Module 6 Choropleth and Proportional Symbol Mapping - or, best places in Europe to blend if you're a heavy wine drinker!

     This module was designed to introduce us to Choropleth and Proportional Symbol Mapping.  It was a great exercise to pull together what we have learned with regard to cartographic principles, data classification, SQL queries and additional time with Adobe Illustrator.
   

Once I imported my data, I used Color Brewer to help select a color ramp for the choropleth portion.  I wanted to use brown tones for the land masses and wanted to make sure the hues were suitable for individuals with colorblindness.  After confirming my choices, I downloaded the style for future use with ArcMap.  My next task was to determine a classification scheme for the population density data.  After previewing several options, I chose a 5 class quantile scheme.  Since we had removed four countries whose small size and large density would have skewed the presentation, I felt that keeping an equal number of features within each class was a good means of presenting the data.  This method allowed me to see density spread among lower ranges much more clearly while still allowing visualization of the higher end densities. With regard to the wine consumption, I chose a deep red/purple color resembling red wine to apply to my graduated symbol choice.  I used graduated symbols because the symbol sizes are discriminated by the range to which they belong making them easily recognizable in the legend.  Classification was a bit trickier here as I did not like the results of the standard options.  While certainly subjective, I chose to use a 5 class manual interval classification to determine a set of ranges which kept consumption levels similar to others in its class (rare, occasional, social, moderate and heavy consumption) while still depicting the high end Vatican City outlier.  Because of the subjectivity of this method, I divided the consumption by weeks, months and days to help determine where to best place my breaks as well as using information on the graph within the classification window.  Before finalizing the map, I inserted a world ocean base layer from ESRI to fill in the blank space where the oceans would be as well as to account for the location of less important, non-European countries.  I placed the basic map elements and labels within ArcMap and then exported to Adobe Illustrator to apply clean-up.  Before beginning any edits in AI, I was careful to organize my layers and move items into more appropriate categories.  I also removed any unnecessary layers, in this case the wine consumption country linework, and clipping planes.  Once my layers were renamed and organized I could begin the clean-up process.  Since I had already translated my country names via my attribute table, all I needed to do with the text in AI was to move and rotate as required to make it legible.  I applied a drop shadow to raise the legend above other items, since space was tight.  I screened the small tightly grouped countries west of Italy and north of Greece on the main map and directed the user to the enlarged, inset map.  The color scheme, symbology an labels for theses countries was only visible in the inset map.  I kept effects to a minimum so as not to shift focus away from the content of the map.  Overall, this was an enjoyable means of implementing choropleth and graduated symbols to compare data.
 

Module 6 Data Classification - At least I'm moving in a positive direction!

This lab was designed to provide practice in not only using data classification methods in ArcDesktop, but also to illustrate the differences between the various methods.  We used Equal Interval, Quantile, Natural Breaks and Standard Deviation in this lab.  Each method needed to be properly symbolized by quantity so that the results were clear to the end user.  All used a color ramp, except for standard deviation which used a divergent color map so that the diverging values above and below the mean were distinguishable.  This lab continued to emphasize the importance of cartographic design to make the information easily interpreted by the end user.  As the map production process evolved, the application of good cartographic design steadily improved the appearance and usability of the map.

I feel the Map presenting the quantity of seniors per square mile is the most accurate means to portray the data.  The population count normalized by area more accurately depicts the distribution of senior citizens because only the senior age group is taken into account with the square mile area, not other age groups.  With this map, I compared the following classification methods:

·        With the Equal Interval method, the range is divided into equal parts along a number line and the data falls within the resulting classes based on its value.  This method did not depict the localized areas of seniors as well because the data was not value grouped.

·        Using the Quantile method, data is rank ordered and an equal number of observations are placed in each class.  Differentiation between data clusters is more visible here, but the high end of the ranges was greatly affected by the outlier.   

·        The Standard Deviation method considers how data is distributed along a number line; classes are created by adding or subtracting the standard deviation from the mean.  This method works very well with normally distributed data.  There is visual evidence of cluster differentiation and the effect of the outlier is visually minimized.

