ArcCollector Part 1: An Introduction to Gathering Geospatial Data on a Mobile Device

Introduction
With the introduction of powerful and easily obtained smartphones, GPS units have largely been replaced when collecting location data. Additionally, smartphones have the ability to access recorded data on the go and monitor survey progress through the study area as its continually updated. In this lab exercised, one such smartphone based survey system would be introduced and utilized, ArcCollector. ArcCollector is an Esri based mobile GIS application allowing for the recording of multiple attributes of survey locations based on predetermined domains by many surveyors at once. Domains are essentially rules that limited what type of data, quantitative or qualitative, can be entered into an attribute field, and what range or descriptive data may be entered.
Study Area
The study area was the University of Wisconsin, Eau Claire campus, particularly the areas surrounding the primary area. As the area was far too large for a student to survey completely in the allotted time, the study area was divided into seven zones, labeled numerically, and each student was assigned to help survey a particular zone (Figure 1).

Figure 1: A map of the study zones of the March 29, 2017, micro-climate survey of the University of Wisconsin, Eau Claire campus. Each zone was assigned two or three students from the class to survey, and positional and micro-climate data would be recorded for each zone using the smartphone application ArcCollector.

There were two or three students assigned for each area, and each student would need to survey roughly twenty points to effectively survey the entire study area. Zone 4 was the zone personally assigned. Using ArcCollector, students gathered positional and micro-climate data from within these zones.
Methods
First, each student was tasked with downloading the smartphone application ArcCollector to their personal smartphone, available on both the Google Play and Apple app stores for free. While this was downloading, the data attributes were overviewed, so each student would know what data they were collected. This was primarily micro-climate data, with supplementary time and group data.
The attributes were:

• Group. Listed in the attribute table as GRP. This would show what group collected the data point
• Temperature. Listed in the attribute table as TP. This would be collected with a personal Kestrel pocket weather meter in degrees Fahrenheit.
• Dew point. Listed in the attribute table as DP. This would also be collected with the personal Kestrel pocket weather meter in degrees Fahrenheit.
• Wind chill. Listed in the attribute table as WC. This would be collected with the personal Kestrel pocket weather meter in degrees Fahrenheit as well.
• Wind speed. Listed in the attribute table as WS. This would be collected with the personal Kestrel pocket weather meter in miles per hour.
• Wind direction. Listed in the attribute table as WD. This would be collected by using a compass, either physical or electronic, to measure the direction the wind was coming from in degrees, with North being the designated 0.
• Time. Listed in the attribute table as Time_. This would describe the time each point was collected during the day, and would be listed in military time with the colon removed.
• Notes. A general notes tab for use later. We were instructed to keep this area blank when  collecting data

In order to normally collect data of this kind in ArcCollector, a Geodatabase with the appropriate shapefiles for both the group zones and the survey points must be created, with the appropriate attribute fields and corresponding domains, and uploaded to ArcGIS Online for group use. For the purpose of this exercise, the geodatabase and its shapefiles had already been constructed, uploaded to, and been made available on ArcGIS online for the class. Each student needed to only been given access by the instructor.
Once each student had access to the Online ArcGIS database, they were sent out to gather data points for locations within the zones. This would create point data for the micro-climate point shapefile feature class within the geodatabase. Each student had between 4:10 PM and 5:00 PM on March 29 to gather points data for the feature class. By pushing the "add point" tab within the application, it would create a new datapoint within the shapefile. It would then ask to fill in the attributes listed above. The TP, DP, and WC fields all had domains requiring the input to be a whole number in the normal range of temperatures for this time of year within Eau Claire County. The WS was limited by a domain requiring the input to either be a whole number greater than or equal to 0. The WD field was limited by a domain requiring the input to be a whole number between 0 and 359. However, do to an error created when generating the domains, the time and notes field were not properly limited. The Time field was supposed to have a domain that required the input to be a whole number between 0 and 2359, military time without the colon. However, there was no such domain present. As a result, the field ended up populated by correctly formatted entries (ex: 1636), incorrectly formatted entries (ex: 16:36, 4:36), and null values. In addition, the domain set for the Notes field would demand a form of entry if the field was accidentally accessed during the survey. As a result, this field was occasionally populated by an assortment of values (anything a student could consider a note on the location) and the proper null values. Each student completed roughly 20 survey point in their allotted area before returned to the lab.
Upon the completion of the survey, the data was viewed in ArcGIS online. Using the built in functionalities, it was possible to view the point density as a heat map, for reference. Once it was certain that all data recorded was present, the database was downloaded from ArcGIS online and turned into an offline file geodatabase. The data from the survey was brought into an ArcMap viewer so the data points could be properly viewed (Figure 2).

Figure 2: A map showing the micro-climate survey points collected during the ArcCollector Survey on March 29, 2017, between 4:10 PM and 5:00 PM on the University of Wisconsin, Eau Claire, campus. Each student surveyed roughly twenty points for micro-climate data which including group number (based on group zone), temperature (in degrees Fahrenheit), dew point (in degrees Fahrenheit), wind chill (in degrees Fahrenheit), wind speed (in miles per hour), wind direction, and the time of point collection (in military time).

