M4: Data Classification

 For the fourth lab in Cartography the assignment was to display 2010 Miami Dade census data as a choropleth map with four different data classification methods. When we choose to use a color gradient, there are a few ways that the data can be broken up into different color ranges. The way that the data is classified into different ranges for the choropleth can considerably change how the data is interpreted, and the "main idea" of the map. This assignment provided a great comparison of the different classifications, and practice normalizing data for area or sample size - something that is crucial to make choropleth maps that are not misleading. 

For the map I used the cartographic principles we had reviewed in other lessons to make the data stand out. 

The first map layout I created was one that looked at the percentage of seniors (65 and above) in census tracts. This layout was composed of four maps, one that classified the data with Natural Breaks, one with Quantile, one with Standard Deviation, and one with Equal Interval method. Though these are each mathematical ways for classifying the data, they were each already familiar to me with previous GIS work, and can be calculated as options in the symbology pane. I manipulated the color ramp and tone, as well as added a background to the layout to make my data stand out and be easy to read. 

This map shows data that has been presented with a normalization, however it could have benefitted from additional class numbers; perhaps 8-10 instead of 5. 

The second map layout was structured the same as the first, but instead of a percentage this set of maps displayed senior citizens as counts of individuals, normalized (adjusted for) the size of the census tract they were in. This gives a better "big picture view" statistically speaking, for the distribution of seniors through the general population of Miami Dade county. For this map layout I was unsure if I could use a custom number of classes for the data, I feel that some of the classes were swallowed up a bit by only having 5 classes, with most of the maps appearing largely light blue or periwinkle. However, the instructions were not clear on whether or not I could add classes to improve the display. The normalized data showed a belt of areas in the middle of the county where there were higher concentrations of seniors, while the percentage data highlighted an outlier census tract, along with (generally) a similar belt. The least accurate in the displays was the use of Equal Interval for the percentage data, as it makes it appear seniors are only found in on census tract in Miami-Dade County. 

M3 Cartography

In module 3 of cartography we continued our investigations and application of appropriate map techniques and the principles behind them. In this assignment we were given a packet of data spatially describing Ward 7 in Washington DC, and directed to create an aesthetically pleasing map using skills we learned from reading and lecture. Gestalt's Design Principles, visual hierarchy, and the mechanics of certain ArcPro labeling and scale tools were the forefront of this assignment. 

For this assignment we created maps of Ward 7 public schools, with specific requirements for certain symbols and labels to display. 

One of the first things I did with my map was use the Clip tool to create small subsets of the road, school, and park shapefiles. I clipped these datasets to just the boundaries of Ward 7, which allowed me to focus detail in that area. 

For the assignment we also needed to label one neighborhood in each of the seven neighborhood clusters within Ward 7. I knew I didn't want to keep the neighborhood cluster delineations on the map, because it would create too much distraction. I assigned a boundary-only symbology to the neighborhood clusters, then created unique-feature color symbology for the neighborhoods themselves. This allowed me to see each neighborhood border within the neighborhood cluster. By mapping the schools over both of these layers, I was able to make a list of the seven neighborhoods I wanted to label based on which ones seemed most relevant to school locations. Labelling just seven neighborhoods could have been accomplished a number of ways - from annotating, to SQL lines, to creating a new label class based on feature selection. For my map I decided to add an attribute column called "Name Selected" and only populate the column for neighborhoods I had picked to label. This wouldn't have worked for a larger dataset, but for this size it was handy to be able to manipulate the labels by selecting that new attribute. 

After toying with color and symbology, the last challenge in the map creation was inserting text that followed the shape of the river for Anacostia River. During map creation I had located the Anacostia, created a new attribute column for naming it, and then used the River Polygon label defaults to label the river. This gave me the results I expected during most of my map work, but once I got to layout the labelling proved very difficult to control and did not always line up with the feature like I expected. This led me to use an annotation, where I went to Layout->Insert->Curved Text and traced the shape of the river before fixing the font and color for my new curved river label. 

Overall this was a great exercise in map customization and aesthetics. I think I spent more time on this map than almost any of the maps in the certificate program so far. 

One thing I wish I had done was put a shadow on the border of my Washington D.C. shape, since this would have added a new layer of visual hierarchy and helped better establish Feature Ground Relationship. 

GIS Internship - GIS Day

 GIS day was first created in 1999 by the spatial software company ESRI, to celebrate geospatial technology, capabilities, and look forward to a future where new geospatial tools could be used to solve problems. Though GIS day is typically celebrated in the Fall (November 16th) the spirit of GIS day is effectively any day where you look to geospatial information or technology to make life easier. 

A page from the ESRI website on GIS day, and how to get involved.

For my GIS day event, I decided to treat my coworkers to a map-making workshop, and show off some of the ways GIS could be used to make our recreational diving more fun. We often have an issue with our favorite dive sites in that we know their layout very well, but struggle to communicate where exactly we want to explore if we have a loose dive plan. I let my coworkers know in honor of GIS day I would make some maps of our two main dive sites for the upcoming weekend. In my living room after work we brainstormed what features we wanted on the maps, and I used NOAA bathymetric data as my base to show depth contours. Once we agreed on which landmarks to show and the map extents, we "assigned" distinct landmark symbol sketches to the most artistic person, and imported those sketches as jpgs into ArcPro symbology. I had the two maps printed onto waterproof paper, and attached them to pvc slate so we could carry them with us during the dive. 

