Lab 11: Navigation with a GPS device using a UTM Coordinate System

Introduction

In this lab the field navigation maps created in Lab 4 were used to locate supplied UTM coordinates within the Priory.  The coordinate locations were located by using the navigation maps and a digital GPS unit.  The UTM coordinates were pre plotted on the map and then located.  Once the coordinate location was located spray paint and ribbon were placed on the tree at that location to mark the location.

Study Area

The study area that was navigated through to find coordinate locations was a piece of land called the Priory.  The Priory is an area of land owned and operate by the University of Wisconsin- Eau Claire.  This land is used as a private dormitory and a 120 acre wooded area that is used as a children's nature preserve.  Maps of the Priory in relation to the University of Wisconsin- Eau Claire and the Priory itself are in Figure 1 and Figure 2.

Figure 1. Image highlighting the location of the Priory in reference to the University of Wisconsin- Eau Claire main campus.

Figure 2. Image displaying the appearance of the Priory campus.

Methods

Before arriving at the Priory study location the iPhone application was downloaded onto each student's iPhone from the iTunes app store.  What the app looks like can be seen in Figure 1.

Figure 1. What the iPhone application Bad Elf GPS looks like on an iPhone device.

Upon arriving at the Priory study area, the class was divided into a few groups and each group was given a printed off field navigation map created in Lab 4 and a list of five UTM coordinate locations.  Each group plotted their five UTM coordinates on the navigation map with a pen.  The list of five coordinates that were located by this group of students can be seen in Figure 2.  The navigation map used can be seen in Figure 3.

                                                                                                   Figure 2. List of the five 

UTM coordinate locations to be 

located by this group in this lab. 

                                                                               Figure 3. Navigation map used to                                                                                             locate five UTM coordinate locations                                                                                                       on the Priory property. 

Once the UTM coordinate locations were plotted on the navigation map, a digital GPS unit was given to each group to give the exact UTM coordinates of the unit to help with the accuracy of the navigation.  The GPS unit can be seen in Figure 4.  A tracking device was also given to each group so a track log of the path to each coordinate location could be generated after the navigation.



Figure 4. Digital GPS unit used to
help improve accuracy of the
navigation.








The Bad Elf GPS iPhone application was also opened and the map units were changed from decimal degrees to UTM.  The application was helpful by giving a satellite image map that in real time gave the location of the iPhone.  It also gave UTM coordinate locations much like the mobile GPS unit did.  Figure 5 shows what the home interface of the Bad Elf GPS iPhone application looks like.

Figure 5. Screen shot of what the Bad 

Elf GPS iPhone application looks like. 

To locate all of the UTM coordinate locations the GPS digital device, UTM navigation map, and Bad Elf GPS iPhone application were all used.  The navigation map gave the idea of the general direction to travel to find the coordinate point.  The iPhone application was used to give reference to the exact point the group was located at live time.  The digital imagery in the application was compared to the navigation map to give reference to how close the group was to the coordinate location.  The digital imagery on the application can be seen in Figure 6.  The GPS digital device was used once the group was close to the correct location to finalize and check that the UTM coordinates on the GPS unit matched exactly with the provided UTM coordinate location.    

Figure 6. Digital imagery used in the

 Bad Elf GPS iPhone application. 

Upon arriving at the correct UTM coordinate location.  The tree at that location was sprayed with orange spray paint and tagged with a yellow ribbon.  Figures 7 through 10 show the trees that were tagged by this group in the order that they were tagged.      

Figure 7. The tree that was tagged to represent the fourth coordinate location.  

The fourth coordinate location was tagged first.  This tree was located in a 

clearing in the forest.   

Figure 8.  The tree that was tagged to represent the third coordinate location.

The third coordinate location was tagged second.  This tree was located 

in a flat area of thick forest cover with a lot of vegetation.

Figure 9.  The tree that was tagged to represent the second coordinate location.  

The second coordinate location was tagged third. This tree was located 

in a flat area of thick forest cover with a lot of vegetation. 

Figure 10.  The tree that was tagged to represent the first coordinate location.

The first coordinate location was tagged forth.  This fallen tree was 

located on a steep slant on the side of a hill in an area 

of thick forest cover and with a lot of vegetation.   

