Yearly Traffic Safety Analysis

274 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Crawford County recorded 274 total traffic crashes, a 22.3% increase from the 224 crashes reported in 2016. Despite the rise in total collisions, the number of fatalities fell to zero in 2017, down from three in the prior year. The number of injuries remained nearly stable, with 94 people injured in 2017 compared to 95 in 2016.

274

▲ 22.3%was 224

Total Crash Events

0

▼ -100.0%was 3

Persons Killed

94

▼ -1.1%was 95

Persons Injured

0

▼ -100.0%was 2

Fatal Crash Events

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) 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

Traffic crashes in Crawford County increased year-over-year, rising from 224 incidents in 2016 to 274 in 2017, which represents a 22.3% increase. While the total number of collisions went up, the outcomes were less severe on average. The number of injuries saw a minor decrease from 95 to 94, and fatalities dropped from three to zero.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 3↓-100.0%

1

Pedestrians Injured

Prior: 1→0.0%

93

Motorists Injured

Prior: 94↓-1.1%

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 showed some consistency and some shifts between 2016 and 2017. Friday remained the peak day for crashes in both years, with 48 incidents in 2017 compared to 55 in 2016. Similarly, the 3 PM hour was the most frequent time for crashes in both periods. However, 2017 saw a notable increase in crashes on Saturdays, which rose from 29 to 46, and a significant spike in collisions during the 2 AM hour, increasing from 2 to 12 incidents.

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 improved notably in 2017, with zero fatal crashes recorded, down from two fatal crashes in 2016. The proportion of crashes resulting in any injury decreased slightly, from 29.5% in 2016 to 29.2% in 2017. Within injury-related collisions, the number of serious injury crashes was unchanged at nine, while minor injury crashes increased from 16 to 26. The share of no-injury crashes held steady at approximately 71% in both years.

Outcome by Severity (Crash Events)

Serious Injury9serious injury crashes3.3%
→0.0%prior 9
Minor Injury26minor injury crashes9.5%
↑62.5%prior 16
Possible Injury45possible injury crashes16.4%
↑15.4%prior 39
No Injury194no injury crashes70.8%
↑22.8%prior 158

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 leading contributing factor in both periods, increasing from 35 incidents in 2016 to 40 in 2017. 'Lost Control' was the second-most cited factor, with its count rising from 19 to 27. 'Followed too close' also saw an increase, growing from 13 to 19 incidents to become the third-most common factor in 2017. Conversely, crashes attributed to 'Failure to Yield Right of Way from a stop sign' decreased from 17 to 13 incidents.

Officer-Reported Primary Contributing Cause

Animal40 (14.6%)↑14.3%prior 35
Lost Control27 (9.9%)↑42.1%prior 19
Followed too close19 (6.9%)↑46.2%prior 13
Ran off road - straight16 (5.8%)↑45.5%prior 11
Ran off road - left14 (5.1%)↑7.7%prior 13
FTYROW: From stop sign13 (4.7%)↓-23.5%prior 17
Other (explain in narrative): Other12 (4.4%)↑71.4%prior 7
Operating vehicle in an reckless, erratic, careless, negligent manner12 (4.4%)↑140.0%prior 5
Improper Backing10 (3.6%)↑11.1%prior 9
Driving too fast for conditions9 (3.3%)

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

Road & Environmental Conditions

The proportion of crashes occurring in clear weather and on dry roads remained relatively stable year-over-year, accounting for approximately 60% and 64-66% of all incidents, respectively. There was a significant shift in lighting conditions, as crashes in darkness on unlit roadways more than doubled, increasing from 24 incidents in 2016 to 57 in 2017. Consequently, the share of crashes happening in daylight decreased from 65.2% in 2016 to 55.8% in 2017.

Weather

Clear166 (67.8%)
↑23.0%prior 135
Cloudy50 (20.4%)
↑38.9%prior 36
Rain10 (4.1%)
↓-9.1%prior 11
Snow9 (3.7%)
↑50.0%prior 6
Freezing rain/drizzle5 (2.0%)
Other (explain in narrative)1 (0.4%)
Fog, smoke, smog1 (0.4%)
Severe Winds1 (0.4%)
Blowing Snow1 (0.4%)
Blowing sand, soil, dirt1 (0.4%)

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

Lighting

Daylight153 (61.4%)
↑4.8%prior 146
Dark - roadway not lighted57 (22.9%)
↑137.5%prior 24
Dark - roadway lighted23 (9.2%)
↑76.9%prior 13
Dusk7 (2.8%)
↑16.7%prior 6
Dawn7 (2.8%)
Dark - unknown roadway lighting2 (0.8%)

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

Road Surface

Dry180 (72.6%)
↑25.9%prior 143
Wet21 (8.5%)
→0.0%prior 21
Snow16 (6.5%)
↑23.1%prior 13
Ice/frost16 (6.5%)
↑60.0%prior 10
Gravel12 (4.8%)
↑100.0%prior 6
Slush1 (0.4%)
Other (explain in narrative)1 (0.4%)
Mud, dirt1 (0.4%)

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

Vehicles & Demographics

Ford and Chevrolet remained the two most common vehicle makes involved in crashes, with the count of Fords increasing from 59 to 86 and Chevrolets increasing from 77 to 82 year-over-year. In contrast, the number of Dodge vehicles involved in collisions decreased from 44 in 2016 to 33 in 2017. An analysis of persons involved in crashes shows a notable increase in the 65+ age group, which grew from 47 individuals in 2016 to 66 in 2017, and the 55-64 age group, which rose from 43 to 55.

Top Vehicle Makes (432 vehicles)

1
FORD86 (19.9%)
↑45.8%prior 59
2
CHEV48 (11.1%)
↑17.1%prior 41
3
CHEVROLET34 (7.9%)
↓-5.6%prior 36
4
DODG23 (5.3%)
↑4.5%prior 22
5
GMC21 (4.9%)
↑31.3%prior 16
6
JEEP16 (3.7%)
↑6.7%prior 15
7
PONT16 (3.7%)
↑220.0%prior 5
8
CHRY14 (3.2%)
↑75.0%prior 8
9
NISS12 (2.8%)
10
BUIC11 (2.5%)
↑10.0%prior 10

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

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

Sex Distribution (306 persons with recorded sex)

Male188 (61.4%)
↑11.2%prior 169
Female118 (38.6%)
↑6.3%prior 111

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: 274
  • Total persons involved: 506
  • Total vehicles involved: 432

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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