Cartographic Skills Module 6: Data Classification

This week was all about data classification, or how to categorize your data in order to display it on a map. For the lab assignment, we worked with census data for Miami-Dade County, FL to map two related demographic variables using four different classification methods. As you can see, the classification method makes a big difference in how the data is ultimately visualized, which is why it's important to fully consider the nature of the dataset and the purpose of the map before choosing a classification method.

Pictured here are the equal interval, quantile, standard deviation, and natural breaks methods of classification. All four maps use the same dataset and show the number of senior citizens per square mile in each census tract (the other set of maps from this lab illustrated the percentage of the population over 65). In each case the data classification was performed automatically within ArcMap. In equal interval classification, the data is divided into classes that each cover the same range of data values, which in this case shows us that most of the county has a low density of people over 65, but doesn't show a lot of variation. The quantile method divides data into classes that each have an equal number of data observations, which definitely brings out the subtle variation in this dataset, although you have to look closely at the legend and realize that most of the classes are pretty close together in terms of the values they contain. The natural breaks method tries to group like values and make each class as different from the others as possible, which can lead to some weird ranges of values for those classes, but does bring out some variation in the data without overstating it. Standard deviation is a bit different. It classifies data values according to how far they fall from the mean (i.e. within one or two standard deviations above or below the mean). In this case, that shows us that the density for most of the county is below the mean, with a cluster of tracts in the northeast that are around or above the mean.

GIS Week 6: Map Projections, Part 2

This week's lab was a continuation of our exploration of spatial referencing and map projections. For this complex assignment, we were required to download data in various formats, from various sources, and make sure all of it ended up in the same projection. Some of the datasets were already ArcMap ready and just needed to be re-projected using the Project tool provided in ArcMap; in other cases we needed to define the coordinate system for a raster image file and import x,y data from a spreadsheet, define its coordinate system, create a shapefile, and re-project it to the correct projected coordinate system (in this case, State Plane, Florida North).

The screenshot below shows the end result, with all the data lined up correctly in the right place. Pictured is aerial imagery for USGS quad 5160 in the far southwestern tip of Escambia County, FL, overlain by Florida county boundaries and the USGS quad index, along with major roads and point data indicating the location of petroleum storage tank contamination monitoring sites.

Cartographic Skills Module 5: Spatial Statistics

This week in Cartographic Skills, we went through an ESRI training course on using spatial statistics to explore data in GIS before performing any analysis. ArcMap includes various tools that allow you to look at things like how your data is distributed, whether it's autocorrelated (meaning whether values are dependent on location -- pretty important for spatial analysis!), how variable it is across your study area, and whether there are any identifiable trends. All of this information can then be used to make an informed decision about what kind of analysis would be best to use. These tools also offer several ways to spot outliers in the data so that they can be corrected or removed so as not to skew the analysis.

While exploring the statistical tools available in ArcMap, we worked with temperature data collected from weather monitoring stations in western Europe. The map below shows the geographic distribution of those stations, as well as the mean center and median center calculated from the locations of all the monitoring stations in the dataset. The red oval indicates the directional distribution (roughly east-west) of the location data, and encircles all the stations whose location is within one standard deviation of the mean center. All of these were calculated within ArcMap using tools found in the standard spatial statistics toolbox.

GIS Week 5: Map Projections, Part 1

This week we started learning about map projections and how to re-project spatial data in ArcMap. This week's lab assignment was to display Florida counties using three different map projections, thereby illustrating the way different projections display (and distort) information differently. As you can see from the map below, where all three projections are lined up side by side, Florida's shape and orientation look slightly different depending on which coordinate system is used. More than that, sizes are affected. We used ArcMap to calculate the area for each of four counties in each map projection, and came up with three very different sets of numbers. Some parts of the state aren't affected too much, but others, specifically those farther away from a particular projection's most accurate region, vary a lot. Based on this, it's easy to see how important it is to make sure all of your data uses a consistent coordinate system before trying to do any spatial analysis.

Module 4: Cartographic Design

In this lab assignment for Cartographic Skills, we were tasked with making a map showing the locations of public schools in Ward 7 of Washington, D.C. We also needed to display the schools by type (elementary school, middle school, and high school). Since this week's lesson was about good cartographic design, this map is also an opportunity to showcase the principles of visual hierarchy, contrast, figure-ground relationship, and map balance.

