LAB 5 Surface Interpolation

The use of interpolation for water quality in Tampa Bay is actually a excellent use of this kind of data. Interpolation is the creation of new data based one actual data points. For this particular scenario four different interpolation techniques were used on this data IDW, Thiessen, and Spline( Regularized and Tension). Each of the methods are unique in their approach in creating a layer suing the given points. For instance Spline creates data based off a line that curves through the points and uses that information to create the rest of the data with the regularized creating a smoother surfaces and the tension creating stiffer layer that are more constrained with the sample data (as seen above). While Thiessen creates polygons based on their proximity to the closest point and IDW runs off the assumption that proximity means there more things in common which is similar to dispersion. 

   

  

LAB 3 Data Quality - Assessment

The goal of the accuracy assessment is to determine the completeness of the roads compared to each other in each gridcode in the study area. Specifically how different the Centerline shapefile is from the Tiger shapefile in the grided area. In order tot do this I had to clip the two shapefiles by the grid to only calculate the roads in that area. Then I used the intersect tool between the grid layer with each of the road layers. Then after that I used the percent difference formula in excel with the centerline as the base line to calculate the difference in length within each grid between the two road shapefiles.

LAB 2 Data Quality - Standards

In order to the begin the process for the accuracy statement first I had to create reference points based off the satellite images. Then I created IDs for each of the sets of points from the intersections of the lines and for the reference points that were created from the visuals. After that I created columns in the attribute tables of each set of points for their XY coordinates then I inserted the coordinates into the worksheets that calculated the data and from that I determined the accuracy.

ABQ CITY DATA
Using the National Standard for Spatial Data Accuracy, the data set tested 13 feet horizontal accuracy at 95% confidence level
STREETMAP DATA
Using the National Standard for Spatial Data Accuracy, the data set tested 180 feet horizontal accuracy at 95% confidence level

LAB 4 Surfaces: TINs and DEMs

During this lab I worked with DEMs and TINs in ArcGIS Pro. I learned about the difference between DEM and TIN where I learned that TINs are created from elevation points and they create face of triangles on the surface. While the DEMs represent a continous surface and the cell sizes are all the same unlike a TIN. Each though can make contour lines with some differeneces for example the TIN contour lines have an index lines and the lines are more jagged. The DEM contour line are much smoother though surprsingly their accuracy is similar.

Calculating Metrics for Spatial Data Quality LAB 1

 Horizontal precision : 4.5m, Horizontal Accuracy: is over 99% accurate

Vertical precision : 5.9m, Vertical Accuracy: is about 73% accurate

Accuracy is measured by taking the acutal value and comparing it to the calcualted/observed value then calculating the percent difference and subtracting from 100. Precision is measured how closely the calculated results are and is found by finding the point where the 68th percentile of the data is located. 

Lab 6 Proportional & Bivariate

As you can see from the first map proportional images can be quite useful for mapping information that can be negative and positive. For this map we created two layers in order to show a negative growth and a positive one with appropriate symbology for both and legend that accurately shows the size of differences. For the second map we used a  bivariate choropleth mapping method to show the relationship between two variables. In order to do this correctly the data had to be normalized and had to be reorganized in a way to create a new symbol for the new relationship data. This also involved creating a 3X3 color chart based on the percentage of each variable the color had to be complementary and be able to blend well so that any patterns could be easily distinguished in the map.

 

Analytics Lab 5

 

For my map I chose to have simple but unique color palettes for each variable so they could be distinguished from each other. Additionally I chose to make the corresponding infographics and charts the same color to avoid confusion and enhance the understanding.  I used simple charts the both shared similar designs so that they were understood they were showing similar data but the data was labeled clearly so that it was clear what the information was. In general I used the five principles of map making such as visual contrast, and balance so that the map is easy to understand. Though in hindsight the scatterplot could have been better in terms of color design and title as it appears to stick out.