Data Classification

The module this week had us exploring different classification methods for data.  The ones we looked at most closely and used for this lab were equal interval, quantile, natural breaks, and standard deviation.  These different classification systems determine what groups data are divided into and thus was color they correspond to on a choropleth map.  Therefore, the classification style chosen for a map can greatly effect the message that it conveys to the audience.  It is important to know what each classification system does and when best to apply it as to not create maps that are misleading.

The equal interval system divides the data into range breaks of equal size based off of number of classes desired.  The range breaks will get however much data there is within the ranges - whether they contain most of the data or none at all.  The ranges are thus almost always going to be unequally filled because of how they are equally spaced out.

The quantile system classifies data into categories with an equal number of data values within each category.  The spacing within the categories will be uneven, but the quantity of content within each class is equal.

The natural break system uses a mathematical algorithm to calculate natural groupings within the data.  The algorithm computes a unique grouping of classifications for each data set. Because how tailored it is this is a preferred choice for many maps.

For standard deviation, classes are created by adding or subtracting the standard deviation from the mean of the dataset. Standard deviation does a good job at creating (statistically) evenly spaced classes.  The way that the data is broken into classes is statistically logical. However, a casual audience may not understand what standard deviation is.

For this project we used census tract data for Miami-Dade county to create maps showing the senior population using different classification methods.  I created eight maps in all.  The first set was the percentage of seniors in each census tract displayed via equal interval, quantile, natural breaks, and standard deviation classifications.  The second used the same grouping of classifications but normalized the population based off of square mileage to find the density of the senior population in each census tract.  The purpose of the lab was to get us to understand the differences between classification systems, how to normalize data, and the overall usage of choropleth maps.  All of this work was performed using ArcGIS Pro.

The percent of senior citizens living within different census tracts of Miami-Dade County.

The population density living within different census tracts of Miami-Dade County.  This data took the count of senior living in each census tract and normalized it based off of the square mileage of each tract.

Most of this maps all use the same graduated bluish-purple color ramp except for the standard deviation maps.  This is because standard deviation maps typically use a diverging color ramp - one color for the classes below the mean and another for those above.

The natural breaks map normalized by square mileage isolated.  This is a strong map for both presentation and for data interpretation. 

I organized the map frames in this order because I believe that they go from conceptually easiest to understand to hardest.  Equal interval is very straight-forward, and quantile is one step of complexity above that.  The actual algorithm behind natural breaks is fairly sophisticated, but it is easy to explain to someone that they are grouped into most alike classes.  Standard deviation is harder to explain to someone without a statistics primer first.  If your audience isn't mathematically inclined using this methodology requires an extra layer of explanation in a way that the others do not. 

The best classification method for this map is natural breaks.  Since the algorithm finds the best class sections within the data it creates a map with good contrast while taking into account possible outliers.  The equal interval maps for both percent and density make it look like there are hardly any seniors at all. While the standard deviation classification is mathematically sound, it is difficult to explain to an audience who is not mathematically oriented and may not be interested in a lengthy explanation.  Quantile also produces a pretty solid looking map, but I like how natural breaks takes into account the grouping of the data. Where people live is influenced by a lot of factors, such as cost of living and access to health care so to divide the data up arbitrarily in quantiles has the potential to be problematic.  It makes more sense to use natural breaks which is able to take into account natural clusters of data.

Additionally, using the natural breaks map that is normalized by square mileage makes the most sense. Normalized data is a more useful depiction than the percentage maps.  If a county official was using a map to determine funding for geriatric-specific medical care, it makes more sense for that official to consult a population density map.  The density map makes comparisons between counties equal, while a percent map the comparisons do not easily transfer between counties.

To further drive this point, imagine if the city official was tasked with allocating funding to ambulances for each tract.  If the official looked at just the percentage, they would assign a large number of ambulances to that rather small dark purple tract that shows up in the Northeast quadrant of the county, but hardly any to the much larger country directly West of it.  When consulting the normalized natural breaks map, these two quadrants are in the same class. Resources should probably be allocated approximately equally between them instead making that decision off of the less balanced population map.

