Yearly Traffic Safety Analysis

207 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In 2019, Chickasaw County recorded 207 total crashes, a 29.4% increase from the 160 crashes documented in 2018. Despite this significant rise in total collisions, the number of fatalities decreased from two in the prior period to zero in the current period. Concurrently, the total number of injuries rose from 42 to 53.

207

29.4%was 160

Total Crash Events

0

-100.0%was 2

Persons Killed

53

26.2%was 42

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 · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash incidents in Chickasaw County demonstrated a rising trend year-over-year. The county experienced 207 crashes in 2019, an increase of 47 incidents from the 160 recorded in 2018. This represents a 29.4% increase in the total number of collisions during the period.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 00.0%

0

Cyclists Killed

Prior: 00.0%

0

Motorists Killed

Prior: 2-100.0%

1

Pedestrians Injured

Prior: 10.0%

1

Cyclists Injured

Prior: 10.0%

51

Motorists Injured

Prior: 4027.5%

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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 2018 and 2019. While the peak hour for crashes remained the 5 p.m. hour with 19 incidents in both years, the peak day of the week changed. In 2019, Monday and Tuesday were the joint peak days with 34 crashes each, which was a notable increase for Tuesday from only 13 crashes in 2018. The previous year's peak day was Monday, with 29 crashes.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

While total crashes increased, the most severe outcomes decreased year-over-year. The number of fatal crashes dropped from two in 2018 to zero in 2019, and crashes resulting in serious injuries fell from six to one. However, less severe incidents increased, with minor injury crashes rising from 16 to 27 and possible injury crashes increasing from 12 to 18. Overall, the proportion of crashes involving any injury remained stable at approximately 22% in 2019 compared to 21% in 2018.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes0.5%
-83.3%prior 6
Minor Injury27minor injury crashes13%
68.8%prior 16
Possible Injury18possible injury crashes8.7%
50.0%prior 12
No Injury161no injury crashes77.8%
29.8%prior 124

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving animals remained the top contributing factor in both periods, with the count of such incidents increasing from 76 in 2018 to 89 in 2019. The number of crashes attributed to 'Driving too fast for conditions' saw a 75% increase in count, rising from 8 to 14 incidents, making it the second-leading factor in 2019. 'Lost Control' as a factor also grew in count from 8 to 13 crashes.

Officer-Reported Primary Contributing Cause

Animal89 (43%)17.1%prior 76
Driving too fast for conditions14 (6.8%)75.0%prior 8
Lost Control13 (6.3%)62.5%prior 8
Ran off road - left10 (4.8%)66.7%prior 6
Followed too close9 (4.3%)0.0%prior 9
Other (explain in narrative): Other8 (3.9%)
Ran Stop Sign8 (3.9%)
FTYROW: Other (explain in narrative)7 (3.4%)
Ran off road - straight7 (3.4%)16.7%prior 6
FTYROW: From driveway4 (1.9%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

While clear weather and dry roads remained the most common conditions for crashes, the number of incidents in adverse conditions grew significantly. Crashes occurring on snowy road surfaces more than doubled, increasing from 10 in 2018 to 27 in 2019. Similarly, collisions in dark, unlit conditions rose from 22 incidents to 46, and crashes in cloudy weather increased from 13 to 27.

Weather

Clear83 (57.2%)
38.3%prior 60
Cloudy27 (18.6%)
107.7%prior 13
Snow14 (9.7%)
100.0%prior 7
Rain6 (4.1%)
20.0%prior 5
Blowing Snow5 (3.4%)
Freezing rain/drizzle4 (2.8%)
Fog, smoke, smog4 (2.8%)
Severe Winds2 (1.4%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash

Lighting

Daylight88 (59.9%)
49.2%prior 59
Dark - roadway not lighted46 (31.3%)
109.1%prior 22
Dark - roadway lighted7 (4.8%)
-22.2%prior 9
Dawn5 (3.4%)
Dusk1 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field

Road Surface

Dry82 (56.6%)
54.7%prior 53
Snow27 (18.6%)
170.0%prior 10
Wet18 (12.4%)
125.0%prior 8
Ice/frost13 (9.0%)
116.7%prior 6
Gravel4 (2.8%)
-60.0%prior 10
Slush1 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Road surface condition field

Vehicles & Demographics

Analysis of involved persons and vehicles reveals demographic shifts. The number of persons aged 65 and older involved in crashes more than doubled, from 26 in 2018 to 62 in 2019. In contrast, the 16-20 age group saw its count remain stable (51 to 48). Among vehicle makes, Chevrolet-branded vehicles were involved in 75 crashes in 2019, up from 59 in 2018, overtaking Ford (44 crashes in 2019 vs. 43 in 2018) as the most common make in collisions.

Top Vehicle Makes (280 vehicles)

1
CHEV58 (20.7%)
70.6%prior 34
2
FORD44 (15.7%)
2.3%prior 43
3
CHEVROLET17 (6.1%)
-32.0%prior 25
4
GMC15 (5.4%)
150.0%prior 6
5
DODG13 (4.6%)
30.0%prior 10
6
CHRY12 (4.3%)
7
TOYT9 (3.2%)
8
BUIC7 (2.5%)
-30.0%prior 10
9
CHRYSLER7 (2.5%)
10
JEEP6 (2.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records

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

Sex Distribution (257 persons with recorded sex)

Male149 (58.0%)
65.6%prior 90
Female108 (42.0%)
83.1%prior 59

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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: 2019-01-01 through 2019-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2019-01-01 through 2019-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 207
  • Total persons involved: 410
  • Total vehicles involved: 280

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: 2019." Published September 9, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2019-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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Chickasaw County, IA Crash Report — 2019 | ThatCarHitMe.com