Lab 10: Distance Azimuth Survey

Introduction: 

GPS technology can be a great tool when completing field work. However, technology isn't always the most reliable and can fail at the worst of times. Learning how to conduct fieldwork using a non-technology based method could be very beneficial in the time of need. The objective of this lab was to conduct a distance azimuth survey. Tree data was collected along Putnam Drive at the University of Wisconsin-Eau Claire. The data was then organized and entered into an Excel spreadsheet to be imported in ArcMap and used to create useful maps.

Methods: 

Ten samples were collected at the first stop on Putnam Drive and then another six samples were collected about 15 meters away. A designated spot was used for a student to stand to take measurements for all of the samples to ensure accuracy for the latitude and longitude coordinates. There was a designated spot for the first ten samples and then a separate spot for the second six samples.  For each tree, the following attributes were collected: lat/long coordinates, distance, circumference, azimuth and tree type. To record the data, a series of instruments were used that don't all rely on technology: BadElf GPS, tape measure (Figure 1), compass (Figure 2), and a laser.

Figure 1: Compass to Measure Azimuth                                      Figure 2: Tape Measure Recording Circumference

The BadElf GPS was connected to an iPhone via bluetooth to collect the lat/long coordinates. The circumference of the trees were recorded in centimeters using a tape measure. The compass was used to record azimuth. This is where the designated spot was utilized. One student stood in the same spot on one side of Putnam Drive during the data collection of the first ten trees to ensure the azimuth was correct and that another student could then repeat the same steps and find the right trees that were analyzed in this lab. The direction shows on face of the compass but it is more accurate to look through the eye hole on the side. For measuring distance, the student standing at the designated spot held a laser that determined the distance to a sample tree in meters. All of the data was recorded in a field notebook during the process and was then later transferred into an Excel document (Figure 3). 

Figure 3: Data Organized in Excel

Then, the Excel table was imported into ArcMap and the Bearing Distance to Line tool was utilized (Figure 4). This tool creates a new feature class representing a line from the attributes. The azimuth data goes into the Bearing Field for this tool. The Feature Vertices to Points tool was also used on this data. This tool creates a point feature class containing all of the attribute data that was collected for each of the trees.

Figure 4: Bearing Distance to Line Tool

Results: 

Using graduated symbols, a map was created to show the difference of circumference between all of the trees (Figure 5).  The smallest recorded circumference from this data set was 12 centimeters to the largest at 116 centimeters, so there was quite the range. Looking at the map, one of the vertexes appears to not be located on the edge of Putnam Drive like the other; this was an error in our data. It was hard to find the problem that caused this slight shift but overall the rest of our data was relatively accurate. 

Figure 5: Tree Circumference

 Figure 6 shows the difference of azimuth in each tree with graduated colors. Most of the trees were recoded within 2 - 80 degrees.

Figure 6: Azimuth of Trees

Conclusion: 

Although technology is a fast and handy tool, it may not always be the most reliable. In this lab, a distance azimuth survey was completed using mostly non technological field tools. It is important to be able to know how to conduct research this way in the scenario that technology fails. Luckily, there are high-tech tools that can be used when completing surveys that have a much larger area to survey. GPS tools today have very high capabilities in recording extremely accurate locations amongst other abilities. 

Lab 9: Arc Collector Part 2: Research Project

Introduction:


In Lab 8, the class split up into groups and collected microclimate data in one of the seven designated zones on the University of Wisconsin-Eau Claire campus and then compiled all the data together. The data that was collected in ArcCollector on the smart phones was then accessed on ArcGIS Online and exported to be opened in ArcMap. Different maps were created to show each of the attributes of data collected. More information regarding the previous lab can be found here. The objective of this lab was to create individual projects that answered a spatial question. The requirements were to think of a point feature to gather and then decide what attributes would be associated with that particular point.

