Yearly Traffic Safety Analysis

244 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Appanoose County recorded 244 total crashes, an increase of 13% from the 216 crashes reported in 2016. Despite the rise in total collisions, the number of fatalities decreased from five in 2016 to two in 2017. Total injuries, however, increased from 76 to 88 during the same period.

244

▲ 13.0%was 216

Total Crash Events

2

▼ -60.0%was 5

Persons Killed

88

▲ 15.8%was 76

Persons Injured

2

▼ -50.0%was 4

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 · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash trends in Appanoose County show an overall increase in collision volume from 2016 to 2017. Total crashes rose by 13%, from 216 to 244. While the number of fatalities decreased from five to two, the number of people injured in crashes increased by nearly 16%, from 76 to 88.

Vulnerable Road User Casualties

2

Motorists Killed

Prior: 5↓-60.0%

88

Motorists Injured

Prior: 71↑23.9%

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 showed some consistency and some shifts between 2016 and 2017. Friday remained the peak day for crashes in both years, with 44 incidents in 2016 and 43 in 2017. The peak hour for crashes in 2017 was 7 p.m. with 23 incidents, a notable increase from 2016's peak of 16 crashes, which occurred at both 6 p.m. and 7 p.m.

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

The severity of crashes shifted between the two periods, with a notable decrease in fatal incidents. In 2017, there were two fatal crashes, down from four in 2016, and the fatal crash share dropped from 1.9% to 0.8% of all crashes. While the number of serious injury crashes remained stable (11 in 2016 vs. 10 in 2017), minor injury crashes saw a significant increase, rising from 15 to 28. The proportion of crashes resulting in no injuries was nearly unchanged at approximately 72% in both years.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.8%
↓-50.0%prior 4
Serious Injury10serious injury crashes4.1%
↓-9.1%prior 11
Minor Injury28minor injury crashes11.5%
↑86.7%prior 15
Possible Injury28possible injury crashes11.5%
↓-6.7%prior 30
No Injury176no injury crashes72.1%
↑12.8%prior 156

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 remained the leading contributing factor in both years, with the count increasing from 75 in 2016 to 86 in 2017. The count for 'Lost Control' decreased from 25 to 22 incidents, and 'Ran off road - straight' incidents also fell from 21 to 14. Conversely, crashes attributed to 'Followed too close' nearly doubled, rising from 7 in 2016 to 13 in 2017, making it a more prominent factor.

Officer-Reported Primary Contributing Cause

Animal86 (35.2%)↑14.7%prior 75
Lost Control22 (9%)↓-12.0%prior 25
Ran off road - straight14 (5.7%)↓-33.3%prior 21
Followed too close13 (5.3%)↑85.7%prior 7
Ran off road - left13 (5.3%)↑30.0%prior 10
Ran Stop Sign9 (3.7%)↑80.0%prior 5
FTYROW: Making left turn8 (3.3%)
FTYROW: From stop sign8 (3.3%)↑33.3%prior 6
Driver Distraction: Other interior distraction7 (2.9%)→0.0%prior 7
Other (explain in narrative): Other7 (2.9%)↓-12.5%prior 8

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

Road & Environmental Conditions

Crash conditions remained broadly similar year-over-year, with the majority of incidents in both 2016 and 2017 occurring in daylight and on dry roads. The proportion of crashes in daylight increased from 47% in 2016 to 50% in 2017. Crashes occurring on unlit dark roadways accounted for 46 incidents in both years, representing a smaller share of the higher total in 2017. Incidents during snowy weather decreased from 9 in 2016 to 5 in 2017, and crashes on snow-covered road surfaces also declined from 15 to 12.

Weather

Clear115 (61.5%)
↑10.6%prior 104
Cloudy52 (27.8%)
↑18.2%prior 44
Rain8 (4.3%)
↓-11.1%prior 9
Snow5 (2.7%)
↓-44.4%prior 9
Freezing rain/drizzle5 (2.7%)
Fog, smoke, smog1 (0.5%)
Blowing Snow1 (0.5%)

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

Lighting

Daylight123 (65.8%)
↑21.8%prior 101
Dark - roadway not lighted46 (24.6%)
→0.0%prior 46
Dark - roadway lighted11 (5.9%)
→0.0%prior 11
Dusk4 (2.1%)
↓-20.0%prior 5
Dawn3 (1.6%)
↓-50.0%prior 6

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

Road Surface

Dry138 (73.8%)
↑11.3%prior 124
Wet20 (10.7%)
↑25.0%prior 16
Snow12 (6.4%)
↓-20.0%prior 15
Gravel9 (4.8%)
→0.0%prior 9
Ice/frost5 (2.7%)
↓-28.6%prior 7
Sand1 (0.5%)
Mud, dirt1 (0.5%)
Slush1 (0.5%)

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

Vehicles & Demographics

An analysis of vehicles and persons involved shows an increase in volume and some demographic shifts. The total number of vehicles in crashes grew from 304 to 345, and the total persons involved increased from 355 to 430. Combining abbreviated and full names, Chevrolet became the most common vehicle make in 2017 with 89 vehicles involved, surpassing Ford's 59. In terms of demographics, the number of individuals aged 65 and older involved in crashes nearly doubled, increasing from 31 in 2016 to 60 in 2017.

Top Vehicle Makes (345 vehicles)

1
CHEV61 (17.7%)
↑84.8%prior 33
2
FORD59 (17.1%)
↑18.0%prior 50
3
CHEVROLET28 (8.1%)
↓-12.5%prior 32
4
DODG23 (6.7%)
↑109.1%prior 11
5
PONT14 (4.1%)
↓-6.7%prior 15
6
TOYT14 (4.1%)
↓-22.2%prior 18
7
DODGE14 (4.1%)
↑16.7%prior 12
8
BUIC11 (3.2%)
9
JEEP11 (3.2%)
↑10.0%prior 10
10
NISS8 (2.3%)

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

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

Sex Distribution (246 persons with recorded sex)

Female123 (50.0%)
↑16.0%prior 106
Male123 (50.0%)
↑8.8%prior 113

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: 244
  • Total persons involved: 430
  • Total vehicles involved: 345

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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