Lab Six: Parcel Forum

 This post, unlike the others in this series, is not about a lab. Instead, it focuses on a Parcel Mapping forum the occurred at UWEC on Tuesday March 15, 2016. Attending this forum was an opportunity for students to rub shoulders with people in the PLSS parcel mapping and land records industry. I arrived at this session as it was moving into its second half at ~12:45pm, meaning that I was able to listen to four people presenting on their work and even participate in a breakout session where groups attempted to solve complex issues relating to the field.

Jason Poser of Buffalo County, Wisconsin gave the first presentation I attended. As with many of the presentations I attended during this forum, his was ripe with jargon, explanations of how the advance of technology has drastically changed, and how land records are kept and how surveys are conducted. An example of how the advance of technology has changed and streamlined land records was that in Buffalo County there was as much data digitized in 2015 and early in 2016 as was digitized in from 2006 to 2014. This was due to a shift from using Autocad to ESRI’s parcel fabric. Poser describes this as being functionally better, more accurate, and easier to change parcels. He also said that it was quite convenient to use historical data to see changes made to parcels in the past.

Two other presentations were much akin the first. Brett Budrow of St. Croix County and Dan Pleoger of Sawyer County spoke briefly on how GIS has been implemented within their districts. Budrow even included a little history as to how parcel mapping was originally done with ink and mylar before Autocad or the use of designated GIS software products.

The presentation by Mark Netterlund of Barron County was by far my favorite and most informative. As someone who has not worked with parcel mapping as a career, I appreciated Netterslund’s avoidance of jargon and interjection of advice that can be useful for all different career types. He spoke about how the turnover rate for the surveyors in the county was low, which allowed a community-like atmosphere to develop. He also mentioned how important it is to create relationships with other departments as favors are often exchanged. Finally, he suggested to just talk with people and let them know what it is that you are up to, often times they will be quite interested and willing to help with the job.

The final part of the forum was where the people present formed breakout groups. As this forum was designed for PLSS and county surveyor people, I was able to observe (but not contribute to) one of these breakout groups. I wasn’t prepared to contribute much as much of the material went right over my head, even having spent a few hours doing simple parcel mapping in a GIS class.

During this breakout session, the idea of group polarity kept coming to mind. This is where the ideas of likeminded individuals are amplified when they are put in a group setting. The main ideas that I got from a good majority of people was how they needed more funding to complete what surveying and mapping projects they want to. Despite this, there were few who see the value in educating the people who write their checks the reason why they would like the money. One cannot blame them too much for this, as interpersonal communication has typically been a shortcoming for those in this type of discipline.

As a whole, I would have to comment that this was a fairly exclusive, yet indecisive group; with exceptions of course. They seemed to be bonded via the knowledge of common industry jargon and similar work tasks. Despite this they were usually unable to agree on ways to solve a problem as everyone who had a solution seemed to believe that their solution was best and did not wish to entertain other’s ideas. Yet from this mass of ideas, I was able to pick up some sound advice about ways to work with others instead of against them. 

Lab Five: Introduction to Arc Collector

Objective:

To use arc collector to collect data with the use of a mobile device running on IOS or Android. This lab introduces students to the creation of a web map to be shared across ArcGIS with Arc Collector. Students then are tasked to collect more micro climate with the use of Kestral wind gauges in the same fashion as the previous lab.

Methods: 

Figure 1. Classmate Audrey Bottolfson poses with an Arc Collector enabled smart phone and Kestrel wind gauge.

For the first step of this project the same feature class used in the previous lab was made available for use in this one. No changes were made to domains or coded index items as it was warmer this day (March 8^th, 2016) and adjustments were unnecessary.

This feature class was then deployed to ArcGIS Online with an accompanying base map, making it ready for collecting data with a mobile device through ESRI’s Arc Collector mobile application. As with a great deal of equipment initially it didn’t work, but after intensive troubleshooting by the course instructor it became functional.

As like the previous lab, microclimate measurements were taken with the use of a Kestral wind gauge. These readings were then entered as data points into the feature class via the use of a personal mobile device. After about 45 minutes of data collection the entire class reconvened to upload the data into a shared drive for all to access.

Results: 

Figure 2. Compiled temperature readings across the UWEC campus (five category Jenks natural breaks classification). 

