Deer Crashes in Jackson County: Did the topography play a factor?
Deer Crashes in Jackson County:
Did the topography play a factor?
(Photo South of Humbird, Wisconsin on Highway 12)
Introduction
A person is driving when a deer jumps out and wrecks the car. Deer crashes happen very often in Wisconsin, about 18,000 a year (Community maps). For Wisconsin residents there is a 1 in 57 chance of hitting a deer, that puts Wisconsin 7th in the nation when comes to number of deer crashes (State Farm). If you hit a deer the insurance claims average about $3362 (Taschler). Sometimes those crash sites seem to have similar topographies and maybe they play a factor in increasing the chance of a deer crash. This project will investigate topographical similarities of deer crashes in Jackson County, Wisconsin
Background:
This project was inspired because of a deer crash that the author had in June 2020. After the deer crash, analysis of the satellite imagery showed that the topography was very similar to something else that was read about in the preliminary research.
Figure B1.
. 
Figure B1 shows were the crash that occurred in June 2020. There was forest on one side, and water and crops on the other side.
Background: Preliminary Research.
Before conducting the field test, it was decided to see if there had been previous research into this. Luckily, it is a very common problem so there are thousands of studies on the topic. This article was a news release from 2010 from the Iowa Department of Transportaion official Troy Jerman, "Be most alert for the presence of deer at locations where three factors converge: Food (corn fields or recently harvested corn fields),Shelter (woods),Water (streams, culverts and river crossings).” (Jerman).
An interview that Aaron Dekker conducted with Jeff Pritzl, a deer specialist from Wisconsin DNR, also shed some light on deer behavior. His response was that “Deer are creatures of habit. You can see this with deer paths in the woods. Their average range is about one square mile.” (Pritzl)
Figure P1
Figure B1:
Shows a deer path highlighted in green.
Background: Hypothesis formed.
After reading that article with Jeff Pritzl, it was noticed that he said that deer are creatures of habit, this could explain the similar topographies.
The article with Troy Jerman formed the main part of my hypothesis when he mentioned. "Be most alert for the presence of deer at locations where three factors converge: Food (corn fields or recently harvested corn fields), Shelter (woods), Water (streams, culverts and river crossings).” (Jerman).
The project’s hypothesis is that “When Crops, Forest, and Water combine, do they increase the chance of hitting a deer?”
Methods
This project leans towards qualitative since it was not just numbers.
Figure M1:
Figure M1: Shows the steps that were taken in making the project.
Jackson County was picked because of frequent close calls with deer in that county from previous experiences. The crash data source that was used was from Community Maps, a website that UW-Madison runs with crash data from local sheriff departments. The data collection program was Survey123. The other key method was to separate Crops & Forest & Water to make comparison easier to see which factor was the biggest on a smaller scale instead of the three combined.
Methods: Community Maps Selection
The years selected were from 2001 to 2021. From there select Jackson County and deer crashes only. Leave the rest of the fields blank. Change imagery to satellite, this will make the field work far easier.
Figure M2:
Figure M2:
The final image should have looked like this after finishing the settings on Community Maps.
Methods: Survey 123
The next step was to make a form on Survey123.
Figure M3:
Figure M3:
Shows what the first part of the Survey form on Survey123. The other two counties were included because to get to Jackson County, someone has to drive through Eau Claire and Trempealeau Counties, but those county’s data was thrown out after the survey was completed. The map location setting was set to automatically take the location.
Figure M4:
Figure M4:
This shows part two of the form.
Date and time were important for comparison in case someone said that the terrain does not look like that in the fall, this will show the photo was taken in the spring.
The photo was taken through the camera to go directly the Survey123. The photo was very important as it proved what the terrain looked like at the time of taking the photo.
Figure M5:
Figure M5:
This is the last part of the form. The topography was logged into one of a few categories. This was done for efficiency when out in the field. Since it would be highly likely that one of these combinations would exist.
Figure M6:
Figure M6:
Shows the spreadsheet after the survey was done. The last three columns, Water & Forest & Crops, were added after the survey was completed to make calculations easier.
Graphing Methods
From Figure M5, take the Water, Forest, Crops separately and select insert graph. From there take the number of times each one is mentioned and divided by the number total crashes multiply by 100 and we get the answers. Also, the number was rounded up.
Formula: V/T*100= percentage
Example
Variable (IE, fields were nearby) | Total |
Yes | 59 |
No | 23 |
Total | 72 |
Formula: 59/72*100=68%
Formula 23/72*100=32%
Forest | Percent total |
Yes | 68 |
No | 32 |
Total | 100 |
Insert Graph
Part 2:
Field Collection
Used a Samsung smartphone to collect points on with the Survey123 app.
STOP driving when taking field notes on the phone.
Wear a reflective vest.
Complete during daylight hours.
Match locations from Community maps.
If the topographies where within a half-mile, it counted.
The field visit was conducted on March 26,2021.
Results
Figure R1
Figure R1: Shows the totals of the numerical and percentage combination of topography.
