ArcCollector 2

Introduction
     This lab is a continuation on the development of ArcCollector skills. The previous lab had a pre-created database set up prior to the beginning of the lab. This weeks lab answers a spatial question created by each individual student. This particular blog investigates where houses are located that put up Holiday lights early and how many different colored lights were used per house.

Study Area

     The study area that was chosen for this lab was a two block section to the east of the University of Wisconsin - Eau Claire campus. This area is part of the downtown area of Eau Claire and consist of various old and nicer house. It was believed that the owner of these houses would have been more likely to put up Holiday lights earlier.

Methods
     The first step in answering the study question was to create a geodatabase with specific domains to help normalize the data collected in the field. The project in this lab had two different domains, the first was created for the quantitative attribute, the number of different colors used in Holiday decoration per house, that created a range between 0 and 15. It was very unlikely that a house had 15 different colored lights but it was better to guess a little high than not high enough. The other domain was for the qualitative attribute, which was the amount of lights used for each house. This was broken down into 4 categories Lights Everywhere, They Tried, Meh Holidays, and No Lights. Lights Everywhere represented the most lights and the further down the list the less lights the house had. With the domains created a point feature class could be made. The proceeding step was to create a polygon of the study area. This is helpful for when the data collector is in the field. They can easily see on there tablet of smart phone whether or not they are within the study area. With the two feature classes created they could be uploaded to ArcGIS online where they can be added to an online map that can be accessed remotely from a smart phone or tablet.
       In the field a GPS point was taken at every house in the study area and the number of lights per house and the amount of lights per house was collected. This data was then uploaded back to ArcGIS Online. From here an embedded map was created of the data. Embedded map is an online map that can be manipulated by the viewer, Google maps is the most famous embedded map. The embedded map below depicts a hotspot map of the quantitative variable. Another function that can be down with the data in ArcGIS online is to download it to a desktop version of ArcMaps where it can be cleaned and manipulated into static maps, or non-interactive maps.

Results
     Unfortunately the results where a little disheartening. The majority of the houses in the study area did not yet have Holiday lights set up yet. However the ones that did have lights set up tended to have a larger variety of colors. Figure 1 below shows the amount of lights per house in the form of an embedded heat map. Areas that have higher colors are areas with larger amounts of Holiday lights. Interestingly the areas further away from campus tended to have more lights than the areas closer to campus.

Conclusion

     This lab really stresses the importance of properly setting up a project before heading out into the field. It is necessary to think through all aspects of the project beforehand and what would be the best way to address these aspects.

    

ArcCollecter and Micro Climates

Introduction
     This lab used ArcCollecter to gather data remotely in the field and transfer it into the Esri online web service. This method of data collection is exceptionally powerful because it allows for multiple people to gather data in various locations at the same time and upload it to the same online document. This lab looks at the micro climate for the University of Wisconsin - Eau Claire, specifically the temperature, dew point, wind speed, and wind direction. For this lab the class was broken up into 9 different groups and assigned different locations around campus where the groups gathered the weather attributes. Figure 1 below shows the campus of the University of Wisconsin - Eau Claire broken up into 5 different zones. The author of this post collected data from zone three. The terrain for zone 3 was a mix of woods, bog, and

 (Figure 1: A map of the 5 different zones that were mapped out for the micro climate lab)

the campus itself. The area in the eastern half of zone three is woods that has a small creek, called the Little Niagara, traveling threw it, Little Niagara travels all the way threw zone 3 then cuts north through zone 4 until it hits the Chippewa River. Certain areas around the creek have turned into a bog or wetlands, these terrains are solely located in the eastern half of zone 3. The western half of zone 3 contains the campus science building, the student union, and the nursing building. The terrain around these buildings is well kept with trees planted artistically around the buildings. Other notable terrains in the study area include the area between zones 5 and 3/4, which is a wooded hill with a very drastic elevation change. The southern portion of zone 1 is the flood plains and also contains a slight elevation change from the water to inland. The northern portion of zone 5 is Putnum park, Putnum Park is on a cliff that overlooks the Chippewa River and is very wooded. Little Niagara discharges into the Chippewa in Putnum Park.
   

