Cartography and Classification Methods

Good day GIS enthusiasts and other viewers,
         The topic of this post will be Classification Methods when visualizing map data. My completed assignment map below is based on the percentage of those above age 65 by census tract for Escambia County, FL. The data source originated from the 2010 Census, but was provided by UWF. The data and map were completely integrated / created in ArcMAP. Before we get into that, lets look at some of the learning objectives that drove this assignment.

•  Demonstrate four common data classification methods.
• Utilize ArcGIS to prepare a map with four data frames.
• Symbolize a map for intuitive data acquisition.
• Compare and contrast classification methods.
• Identify classification method best suited to represent spatial data.

Based on the above let me give you a brief explanation on each of the classification methods you'll see in my finalized map.

Equal Interval:

The equal interval classification method presents your data by separating it into classes by dividing the total range (Max – Min value) by however many classes you’ve created. This method is one of the simplest and can be done by hand if necessary. However this method doesn’t take into account how the data falls along a number line and could lead to classes with no values in them. But, a positive to this is that there are no gaps in your legend data to get confused on.

Quantile:

Classifying your data into quantiles separates it into equal numbers of observations per class. This is done through rank ordering (lowest to highest number etc) until all data has been equally dispersed among your classes. This does not take into account any data clustering or “Natural Breaks” observed by seeing where values are in relation to each other on a number line. However unlike equal interval there is no chance of an empty class skewing your map. 

Standard Deviation:

This method of classification does take into account where the numbers lie on a number line. One of the big advantages and disadvantages to this method is that it relies on having normally distributed data. So in the event of having data that follows a Gaussian or standard bell curve this is an excellent choice. When not dealing with this style data that has roughly equal amounts of data points on either side of the mean standard deviation you’re likely to once again end up with empty classes, or skewed data. 

Natural Break:

This method as alluded to above takes into account where your data is along a number line and tries to group data items based on where they occur most frequently. This tries to keep like values together and unlike values in separate classes. This is the default for ArcMAP likely because it attempts to logically compute which values should be grouped (classed) together, and which should not. This is both editable and subjective by the person creating the map.  

Now that you have a little more understanding or refresher on the above classification methods please look at my finalized map below:

 One of the intended take-a-ways in creating this map was to explore the different classification methods but to also learn through comparison of how they interrelate or function independently. Upon some intense studying you can see that generally the lower percentages are all fairly similar across the maps, this is mostly true with the high percentages as well. So the real delineation of which is the best portrayal of the data comes from the middle classes. So it really comes down to which percentage point representation is best for the data. Looking only at the middle classes we have a range of  approximately 2.5% (Quantile), 3% (Natural Breaks), 4.5% (Standard Deviation (+/- .5)), or 5% (Equal Interval) respectively. So which is the best?

Well if you aren't statistically inclined I'd say lets forgo the Standard Deviation discussion, and focus on the best of the remaining worlds. The Natural Breaks. Not only does its name make it sound like the natural choice, the method in which it portrays the data makes it so as well. It does the best job of showing the natural groups associated with this data set. Once again going back to a few sentences ago, its all in the middle classes. You can see by the small percentage range in the middle class and relatively larger range on either side that the data is clustered about the center, and tapers off at the ends somewhat representative of a bell curve. This representation is more fluid than the other choices. 

This discussion could go on for quite some time, but above all its open to your interpretation of the data. I personally like how logical the Standard Deviation method presents the data. But it is in a separate class compared to the other three. So in an effort not to drone on about statistics we default to the optimum of the other three the Natural Break method. I hope you took something away from this other than a few lost minutes in reading. Thank you.

Projections Part 2, The Storage and Contamination Monitoring Project

Happy Valentines day wonderful page viewers.
What better way to herald Valentines day with a post about contaminated lubricants. More specifically, this post will give highlight to the second week of Projections work in Intro to GIS by discussing the Escambia County FL Petroleum Storage and Contamination Monitoring project I have created. First lets look at some of the learning objectives that led the way for this project. 

