Yearly Traffic Safety Analysis

175 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Chickasaw County, total crashes decreased from 192 in 2016 to 175 in 2017, representing an 8.9% reduction. The most significant year-over-year change was the complete elimination of traffic fatalities, which dropped from 5 in the prior period to zero in the current period. Total injuries also saw a decline from 52 to 43.

175

▼ -8.9%was 192

Total Crash Events

0

▼ -100.0%was 5

Persons Killed

43

▼ -17.3%was 52

Persons Injured

0

▼ -100.0%was 3

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 incidents in Chickasaw County showed a positive downward trend year-over-year. The total number of crashes fell by 8.9%, from 192 in 2016 to 175 in 2017. This improvement extended to crash severity, as total injuries declined by 17.3% from 52 to 43, and traffic fatalities were reduced from 5 to 0.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

0

Motorists Killed

Prior: 5↓-100.0%

1

Pedestrians Injured

Prior: 0→%

2

Cyclists Injured

Prior: 0→%

40

Motorists Injured

Prior: 52↓-23.1%

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 shifted between the two periods. The day with the most crashes moved from Thursday (33 crashes) in 2016 to Friday (30 crashes) in 2017. The peak hour for collisions also changed, shifting from the 5 PM hour in the prior year (25 crashes) to a dual peak at the 4 PM and 7 PM hours in the current year, which each recorded 19 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 improved significantly, with fatal crashes dropping from 3 in 2016 to 0 in 2017, and fatalities falling from 5 to 0. The proportion of crashes resulting in any level of injury remained relatively stable at 18.3% in 2017 compared to 19.3% in 2016. While the count of minor injury crashes decreased from 18 to 12, the number of serious injury crashes increased slightly from 4 to 6.

Outcome by Severity (Crash Events)

Serious Injury6serious injury crashes3.4%
↑50.0%prior 4
Minor Injury12minor injury crashes6.9%
↓-33.3%prior 18
Possible Injury14possible injury crashes8%
↓-6.7%prior 15
No Injury143no injury crashes81.7%
↓-5.9%prior 152

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 animals were the leading contributing factor in both years, but the count of these incidents grew by 38.1%, from 63 crashes in 2016 to 87 in 2017. As a result, this factor's share of all crashes increased from 32.8% to 49.7%. In contrast, crashes attributed to 'Lost Control' were more than halved, with the count falling from 20 to 9, while crashes from 'Driving too fast for conditions' decreased from a count of 14 to 6.

Officer-Reported Primary Contributing Cause

Animal87 (49.7%)↑38.1%prior 63
Lost Control9 (5.1%)↓-55.0%prior 20
Ran Stop Sign7 (4%)↑40.0%prior 5
Driving too fast for conditions6 (3.4%)↓-57.1%prior 14
Swerving/Evasive Action5 (2.9%)
FTYROW: From stop sign5 (2.9%)↓-28.6%prior 7
Driver Distraction: Other interior distraction4 (2.3%)
Ran off road - left4 (2.3%)↓-33.3%prior 6
Ran off road - straight4 (2.3%)↓-33.3%prior 6
Ran off road - right4 (2.3%)

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

Road & Environmental Conditions

There was a notable decrease in the proportion of crashes occurring on adverse road surfaces, which fell from 35.5% in 2016 to 25.2% in 2017, largely due to fewer incidents on snow and ice. Consequently, the share of crashes on dry roads increased from 57.4% to 68.7% year-over-year. The proportion of crashes in daylight decreased from 65.2% to 56.5%, while the share of crashes on dark, unlighted roadways increased from 19.9% to 25.2%.

Weather

Clear72 (63.7%)
↓-20.9%prior 91
Cloudy22 (19.5%)
↓-8.3%prior 24
Rain7 (6.2%)
↑40.0%prior 5
Freezing rain/drizzle5 (4.4%)
Blowing Snow4 (3.5%)
↓-33.3%prior 6
Sleet, hail2 (1.8%)
Other (explain in narrative)1 (0.9%)

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

Lighting

Daylight65 (56.5%)
↓-29.3%prior 92
Dark - roadway not lighted29 (25.2%)
↑3.6%prior 28
Dark - roadway lighted9 (7.8%)
↑28.6%prior 7
Dawn7 (6.1%)
↑40.0%prior 5
Dusk4 (3.5%)
↓-50.0%prior 8
Dark - unknown roadway lighting1 (0.9%)

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

Road Surface

Dry79 (68.7%)
↓-2.5%prior 81
Wet13 (11.3%)
↑18.2%prior 11
Ice/frost12 (10.4%)
↓-25.0%prior 16
Gravel6 (5.2%)
↓-40.0%prior 10
Snow3 (2.6%)
↓-83.3%prior 18
Mud, dirt1 (0.9%)
Slush1 (0.9%)
↓-80.0%prior 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 between periods. After consolidating name variations, Chevrolet was the top make in 2016 with 75 vehicles, but its count fell to 44 in 2017. Ford became the most common make in 2017 with 49 vehicles involved, an increase from 39 in the prior year. The age distribution of persons involved in crashes remained largely consistent, showing only minor proportional shifts between the two years.

Top Vehicle Makes (235 vehicles)

1
FORD49 (20.9%)
↑25.6%prior 39
2
CHEVROLET29 (12.3%)
↓-44.2%prior 52
3
DODGE16 (6.8%)
↓-15.8%prior 19
4
CHEV15 (6.4%)
↓-34.8%prior 23
5
GMC14 (6%)
↓-36.4%prior 22
6
TOYOTA11 (4.7%)
↑10.0%prior 10
7
PONTIAC9 (3.8%)
↑12.5%prior 8
8
BUIC7 (3%)
9
KIA7 (3%)
10
DODG7 (3%)
↑40.0%prior 5

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

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

Sex Distribution (179 persons with recorded sex)

Male118 (65.9%)
↓-15.7%prior 140
Female61 (34.1%)
↓-35.1%prior 94

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: 175
  • Total persons involved: 270
  • Total vehicles involved: 235

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

ThatCarHitMe.com · An Injuria.ai Company