Yearly Traffic Safety Analysis

123 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Keokuk County recorded 123 total crashes, a 15.0% increase from the 107 crashes documented in 2016. During this period, the number of injuries rose from 45 to 52, while fatalities remained stable with one death reported in each year. The most notable year-over-year change was a 173% increase in the count of crashes attributed to drivers losing control, which grew from 11 incidents in 2016 to 30 in 2017.

123

▲ 15.0%was 107

Total Crash Events

1

Persons Killed

52

▲ 15.6%was 45

Persons Injured

1

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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 crash data for Keokuk County indicates a rising trend from 2016 to 2017. The total number of crashes increased by 15.0%, from 107 to 123. Correspondingly, the number of persons injured in these incidents grew by 15.6% from 45 to 52, while the number of fatalities was unchanged at one for both years.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

1

Motorists Killed

Prior: 1→0.0%

1

Pedestrians Injured

Prior: 0→%

51

Motorists Injured

Prior: 45↑13.3%

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. In 2016, the peak day for collisions was Sunday with 17 incidents, whereas in 2017, Wednesday became the peak day with 24 crashes. The most frequent time for crashes also changed, moving from the 10 PM hour in 2016 (10 crashes) to the 3 PM and 5 PM hours in 2017, which each recorded 13 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

While the total number of fatal crashes remained constant at one for both years, the fatal crash rate per 100 crashes declined from 0.93 in 2016 to 0.81 in 2017. The distribution of injury severity changed, with crashes involving serious injuries decreasing from 7 to 5. In contrast, crashes resulting in minor injuries increased from 11 in 2016 to 19 in 2017, representing a rise from 10.3% to 15.4% of all crashes.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.8%
→0.0%prior 1
Serious Injury5serious injury crashes4.1%
↓-28.6%prior 7
Minor Injury19minor injury crashes15.4%
↑72.7%prior 11
Possible Injury17possible injury crashes13.8%
↑13.3%prior 15
No Injury81no injury crashes65.9%
↑11.0%prior 73

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 with animals were the leading contributing factor in both years, though the count decreased slightly from 44 in 2016 to 42 in 2017. The most significant shift was in crashes where a driver 'Lost Control,' which saw a 173% increase in count from 11 incidents in 2016 to 30 in 2017. This change elevated it from the second-ranked factor to a more prominent position. Additionally, crashes from running a stop sign increased from 2 to 6, while incidents of running off a straight road decreased from 9 to 6.

Officer-Reported Primary Contributing Cause

Animal42 (34.1%)↓-4.5%prior 44
Lost Control30 (24.4%)↑172.7%prior 11
Ran off road - straight6 (4.9%)↓-33.3%prior 9
Ran Stop Sign6 (4.9%)
Other (explain in narrative): Other4 (3.3%)
Operating vehicle in an reckless, erratic, careless, negligent manner4 (3.3%)
FTYROW: From stop sign4 (3.3%)↓-33.3%prior 6
Driving too fast for conditions3 (2.4%)
Made improper turn2 (1.6%)
Driver Distraction: Other interior distraction2 (1.6%)

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 both 2016 and 2017 occurred predominantly during daylight hours on dry roads under clear skies. The absolute number of crashes under these favorable conditions increased year-over-year, with daylight crashes rising from 46 to 57 and dry-road crashes increasing from 53 to 69. Crashes in cloudy conditions also saw a notable increase from 18 to 31 incidents. The proportion of crashes occurring in adverse weather or on wet, snowy, or icy surfaces did not change significantly between the two periods.

Weather

Clear46 (54.8%)
↑12.2%prior 41
Cloudy31 (36.9%)
↑72.2%prior 18
Fog, smoke, smog3 (3.6%)
Snow2 (2.4%)
Rain1 (1.2%)
Blowing Snow1 (1.2%)

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

Lighting

Daylight57 (67.9%)
↑23.9%prior 46
Dark - roadway not lighted13 (15.5%)
↑8.3%prior 12
Dark - roadway lighted5 (6.0%)
Dusk4 (4.8%)
Dawn3 (3.6%)
Dark - unknown roadway lighting2 (2.4%)

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

Road Surface

Dry69 (81.2%)
↑30.2%prior 53
Wet7 (8.2%)
↑16.7%prior 6
Gravel3 (3.5%)
Snow3 (3.5%)
Ice/frost2 (2.4%)
Mud, dirt1 (1.2%)

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

Vehicles & Demographics

Ford, Chevrolet, and Dodge were the most common vehicle makes involved in crashes in both years, with the count for each increasing from 2016 to 2017. The involvement of Jeeps in crashes saw a notable increase, rising from 3 vehicles in 2016 to 10 in 2017. The age demographics of people involved in crashes also shifted; there was a higher number of individuals in the 21-25 age group (from 16 to 29 people) and the 26-34 age group (from 26 to 35 people). Conversely, the number of people involved from the 45-54 age group decreased from 30 to 22.

Top Vehicle Makes (160 vehicles)

1
FORD27 (16.9%)
↑17.4%prior 23
2
CHEV18 (11.3%)
↑38.5%prior 13
3
CHEVROLET18 (11.3%)
→0.0%prior 18
4
DODG13 (8.1%)
↑44.4%prior 9
5
JEEP10 (6.3%)
6
GMC6 (3.8%)
↓-14.3%prior 7
7
TOYOTA5 (3.1%)
8
DODGE5 (3.1%)
↓-28.6%prior 7
9
CHRY4 (2.5%)
10
KIA3 (1.9%)

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

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

Sex Distribution (104 persons with recorded sex)

Male66 (63.5%)
↑15.8%prior 57
Female38 (36.5%)
↓-19.1%prior 47

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: 123
  • Total persons involved: 197
  • Total vehicles involved: 160

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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