Final Project - GIS 3015

The final task for GIS 3015 was to gather data for 2013 college placement exams (a choice of either ACT or SAT) and present it on a map. The map is intended to be an infographic for a fictional newspaper article in The Washington Post. There were two main objectives with this map. First, the map must be visually appealing, use the best possible thematic data representation, and adhere to basic cartographic design principles. Second, the data presented in the map must be done in the best possible way, with the choice being ours as to what (if any) data classification method to use or which thematic map style(s) to display.

Map infographic depicting 2013 ACT Data.

Technical Details

Graduated picture symbols (representing a mortarboard) were used to depict the composite ACT scores, and a choropleth map was used to represent the percentage of students who took the exam. Since the data focus is mainly on the composite ACT scores, those took first priority (and thus the is the first item visible on the map). The graduated symbols were range graded using manual breaks divided into seven classes – I wanted to preserve the idea of the whole number composite scores, since most people think of the scores in that way. The choropleth data was divided into five separate classes using the quartile method. The percentage dataset was evenly distributed (more or less), with the only ‘outlier’ being the eight states that have mandated 100% participation for high school students. Information on the 2013 ACT was added to an excel table which was later joined to the state shapefile – thus allowing me to use the same shapefile in two different ways on my map.

Since the map is intended to be an infographic, I wanted to catch the reader’s eye and give them something to think about in the few seconds that I have their attention. To do this I steered well clear of any tables on the map – my own eyes glaze over when I see that, and it would require a bit more effort on the reader’s part to make sense of my map (not to impugn the good readers of The Washington Post!). To create visual interest I used a picture symbol for the ACT score data, changed the map neatline to an oval, and arranged my map items accordingly. It is hoped that the deviation from a non-square map might be visually appealing. To further enhance the prominence of the choropleth map (as opposed to the surrounding map text) I used a drop shadow on the contiguous U.S. outline, and also for Alaska and Hawaii.

Final Thoughts on the Class

I found the class to be very beneficial overall. One of the (many) reasons that I had entered into this GIS Certificate program was to learn how to create better maps – what better way to do that than to take on the principals of map design? I found it a relief to learn that I was at least on the right track with my pre-GIS Certificate program maps… and to learn ways to improve on them. It was fun (even in the depths of CorelDraw hell) to try my hand at all those crazy thematic maps – and to pick up some very useful graphic arts knowledge along the way. I know that any maps I will make in the future will be 100% better for having taken this course.

Week 12 - Google Earth

This week in GIS3013 we created maps using Google Earth... or more specifically, created tours using Google Earth.  Data was added from ArcMap by converting our dot map density maps and associated data (from Module 10) to KMZ files.  Afterwards we created a video tour of various urban areas in southern Florida.

A typical street scene of downtown Tampa, as depicted by Google Earth.

Some thoughts...

The main objective with this lab was to familiarize ourselves with Google Earth, and become comfortable adding information generated in ArcMap to the Google Earth program. Converting the maps and individual shapefiles was easy enough, although the difference in output within Google Earth was striking. I found it interesting that there are more options to manipulate the data when it's been converted to KML as an individual layer as opposed to having been imported as group of layers associated with a map.

My biggest struggle with this lab was with the video tour... I mean, that should have been so easy, I'm almost ashamed of how difficult I found it to be! I did my best to keep the view going smoothly - not too fast, not many sudden movements - but that turned out to be for naught when nothing played back the way I thought it would.  During all my video takes I had turned off the southern Florida dot density data (reused from Module 10) during the zoom in to downtown Miami, which was the first stop on our list.  However you'd never know it because that data either wouldn't show up at all during subsequent playbacks, or it would stay turned on the entire time.  What gives?

Another minor snafu was with the importation of the dot density data from Module 10... I had suppressed the mini-dramas involving the dot density mask and so it took a while to figure out why those dots weren't showing up the way I had remembered them.  Plus the legend or the labels wouldn't import either, as they had been converted to graphics/annotation.  Since we had an example dot map included with this lab I used that, but I believe that I could have gotten around my issues by importing the mask as an individual shapefile... and also by completely re-doing the legend and labels on my original map.

