Module 4

 The purpose of this weeks lab was to explore TIN and DEM data sets. I had to create 3d visualizations of elevation model. I learned to purpose of TIN data and how it can be used as an elevation source creating these 3d models. Using different symbology's and the reclassify tool allowed me to make the models more presentable. In the image provided I used a DEM to develop a Ski Run Suitability Map:

Module 3

 In this blog I did data analysis to see which road dataset was more complete within a grid. The first thing I did was use the Projection  tool to project the Tiger Road data to be the same as the Centerline.

 Next I used the summarize within tool. When using this tool I set the input polygon as grid and the input summary feature at the tiger roads polygon. For the summary fields I used the LengthinKM field I created to generate the length in Km of the roads and for the statistic I chose sum. In shape unit I used KM. This left me with a table with the sum of roads within each grid. I did the same analysis for the centerline roads data. When both of these were complete I created 2 new fields in the grid: Tiger, for the tiger roads sum, and centerline, for the centerline roads sum.

I joined the summary table of each of the roads to the grid and calculated the field with the summary data. I then created another field for my percent difference calculation. I used the calculate field tool to run this formula.

For symbology I wanted to have a graduated color ramp to show the difference in percentage. I chose to use Natural Breaks with 7 different breaks in the data.

Module 2

 

Location of 20 test points

In this lab I used the Positional Accuracy Handbook to find the horizontal accuracy of 2 datasets of streets in Albuquerque: the cities street data and StreetMap USA's data. To complete my analysis I found 20 test points. In finding the right test points I looked out for right angle intersections. I placed a point for each dataset at all 20 locations, then using aerial imagery provided, I placed another point where the intersection should be. I got the latitude and longitude of all points and exported the 3 new tables into .csv tables.

I used the worksheet provided in the Positional Accuracy Handbook to find the RSME and the National Standard for Spatial Data Accuracy.

Formal Accuracy Statements:

Using the National Standard for Spatial Data Accuracy, the street data from the city of 

Albuquerque tested 28.93 meters horizontal accuracy at 95% confidence level

Using the National Standard for Spatial Data Accuracy, the street data from StreetMap USA

tested 206.30 meters horizontal accuracy at 95% confidence level

Module 1

 

The distance between the average location and the reference point is 3.18 meters. This lands in the 68% buffer. This tells me that the gps data averages to be within 68% of the actual point location.

For horizontal accuracy the distance from the average and the reference was 3.14 meters. For the results of the horizontal precision looking at the 68% buffer value the distance is 4.4 meters. These 2 values are very close giving a difference of 1.26 meter. This could mean that the gps data is more accurate with horizontal accuracy than it  is precise.

Horizonal accuracy and precision are measured by first getting the average of all of the points longitudes and latitudes. This will leave you with one average waypoint. To get the horizontal precision you will find the difference between the average waypoints longitudes and latitudes and all of the gps points longitudes and latitudes. you will then section these differences out into percentiles, 50th, 68th, and 95th. Using the percentiles you create buffers around the average points. the get the Horizontal accuracy you find the distance between the actual point location and the average of all the gps points.