Lab 11: Topographic Survey with Total Station

Dr. Hupy and group partner with equipment

Introduction:

    Lab this week was in comparison to the distance azimuthal lab two weeks prior which was about simple survey methods using lower grade technology.  Here the class broken into groups of two, used a total station, the ___ to collect various points with attached elevation data  throughout the University of Wisconsin - Eau Claire's  campus mall on lower campus in order to create a  digital elevation model or DEM of the study area (see Figure 1).

Figure 1 Location of survey on UWEC lower campus

Methodology:

Figure 3 GPS

Figure 2 Total Station

Figure 4 Stadia rod facing total station

    ​The class gathered around Dr. Hupy and the  total station accompanied by the geography department (technology person) Martin Goettl, there Dr. Hupy  introduced the _  (see Figures 2 & 3)and the lab before instructing the class to break down into their groups of two so each group may once again take turns joining him to record several points. The total station must remain in a single location throughout the survey at a known static point or occupied point. This selected destination point is recorded when (60) "mini" points are collected and averaged for high accuracy. Before beginning the survey the total station needs to have some priorities set like how high the station and the stadia rod's prism  is from the the ground and the orientation of the station itself.   To do this one can measure out the heights and input them into the GPS then a couple

Figure 5 Prism on stadia rod

locations are selected as backsights. for this lab 2 backsights were chosen and were marked with an orange flag. The orientation angle is calculated through the coordinates of the station and the backsights by measuring angle and distance of the backsights to the station using the stadia rod. Once ready to record a point one group member would go out in the area being surveyed with the stadia rod and places it in locations every few feet from each other. when a point's location is decided the stadia rod must be leveled (see Figure 4). Then the prism atop the stadia rod needs to be positioned facing the total station while the group member at the station focuses the magnifies lense over the center of the prism on the stadia rod. (see Figure 5)

Figure 6 Using display XY data tool

When centered the group member at the station can you the GPS to record the point. Once each group had its turn and all the points were recorded Dr. Hupy sent the data through notepad to the class. From here the notepad could be brought in directly to ArcMap and was easily edited in excel after using the table to excel command found by using the search box on the right of the screen. Figure shows the edited excel file which was brought in and replaced the previous table. The table was then converted into XY data (see Figure 6) to visualize the point features (see Figure 7). From there the kriging tool used 3D Interpolation

Figure 7 After using Kriging tool

used to create a continuous surface. This tool is found under the Raster Interpolation of the 3D Analyst folder in the Toolbox. To be able to view the terrain in 3D the feature is saved as a layer file and brought into ArcScene where its properties are edited by checking the Float box and raise its base level from 0. The point layer file is also added and then both were brought back into ArcMap to create the final map below in the results section.





Here is an equation and model from ESRI's website to better understand how kriging works:

                          

 

Results:
    Below is the final map created by this survey. The darkest areas are deepest in elevation while the light areas show the higher.  THe orange flags represents the backsights and the blue pin point marks the location of the total station during the survey. This collected data and interpolation model turned out to be very accurate to the real terrain. 

Conclusion:

     This lab showed itself to be in quite a contrast to the previous lower grade technology lab. This survey turned out very accurately and was simple efficient and effective.  

Lab 10: Surveying of point features using Dual Frequency GPS

Dr. Hupy and group partner with GPS.

Introduction:
   This week's lab centered around a simple survey method of an area. Using a high precision GPS unit, the Tesla Topcon and  the Hiper SR, (see Figure 1.) unique point

Figure 1. Tesla Topcon GPS unit used to record points.

 features were collected of several objects behind and throughout the parking lot of the Davies Center of the University of Wisconsin - Eau Claire's lower campus. Some of the objects recorded included various planted trees, lampposts and fire hydrants. With these collected points and better knowledge of the method and equipment the students each made a few maps representing the recorded data.

Methodology:

Figure 2. Simple map of survey location.

