Module 7 - Cartography - Choropleth Mapping

Classification of Percent Change in Population by State

This week in Cartographic Skills class, we learned about making meaningful and honest choropleth maps.  We classified population data from the US Census in two ways: percent change by state, and percent change by division.
We also had to establish a classification method for each map, that would properly take into consideration the distribution of the data.  Both of these maps were classified using the Natural Breaks method, in which naturally-occurring groups of data are classed together.

Classification of Percent Change in Population by Division

Besides learning about Choropleth Maps this week, I also got a lot of practice in organizing Adobe Illustrator layers.

Module 6 - Cartography - Data Classification

Comparison of Several Methods of Data Classification

Week 6 of our Cartographic Skills course demonstrated how one set of data can produce different-looking maps, depending on how the data has been divided into classes.  The objective of making the maps to the left was to utilize four different data classification methods to display a single set of data: the percentage of African Americans living in the various census tracts of Escambia County, Florida in 2000. We used ArcMap's data classification tools, in the Layer Properties, to create the data classes and make the maps.

The Equal Interval (blue map), Quantile (green map), and Standard Deviation (red and green map) data classification methods are all methods that do not take into account the real distribution of the data values along range.  Respectively, they divide the data among equal divisions of the total data range, equal classes according to numbers of observations, and into classes that assume that most of the observations fall near the average of the range.

Our dataset has a large number of its observations in the low-percentage range, but also has some “outliers,” a few observations of unusually high percentage.  For this reason, the three methods above do not accurately represent the percentages of African Americans in the census tracts of the county.   

The remaining map, which is shown as red in the composite map above, and also alone in the larger single map below, demonstrates the Jenks Natural Breaks method of data classification.  Simply put, the observations can be visually grouped into classes according to the natural groups that they seem to fall into along the continuum of percentages.  In actuality, mathematical algorithms are used to discover the groups of values that are most similar to each other, and these groups then define the classes.  This method is good for data distributions which are not evenly dispersed along their ranges.  If there are discrete groups of values that are very high or very low, they are grouped into their own classes, apart from the other data.  If there are gaps in the range of data values, these are used to divide classes.

So, this method, Jenks Natural Breaks, is best for this dataset of percentage of Black population in the census tracts of Escambia County.  It takes into account the few tracts which have very high percentages, and also those that lie in the middle.  It divides the lower percentages into more classes, because there are many data values that lie within that range, falling into several groups.  

So, why should we care about how data observations are divided and classified?  

From examination of these maps and their data classification methods, we can see the ease with which a single set of data can be manipulated to anyone's particular agenda.  For example, at first glance, the Equal Interval map gives us the idea that most of the tracts of Escambia County have a low percentage of African Americans.  One has to study the legend to find out the real values, and there is still not enough detail for us to learn much about those tracts' populations.  In fact, a different method of classification reveals that some of these tracts have more medium-sized percentages of African Americans.

 The Natural Breaks map is a more honest representation of our data.

Jenks Natural Breaks Method of Data Classification

Week 6 -GIS - Projections Part II

This week's lesson was really involved and really educational!   I gained a lot from it.

It was quite a detailed project, involving downloading aerial orthographic raster images, shapefile data, and data from an Excel file.  The aerial data was added first, so its projection defined the projection for the whole Dataframe.  We then had to convert the Excel file to a shapefile, then reproject the all the shapefile vector datasets for Florida county lines, major roads, quadrangle boundaries and locations of petroleum storage tanks that were being monitored to make sure they were not contaminating their surroundings.  I found that because of my sufficient preparation, organization, extreme attention to detail and paranoia, this was not difficult.

I had no problems with making the map and had time to add inset map dataframes and experiment with the format. It was so helpful to read the lab instructions very carefully ahead of time,  and understand what needed to be done, then lay out a written procedure with necessary transformations of projections listed for all layers before starting.  