The Eau Claire Planet Walk is a one-mile scale model of the solar system that has a beautiful view of the Chippewa River along the route. Throughout the mile walk, there are a small monuments representing the planets along the route that are located along the scaled model of where they would be in relation to distance in the solar system. Each monument (Figure 1) includes a very detailed description and other fun facts about that specific planet as well who the monument was donated by. There is also a small map on each monument to show where how far long the route that planet is.The spatial question for this lab was determining whether the scale model of the solar system is an accurate representation of the real life distances of the planets to the sun. Attributes that were collected for this lab were planet name, distance from the sun in astronomical units, radius of each planet, condition of the monuments, and a notes section that included information regarding who the monument was donated by. To complete the lab, the long process of creating a geodatabase needed to be attained. This was a very important step in the process in collecting accurate data. ArcCollector was used to retrieve the data so then it could be analyzed on ArcGIS online. 

Figure 1: Saturn Monument

Study Area: 

The study area for the research project spanned from Phoenix Park to the end of Randall Park (Figure 2). The route is paved and ends up leading to a bridge that crosses the river. The Sun is in Phoenix Park and Pluto is in Randall Park. The walk is welcome to start at either park depending on which end of the solar system the walker would like to start at.

Study Area: Eau Claire Planet Walk

Methods: 

After deciding what the spatial question and attributes would be, it was time to create fields and domains to be used in ArcMap. A detailed step by step process on ArcGIS Online demonstrates how to create this. Going to properties in the database, the domains and their descriptions could be added (Figure 3). The domains could be set to different types. For example, the Condition domain was set to coded values: perfect, good, fair, inadequate. The field type was also changed to either text, long integer or float. During the process, a decision was made to record the distance from the planets to the sun by astronomical units because that is the commonly used measurement for solar distance. Each domain helped to represented each monument while aiding with the spatial question.

Figure 3: Creating the domains 

After the domains were created, the data needed to be shared to ArcGIS online to be used for the online map. Once signed in to ArcGIS in ArcMap, the option to connect to My Hosted Service allowed the data to be shared (Figure 4). Another important step was to the check the boxes of Create, Update, Delete, and Sync in the Feature Access tab to allow changes to be made while collecting the data. Once all of the necessary steps were completed, the data could be published to ArcGIS Online to then be used in ArcCollector.

Figure 4: Publishing process to ArcGIS Online

Using ArcCollector on a smart phone, the data points were recorded at each planet monument along the Eau Claire Planet Walk route (Figure 5). Data was recorded within the appropriate domains. 

Figure 5: Recording data in ArcCollector

Results: 

Once all the data points were collected, they were analyzed in a map in ArcGIS Online. The link to this online map can be found here. There is an option to select the individual points to where a pop-up window appears displaying the attribute information about each of the monuments (Figure 6). The figure below shows the Neptune monument with all the information of each attribute except for the Notes section. This was a user error that occurred within the process. Who the monument was donated by was recorded in the notes section for each planet in ArcCollector but it did not save or show up in ArcGIS Online. It was discovered that the field type was set to Float instead of Text while creating the domains back in the beginning. This error just proves that the initial domain creation is very important when collecting data because one wrong step could end up disrupting the results.

Figure 6: Pop-up window in ArcGIS Online

Next, the data was exported to be used in ArcMap to create several maps. Figure 7 shows the difference of condition between each of the planet monuments. Condition was determined whether there were scratches or dents on the surface of the monument where the text was located. It appeared that most of the monuments were in perfect or good condition. The fact that all the monuments are outside and exposed to the atmosphere and people passing by makes it difficult for them to stay in perfect condition. The only monument that is free from outside conditions is the sun monument in the Phoenix Park (Figure 8). The monument plaque is enclosed in a glass case free of outside conditions.

Figure 7: Monument Condition

Figure 8: Sun Monument at Phoenix Park

The next two maps show distance (Figure 9) and radius (Figure 10) in relation to the planets. The radius is represented in miles and is shown in different sizes along the route. Each monument is the same size except for the sun so it is beneficial to compare model scale real life measurements on a map to compare how the sizes of the planets look in the solar system. The map shows how the planets closer to the sun are smaller and then further out they get bigger and then start to get small again. The real life distance from the sun was measured in astronomical units because that is a commonly used used measurement relating to the solar system. One astronomical unit is equal to about nine million miles which is shown on the map. This map helps to answer the study question by tracking the planet monuments and then recording the real life distance. 