• Explore and download aerials, topographic quadrangles, shapefiles and tabular xy data from two different online data sources for Florida
• Define a spatial reference for an unknown data set
• Reproject GIS data to a common coordinate system and projection (understanding the difference between defining and projecting the coordinate system)
• Identify UTM and state plane zones for a specific area 
• Decipher Federal Information Processing Standards (FIPS) code
• Create/Convert x,y data using Microsoft Excel and import to ArcGIS
• Locate important accuracy information regarding GIS/GPS data
• Relate coordinate values to the appropriate earth hemisphere and double that check calculations make geographical sense
• Generate a map displaying aerials, topographic quads, shapefiles and tabular xy data

 This project focused on being a guided trial run for the impending midterm. As such the key tasks were to gather imagery (Quarter Quads) from reputable sources, and then convert and project provided tabular data (excel spreadsheet) for ArcMAP use, and build a map (exclusively in ArcMAP) incorporating these and other base information while ensuring all of the various data and input layers were projected in a common coordinate system. My version of the project is seen below.

One of the particulars of the assignment was to incorporate two adjacent 7.5 minute quarter quad pictures. For my project I chose two North - South oriented quads, those for Pace and Pensacola proper, which give you an entire overview of eastern Pensacola as it rests on the Pensacola and Escambia Bay's. As such this very data populated area is the result. This provides a good overview for you to look at to see where major clusters of certain types of monitoring sites are. We could very easily make the scale larger and focus in on any central area to further explore this data, it also lends you to believe the areas not pictured to the west would be as populated whereas the eastern residential shore has less sites. Please enjoy this overview map. Thank you.

Cartography and Spatial Statistics

Good day all,

     Here we have a post about how Spatial Statistics interrelate into cartography. This specifically applies to the analyses of data on a map. The integration of these two items is seen through this weeks learning objectives below:

• Define key spatial statistics terms
• Know what questions to ask about your data before choosing an analysis tool.
• Examine the spatial distribution of a data set to identify clusters and spatial relationships in the data.
• Interpret a histogram to determine the frequency distribution data set.
• Find outliers in your data using a semivariogram cloud, Voronoi map, histogram, and normal QQ plot.
• Use a trend analysis graph to identify patterns in your data.
• Assess which analysis tools are appropriate given the spatial distribution and values of your data. 

The majority of the above were exercised through Esri.com specific user training "Exploring Spatial Patterns in Your Data Using ArcGIS." The examples used in this weeks assignments covered map data for the depth of wells across an area, weather station & temperature readings, and murder rates across various cities. You can see that that data requiring statistical/spatial analyses can vary to just about any topic you can imagine. Below we will look at some of these particular tools that I delved into using ArcGIS this week. 

Lets start with some of the basics. The Mean, Median Center, and general distribution of data. 

Recall;

Mean = The average of all of your data points

Median Center= The middle value of all of your data

Distribution= how your data is graphically oriented in reference against a particular standard, Ie Normal distribution is judged against a bell curve.

The above data was provided by ESRI, with the map being created by myself exclusively in ArcGIS. I first took the data and had ArcGIS compute the Mean center, and Median center, which show there is only a slight difference in location between the two. This difference lends you to see how the more densely packed areas in the north and south, south-east drive the mean and median locations slightly apart. Also you can see an overall directional distribution computed to 1 standard deviation (meaning approximately 68% of the overall data falls in this area). We can see with this overlay that the station locations have a roughly east-west distribution. Overall this portion of the lab essentially tells us where and how the data is located.

The next thing I did with this data was to determine if it was normally distributed. This was done in two ways, showing a histogram and QQ Plot of the data. You can see these computations and graphics below, as generated in ArcGIS.

Luckily for me, ArcGIS does all of the heavy computations. The statistics side would make my brain hurt. Above we have a Histogram with potential outlier on the right side. But in general you can see that this does lend itself to a mostly normal distribution with only slight positive skew due in part to that potential outlier. 