Methods
     In order to measure the different weather attributes around the Eau Claire campus different tools were used. The main tool to measure the weather was a Kestrel weather meter, which is able to measure the wind speed, temperature, and dew point, to name a few of the functions. A compass was used to find the direction that the wind was blowing in degrees. The final tool was a smart phone with GPS capabilities that had the ArcCollecter app downloaded. This app allow the user to access their esri online profile from their phone and collect GPS data points.
     Every group had a smart phone and were assigned a zone in which to collect the weather measurements. These points would then be uploaded to a pre-created basemap of the area that was provided by the professor, Dr. Joe Hupy. After all the points were collected each person was able to download the online maps to a desktop version of ArcMaps and use the field data. This lab called for surface maps of every attribute mentioned above. The inverse distance weighted interpolation method was used to create a surface maps of the attributes collected. Wind direction was a special case, instead of creating a surface map the GPS symbols were turned to arrows and rotated to face the direction that the wind was blowing. These arrows were overlayed on the wind speed surface map.

Results
     Below are all of the surface maps that were created. The first image, Figure 1, shows surface temperature map for the Eau Claire campus. This map is particularly interesting due to the patterns that formed. The Pink blobs in the south eastern portion of the map represent hot spots where the temperature was between 63 and 64 degrees Fahrenheit. These three building represent the where the student union, science building, and nursing buildings are located. Another hot spot is in the north which represents the Eau Claire arts center. The green areas to the east of there represent cold spots. These cold spots represent the wetlands in the woods where sunlight was reduced, lowering the temperature.

(Figure 2: Surface Temperature Map of the Eau Claire campus)

 
     Figure 3 show the dew point surface map. This map shows an obvious pattern where a diagonal area of higher dew point separates 2 different areas of lower dew points. This is caused, in part, by the Little Niagara which follow the path of high dew points. Also the forest ridges follows this same path and can also be a cause the higher dew points.

  (Figure 3: Surface Dew Point Map for the Eau Claire campus)

 
     The final map is the wind direction and wind speed map, which was briefly explained above. Figure 4 is an image of this map. The wooded ridge is a huge factor to wind speed and direction. There is a green path, which represents wind speeds between 0 to 2.2 mph, that follows the ridge line. The areas above and below have significantly higher wind speeds than the areas around the ridge. There are lower wind speeds in the north eastern corner as well. This could due to the surrounding houses and trees blocking the wind.

(Figure 4: Wind speed and wind direction surface map of the Eau Claire campus)

Conclusion

     This project was an excellent success. The data transfer from every ones phones to the online Esri mapping site went very smoothly. Having multiple people collecting data points simultaneously saved a considerable amount of time and was overall more efficient. ArcCollecter is an amazing resource that is significantly cheaper and more accurate than most GPS units. Also it makes getting the data from the field to ArcMaps quick and easy. It would be unsurprising if smart phones and ArcCollecter replace most GPS units in the very near future. 

Navigation Courses at the Priory

Introduction
     The latest project for field methods is a continuation of the previous lab titled Navigation Maps. Navigation Maps was a lab where two maps of the Priory were created that would be used to navigate through the Priory to find certain location marks. This lab used the navigation maps of the Priory to locate 5 markers that were scattered throughout the Priory. Along with the maps a combination of pace counts and compasses were used to help with the navigation process. The Priory contains 5 different sets of marks, also known as a course, for groups to find, however do to the overly large class size this semester there was a 6th group. Group 6 was assigned markers from multiple courses.