   For this activity the class was broken down into groups of two and each group took its turn joining Dr. Hupy with the GPS to collect point features behind the Davies Center. Figure 2. shows a map of the area where the data was collected. Once an object was selected to be recorded the feature is selected

Figure 3. GPS against tree about to record point.

 on the GPS before recording the point. When using the Tesla Topcon and the Hiper SR  it is important to keep the center of the GPS as close to the object being recorded for ultimate precision. The GPS had two options for recording the points (see Figure 3.). The first was the Precise option where the GPS collects at minimum sixty points where the average is calculated to intensify the accuracy

Figure 4. The original table received from Dr. Hupy

of the point collected. The second option for recording the point is the quick solution, where at least twenty points are collected and averaged. For this activity the second option for quick solution was used to record the data points. Objects recorded began in between the Phillips Science Hall and the Davies Center in the small Future Blugolds parking lot continuing behind the Davies center and through the parking lot to the east. Once each group got a chance to record their points Dr. Hupy sent the information through a table

Figure 6. Attribute table in ArcMap.

Figure 5. Navigation to Display XY data of table.

 including X and Y columns where the locations were recording with the feature attribute associated with them (see Figure 4.). The X column represented the latitude while the  Y column was the longitude. The table was then imported into ArcMap(see Figure 5.). From there the tables X, Y data is is used in Display XY data to show its location (see Figure 6.).  A topographic base map was added before creating the map below.

Results:
   With a total of 33 various feature points there were 14 trees, 11 lampposts, 2 fire hydrants, 1 telephone, 1 mailbox, 2 campus signs and 2 garbage/recycling bins collected. Below is the map created from the GPS points and their features.

Figure 7. Map created from recorded data.

Conclusions:
This lab let students use a high accuracy GPS to take a simpler point feature data survey and work on their mapping skills. The only issue the class ran into was that while completing this lab when a tree was recorded its' diameter was also listed but the data was not able to recorded and did not make it into the system when transferred from the GPS to the computer. It would have been interesting to be able to look at the tree diameters to show more attribute data along with the points.

Lab 9: Distance Azimuth Survey

Introduction:
             Technology has helped science advance, helped people be able to record data faster, more accurate and to answer many more intricate questions. GPS and superbly accurate survey stations are typically used these days but as many of us know, technology can’t always be relied on and whether you’re on the job or working on a school project the task at hand still needs to be completed. For this week’s lab the class focused on an older data collection survey method called Distance-Azimuth. This method uses one pin-point location where the data is recorded and the object(s) being documented is surveyed by calculating the distance of the object(s) from the point. This way a slightly less accurate survey of an area can still be performed to document where things are and the attributes associated with them. For this lab the surveying centered around trees near the Little Niagara stream running through the University of Wisconsin-Eau Claire’s campus. The data collected for the trees were its species and diameter at chest height along with its azimuthal direction to the selected Lat/Long point at 44' 47" 50.99*  91' 30" 1* near a small bridge over the stream between the Phillips Science Hall and the Davies Center of lower campus. Below in Figure 1.  is a small map of the selected point’s location. Data for 17 trees was recorded facing the Phillips Science Hall. This activity was performed as a class before returning to the computer lab to create maps from the data collected.

Figure 1. Showing the location selected for data recordation.

Methodology:

Figure 2. image of the True Shot 360

            To complete this distance-azimuth survey a True Shot 360 range laser, seen in Figure 2. was used to gather the distance in feet and the azimuthal direction of the tree from the selected point. Figure 3. Is an image of the scope view of the True Shot 360. Not much preparation was needed for this activity other than bringing along the devices, measuring tape and a pen and paper to record the data, the class and Dr. Hupy left the classroom around 3:30pm on Tuesday April 5^th and selected a data recordation point at 44' 47" 50.99*  91' 30" 1* as seen in Figure 1. While the survey was taking place it was lightly raining and was about 36 degrees Fahrenheit. The class was put into groups of two to either record the species and diameter of the tree, read the distance, azimuth or record the data