Overall I do feel more comfortable adding data to Google Earth and using it generally.  It is an internet needy program though - on my slower connection all the downtown scenes looked like warped views of Gotham city, with small items eventually popping up like bugs once the graphics were loaded... which was kind of neat to me, but probably not in the way the designers intended.  To compensate for this I tried to steer my video tour well clear of too many 'detail' views!

Week 12 - 3D Mapping

This week in GIS3015 we explored the world of 3D mapping with ArcScene and Google Earth. Most of our time was spent with ArcScene within the context of an ESRI online training course. The course provided an overview of how to use ArcScene, how to extrude features, set lighting effects, and much, much more. The course also introduced the concepts of terrain datasets and multipatches - both of which are used extensively within ArcScene to model features and provide a 3D-like experience.

Outside of the ESRI course we created 3D images from 2D features - in our lab example it was the buildings of downtown Boston. The buildings were made using created mass points and building roof elevation values. Our final result was exported as a KML file, for viewing in Google Earth.

Gray 3D buildings add dimension to downtown Boston.

Using 3D maps definitely has a 'wow' factor attached to it, so if attracting an audience is your goal then this is the medium to use. Its analytic capabilities are also pretty cool - some of the examples provided in class included showing varying rates of sun exposure on buildings, or views of available space for rent by floor within individual buildings. One use that really popped out at me is how it can show 'below ground' features and enhance the elevation of a dataset with vertical exaggeration effects. To me this would make a pretty neat way to show archaeological excavations in context. Seriously, people spend hours upon hours analyzing this type of stuff - how awesome would it be to see it fully realized in 3D?

There are some drawbacks to using 3D however - first and foremost being the creation of 3D objects. Essentially, the learning curve can be steep (and costly) when it comes to creating the individual elements that make up a 3D map. For realistic looking data one might need to spend a lot of time and money creating it. And 3D is not always the answer for all situations - its very appearance means that the map can only be viewed on a computer. Sure, there are screenshots that can be taken and printed, but why? If you were going to print out the map anyway then why go through all the trouble of creating one in a specialized 3D format? On the whole though I think that for the average data analysis-minded user the benefits outweigh the costs - especially since for basic analysis one does not need fancy graphics, and learning the basics is no more difficult than learning ArcMap itself.

Week 11 - Dot Mapping

This week in GIS4043 we created dot density maps. Dot maps are great for showing the distribution of raw totals of conceptual data, such as for example income distribution or ethnicity within a city. The key here is to keep the enumeration unit small (so census tract areas would be better than a county, which would be better than a state, etc.). The map deliverable for this week shows the population density of southern Florida.

Dot map showing population density of southern Florida.

Technical Details

The dots are meant to be the main focus of the map, so the symbols for my polygon layers (the urban and water areas, and the Florida counties) are in fairly low key pastels. The water and urban area symbols are set to a 45% transparency, and the Florida counties layer is set to 10% transparency. The cities layer isn't transparent, but the symbol is 80% gray as opposed to being solid black. Incidentally the dots showing the population density and the Florida counties come from the same data layer - I just have the symbols separated out into two layers in order to be able to manipulate the map view a bit better.

The dots shown above were generated within ArcMap, under Symbology > Quantities > Dot density. Instead of keeping the default settings I opted to have the dots stay within a fixed location and applied a mask to the data. This mask stipulates that the dots will draw only within an urban area (enter the urban area layer!). Before applying the mask I had messed with the dot size vs. dot value, and eventually settled on a size of 1.87 with each dot representing 25,000 people. I had wanted to keep the dot value within increments of 5,000 and it seemed to me that 25,000 was the best fit while still allowing a reasonable dot size.

