividly see the concentration of India’s population without these key amenities in the states of Uttar Pradesh, Bihar, and West Bengal. Although the colors are the same as they would be in a flat choropleth, the height parameter adds an entirely new dimension to the story.
3D Choropleth Weaknesses
Not surprisingly though, the 3D choropleth suffers from a common dilemma for any kind of 3D visualization. It can be difficult to see in its entirety. The view is routinely blocked or obscured by other parts of the visualization. Without being able to rotate and tilt the visualization at will, and sometimes even then, it is very difficult to comprehend the details of the entire map. In contrast, the other thematic mapping options in this blog are perfectly viable as static maps.
mapdeck library and a single source of data, this post has attempted to highlight the strengths and weaknesses of the most common thematic maps, including choropleths, dot density maps, proportional symbols maps, and 3D choropleths.
Although this is far from an exhaustive list of thematic mapping options, hopefully it has introduced the idea of tradeoffs inherent in visualization choices depending on the type of data at hand and the story that you hope to communicate. At the same time, I hope it has helped to unearth some of the most important stories in the history of global development.
Read More: For more resources on geospatial data visualization, be sure to check out some of the links below.
- For more examples on the mechanics of mapmaking in R, please see my contributions in Lessons 3 and 4 of Atlan’s free course, Introduction to GIS in R.
- Axis Maps has an excellent cartography guide covering principles of map design, pertaining to all of the thematic maps covered in this post.
- Claus Wilke’s chapter “Visualizing geospatial data” in his open-source book Fundamentals of Data Visualization introduces the idea of projections and choropleth mapping.
- Kieran Healy’s chapter on maps in his open source book Data Visualization: A practical introduction tests out a wide range of choropleths with a lot of in-text R code.
- Although not strictly geospatial, the gold standard of data storytelling in international development is the late Hans Rosling’s country comparison of life expectancy and income. His animated graphics inspired a number of contributors to recreate his visualizations with a variety of programming languages and tools.
Two types of thematic maps can be used for showing the population distribution of any given area. These are dot distribution maps and choropleth maps. 1) Dot distribution maps - Uses dots to represent pre-calculated values of chosen variable.For which purpose would a thematic map be most appropriate? ›
Thematic maps can be used for exploratory spatial data analysis, confirming hypotheses, synthesizing spatial data by revealing patterns and relationships, and data presentation.Which four main types of thematic data can be distinguished on the basis of their measurement scales? ›
... property layers S-map thematic data are divided into 4 types: correlation properties, base properties, derived properties and interpretations (Table 1).What are six visual resources available for thematic mapping? ›
These types include isoline maps, cartogram maps, choropleth maps, graduated symbol maps, heat maps, dot-density maps, and flow-line definition maps.Which type of map is most accurate? ›
AuthaGraph. The AuthaGraphy projection was created by Japanese architect Hajime Narukawa in 1999. It is considered the most accurate projection in the mapping world for its way of showing relative areas of landmasses and oceans with very little distortion of shapes.What is thematic map very short answer? ›
A thematic map is a type of map that portrays the geographic pattern of a particular subject matter (theme) in a geographic area. This usually involves the use of map symbols to visualize selected properties of geographic features that are not naturally visible, such as temperature, language, or population.What is the most common type of thematic map? ›
Choropleth maps are perhaps the most frequently used thematic maps for portraying statistical data, and therefore benefit from their familiarity across general audiences. A proportional symbol map is a thematic map that scales the size of a point symbol proportional to the represented value.What types of things can thematic maps show give 3 examples? ›
Weather, population density and geology maps are examples of thematic maps.What is a good sample size for thematic analysis? ›
For small projects, 6–10 participants are recommended for interviews, 2–4 for focus groups, 10–50 for participant-generated text and 10–100 for secondary sources. The upper range for large projects is '400+'.How do you analyze qualitative data through thematic analysis? ›
- Step 1: Familiarization. The first step is to get to know our data. ...
- Step 2: Coding. Next up, we need to code the data. ...
- Step 3: Generating themes. ...
- Step 4: Reviewing themes. ...
- Step 5: Defining and naming themes. ...
- Step 6: Writing up.
