Yearly Traffic Safety Analysis

213 CRASHES IN
IOWA, IA
2018

All metrics benchmarked against2017

In Appanoose County, total traffic crashes decreased from 244 in 2017 to 213 in 2018, a 12.7% reduction. During this period, the number of people injured in crashes also fell by 27.3%, from 88 to 64, while fatalities remained unchanged at two. The most notable year-over-year shift was the significant drop in the number of people injured, even as the number of fatal crashes held steady.

213

▼ -12.7%was 244

Total Crash Events

2

Persons Killed

64

▼ -27.3%was 88

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 safety trends in Appanoose County showed improvement from 2017 to 2018. The total number of crashes declined by 12.7% (from 244 to 213). This was accompanied by a 27.3% decrease in total injuries (from 88 to 64), while the number of fatalities remained stable at two for both years.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 2→0.0%

64

Motorists Injured

Prior: 88↓-27.3%

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 timing of crashes shifted between the two periods. In 2018, the peak day for crashes was Wednesday with 38 incidents, a change from 2017 when Friday was the peak day with 43 incidents. Similarly, the peak time for crashes moved earlier, from 7 p.m. in 2017 (23 crashes) to a tie between 5 p.m. and 6 p.m. in 2018 (19 crashes each).

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

While the number of fatal crashes remained constant at two in both 2017 and 2018, the fatal crash rate per 100 crashes increased slightly from 0.82 to 0.94 due to the lower overall crash volume. The number of crashes resulting in injury saw a marked decrease across all categories: serious injury crashes fell from 10 to 8, minor injury crashes dropped from 28 to 18, and possible injury crashes declined from 28 to 22. Consequently, the share of all crashes involving an injury decreased from 27.1% in 2017 to 22.5% in 2018.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.9%
→0.0%prior 2
Serious Injury8serious injury crashes3.8%
↓-20.0%prior 10
Minor Injury18minor injury crashes8.5%
↓-35.7%prior 28
Possible Injury22possible injury crashes10.3%
↓-21.4%prior 28
No Injury163no injury crashes76.5%
↓-7.4%prior 176

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 with animals remained the leading contributing factor in both years, though the count decreased slightly from 86 crashes in 2017 to 81 in 2018. The count for crashes attributed to 'Lost Control' was unchanged at 22 incidents. Other key factors saw decreases, including 'Followed too close' (down from 13 to 9 crashes), 'Ran off road - straight' (down from 14 to 9), and 'Ran Stop Sign' (down from 9 to 3).

Officer-Reported Primary Contributing Cause

Animal81 (38%)↓-5.8%prior 86
Lost Control22 (10.3%)→0.0%prior 22
FTYROW: From stop sign9 (4.2%)↑12.5%prior 8
Followed too close9 (4.2%)↓-30.8%prior 13
Ran off road - left9 (4.2%)↓-30.8%prior 13
Ran off road - straight9 (4.2%)↓-35.7%prior 14
Driver Distraction: Other interior distraction7 (3.3%)→0.0%prior 7
Driving too fast for conditions7 (3.3%)↑16.7%prior 6
FTYROW: From driveway6 (2.8%)
Other (explain in narrative): Other6 (2.8%)↓-14.3%prior 7

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

Road & Environmental Conditions

Crash conditions showed a notable shift regarding road surface and weather. While crashes on dry roads decreased from 138 to 114, crashes on wet roads increased from 20 to 26, representing a rise in share from 8.2% to 12.2% of all crashes. Correspondingly, crashes occurring during rain increased from 8 to 13. The distribution of crashes by lighting conditions remained relatively stable, with daylight crashes accounting for approximately half of all incidents in both years (51.2% in 2018 vs. 50.4% in 2017).

Weather

Clear105 (65.6%)
↓-8.7%prior 115
Cloudy32 (20.0%)
↓-38.5%prior 52
Rain13 (8.1%)
↑62.5%prior 8
Freezing rain/drizzle7 (4.4%)
↑40.0%prior 5
Fog, smoke, smog2 (1.3%)
Snow1 (0.6%)
↓-80.0%prior 5

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

Lighting

Daylight109 (67.3%)
↓-11.4%prior 123
Dark - roadway not lighted38 (23.5%)
↓-17.4%prior 46
Dark - roadway lighted11 (6.8%)
→0.0%prior 11
Dusk3 (1.9%)
Dawn1 (0.6%)

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

Road Surface

Dry114 (70.8%)
↓-17.4%prior 138
Wet26 (16.1%)
↑30.0%prior 20
Snow8 (5.0%)
↓-33.3%prior 12
Ice/frost6 (3.7%)
↑20.0%prior 5
Gravel5 (3.1%)
↓-44.4%prior 9
Slush1 (0.6%)
Mud, dirt1 (0.6%)

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent, with Chevrolet, Ford, and Dodge being the most common in both 2017 and 2018, and all three saw a decrease in total crash involvements. An analysis of persons involved shows a decrease in the 16-20 age group (from 68 to 50 individuals) and the 26-34 age group (from 68 to 54). In contrast, the number of individuals aged 65 and older involved in crashes saw a slight increase from 60 to 63.

Top Vehicle Makes (304 vehicles)

1
CHEV54 (17.8%)
↓-11.5%prior 61
2
FORD54 (17.8%)
↓-8.5%prior 59
3
CHEVROLET25 (8.2%)
↓-10.7%prior 28
4
DODG22 (7.2%)
↓-4.3%prior 23
5
JEEP20 (6.6%)
↑81.8%prior 11
6
TOYT20 (6.6%)
↑42.9%prior 14
7
DODGE11 (3.6%)
↓-21.4%prior 14
8
BUIC10 (3.3%)
↓-9.1%prior 11
9
GMC10 (3.3%)
↑25.0%prior 8
10
CHRY8 (2.6%)
↑33.3%prior 6

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

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

Sex Distribution (227 persons with recorded sex)

Male146 (64.3%)
↑18.7%prior 123
Female81 (35.7%)
↓-34.1%prior 123

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: 213
  • Total persons involved: 363
  • Total vehicles involved: 304

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

ThatCarHitMe.com · An Injuria.ai Company

Appanoose County, IA Crash Report — 2018 | ThatCarHitMe.com