Use of the mask was fraught with all kinds of mini-dramas for my poor little laptop. The mask seems to pull on all available computer resources, and when it wasn't getting enough it caused ArcMap to crash! Repeatedly. This became an issue when I wanted to see how a different setting with the mask would look on my map, and also when I wanted to export the image. To get around this I did the following:

• Got over my need to see minor changes to the mask displayed on the whole map. It just wasn't worth it, and I found that those little boxes in the Symbol settings window were there for a reason.
• I decided to drag and drop a new population density layer onto my original map. To do this I re-created the exact original layer settings (even down to the scale) on a blank map.
• I then converted my original legend to graphics, so that I didn't have to mess with making or editing a new legend once I added my fresh population density layer (yes, ArcMap was that touchy).
• Once everything was in place and with two maps open, I then dragged and dropped the new layer onto the old map. After waiting a few minutes for it to draw I then exported it. Several more minutes later I had the .png file that you see above!

At no time during this process did I do anything else on my computer. And once my new population density layer drew correctly, I closed its source map in order to allocate even more computer resources for the map I was trying to export.

Week 9 - Flow Line Mapping

For this lab we returned to CorelDraw, as it is the easier option with which to create flow line maps.

Our goal this week was to create a map showing the flow of immigration to the United States in 2007. We we were given two options with which to present our data - either an overall worldview map showing flow into the United States, or to show disembodied continents surrounding the U.S. (still with rates of flow into the U.S.). Either way, the goal was to effectively place curved flow lines without covering up other features. Other map requirements included a choropleth map of the United States showing percentage of immigration per state, and two special stylistic effects applied to the overall map.

The flow map.

Technical Details

Let's start with the main focus of the map - the flow lines! These were created using a Bezier tool; the colors were then changed to reflect their host region, and a drop shadow effect was applied to make them stand out even further. I will admit that I let the idea of working with the Bezier tool get to me - but once I practiced with it several times I kind of got the hang of it... so much so that I incorporated the Bezier tool for my flow line legend.

The drop shadows were only one of my 'special' style effects... the other big one was the extrude effect on the United States. I had wanted my U.S. choropleth map to stand out a bit more and I ultimately decided on using the extrude function. I say ultimately because I had originally started to apply a drop shadow effect... and it didn't work out. Plus I already had drop shadows for my flow lines, so that probably would have been overkill.

To complete the extrude I had to first draw a quick outline of the United States - there was simply too much going on with my map depiction of the U.S. for the tool to even work, and going line by line trying to figure out which one represented the U.S. boundary was not a realistic option. After applying the extrude function the entire eastern seaboard was covered up by the effect. To mitigate this I placed a copy of the U.S. map in a new layer over the original extruded layer. The U.S. still isn't popping out like I'd hoped it would but I suspect I'm on the right path... one day I'll get it!

Other fairly minor effects were applied to the map: text on a path for the flow line legend, different colors for the continents, a two-toned blue circular color gradient for the map background, letter spacing and different outline color for the the region labels, etc. Basically, this map took a while to make and there are still little things I'd like to do with it... but sometimes you just have to know when to say when!

Week 8 - Isarithmic Mapping

This week's lab involved creating isarithmic maps - basically, the kind of flowing line maps that are usually seen with weather-related data. We were tasked to create two maps - each essentially the same, except one shows precipitation data using continuous tones and the other with hypsometric tints. Both maps needed to employ the hillshade effect, and one of the maps needed to show the display with contour lines. A final twist on both these maps was to employ a certain level of craftsmanship - essentially, to own our maps!

The continuous tone map.

The hypsometric map.

Technical Details

I settled on a landscape view, as I felt that would depict the data the best. An overall light gray background was used so that the really important map elements (the map itself and the legend) could further stand out with a plain white background.

The symbology presentation is what is so different about each map. To create a continuous tone map, one uses a stretched symbology, and for this map I had standardized the labels to the nearest multiple of 5 to show the beginning, center, and end of the range. After a few minutes with the legend properties (specifically, modifying the patch settings so the bar wasn't compact) I was able to get my legend to display correctly. What I couldn't do was get the values and color ramp to flip, so that the bar would read from '5' to '260'. Perhaps that's just not the done thing with these types of maps?


