Yearly Traffic Safety Analysis

134 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In 2020, Chickasaw County recorded 134 total crashes, a 35.3% decrease from the 207 crashes reported in 2019. Total injuries also declined by 15.1%, from 53 to 45. Despite the overall reduction in collisions, the most notable shift was the occurrence of one fatal crash in 2020, resulting in one fatality, whereas no fatal crashes were reported in the prior year.

134

▼ -35.3%was 207

Total Crash Events

1

Persons Killed

45

▼ -15.1%was 53

Persons Injured

1

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic safety in Chickasaw County showed a significant improvement in 2020, with total crashes falling by 35.3% from 207 in 2019 to 134. This downward trend included a 15.1% reduction in injuries, which decreased from 53 to 45. However, the county registered one fatality in 2020, a metric that was zero in the previous year.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 0→%

1

Cyclists Injured

Prior: 1→0.0%

44

Motorists Injured

Prior: 51↓-13.7%

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes shifted year-over-year. In 2020, the peak days for collisions were Thursday and Friday, with 22 incidents each, moving from Monday and Tuesday (34 incidents each) in 2019. The peak hour for crashes also shifted an hour later, from 5 p.m. in 2019 (19 crashes) to 6 p.m. in 2020 (17 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes decreased, the severity profile changed with the appearance of one fatal crash in 2020, which accounted for 0.7% of all crashes; there were no fatal crashes in 2019. The overall proportion of crashes involving any injury saw a slight decrease, from 22.2% in 2019 to 20.9% in 2020. Crashes resulting in serious injuries increased from one to three, while those with minor or possible injuries saw their counts decrease.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.7%
Serious Injury3serious injury crashes2.2%
↑200.0%prior 1
Minor Injury16minor injury crashes11.9%
↓-40.7%prior 27
Possible Injury9possible injury crashes6.7%
↓-50.0%prior 18
No Injury105no injury crashes78.4%
↓-34.8%prior 161

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving animals remained the top contributing factor in both periods, though the count of such incidents decreased by 30.3% from 89 in 2019 to 62 in 2020; this factor's share of total crashes rose from 43.0% to 46.3%. 'Driving too fast for conditions' saw a significant drop, falling from the second-leading cause with 14 crashes in 2019 to only 3 in 2020. Crashes due to 'Lost Control' also declined in count from 13 to 8 but became the second most frequent cause in 2020.

Officer-Reported Primary Contributing Cause

Animal62 (46.3%)↓-30.3%prior 89
Lost Control8 (6%)↓-38.5%prior 13
Other (explain in narrative): Other7 (5.2%)↓-12.5%prior 8
Ran off road - straight7 (5.2%)→0.0%prior 7
Followed too close4 (3%)↓-55.6%prior 9
FTYROW: From yield sign4 (3%)
FTYROW: Making left turn4 (3%)
Other (explain in narrative): No improper action4 (3%)
FTYROW: From driveway3 (2.2%)
Ran off road - left3 (2.2%)↓-70.0%prior 10

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes in 2020 were more likely to occur under favorable conditions compared to the prior year. The proportion of collisions on dry roads increased from 56.6% of reported incidents in 2019 to 71.3% in 2020. Similarly, the share of crashes happening in daylight grew from 59.9% to 70.5%. Crashes during adverse weather, such as on snowy or icy roads, constituted a smaller percentage of the total in 2020 than in 2019.

Weather

Clear63 (72.4%)
↓-24.1%prior 83
Cloudy13 (14.9%)
↓-51.9%prior 27
Rain5 (5.7%)
↓-16.7%prior 6
Snow4 (4.6%)
↓-71.4%prior 14
Fog, smoke, smog2 (2.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Weather condition at time of crash

Lighting

Daylight62 (70.5%)
↓-29.5%prior 88
Dark - roadway not lighted13 (14.8%)
↓-71.7%prior 46
Dark - roadway lighted7 (8.0%)
→0.0%prior 7
Dawn2 (2.3%)
↓-60.0%prior 5
Dark - unknown roadway lighting2 (2.3%)
Dusk2 (2.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Lighting condition field

Road Surface

Dry62 (71.3%)
↓-24.4%prior 82
Wet8 (9.2%)
↓-55.6%prior 18
Snow6 (6.9%)
↓-77.8%prior 27
Ice/frost5 (5.7%)
↓-61.5%prior 13
Gravel5 (5.7%)
Slush1 (1.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Road surface condition field

Vehicles & Demographics

The most common vehicle makes involved in crashes, Chevrolet, Ford, and GMC, maintained their top rankings in 2020, although the absolute number of vehicles involved decreased in line with the overall trend. An analysis of persons involved in crashes shows a significant shift in age demographics; the 16-20 age group's share of persons involved fell from 12.8% in 2019 to 5.5% in 2020. In contrast, the 26-34 age group's representation increased from 19.3% to 24.8% over the same period.