Step 1: Become familiar with the data, Step 2: Generate initial codes, Step 3: Search for themes, Step 4: Review themes, Step 5: Define themes, Step 6: Write-up. 3.3 Step 1: Become familiar with the data.What are the 4 types of map data? ›
According to the ICSM (Intergovernmental Committee on Surveying and Mapping), there are five different types of maps: General Reference, Topographical, Thematic, Navigation Charts and Cadastral Maps and Plans.What are 3 features of a thematic map? ›
Thematic maps are single-topic maps that focus on specific themes or phenomena, such as population density, rainfall and precipitation levels, vegetation distribution, and poverty.Which symbols are used in thematic map? ›
Common thematic mapping techniques using point symbols are dot maps and proportional symbol maps. On a dot map one dot represents a unit of some phenomena, and dots are placed at locations where the phenomenon is likely to occur (Slocum et al., 2005).How do you draw a thematic map? ›
- On the Create ribbon, click Themes.
- Choose a set from the drop-down menu.
- Choose a field from the drop-down menu.
- Check Use Label check box (optional). ...
- Define the label options: ...
- Check the Show Lines check box (optional). ...
- Check the Color Areas check box (optional).
Competition on the market resulted on the production of increasingly better atlases, including increasingly more thematic maps.Which mapping is best? ›
- Google Maps. The granddaddy of GPS navigation options for almost any type of transportation. ...
- Waze. This app stands apart due to its crowd-sourced traffic information. ...
- MapQuest. ...
- Maps.Me. ...
- Scout GPS. ...
- InRoute Route Planner. ...
- Apple Maps. ...
- MapFactor Navigator.
The accuracy of any map may be tested by comparing the positions of points whose locations or elevations are shown upon it with corresponding positions as determined by surveys of a higher accuracy.How can I make my maps more accurate? ›
- On your Android phone or tablet, open the Settings app .
- Tap Location.
- At the top, switch location on.
- Tap Mode. High accuracy.
Thematic maps are called so because they show features relating to a particular theme or aspect of geography.
Answer: A map which gives focus on specific information is known as thematic map. For example, road maps, maps showing distribution of industries, etc.What is called thematic map class 6? ›
- Maps showing specific information are called thematic maps. - Different themes like road maps, rainfall maps, forest distribution maps, industry maps, etc are examples of thematic maps. - All the naturally occurring resources can be pointed by making thematic maps.Which Colour used in thematic map? ›
Sequential color schemes are the most popular color schemes used in thematic mapping, as they are excellent for demonstrating the order of data values.What are the 5 themes of a map? ›
Geographers study the processes that cause changes like these. To help you understand how geographers think about the world, consider geography's five themes—location, place, region, movement, and human-environment interaction.How many types of thematic map are there? ›
5 Popular Thematic Map Types and Techniques for Spatial Data.Is 10 participants enough for qualitative research? ›
It has previously been recommended that qualitative studies require a minimum sample size of at least 12 to reach data saturation (Clarke & Braun, 2013; Fugard & Potts, 2014; Guest, Bunce, & Johnson, 2006) Therefore, a sample of 13 was deemed sufficient for the qualitative analysis and scale of this study.Is 50 participants enough for qualitative research? ›
Dworkin (2012) points out that most authors suggest sample sizes of 5 to 50. This leaves a lot of room for error and does not, in advance, propose a reasonable estimate. He also reminds us that in qualitative research of the “grounded theory” type, having 25 to 30 participants is a minimum to reach saturation.Is a sample size of 300 enough? ›
As a general rule, sample sizes of 200 to 300 respondents provide an acceptable margin of error and fall before the point of diminishing returns.What are the 2 types of thematic analysis? ›
An inductive approach involves allowing the data to determine your themes. A deductive approach involves coming to the data with some preconceived themes you expect to find reflected there, based on theory or existing knowledge.What makes a good thematic analysis? ›
Themes should be identified from the data, not your research questions. Don't fall into the trap of structuring your data according to research questions. This will lead to your themes just being representation of your research questions. Make sure you're actively finding patterns and meaning from your data.
Thematic analysis is one of its key features. It makes it easy to manually analyze text, tag specific parts of feedback with themes and then organize these themes.What is thematic analysis example? ›
Example of Thematic Analysis
An interview transcript. A researcher will have to go through the entire transcript and look for meaningful patterns in themes across the data. The patterns can be analysed by repetitive data reading, data coding, and theme creation.
