Module 6: Data Classification

This week the assignment was to utilize ArcGIS to create two maps with 4 data frames each.  4 data classification with the same data, population over 65 in Miami Dade County, FL. The first map utilized % of population over 65.  The second map utilized a count of those over 65 normalized by square mile.  One of the learning outcomes is to gain awareness of the different distribution of the same data by the different classification methods and different aggregate of the data (% total verses count normalized by square mile).  The 4 data classification methods utilized in this lab are:  Natural Breaks, Standard Deviation, Quantile, and Equal Interval.

Equal Interval: Takes the maximum value and subtracts the minimum value to get the range of the data.  The range is then divided into equal range classes.  The number of classes to be assigned by the map maker.  This option leaves no gaps in the data range and is fairly easy to understand.  However it can force same or similar values being divided into different groups and/or dissimilar values groups together.

Quantile: Takes all the data, ordered numerically, and divides it into classes with equal observations. Each group has the same number of observations.  This option is again fairly easy to understand.  However, this option can leaves gaps in the data range and can force same or similar values into separate categories and/or group dissimilar values into the same category. 

Natural Break: Takes data and runs mathematical algorithms to place similar values together and maximize differences between classes.  This option is much more complicated to explain how mathematical equations determine the class breaks. There option does not allow same values to be put in different classes and should not class values drastically different together.  This classification method is popular among cartographers, and is the default classification method used by ArcMap. 

Standard Deviation:  Takes data and a bell curve approach to classification with equal sections. The majority of data will be in the middle class around the average value, other classes will have fewer and fewer data points as they get farther away from the mean.  This option requires basic understanding of statistics to understand bell curve data and the percent of deviation from the mean to understand the class differentiation.  This method would not allow for gaps in the data range.  This method would not force same or similar values into different classes or dissimilar values into the same class.

Symbolized map for intuitive data acquisition by using graduated color was utilized to symbolize the data.  Lighter color for lower numbers ranging to darker color for higher numbers.   Implemented cartographic design principles by positioning the page title in the largest font at the top of the page and individual data frame titles in smaller font within their frames.  Data information, author and date are all positioned in smallest font at the bottom of the page. 

In my opinion the presentation method best suited to present the distribution is the data that is % over 65 presented in natural breaks classification.  The count per square mile data seemed to wash out the data.  The values were lower resulting in lower values for the map.  The percent over 65 takes into account the areas that are populated and the section of that population that meets the criteria of over 65.  The classification method of Natural Breaks allows for categories to be formulated based on the data.  Forcing the values into equal ranges for equal interval distorts the data on the high end of the values.  Quantile forces a equal count of values into categories and then the rages are set from the data.  This doesn't allow for an natural separation in the data. Standard Deviation assumes a bell curve to the data and this data set is more weighted in the lower range and one outlier on the high end that skews this classification.
 

Module 5: Spatial Statistics

This week I accessed ESRI’s My Virtual Campus Training to complete Exploring Spatial Patterns in Your Data Using ArcGIS.  I downloaded the data provided.  I used the toolbox: spatial statistic tools>measuring geographic distributions>Mean Center, Median Center and Directional Distribution (shown in the map above).  Utilized Geostatistical Analyst Extension.  I added it from customize>extension>geostatistical (checked) and then customize>toolbars>geostatistical (checked).  From the geostatistical analyst drop down box Explore data> Histogram (bar graph) and Normal QQ Plot (data points compared to normative distribution line).  Continuing the exploration from the geostatistical analyst drop down box Explore data>Voronoi Map to determine the variation in data and Expore Data>Semivariogram Cloud to determine if spatial autocorrelation is present.  Lastly Explore Data>Trend Analysis.  Everything beyond the Mean Center, Median Center and Directional Distribution I am not sure what I was doing.  The training indicated that these are all ways to look at and examine data.  

Module 4: Cartographic Design

Several objectives in this week's lab.  I chose to implement visual hierarchy by making the study are as large as possible to fit the page.  They symbols for the schools are large and contrast in color to the study area color.  The title is centered at the top of the page, in large font with a halo to further diminish back ground interference with the text. The legend is also fairly large with the background matching the study area to further emphasize it's importance.  The locator map is at the top part of the page to lend additional information.  The bar scale and north arrow are easily located but not as visually dominant as other elements.  Finally the source data, my name and date are located at the bottom of the page in small font.

More objectives were also incorporated.  Contrast was used in the color of the study area and the color of the symbology (opposite on the color wheel).  Figure ground was employed by the study area being a lighter color than the surroundings with more detail (local roads and neighborhood names) giving the illusion with lighter color and more detail that the study area is closer to the viewer than the surroundings.  Attempts at balance were made by placing the large elements first (the main map in the largest format that the page would allow, the legend and insert to make sure adjustments to the main map were not required, and the title as another large element.  The lesser dominate map elements (scale bar, north arrow, source data, author and date) were placed in the non map space to help balance the page.  Graduated symbols for the schools;  Elementary schools have a smaller symbol than Middle schools which in turn is smaller than the High schools.  Inset map has an extent indicator to show the area of study in relation to the surrounding area of Washington, DC.

I started the map in ArcGIS desktop and then exported it to Adobe Illustrator.  I continued to work in both for most of the project.  There are pros and cons for me already with each.  I feel more control over the layers and information in ArcGIS, but Adobe offers more color and symbol options.  I chose to keep the ArcGIS school symbol for it's simplicity and maybe more recognizable.    I struggled with color choice vacillating often and changing frequently.  I am not totally committed to the yellow cream for the back ground but it seemed to contrast the green color choices of the study area and DC area.

Module 3: Typography

This week I took county data provided in lab from FDGL in Albers Conical Equal Area projection and mapped Marathon Key, Florida.  This required the assistance of Bing Maps as I didn't know Marathon Keys are the middle Keys off the southern tip of Florida.  One of the challenges was to get all of the labels on the map in a clear way, utilizing typographical guidelines from our text.  Water features can be italicized to more resemble water.  Larger structures utilize larger font.  The islands (Keys) are labeled with all caps at 14pt to distinguish them from cities that are at 13pt and the location of interest that are at 12pt.  The type font I chose was Bell MT as it appeared one of the cleanest.  The goal of typology is to utilize the text shape and size to further distinguish items within the map.  Still a bit of a struggle to get all the parts in the right format, fit into the area available and still clearly communicate.  Leader lines should be similar in angle to avoid chaotic looking map (this is more difficult that it sounds) and should not touch points or text while being in close enough proximity to clearly communicate the intent.  Adobe Illustrator (AI) is still new and I find myself overwhelmed at the seemly unlimited amount of options to customize.  There is a limited number of map symbols in AI, so I was able to down load one for the state park, airport and country club.  I chose to further customize my map with 1) a halo on the title adding emphasis as well as tying colors together  2)a border for the data frame to finish and tie colors together 3) added a small light drop shadow to the islands to add depth but limit interference with small water feature labels.