Yearly Traffic Safety Analysis

571 CRASHES IN
IOWA, IA
2020

All metrics benchmarked against2019

In Jasper County, total vehicle crashes decreased by 19.4% from 708 in 2019 to 571 in 2020. While total fatalities also declined from 6 to 4, the number of injuries remained stable, increasing slightly from 183 to 185. The most significant year-over-year change was a 38.6% reduction in crashes where "Driving too fast for conditions" was a contributing factor, with incidents falling from 70 to 43.

571

▼ -19.4%was 708

Total Crash Events

4

▼ -33.3%was 6

Persons Killed

185

▲ 1.1%was 183

Persons Injured

4

▼ -20.0%was 5

Fatal Crash Events

Note: "Persons Killed" (4) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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 crashes in Jasper County showed a notable downward trend from 2019 to 2020. The total number of crashes fell by 19.4%, from 708 to 571. This decline was accompanied by a 33.3% decrease in fatalities, from 6 to 4, although the total number of injuries saw a marginal increase of 1.1%.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Cyclists Killed

Prior: 0→%

3

Motorists Killed

Prior: 6↓-50.0%

3

Pedestrians Injured

Prior: 0→%

2

Cyclists Injured

Prior: 5↓-60.0%

180

Motorists Injured

Prior: 177↑1.7%

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 temporal patterns of crashes shifted between the two periods. The peak day for crashes moved from Monday in 2019, with 131 incidents, to Friday in 2020, with 101 incidents. The afternoon commute remained the most frequent time for crashes, with the peak hour shifting slightly from 3 p.m. in 2019 (48 crashes) to a tie between 3 p.m. and 5 p.m. in 2020 (42 crashes each).

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

Year-over-year, the number of fatal crashes decreased from 5 to 4, but the fatal crash rate as a percentage of all crashes remained stable at 0.7%. While the absolute number of injury-related crashes was nearly unchanged (152 in 2019 vs. 149 in 2020), their proportion of total crashes increased. Crashes involving any injury accounted for 26.1% of all incidents in 2020, up from 21.5% in the prior year.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.7%
↓-20.0%prior 5
Serious Injury17serious injury crashes3%
↑6.3%prior 16
Minor Injury65minor injury crashes11.4%
↓-3.0%prior 67
Possible Injury67possible injury crashes11.7%
↓-2.9%prior 69
No Injury418no injury crashes73.2%
↓-24.1%prior 551

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 an animal remained the top contributing factor in both 2019 (122 crashes) and 2020 (110 crashes). A significant change occurred with "Driving too fast for conditions," which dropped from the second-ranked factor in 2019 with 70 crashes to the fourth-ranked in 2020 with 43 crashes, representing a 38.6% decrease in count. Similarly, crashes attributed to "Lost Control" fell from 69 to 59, and those from "Ran off road - straight" decreased from 67 to 55.

Officer-Reported Primary Contributing Cause

Animal110 (19.3%)↓-9.8%prior 122
Lost Control59 (10.3%)↓-14.5%prior 69
Ran off road - straight55 (9.6%)↓-17.9%prior 67
Driving too fast for conditions43 (7.5%)↓-38.6%prior 70
Ran off road - left31 (5.4%)↓-13.9%prior 36
Followed too close29 (5.1%)↓-38.3%prior 47
Other (explain in narrative): No improper action24 (4.2%)↑118.2%prior 11
FTYROW: From stop sign23 (4%)↑4.5%prior 22
Ran Stop Sign20 (3.5%)↓-4.8%prior 21
Other (explain in narrative): Other18 (3.2%)↓-73.5%prior 68

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

Road & Environmental Conditions

The conditions under which crashes occurred shifted toward better weather and road surfaces in 2020. The share of crashes on dry roads increased from 54.9% in 2019 to 60.9% in 2020. Conversely, the proportion of crashes on snow or ice-covered roads decreased significantly, from a combined 23.2% in 2019 to 13.2% in 2020. Crashes in daylight conditions made up a slightly smaller share of the total, falling from 57.6% to 55.3%.

Weather

Clear337 (67.9%)
↓-5.1%prior 355
Cloudy68 (13.7%)
↓-43.8%prior 121
Snow35 (7.1%)
↓-52.1%prior 73
Rain22 (4.4%)
↓-21.4%prior 28
Blowing Snow13 (2.6%)
↓-50.0%prior 26
Freezing rain/drizzle12 (2.4%)
↓-47.8%prior 23
Fog, smoke, smog4 (0.8%)
Other (explain in narrative)2 (0.4%)
Severe Winds2 (0.4%)
Sleet, hail1 (0.2%)

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

Lighting

Daylight316 (63.8%)
↓-22.5%prior 408
Dark - roadway not lighted113 (22.8%)
↓-24.2%prior 149
Dark - roadway lighted36 (7.3%)
↑2.9%prior 35
Dawn12 (2.4%)
↓-40.0%prior 20
Dusk12 (2.4%)
↓-14.3%prior 14
Dark - unknown roadway lighting6 (1.2%)
↓-14.3%prior 7

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

Road Surface

Dry348 (70.0%)
↓-10.5%prior 389
Wet48 (9.7%)
↓-20.0%prior 60
Snow38 (7.6%)
↓-54.8%prior 84
Ice/frost37 (7.4%)
↓-53.8%prior 80
Gravel16 (3.2%)
↑23.1%prior 13
Slush7 (1.4%)
Other (explain in narrative)2 (0.4%)
Mud, dirt1 (0.2%)

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, Ford and Chevrolet, maintained their top rankings in 2020, though their involvement decreased in absolute numbers from 160 and 160 respectively in 2019 to 130 and 107. An analysis of persons involved shows a shift in age demographics; the proportion of individuals aged 65 and older involved in crashes decreased from 12.0% in 2019 to 9.8% in 2020. Meanwhile, the 16-20 age group saw its share increase slightly from 14.5% to 15.5%.

Top Vehicle Makes (826 vehicles)

1
FORD130 (15.7%)
↓-18.8%prior 160
2
CHEV107 (13%)
↓-33.1%prior 160
3
CHEVROLET63 (7.6%)
↓-21.3%prior 80
4
DODG30 (3.6%)
↓-36.2%prior 47
5
FREIGHTLINER27 (3.3%)
↓-35.7%prior 42
6
JEEP26 (3.1%)
↓-31.6%prior 38
7
TOYT26 (3.1%)
↓-18.8%prior 32
8
DODGE22 (2.7%)
↓-15.4%prior 26
9
GMC21 (2.5%)
↓-36.4%prior 33
10
TOYOTA20 (2.4%)
↓-23.1%prior 26

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

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

Sex Distribution (753 persons with recorded sex)

Male462 (61.4%)
↓-25.1%prior 617
Female291 (38.6%)
↓-21.4%prior 370

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: 571
  • Total persons involved: 1,145
  • Total vehicles involved: 826

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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