Field Exercise 8: Data Collection with ArcCollector

Introduction:

The purpose of this exercise was to gain experience creating domains and feature classes to use in ArcCollector. The feature class was used in the ArcCollector app to collect data about on-campus and off-campus features using a smartphone. Information about cars parked on campus was collected to determine similarities and differences between cars owned by faculty and students. Information about cars parked at a local grocery store, Festival Foods, was used to add to knowledge of cars driven in Eau Claire. Knowledge of online data collection is very useful in today's modern age, and knowing how to build feature classes for easy data entry is important for conducting efficient surveys.

On-Campus Collection:

Background:

Cars on the UW-Eau Claire campus were surveyed to determine any patterns between student and faculty cars. The campus has 18 parking lots, and 8 were surveyed (Figure 1). Lot types include Faculty Staff (F), Student (S), Guarenteed Facutly/Staff (G), Residence Hall/Student (R), Bollinger Lot (B), Recreation Lot (E), and Public Meter. The Bollinger Lot and  Recreation Lot were not surveyed because they were off campus.

Figure 1: The parking map shows all parking lots on the UW-Eau Claire campus. Eight parking lots out of eighteen were surveyed to determine what similarities and differences existed by cars driven by students and faculty. The map was obtained from the UW-Eau Claire Parkig Office http://www.uwec.edu/about/maps-directions/upload/campus-parking-map.pdf

In order to determine similarities and differneces between student and faculty cars, lot types were combined. Student cars included cars parked in Student and Residence Hall/Student lots.Faculty cars included cars parked in Faculty Staff and Guaranteed Faculty/Staff lots. Public Meter was its own entity and was surveyed to see if any patterns existed in those lots.

Methods:

The survey began with the creation of domains in ArcCatalog. Domains are defined values that are acceptable for attribute values. There are two types of domains: range and coded. Range domains set allowable number values for an attribute while coded domains set codes for any type of data, which include text, number, and date (ESRI, 2015). Both range and coded domains were used for the exercise. Instructions on how to properly format domains were found on ESRI's help website (ESRI, 2015c). The domains used in the on-campus collection included the following:

• Make
• Color
• Size
• Type 
• Condition
• Lot Type
• Date
• Temperature

The date and temperature domains were range domains while the other domains were coded. Setting domains increased efficiency and prevented errorrs in data collection. 

The next part of the exercise was to use ArcCatalog's feature class builder to create feature classes using the recently created domains. A point feature class was made for the car survey, which would be used to collect points in the ArcCollector app (Figure 2). The coordinate system was set to WGS 1984 Web Mercator (auxillary sphere) and the data was themed. Since the feature class domains were already made, creating the feature class was easy. Instructions about how to format the feaure class was obtained from ESRI's help website (ESRI, 2015c).

Figure 2: The picture shows the ArcCollector interface, as used on a smart phone. Attributes can be designated using the app , and domains control the allowable values of the attributes.

Once the feature class was created, the feature class was published to my ArcGIS server for use in ArcCollector online. Before publishing, I had to make sure I was signed into UW-Eau Claire's enterprise account. Information about how to publish to ArcGIS online was found on ESRI's help website (ESRI, 2015c).

After the data was successfully published, data was collected in the field. This required going to each car and recording attribute information. Data collection took two hours, and positional accuracy ranged from 3-5m on campus (Figure 3). Instructions on how to collect data using ArcCollector was obtained from ESRI help website (ESRI, 2015b).

The final part of the exercise was downloading the collected data from ArcGIS online and analyzing it in ArcMap 10.3.1.

Metadata:

Figure 3: Metadata for the on-campus vehicle survey.

Figure 4: Metadata for the off-campus vehicle survey at Festival Foods in Eau Claire, WI.

Discussion:

The data shows that most similarities and differences between cars driven by students and faculty resided in parking lot location, car type, and car make. Figure 5 shows that most student parking is on upper campus while most faculty parking is on lower campus. This is because students live in the residence halls and faculty teach in academic buildings.

