Module 3 - Data Collection and Projections

     This week, in the third GIS lab, concerns learning to collect data points and utilizing projections as the objectives for lab. 

    The lab was split into two parts, one concerning data collection and the other was projections. Starting with the data collection, it was expanding on how to program and utilize domains in ArcGIS. This had us make domains and feature classes, in order to make it easy to collect data points without worry. There was also use of last weeks cartography lab, with setting up the symbology we would need for out data points. They would be labeled and symbolled as new, good, or bad concerning the quality of the features we would collect. Continuing on from that, we learned to utilize ArcGIS online, which would allow us to upload our collected data points. Lastly I went on a walk and sought out features to use for the lab and viewed  my results once finished. This was a good lesson as it was followed up with all the many different ways possible to share the collected data. 

    The second part of the lab was about projections and the different ones that we utilize them. It started with setting up the first map using the Albers Conical Equal Area. Then, with the next few steps in the lab, we set up the UTM 16N map and the Florida State Plane North map. As you can see below, each is slightly different with the compactness of the map, and the angle of the state. Next was comparing each projections quantitatively, as we learned to use ArcGIS Pro to calculate the area in square miles for each map. Afterwards we selected four counties to compare, these being Alachua, Escambia, Miami-Dade, and Polk. This culminated in the map you see below where each projected map of Florida is compared physically and quantitatively, with the listed four counties. Lastly, the lab touched upon raster projections, where we corrected and fixed an image map of UWF. This was because in the Albers map it was in the incorrect location, and we needed to put it in the correct location it is supposed to be. We also did more editing of the image map with UWF with making a more correct projected raster.



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