Top Vehicle Makes (183 vehicles)

1
CHEV36 (19.7%)
↓-37.9%prior 58
2
FORD25 (13.7%)
↓-43.2%prior 44
3
CHEVROLET24 (13.1%)
↑41.2%prior 17
4
GMC16 (8.7%)
↑6.7%prior 15
5
CHRYSLER7 (3.8%)
→0.0%prior 7
6
JEEP7 (3.8%)
↑16.7%prior 6
7
DODGE6 (3.3%)
8
TOYOTA5 (2.7%)
9
DODG5 (2.7%)
↓-61.5%prior 13
10
HONDA4 (2.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Vehicle unit records

26 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (169 persons with recorded sex)

Male89 (52.7%)
↓-40.3%prior 149
Female80 (47.3%)
↓-25.9%prior 108

Source: Iowa Crash Data · ArcGIS Open Data · 2020-01-01 to 2020-12-31 · Person-level records linked to crash events

Data Sources & Methodology

Primary Data Source

All crash data in this report is sourced from Iowa Crash Data, accessed programmatically via the ArcGIS Open Data API (SODA). This dataset contains official police-reported motor vehicle traffic crash records maintained by the reporting jurisdiction's law enforcement agency. Records are published to the open data portal by the municipality and are subject to the portal's terms of use.

Data Retrieval

  • Access method: ArcGIS Open Data API (SoQL queries)
  • Data format: Structured JSON via REST API
  • Record types queried: Crash events, person records, and vehicle unit records
  • Date filter applied: 2020-01-01 through 2020-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2020-01-01 through 2020-12-31 (366 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 134
  • Total persons involved: 295
  • Total vehicles involved: 183

Analytical Methodology

  • Severity classification: Uses the KABCO injury scale (K=Fatal, A=Incapacitating injury, B=Non-incapacitating injury, C=Possible injury, O=No injury/property damage only), the standard classification in U.S. Model Minimum Uniform Crash Criteria (MMUCC). Severity is assigned per crash event based on the most severe injury in that crash. A single fatal crash (K) may involve multiple fatalities; therefore the "Persons Killed" count in the headline KPIs may differ from the "Fatal" crash count in the severity breakdown.
  • Contributing factors: Reflect the officer-determined primary contributory cause recorded at the time of the crash report. These are preliminary determinations and may not reflect final investigation findings.
  • Hit-and-run classification: Based on the hit-and-run indicator field in the official crash report, as determined by the responding officer at the scene.
  • Temporal analysis: Day-of-week and hour-of-day distributions are computed from the crash date/time timestamp in each record.
  • Demographics: Age and sex distributions are drawn from person-level records linked to each crash event. A single crash may involve multiple persons.
  • Vehicle data: Make information is drawn from vehicle unit records linked to each crash event.
  • AI commentary: Narrative sections are generated by Google Gemini (large language model) based on the structured data. Commentary is descriptive, not predictive, and should not be interpreted as expert opinion.

Limitations & Disclaimers

  • Only crashes reported to and documented by law enforcement are included. Minor incidents, unreported crashes, and near-misses are not captured in this dataset.
  • Data reflects conditions at the time of the initial police report and may be subject to subsequent corrections, reclassifications, or supplements by the reporting agency.
  • Open data portal records may experience a publication lag - recently occurring crashes may not yet appear in the dataset at the time of report generation.
  • AI-generated commentary is produced by a large language model and is intended to highlight patterns in the data. It does not constitute legal, medical, or professional analysis.
  • Percentages are calculated from reported data and are subject to rounding.

Non-Affiliation Disclosure

This report is produced independently by ThatCarHitMe.com (Injuria.ai). It is not affiliated with, endorsed by, or produced in partnership with any law enforcement agency, municipal government, state department of transportation, or the National Highway Traffic Safety Administration (NHTSA). Data is sourced from publicly available government open data portals.

Data License

The underlying crash data is provided under the municipality's Open Data Terms of Use and is made available to the public for unrestricted use. This analysis and report is © 2026 Injuria.ai and may be cited with attribution using the suggested citation below.

Corrections & Feedback

If you believe any data in this report is inaccurate or have questions about our methodology, please contact: data@injuria.ai. We are committed to accuracy and will issue corrections promptly.

Suggested Citation

ThatCarHitMe.com (Injuria.ai). "iowa, IA Crash Intelligence Report: 2020." Published September 9, 2026. Reporting period: 2020-01-01 to 2020-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2020-annual-report

About the Publisher

ThatCarHitMe.com is a crash data intelligence platform developed by Injuria.ai, a legal technology company specializing in traffic safety analytics. We aggregate and analyze publicly available government crash data to produce structured intelligence reports for communities, researchers, journalists, and legal professionals. Our reports combine programmatic data retrieval from official open data portals with AI-assisted narrative analysis.

Questions about this report's data or methodology: data@injuria.ai

ThatCarHitMe.com · An Injuria.ai Company