The fifth coordinate location was located on an extremely steep slope with thick tree brush and both the Bad Elf GPS iPhone application and the digital GPS unit were not able to get a signal to help locate the exact coordinate location.  No tree was marked for the fifth coordinate location because there was not enough accuracy to confidently mark a tree.

Results/ Discussion

Figure 11 shows a map the track log of the path traveled to locate the UTM coordinates along with the correct coordinate locations.  The path traveled does match up with the coordinate locations.  Every coordinate location was placed in the generally correct spot according to the track log path.  It can be seen that the most direct path to each coordinate location was not always taken.  There are many loops and backtracking that occurred while locating the coordinate locations.  Even with considering that coordinate location five was not marked due to fear of inaccuracy and problems with the location service devices, the track log showed that the group was in the correct location and was not far off from the place where the tree should have been marked.  The terrain of the priory is very hilly with steep slopes.  The vegetation is very thick making a direct path almost impossible when navigating.  A possible source of error that could have occurred during this lab is if there was not a tree present at the exact location of the UTM coordinate.  The tree closest to the coordinate location was marked if no tree was present at the exact spot.  That could cause a minimal source of error.

Figure 11. Map of the path taken through the priory to locate the UTM coordinate locations.

The path taken suggests all marked locations were placed in the right place. 

Conclusions

Reverting back to locating UTM coordinate locations using a simple UTM navigation map was more difficult than expected.  Modern day technologies can make it appear that basic navigation map reading is a no longer crucial skill, but it is.  Technology is not always reliable, so it is important to know how to read a basic navigation map in case advanced technology is malfunctioning or not available.

Sources

Priory Hall. (n.d.). Retrieved March 01, 2017, from http://www.uwec.edu/Housing/residencehalls/priory/priory-hall.htm.

Lab 10: Using dual frequency survey grade GPS to collect soil data on a community garden

Introduction

The purpose of this lab was two part.  Part one was to practice using dual frequency survey grade GPS to collect data in the field.  The survey grade GPS was used to collect attributes on a community garden in a neighborhood in Eau Claire, WI. The soil pH, soil temperature, and soil moisture were measured at 28 dispersed points within the garden.   Part two was to use the Survey GPS to gather GCP marks and use an unmanned aerial system (UAS) to collect imagery to use in visualizing the results from part one data collection.






Study Area

The study area for this lab was a community garden called the South Side Gardens.  Figure 1 shows the location of the garden.  The plots within the garden are 10' by 20' or 20' by 20'.  The garden is enclosed with a 9 foot deer fence, which can be seen in Figure 2.  Evenly spaced samples were taken down the rows of plots.    

                                                                             Figure 1. The study area of the South Side 

                                                             community garden.

Figure 2. A picture taken at the South Side 

Gardens of the garden plots.  

Methods

Part 1:
Four key pieces of equipment were used to complete this lab:

• TDR Probe
• pH Probe
• Temperature Probe
• Topcon Survey Grade GPS

First, 28 orange flags were placed around the garden study area marking the locations that samples will be taken.

TDR Probe

A Time Domain Reflectometry (TDR) Probe was used to measure the volumetric water content of the soil as a percentage.  The probe had two long metal prongs that stuck out of a purple box with a screen and two handles.  The probe worked by sticking the two prongs into the soil and pressing the READ button on the purple box.  The probe works by measuring the travel time of waves along the probe in the soil to give the moisture measurement.  The screen rendered two numbers, the HiClay vWC% measured number of the top number on the screen was recorded after taking a few measurements around the sample point and averaging the values.  Figure 3 shows the probe in use and Figure 4 shows what the screen on the device looks like after taking a measurement.  






Figure 3. Classmate Kayla using the TDR probe by sticking the prongs into the ground of the sampling location.

              

                                                                                                                                                                      Figure 4.  The appearance of  the TDR probe with the prongs sticking into the ground.  The top number on the screen is the value that was recorded.