Figure 3. Scope view of the True Shot 360

being gathered. The trees were recorded from closet and to the left of the selected point facing the Phillips Science Hall to the farthest and to the right on each side of the stream, although the last three trees were originally skipped over four trees prior, they were added at the end. Each two person buddy group took turns measuring the diameters of the trees at chest height with measuring tape while Dr. Hupy identified each tree species. The information was shouted back to the rest of the class standing around the selected point while other students were announcing the distance and azimuthal direction of the trees. In order to calculate the distance the True Shot 360 had to face its second half held by one of the students with Dr. Hupy against the tree. Each half needed to face the other for the True Shot 360 to calculate the distance between them. After recording 17 tree’s species, distance, diameter and azimuthal direction around the Little Niagara stream the class reconvened in the computer lab. There the recorded data was inputted into an excel file by one of the students and shared with the class in a temporary all access folder (See Figure 4.).

Figure 4. Class shared Excel file.

 After having all the data on an excel file it was formatted for the finicky Esri ArcMap by ensuring each number field was listed as a number field, no extra or unneeded data was left on the file, the longitude column was made negative (as we are west of the prime meridian) and that the data recordation point’s location was converted to decimal degrees from its original degrees, minutes and seconds by simply dividing the minutes by 60 (See Figure 5.).

Figure 5. Formatted Excel file

 In ArcCatalog a file geodatabase was created (along with a feature class) in this lab’s specific folder where the file was then imported as a table (See Figure). Then in ArcMap from the catalog window the geodatabase with the table was added. At this stage the data is shown as the single point. To display the distance associated with each tree the Bearing Distance to Line command or tool, located in the Features section of the Data Management folder in the ArcToolbox must be applied (See Figure 6).

Figure 6. Commands used from ArcToolbox

 Then in order to add corresponding points to the locations of the trees at the end of these distances another tool is applied, the Feature Vertices to Points command which was found using the search tab above catalog to the right of the screen in ArcMap. It can also be found in the ToolBox under the Features section of the Data Management folder like the previous tool (See Figures 7. & 8.). Then a detailed topographic base map was chosen rather than an outdated satellite image taken before construction and relocation of the Davies Center.  Finally an additional map was created showing the diameter and species of each recorded tree (Shown in Figure below in the Results section).

Figure 8. Result of Vertices to Points command

Figure 7. Result of Bearing Distance to Line command 

Results:
      Two maps were created from the recorded data. Figure 9. below shows the various identified species of the trees around the Little Niagara stream on lower campus of UWEC. 11 different species were recorded.

Figure 9. Species of recorded trees.

     The second map (See Figure 10.) visualizes the diameter at breast height of each of the trees in inches. As far as the complications encountered during this lab the rain soaking most of the class and their notebooks for recording the data was the first. Although thankfully a waterproof field notebook from Dr. Jol (another professor in the Geography department) and a pencil allowed for a mess free recordation of the data, whilst other students pages turned to mush and their pen ink smeared all over the pages. While the pencil worked out this time, it is a bad habit to takes notes in pencil as it has the ability to be erased and rewritten suggesting that data could have been changed after the fact. Most of the issues and frustrations of this lab happened due to instructions being different as the weather prevented an additional survey method, Point-quarter surveying, and an insane amount of glitching on the computer while in ArcMap. Only 2 issues with the data, the first seemed to be the azimuthal directions. Clearly the trees in the maps above were not in the building or stream but their exact locations skewed. This could have been from incorrect data stated or due to interference of the building, Phillips Science Hall. The "fan" as it could be described would need to be compressed vertically produce a more accurate representation. The second issue, a simple one, was that the diameters of the trees were recorded at breast height which was only an issue because the students took turns measuring the trees diameters at each their own breast height. This could easily be fixed by having one person gather this data but as this was a lab, each student needed a chance to be involved and understand the survey method being used.

Figure 10. Measurement of tree diameters at breast height.

Conclusion:
     All in all this was a beneficial lab showing the importance of simpler survey methods. In situations where higher grade technology may fail or none is or can be present this method can provide good data recordation and effectiveness.

