Statistics...ewww but not ewww

This week we worked with STATISTICS statics...actually, despite the use of the word statistics, we worked with a GIS tool that uses statistics to help discern if the given data is displayed or distributed correctly.We used three different scary sounding but easy to use tools (geostatistical analyst) to determine how are data related to each other. For this week, we used an ESRI course. Prior to UWF, I have done a few of the more basic (i.e. FREE) ones and they are dull. Somewhere between the monotone voice of the video and the beyond dry writing, those less than 3 hours of training, seemed like a good day's worth. ESRI makes a great software suite, but they need work in the keeping things lively department.

The primary focus of the lesson (I hope) is determining if the data we have is correct in a statistical sense. i.e. does the data relate to each other correctly. What do I mean correctly? I mean, "normally distributed". Normally distributed means data that when displayed falls along a bell curve. The data shows a logical spatial relationship with each other. In determining the bell curve, we used a histogram which displays a bar graph. If the data is mostly normally distributed, then the bars are displayed with the tallest bar in the center and the remainder creating a pyramid esque look on either side. We then learnt to use a QQ Plot which uses a plot line to show if the data falls along said line. If the data is on or near the plot, then the data is "normally distibuted". In our exercise, we determined that there were a few outliers ( i.e. data that wasn't in line with the majority.) My over all take away was that we used these tools and graphs to figure out if the data we were given is good. Outliers may be the result of bad data entry so to speak or a special phenomenon .

In the exercises, we had to use weather stations in western Europe. Of course, temperature readings in high altitude areas (the Alps) were much colder than other areas. Using the QQ plot and Histogram we found an outlier in Switzerland, Most reported temperatures where somewhere in the winter conditions where as this single station was in the 70s. So there was an obvious problem. The whole exercise was essentially learning to use statistics to figure our if our data is good or not. Bad data used can create issues down the line or at least make a GIS technician's life harder.

The map below shows the distribution of temperature across western Europe. You'll notice an X and a cross, these display the median and mean center of the stations. Then the directional distribution shows the direction in which the stations are statistically distributed. I used a color ramp to help display the temperature range better.

Week 5 Projections GIS 4043

This week we were working with coordinate systems. It's important to know these systems and to learn how to use them and/or change them in ArcGIS. My take away this week is that I need to ensure my data is projected in a similar fashion across the board. Though on a larger scale coordinate systems may not make much of difference if there is a conflict or if the chosen system is an ill fit for the area in question, however, it does begin to matter when working with data in detail.

For the lab, we had to work with the Florida county maps and change the projections. (See below).  When I changed the format to UTM from Albers, I noticed that the UTM map became more compacted. The State Plane N, coordinate seem to distort the counties in the south while keeping the northern counties, particularly in the panhandle, un-distorted (not stretched out or compacted.) We then worked with a raster image of UWF. It had no assigned coordinate system. We had to define the system for it, by going to the properties and clicking the edit button to change the features. State Plane N seem to place the image in the correct location. When I did it in Albers, the image was no where near where UWF is in reality. It was interesting to see how different projections impact the map and the data over all. (Which also means that programs like Google earth, must be constantly changing projection as you zoom in and out.) State Plane N obviously worked the best with the raster image it was projected primarily for northern Florida. 

This was a week in which we dug deeper into ArcGIS and understanding how to prepare data we'll be working with in Arc. Of the lessons thus far, I feel this was probably one of the most important to learn and understand.

Comparison of Coordinate Systems using the State of Florida and selected counties. It shows how the square mileage of a given area can change (quite a lot) as the projection changes. At first it's hard to see the the changes, but looking from a slight distance, you can see the distortions.