Yearly Traffic Safety Analysis

103 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In 2018, Decatur County recorded 103 total crashes, a 5.1% increase from the 98 crashes reported in 2017. Despite the rise in total incidents, the number of fatalities decreased from 7 in the prior period to 4 in the current period. The number of fatal crashes similarly declined from 6 to 4 over the same timeframe.

103

▲ 5.1%was 98

Total Crash Events

4

▼ -42.9%was 7

Persons Killed

28

▲ 7.7%was 26

Persons Injured

4

▼ -33.3%was 6

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 · 2018-01-01 to 2018-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crashes in Decatur County saw a slight increase in 2018, rising by 5.1% to 103 incidents from 98 in the previous year. While total crashes and the number of people injured (28, up from 26) trended upwards, the number of fatalities saw a notable decrease, dropping from 7 in 2017 to 4 in 2018.

Vulnerable Road User Casualties

4

Motorists Killed

Prior: 6↓-33.3%

28

Motorists Injured

Prior: 25↑12.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · 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 2018, Friday was the most frequent day for crashes with 20 incidents, a change from 2017 when Saturday, Sunday, and Wednesday tied for the peak day with 16 crashes each. The peak hour for crashes also shifted, moving from several hours tied with 8 crashes in the prior period (including 7 p.m.) to 10 p.m. in the current period, which also recorded 8 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes decreased year-over-year, with the fatal crash rate falling from 6.1% of all crashes in 2017 to 3.9% in 2018. The proportion of crashes resulting in serious injuries also declined from 3.1% to 1.9%. Conversely, the share of crashes involving minor injuries increased from 6.1% to 11.7%, with the count of such incidents doubling from 6 to 12. The percentage of no-injury crashes remained stable at approximately 75.6% in both periods.

Outcome by Severity (Crash Events)

Fatal4fatal crashes3.9%
↓-33.3%prior 6
Serious Injury2serious injury crashes1.9%
↓-33.3%prior 3
Minor Injury12minor injury crashes11.7%
↑100.0%prior 6
Possible Injury7possible injury crashes6.8%
↓-22.2%prior 9
No Injury78no injury crashes75.7%
↑5.4%prior 74

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the top contributing factor in both periods, holding steady at 42 crashes. The second most common factor, 'Lost Control,' saw a significant increase in incidents, rising from 11 crashes in 2017 to 19 in 2018. 'Ran off road - straight' was the third-ranked factor in 2018 with 7 crashes, a slight decrease from 8 crashes in the prior year. Crashes attributed to 'Followed too close' were unchanged at 4 incidents in both periods.

Officer-Reported Primary Contributing Cause

Animal42 (40.8%)→0.0%prior 42
Lost Control19 (18.4%)↑72.7%prior 11
Ran off road - straight7 (6.8%)↓-12.5%prior 8
Followed too close4 (3.9%)
Ran off road - left4 (3.9%)
Driving too fast for conditions3 (2.9%)
Driver Distraction: Other interior distraction3 (2.9%)
Other (explain in narrative): No improper action2 (1.9%)
Other (explain in narrative): Other2 (1.9%)
FTYROW: From driveway2 (1.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The distribution of crashes by lighting conditions remained consistent year-over-year, with most incidents occurring during daylight (37 in 2018 vs. 36 in 2017) or on unlit dark roadways (26 in 2018 vs. 25 in 2017). Crashes on dry roads increased from 44 to 48, while incidents on wet roads rose from 6 to 10. Regarding weather, there was a decrease in clear-weather crashes from 44 to 39, alongside an increase in crashes during cloudy conditions (from 10 to 16) and snow (from 3 to 7).

Weather

Clear39 (55.7%)
↓-11.4%prior 44
Cloudy16 (22.9%)
↑60.0%prior 10
Snow7 (10.0%)
Rain5 (7.1%)
→0.0%prior 5
Fog, smoke, smog2 (2.9%)
Blowing Snow1 (1.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Weather condition at time of crash

Lighting

Daylight37 (53.6%)
↑2.8%prior 36
Dark - roadway not lighted26 (37.7%)
↑4.0%prior 25
Dawn3 (4.3%)
Dusk3 (4.3%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Lighting condition field

Road Surface

Dry48 (68.6%)
↑9.1%prior 44
Wet10 (14.3%)
↑66.7%prior 6
Snow7 (10.0%)
→0.0%prior 7
Ice/frost3 (4.3%)
Gravel2 (2.9%)
↓-60.0%prior 5

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Road surface condition field

Vehicles & Demographics

Analysis of persons involved in crashes shows a significant demographic shift, with the 16-20 age group's involvement decreasing from 24 individuals in 2017 to 12 in 2018. Conversely, involvement for the 55-64 age group increased from 15 to 26. Regarding vehicle makes, Ford and Chevrolet remained the most common brands in both periods, with Ford-involved vehicles increasing from 21 to 24. Freightliner trucks became more prominent in crash data, with their count tripling from 3 in 2017 to 9 in 2018.

Top Vehicle Makes (130 vehicles)

1
FORD24 (18.5%)
↑14.3%prior 21
2
CHEV14 (10.8%)
↑55.6%prior 9
3
FREIGHTLINER9 (6.9%)
4
DODG8 (6.2%)
5
DODGE7 (5.4%)
↓-30.0%prior 10
6
CHEVROLET6 (4.6%)
↓-50.0%prior 12
7
KIA5 (3.8%)
8
HONDA5 (3.8%)
9
GMC4 (3.1%)
↓-42.9%prior 7
10
JEEP4 (3.1%)
↓-33.3%prior 6

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Vehicle unit records

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

Sex Distribution (90 persons with recorded sex)

Male65 (72.2%)
↑10.2%prior 59
Female25 (27.8%)
↓-28.6%prior 35

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · 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: 2018-01-01 through 2018-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2018-01-01 through 2018-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 103
  • Total persons involved: 162
  • Total vehicles involved: 130

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: 2018." Published September 9, 2026. Reporting period: 2018-01-01 to 2018-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2018-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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