To that end, my first step after assembling all the data was to weed out unnecessary information so as not to distract from the map's purpose (the schools in Ward 7). I clipped the schools and parks layers to display only those in Ward 7, and I chose to display all the roads within Ward 7, but only major roads and highways in the surrounding area. I also made the Ward 7 polygon a light grey in contrast to a darker grey for the rest of D.C. and a light purple for the background. All of this was to establish figure-ground, where the eye is drawn to the area of focus. The roads and neighborhood labels are also shades of grey, dark enough to be seen but similar enough to the other base information that they aren't distracting, while the stark contrast of the red school symbols makes them stand out as the focus of the map. (I opted to let the parks stand out a little, too, to show their distribution in relation to the schools, but the schools still stand out most because of the bright color.) In other words, the visual hierarchy emphasizes the schools over the base information. In order to distinguish between the different types of schools, I employed contrast again in making the symbols different sizes. Finally, to achieve good balance, I centered the title and placed the legend and inset map so that each was occupying one large empty space, and spread out the remaining map elements to fill in the rest of the available space at the bottom of the page.

I was a little pressed for time this week and since using Adobe Illustrator wasn't required, I opted to make and finish my map entirely in ArcMap, with which I'm more familiar.

GIS Week 4: Map Sharing

In this week's GIS lab, we learned about different ways of sharing map data with others and the public. The assignment was to create a geocoded "Top 10" list (in the form of a table of addresses tied to a map layer of point features) that was saved/presented three different ways: as a publicly viewable webmap on the ArcGIS website, as a map package that can be shared to use within ArcGIS Desktop, and as a KMZ file for use with Google Earth.

My Top 10 list is simply a list of some of the breweries and wineries in the area of central Virginia where I lived until recently. I thought that was a fun subject, and my familiarity with the area made it easy for me to see that my data was geocoded and displayed correctly.

If you're interested, you can view my webmap here: http://arcg.is/2l7ulzx.

Cartographic Skills Module 3: Typography Lab

This week's assignment for Cartographic Skills was to create and label a map of Marathon, in the Florida Keys, in accordance with the lesson material on good cartographic design and typography. The base map and inset map were created in ArcMap and exported to Adobe Illustrator. I consulted with Google Maps to locate each of the places that needed to be labeled.

I first labeled the islands, in all caps as is suggested for areal features, using AI's "type on a path" function to place labels directly on the islands where they would fit, and using leader lines for those that were too small. Bodies of water are also labeled in all caps, and I made the type blue and made the customization of shearing the text to give it the appearance of being italicized, both of which are conventional for labeling hydrographic features. Since the city of Marathon actually covers most of the islands pictured, I opted to indicate it with areal color (green) rather than as a particular point. Finally, I placed point symbols at specific locations on the map to indicate towns, the airport, a state park, and a country club, and labeled these using "normal" text (but black to differentiate from the dark grey used for the island labels) and leader lines, since none of them could be labeled without the label overlapping the coastline or another feature. I made two modifications to the symbols, all of which were from the libraries included in AI: 1) I made the state park symbol a darker green than the default to make it stand out better, and 2) I gave all the symbols a little bit of a drop shadow to help them pop out to the reader, since they're kind of small and this is a moderately busy map.

While Illustrator is a powerful tool, I'm not finding it to be particularly intuitive to use, and it seems to me that it's a lot more work to label by hand and create a legend from scratch than it would be to do those things in ArcMap first and then use AI to edit them and add finishing touches. Hopefully as the course progresses I'll get better at using AI, or if not then at least get to experiment with other approaches to finishing maps.

GIS Week 3: Cartography Lab

This week was all about incorporating cartographic best practices into our GIS outputs, and the lab assignment walked us through creating three different maps of Mexico, each highlighting different types of data and how we could use ArcMap to present that data clearly and attractively. This week was also the first time in this course that we looked at raster data (a DEM) and delved into customizing a map's legend.

The map I'm choosing to share (below) isn't my favorite from this week, but it's the one I feel I got the most out of making. For this map we were tasked with simplifying a rather complex (and therefore difficult to read) set of transportation features, by using the attributes tables to single out only the most important rivers and highways for display and ordering the map layers so that everything was as visible as possible. (I have mixed feelings about my red road network, but needed it to be distinct from the black railroads and also needed it to be easy to see without being easily mistaken for a natural feature.) We also played with the city labels to make them more readable and to make the Distrito Federal stand out. The inset map shows which area of Mexico the main map displays, and the coolest thing I learned this week was how to make ArcMap automatically highlight the extent of the main map on the inset map.