                                                                                             

pH Probe

A pH probe was used to measure the pH level of a soil sample at each sample point in the garden.  The probe was calibrated by using special solutions.  The probe was always rinsed with distilled water before a measurement was taken.  A sample of soil was taken in the cap of the probe and mixed with distilled water to form a solution.  Next the tip of the probe was inserted into the solution resulting in a pH measurement of how acidic or basic the soil being sampled was. Figure 5 shows the pH probe being used to measure the pH of a soil sample. 

Figure 5. Classmate Alex using the 

pH probe to get a pH measurement 

of how acidic or basic the soil     

sample was.                    

Temperature Probe

A temperature probe was used to measure the temperature of the soil in Celsius.  The probe was used by inserting the probe end directly into the soil and reading the temperature value off of the attached box.  Figure 6 shows the soil temperature being taken.

Figure 6. Temperature probe is inserted into the ground to 

get the soil temperature.

Topcon Survey Grade GPS

A Topcon Survey Grade GPS unit was used to gather the coordinate location of the sample point.  The unit was able to get the coordinate location within centimeters of the exact location.  The unit was placed directly over the flagged location where samples were taken.  After the coordinate location of the sample point was recorded.  All of the other attributes, soil moisture, pH, and temperature were manually entered in the electronic unit at each point as well.

Figure 7. The Topcon Survey Grade GPS unit used to record the 

coordinates of the sample point and record all of the attribute 

data of the samples taken at the location. 

The TDR soil moisture measurement, temperature, and pH measurements were all recorded on paper in a notebook as a hard copy and inputted into the Topcon survey grade GPS equipment for each location samples were taken.  Hard copies should always be taken in case of a technological error or malfunction.

Following the lab the flags marking sample locations were collected.  The data recorded in the Topcon Survey Grade GPS unit was downloaded and normalized for use in ArcMap.

Part 2:

In part two a Matrice 600 (M600) platform was used to collect aerial imagery of the study area.  An image of the M600 can be seen in Figure 8.    

Figure 8. Matrice 600 platform used to collect aerial imagery of the garden study area. 

Two other components were used to fly the M600.  A RTK was used to give accuracy to the flight.  The RTK can be seen in Figure 9.  A controller was also needed to be able to control the M600 while in flight.  An iPad was attached to the controller with a preset flight path that the M600 followed during its flight.  The controller and iPad can be seen in Figure 10.  

Figure 9. RTK used to improve the                                  Figure 10. Controller and iPad used to

accuracy of the flight.                                                               control the M600 while in flight.

Before the M600 could be flown a total of nine ground control points (GCPs) needed to be laid out in the garden.  At each location where a GCP was placed the Topcon Survey Grade GPS unit was used to record the exact coordinate location of the GCP.  This was done by placing the Topcon Survey Grade GPS unit was placed directly over the center of the pink and black GCP.  The unit being properly placed over a GCP can be seen in Figure 11.  When placing the survey grade GPS unit it was important to make sure the center spoke was dead center over the GCP and the unit was level by making sure the bubble on the unit was placed center to a circle guaranteeing the unit was level.  The bubble that needed to be perfectly positioned can be seen in Figure 12.

Figure 11. Professor Hupy demonstrating how properly center the Topcon Survey Grade GPS unit over a ground control point.  

Figure 12. The bubble on the Topcon Survey Grade GPS unit that needed to appear within the black circle to insure that the unit was level and accurate coordinate locations would be recorded.














After all GPS were in place with coordinate locations recorded, flight checks on the M600 were conducted.  Software updates were required and a few other glitches needed to be resolved before the M600 could fly, like remembering to place the SD card in the apparatus.  Once all of the proper adjustments were made the M600 flew a predetermined flight path.  The M600 taking off for flight can be seen in Figure 13.  In Figure 14 the max altitude the flight was flown at.  The M600 can reach very high altitudes.  A video of the M600 landing after its flight can be seen in Figure 15.

Figure 13. The M600 taking off from the                                 Figure 14.  The M600 at the max 

ground to fly its flight path.                                                    preset altitude for this flight path. 

Figure 15.The M600 landing after completing its flight path.

The flight imagery was processed in Pix4D software following the M600 flight.  Both the Pix4D extracted imagery and the data coordinate points and attribute data collected in part one were imported into ArcMap for data interpretation and map creation.  The natural neighbor interpolation was performed on four attributes, elevation, soil pH, soil moisture, and soil temperature, for data interpretation.    