Figure 5: The map shows the location of all cars surveyed during the field exercise. Lot types were combined to form the categories of student cars, faculty cars, and metered parking cars. Student parking is mostly on upper campus, while faculty parking is mostly on lower campus.

Results show the most popular type of car driven by both students and faculty was a sedan (Figures 6 and 7). In fact, 17 out of 39 students drove sedans, while 16 out of 48 faculty drove sedans. The next most popular types of cars were SUV's, with students driving 8 and faculty driving 13. A close third for students were hatchbacks (7). It is likely sedan was the most popular type of car because sedans are stylish and less expensive than trucks or sports cars.

Figure 6: The map shows the most popular types of car driven by students were sedans, SUV's, and hatchbacks.

Figure 7: The map shows the most popular type of cars driven by faculty were sedans and SUV's.

To continue, the make of car for students and faculty showed similarities. The most commonly driven cars among students (39 total) were Toyota (7),  Ford (7),  Chevy (7), and Subaru (5) (Figure 6). Faculty showed similar results (48 total) with Toyota (12), Honda (7), Ford (5), and Chevy (5) (Figure 9). Toyota, Ford, and Chevy are cars brands sold widely in the U.S., and are not too expensive. This can help explain why faculty and students drive these brands of cars.

Figure 8: The most common makes among student cars were Toyota, Ford, Subaru, and Chevy.

Figure 9: The most popular makes of faculty cars were Toyota, with 12 out of 48 cars. 

Since data was only collected for 16 cars parked in public meter spaces, the results should be taken with a grain of salt. The most significant trend I found was the most popular make of car in metered spots was Ford ,with 6 out of 16 (Figure 10) . Many other makes were present, but in much smaller quantities.

Figure 10: The most popular make of vehicle in public meter parking was Ford, with 6 out of 16 vehicles.

Considerations and Future Improvements:

After completing this lab, I realized I could have done a few things differently to improve the lab. For one, I really could have used a notes field for data collection. For example, I did not make the brands Buick or Fiat as domains in my feature class. I could have used notes to record these brands of cars, but I forgot to add a notes field before I went out and did my survey. A second thing I could improve upon would be to take pictures with ArcCollector app, which would tag a photo to a location. I could have used this to record unlisted car brands and provide better details in my lab report.

The results of the survey should be taken with a grain of sand since only 89 out of hundreds of parked cars on campus were surveyed. I did not have time to survey every car, so I did my best to survey randomly by walking diagonally between surveyed cars. In order to obtain more accurate results. I would have to survey more cars if not all of them.

Off-Campus Vehicle Survey:

Seventy-six (76) vehicles were surveyed at the Festival Foods grocery store in Eau Claire a week after the campus vehicle survey (Figure 11). The purpose of the off-campus survey was to use the experience from the on-campus survey to improve feature class design, data collection, and results. In addition, the survey would help determine trends among Eau Claire drivers.

Figure 11: The yellow box indicates where the Festival Foods parking lot is located. 

In order to improve both feature class design and data collection, more makes of vehicles were added in the make domain. The following makes were added: Buick, Cadillac, GM, GMC, Infiniti, Kia, and Land Rover. A notes field was added to the feature class prior to collection so notes could be recorded in the field if need be. Finally, the Parking Lot Type domain was deleted since there was only one parking lot for Festival Foods.

Methods from the on-campus vehicle survey were used in the off-campus survey to create domains, a feature class, and an ArcGIS online map. Collection and exporting of data also followed the same procedure. The added notes field was handy in recording the makes of vehicles that were still not in the feature class, such as Misubishi and Volkswagen.

Results show many vehicles (76 total) at Festival Foods were Ford (12), Toyota (11), and Chevy (9) (Figure 12). In addition, collected data demonstrates the most popular types of vehicles at Festival Foods were Sedans (35) and SUVs (19) (Figure 13). I believe Ford, Toyota, and Chevy were the most popular makes because these cars are relatively inexpensive, durable, and handle well in the snowy conditions of Eau Claire. Sedans and SUV's were the most popular types of cars because sedans are convenient for family travel and SUV's are well equipped for handling snowy conditions.