Cartographic Design

This week's lesson introduced us to the elements of cartographic design.  This involved learning about the Gestalt principles of things like visual hierarchy, contrast, figure-ground, and balance.  Utilizing these principles serves as a framework for learning how to properly construct a map that is visually beautiful through its layout and how well it conveys its primary goals to the viewer. 

The goal of this map was to display the location of the public K-12 schools of Ward 7 in Washington, D.C. using the cartographic principles of design we had just learned.  The base data was provided for us, but I had to decide how to lay out the content in such a way to make it visually useful.  The bulk of the map work was preformed in ArcGIS Pro with some final touch up work being completed in Adobe Illustrator.

The location of K-12 public schools located within Ward 7 of Washington, D.C.  The primary objective of this lab was to understand and implement the various principles of proper cartographic design.

In creating a map that will show the schools of Ward 7, part of the assignment was to create appropriate symbology for the schools.  I made the school symbols increase in size with the elementary schools having the smallest icons and the high schools having the largest.  The icons are a vibrant purple and yellow which make them contrast against the duller neutral earth tones of the map.  The assignment did not explicitly request the the names of the schools be listed, but without that information the utility of the map is limited.  So I took the symbology another step and coded the shade of yellow to the school type.  This makes it so looking at the list of schools it is easy to pick out the school's designation and unite it to its symbol.

Another thing I learned in this lab was the use of annotations in ArcGIS.  I used this to create text for the neighborhoods within Ward 7.  The default placement of the text contested with some of the other map elements so I carefully had to change their placement so they appropriately blended in with the rest of the map.  A large part of this lab was dealing with the design principle known as "visual hierarchy" which means placing the most important elements in such a way that they are most visible while deemphasizing the background information.  The names of the neighborhoods provides great contextualizing content - especially considering what school a child goes to often depends on what neighborhood they live in.  However, the neighborhoods are not the most important part of the map so they are displayed in a more washed-out gray text.

The concept of figure ground is the notion of making the most important part of the map distinct against the background.  This put focus on the area of interest.  For this lab, I used the screening technique to make Ward 7 lighter than it surroundings and pop out against the darker background.

The shape of Ward 7 naturally lends itself to creating empty space for the placement of other map elements.  By putting content such as the legend and the scale bar in these places it creates a sense of balance within the map.  I tried not to cluster too much information in one particular place so that no part of the map seemed too heavy but instead complemented the central focus of the map.

Typography

The focus of this week's module was typography. This involved learning a lot about the anatomy of a font, when it is appropriate to use certain kinds of fonts, and standard cartographic practices in order to create maps that are easy to understand. The objective of this module was to design a map of Marathon, Florida using the typographic principles we have learned to label designated features in a way that makes sense to the viewer.  I used ArcGIS Pro to create the initial map which was then shared to ArcGIS Online.  Using the ArcGIS Maps extension  I was able to import the map into Adobe Illustrator Creative Cloud where most of the cartographic work was performed.

A map of Marathon, Florida showing some key landmarks such as the airport. The vector file for the land was exported from ArcGIS Pro and most of the map creation was done using Adobe Illustrator Creative Cloud.

For my map of Marathon, Florida I used the font Trebuchet MS. This sans serif font is not only easy to read but one of the most popular fonts in web design making it familiar to viewers. Additionally, the font family includes bold, italic, and a bold italic style in addition to its regular style giving it versatility.

Varying how type is used creates a visual hierarchy for the map. This is important so that the reader of the map can quickly seek out and determine what is the most important information for them. Most of the visual hierarchy on this map was created using ordinal differences of size. For example, the text used for the cities is larger than the text used for the points of interest.

Due to the narrow nature of the keys themselves, I had to make use of leader lines to point to key features on the map. All the leader lines are the same color and thickness. I made sure to place the leader lines on only a few different angles as not to overly clutter up the map. I also used different symbols for places like the airport or the state park to make them easy for the viewer to seek out.