Results

The results from the unmanned aerial system (UAS) flight done by the M600 platform turned out rally well.  Some of the imagery collected by the M600 can be seen in Figure 16.  The garden that was the area of study can be seen just below the line of trees in the center of the imagery.

Figure 16.

Once imported into ArcMap the soil data collection points were visible in their correct coordinate location recorded by the Topcon Survey Grade GPS unit.  The locations of where the soil attribute data was collected within the community garden can be seen in Figure 17.

Figure 17. The points were soil attribute data was collected within the community garden.

Figure 18, shows how the elevation changes through the sampled area in the community garden.  Moving west to east through the garden the elevation increased.  The highest elevations are located on the east side of the garden and the lowest elevations are located on the west side of the garden.  One could expect that during a heavy rainfall the plots on the west side of the garden located in the lower elevations would receive much of the runoff water and become over saturated.  

Figure 18. Elevation of the community garden.  Elevation increases west to east 

across the garden plots.

Figure 19, shows the distribution of soil pH through the sampled area of the community garden.  A general observation is that the more acidic pH locations are on the outer edges of the sampled area with a higher concentration located on the west side of the garden.  The more basic pH levels are located in the center of the study area in the center of the community garden.

Figure 19.  Soil pH in the community garden.  Soil pH is generally more acidic on 

the outer edges and the west side of the community garden, while 

the center of the sampled area has a more basic pH.

Figure 20, shows the distribution of soil temperature through the sampled area of the community garden.  The soil temperature was higher on the east side of the garden than the west side.      

Figure 20. Soil temperature in the community garden.  Soil temperature was lower 

on the west side of the garden and higher on the east side of the garden.

Figure 21, shows the distribution of soil moisture content through the sampled area of the community garden.  The soil moisture content was higher on the east side of the garden than the west side of the garden.  This is unusual because the west side of the garden is at a lower elevation than the west, so any runoff water from any precipitation would be moving toward the west side.  The soils on the east side of the garden must have more porosity and hold water more tightly than the soils on the west side of the garden.

Figure 21. Soil moisture content in the community garden.  The soil moisture 

was generally higher on the east side of the garden.

Overall the best place to have a garden plot in this community garden depends on what type of crop is being grown.  If a crop requires more water it should be grown on the east side of the garden unless the crop is being grown during a rainy season which due to elevation and water runoff, the west die of the garden would be expected to have a higher moisture content.  Plants generally speaking grow best in soils of pH between six and seven.  A plot located more on the edges or more on the west side of the garden would provide the best pH levels.  If the crop being grown requires warmer temperatures plots on the east side of the garden would provide warmer temperatures.  Overall, the east side of the garden provides the most ideal locations for plant growth.    

Conclusions 

A significant amount of data was collected in this lab.  It is important to always have hard copies of the data in case an error with the electronic data occurs.  The sector of geospatial techniques that works with using UAS platforms is a growing sector with many applications.  Learning the basics behind this technology and the possibility of the applications is very valuable.

Sources

A method of measuring soil moisture by time-domain reflectometry. (n.d.). Retrieved May 01, 2017, from http://www.sciencedirect.com/science/article/pii/0022169486900971.

Eau Claire Community Gardens. (2016). retrieved May 01, 2017, from http://www.eauclairecommunuitygardens.com/southside/southside.htm.

Lab 9: Arc Collector 2- Creating your own database, features, and domains for deployment and use in Arc Collector.

Introduction

This lab builds on the skills developed in Lab 8, using the Arc Collector app to collect data using a previously created database.  In this lab each student was to generate their own research question, create the database to be deployed to the Arc Collector app, and use the Arc Collector app to collect data to answer that research question.

For this project the research question was: which street of the main off campus student housing area at the University of Wisconsin-Eau Claire is the biggest party street?  

To collect data on this research question the main 20 blocks of off campus student housing at the University of Wisconsin-Eau Claire were walked and data was entered in the Arc Collector app for every discarded alcohol bottle or can spotted.