Figure 12: The off-campus surveyed revealed the most popular makes of vehicle were Ford, Toyota, and Chevy.

Figure 13: The survey revealed most people drove sedans and SUV's.

Conclusions:

The field exercise helped me learn how to survey data in the field using the ArcCollector app on my smartphone. Before conducting the survey, I had to carefully create domains that would make data collection easy and efficient. This made me think about what data I wanted to survey and how I wanted to survey the data before I ventured into the field. This taught me preparation is critical to the success of data collection. From the on-campus survey, I learned students and faculty often drive sedans and SUV's that are Toyota, Ford, or Chevy. The results should be taken with a grain of salt since only 89 out of hundreds of cars parked on campus were surveyed. From the off-campus survey, I learned many people drive Ford, Toyota, and Chevy vehicles that are commonly either sedans or SUV's. Overall, knowledge of feature class design, data collection, and ArcCollector will benefit me greatly in a geospatial career.

Works Cited:

ESRI. (2015a). "An overview of attribute domains." Retrieved from: http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?TopicName=An%20overview%20of%20attribute%20domains

ESRI. (2015b). "Collect data". Retrieved from: http://doc.arcgis.com/en/collector/android/collect-data/collect-tutorial.htm

ESRI. (2015c). "Prepare your data in ArcGIS for Desktop". Retrieved from: https://doc.arcgis.com/en/collector/android/create-maps/prepare-data-desktop.htm

Field Exercise 7: Topographic Survey

Introduction:

The purpose of this exercise was to conduct a topographic survey of the UW-Eau Claire campus mall using a dual-frequency GPS and Total Station. Data collected was used to create 2D and 3D surfaces of the mall. Experience gained in using high-grade survey equipment, as well as trouble shooting skills developed in the exercise, will benefit any career in the geospatial workforce.

Study Area:

The UW-Eau Claire campus mall was the main survey area for this exercise (Figure 1). The area is surrounded by buildings, including the Davies Student Center, Phillips Science Hall, Schnieder Business Hall, Centennial Hall, Schofield Hall, and the McIntyre Library. The mall area has many sitting stones, small trees, walking paths, and even a small creek. An important note is the mall has little elevational change (Figure 1).

Figure 1: The study area is the UW-Eau Claire campus mall, which is surrounded by academic buildings.

Figure 2: The campus mall has relatively little change in elevation throughout the landscape.

Methods:

High-grade survey equipment was used to collect distance, GPS, and elevation data for the study area. Equipment used in the exercise included the following:

Equipment list:

• Topcon Hiper
• Topcon TESLA
• Topcon Total Station
• Prism rod (used with the Total Station)
• MiFi wireless router
• Flags and pink tape for marking 

Surveying Using the Hiper

For the first part of the lab, we used the Topcon Hiper and Topcon TESLA units to collect elevation and GPS data of the study area (Figure 3). Before surveying could occur, the equipment had to be set up. The Topcon Hiper was screwed to the top, the TESLA unit was attached to the surveying rod, and the Mifi unit was attached to velcro on the surveying pole. A particular sequence was used to power up the equipment: Hiper first, Mifi second, and TESLA third.

After the equipment was set up and powered on, a folder was created on the TESLA to collect the elevation  and GPS data. The folder contained a file name and properties for collecting the data. The TESLA was set to collect elevation and GPS data in two different modes: solution and quick. Solution mode was set to take 10 points for each data type (elevation, and GPS), then average all 10 points to create one averaged point. Quick mode was set to average 5 points into one. The TESLA was set up to collect GPS locations in NAD83(2011), UTM Zone 15.

The last step to complete before starting the survey was to connect the TESLA to the Hiper using Mifi wireless router. The TESLA was connected to the TESLA once the rectangle at the top left was green and the Hiper icon appeared in the top right of the TESLA screen.

