Yearly Traffic Safety Analysis

349 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Harrison County, total traffic crashes increased by 4.8% from 333 in 2016 to 349 in 2017. This period also saw a significant rise in crash severity, with total fatalities increasing from one in 2016 to five in 2017. The number of people injured in crashes also rose from 115 to 139, a 20.9% increase.

349

▲ 4.8%was 333

Total Crash Events

5

▲ 400.0%was 1

Persons Killed

139

▲ 20.9%was 115

Persons Injured

4

▲ 300.0%was 1

Fatal Crash Events

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (4) 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

Overall traffic safety trends in Harrison County worsened from 2016 to 2017. The total number of crashes rose by 16 incidents, a 4.8% increase from the previous year. More significantly, the number of fatalities increased from one to five, and total injuries grew by 20.9% from 115 to 139.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

5

Motorists Killed

Prior: 1↑400.0%

3

Pedestrians Injured

Prior: 0→%

136

Motorists Injured

Prior: 115↑18.3%

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 shifts between the two years. In 2017, the peak days for crashes were Wednesday and Thursday, each with 59 incidents, whereas in 2016 the peak was shared across Thursday, Friday, and Saturday, each with 55 incidents. The single busiest hour also shifted earlier, from the 5 p.m. hour in 2016 (25 crashes) to the 3 p.m. hour in 2017 (35 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 increased notably from 2016 to 2017. The number of fatal crashes rose from one to four, and total fatalities increased from one to five. While the number of serious injury crashes remained constant at 16 in both years, their share of all crashes slightly decreased from 4.8% to 4.6%. Conversely, crashes resulting in minor injuries increased from 26 to 37, and possible injury crashes rose from 35 to 43.

Severity is per crash event (most severe injury). 4 fatal crash events resulted in 5 persons killed.

Outcome by Severity (Crash Events)

Fatal4fatal crashes1.1%
↑300.0%prior 1
Serious Injury16serious injury crashes4.6%
→0.0%prior 16
Minor Injury37minor injury crashes10.6%
↑42.3%prior 26
Possible Injury43possible injury crashes12.3%
↑22.9%prior 35
No Injury249no injury crashes71.3%
↓-2.4%prior 255

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

The leading contributing factors to crashes remained consistent year-over-year, with collisions involving an animal being the most common cause in both periods, increasing in count from 81 in 2016 to 89 in 2017. The second most frequent factor, "Lost Control," saw a slight decrease in count from 44 to 41 incidents. Other top factors like "Followed too close" (21 incidents in both years) and "Driving too fast for conditions" (decreasing from 23 to 21) showed minimal change in their occurrence.

Officer-Reported Primary Contributing Cause

Animal89 (25.5%)↑9.9%prior 81
Lost Control41 (11.7%)↓-6.8%prior 44
Ran off road - straight23 (6.6%)↑9.5%prior 21
Followed too close21 (6%)→0.0%prior 21
Driving too fast for conditions21 (6%)↓-8.7%prior 23
Ran off road - left15 (4.3%)↓-16.7%prior 18
Exceeded authorized speed11 (3.2%)↑57.1%prior 7
Other (explain in narrative): Other11 (3.2%)↑10.0%prior 10
FTYROW: From stop sign11 (3.2%)↓-8.3%prior 12
Other (explain in narrative): No improper action9 (2.6%)

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 largely similar between 2016 and 2017. Crashes in daylight and on dry roads constituted the majority in both years, with counts of 177 and 200 respectively in 2017, compared to 171 and 185 in 2016. There was a slight increase in crashes occurring on dark, unlit roadways, which rose from 55 incidents in 2016 to 66 in 2017. Additionally, crashes on roads with ice or frost increased from 18 to 22.

Weather

Clear182 (67.9%)
↑2.2%prior 178
Cloudy48 (17.9%)
↓-11.1%prior 54
Rain11 (4.1%)
↑57.1%prior 7
Freezing rain/drizzle10 (3.7%)
↑66.7%prior 6
Snow9 (3.4%)
↓-55.0%prior 20
Severe Winds3 (1.1%)
Blowing Snow3 (1.1%)
Sleet, hail1 (0.4%)
Fog, smoke, smog1 (0.4%)

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

Lighting

Daylight177 (64.4%)
↑3.5%prior 171
Dark - roadway not lighted66 (24.0%)
↑20.0%prior 55
Dark - roadway lighted18 (6.5%)
↓-28.0%prior 25
Dawn7 (2.5%)
↓-41.7%prior 12
Dusk7 (2.5%)

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

Road Surface

Dry200 (74.1%)
↑8.1%prior 185
Wet22 (8.1%)
↓-4.3%prior 23
Ice/frost22 (8.1%)
↑22.2%prior 18
Gravel17 (6.3%)
↑30.8%prior 13
Snow7 (2.6%)
↓-68.2%prior 22
Slush1 (0.4%)
Mud, dirt1 (0.4%)

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

Vehicles & Demographics

An analysis of vehicle and person data reveals shifts in crash demographics. Ford and Chevrolet remained the top two vehicle makes involved in crashes in both years. However, the number of persons in the 16-20 age group involved in crashes saw a substantial increase, rising from 62 individuals in 2016 to 91 in 2017. In contrast, involvement for most other age groups remained relatively stable or saw minor fluctuations.

Top Vehicle Makes (488 vehicles)

1
FORD82 (16.8%)
↓-5.7%prior 87
2
CHEVROLET56 (11.5%)
↑1.8%prior 55
3
CHEV55 (11.3%)
↑44.7%prior 38
4
DODGE26 (5.3%)
↑13.0%prior 23
5
JEEP18 (3.7%)
↑28.6%prior 14
6
TOYOTA15 (3.1%)
↓-21.1%prior 19
7
GMC15 (3.1%)
↑25.0%prior 12
8
DODG13 (2.7%)
↑44.4%prior 9
9
KIA12 (2.5%)
↑50.0%prior 8
10
HONDA11 (2.3%)
↓-15.4%prior 13

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

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

Sex Distribution (326 persons with recorded sex)

Male214 (65.6%)
↓-2.3%prior 219
Female112 (34.4%)
↓-12.5%prior 128

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: 349
  • Total persons involved: 587
  • Total vehicles involved: 488

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