Arc Collector Part II: Building a Database, Domain, and Attributes

Introduction
The purpose of this lab was to propose a geospatial question and then use Arc Collector to build a database, domain, and attributes to solve the problem. The question addressed in this project was, "Where are the most hazardous areas to rollerblade on common pathways in Eau Claire?" Three different pathways were analyzed. Hazards included twigs, rocks, dirt and sand cover, cracks, and bumps. Twigs and rocks were combined into one attribute and cracks and bumps were joined as well. Proper project design is important because there must be a problem to be solved, objectives that will help appropriately solve that problem, and attributes/data collection methods that pertain to the problem. Poor design can result in inaccurate data and unclear results.

Study Area
The study area chosen for this project was all on the lower campus residency area of Eau Claire, WI. Two of the three pathways were along the river, and the third was on a pathway near Half Moon Lake and down Water Street. These pathways were chosen because they are well-known and common pathways for rollerbladers and bikers.

Methods
Common hazards for rollerbladers include anything obstructing the pathway. Twigs, rocks, sand/dirt, bumps, and cracks were there attributes collected. Twigs and rocks were counted and collected as integer fields, as well as bumps and cracks. Sand and dirt cover was collected as a percentage to be translated into light (0-5%), moderate (6-15%), and severe cover (>15%). A point feature layer was created in ArcMap desktop and brought into ArcGIS online so that data could be collected via Arc Collector, just as was done in the previous lab. (See Fig. 1 & 2 below)

Figure 1. Appearance of attribute collection in Arc Collector

Figure 2. The map after data collection was complete.

Results and Discussion
The resulting web map from ArcGIS online can be found here.

Figure 3. Map displaying recorded amounts of cracks and bumps obstructing rollerblading paths in Eau Claire, WI. Three sizes of symbols represent different ranges of cracks and bumps, with the largest symbol representing the most obstruction. Each pathway is labeled with a number (1-3) for referencing purposes.

Pathway number 2 had three areas with much obstruction from cracks and bumps, however pathways 1 and 2 also had a lot of bumps and cracks but they are more spread out. Overall, pathway number 1 seems to be the safest in terms of crack/bump obstruction (Fig. 3).

Figure 4. Map showing different levels of dirt cover on three different rollerblade pathways in Eau Claire, WI. Each pathway is labeled with a number (1-3) for referencing.

Pathway 3 had multiple severe areas of dirt cover which is very dangerous for rollerbladers because it is very slippery under the wheels. Pathways 1 and 2 had a few areas of moderate sand/dirt cover but nothing really as heavy as pathway 3. Taking dirt cover and bumps/cracks into account, pathway 1 still appears to be the safest (Fig 4).

Figure 5. Map representing the amount of twigs and rocks obstructing the pathways in Eau Claire, WI. Each pathway is labeled with a number (1-3) for referencing.

In Figure 5, each pathway contains a severe patch of twigs and rocks which is at least 10. Pathway 1 had a consistent amount of twigs along the whole trail due to most of it being within the forest. Pathway 3 had the least amount (except for the short bit on Chippewa street) because it was mostly on water street sidewalk where there are not many trees or rocks around. Pathway number 2 had a moderate amount on its northern portion, also due to surrounding density of trees. Taking bumps/cracks, dirt cover, and twigs/rocks all into account, pathway number 1 seems to be the best route for a relatively hazard-free rollerblade ride, mostly because its levels of obstruction are mostly low to moderate, with very few areas of severity.

Conclusions
Proper project design was very important in solving this geographical problem because useful data needs to be collected in order to come to any conclusions. Latent variables may be determined by using other attributes to understand them and their affect on the problem. If this project were to be redone, it would be a good idea to collect a few more attributes, such as road crossings, steep hills, and obstacles such as tables/chairs outside of restaurants and coffee shops (mostly pertaining to Water St.). These are all important hazards that were not realized until data was being collected out in the field. This idea could be expanded upon by including more potential rollerblading pathways around lower campus as well as on upper campus or the downtown area.