Figure R2:
Figure R2: This graph shows the percentage of deer crashes that had Crops nearby.
Figure R3:
Figure R3: This graph shows the percentage of deer crashes that had Forest nearby.
Figure R4:
Figure R4: This graph shows the percentage of deer crashes that had Water nearby.
Figure R5:
Figure R5: This map shows the satellite crash map.
Figure R6
Figure R6: Shows the map of road view of crash sites.
Discussion
For this survey, all but water cleared 80 percent of the total of cases. The breakdown by topography was important because when it was separated a stronger picture emerged. That each one does a play a factor on their own. Just like the starters for a basketball team. Some score more than others. This part will break down who was the bigger scorers.
Figure R1 showed that Crops/Forest/Water combined created the highest number of combined crashes, almost hitting 50 percent. With runners up, forest/crops and water/crops being equally at 15 percent each.
Water (See Figure R4) did clear 60 percent which shows that water definitely contributed to crashes in this survey but was not quite a major factor.
Forest and fields both cleared 80 percent (See Figure R2 and Figure R3), this shows that both are major factors in a deer crash. It was surprising how their percentages were almost equal. But forest barely edged out crops by two percent to be the biggest factor.
In Figure F6 and in Figure F5, the route of the roads also mattered as if the road was running through woods with creeks nearby, it increased the water and wood ratios, and whatever combination the road ran through in other cases.
In Figure R5, the county's topography also varied to the point where some areas where highly predictable, other areas needed more time to decide on. It was noticeable how the northern stretch of road was almost a full sweep of Crops/Forest/Water. Just a few exceptions of field/water or forest/water. The stretch south of Humbird was mostly Field/Water. The stretch of I-94 west of Black River Falls was mostly Forest/Water. The other stretch west of Northfield was all Crops/Forest/Water.
Limitations
The first limitation is that not all crashes are reported. If every crash did get reported, that would either confirm my hypothesis or deny my hypothesis by bigger margins. Mr. Pritzl said, "For every crash that gets reported, two do not get reported.” (Pritzl)
The other limitation was that the terrain could have changed since 2001 in some locations. That could create some minor discrepancies.
The other limitation was that crashes are not always reported in the correct spot. Like the crash happened at xyz and it was reported as yxz because either person drove further down the road or they just gave the wrong location in the report.
Future Projects
For future projects some things were noticed. First, it seemed like crashes were in hotspots. It would be interesting to see if deer crashes are in hotspots or just random.
The second thing noticed was the sheer number of crashes of some two-lane highways versus four lane highways. The next project would be is to see how traffic counts played a role in the topography. Some topography along the four lane highways showed different results of crashes. The next step involving traffic counts would be to get the number of crashes and divide that by the number of traffic to get the probability of hitting a deer. There were also some two-lane highways that had a similar count to the four lane highways with deer crashes.
The other thing noted in the crash reports was the month of the crash. It would be interesting to see what time of day and month the most common time would be to hit a deer.
It would also be interesting to see if other Wisconsin counties have similar patterns. Like maybe comparing Jackson to Washburn and Iowa counties in Wisconsin for topographical comparison. Even other political territories would be an interesting case study.
Conclusion
Overall, the hypothesis was mostly accepted. But water was not as big of a factor as predicted is why I say “mostly” accepted. Forest and crops were the bigger factors. But the results showed that when forest, water, and crops came together, it did make deer crashes more likely to occur.
If you see Forest, Crops, and Water nearby, be careful as you might be in deer crash alley. With that, the buck stops here.
References
- Community Maps - Crash. https://transportal.cee.wisc.edu/partners/community-maps/crash/search/BasicSearch.do;jsessionid=00556FBC56703952046F7E82DA0AE5A2 (last accessed 1 May 2021).
- Jerman , T. 2010. Iowa DOT News Release. Iowa Department of Transportation. https://www.news.iowadot.gov/newsandinfo/2010/10/increased-deer-movement-means-extra-caution-needed-on-iowa-roadways.html (last accessed 1 March 2021).
- State Farm insurance S. F. S. 2020. Where are Animal Collisions Most Likely? - State Farm®. State Farm. https://www.statefarm.com/simple-insights/auto-and-vehicles/how-likely-are-you-to-have-an-animal-collision (last accessed 11 May 2021).
- Taschler, J. 2018. Every day, a car hits a deer in Wisconsin. This week is among the most dangerous on our roads. Milwaukee Journal Sentinel. https://www.jsonline.com/story/money/business/2018/11/08/deer-crashes-wisconsin-peak-november/1847638002/ (last accessed 4 May 2021).
Acknowledgments
Thank you Dr. Rozario for teaching this class and preparing me for fieldwork. Thank you to the deer that I hit in 2020 for inspiration for this project, I hope it is doing well as it ran off after I hit it. I would also like to thank my parents for pushing me to strive for excellence. I would also like to thank Martin Goettl, Yvonne Plomedahl, and the UWEC geography department for all the hard work they do.
Further readings.
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