Data Classification

This week we looked at how different classifications presents the same data sets.  We also looked at how these same classifications presented a 2nd similar set of data.  The map I will share will focus on the percent population of individuals over the age of 65 residing in Miami-Dade County. 

I chose to share this map because it actually shows the greater variation of classification representation on all the maps.  I fell that this is due to the values of the datasets from the two different maps.  The two different data sets were percent of citizens over 65 and the total number of citizens over 65 normalized per square mile.  So you can see that the class ranges for the percent values when from 0 to just under 80%.  In the other map, the values went from 0 to 13,190.  This wider range of values led to more washing out of the census tracts on that map when compared to the percent over 65 map. 

For fun, here is the total population over 65 map as well.  Enjoy!

Spatial Statistics

This week we learned about how to apply spatial statistics to data inside a map.  The training provided by ESRI covered different aspects of geostatistics.  The first things learned in the training were how to apply a mean center, median center, and the directional distribution to a map in ArcGIS and what this actually means in the context of your data.  These tools basically show you where your data lies directionally and where it is centered.  You will see these applied to the map below. 

Next the training focused on histograms and QQ plots and how to interpret what those graphs display.  These are good tools to determine if your data is normally distributed or not and they are also good for finding outliers in your data. 

Finally the training focused on identifying outliers and conducting trend analysis.  Some tools to assist in identifying outliers included using Voronoi maps and semivariogram clouds in addition to the histograms and QQ plots learned earlier in the training.  Trend analysis was also conducted in ArcGIS using the Explore Data > Trend Analysis tool in the Geostatistical Analysis toolbar. 

The map I created below shows the weather monitoring stations in western Europe.  This map highlights the mean center and median center location as well as the general directional distribution of all the points.  A full explanation is included in the map itself. 

Cartographic Design - Ward 7 Public Schools

The focus of this lab was to apply Gestalt's Principles into creating a map of all public schools of Ward 7 in Washington D.C.  These principles are visual hierarchy, contrast, figure-ground relationship, and balance.  Here is how I achieved each principle:

• Visual Hierarchy - The most important items should stand out and that is the case here with Ward 7 itself, the schools we are identifying, the titles, and finally the legend.  All other elements are lower on the scale of importance and are therefore subdued or de-emphasized so they don't draw the reader's attention away prematurely.

• Contrast - Contrast was mostly applied to the schools.  The red color stands out against a white and grey background.  The school symbols have contrast within themselves as well as their different sizes indicate what type of school they are.  The call out box in the locator map is also a form of contrast as it is bright yellow and that stands out from its surroundings.  

• Figure-Ground Relationship - For this a couple of techniques were used.  1.  Ward 7 is brighter than the surrounding areas.  2.  The roads are mostly subdued (grey in color) to allow the school icons to stand out better. 

• Balance - Balance was achieved by placing Ward 7 in a location that best filled the map space.  Then the locator was placed in the empty area in the top left of the map space.  The legend, scale bar, north star, and additional information was placed in the bottom right empty space.  For the top right empty space there wasn’t much left to add to the map, balance was achieved from the top left and bottom right corners, but the space felt empty.  So I decided to label that the colored space (matching the locator) was still indeed Maryland.  

Overall I think the map came out ok and some new skills were learned in Illustrator.  One thing I would like to learn in the future is how to make the roads that have dead ends round out vice remain open as they are in my map.  I did learn the neat trick of using multiple strokes in Illustrator to achieve the hollow road appearance, so I was happy with that.  I do hope that my color choices are acceptable as I always caveat that I am color blind, so these seem ok to me.  Leave your thoughts, comments, and suggestions below as they are always welcome.  

Cheers!

Learning the Typ-to-the-Ography

This week's lab focus on Typography and reinforcing essential map elements.  Our instruction focused around creating a map, which you will see below, of Marathon, Florida located in the Florida Keys. 

An initial base map was created in ArcGIS which was then exported as an Adobe Illustrator (AI) file.  AI was where the predominance of work was done on this map.  The focus of work was to create a map of Marathon, identifying key (no pun intended) features utilizing typographic principles, and ensuring that we properly included key map features.  An inset map, indicating where Marathon is in relation to southern Florida and the rest of the Keys was included as reference. 

My map, again below, focused on 4 labeling standards.  All cities are Arial 12pt.  All keys (islands) are Arial 10pt.  All water features are Arial Italic, 60% grey in color, and sized to fit the feature.  I also used symbols, identified in the legend, to identify random features and to reduce word clutter on the map. 

Some custom features to this map include drop shadows, path text for the title/subtitle, and a unique border. 

Without further ado, here is my map.  As always, comments and suggestions for improvement are welcome below.