Lab 8: Personal ArcCollector Project

Butts Around Campus

                

    Introduction:
           For this lab the class was instructed to think of their own research question and gather point data to answer those questions. As in the previous labs this lab included creating a normalized geodatabase. This time it was to be normalized and specified to the question at hand. The question this study is based on revolves around cigarette smoking and the campus's tobacco policy. The University of Wisconsin-Eau Claire is not a tobacco free campus and it is common to see several students smoking within campus grounds on a daily basis. In 2010 Chancellor Brian Levin-Stankevich received two separate proposals regarding smoking restrictions. One from the University Senate, comprised of faculty and staff calling for a tobacco free campus and the other by the Student senate objecting the tobacco ban but recommending that smoking be shifted away from areas heavily traveled by non-smokers, building entrances and air intakes. The chancellor's committee was charged with creating a policy that, as proposed by the Student Senate, would establish fewer smoking areas on campus. (Campus Smoking Policy 2016) Below is a map of these designated smoking areas provided on the policy’s page on the UWEC website. (See figure 1.) This policy was put into effect on may 1st of 2012.

Figure 1. Map of designated smoking areas on UWEC campus found on the school's website.

This study aims to look at students smoking patterns on campus, how they relate to the identified areas and seeing how proximity to building doors and high traffic or lower traffic areas play a role in wear students smoke. This study was completed using the ArcCollector phone application by Esri, cigarette butts found around the UWEC campus, Esri online and ArcMap.

Figure 2. Normalizing the geodatabase in ArcCatalog  

Methodology: 

        The first step in beginning this study once the research question was set was to create a normalized geodatabase in ArcCatalog. (See figure 2.)  The field sections included in this database were number of cigarette butts, proximity to the closet door, brands, whether the area was high traffic or not and if there was anyone presently smoking. (See figure 3.)Once the geodatabase was created it was shared to ArcGIS Online where it could be downloaded onto the ArcCollector phone application. From 2 to 5pm 38 data points around lower campus were collected on April 1^st of 2016. Data collection began on the west side of the Philips Science Hall and wrapped clockwise around lower campus ending behind the Davies Center. 

Figure 3. Fields within the geodatabase  

  Not every cigarette butt was documented, only significant piles and groupings of the butts were collected.  The ArcCollector app once again proved to be a simple and useful tool in the data recording process. Figures 4. and 5. show screenshots of the app and how the data is inputted. After finishing the data collection which updates automatically after each point is recorded, the data had to be imported into ArcMap from ArcGIS Online and then transformed into an attractive informational map of the collected data.(See figure 6.)

                 

 

        Figure 4. Screenshot of ArcCollector app and recorded data points, Figure 5. Screenshot of data entry screen

Results:

Figure 6. Map of the number of Cigarette butts around the UWEC campus.

       It seems that the points with the highest number of cigarette butts were located in more secluded areas of campus and near the designated smoking areas. It was speculation that the higher number of butt points would be closer to the doors but surprisingly these points weren't. The points with the highest number of cigarette butts were 50 to about 150 feet from the closet building doors. Which is good following closer to the hopes of the tobacco policy established in 2012. They were actually several people smoking while the data was being recorded as well and some in or near the designated smoking areas.(See figure 7.)

Figure 7. A map showing points proximity to the closet door in feet and number of people smoking.

 Marlboro seemed to be the most popular brand followed by Camel. 4 cigars and one rolled cigarette were found. Rarely any cigarettes butts were found in grassed areas like in the center of campus.(See figure 8.)

Figure 8. A map showing the number 1 brand of cigarette butts at each data point.

Conclusions:
      Overall this was an informational study  showing the main smoking spots on campus were in more secluded areas, under the L.E. Phillips Library, on the side of Davies, and behind the buildings of Hibbard and east side door of Phillips Science Hall.  Some minute issues became present in the data recordation process do to the way the geodatabase was normalized. An additional "other" option for the brand field would have prevented nulls and higher set range for the number of butts and feet for the proximity to doors would have improved this study. (See figure 9.)

Figure 9. Attribute table of collected data.

References:

    "Campus Smoking Policy." Campus Smoking Policy. UW-Eau Claire and the Board of Regents of the University of Wisconsin System, 11 Jan. 2016. Web. 04 Apr. 2016.