We were instructed to make three customizations on the map to make it visually our own. One of the customizations I made was a water color paper effect on the blue water background. This was added with the Effect>Artistic>Watercolor. This makes the edges around the map lighter and giving it more visual intrigue. In the same vein, I added a light drop shadow around the islands as well.

Another addition I made was to add in a line for the overseas highway. Without the highway Marathon looks completely disconnected from the rest of Florida which is not accurate to life. I made the road red which is an intuitive standard for roadways. I also added in highway shields so that it is clear which road it is.

My third customization was to place an outer glow behind the text for each of the keys. The keys already are in all uppercase and have a modified kerning so that even though it is the same font family as all the other text on the map they already look distinct. I added the glow to make it even more apparent that they have a different geographic meaning (they indicate the islands) than other text.

Final Project

Our final project was to use out GIS skills developed over the course of the class to determine if a potential electrical transmission line meets its various goals.  These goals included minimal impact on the environment, homes, schools, and the overall length of the transmission line.  I made a different map for each of these criteria as well as a basemap for the region.  The created story map and transcript for the presentation can be viewed below:

Bobwhite-Manatee Transmission Line Story Map

Bobwhite-Manatee Transmission Line Transcript


Study area of FPL's proposed Bobwhite-Manatee transmission line that will connect Manatee and Sarasota county.

Georeferencing, Digitizing, and 3D Scenes

Georeferencing 

This week's lab was georeferencing - the process of taking an unknown (without coordinate data) raster image and placing it correctly on the map by using vector data of known provenience.  I took satellite images of the North and South campuses and georeferenced them using common points from the shapefiles of the campus buildings and roads such as intersections.  This process creates a stitched together map of the two places that has a placement accurate to the real world.

The more links (control points) between the unknown raster and the known shapefiles the more data ArcGIS Pro has to work with.  The method to evaluate the quality of an individual control point is to look at the residual values of the control points.  If a point has a high residual value it may be misplaced - or if it seems perfectly accurate than the other points may be the source of the inaccuracy.  It is also important that the points are evenly spaced around the image and not clustered in a certain area or all in a line.

Root mean square error (RMSE) is a means of measuring the accuracy of the georeferenced image as a whole.  A lower RMSE means an image is more accurately georeferenced.  Another way to get a an image more accurate is to have enough control points to have a 2nd or 3rd order polynomial transformation.  This gives ArcGIS Pro a greater ability to flex and bend the image it is working with so it can better distort to the control points.  Though sometimes this can distort an image too much so even if the RMSE is low it is important for a human to evaluate the final look of the geoferenced image.

Digitizing

Digitizing is the process of drawing in new features on the map.  In this part of the lab I drew in a polygon for the gymnasium that did not already exist as a building as well as Campus Lane based off of aerial photography.  When making the road it was important to activate edge snapping so that the new road connected to the other roads.

This lab also provided an opportunity to use buffers again as we had to put a buffer around a bald eagle's nest so that future campus construction can be planned around the protected area.  I made a multi-ring buffer - one ring at 330 feet for the conservation easement placed around the nest, and 660 feet protection area that is required by the FWC for certain kinds of development.  Ultimately, the map produced for this part of the lab shows where the eagle's nest in relation to current campus structures so that developers can work around it in the future.

A map of UWF's campus buildings and roads in relation to a known eagle's nest.

3D Scene

The final part of this lab was to make a 3D scene.  This means overlaying the roads, buildings, and georeferenced images over a digital elevation model (DEM).  To create the DEM I took a LiDAR layer and used the geoprocessing tool "LAS Dataset to Raster."  Using this created DEM as a ground for the scene superimposes the layers over it to create the 3D effect.  To make it more striking, I also made the buildings 3D by setting the building height provided by their attribute table to their maximum height in the appearances tab.