Now that the equipment was set up, powered on, and had a folder created for the data, we conducted the survey of the study area. We surveyed 100 points with the units. Before collecting each data point, the surveyor had to make sure the surveying rod was level with the ground. A bubble level built into the surveying rod helped level the rod. Our group had to make four separate folders to survey 100 points because the TESLA unit could only store up to 25 points in one folder. The TESLA unit automatically saved each point to the specified folder every time a point was collected.

The survey took approximately one hour, including setup, surveying, and take-down. Once the survey was finished, the TESLA was disconnected from the Hiper. All equipment was then powered down and put away in Phillips Hall.

Figure 3: The equipment used for the Hiper survey included the Hiper (top of rod), TESLA (middle unit), and surveying rod. The unit was relatively easy and efficient to use.

Surveying using the Total Station:

For the second part of the lab, we used the Topcon Total Station to collect elevation, distance, and GPS data of the study area (Figure 4). This is similar to the distance azimuth survey completed in Field Exercise 3. We used the Topcon Hiper and Topcon TESLA units with the Total Station to complete the survey.

The first step of collection was to set up all the equipment. The same procedure as discussed above was used to set up the Topcon Hiper and Tesla. Setup of the Topcon Total Station will be discussed later. After the initial setup of the equipment was complete, the Hiper, Mifi, and Tesla were turned on. The Mifi was then used to connect the Tesla to the Hiper.

Before setting up the total station, it was critical to collect GPS data for the occupied point and multiple backpoints. The occupied point is where the Total Station is grounded upon. Backpoints are points taken in the study area for the Total Station to reference when determining the location of other points. Our group collected three backpoints so that our data would be accurate, and we would have a variety of backpoints to choose from. It was important to collect very accurate backpoints and an occupied point because the accuracy of these points would determine the accuracy of our resulting data.

After collecting our occupied point and backpoints, we connected the Tesla and Total Station using the bluetooth on both devices. The bluetooth was first turned on for the Total Station, and then the Tesla was connected to the Total Station. This process had to be followed in this order because it would not work otherwise.

Setting up the Total Station was the most time-consuming process in the field that had many steps, as discussed below:

Tripod Setup (Figure 4):

• Extend legs equally to a height that will position the instrument at a comfortable height for viewing the instrument
• Attach the instrument to the tripod; ensure the instrument is centered over the occupied point
• Spread the tripod's legs equally to ensure a solid base. 
• Lock the locking mechanisms on the tripod 
• Step on footpads to ground the tripod over the occupied point

Instrument Setup (Figure 4):

• Take lens cover off the instrument and wipe lens with a gentle cloth
• Twist the knobs on the instrument to neutral. This will help in leveling the instrument
• Level the instrument on the first plane (over one of the tripod legs) using the two levels on the instrument
• Use knobs to level the instrument. If the knobs can't adjust the instrument enough, adjust the legs and start over. 
• Repeat the leveling procedure over the two other legs (planes)
• Use the "laser plummet" feature on the instrument to ensure the instrument is centered over the occupied point. 
• Adjust the instrument if the plummet is not directly on the occupied point
• Measure the height from the occupied point (ground) to instrument (measure to the yellow line on the side of the instrument)

Surveying Procedure (Figures 5 and 6):  

• Create a new job in Field Magnet to collect your data
• Go to Survey
• Enter the following:
• Instrument height (m)
• Prism rod height (set the rod to 2m)
• Set the occupied point and backsight point (using the points collected earlier in the exercise)
• Collect points
• Set the quick and solution settings (determines how many measurements the instrument will average to determine one point)
• Set your naming/numbering sequence for your points you are collecting
• Have one person take the prism rod to the site you want to collect a point 
• the person must hold absolutely still while taking the point 
• Sight the Total Station onto the prism rod
• Use the large knobs on the side of the instrument to unlock the head. This will allow you to turn the head up/down and left/right, which will make macro adjustments easier. Turn the knobs again to lock the instrument when relatively close to the prism rod.
• Use the smaller knobs on the side of the instrument to make micro adjustments to sight in the Total Station to the prism rod
• Once sighted in, hit the collect button on the TESLA, which will tell the Total Station to take the point
• Collect 25 points
• Either end the survey after 25 points, or create a new job to keep collecting
• Take down
• Disconnect the Hiper from the Total Station
• General tab, disconnect
• Disassemble all equipment and put away.