To create the hypsometric map I used a classified scheme. This is very similar to what I had done with the choropleth maps, except this was created using a raster file. The end result was still a color ramp being employed for the symbology. Getting the legend to display properly was a bit tricky - not only did I mess with the patch settings but I also needed to mess with how the text for the values displayed as well. This may have been a bit easier if I had a vertical legend display, but I had already set something of a style precedent with my continuous tone map... and in the end I finally got things to work without having to resort to fiddling with it in CorelDraw.

I kept the contour lines for both maps... to my mind each looked flat without it (bad pun, I know). I suppose I could have gotten away with not having contours for the hypsometric map, but after I had seen it with contours... well, why go back? Oddly enough the default color ArcMap gave me for the contours worked out, although I did some experimenting with just about every other color under the sun before realizing it. Sometimes... well, very infrequently... the default settings are good!

Week 7 - Choropleth and Proportional Symbol Mapping

The focus this week in GIS3015 was on choropleth and proportional/graduated symbol mapping. The overall goal of the lab was to create three choropleth maps using standardized data (in this case population density and demographic percentages). One of the choropleth maps needed to be overlaid with our choice of either proportional or graduated symbols representing a count (for our lab, this was to be wine consumption in liters per capita). We also needed to choose an appropriate classifcation scheme for each dataset.

View of three choropleth maps and one map with graduated symbols.

Technical Details

The map above is more of a 'think-piece' than anything else... quite a bit of time was spent pondering such questions as what color schemes to use? What is the best classification scheme for each dataset? How to account for all those outliers and null values, and what to do about all those invisible micro-countries? And how do I bring this all together visually?

The final product above was one I could live with... of the three data frames, I decided to make the population density/wine consumption map the centerpiece, with the two demographic maps a bit smaller. Linking all of the maps together is my neatline and the sandy/rose background color. Information that pertained to all three maps was purposely placed within the neatline but outside of the other data frames.

For each inset map I carefully considered the classification scheme. Since I had some crazy values for each dataset (and no space whatsoever with which to represent the tiny countries) I put in a separate text box for each map that hopefully explained the situation. It also just worked out that the Natural Breaks (Jenks) scheme represented all of my datasets the best, although in the case of the population density map I used 8 classes instead of the default 5. This choice was made completely due to the range of values I had... it went from 3 to 18,353 people per square kilometer. Due to the extreme range a 5 class system completely glossed over the variation on the low end which comprised the majority of the countries (with values from 3 to 518 people per square kilometer). In my opinion a quartile classification would have also showed the variation, but I wasn't too crazy about the breaks with that scheme, especially since it lumped all my outliers with what I considered to be my 'main' dataset (for example, the last class for the quartile scheme ranged from 233 to 18,353).

Each map has a circular gradient background set to a custom made blue green color ramp... it took me a bit to find, but I'm glad I was able to eventually change the default color gradient settings! For each dataset I wanted a color scheme that not only showed easily visible variations between classes but that also wouldn't clash when compared with the other data frames. In the end I kept a yellow/brown scheme for the main map, and off-set it with berry tones and blue tones for the two smaller maps. I'm not too proud of using gendered colors to represent, well... gender, but both color schemes went together and are culturally easy to interpret.

Other Thoughts...

I spent several days, off and on, working on this particular lab. I found that working on it in smaller pieces was less frustrating than trying to solve all my problems in one sitting (such as pondering what classification scheme(s) to use!).

A bonus option had also been presented with this lab - if we could successfully pull off a picture symbol (for the proportional/graduated symbols), then we'd get some extra points. Well I wasn't successful - not that I mind though. Personally, I'm not a big fan of proportional/graduated symbol maps as it is. I've never liked them, and to my mind the picture symbols are even worse. Since I already have troubles telling symbol size apart on professionally made proportional maps it was probably predestined that I would not have enjoyed whatever picture creation I came up with. Truth be told I'm not a huge fan of the graduated symbols on my map, but at least these work better for me than the pictures!