Arc Collector Part One: Microclimate

Introduction
In this lab, Arc Collector was used to collect microclimate data on the UW-Eau Claire campus. Arc Collector has an app which allows one to enter attributes using the cell phone's GPS. This is very useful for collecting data quickly and efficiently out in the field, as well as allowing for multiple people to be inputing data at once.

Study Area
The chosen study area was UW-Eau Claire campus. The campus was split into seven zones, and groups of 2-3 students collected data from each zone. I was assigned to collect from Zone 2.
(Fig. 1 & 3). Data was collected around 16:00 on Wednesday, April 5th.

Figure 1. Map of 7 zones used to split up and collect microclimate data on UW-Eau Claire campus

Figure 3. A view of the zone map in
the ArcCollector app

Figure 2. The Kestrel 3000
weather meter

Methods
The attributes collected in the microclimate survey were temperature, dew point, wind chill, wind speed, and wind direction. A compass was used to measure cardinal wind direction, and all other attributes were collected using Kestrel 3000 weather meter (Fig. 2).

Figure 4. Data input function in
ArcCollector app

The zone map for the study area was provided by the UWEC Geography Department so data collection could begin as soon as the ArcCollector app was downloaded. To record a point, tap the plus symbol at the top, and then a screen pops up with a spot to record each attribute (Fig. 4). Once all groups collected at least 20 points in their zone, the maps were downloaded from ArcGIS online and opened in ArcMap desktop in order to create continuous surface maps. The interpolation method used in this lab was Natural Neighbors.

Results/Discussion

Figure 5. Map displaying the dew points at UW-Eau Claire campus. Measured in degrees Fahrenheit. 

 The dew points ranged from 30 to 58. The highest dew points were found in the parking lot behind the Davies center. The coolest places were in the corners and edges of campus.

Figure 6. Map showing the temperatures at UW-Eau Claire campus. Measured in degrees Fahrenheit.

 Temperatures ranged from 49 to 62 degrees. The warmest temperature areas were behind Phillips hall, right in front of Hubbard hall, and outside Towers hall. Coolest temperatures were recorded from north of the Chippewa river as well as Governor's parking lot.

Figure 7. Map showing the wind direction at UW-Eau Claire campus. Measured in degrees from North. 

 Wind direction ranged from 0.5 degrees to 340 degrees. Most of the wind directions were in the 120-150 range. The areas with higher wind directions were mostly found on lower campus and near the river.

Figure 8. Map of the wind chill variance at UW-Eau Claire campus. Measured in degrees Fahrenheit.

 Windchill measurements ranged from 45 to 62 degrees. Just outside of Haas was the coldest windchill, and Towers hall area had the highest windchill. As you can see, majority of campus had a windchill within the range of 50-53 degrees.

Figure 9. Map of wind speed at UW-Eau Claire campus. Measured in miles per hour.

Windspeed appeared to vary from 0 to 30 mph, however being in the range 15-30mph was rare. Wind speeds were greatest in the Oakridge parking lot on upper campus. Most of campus appears to have a windspeed of 0 to 4 mph. Upper campus also appears to have higher wind speeds than lower campus.

An anomaly that was noticed is the wind speeds in the range of 22-30 mph. On a day when the average wind speed was 1 mph, it does not make sense that there would be a microclimate wind speed that high. This could be due to human error or an error in the Kestrel device. Voids in the dataset include a time recording for each point (some were forgotten or not recorded correctly), and lack of elevation data. Elevation may have a significant impact on microclimate and it is important to take this into account when making inferences about the area.

Conclusions
 This lab demonstrated how to use ArcCollector to quickly enter data while out in the field and have it immediately be brought into a map. ArcCollector is very effective for surveying in groups and being able to gather a lot of data in a short amount of time. It allows for a wide range of functions and uses for the data and even automatically creates a web map (viewable here).