Our labs on Surfaces continued on with Lab 5 with Interpolation with Thiessen Polygons, IDW, and Spline.
Thiessen Polygons - as the name suggests - creates polygons between the sampling points.
IDW (Inverse Distance Weighting) - a DEM that gives weight to its nearest neighbors but can be "spotty".
Spline - DEM that smooths the differences between points.
This week we in our Internship class we had to perform a GIS Job search.
I started with a Google search, “GIS 911 jobs”, which led to Indeed.
I started with these broad terms because I was already aware that the technical job names for what I am wanting varies across the nation. As expected, there were various different job titles – 911 GIS Technician, GIS Manager, CAD/E911 GIS System Administrator, 911 GIS Coordinator, etc.
I went through quite a few of them and they all had about the same requirements. Most were completely dedicated to GIS tasks and a few mentioned working with IT.
The closest I could find to what I am looking for was for Geographic Systems Administrator for a 911 center in Texas.
The only requirement across the various job descriptions that I think I would struggle with at the start is SQL Servers, but once I learn the basics and get some hands on experience I think I would be fine.
Ideally, I would want a role that is dedicated to GIS for 911 and assists with CAD/E911, IT, Dispatch Training, and GIS projects for Public Safety.
Our 3rd and final lab in Data Quality was on Assessment.
We compared the Jackson County road network to the US Census TIGER road network data.
Before confining the roads to 1 km by 1 km grids for analysis the difference in street length between the two networks was about 576 kilometers, with the TIGER data seeming more complete.
After clipping the roads to the grids the difference between the two networks rose by approximately 6300 kilometers.
I then used the Pairwise intersect tool at the suggestion from a classmate. I had to do some trial and error with the summarize tool to get all the required information. There was 1 grid that had zero roads and 1 grid with only TIGER road data.
I ended up with percentages in the high negatives. The highest being in negative 500 to 1000. A visual inspection shows that the TIGER road data may be roads that were planned and had not been developed yet or trails. I redid things a few times and kept getting the same results.
Our 2nd lab in GIS4930 builds off skills we learned in the first lab as it pertains to accuracy.
Since I've been playing catch up from last week, I was able to review the discussion board before starting the lab and take advantage of the various tips that had been posted (mainly the missing orthros and the excel sheet)
Overall no issues with the lab. I manually labeled the three sets to match each other and since the excel sheet already had the formulas, the data entry and analysis were a breeze.
I split the study area up into 4 quadrants and randomly selected from there. A few points had to be moved due to lack of viable streets. I used Calculate Geometry to get the correct points on each set and then copied and pasted into excel from there.
Albuquerque City
Positional Accuracy: Tested 18.41 feet
horizontal accuracy at 95% confidence level.
StreetMap USA
Positional Accuracy: Tested 175.76 feet
horizontal accuracy at 95% confidence level.
I passed Security+, so that journey for now has come to an end. Thinking Cloud, Data, or Project for next year though.
Welcome back blog readers! It's been.. not that long. But we're at the beginning of the end! Its officially the Fall Semester. I am taking 3 courses this semester. Special Topics in Geographic Science, Aerial Photography and Remote Sensing, and the GIS Internship course.
Since the GIS Internship course runs the whole semester, a few blogs from that course will be scattered through our usual line up.
But the first blog due is for Special Topics.
This first week in GIS4930 we went over the Fundamentals of Data Quality, specifically, Calculating Metrics for Spatial Data Quality to better understand the difference between precision and accuracy.
We learned how to calculate the vertical and horizontal position accuracy and precision, calculate root-mean-square-error (RMSE) and cumulative distribution function (CDF)
The only "new" tool I don't think we have been specifically told to use before was the measure tool, but I use this frequently at work, so no issues.
Honestly, besides the math, the thing that gave me the most pain was remembering how to edit a chart in excel (which prior blog posts have address our love-hate relationship).
The map below shows the projected waypoints, the average location, and buffers for the precision estimates.
|
Horizontal
accuracy |
-6.066 |
|
Horizontal
precision |
4.293 |
|
Difference |
10.359 |
Horizontal accuracy measures how close the GPS measurements are to the true location. Horizontal precision measures the spread of the GPS measurements around the mean location. I believe our lab instructions said it best - The larger the value, the lower the precision. The larger the distance, the lower the accuracy.
Thankfully this lab was fairly direct. I have been studying for the CompTIA Security+ Exam and I'll be taking it tomorrow (Wednesday) so hopefully my next blog I can report that my CompTIA journey for this year is complete and I can completely focus on GIS again.