Week 6 - Data Classification

This week's lab highlighted the differences between various data classification schemes. The classification schemes we compared were Equal Interval, Quantile, Natural Breaks (Jenks), and Standard Deviation.

View of four different data classification schemes for the same dataset.

Technical Notes

The map is a compilation of four different inset maps arranged on a single page... and is very similar to what we created in lab last week. To make my inset maps stand out, I put a data frame around all of the map elements, then set the background to light brown (ArcGIS calls it 'Sand'). Each of the data frame backgrounds were then set to white so that they sort of pop off the page, so to speak.

I relied heavily on the guidelines for this exercise - not just to line up my individual data frames, but to also line up Escambia County, the legends, and each data frame title. Without them I'm pretty sure I wouldn't have been able to place everything with such precision by hand! 

Initially I had used a default color ramp (the Blue Green Bright ramp), but I was not satisfied with the results. So I went to ColorBrewer, and came up with what I think is a fairly decent multi-hue scheme of green. I did keep the default color ramp choice for the Standard Deviation map, but flipped the values so that my legend started at 'less than' and went down the line to 'greater than'.

Other Thoughts...

This week's lab was one of the quicker labs I've had in recent memory, however the concepts behind the map are definitely not something I'd want to rush. I personally think that the Standard Deviation map shows the variation in the data the best, although some good runners up would be either the Natural Breaks or the Equal Interval classification maps. One thing I'm fairly certain of is that the Quantile classification does not represent the data well, and in fact can be very misleading if one doesn't study that legend!

Week 5 - Spatial Statistics

This week's lab took place via ESRI's Virtual Classroom - we completed the 'Exploring Spatial Patterns in Your Data Using ArcGIS' course. We covered QQ Plots, Median and Mean Centers, Voronoi Maps - all kinds of little analysis tools to help determine the nature of your data.

View of the map created during the 'Explore the values of your data' exercise.

Several maps were created during the ESRI course but I decided to share this one. Above is a view of weather stations in western and central Europe (symbolized by the dots). I found the mean and median centers by running the appropriate tool scripts (found in the Statistical Analysis tool box). The purple ellipse represents the directional distribution, which is also a tool found under the Statistical Analysis toolbox.

The mean and median centers are fairly close together, which is a good thing - I don't have any obvious outliers here in terms of spatial extent, although the sheer amount of weather stations in the Alps did pull the median center a bit south. The directional deviation ellipse was set to 1 standard deviation away from the mean center, and it shows that the majority of my weather stations trend east/west within the center of Europe. The ellipse is fairly large, and there are several weather station locations outside of it, which indicates that my data is fairly well spread out.

I found the ESRI course to be quite interesting, once I figured out exactly what was going on with all those tools! I still would like to do a bit more reading up on what each tool does to the data before I start going blindly forth using them - but it is very heartening to know that the tools presented in the course are very easy to use, and it's generally easy to understand their results.

Week 4 - Typography

View of Marathon, FL

This week's lab for GIS 3015 covered typography, hence the text heavy map shown above. Some of the objectives for this week included a map with at least 3 specially created elements or effects, and 17 labels for 17 features, all following proper typographic style rules.

Technical details

The map was created primarily with CorelDraw X7. Certain elements had been added from elsewhere (the inset map from Florida Scenic Highways, the islands from UWF provided data, and the majority of the 'Local Attractions' symbols from ESRI ArcMap) but the rest were created by me. That includes the little sand dollar figures in each of the map corners... yes, I had some fun with Corel!

My approach to this map was to treat it as if it were a general tourist guide map. Ideally, this map would have also had the highway on it, but nothing is perfect. Since the majority of the view is of the ocean, I decided to play up the beach theme - hence the sand dollars in the corners. The corners of my original neat line were scalloped to accommodate the oft mentioned sand dollar figures (created with a series of ellipses and the smoothing tool), and the interior map fill was modified using an elliptical fountain fill. I also managed to get the extrude tool to work this time around, and that was used to modify the star symbols for the cities.