The part that taught me the biggest lesson this week also involves organization:  the best way to assemble all metadata and file paths as you proceed with making the map.  I don't know that I would have known just what I needed to do before I started.  I made notes, but they weren't quite good enough.   It was a big job to get it all together at the end.  In the future, I will now know what I need to do, and the way I need to list filepaths for all data, as I go along.

Just how important it is to continuously organize the metadata and list all the paths for all the files, as part of the map-making process,  is the most useful lesson I learned this week.

Week 5 -GIS - Projections Part I

This week in Intro to GIS we started learning about map projections, in the first of a two-part section.

This map and areal data on four counties in Florida demonstrate how ArcGIS can be used to transform a spatial dataset from one map projection to another.  The table shows how the apparent square-mile area of the counties changes, depending on the map projection use.  So, it is important to know which map projection to use for the region of interest when making a map.

Module 5 - Cartography - Spatial Statistics

This week we studied spatial statistics, analyzing the distribution of spatial data in the form of weather stations across Western Europe and their temperature values.

Using an online ESRI course and the Spatial Statistics toolbox, we calculated the mean an median centers and the directional distribution of the weather stations.  We also examined the distribution of the temperature data using histogram, normal QQ plot, Voronoi map, and semivariagram cloud.


Included here is the Normal QQ plot for the data.  It compares our dataset with a normal distribution, represented by the straight line.  I found this plot to be quite elegant, because it very intuitively shows how close to normal our data is.  We can see that it is almost normal, because it closely follows the ideal line. However, we can also see that there are some outliers, data points that lie outside the normal distribution, at the right end of the plot.  A few temperature values are unexpectedly high.  We also saw those points on the histogram, maps and semivariagram cloud.

Module 4 - Cartography - Typography

This is a cartography exercise using Adobe Illustrator in which we learned about standardized selection and placement of labels: type families, styles, sizes.

This map would be useful in a brochure advertising Marathon to tourists, who might be interested in a couple of fun activities, including golf at the country club, and picnicking and swimming at the state park.  If the tourists decided to visit, they would want a more detailed map which includes a scale bar and roads, for example.


Cultural features are labeled here with a sans serif type, while natural features (including the keys) are labeled with a serif type. The hydrologic natural  features are additionally labeled in blue italics.

 I imported the symbols for the airport and country club from Google Images, and then removed the white background with the Tracing tool in Adobe Illustrator

I used a simple yellow triangle polygon for the state park.  The more standard green tree symbols that I wanted to use were not conspicuous enough on this map.  A couple of rules regarding placement of labels is that they should not stretch across the shorelines.  If there is no room for the label of a land feature to be placed completely on land  (for example, on these small islands), it's okay to put it in the water, with a lead line.  The best place to put a label for a point or very small areal feature is to the right, and slightly above it. If there's no room there, to the right and slightly below is the second choice.  When labeling larger areal features, such as the ocean, it is nice to stretch the text out a little to emphasize the breadth of the feature.

I really liked this lab and found all the cartographic standards interesting.

Week 4 -GIS - ArcGIS Online and Map Packages

This exercise in creating a Map Package and sharing it at ArcGIS Online turned out to be fairly simple and straightforward.  The ESRI course that explains it was very helpful.  I'm impressed by some of the clever features of the mapping software, for example, the option of displaying the map at a detailed or generalized scale.  The ability to upload it into ArcGIS online is impressive.

One of the biggest lessons learned this week in Intro to GIS:  If all else fails, read the instructions.

You have to read through ALL the directions at the beginning, REALLY carefully.  We also gained valuable experience in the realities of dealing with glitches in software, and conflicts between various platforms and software packages.

This is the Map Package summary for Yosemite rock climbing site data.

It includes only the point data for the climbing sites.  A raster base map layer

can be added once you download this data.  

This is the Map Package summary for the Aguirre Springs, NM,  ponderosa
pine tree study area.  The package includes only the stream drainage linear vector data
and the location data (points) for the individual trees.   A raster base map layer can be added.