Figure 4: The Topcon Total Station was used to collect distance, elevation, and GPS data. The unit took a long time to set up, and collecting data also took a considerable amount of time.

Figure 5: A lab partner holds the prism rod, which was used to collect distance, elevation, and GPS data. The rod is placed in the location where you want to collect data, and the Total Station is sited in on the prism to collect the data.

Figure 6: A teammate sights the Total Station onto the prism rod in the distance, which is held by another teammate. The third teammate operates the TESLA (located on the right side of the picture) to collect the acutal point, 

Metadata:

Figure 7: Metadata for the Hiper GPS survey.

Figure 8: Metadata for the Total Station GPS survey.

Data extraction:

The data had to be extracted from the TESLA unit and stored on a removable flash drive after the surveys were completed. The following steps were used to extract the data:

• Go to Magnet Field
• Go to Exchange
• Select your file(s) for export
• Select the export format as .txt
• Select your flash drive as the device you want to export the data to
• Name the output folder
• Set the delimiter and plane coordinate position settings
• Finish

2D Rendering:

The exported text file was edited on a computer to delete any unnecessary text. The exported data was then used in ArcMap to create a feature class that contained x,y,z data. The Kriging interpolation method was used to create a raster surface of the elevation data for both the Hiper and Total Station survey (Figure 9).

3D Rendering:

Data for both the Hiper and Total Station was used to create 3D surfaces of the UW-Eau Claire campus mall (Figures 10 and 11). ArcScene was used to build the 3D models. In ArcScene, the interpolated surfaces (kriging) from both surveys were brought into the scene, and the baseheights were rendered on a floating surface. Since the campus mall had little elevation change, the rendering was set to a factor of 3.

Discussion: 

The data collected for the campus mall was relatively accurate. Figure 9 shows the Hiper survey produced more detailed results than the Total Station survey. This is because 100 points were collected using the Hiper, while only 25 points were collected using the Total Station. Our group only collected 25 points using the Total Station because by the time we had all the equipment running and had solved many issues, it was time for our group to go. In addition, the Hiper could only survey 25 points for each project. If we wanted more points for the Total Station survey, we would have had to retake our occupation point and backsight point.

The 3D surfaces in Figures 10 and 11 show changes in elevation of the campus mall relatively well, but the base rendering had to be set to a factor of 3 to show the surface. It was hard to see any 3D elevation changes without exaggerating the rendering. This is because the campus mall has very little elevation change. An inaccuracy can be seen in Figure 11 at the top right-hand portion of the image. This is because only 1 point was collected in this area. To fix the error, we should take more points in that area. The 3D model in Figure 8 is very accurate since we took 100 points in many different places.

Figure 9: The two maps show elevation data for the UW-Eau Claire campus mall determined using two different methods. The top map demonstrates the accuracy of the Hiper survey, and the bottom map documents the accuracy of the Total Station survey.

Figure 10:The 3D surface for the Hiper survey was created in ArcScene by rendering base heights on a floating surface by a factor of 3. Since 100 points were used to construct the model, the model is very accurate.

Figure 11: The 3D surface for the Total Station survey was created in ArcScene by rendering base heights on a floating surface by a factor of 3. Since only 25 points were collected for the model, the model was not very accurate in some places. For example, only one point was used to represent the elevation of the top right portion of the model. This resulted in a jagged elevation suface in the model, as seen above.

Conclusions: 

The exercise taught me how to conduct geospatial surveys using a dual-frequency GPS and a Total Station. By conducting a survey with each method, I learned the dual-frequency GPS takes much less time to use and still produces quality data. Although the Total Station produces quality data, it takes a very long time to set up and there are many opportunities for errors to arise in the technology when conducting the survey. Overall, the exercise added to my knowledge of geospatial tools and increased my problem-solving skills, which will be valuable in the geospatial workforce.