A 3D map of UWF's campus

XY Coordinates and Geocoding

XY Coordinates

This section of the lab served as a way to learn how to take known coordinates and import them as points into ArcGIS.  I started by making an excel spreadsheet with known locations of eagle nests in Santa Rosa County, Florida.  I used the spreadsheet to convert the coordinates from the degree, minutes, seconds format to the decimal degrees format that works better with ArcGIS.  I then added this new data into ArcGIS by using the "XY Point Data" tool.  I then reprojected the data to the NAD 1983 (2011) StatePlane Florida North FIPD 0903 (US Feet) projection.

Location of Eagle Nests in Santa Rosa County, Florida

Geocoding

In this lab we generated our own data from addresses pulled off of the Florida Department of Education website.  I took the list of schools located in Brevard county and pasted them into a excel spreadsheet.  From there, I had to clean up the data so that individual pieces of information were parsed out into their own cells.  To do this, I used excel formulas such as =offset, =right, and =mid to pull out parts of the addresses.  I saved this cleaned up file as a .csv.

I then got all lines shapefile Brevard county that I would later use to find addresses from the US Census Bureau.  I also got a shapefile for the county so that I could show the county's outline on the map.  I reprojected these files to the to NAD1983 Harn State Plane Florida East FIPS
0901 Feet projection.

The next step was to set up the address locator using the "Create Address Locator" tool with the all lines file for the reference data.  After that, I geocoded the imported table with the addresses of the schools.  Most of the schools matched automatically, but there were a handful that did not.  For these I took the street name and searched the attributes table of the all lines file for a match.  Then I would also put the address in to Google Maps to see the exact location of the school.  From there I could find where the point for the school should be and added it into my own map.

Location of Schools in Brevard County, Florida

The completed version of this map can be view here.

Vectors and Buffers

Vectors

This week's lab was all about understanding vectors and how to use them in spatial analysis.  I learned how to preform attribute and spatial queries to answer questions about data.  This process is useful for determining how close certain types of features are to other types (for example: how many schools in a county are within a mile of the nearest hospital.  Or what land parcels are more than a mile away from a railroad).  Multiple queries can be combined to make a more sophisticated question.  It is also possible to run queries for features that are completely within or only partially within other features, such as properties that are within a flood zone.

Additionally, it is possible look at the attribute tables from these selections to learn other things, such as the sum of the property values of the buildings within flood zones.  The spatial join tool takes this even further by taking attributes from one layer and appending them to the corresponding attribute from another layer.  For this lab we used the spatial join tool to find out which drainage basins have the most contamination risk features within them by joining a risk features layer to a drainage basins layer.

Buffers

In the second half of the lab we learned about buffers.  A buffer is an area of space around a point, line, or polygon.  Buffers can be variable, such as giving one type of body of water a larger buffer than another type of body of water.  Buffers can also be multilayered when it is important to see multiple distances around a particular feature.

For this lab we used buffers to determine ideal campsites in the De Soto National Forest near Hattiesburg, Mississippi.  We were provided with a layer for the roads, and another with lakes and rivers.  The best campsite has to be within 300 meters of a road and also within 150 meters of a lake or 500 meters of a river so that it is easy to get to but also near enough to water for activities such as fishing and swimming.

The first step was to create a variable distance buffer around the water features.  I added a new field to the water features table called "buffdist" with set the appropriate buffer distance for the body of water.  Then, I used the buffer tool to draw the buffer using "buffdist" to set the distance.  At this point I also created a buffer around the roads.

The next step was to create a union between these two layer to see the overlap between them.  This was done using the union tool to determine the intersect between the two of them.  To ensure that campers are discouraged from staying in areas with rare plants and animals with used a layer with the conserved areas and the overlay tool set to "erase" to eliminate the protected areas.

At this point, I now want to change are multipart polygons to single polygons so we can better assess each camping area.  The multipart to singlepart tool is designed for exactly this.  By adding a new field called area and using the calculate geometry tool I added the area of each feature in hectares.  I also split the polygon symbology into three equal intervals to help highlight the largest suitable campsite polygon.

Suitable Camping areas in the De Soto National Forest