Figure 9: Planet Real Life Radius Size in Miles

Figure 10: Real Life Distance from the Sun in AU

Conclusion:

This lab demonstrated the importance of the initial planning and proper project design of data collection. The process is long and detailed and steps can easily be missed or performed inaccurately. Personal error caused missed data results because of a mix up while creating the domains. The actual data collection is the most exciting part of a research project but it is extremely important to have everything set up correctly so that the results are the most accurate they can be. For this research project, the Notes attribute were not the most important attribute so that it didn't really affect the results but that information would have been nice to have. The answer to the spatial question was that the scale model of the solar system does not perfectly represent the real life version. However, the Eau Claire Planet Walk is still a route that provides people descriptive information about the planets in our solar system while presenting beautiful views of the Chippewa River. For a future research project, it would be interesting to determine a study question that would involve collecting more data points in small area compared to fewer data points in a large area.

Lab 8: Arc Collector Part 1: Microclimate

Introduction: 

Smart phones are a useful tool for GPS data collection because they actually have a higher computing power than most GPS units. Because smart phones have the ability to access online data, the points update as they are being collected so the user can see the progress. For this lab, ArcCollector was downloaded so that each student has the ability to collect data from his/her phone. The objective of this lab was for the class to split up into groups and collect microclimate data in one of the seven designated zones on the University of Wisconsin-Eau Claire campus and then compile all the data together. This group collected data in zone four. The data that was collected in ArcCollector on the smart phones was then accessed on ArcGIS Online and exported to be opened in ArcMap.

Study Area:

The area of study for this lab was within the seven zones located at the University of Wisconsin-Eau Claire campus (Figure 1). The zones were situated on upper and lower campus, as well as across and on the walking bridge.

Figure 1: Study Area Located at the University of Wisconsin-Eau Claire

Methods:

First, a geodatabase was created before the data collection could be stared. Then, eight attribute fields were created: surface temperature, temperature at 2 meters, dew point, wind speed, wind direction, surface type, and notes. This helps to avoid errors because the data collected will fall under each domain in the attributes. For example, integers are rounded and wind direction is limited to 0-360 degrees.

Before the points could be collected, the last task was to connect to the online map through the ArcCollector app. This way the data points could be viewed and analyzed on ArcGIS Online to then be exported. Two different tools were used to help in collecting the data: a compass and a Kestrel. Each group was assigned one of the 7 group zones and then headed to that designated area. Data points were then collected evenly throughout the zone. The data points were updated on ArcGIS online as each group were collecting the points (Figure 2).

Figure 2: Data Points on ArcGIS Online

After all the points were collected by each group, the data could be analyzed online. ArcGIS online gives the user the option to analyze the different attributes on the site. For example, Figure 3 shows the different wind speed values that each data point collected. However, the data points were exported to be used in ArcMap to create a few maps to represent the data.

Figure 3: Wind Speed Data in ArcGIS Online

Results:

Figure 4 is comparing the surface temperatures and the dew point temperatures at each data point. For the most part, it looks like trends with surface temperatures seem to correspond with the dew point temperatures. A noticeable trend with the data points is that the warmer temperatures seemed to be collected in areas where the surface type was pavement. The pavement heats up faster than bare ground under direct sunlight so that could be the cause for high temperatures in those regions. From looking at the colors on the map, it appears that the most common temperatures that were recorded fell between 26-42 (light green) and 43-47 (yellow).  Groups of similar temperatures tend to cluster in the same areas with few outliers. These outliers could be user error or could have been effected by shade, wind, surface type, or snow cover. Referring back to Figure 3, wind speed also appears to correlate with temperatures. Data points that collected higher wind speed also tended to record more cooler temperatures. A great example of that is located along the walking bridge. The data points across the bridge were overall collecting cooler temperatures and also the highest wind speeds. However, as said earlier, there are a few outliers that could be user error or field error.

Conclusions:

Being able to collect data points on a smartphone makes it easy and efficient to gather data points out in the field. The goal of the lab was to collect microclimate data in each of the seven group zones on the University of Wisconsin-Eau Claire campus. ArcCollector was a great app to use for this lab. It was very user friendly and sufficient. Accessing the data points on ArcGIS Online was a great feature. The site itself allows the user to compare the attributes of each point but exporting the data to use in ArcMap was more useful to create maps with all of the necessary map components. ArcCollector would be a great tool to use for future projects.

Sources:
http://uwec.maps.arcgis.com/home/index.html
http://arcg.is/1CK5Sj