Yearly Traffic Safety Analysis

160 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In 2018, Chickasaw County recorded 160 total crashes, a decrease of 8.6% from the 175 crashes reported in 2017. While overall crashes and total injuries (42 in 2018 vs. 43 in 2017) saw a slight decline, the most notable change was the emergence of fatal incidents. The county experienced two fatal crashes resulting in two deaths in 2018, compared to zero in the prior year.

160

▼ -8.6%was 175

Total Crash Events

2

Persons Killed

42

▼ -2.3%was 43

Persons Injured

2

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crashes in Chickasaw County showed a downward trend, decreasing by 8.6% from 175 incidents in 2017 to 160 in 2018. The number of total injuries remained nearly stable, with 42 injuries reported in 2018, just one fewer than the 43 injuries recorded in the previous year.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

2

Motorists Killed

Prior: 0→%

1

Pedestrians Injured

Prior: 1→0.0%

1

Cyclists Injured

Prior: 2↓-50.0%

40

Motorists Injured

Prior: 40→0.0%

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · 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 shifts between the two periods. The peak day for crashes moved from Friday (30 incidents) in 2017 to Monday (29 incidents) in 2018. Similarly, the peak hour for collisions shifted from a tie between 4 p.m. and 7 p.m. in 2017 (19 crashes each) to the 5 p.m. hour in 2018, which also recorded 19 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity increased in 2018, marked by the occurrence of two fatal crashes, which accounted for 1.3% of all incidents, compared to zero fatal crashes in 2017. While the number of serious injury crashes was unchanged at six, their proportion of total crashes rose slightly from 3.4% to 3.8%. The number of minor injury crashes increased from 12 in 2017 to 16 in 2018, representing a rise in share from 6.9% to 10.0% of all crashes.

Outcome by Severity (Crash Events)

Fatal2fatal crashes1.3%
Serious Injury6serious injury crashes3.8%
→0.0%prior 6
Minor Injury16minor injury crashes10%
↑33.3%prior 12
Possible Injury12possible injury crashes7.5%
↓-14.3%prior 14
No Injury124no injury crashes77.5%
↓-13.3%prior 143

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving animals remained the leading contributing factor in both years, though the count decreased from 87 incidents in 2017 to 76 in 2018. 'Followed too close' became a more prominent factor in 2018, with its count increasing from 3 to 9 crashes. Conversely, crashes attributed to 'Ran Stop Sign' decreased significantly, from 7 incidents in 2017 to just 2 in 2018. Crashes due to 'Lost Control' remained relatively stable, with 9 in 2017 and 8 in 2018.

Officer-Reported Primary Contributing Cause

Animal76 (47.5%)↓-12.6%prior 87
Followed too close9 (5.6%)
Lost Control8 (5%)↓-11.1%prior 9
Driving too fast for conditions8 (5%)↑33.3%prior 6
Driver Distraction: Inattentive/lost in thought6 (3.8%)
Ran off road - left6 (3.8%)
Ran off road - straight6 (3.8%)
Made improper turn4 (2.5%)
Other (explain in narrative): Other4 (2.5%)
Operating vehicle in an reckless, erratic, careless, negligent manner3 (1.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes in daylight conditions decreased from 65 in 2017 to 59 in 2018, and collisions on dark, unlighted roadways also fell from 29 to 22. While crashes on dry road surfaces saw a significant drop from 79 to 53, incidents on adverse surfaces showed a mixed trend. Crashes on snow-covered roads increased from 3 to 10, and incidents on gravel roads rose from 6 to 10 year-over-year.

Weather

Clear60 (64.5%)
↓-16.7%prior 72
Cloudy13 (14.0%)
↓-40.9%prior 22
Snow7 (7.5%)
Rain5 (5.4%)
↓-28.6%prior 7
Freezing rain/drizzle4 (4.3%)
↓-20.0%prior 5
Sleet, hail3 (3.2%)
Blowing Snow1 (1.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Weather condition at time of crash

Lighting

Daylight59 (63.4%)
↓-9.2%prior 65
Dark - roadway not lighted22 (23.7%)
↓-24.1%prior 29
Dark - roadway lighted9 (9.7%)
→0.0%prior 9
Dawn2 (2.2%)
↓-71.4%prior 7
Dusk1 (1.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Lighting condition field

Road Surface

Dry53 (57.6%)
↓-32.9%prior 79
Gravel10 (10.9%)
↑66.7%prior 6
Snow10 (10.9%)
Wet8 (8.7%)
↓-38.5%prior 13
Ice/frost6 (6.5%)
↓-50.0%prior 12
Slush4 (4.3%)
Mud, dirt1 (1.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Road surface condition field

Vehicles & Demographics

The distribution of vehicle makes involved in crashes showed a shift, with Chevrolet vehicles (including 'CHEV') increasing from 44 in 2017 to 59 in 2018, overtaking Ford as the most common make. The number of Ford vehicles involved decreased from 49 to 43. A notable change also occurred in the age of persons involved in crashes; the 16-20 age group became the largest cohort in 2018 with 51 individuals, an increase from 39 in the prior year.

Top Vehicle Makes (211 vehicles)

1
FORD43 (20.4%)
↓-12.2%prior 49
2
CHEV34 (16.1%)
↑126.7%prior 15
3
CHEVROLET25 (11.8%)
↓-13.8%prior 29
4
DODG10 (4.7%)
↑42.9%prior 7
5
BUIC10 (4.7%)
↑42.9%prior 7
6
GMC6 (2.8%)
↓-57.1%prior 14
7
DODGE5 (2.4%)
↓-68.8%prior 16
8
PONT5 (2.4%)
9
JEEP4 (1.9%)
↓-20.0%prior 5
10
NISSAN4 (1.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · Vehicle unit records

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

Sex Distribution (149 persons with recorded sex)

Male90 (60.4%)
↓-23.7%prior 118
Female59 (39.6%)
↓-3.3%prior 61

Source: Iowa Crash Data · ArcGIS Open Data · 2018-01-01 to 2018-12-31 · 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: 2018-01-01 through 2018-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2018-01-01 through 2018-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 160
  • Total persons involved: 263
  • Total vehicles involved: 211

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: 2018." Published September 9, 2026. Reporting period: 2018-01-01 to 2018-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2018-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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