ArcCollector 2

INTRODUCTION

This lab explores geodatabase creation and data collection using ArcCollector. Unlike the previous ArcCollector lab, the area of study and theme of the project is decided by the student. The geospatial question explored in this lab was: what areas of the Third Ward in Eau Claire affected most by potholes?

An overview for the flow for the answering this question is as follows: A geodatabase was created with suitable domains, data was collected, data was brought into ArcMap, data was interpreted.

Data was collected on the following variables for each pothole:

• Location
• Size (in cm)
• Cluster size
• Overall road condition
• Area type (Residential, commercial, etc.)
• Traffic amount

While the data collection and interpretation went smoothly, there were some concerns that arose during interpretation. This will be discussed later.


STUDY AREA

The area of interest is a section of the Third Ward in Eau Claire, Wisconsin. This area was chosen because of the diverse road conditions, the mixture of commercial and residential properties, and the density of potholes. The study area is shown below in figure 1. The Third Ward is considered the downtown of Eau Claire. It is largely small businesses such as restaurants, bars, banks, etc. On the outer edges, particularly on the southern side of the area studied, is more residential. There is a mix of high and low traffic streets.

Figure 1: Study area

METHODS

An overview of the workflow was: geodatabase creation, setting up domains, creating fields and assigning domains, data collection, and data interpretation.

The first step was geodatabase creation. A suitable folder for the project was made, and a geodatabase was created using ArcCatalog. Next, still using ArcCatalog, domains were created. A screenshot of this menu appears below in figure 2. For each data type needed, a domain was necessary to determine the allowable values for each variable. This would minimize error. For example, the traffic amount was assigned as text, and options were given so no text had to be manually entered. Similarly, for number variables, such as pothole size, a range of allowable values were given.

Figure 2: Creation of domains within the geodatabase

Once the domains were created, a feature class was created for data collections. This feature class was assigned as points, which would be the locations of potholes. WGS1984 Auxiliary Sphere was used as the coordinate system, as this is necessary for data collection in ArcCollector. Fields were added to this feature class, and the previously created domains were assigned to fields. After this was completed, the feature class was uploaded to ArcGIS Online so it could be accessed in ArcCollector.

With the ArcCollector app installed on the iPhone used for this lab, the feature class was accessed by logging into an ESRI account. With the collection device ready, all streets within the study area were navigated and potholes were documented. An hour was chosen that would have little traffic, so the location could be collected more accurately by standing directly next to the pothole, as shown below in figure 3. For potholes on busier streets, however, the location was collected from the sidewalk. A few meters difference would have a negligible effect on the results, as it is generally within the uncertainty of the GPS signal anyway.

Figure 3: Data collection

Two screenshots appear below in figure 4 and 5 to show the interface of the ArcCollector app. When a pothole was found, the plus icon at the top of the screen was used to create a new data point. This would bring up the screen shown in figure 5, where variables could be assigned for the new data point. For variables such as TrafficRank, a list of allowable variables was shown. For number variables such as size, the value had to be within a certain range. If the value was outside this range, an error would be given.

 Figure 4: Map view within ArcCollector             Figure 5: Collecting data for a single point

Once all the data was collected, ArcGIS Online was used to download the data to ArcMap Desktop.



RESULTS

Shown below in figure 6 is a density map of potholes. The areas affected most by high pothole density are the streets with the highest traffic. This is likely due to a higher frequency of cars driving over the street causing an increased wear on the road surface.

Figure 6: Pothole density

Shown next in figure 7 is the distribution of average pothole sizes. Notice the largest potholes tend to be on the northeastern side of the study area. This area is residential with very little traffic. This makes sense, as less frequented streets will have a lower demand for fixing problematic potholes, so potholes in these areas could grow larger before there is a large demand to fix them.

Figure 7: Average pothole sizes

CONCLUSION

This lab proved to be an excellent learning opportunity. Much was learned about the theme and subject matter that can be collected and interpreted with the assistance of ArcCollector. This lab showed that data with discrete locations and mainly categorical variables are not ideal for interpolation. Turning discrete locations into continuous surfaces demands a range of numeric variables, as was learned in this lab. Adapting traffic data based on three categories (low, medium, high) into a continuous surface map is not ideal. If this lab were to be redone, a different theme would be chosen to study a variable with a more continuous nature.