- Read the transcripts. ...
- Annotate the transcripts. ...
- Conceptualize the data. ...
- Segment the data. ...
- Analyze the segments. ...
- Write the results.
- Physical Maps.
- Topographic Maps.
- Political Maps.
- Weather Maps.
- Economic Maps.
- Resource Maps.
- Population Maps.
- World Maps.
Cartographers make many different types of maps, which can be divided into two broad categories: general reference maps and thematic maps.What are the four 4 types of thematic maps select four answers? ›
What are the 4 types of thematic maps? Four of the most common types of thematic maps are choropleth maps, dot maps, proportional symbols maps, and flow maps, though there are many other ways to display statistics on maps.What is the main purpose of a thematic map? ›
The primary purpose of a thematic map is to visually portray a non-visual phenomenon, usually the attributes of geographic features (e.g., the median income of a county). A good thematic map clearly shows geographic patterns that mirror patterns in the real-world phenomenon.What are the 4 main features of a map? ›
Border – where the edges of the map are. Orientation – what direction is north (up, down etc). Legend – shows what the symbols mean. Title – describes the map.What are the 4 types of map symbols? ›
The four most popular thematic map types are choropleth, isopleth, proportional symbol, and dot maps.What are the 3 types of map symbols? ›
- Point Symbols= buildings, dipping tanks, trigonometrical beacons.
- Line Symbols= railways, roads, power lines, telephone lines.
- Area Symbols=cultivation, orchards and vineyards, pans.
Population distributions are commonly displayed using choropleth maps of decennial census data. Choropleth maps aggregates population data with administrative units (census tracts or block groups) whose boundaries do not always reflect the natural distribution of human populations.Which type of map is distribution of population? ›
2 Choropleth maps: The distribution of population in different areas can be shown by different shades on a map. This is called a choropleth map.What kind of map should be used to show population? ›
Dot map is a map type that uses a dot symbol to show the presence of a feature or a phenomenon. The population of a region, the distribution of cattle, etc. are usually shown through dot maps.What type of map is used for population? ›
Population maps are thematic maps used to track the number of people of different groups in an area. These maps can show the number of people in an area by using a shaded scale with lighter colors representing less populated areas and darker colors for more densely populated areas.Is a population map a thematic map? ›
Weather, population density and geology maps are examples of thematic maps. Two very different thematic maps on the same topic – Australia's Maritime Boundaries. They illustrate the principle that maps are made for a specific reason, and this dictates the amount of detail they contain.What are the 3 methods of map classification? ›
Maps are generally classified into one of three categories: (1) general purpose, (2) thematic, and (3) cartometric maps.Which method is used to distribution of map? ›
The method by which the distribution of any attribute is shown with the help of lines of equal values, in a map are called isopleth method. For these maps we need the accurate data of a particular attribute such as altitude, temperature, rainfall, etc of some place of a particular region.What map is used for density mapping? ›
One common map type for this is a density map, also called a heatmap. Tableau creates density maps by grouping overlaying marks and color-coding them based on the number of marks in the group. Density maps help you identify locations with greater or fewer numbers of data points.What does a thematic map look like? ›
Thematic maps are single-topic maps that focus on specific themes or phenomena, such as population density, rainfall and precipitation levels, vegetation distribution, and poverty. This differs from reference maps which include a number of different elements like roads, topography, and political boundaries.How do you make a thematic map? ›
- On the Create ribbon, click Themes.
- Choose a set from the drop-down menu.
- Choose a field from the drop-down menu.
- Check Use Label check box (optional). ...
- Define the label options: ...
- Check the Show Lines check box (optional). ...
- Check the Color Areas check box (optional).
Thematic maps cover a wide variety of mapping solutions, and include choropleth, proportional symbol, isoline, dot density, dasymetric, and flow maps as well as cartograms, among others.What is the importance of thematic map? ›
The primary purpose of a thematic map is to visually portray a non-visual phenomenon, usually the attributes of geographic features (e.g., the median income of a county). A good thematic map clearly shows geographic patterns that mirror patterns in the real-world phenomenon.