To answer this question data was collected for each alcohol container on the following variables:

• Alcohol Percentage
• Container Type
• Name of Beverage
• General Type of Alcohol
• How full the container was
• Date
• Time 

Study Area

The study area, Figure 1, selected was the main 20 blocks of off campus student housing for University of Wisconsin- Eau Claire students.  This section of housing is directly across the Chippewa River from campus and the houses located here are by majority filled with college students.  The study area runs north to south from Water Street to Lake Street and east to west from 1st Avenue to 5th Avenue.  On Water Street there are two campus buildings on the eastern side of the study area and on the western side of Water Street in the study area is a small down town area with restaurants, shops, and bars.

When data was surveyed, the sidewalk on both sides of every street were walked and any alcohol containers that were found in the street or lining the sidewalk in the front yards along the streets were used as a data point.  Alley ways and deep into the yards of the houses were not used as locations to collect data from.

Figure 1. The study area used to collect data on discarded alcohol containers in off campus University of Wisconsin- Eau Claire student housing. 

Methods

After developing the research question the first step to create a geodatabase that could be deployed to the Arc Collector app to collect the data for the project.  To begin a file geodatabase specifically for the project was created.  Within the geodatabase under properties in the domains tab, domains were set for the fields of data that will be collected.  Domain names were created with a description, an appropriate field type was set, and coded values were created.  Domains help enforce data integrity and make data entry easier by providing a list of valid values for the field.

After the domains were set within the geodatabase, a new point feature class was created within the geodatabase.  The coordinate system was set to WGS_1984_Web_Mercator_ Auxiliary_Sphere.  Field names were generated to match the domains created in the geodatabase.  Most defaults were accepted and the feature class was generated.  A summary of the field names generated matched with their geodatabase domains can be seen in Figure 2.  In the table if no domain name or coded value is specified that means no domain was created and the value for that field was manually entered during data entry.  These fields were selected as the data to collect because they will give insight into different factors that might help uncover which street is the biggest party street.  It could be expected that the containers found with the greatest alcohol percentage could denote an area of greater partying.  Areas with greater varieties of types of alcohol and greater variety of alcohol container types can also be assumed to be bigger party areas with more people bringing many different types of alcohol and containers.  How empty or full a container is can help decipher why the container was there, was it discarded or left behind at this location?  These fields will help answer the research question of which street is the biggest party street.      

Figure 2. Table summarizing the domains that were created in the geodatabase.

Once the feature class was created, ArcMap was opened and the Map Document Properties were set to the newly created geodatabase.  ArcGIS Online was logged into in ArcMap using the University of Wisconsin- Eau Claire enterprise account in the ArcMap viewer.  A topographic basemap was added to the viewer and a new feature class was created in the project database by using the editor toolbar and creating a polygon feature encompassing the study area.  The polygon was then symbolized as hollow to become a boarder of the study area.  The new point feature class was added to the data frame and a test point was created to verify that all of the fields and domains were correctly operating, any problems were corrected in ArcCatalog.  The basemap was removed from the viewer, and the ArcMap document was saved as a .mxd file.  In ArcMap under File, Share, and then it was selected to share as a service.  In the Service Editor Window all fields were filled out.  Under Feature Access all boxes (create, delete, query, sync, and update) were checked, the item description was filled out, and it was shared as a service.

Next, ArcGIS Online was opened in a web browser and the University of Wisconsin- Eau Claire enterprise account using the student's credentials.  Under My Content the map that was deployed can be seen.  The map was saved and shared with a desired group if desired.  The map then became available for data collection on the Arc Collector application.  An iPhone was used for data collection.  The data was collected on a Sunday morning as to maximize the chance of finding a greater number of alcohol containers after two generally big drinking days, Friday and Saturday.  In Figure 3 all of the points of data collection (alcohol containers) can be seen the way they appeared in Arc Collector.  In Figure 4 is the data collection window with all available fields.  The data was collected by selecting the plus sign in the main window of the app over the alcohol container to record its coordinate location.  Next the container was analyzed and the data collection fields were filled in with information corresponding to the alcohol container.      

            Figure 3. The points of data collection                      Figure 4. The data collection  

            as they appear in the Arc Collector app.                    window as it appears in the                                                                                                          Arc Collector app.