While collecting data it was obvious that the locations recorded by the mobile devices were not as accurate as those recorded by the dedicated GPS devices used for the previous lab. There were several instances where the recorded point was off by tens of meters even after allowing the device to focus in on a stationary location. Data entry on the mobile device was far easier to use than with the dedicated Juno GPS due to the far more intuitive and user friendly design of the smartphone and the collector app. In my opinion the relative poor accuracy of my mobile device compared to the Juno was not a bad compromise for the ease of data entry. There is also the advantage of the data being uploaded automatically to ArcGIS Online as data connections become available, meaning that data is rarely lost and available for viewing and editing from remote locations almost immediately. 

Figure 3. Screen shot of user interface of Arc Collector. 

This lack of accuracy led to several issues when combining data with the other members of my class. For instance, not all mobile devices are built using the same GPS receiver meaning that some are far more accurate than others. The GPS receiver in my mobile device was accurate within about 20 meters. In figure three this lack of positional accuracy was apparent while collecting data. The GPS said that it was located at the blue and white arrow dot in the center of the image. My real location was actually at the northern green dot to the left of the blue one. A classmate’s device was newer and was more accurate than mine; within about 15 meters. This difference in spatial accuracy across devices does not help the data’s integrity as a whole, but for this project it isn’t very important as locational accuracy is not the focus for the exercise.

Conclusion:

Microclimate data collection via Arc Collector is far superior to using a GPS in how it is convenient and able to compute and update data quickly. The time it takes most mobile devices to acquire satellite signals is far quicker than a good majority of commercial GPS units. The drawbacks for this tool in collecting data is that the locational accuracy is not quite comparable to a modern designated GPS unit and some mobile devices use better GPS receivers in their construction than others which makes them more accurate than their counterparts. But this is often not a problem for data collection that doesn’t require specific precision.

Lab Four: Geodatabases and Domains

Introduction:

This lab was centered on creating a geodatabase with custom defined domains for different data types. This database was then downloaded to a handheld GPS unit and used to collect weather observations around the UWEC campus mall.

Methods:

This geodatabase needed to store domain information for a feature class with custom made fields to store microclimate information; for example, temperature, dew point, relative humidity, wind speed, and wind direction are all examples of the data types collected. For each attribute it was necessary to set a domain or subtype to normalize the data across multiple surveyors. An example of a domain would include setting a range for the wind speed field; it would be outrageous to accept a wind speed of -2 or 6666666, so the range was set to only accept values between 0 and 60, inclusive. The other type of domain setting was for a coded index domain, essentially an attribute field where there are a few predefined choices available for use.

Field Name	Data Type	Domain Type
Group Number	Text	Coded Values
Point Number	Short Integer	Range: 1 to 100
Temperature	Float	Range: 15 to 60
Dew Point	Float	Range: -20 to 100
Relative Humidity	Float	Range: 1 to 100
Wind Speed	Float	Range: 0 to 60
Cardinal Wind Direction	Text	Coded Values
Azimuthal Wind Direction	Short Integer	Range: 0 to 360

Table 1. All domains and associated data types used to create this geodatabase. Note that Dew point has a range that was changed to a negative value.

 An example of this was from the group number attribute; allowing the user to only select from a pre-defined list of options. After entering domains, we tested the functionality of the geodatabase and feature data set described earlier by importing it to a Trimble Juno GPS unit set up to run ESRI’s ArcPad GPS program. Observations were collected from the UWEC Campus Mall, an area roughly 500 by 200 feet in the center of the UWEC campus located in Wisconsin. Using the GPS unit and a Kestrel 3000 wind meter, a handheld weather monitoring device capable of collecting all required data, a couple points were created and the attribute table was populated.

Results/Discussion

When entering data points to the feature dataset it became apparent that several adjustments were needed; for example, the dew point attribute domain needed to have an extended range as several dew point measurements had negative values. Because of this the correct value wasn’t able to be entered for the points in the dew point field. All other fields worked well with the domains being set up for the weather present on that particular day. Unfortunately there were only two points collected for the time that was allotted for data collection. This was due to the fact that the GPS unit I was assigned failed to acquire a strong enough signal from satellites meaning that what points I did collect were recorded as being several kilometers south of where they should have been. 

 

Conclusions:

During this lab the processes required to create a geodatabase with relevant range and coded value domains were completed. From here it was a relatively simple process to export this geodatabase with its point feature class into a Trimble Juno GPS unit for a short test. During the test it was apparent that the domain range for the dew point attribute needed to be extended and that one cannot always trust their equipment to work correctly once in the field.