Yearly Traffic Safety Analysis

303 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Tama County recorded 303 total crashes, a 2.0% increase from the 297 crashes documented in 2016. While the overall number of crashes remained relatively stable, the number of fatalities tripled, rising from 2 in the prior year to 6 in the current year. These incidents resulted in a total of 86 injuries and 6 deaths in 2017.

303

▲ 2.0%was 297

Total Crash Events

6

▲ 200.0%was 2

Persons Killed

86

▲ 14.7%was 75

Persons Injured

5

▲ 150.0%was 2

Fatal Crash Events

Note: "Persons Killed" (6) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) 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

Crash trends in Tama County showed a slight increase in volume but a significant rise in severity year-over-year. Total crashes grew by 2.0% from 297 to 303. More notably, the number of people injured increased by 14.7% from 75 to 86, and total fatalities increased from 2 to 6.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 2↑150.0%

0

Pedestrians Injured

Prior: 0→0.0%

2

Cyclists Injured

Prior: 0→%

84

Motorists Injured

Prior: 75↑12.0%

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 timing of crashes shifted between the two periods. The peak day for collisions moved from Saturday (52 crashes) in 2016 to Sunday (54 crashes) in 2017. The most frequent crash hour also occurred earlier, shifting from 6 p.m. in the prior year (30 crashes) to 5 p.m. in the current year (34 crashes).

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 year-over-year. The number of fatal crashes increased from 2 in 2016 to 5 in 2017, causing the fatal crash rate to more than double from 0.7% to 1.7% of all incidents. The proportion of crashes resulting in any type of injury also grew, accounting for 21.5% of all crashes in 2017 compared to 18.5% in 2016.

Severity is per crash event (most severe injury). 5 fatal crash events resulted in 6 persons killed.

Outcome by Severity (Crash Events)

Fatal5fatal crashes1.7%
↑150.0%prior 2
Serious Injury5serious injury crashes1.7%
↑66.7%prior 3
Minor Injury32minor injury crashes10.6%
↑18.5%prior 27
Possible Injury28possible injury crashes9.2%
↑12.0%prior 25
No Injury233no injury crashes76.9%
↓-2.9%prior 240

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, with the count increasing from 117 in 2016 to 128 in 2017. While 'Lost Control' stayed the second-most cited factor, its count decreased from 31 to 29. Crashes attributed to 'Driving too fast for conditions' saw a notable drop, falling from 26 incidents to 17. Conversely, crashes where a vehicle ran off the road to the left increased from 5 to 14.

Officer-Reported Primary Contributing Cause

Animal128 (42.2%)↑9.4%prior 117
Lost Control29 (9.6%)↓-6.5%prior 31
Driving too fast for conditions17 (5.6%)↓-34.6%prior 26
Ran off road - straight14 (4.6%)↑16.7%prior 12
Ran off road - left14 (4.6%)↑180.0%prior 5
Other (explain in narrative): Other13 (4.3%)↑160.0%prior 5
FTYROW: From stop sign9 (3%)↑50.0%prior 6
Followed too close8 (2.6%)↑60.0%prior 5
Operating vehicle in an reckless, erratic, careless, negligent manner7 (2.3%)→0.0%prior 7
FTYROW: Making left turn6 (2%)↑20.0%prior 5

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 distribution of crashes by environmental conditions saw several shifts year-over-year. The number of crashes occurring in cloudy weather increased from 28 to 48, while those in clear weather decreased from 122 to 109. Collisions on wet roads rose from 10 to 18, whereas crashes on snowy or icy surfaces declined from a combined 30 to 19. Regarding lighting, crashes during daylight hours increased from 109 to 117.

Weather

Clear109 (58.9%)
↓-10.7%prior 122
Cloudy48 (25.9%)
↑71.4%prior 28
Rain10 (5.4%)
↑66.7%prior 6
Snow9 (4.9%)
↓-25.0%prior 12
Freezing rain/drizzle4 (2.2%)
Fog, smoke, smog3 (1.6%)
↓-57.1%prior 7
Severe Winds1 (0.5%)
Blowing Snow1 (0.5%)

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

Lighting

Daylight117 (62.9%)
↑7.3%prior 109
Dark - roadway not lighted41 (22.0%)
↓-14.6%prior 48
Dark - roadway lighted21 (11.3%)
↑50.0%prior 14
Dawn4 (2.2%)
Dusk2 (1.1%)
↓-71.4%prior 7
Dark - unknown roadway lighting1 (0.5%)

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

Road Surface

Dry133 (71.1%)
↑6.4%prior 125
Wet18 (9.6%)
↑80.0%prior 10
Snow12 (6.4%)
↓-33.3%prior 18
Gravel10 (5.3%)
↓-23.1%prior 13
Ice/frost7 (3.7%)
↓-41.7%prior 12
Mud, dirt3 (1.6%)
Other (explain in narrative)2 (1.1%)
Water (standing or moving)2 (1.1%)

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

Vehicles & Demographics

Combining abbreviations with full names, Chevrolet became the most common vehicle make in crashes in 2017 with 103 vehicles involved, up from 95 in 2016. This surpassed Ford, which saw its involvement remain stable at 78 vehicles compared to 79 previously. An analysis of persons involved in crashes reveals a significant increase in the 65+ age group, which grew from 40 individuals in 2016 to 64 in 2017.

Top Vehicle Makes (406 vehicles)

1
CHEV80 (19.7%)
↑95.1%prior 41
2
FORD78 (19.2%)
↓-1.3%prior 79
3
CHEVROLET23 (5.7%)
↓-57.4%prior 54
4
DODGE15 (3.7%)
↓-28.6%prior 21
5
JEEP14 (3.4%)
↑100.0%prior 7
6
BUIC12 (3%)
7
CHRY11 (2.7%)
8
DODG10 (2.5%)
↓-41.2%prior 17
9
PONT10 (2.5%)
↑66.7%prior 6
10
HONDA9 (2.2%)
↓-47.1%prior 17

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

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

Sex Distribution (270 persons with recorded sex)

Male159 (58.9%)
↓-16.3%prior 190
Female111 (41.1%)
↑7.8%prior 103

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: 303
  • Total persons involved: 477
  • Total vehicles involved: 406

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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