As the data was recorded all of the alcohol container were collected and recycled.  The collection of all 100 surveyed containers can be seen in Figure 5.

Figure 5. All 100 surveyed alcohol containers were

recorded as data points, collected, and recycled.

After all of the alcohol containers were surveyed the data was used in ArcGIS Online to analyze the data in various web maps.  Also the data was opened in ArcGIS Desktop where static maps were created to visualize the results.

Results/Discussion

ArcGIS Online also allows for the creation of web maps and visualization of collected data.  Below is a web map created in ArcGIS Online has been embedded.  This feature of ArcGIS allows for easy data interpretation if ArcGIS Desktop is not available.  This one is for the simple use of locating the data points collected in this exercise.  Each point should be able to be clicked to read about the attributes of the alcohol container found at that point.  It was just by chance that exactly 100 alcohol containers were located and recorded as data.  The number 100 was not a number desired to attain or maximum/minimum number that was set.  From looking at this web map it can be seen that most of the alcohol containers were found in the southwest corner of the study area.  This area is both close and far away from the University of Wisconsin Eau-Claire campus which is located closest to the study area in the southeast corner.  The most alcohol  containers were located close to Water Street which is where all the bars and a small downtown are located.  The increased number of alcohol containers near the bars could be due to people discarding their alcohol bottles on the street before entering the bars.  But in contrary with this conclusion is the minuscule number of alcohol containers located on Water Street.  An explanation for this could be that the city of Eau Claire does a good job keeping the downtown clean by cleaning up the alcohol containers.  Overall the most alcohol containers were located closer to campus, specifically at Chippewa Street and 4th Avenue. 

Figure 6 shows the spatial patterns of where discarded alcohol containers were found based on how full the container was when it was found.  The more full containers were found closer to campus, closer to Water Street.  This could be due to people discarding their containers before moving to Water Street for the bars.  The most half full containers were located in the middle street of Broadway.  The empty containers were scattered overall all over the 20 block study area, with no observable pattern.

Figure 6. The locations of surveyed alcohol containers by the fullness of the container when it was found to be surveyed.

Figure 7 shows the spatial patterns of what the collected alcohol container was made out of.  It appears that the most popular container is an aluminum can, the second most common is the glass bottle and the least common is the plastic bottle.  This is interesting and leads someone to believe that this is a mostly beer drinking campus, with beer being a common alcohol drank out of an aluminum can.  Glass bottles were located closer to campus with none being located in the farthest two blocks from campus. The greatest variety of container types falls along Chippewa Street suggesting it is the biggest party street, bringing in a larger number of people who would bring in a variety of containers. 

Figure 7. The locations of surveyed alcohol containers by the type of container found when surveyed.

Figure 8 shows the spatial patterns of what type of alcohol the collected container was for.  It can be seen that beer is the most popular beverage of choice making up 78% of the collected containers.  Other forms of alcohol with no specific category make up 16%, vodka makes up 4%, and rum and wine both make up 1% of the collected alcohol containers.  This area of off campus student housing prefers beer over other types of alcohol.  Looking at the locations of the non-beer containers shows a pattern of more varieties of alcohols being closer to campus falling mostly along Chippewa Street. 

Figure 8. The locations of surveyed alcohol containers by the type of alcoholic beverage the container is for.

Figure 9 shows the spatial patterns of the alcohol percentage of the alcohol containers collected.  The greatest number of containers fell in the range of 4.0% to 4.6%.  This is due to the large number of beer cans collected, beer alcohol percent usually fall within this range.  The greatest number of high alcohol percents fell along Chippewa Street, Niagara Street, and 4th Avenue.  All of these areas lie closer to campus within the first three streets from campus.  Conclusions can be drawn that these streets are the biggest party streets because typically hard alcohol would be consumed in a social setting like a party.

Figure 9. The locations of surveyed alcohol containers by the percentage of alcohol in the beverage found on the container.

