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 6 - Projections Part 2

 ... or was it Data Search Part 1?

This week's lab had us gathering spatial information off the internet, reprojecting it, and then putting it all together into a final cohesive whole (the map). We also created spatial data from excel spreadsheets, and converted coordinates from degrees minutes seconds to decimal degrees using a formula entered on the excel spreadsheet. The results of this lab can be seen below.

The majority of the data on this map came from internet derived sources.

Technical Notes

While there is quite a bit going on with the visual part of the map, such as the two insets to show the map location, the real story took place behind the scenes.

My first step was to import an excel spreadsheet of the petroleum storage tank locations. I ran into some snafu's when it came time to put a projection on it, and if it wasn't for the class discussion board I'm not sure how long it would have taken me to treat this as a two-step process. My issue was with defining a correct initial projection - I was trying to fit data from a lat./long. source into a State Plane grid! That won't happen (well, correctly anyway) without first taking the proper steps. Since I had converted degrees minutes seconds into decimal degrees, I needed to assign a lat./long. projection - in this case, WGS84. Only after I set up the GCS to an appropriate system could I then reproject to State Plane Florida North US Feet (the projection required for this lab).

After viewing the storage tank data, I decided to focus my map on the Bratt and Century quads... but it quickly became very clear that in order to have some semblance of consistency, I needed to add more aerials! So the majority of my time was spent downloading aerials from Labins.org (a website of survey data for Florida, hosted by the Florida Department of Environmental Protection), then extracting them. In all there are 8 quads represented on my map - only 2 of which were absolutely required.

The roads, quad index, and county boundary data were also obtained online... this time from the Florida Geographic Data Library (FGDL). It's a source that had already been used several times over in this class. All of the data had been converted from Albers to State Plane Florida North Zone NAD 1983 US Feet, using the project tool.

In all I found this lab very useful - and I've already started to bookmark credible sites with free spatial data downloads for the western U.S.!

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 5 - Projections Part 1

This week's lab covered the difference between map projections and geographic coordinate systems, which tool to use under the Projections and Transformations toolbox to either re-define or assign a map projection, and touched briefly on geographic transformations. Our map for this week shows the state of Florida, set to three different projections.

The state of Florida, shown with three different map projections.

Technical Details

To create the three different views of Florida, I inserted three separate data frames. To each data frame a different projection was made to each shapefile that represents the state of Florida (technically the shapefile is a collection of all the counties in Florida, not simply a view of the state only). To highlight the changes that each projection had on the state of Florida, a sample of four counties is provided in the legend with the square miles for each listed. For continuity the symbology, labeling, and map extent used for each frame was made exactly the same. The background for each data frame was set to light blue, in order to make this section of the map stand out even more.

The creation experience, in review

Creating the map was fairly easy, but somewhat tedious - as a lot of map creation can be. I enjoyed using the projection tool with confidence, as in the past I felt like I barely knew what I was doing. To be honest, the majority of my lab experience was spent reading all those ArcGIS Help topics on geographic coordinate systems, map projections, and geographic transformations. My personal favorite topic was the one covering what to do when you don't know the original projection of your data, as I've run into that a few times myself... and hopefully next time around I'll be able to figure it out on my own, instead of running to the GIS folks for help!

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 4 - ArcGIS Online & Map Packages

This week’s lab in GIS4043 covered how to create and share map packages through ESRI ArcGIS.

A map package is a collection of vector data, along with any supporting .pdf or .txt file documents associated with a map. The design of the map, along with a working copy of the data, is what constitutes a map package.

Screenshot1 for the 'Use and Modify Map' exercise.

For the first exercise we modified an existing map package, then uploaded a new map package to our ESRI ArcGIS accounts. This sounds fancier than it was – the exercise was really about poking around with map and tile packages. The view to the left is a screenshot of my uploaded map package for this portion of the lab.

Screenshot 2a for the 'Optimize a Map Package' exercise.

The second exercise allowed us to add and edit data, as well as specifying certain data spatial extents, layer symbology, etc. The maps were then packed up and shared on the ESRI website as a map package. The views below are screenshots of my second uploaded map package.

Screenshot 2b for the 'Optimize a Map Package' exercise.

I found this exercise to be quite informative – I really had no idea one could send data on like that. I can definitely see the usefulness of this type of data sharing. These packages do not need to be directly uploaded to ESRI ArcGIS online either – I couldn’t upload directly to my ESRI account, so I had to save it locally and add it to my account manually… that’s pretty neat!

On a personal note, I found that old screen names can haunt you… I had an ESRI account many many moons ago, so I didn’t need to create a new one. Unfortunately the screen name wasn’t quite as professional as one would hope… thus the highly edited screenshots. Oh well, you live and learn.

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?