Module 5 - Interpolation

    This week, in the fifth Special Topics in Geographic Science lab, was about Interpolation with concerns to surfaces.

    This lab was broken into two parts, the first half had us comparing Spline and IDW Interpolations to each other and the second half had us analyzing water quality with different interpolations. 
    
    With the first half, we were introduced to Spline and how to use it properly while also comparing it to IDW/DEM interpolation. Once we had produced the Spline, we then subtracted it with the Raster Calculator tool from IDW in order to see the differences between the two. What we saw was areas that were more negative were more aligned to IDW but areas that were more positive where aligned to Spline. This was in part because of the elevation differences between the two Interpolation methods and their variance on the points they came from. This is especially true for areas where they were equal, which causes a dot pattern on the whole of the map where both are equal from the dots the Interpolation came from.

    In the next part of lab we then used a few differing interpolation methods on the water quality of Tampa bay with concerns to Biochemical Oxygen Demand (BOD). This was done with Non-spatial, Thiessen, IDW, and Spline regular and tension interpolation by analyzing the minimum, maximum, average and standard deviation of the methods. The first three, Non-spatial, Thiessen, and IDW, were around the same ballpark with their amounts but the maps did differ. However, the real challenge came with spline which is more sensitive to elevation changes. As the lab had noted and what was observed, there was an area where the elevation changed drastically without any points within the area. Upon further investigation it was found that two points that were extremely close to each other had a magnitude value of difference of one that had caused the sudden change in the area. As spline is a sheet draped over the data, it believed this area when compared to the points around it, was higher than the rest. To fix this, it was deemed necessary to remove the smaller value as the higher values was within a value distance less that +- .5. With that done we can see in the Spline regular and tenson maps below how it fixed the maps to look more uniform. The trouble point is still in this maps as well so you can see how it would look with it still in there. 

Overall, this was another great lab expanding on how surface analysis is done and how it compares to is contemporaries. 


Comments

Post a Comment

Popular posts from this blog

About me

Hi, my name is Reese LeForce, and I wanted to share a bit about myself. I currently work for the Florida Department of Environmental Protection (FDEP) with concerns to Potable Water. I have also worked previously in the Stormwater department for a few years. I got into GIS to hopefully progress my career with the FDEP, and with the completion of the certificate, utilize GIS more within my work. Also, I may continue my education and get an environmental masters. Other than work, if I ever have the free time, I usually try and complete many of my ongoing projects of building model kits or finishing any games I have. I also try to make time to spend with friends and family whenever I can. I also have made a story map that goes a bit further in my past that I have linked  here . I hope you will engage with my blog and enjoy my GIS journey. 
Read more

Module 4 - Data Classification

Image
     This week, in the forth Computer Cartography lab, was about Data Classification.       In this module we used data classifications to differentiate our data. This involved using Equal Interval, Natural breaks, Quantile, and Standard Deviation classifications. With these classifications we are able to see the class distribution change with each map as well as learn what each one does. Equal Interval: groups data into equal grouped ranges that are uniform, however this provides errors due to masking clusters.  Natural breaks: groups data into natural groups where the data looks to have similarities in their clusters. Quantile: groups data similar to equal interval but equally distributes data into their category tracts, however this also conceals the data's true nature.  Standard Deviation: groups data on the deviation from the mean point of all of the data, this provides more errors if not normalized and also reads a bit harder.  ...
Read more

Module 1 - Fundamentals

Image
    This week, in the first Special Topics in Geographic Science lab, was about Fundamentals of data accuracy and precision.      In this classes lab we preformed analysis on a GPS data in order to determine how precise and accurate our data was to the reference points. As you can see in the map below we used the points given to make an average point which we used to make a precision buffer of 50, 68 and 90 percentiles. The 68 percentile is what we use for the majority of the lab but it does later lie close horizontally to our reference point.       For my horizontal accuracy, the distance was 3.25 m. For the horizontal precision, we got 4.4 m. Between these two sets of data we know that out data is accurate with as our reference points lies within out horizontal precision, but due to the rest of our waypoints being farther away from each other does mean our precision is lacking.      Why this is is because with accuracy, ...
Read more