An error that was encountered while collecting data in the field was that the field that was created in ArcMap that was going to be used to record how full the discarded alcohol container was when it was located was not in the app.  The notes field that was created was used to record this data.  Another problem encountered was that photos of the alcohol containers were meant to be taken at each site as an attribute.  Unfortunately that was not an available function within the app when the data was collected because it drained the iPhone's battery used to collect the data too quickly.  As it was, the study area was rather large and the location services utilized by the app drained the cellphone used or data collection rather quickly, and the device had to be recharged twice to complete the data collection.    Also the alcohol percentage of every beer can or bottle was not on the vessel, for those data points the alcohol percentage was estimated at 4.0%.

A concern that could result from using discarded alcohol containers as the method of determining which street of off campus housing is the biggest party street is that the streets that have the most parties are clean and remove all alcohol containers are removed from the yards.  Even given that concern there were some interesting spatial patterns that yield some significant results.

Overall the results displayed that the streets closest to campus, except Water Street, are the bigger party streets of off campus housing.  These streets have the greatest variety of alcoholic beverage types, container types, the highest alcohol percentages, the greatest number of discarded containers, and the largest variety of how full the container was when data was surveyed.  All of these factors suggest that these streets are bringing in a variety of people with different beverage choices suggesting partying.  These observations can also be supported that drunk college students coming from on campus housing might not want to travel very far into off campus housing an go to parties closer to campus.  Looking further into the results of the two streets Chippewa and Niagara which are the two streets closest to campus, not including Water Street which has minimal alcohol containers suspected to be because of downtown city cleaning, Chippewa Street is most likely to be the biggest party street due to its increased variety of alcoholic beverage types, container types, the highest alcohol percentages, the greatest number of discarded containers, and the largest variety of how full the container was when data was surveyed.  Chippewa Street from this surveyed data is the biggest party street of off campus student housing at the University of Wisconsin- Eau Claire. 

Conclusions

Creating the correct design for a project is very important for successful completion of the project.  It is extremely important to include a notes field so that any concerns or extra data that should be recorded can be noted while in the field.  For this project the notes section was crucial for recording data about a field that was missing after being deployed to the Arc Collector app.  It was concluded that Chippewa Street is the biggest party street due to its increased variety of alcoholic beverage types, container types, the highest alcohol percentages, the greatest number of discarded containers, and the largest variety of how full the container was when data was surveyed.  If this project were to be conducted again a larger study area could be done, there are other areas of off campus student housing at this university.  Also multiple weekends could be surveyed and the data could be compared, maybe other streets have more parties on other weekends and this specific weekend the survey was conducted was just a big party weekend for specifically Chippewa Street.

Lab 8: Arc Collector: An Introduction to gathering geospatial data on a mobile device

Introduction

The purpose of this lab was an introduction to using Arc Collector to gather geospatial data on a mobile device like a tablet or smartphone.  This is an important skill to learn given that modern mobile devices like smartphones contain many times more computing power than most GPS units. It makes more sense to use smart phones and tablets over GPS units.  The fact that modern mobile devices like smartphones area capable of accessing online data, as the data collection is occurring the online data is being updated simultaneously.  In this lab this technology is used to gather microclimate data on the University of Wisconsin- Eau Claire campus.

Study Area

The study area for this lab was the University of Wisconsin Eau Claire campus.  The campus was divided into 7 zones.  Each zone was assigned 2 students from class to survey the area due to time constraints.  The goal within each zone was to evenly distribute collection points within the zone. Zone 6 was the specific zone surveyed by this data collector.

Figure 1. The designated zones used for micro climate data collection.

Methods

To begin the lab first in ArcGIS online each student had to join the group created by the professor to have access to the previously made geodatabase, domains, and feature classes for this exercise.  This was done by getting access in the university enterprise account.  Next, the app Collector for ArcGIS had to be downloaded on the smartphone used by the surveyor.  The app is free and user friendly, the app appears as it does in Figure 2 once the download is complete.  Once the app was downloaded, the university enterprise account was logged into and then the student's personal university account was accessed. In the app the group that was previously joined by the student in ArcGIS online was accessed and was used to collect data.

Figure 2. The appearance of the Arc Collector app on an iPhone. 

At each location data was collected at 7 attributes were recorded, the zone number, temperature, dew point, wind-chill, wind speed, wind direction, any notes on the location, and the time the data was collected in military time.  The temperature, wind-chill, and wind speed were collected on a hand held Kestrel 3000 wind meter pictured in Figure 3.  The wind direction was acquired by using a compass and locating the direction the wind was blowing from, the compass is pictured in Figure 4.  Some field of collection contained domains, which allow for standardization.  Domains allows for minimum and maximum values allowed for specific fields.    