Methods
     The method used to navigate threw the priory was rather simple. First the coordinates for the five markers were given to each group. The groups then marked the location of the markers on their maps to use be used to locate the course markers. Next, a pace count was needed to be created for every group member. A pace count is the amount of paces it takes for a person to walk a given distance, for this exercise meters were the units used for distance. To find the pace count a 50 meter tape measure was laid on the ground and a student would walk next to it while counting their steps. Once the student walked the entirety of the tape they could double their amount of steps or turn around and walk back. The pace counts that were used in group 4 were 110 paces per 100 meters for Payden and 135 paces per 100 meters for Sarah. The third group member had recently broken his leg and was unable to put to much stress on it. So he ended up being the note taker during the exercise. The next tool used to help navigate the Priory was a compass. Each compass was able to locate north and find the direction in degrees from north that the next marker is suppose to be located. The compass also had a 5 cm ruler on the side of it. The ruler was used to measure the distance on the map from one marker to the next. The centimeters would then be converted from map distance to actual distance using the map scale. The last piece of equipment used was a GPS unit. The GPS unit recorded the path of each group through the woods and showed how close to the markers each group got.
     To give an example of how all of this worked lets say that the starting point was in the parking lot and the first point was 11 degrees west of north. The distance from the starting point to the first marker is 7 cm. The scale on the map says that 1 cm = 31.34 meters, so 7 * 31.34 = 219.38 m. If Payden, who has a pace count of 110 pace per 100 meters, was pace counter then he would have to walk (110 paces * 2.1938 = 241.318 paces) 241.318 paces 11 degress west of north to find the first marker. While Payden walked Sarah would stand at the starting point with the compass watching to make sure Payden didn't deviate from the 11 degrees west of north.

Discussion
     Overall the exercise did not go as planned, Group 4 was only able to find 2 of the 5 markers. There are many reasons as to why this exercise was unsuccessful, the main one was the terrain of the Priory. The Priory is a 120 acre rectangle that is filled with large ravines, dense brush, thorns bushes well over 7 feet tall, and drastic elevation change. All of these less than ideal navigation obstacles made the traveling through the Priory very difficult. It was very easy for the pace counter to get off course walking around thorn bushes or thick trees. It was also equally difficult for the compass watcher to keep eyes on the pace counter for more than 20 to 25 meters at a time. Also the maps that were created used a 50 meter grid pattern to navigate with, this turned out to be two course of a grid pattern. When the locations of each marker was marked on the map there was a reasonable chance that the point could have been off by 20 meters or more. 20 meters is a significant distance when the forest was as dense as it was. Also the large changes in elevation made it especially difficult for the pace counter to measure the distance that was walked. The distance from one point to another on the maps is as a bird flies. The pace counter has to walk up and down different inclines that add numerous extra paces that threw off the distances. The final difficulty that was encountered was vandalism whether by animals or people certain markers were ripped off of trees. Marker number 2 was ripped off of the tree and laying a few meters away from it. Image 1 below shows this. The marker is pink tap that is laying next to the bottom part of the tree. Image 2 shows a tree with an intact marker, also featured below.

(Image 1: Marker ripped off of a tree)

(Image 2: Intact Marker)

     To give an overall idea of how unsuccessful group 4 was at locating the markers, due to the variety of issues and obstacles that they ran into, a map was created that showed the location of all 5 of the markers and the path that group 4 took based off of the GPS unit that they carried. Figure 1 below shows this map. 

(Figure 1: The Path of Group 4 in relation to the actual location of each marker.)

     The data received by the GPS unit is not 100% accurate due to the large amount of canopy coverage by the trees. However it is easy to see that Group 4 never got close to points 3, 4, or 5 in the north. This is largely due to the fact that when marker two was ripped down they were unsure of where exactly to start when trying to find the third marker. So they missed all the rest by quite a lot.  

Figure 2 below shows a map that contains all 6 groups and the the courses that they did. 

(Figure 2: All groups and courses, color coded.)

    

Conclusion

    This was a fun albeit difficult exercise that really demonstrated that if one part goes wrong the whole navigation can go wrong. It is better to simply retrace your steps back to a previous recognized point and start over from there. Also make sure that the grid used is an appropriate amount for the area that is being navigated. The Priory really needs a grid that is 25 meters or smaller. It makes it significantly easier to navigate.