·        In the Natural Break method, similar value data is grouped and algorithms are used to minimize the value difference within classes and maximize the value differences between classes.  I preferred this method above the others as it made the population densities easy to distinguish and reduced the significance of the outlier on the overall data.

This lab certainly helped to make sense of the classification methods.  Combining this with proper symbology really helped to present the information in a clear, easy to understand format.

Module 6 - Reprojections, Defining Projections, locating data all while maintaining a calm, pleasant demeanor

This week’s lab certainly put us through our paces for projection/definition of coordinate systems.  I feel it did a good job of explaining the material as we worked through the guided sections.  I did have trouble with the missing step of projecting the aerials from NAD 83 State Plane… to NAD 83 (2011) State Plane in the initial exercises.  Also the lack of described geographic transformation in the drop down as shown for step 7 was a problem.  I used the following link to view the ArcGIS 10.3.1 Geographic Transformation Tables to determine the best transformation

http://resources.arcgis.com/en/help/main/10.1/003r/pdf/geographic_transformations.pdf

I followed the spatial reference check instructions for each piece of data which I defined or projected and feel that my results are correct although I would like to know why the geographic transformation shown in step 7 of the lab was not available since all my entered information matched the example in the lab so that I can avoid potential pitfalls in the future.

Here's how I kept my thoughts in order (I like outlines because they keep me focused!):

I.                Part 3 Process Summary Details

A.     Obtain Data and Project/define as needed

a.      Aerial Imagery from Labins.org

                                                    i.     Quad 5560, Bay Springs

1.      2004 RGB State Plane, Units in Feet, MrSid format

2.      Coordinate System NAD_1983_StatePlane_Florida_North_FIPS_0903_Ft_US

3.      Lambert_conformal_conic Projection

4.      Project to NAD_1983_2011_StatePlane_Florida_North_FIPS_0903_Ft_US

a.      Confirmed spatial reference by inserting in a new map and verifying the Data Frame Properties/Coordinate System Tab/Layers and Projection as well as inserted a base map to confirm alignments.

b.      County Boundary Information from FGDL.org

                                                    i.     U.S. Census Bureau’s Florida County Boundaries – Statewide July 2011

1.      Coordinate System GCS_North_American_1983_HARN

2.      Albers Conical Equal Area Projection

3.      Project to NAD_1983_2011_StatePlane_Florida_North_FIPS_0903_Ft_US

a.      Apply Geographic Transformation.  The one in the lab document “NAD_1983_To_HARN_Florida” was not in the options.  I used the ArcGIS 10.3.1 Geographic Transformation Tables to determine the best transformation (http://resources.arcgis.com/en/help/main/10.1/003r/pdf/geographic_transformations.pdf).  I selected NAD_1983_HARN_To_NAD_1983_2011, area of use USA (all states)

b.      Confirmed spatial reference by inserting in a new map and verifying the Data Frame Properties/Coordinate System Tab/Layers and Projection as well as inserted a base map to confirm alignments.

c.      Major Roads Information from FGDL.org

                                                    i.     FDOT RCI Derived Major Roads – Statewide – January 2016, MAJRDS_Jan16

1.      Coordinate System GCS_North_American_1983_HARN

2.      Albers Conical Equal Area Projection

3.      Project to NAD_1983_2011_StatePlane_Florida_North_FIPS_0903_Ft_US

a.      Apply Geographic Transformation.  I used the ArcGIS 10.3.1 Geographic Transformation Tables to determine the best transformation.  I selected NAD_1983_HARN_To_NAD_1983_2011, area of use USA (all states)

b.      Confirmed spatial reference by inserting in a new map and verifying the Data Frame Properties/Coordinate System Tab/Layers and Projection as well as inserted a base map to confirm alignments.

d.      Quad Index information from FGDL.org

1.      Coordinate System GCS_North_American_1983_HARN

2.      Albers Conical Equal Area Projection

3.      Project to NAD_1983_2011_StatePlane_Florida_North_FIPS_0903_Ft_US

a.      Apply Geographic Transformation.  I used the ArcGIS 10.3.1 Geographic Transformation Tables to determine the best transformation.  I selected NAD_1983_HARN_To_NAD_1983_2011, area of use USA (all states)

b.      Confirmed spatial reference by inserting in a new map and verifying the Data Frame Properties/Coordinate System Tab/Layers and Projection as well as inserted a base map to confirm alignments.

e.      Using the provided EscambiaSTCM.xls, add the required columns and formulas to convert to decimal degrees.

