Distance Azimuth

Introduction 
The concept of distance and azimuth survey technique is a simple but very effective tool for geographers even with today's technology. This is because GPS can be prone to failure due to interference from large buildings and/or tree canopy over. Also there could be issues with the device itself or may have forgotten it before entering the field. Nevertheless, the distance and azimuth method is a very useful and foundational method in field work.

Methods 
To begin this lab the class was divided into groups of 3 and 4 people. Each group was then to complete a survey of a set of trees on the campuses southern end, on Putnam drive. The survey would consist of gathering data on each tree such tree circumference, tree type, azimuth and distance from the origin of the survey.
The Each member of the team was given a different tool to complete the survey. The tools used were two tape measures, a range finder, survey compass, GPS receiver, and field notebook. All the measurements and other data was recorded into the field notebook so that the data could be entered into an excel file to be brought into ArcMap (fig. 3).

Figure 1. GPS unit used to record the origin of the survey

Figure 2. Survey compass used collect the azimuth for each the trees in the survey

Figure 3. Excel spreadsheet that has all the data collected during the survey.

The data from the field notebook was transfered into excel. The excel spreadsheet was transfered into a newly created file geodatabase. Using the Bearing Distance to Line tool was used to give the data its azimuth and the Feature Vertixes to Points tool was used to create the individual points for each of the trees.

Results
The first step in evaluating the accuracy of the survey was to visually inspect the accuracy of the GPS location. Qualitatively the accuracy of the GPS appears to be accurate (fig. 1). The survey taken on to the west may be slightly off to the north. This was likely caused by poor GPS signals from tree cover and the adjacent hill located to the south. The map created below (fig. 1) shows the tree type for each of the trees collected within the surveys. When comparing the results of the surveys there is more homogeneity between the tree species in the western survey compared to the survey taken to the east.

Figure 4. Map created in ArcMap that displays the location and tree type for each of the trees within the survey. The locations of the trees was determined by taking the azimuth each of the trees and measuring the distance from the origin. 

Conclusion/discussion
The distance and azimuth survey method is a very effective method for data collection in the field. By taking the distance and azimuth of objects from a single point of origin, accurate spatial data can be taken in the field even without the aid of more sophisticated technology such as GPS.

Arc Collector II

Introduction 

Arc Collector

Introduction
The purpose of this lab is to use smart phones to collect geospatial data and create climate maps with the newly collected data. Smart phones have far more computing power than most GPS units and therefore it is much for convenient and cost effective to use a smart phone in the field. This lab also introduced domains and their importance for data collection.

Methods
To begin the lab the class was divided into groups and two and asked to collect weather data for the various sections on campus (fig. 1). Each was given a kestrel 3000 to record atmospheric conditions and a compass to record wind direction. The readings were imputed into the Arc Collector where the locations of each of the recordings geolocated using the smart phone's GPS. Domains were created for the different fields to insure that the data was properly imputed into the data set and to minimize normalization issues in the future.

Figure 1. Displays the different sections of campus the various groups were assigned to.

Figure 2. Kestrel 3000 used to collect surface temperature (Fahrenheit), dew point and wind speed 

Each of the groups collected between 20-30 data points in their respective zones giving the data set 154 climatic readings across the campus. Once all the readings were collected they were placed into an ArcGIS Online file (fig. 3). A new personalized map was created using this data so that it could be brought into a geodatabase so that the layers could be brought into ArcMap to create microclimate maps for the campus.

Figure 3. Displays each of groups data points collected in each of their respective zones. 

Results

Below are the four maps created from the data collected using Arc Collector. Looking at the wind speed and wind direction map below a couple patterns are evident. First, the main direction for the wind that day was from the southwest. Areas closure to the river and especially over the walking bridge had the highest wind speeds. Areas in the main campus had lower winds than surrounding areas that had more exposure to the wind. 

Figure 4. Map displaying both the wind direction as well as the wind speed for the campus

Looking at temperature maps below, areas along the river had the lowest surface temperatures and temperature taken at 2 meters. The lower surface temperature along the river is likely a result of the temperature being taken on grass. Areas of blacktop and concrete also had higher surface temperatures. The dew point map (fig. 7) followed trends that would be expected looking at the temperature at 2 meters maps (fig. 6) with areas that had high temperatures also having relatively high dew points and vice versa. 

Figure 5.  Map displaying surface temperature for the campus using graduated symbols.

Figure 6. Map displaying temperature at 2 meters for the campus using graduated symbols.

Figure 7. Map displaying surface dew point for the campus using graduated symbols.

Conclusions

Arc Collector is a very user friendly app that is extremely useful for geospatial analysists. It allows for the user to his/her smart phone to collect data in the field and develop web based applications for that data. Having the ability to have pre-created datasets with domains prior to going into field allows for the user to avoid input errors that may lead to normalization errors when processing the data.