Once it was ascertained that the data could be properly viewed, it was utilized to create a variety of surface and point maps displaying the collected data.
Results
Using the point data, a surface map could be created of the temperature with the IDW interpolation method and the TP (temperature) field (Figure 3). The data collected shows that the temperature in the northern half of the area is generally below 55 °F, except for scattered points close to the river, while the southwestern and southeastern portions of the map are dominated by areas where the temperature is greater that 55 °F. These areas of highest temperature are generally located directly west of buildings in exposed areas. In addition, the points to the south-east are located at the base of a steep embankment, with elevation increasing as the location moves from the north east to the south-west.This means they received the most exposed sunlight during this time of day, increasing the temperature of these areas. The one notable exception to this trend is the area in the far south, which possesses few tall buildings and is comprised largely of sports fields. This abnormality may be explained by combining a combination of the other micro-climate attributes. However, it does not show strong correlation with either high wind speed or low dew point, meaning that explanation does not likely lie with either of those two factors.

Figure 3: A temperature surface map created of the University of Wisconsin, Eau Claire campus using the data collected in the March, 2017 ArcCollector micro-climate survey. The temperature data was collected in degrees Fahrenheit between 4:10 PM and 5:00 PM. The continuous surface map was generated using the IDW interpolation method.

A wind chill map was created by similarly utilizing the point data and the IDW interpolation method, using the WC field instead of the TP field (Figure 4). When looking at this data, it is clear it exhibits the same general trends as the temperature map. Areas of higher temperature and lower temperature for wind chill line up very well with the areas of higher and lower overall temperature. The major difference is that wind chill has a lower minimum wind chill than overall temperature, 45 °F as compared to 49 °F.

Figure 4: A wind chill surface map created of the University of Wisconsin, Eau Claire, campus using the data collected in the March, 2017, ArcCollector micro-climate survey. The wind chill data was collected in degrees Fahrenheit between 4:10 PM and 5:00 PM. The continuous surface map was generated using the IDW interpolation method.

A dew point surface map was also created utilizing the IDW interpolation method and the survey points. The DP field was the field used in this instance (Figure 5). This data shows some very different trends from temperature and wind chill. The southwestern portions of the survey area, along with the far northern locations and an area in the east-central portion of the study area have the lowest dew points, generally less than 36 °F. In contrast, the portions with the highest dew point, generally greater than 48 °F, are located on either the central to southeast and to locations to the north -central portions of the study area. These areas likely have the highest dew point because they have the greatest ambient moisture in the air. They are either at the bottom of a steep embankment (central to southeast) where water naturally collects, or either ring the river to the north or were taken from a bridge crossing the river. In contrast, the areas of the lowest dew point either sit way to the north of the river, southwest and on top of the steep embankment, or in the flat, open area between the embankment and the river. These are the drier locations with the lowest ambient moisture in the air.

Figure 5: A dew point surface map created of the University of Wisconsin, Eau Claire campus using the data collected in the March, 2017 ArcCollector micro-climate survey. The dew point data was collected in degrees Fahrenheit between 4:10 PM and 5:00 PM. The continuous surface map was generated using the IDW interpolation method.

Utilizing the points dataset in conjunction with the wind speed and wind direction fields, a map could be generating showing both the magnitude and direction of the wind quantitatively and qualitatively as angled, graduated symbols. From looking at the data, it can generally be seen that the wind was blowing from the southeast to the northwest. In locations where the wind was not blowing in this direction, the southeastern wind was usually blocked by either buildings or or forested areas. In addition, the areas of greatest magnitude of wind speed are either on the river, to the far west, or to the far north. These areas are the least population by obstructions which would either block or divert the wind.

Figure 6: A wind speed and direction graduated symbols map created  of the University of Wisconsin, Eau Claire campus using the data collected in the March, 2017 ArcCollector micro-climate survey. The wind speed data was collected in miles per hour, and the wind direction was collected in degrees from its point of original and later angled to show the direction it was blowing towards. Wind speed is show as graduated arrow symbols labeled in miles per hour.

While it would have been appreciated if a map could have been generated for the Time field, it is currently not possible, due to the state of the data on this field. Because of errors in both domain and field entry by both designer and surveyors, the data within the fields is not properly formatted so that it can be quantitatively or qualitatively expressed. It is imperative that domains be properly constructed and formatted to eliminate data entry error by surveyors.
Conclusion
ArcCollector is a valuable and easily accessible tool for completing groups surveys across an area too large for a single individual to properly survey. With it, surveyors can collect and record multiple types of data from various locations without the need for complex GPS equipment. Only a smartphone application is required. In this survey, dozens of datapoints were taken of location across the University of Wisconsin, Eau Claire campus by students in under and hour and contained data describing multiple forms of the local micro-climate. Additionally, it allows for the monitoring of surveyors and the survey progress by accessing the geodatabase from ArcGIS online, either using a computer of smartphone. With ArcGIS online, the data can also be effectively shared and distributed to all analysts within an organization for breakdown and study. However, it is critical that the domains for each field be properly formatted. If it is not, data entry errors can occur when surveyors generate the data, as in the case of the time and note fields of the collected data. However, if this error can be avoided, ArcCollector can serve as a valuable tool for a field data analyst.
Sources
Hupy, J. (2017). Arc Collector: An Introduction to gathering geospatial data on a mobile device, such as a tablet or smartphone. Eau Claire, WI.