                      

              Figure 3. Kestrel 3000 used to                                  Figure 4. Compass used to 

                     collect the temperature, wind-chill,                        determine from what direction the                                and wind speed data.                                             wind was blowing. 

Once in a desired location and data should be collected, the plus sign at the top of the Arc Collector app interface should be selected and then the data can be entered in the given fields in the next window that opens.  As the groups of students collected data points the data map was updated in real time with all of the data points in Arc Collector appearing as it does in Figure 5.  

Figure 5. A screenshot of during the data collection inside of the Arc Collector app.  As data points are collected on any smartphone or tablet more green dots of data points are added to the map.

After data collection ArcGIS Online was logged into and the data was opened in ArcGIS Desktop.  The data was then exported and saved in a new file geodatabase.  Different interpolations were conducted on the data used to interpret the data.  There were 239 data points collect by the class.  They were very evenly distributed across all 6 zones of data collection, which can be seen in Figure 6.  For the temperature and dew point interpolation method natural neighbor was used. 

Figure 6. The data collection points taken across campus.  

Results/Discussion

ArcGIS Online also allows for the creation of web maps, below is a web map created in ArcGIS Online has been embedded.  This feature of ArcGIS allows for easy data interpretation if ArcGIS Desktop is not available.  This one is for the temperature data collected in this exercise.

Static maps were created in ArcMap for data interpretation, shown below. 

Temperature
Figure 7 shows the spatial patterns that appeared in the temperature data collected in Arc Collector by the class.  It can be seen that warmer temperature spikes occur around the buildings on campus.  This is best seen over Hibbard Humanities Hall, the building is considerably warmer than the areas of land that surround it.  The north campus separated from main campus by the Chippewa River is warmer along the river than the areas along the river on main campus.  Land areas are generally warmer than water areas.  The data presents no concern for accuracy, but some could have occurred when choosing where to sample.  If the sampling that took place near the buildings was right next to a heating vent those samples may be inaccurate.          

Figure 7. Map showing the collected temperature data displayed in the

 natural neighbor interpolation method. 

Dew Point
Figure 8 shows the distribution of the dew point data on the University of Wisconsin- Eau Claire campus.  It can be seen than the dew point was higher at locations where surface water was present.  On campus along the Little Niagara Creek and measurements taken over the Chippewa River had a significantly greater dew point than anywhere else on campus.  This makes logical sense because the higher the dew point, the more moisture present in the air.  This data presents no concern for accuracy.  The only error that could have occurred is manual error on the part of the surveyor when entering the dew point data in the Arc Collector App.

Figure 8. Map showing the collected dew point data displayed

in the natural neighbor interpolation method.

Wind speed and Direction
Figure 9 shows that the wind was blowing westward on the day the data sampling took place.  The windiest place on campus with the largest wind speeds is on the footbridge crossing the Chippewa River.  This makes sense given that over the Chippewa River there are no buildings or other objects blocking the blowing of the wind the way there is on the parts of campus not above water.  There could be manual error by the surveyor with the wind direction and wind speed.  There could be an error with the surveyor entering in an incorrect value for either field.  There is an even greater chance for error on the part of the surveyor because wind direction was manually tested for by using a hand held compass.    

Figure 9. Map showing the collected wind speed and direction data.

The addition of domains could have reduced any of the error caused by students entering in data.  Domains allow for standardization and control over bad entries.  Some domains were used in this exercise but the use of additional domains could have eliminated even more error.  Overall the data was generally pretty accurate.  

Conclusions

This introductory lab demonstrated that collecting data can be made easy with the use of the Arc Collector app and ArcGIS Online.  There are many advantages to this app including it can be used on any smartphone or tablet making data collection easy and efficient on a personal mobile device.  The fact the app has the capability to show all collected data points in real time makes data collection done in groups simple, making sure data collection points are evenly dispersed through the study area and collect a wider range of data. Arc Collector overall is effective at data collection and was able to address the goals of this lab.