Two text fonts used - the Arial type family for the water features, and the Bell MT type family for everything else. The water features are the only text that I used different colors with, as well as kerning and the text on a path technique. The key features I labelled with a rectangular highlighted background - this may not have been strictly necessary, but it seemed like the text was getting lost out at sea! Hence the highlighted background. The cities were labelled with a bold typeface, and are the second largest size type on the map, outside of the map title. The symbolized attractions were not labelled directly - for those I felt a legend would suffice, and would also help de-clutter the map view.

Coming full circle...

Overall I'd say that this CorelDraw experience was much easier for me than the last time around (see Week 2). I think part of it was that I wasn't trying to directly import an .eps file with various symbols on it. CorelDraw may be a fickle program, but once you get the hang of it it's not so bad!

Week 3 - Cartographic Design

This week in GIS3015 lab we created a map using Gestalt's Principles of map design. Some of the principles I used include contrast between map features, and the creation of a figure-ground effect based on the figure weight and coloration of the map symbols.

Our foundation with which to work was a view of the public schools within Ward 7, Washington D.C. Required map elements include a scaled symbology for the schools within Ward 7, an inset map of the Washington D.C. area, and labels on at least seven neighborhoods within Ward 7.

The map

The finished product - public schools in Ward 7.

For me the central challenge this week was to try and incorporate a sense of balance and the figure-ground effect while working with a funky lopsided central figure (Ward 7!) and several competing map features. In the end I decided to not depict all of provided map layers... for example, there is a layer called "neighborhood clusters" that I chose to show with map labels as opposed to a separate map symbol. I also decided not to show all of the streets within the Washington D.C. area - I felt that by depicting all of the roads within Ward 7 it would better call attention to the prominence of that area in relation to everything else on the map.

Another challenge for this week (and probably for the weeks to come!) involves what could be termed 'intuitive mapping'. The map features need to make sense to the viewer with only minimal reliance on a map legend (this week's rubric specifically stated that only the schools should be on the legend). This meant that I really needed to make Ward 7 stand out without calling attention to it specifically. To achieve this end I used a very light neutral color to represent the ward, and a darker neutral color for the Washington D.C. area. The D.C. color was further muted by setting the transparency for that layer at 60%, otherwise that color would have been dominant. I also used a yellow color gradient for the overall map background to further emphasize the lighter Ward 7 area.

My central figures are the schools, which needed to be depicted by school type. The school types were shown by using varying sizes of the same symbol - I felt that would be visually less complicated than using different symbols/colors for each school type. To make the schools stand out against the neutral background they needed to be depicted in an eye popping color. Happily the symbol I picked came preset to fire engine red!

I noticed that Ward 7 has a northeast/southwest tilt to it - this was great news for me, as I was able to balance out my map by placing the map inset and essential map elements (i.e. legend, etc.) along the exact opposite angle of Ward 7.

...and I'm just now realizing that I've inadvertently used an "X" pattern to balance out my map. "X" always marks the spot, right?

Week 2 - Intro to CartoGraphic Design with CorelDrawX7

The lab assignment

This week's lab for GIS3015 was focused on fine tuning ArcGIS created map products within CorelDrawX7. The purpose of the lab was not only to become more familiar CorelDraw, but to also learn what a graphics software program can do for a final map product.  

Our focus this week was to create a map of Florida for insertion within a children’s encyclopedia. In addition to the basic elements required for every map (e.g. a north arrow), the final maps for this week needed to include the following:

•  At least three state symbols, such as an image of the state animal
•  A unique symbol for the state capital
•  At least two cities labelled on the map, plus a change of the symbol color for the cities
•  At least one graphic style or effect created in CorelDraw, such as a drop shadow
•  A lightly colored background
•  An overall color scheme that is complementary (i.e. not too harsh on the eyes!)      

The final map, showing more than a bit of polish thanks to CorelDraw.

Technical description of my final map product

The map was designed with the idea that an older child is the intended target audience. Working under that theory I tried to keep the map content uniform and light while still being educational. To do this I decided to focus on two sub-themes: the state flower and animals, and major cities within the state.