Again, the beauty of ArcGIS is that it does the calculations and has these amazing displays to save the aspiring Cartographer / GIS Professional from having to do such things by hand, like the wonderful scientists coming before. Here we have the QQ Plot, whose data should predominately fall upon that central line to show if the data favors normal distribution or not. thankfully with few exceptions, this data is normally distributed. 

The scope of the this weeks lesson did go beyond the examples above. But those items are beyond the scope of this post for today. Spatial statistics are a huge portion of the mapping world which i have only scratched the surface. Thanks for viewing this little bit.

GIS Projections Part 1

Good Day GIS'ers and people who randomly found there way here.
This post will be all about map Projections based on this weeks Intro to GIS assignment on the same. This is a two part theme with part one being this week with the second part to come later. some key learning objectives are below

• Recall the basis in which projected and geographic coordinate systems are measured
• Add geographic data and observe software rules and “on-the-fly” functions while adding data with and without a defined coordinate system to a map data frame
• Utilize the Project tool in ArcGIS to reproject a data layer to a common Projected Coordinate System
• Visualize data being displayed in three different projected coordinate systems on a single map and note the differences
• Quantify the difference in area (square miles) between data being displayed between three different projected coordinate systems

 So lets break down a couple of the above. First, this lab was done entirely in ArcGIS Desktop, and its purpose was to display the same data set (size of Florida and its counties) under different projections or coordinate systems. A map projection is a transformation of the 3-dimensional (globe) earth into a 2-dimensional flat map. There are many ways to accomplish this projection, and you can then add many different coordinate systems to it. I wont go more in depth with that here. The lab took county data for Florida and had me change the projection from one projected coordinate system, (that is a coordinate system designed for a flat surface) to another. It also had me observing what and how this is done in ArcGIS Desktop. This is finally portrayed for you the map user to see by showing the same mapped area, in three different projections, highlighting counties across the state as an example, and showing their measurement in square miles compared to each other. See below:

Upon looking at this map you can see that right away, they are essentially 3 of the same map minus the color change to set them apart. The projection used is highlighted at the top of the otherwise identically laid out maps. When you look a little further you can see the same four highlighted counties in each map, and a little closer still shows that the square mileage is slightly different in each. you can see that in the extreme from one projection to the next an entire county is different by 19 square miles. All of this is due to the projection type used and where its origin lies in reference to the map. This can also show you a good reason of why its important to make sure that all the data you gather for a project is in a usable format or can be changed to suit your needs. 
I hope you enjoy referencing the above and at least learned something about map projections. Did you notice the subliminal patriotism?

ArcGIS Online and Map Packages

Hello readers,
      This weeks entry is in regard to my Intro to GIS class assignment about ArcGIS Online and Map Packages. This weeks objectives are to be able to create a Map Package via ArcMap and share it to arc GIS Online, as well as gain a better understanding of the services offered by Arc GIS Online, ESRI, and their interoperability with ArcMap. This was done through two virtual training courses found at ESRI.com in concert with ArcMAP desktop. If this doesnt make sense at first see below.

First: Build your essential map data layers via Arc Map. This should be data that you absolutely need to share to the community at large. The sharing will take place via Arc GIS Online under your personal Content tab. An example of uploaded data is below.

As you can see, this data page is titled Yosemite Operational Data, as in data that I need to get to some user. It has been uploaded to my Content on ArGis Online, and is ready for sharing to the masses.

Here is another example:

This is a variation on the same theme above, just with more detailed background information. So how do these things work for you. Well, these are packaged map data that I have composed and simplified for you to view. Ideally you would view this through ArcGIS online once it was shared with you. A combination of the above map package (which is your important data) with a tile package (which is your background information) and you have something like the below at your fingertips.

Ideally, you would use the combination of this information to assist you in the job at hand. The example above is in regard to environmental sampling data obtained from trees in these various water drainage areas.
The basic concept above is that I can take the data that I need for whatever job, and package it for sharing to anyone on my team, or boss, or customer for their specific use. That is what was learned this week. Thank you.