Yearly Traffic Safety Analysis

98 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Decatur County recorded 98 total crashes, a 9.3% decrease from the 108 crashes reported in 2016. Despite the overall decline in collisions, the number of fatalities increased significantly, rising from 1 in 2016 to 7 in 2017.

98

▼ -9.3%was 108

Total Crash Events

7

▲ 600.0%was 1

Persons Killed

26

▼ -25.7%was 35

Persons Injured

6

▲ 500.0%was 1

Fatal Crash Events

Note: "Persons Killed" (7) counts individual fatalities across all crash events. "Fatal" in the severity table below (6) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash volume in Decatur County decreased by 9.3% from 108 incidents in 2016 to 98 in 2017. The total number of injuries also saw a decline, falling 25.7% from 35 to 26. However, this downward trend did not extend to crash fatalities, which increased from 1 to 7 year-over-year.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

6

Motorists Killed

Prior: 1↑500.0%

0

Other Killed

Prior: 0→0.0%

0

Pedestrians Injured

Prior: 1↓-100.0%

25

Motorists Injured

Prior: 34↓-26.5%

1

Other Injured

Prior: 0→%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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 2016 and 2017. In 2016, the peak day for crashes was Friday with 25 incidents, whereas in 2017, crashes were more distributed, with Sunday, Wednesday, and Saturday each recording a high of 16 crashes. The peak hour for collisions also changed, moving from 8 PM (11 crashes) in 2016 to a three-way tie between 2 PM, 5 PM, and 7 PM (8 crashes each) in 2017.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity worsened significantly in 2017 compared to the prior year. The number of fatal crashes increased from 1 in 2016 to 6 in 2017, causing the fatal crash share to rise from 0.9% to 6.1% of all crashes. While the total number of injury-involved crashes decreased from 24 to 18, the proportion of serious injury crashes fell slightly from 3.7% to 3.1% of all incidents. Crashes resulting in no injuries accounted for 75.5% of the total in 2017, a slight decrease from 76.9% in 2016.

Severity is per crash event (most severe injury). 6 fatal crash events resulted in 7 persons killed.

Outcome by Severity (Crash Events)

Fatal6fatal crashes6.1%
↑500.0%prior 1
Serious Injury3serious injury crashes3.1%
↓-25.0%prior 4
Minor Injury6minor injury crashes6.1%
↓-53.8%prior 13
Possible Injury9possible injury crashes9.2%
↑28.6%prior 7
No Injury74no injury crashes75.5%
↓-10.8%prior 83

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an animal remained the top contributing factor in both periods, though the count decreased by 26.3% from 57 crashes in 2016 to 42 in 2017. 'Lost Control' was the second most common factor in both years, with its count falling from 18 to 11. Conversely, crashes attributed to 'Driving too fast for conditions' increased from 1 to 4, and incidents of 'Followed too close' rose from 2 to 4 year-over-year.

Officer-Reported Primary Contributing Cause

Animal42 (42.9%)↓-26.3%prior 57
Lost Control11 (11.2%)↓-38.9%prior 18
Ran off road - straight8 (8.2%)→0.0%prior 8
Driving too fast for conditions4 (4.1%)
Followed too close4 (4.1%)
Other (explain in narrative): Other2 (2%)
Driver Distraction: Inattentive/lost in thought2 (2%)
Operating vehicle in an reckless, erratic, careless, negligent manner2 (2%)
Swerving/Evasive Action2 (2%)
FTYROW: From stop sign2 (2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes in 2017 occurred more frequently under favorable conditions compared to the previous year. The share of crashes in 'Clear' weather increased from 42.6% in 2016 to 68.8% in 2017. Similarly, the proportion of collisions on 'Dry' road surfaces rose from 51.9% to 67.7%. The distribution of crashes by lighting conditions remained relatively stable, with daylight crashes accounting for 54.5% of incidents in 2017, similar to 53.7% in 2016.

Weather

Clear44 (67.7%)
↑91.3%prior 23
Cloudy10 (15.4%)
↓-33.3%prior 15
Rain5 (7.7%)
Snow3 (4.6%)
Freezing rain/drizzle1 (1.5%)
↓-85.7%prior 7
Fog, smoke, smog1 (1.5%)
Sleet, hail1 (1.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight36 (54.5%)
↑24.1%prior 29
Dark - roadway not lighted25 (37.9%)
↑31.6%prior 19
Dark - roadway lighted2 (3.0%)
Dawn2 (3.0%)
Dusk1 (1.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry44 (67.7%)
↑57.1%prior 28
Snow7 (10.8%)
→0.0%prior 7
Wet6 (9.2%)
↑20.0%prior 5
Gravel5 (7.7%)
Ice/frost2 (3.1%)
↓-75.0%prior 8
Mud, dirt1 (1.5%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

The vehicle makes most frequently involved in crashes shifted year-over-year. In 2017, Ford was the top make with 21 vehicles involved, up from 19 in 2016 when it was ranked second. Chevrolet, the top make in 2016 with 28 vehicles, saw its involvement decrease to 12 vehicles in 2017. Regarding persons involved, there was a notable increase in the 16-20 age group, which grew from 16 individuals in 2016 to 24 in 2017. Conversely, involvement of the 45-54 age group decreased from 27 to 20.

Top Vehicle Makes (123 vehicles)

1
FORD21 (17.1%)
↑10.5%prior 19
2
CHEVROLET12 (9.8%)
↓-57.1%prior 28
3
DODGE10 (8.1%)
↓-9.1%prior 11
4
CHEV9 (7.3%)
5
TOYOTA7 (5.7%)
→0.0%prior 7
6
GMC7 (5.7%)
7
JEEP6 (4.9%)
8
HONDA4 (3.3%)
↓-33.3%prior 6
9
FREIGHTLINER3 (2.4%)
↓-40.0%prior 5
10
NISS3 (2.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

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

Sex Distribution (94 persons with recorded sex)

Male59 (62.8%)
↓-3.3%prior 61
Female35 (37.2%)
↓-10.3%prior 39

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · 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: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 98
  • Total persons involved: 148
  • Total vehicles involved: 123

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: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-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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