Monthly Traffic Safety Analysis

4,279 CRASHES IN
IOWA, IA
AUGUST 2021

All metrics benchmarked againstAugust 2020

In August 2021, Iowa recorded 4,279 vehicle crashes, a 1.2% increase from the 4,229 crashes in August 2020. While total crashes remained relatively stable, the number of fatalities saw a significant year-over-year decrease. Fatalities dropped 22.6% from 53 in the prior period to 41 in the current period.

4,279

▲ 1.2%was 4,229

Total Crash Events

41

▼ -22.6%was 53

Persons Killed

1,588

▼ -2.5%was 1,628

Persons Injured

38

▼ -13.6%was 44

Fatal Crash Events

Note: "Persons Killed" (41) counts individual fatalities across all crash events. "Fatal" in the severity table below (38) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2021-08-01 to 2021-08-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crash volume in August 2021 was nearly flat compared to the previous year, increasing by just 50 incidents to a total of 4,279. However, the outcomes of these crashes improved, with total injuries decreasing by 2.5% from 1,628 to 1,588 and total fatalities declining by 22.6% from 53 to 41.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 1↑100.0%

1

Cyclists Killed

Prior: 2↓-50.0%

37

Motorists Killed

Prior: 50↓-26.0%

1

Other Killed

Prior: 0→%

41

Pedestrians Injured

Prior: 22↑86.4%

27

Cyclists Injured

Prior: 40↓-32.5%

1,515

Motorists Injured

Prior: 1,562↓-3.0%

5

Other Injured

Prior: 4↑25.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2021-08-01 to 2021-08-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes remained broadly consistent year-over-year. Monday was the peak day for crashes in both August 2021 (715 crashes) and August 2020 (789 crashes). The peak hour for crashes shifted slightly later in the day, from 4 p.m. in the prior year (342 crashes) to 5 p.m. in the current year (352 crashes).

Source: Iowa Crash Data · ArcGIS Open Data · 2021-08-01 to 2021-08-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2021-08-01 to 2021-08-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity decreased compared to the prior year. The number of fatal crashes fell from 44 to 38, and their share of all crashes dipped from 1.0% to 0.9%. While the count of crashes involving possible or minor injuries increased slightly, the number of serious injury crashes declined from 150 to 124. Correspondingly, crashes resulting in no injuries increased from 2,806 to 2,853.

Severity is per crash event (most severe injury). 38 fatal crash events resulted in 41 persons killed.

Outcome by Severity (Crash Events)

Fatal38fatal crashes0.9%
↓-13.6%prior 44
Serious Injury124serious injury crashes2.9%
↓-17.3%prior 150
Minor Injury518minor injury crashes12.1%
↑2.2%prior 507
Possible Injury746possible injury crashes17.4%
↑3.3%prior 722
No Injury2,853no injury crashes66.7%
↑1.7%prior 2,806

Source: Iowa Crash Data · ArcGIS Open Data · 2021-08-01 to 2021-08-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2021-08-01 to 2021-08-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors remained consistent between the two periods, with 'Followed too close' and 'Animal' being the top two causes in both years. The count of crashes attributed to 'Followed too close' increased by 17.9%, rising from 458 to 540 incidents. Crashes involving 'Improper or erratic lane changing' also saw a notable increase in count, from 90 to 125 incidents. Conversely, crashes from 'Ran off road - left' decreased from 248 to 236.

Officer-Reported Primary Contributing Cause

Followed too close540 (12.6%)↑17.9%prior 458
Animal342 (8%)↑1.2%prior 338
Other (explain in narrative): Other290 (6.8%)↑1.0%prior 287
Ran off road - left236 (5.5%)↓-4.8%prior 248
FTYROW: From stop sign222 (5.2%)↑1.4%prior 219
Lost Control218 (5.1%)↓-4.0%prior 227
FTYROW: Making left turn209 (4.9%)↑4.0%prior 201
Ran off road - straight152 (3.6%)↑6.3%prior 143
Ran Traffic Signal148 (3.5%)↑7.2%prior 138
Ran Stop Sign145 (3.4%)↑14.2%prior 127

Source: Iowa Crash Data · ArcGIS Open Data · 2021-08-01 to 2021-08-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Driving conditions were largely similar year-over-year, with the vast majority of crashes in both periods occurring in daylight (73.6% in 2021 vs. 74.2% in 2020) and on dry roads (85.2% in 2021 vs. 86.6% in 2020). There was a slight increase in the proportion of crashes happening in adverse weather. Crashes in the rain increased from 101 to 180, and those on wet surfaces rose from 189 to 259.

