Yearly Traffic Safety Analysis

59 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Taylor County, total vehicle crashes decreased by 16.9%, from 71 in 2019 to 59 in 2020. Despite this overall reduction in collisions, the number of people injured in crashes increased by 61.9%, rising from 21 in the prior year to 34 in the current period. The number of fatalities remained unchanged, with one fatality recorded in both 2019 and 2020.

59

▼ -16.9%was 71

Total Crash Events

1

Persons Killed

34

▲ 61.9%was 21

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 data for Taylor County indicates a downward trend in the total number of crashes, which fell from 71 in 2019 to 59 in 2020, a 16.9% decrease. However, this positive trend in crash frequency was offset by a negative trend in outcomes, as total injuries rose 61.9% from 21 to 34 year-over-year. Fatalities held steady at one for each period.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 1→0.0%

34

Motorists Injured

Prior: 21↑61.9%

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 temporal patterns of crashes shifted between the two periods. In 2020, the peak day for crashes was Wednesday with 15 incidents, a change from 2019 when Thursday was the peak day with 19 incidents. Similarly, the peak hour for crashes moved later into the evening, from 4 p.m. (7 crashes) in 2019 to 7 p.m. (9 crashes) in 2020.

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 the total number of fatal crashes remained stable at one in both 2019 and 2020, the fatal crash rate increased from 1.4% to 1.7% of all crashes due to the lower overall crash volume. The proportion of crashes resulting in injury grew, accounting for 37.3% of incidents in 2020 compared to 23.9% in 2019. Notably, the count of serious injury crashes increased from 4 in 2019 to 7 in 2020.

Outcome by Severity (Crash Events)

Fatal1fatal crashes1.7%
→0.0%prior 1
Serious Injury7serious injury crashes11.9%
↑75.0%prior 4
Minor Injury9minor injury crashes15.3%
↑12.5%prior 8
Possible Injury6possible injury crashes10.2%
↑20.0%prior 5
No Injury36no injury crashes61%
↓-32.1%prior 53

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, with a nearly stable count of 25 crashes in 2019 and 24 in 2020; this represented a 35.2% share and a 40.7% share of crashes, respectively. The most significant change was in crashes attributed to 'Lost Control,' which more than doubled in count from 3 incidents in 2019 to 7 in 2020. Conversely, crashes involving failure to yield from a stop sign decreased from 5 in 2019 to 1 in 2020.

Officer-Reported Primary Contributing Cause

Animal24 (40.7%)↓-4.0%prior 25
Lost Control7 (11.9%)
Ran off road - straight4 (6.8%)↓-33.3%prior 6
Driving too fast for conditions4 (6.8%)
Ran Stop Sign3 (5.1%)
FTYROW: At uncontrolled intersection2 (3.4%)
Followed too close2 (3.4%)
Other (explain in narrative): Other2 (3.4%)
FTYROW: Making left turn1 (1.7%)
FTYROW: Other (explain in narrative)1 (1.7%)

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

Road & Environmental Conditions

Comparing conditions, the most notable shift occurred in lighting. The share of crashes happening in daylight decreased from 49.3% in 2019 to 42.4% in 2020. Correspondingly, the proportion of crashes on dark, unlighted roadways increased from 14.1% of all crashes in 2019 to 23.7% in 2020. Proportions of crashes in clear weather and on dry road surfaces remained relatively stable between the two years.

Weather

Clear30 (73.2%)
↓-23.1%prior 39
Cloudy4 (9.8%)
↓-20.0%prior 5
Fog, smoke, smog2 (4.9%)
Rain2 (4.9%)
Freezing rain/drizzle1 (2.4%)
Severe Winds1 (2.4%)
Snow1 (2.4%)

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

Lighting

Daylight25 (61.0%)
↓-28.6%prior 35
Dark - roadway not lighted14 (34.1%)
↑40.0%prior 10
Dawn2 (4.9%)

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

Road Surface

Dry29 (70.7%)
↓-23.7%prior 38
Gravel4 (9.8%)
Wet4 (9.8%)
Snow3 (7.3%)
Mud, dirt1 (2.4%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent, with Ford and Chevrolet models being the most common in both 2019 and 2020. There was a shift in the age distribution of persons involved in collisions. In 2019, the 55-64 age group had the highest involvement with 24 people. In 2020, younger demographics were more represented, with the 35-44 age group being the largest cohort at 26 people, followed by the 16-20, 21-25, and 26-34 age groups, each with 22 people.

Top Vehicle Makes (82 vehicles)

1
FORD20 (24.4%)
↓-20.0%prior 25
2
CHEV13 (15.9%)
→0.0%prior 13
3
CHEVROLET8 (9.8%)
↓-11.1%prior 9
4
DODG6 (7.3%)
↓-14.3%prior 7
5
DODGE3 (3.7%)
6
TOYO3 (3.7%)
7
HD2 (2.4%)
8
CHRY2 (2.4%)
9
JEEP2 (2.4%)
10
KIA2 (2.4%)

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

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

Sex Distribution (79 persons with recorded sex)

Male48 (60.8%)
↓-21.3%prior 61
Female31 (39.2%)
↑3.3%prior 30

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: 59
  • Total persons involved: 138
  • Total vehicles involved: 82

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

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