Overall my event was very informal, but it was nice to spend some dedicated time using GIS for fun instead of strictly for work. I think my coworkers and friends got a good appreciation for the abilities of GIS, since usually only very popular dive sites have existing maps, and those commercial maps tend to be expensive. 

M2: Typography

The goal of the Typography lab was to understand typical naming and style conventions used in map making. General rules like using italics for water features, using font and style to denote nominal and ordinal values, as well as continuing to adhere to the 6 Commandments of Cartography.  During this assignment we were instructed to build a map of Florida from provided datasets, and then make changes to the symbology, typography, and color scheme, overall building the map in our style. 

I built my map and decided right away I wanted to take advantage of the dust color line and stick with natural tones for the land and features. For labeling cities I toyed around with using graduated symbology to show the size of different cities. This worked well for the northern and Panhandle portions of Florida, but in South Florida the cities were too large and too close together, and it became difficult to understand. I also attempted to create separate label classes based on population, so that all cities with a population 250,000 - 499,999 would be labeled in a larger font than cities 50,000 - 99,999. I was able to accomplish this, but the size breaks were a bit abrupt and the map looked crowded with text. In the end I kept the basic convention from the assignment of separating the city symbology by "County Seat" and "Populated Place" but changed the appearance of the points so they fit the map aesthetic. I also created a custom label class so only the cities I wanted to show in the assignment were displayed. 

Customization of the map was the name of the game. I stuck with a National Parks inspired theme, but there are endless color and theme options.

For this assignment I also added Tallahassee as a feature, since it is the capitol and should be displayed, but was not originally included in the assignment dataset. 

Though our classwork left the rivers labeled in a lovely turquoise dust shade, when I reduced the scale of my map to the size I intended to export, I realized the names were too difficult to read. I changed the river label font and color and added a halo to keep the features more readable. I also increased the space between the label and the feature, which I feel made the river features easier to see. The use of italic type for river name helped distinguish rivers from cities, and the use of Tahoma for swamps kept those features visually distinct from cities and rivers. 

Lesson Learned: I am a bit chagrined to admit I've been using ArcPro off and on for five years but heavily for the last two years, and still through this assignment I "discovered" full use of the label ribbon. In the past I've usually saved my labeling for the Layout design window instead of the map view. Properties I needed to change that I couldn't access through the Label Properties pane I would edit by converting the labels to annotations and fix manually from there. The full use of the label ribbon is an excellent tool to have, and will save a good deal of time.

M1: Map Critique

For this module we began orienting ourselves by exploring cartographic styles and evaluating different existing maps to recognize well designed and poorly designed maps. Though basic elements were part of the process, so were the “6 Commandments of Cartography” all of which are based on the work of cartographer Edward Tufte:

6 Commandments:

▪ Commandment 1: Map Substantial Information (Tufteisms 1, 2, 3, 4, and 20).

▪ Commandment 2: Don’t Lie with Maps (Tufteisms 5, 6, 9, 10, 12, and 13).

▪ Commandment 3: Effectively Label Maps (Tufteisms 7 & 8).

▪ Commandment 4: Minimize Map Crap (Tufteisms 11, 14, 15, 16, and 18).

▪ Commandment 5: Map Layout Matters (Tufteism 19).

▪ Commandment 6: Evaluate your Map (Tufteism 17).

I chose maps from the UWF student drive, and found this map of Easter Island I considered well-designed:

 

This Easter Island map appeals to my personal aesthetic with it’s use of natural colors for water and elevation, and all of the colors have about the same value, without jarring contrast. I appreciate the inset on this map that shows the area being mapped relative to parts of South America, and I really like the balance the author chose between highlighting roads and cities while keeping the numerous archaeological sites legible. Some of these things that appeal to me personally are also in line with the 6 Commandments. This map maps substantial information (Commandment 1), minimizes map crap while still be informative (Commandment 2) and is very well laid out to balance all of this information in a pleasing way (Commandment 5). There are some small things that perhaps should be changed on a technical level – the legend and inset boxes don’t line up, there is no clear north arrow, and the map uses italics a bit more often than we have been advised to. Overall this is a very well designed map.

This map of LiDAR survey coverage areas on the other hand was poorly designed:

This map does a poor job with effective labeling and layout (Commandments 3 & 5). As someone with LiDAR natural resource experience and a familiarity with the Pacific Northwest, I should be set right in the target audience for this map. However, the colors are densely packed with borders that are unclear, the legend relies on acronyms, and some of the colors aren’t even labeled. The map extent is a bit skewed because of the dataset way down in southern Oregon, leading almost to a violation of Commandment 1. This “lie” happens because the scale gives the impression that Puget Sound is not well covered by LiDAR, when in fact the Sound itself is the densest area of LiDAR coverage. Since this is a map of Puget Sound coverage, it’s not clear why that southern Oregon dataset or Canadian dataset is included, or why parts of the dataset on the coast is cut off. 

In order to fix this map, I would clip the LiDAR coverage areas to the extent of a Puget Sound shapefile. This would allow me to change the map extent and scale, and focus in on the area densely packed with LiDAR coverage. Additional labelling would be the next priority for this map, further explaining some items in the legend, perhaps removing the county separations, and adding basic map elements like a scale bar and north arrow. While the bright contrasting colors on gray do not agree with my personal aesthetic, they do make the coverage areas stand out, and could stay as long as they are all labeled in the legend.