Weather

Clear3,334 (83.3%)
↓-3.6%prior 3,458
Cloudy467 (11.7%)
↑28.3%prior 364
Rain180 (4.5%)
↑78.2%prior 101
Fog, smoke, smog12 (0.3%)
↑140.0%prior 5
Blowing sand, soil, dirt4 (0.1%)
Other (explain in narrative)2 (0.0%)
Freezing rain/drizzle1 (0.0%)
Severe Winds1 (0.0%)
↓-96.8%prior 31

Source: Iowa Crash Data · ArcGIS Open Data · 2021-08-01 to 2021-08-31 · Weather condition at time of crash

Lighting

Daylight3,151 (78.5%)
↑0.5%prior 3,136
Dark - roadway lighted388 (9.7%)
↑7.5%prior 361
Dark - roadway not lighted323 (8.0%)
↑2.5%prior 315
Dusk75 (1.9%)
↓-12.8%prior 86
Dawn61 (1.5%)
↓-9.0%prior 67
Dark - unknown roadway lighting17 (0.4%)
↑30.8%prior 13

Source: Iowa Crash Data · ArcGIS Open Data · 2021-08-01 to 2021-08-31 · Lighting condition field

Road Surface

Dry3,646 (90.9%)
↓-0.4%prior 3,662
Wet259 (6.5%)
↑37.0%prior 189
Gravel94 (2.3%)
↓-14.5%prior 110
Mud, dirt7 (0.2%)
Other (explain in narrative)2 (0.0%)
↓-80.0%prior 10
Sand1 (0.0%)
Oil1 (0.0%)
Water (standing or moving)1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2021-08-01 to 2021-08-31 · Road surface condition field

Vehicles & Demographics

The most common vehicle makes involved in crashes remained consistent, with Ford and Chevrolet vehicles being the most frequently recorded in both August 2021 and August 2020. The age demographics of individuals involved in crashes showed minimal changes. The total number of people involved in crashes decreased from 10,119 to 9,670, with no significant shifts in the proportional representation of any single age group.

Top Vehicle Makes (7,601 vehicles)

1
FORD1,260 (16.6%)
↑0.9%prior 1,249
2
CHEV803 (10.6%)
↓-8.6%prior 879
3
CHEVROLET602 (7.9%)
↑6.0%prior 568
4
TOYT301 (4%)
↑6.0%prior 284
5
JEEP295 (3.9%)
↑7.3%prior 275
6
NR258 (3.4%)
↑22.9%prior 210
7
GMC238 (3.1%)
↓-4.0%prior 248
8
HOND223 (2.9%)
↓-5.5%prior 236
9
DODGE221 (2.9%)
↑5.7%prior 209
10
TOYOTA217 (2.9%)
↑5.9%prior 205

Source: Iowa Crash Data · ArcGIS Open Data · 2021-08-01 to 2021-08-31 · Vehicle unit records

1,262 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (6,410 persons with recorded sex)

Male3,660 (57.1%)
↓-7.6%prior 3,959
Female2,750 (42.9%)
↑3.0%prior 2,671

Source: Iowa Crash Data · ArcGIS Open Data · 2021-08-01 to 2021-08-31 · 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: 2021-08-01 through 2021-08-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2021-08-01 through 2021-08-31 (31 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 4,279
  • Total persons involved: 9,670
  • Total vehicles involved: 7,601

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: August 2021." Published September 9, 2026. Reporting period: 2021-08-01 to 2021-08-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/august-2021-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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