In keeping with the educational vein I kept my fonts somewhat traditional (a mix of Times New Roman and Cambria), but played around a few of the non-technical map elements such as the title. Nearly all of the map text was created within CorelDraw – the exception was the Legend text which had been imported from ArcGIS. The map title was modified using the text properties docker. I spaced the title text far apart, and I found that I could choose to change only certain words or letters within any given text string – hence the yellow letters for the ‘sunshine’ part of my sub-title.

A drop shadow effect was created for the entire state of Florida. The picture insets were created by using the Power Clip Inside tool with an ellipse. The text around the pictures was then added with the text tool – all were created using the ‘text on a path’ technique that had been covered in the lab. In working with the picture text I found that it was automatically grouped with my image, so everything moved together when I wanted to adjust the position of the images… pretty neat, right? 

Personal thoughts on the lab exercise

I found this lab exercise to be deceptively simple… I’ve worked with both ArcGIS and CorelDraw before, but not necessarily in tandem with the same map, and admittedly my CorelDraw experience had been limited to sprucing up hand drawn sketch maps, not necessarily anything too complicated. There were some initial snafu’s with importing the point symbols from ArcGIS that took me hours to resolve. While working in CorelDraw I found that one really needs to have a workflow plan of action, otherwise things get crazy frustrating (such as realizing that I needed to move and resize the Florida… but I had already locked down my Legend box with the map scale!). To top it all off CorelDraw kept crashing on me – luckily I saved early and often!

In the end I spent a lot of time on this lab – but despite the time setbacks it was worth it. If I hadn’t encountered all the above issues, I wouldn’t have learned half as much about CorelDraw – or about critically thinking about how to best put together map.

Week 1 - Map Critique

Critique of map design using Edward Tuft's "20 Tufteisms"

 

The Good:

[Image Source: UWF R:\Example Maps]

The map above is an example of good map design.  The point of the map is clear almost before one read the text labels (Tufteism’s 1, 2, 3, 20) – this is a map concerning specific features on an island (in this case, archaeological features on Easter Island).  The map makes excellent use of the space available (Tufteism’s 3, 14), with helpful keys and map elements (scale bar, map keys, inset map) placed in the open areas around the main focus of the map.  The map is well labelled, with the label text presented in different fonts and sizes based on their importance to overall message of the map (Tufteism 7).  The labelling is uniform for each symbol type throughout the map, so it is not distracting or showing design variations (Tufteism 9).  There also isn’t anything redundant or extraneous on the map (Tufteism’s 15 -19).  The map could benefit from a north arrow, although perhaps that isn’t necessary given the latitude/longitude lines placed on the map.

The Bad: 

 

[Image Source: UWF R:\ Example Maps]

The map above is an example of poor map design.  According to Edward Tuft’s “20 Tufteisms”, a well-designed map should include the presentation of information in a clear and concise way (Tufteism’s 2 & 3), showing only what is necessary without a lot of junk (Tufteism’s 13, 15, 16, 17, 18, 20). Labeling on the map should be easy to follow, and should also explain what is on the map (Tufteism 7).  To start, it is unclear what the map is supposed to be a representation of. The above map is not easy to read (violation of Tufteism’s 1-3, 7, 20), and the sheer amount of symbols (all of which do not have a discernable key) are overwhelming to look at (violation of Tufteism’s 7, 13, 15 – 18).  There is an arrow pointing to something on the map, but it is unclear what that item might be, or what its significance is (violation of Tufteism 15, 18).  A scale bar is present, but for some reason had been placed over the map view itself – this makes it very difficult for the viewer to use, assuming that the viewer was able to find the scale in the first place (violation of Tufteism 17, 18).   The map could be improved by removing some of the symbols on the map.  The use of labels should be restricted to what is absolutely necessary, and the addition of a descriptive title, map key, north arrow, and scale bar (all placed off the map view) would help the viewer tremendously.