                                                    i.     Add xy data to a blank map by choosing the sheet and assigning values of xcoord for x field and ycoord for y field

1.      Correct the unknown coordinate system by defining GCS_WGS_1984

                                                   ii.     Add points to map and export to shp file.

1.      Shp file needs to be Projected to NAD_1983_2011_StatePlane_Florida_North_FIPS_0903_Ft_US

a.      Apply Geographic Transformation.  I used the ArcGIS 10.3.1 Geographic Transformation Tables to determine the best transformation.  I selected WGS_1984_(ITRF00)_To_NAD_1983_HARN, area of use USA (all states) + NAD_1983_HARN_To_NAD_1983_2011, area of use USA (all states)

b.      Confirmed spatial reference by inserting in a new map and verifying the Data Frame Properties/Coordinate System Tab/Layers and Projection as well as inserted a base map to confirm alignments.

B.     Create mxd containing the aforementioned data sets

a.      Insert all items into the mxd

b.      Confirmed spatial reference by inserting in a new map and verifying the Data Frame Properties/Coordinate System Tab/Layers and Projection as well as inserted a base map to confirm alignments.

c.      Adjust layer hierarchy to improve visibility.

d.      Adjust symbology to improve visibility

e.      Create bookmark of quad extents for quick access

f.       Take required screenshots and save mxd

Week 5 Introduction to Projections - Would you like your map as Albers, UTM, State Plane? Side of fries? Proper projections are important, allow time to wait when you place your order....

OOPS...I shoud have included Florida somewere in the title.  Always something.....

This week was our first foray into coordinate systems and projections.  The projections chosen for us to use did an amazing job illustrating the importance of choosing one appropriate for you subject area.  Having us create three data views as a side by side comparison was brilliant, allowing us to see the subtle and not so subtle changes from one to another.  It also showed how error can distribute the further you are from a zone/limit boundary of your projection choice.  The Albers did the best preserving area since it is, after all, an equal area projection. Since it is a conformal projection, UTM maintained the overal shapes but area and distance suffered.  The results within UTM 16N were also significantly worse the further away it was from its zone limit (panhadle area of FL.  Although NAD83 State Plane Florida North FIPS 0903 did a better job locally, there was still distortion outside of its zone as well (mainly the panhandle and a portion of northwest Fl fall in this zone).  The least affected by all of the projections was Escambia county since it fell within the localized areas for two zones and of course, Albers being global was good.

Module 5 Spatial Statistics ~ Math Mayhem

Whew, I made it through this module.  I started by reading, or attempting to read Chapter 3 in our textbooks during my lunch hours.  During an already hectic, brain scrambling day this was a poor choice!  I shifted my focus that evening to the lecture content and moved on to the lab assignment feeling slightly better and ready to analyze some data.  The ESRI training was well organized, with a nice overview including key terms which I printed and saved for future use.  After each module introduction we were provided step by step instructions to repeat the process using different data so that we could form our own conclusions and check them against the ESRI response.  The first module focused on spatial distribution using Mean Center, Median Center and Directional Distribution tools.  This allowed me to determine that my mean and median were similar and that the majority of my data ran in an east westerly direction.  It was important to note that the Median Center was located southeasterly of the Mean Center, most likely due to the cluster of weather stations in that area.  The remaining lessons in the training expounded on the use of additional Geostatistical Analysis Data Exploration Tools to determine if the data fell into a normal distribution, were there any outliers, was it stationary and did it have autocorrelation.  I'm still a bit confused about some of these tools, but can see that they aid in determining areas that might need further study before performing analysis.  In this case, all the tools pointed to La Fretaz in Switzerland as my outlier.  I look forward to more practice and education with regard to spatial statistics.