Yearly Traffic Safety Analysis

143 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Greene County recorded 143 total crashes, a 15.9% decrease from the 170 crashes reported in 2016. This overall decline in collisions was accompanied by a reduction in both fatalities and injuries. The most notable shift in contributing factors was a 23.5% increase in the count of crashes involving animals, which became the primary factor in nearly 30% of all incidents in 2017.

143

▼ -15.9%was 170

Total Crash Events

1

▼ -66.7%was 3

Persons Killed

42

▼ -12.5%was 48

Persons Injured

1

▼ -50.0%was 2

Fatal Crash Events

Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) 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

Traffic safety metrics in Greene County improved from 2016 to 2017. Total crashes decreased by 15.9% from 170 to 143. The number of people killed in crashes fell from 3 to 1, and total injuries declined by 12.5% from 48 to 42.

Vulnerable Road User Casualties

1

Motorists Killed

Prior: 3↓-66.7%

42

Motorists Injured

Prior: 46↓-8.7%

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 shifted year-over-year. The peak day for crashes moved from Friday (37 incidents) in 2016 to Thursday (25 incidents) in 2017. Similarly, the peak hour for collisions shifted from the 5 p.m. evening commute hour in 2016, which saw 18 crashes, to the 7 a.m. morning hour in 2017, with 13 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 decreased in 2017 compared to the prior year. The number of fatal crashes was halved from 2 to 1, and serious injury crashes fell from 10 to 6. The share of crashes resulting in no injury increased slightly from 76.5% in 2016 to 77.6% in 2017, while the proportion of all injury-related crashes (fatal, serious, minor, and possible) decreased.

Outcome by Severity (Crash Events)

Fatal1fatal crashes0.7%
↓-50.0%prior 2
Serious Injury6serious injury crashes4.2%
↓-40.0%prior 10
Minor Injury12minor injury crashes8.4%
↓-7.7%prior 13
Possible Injury13possible injury crashes9.1%
↓-13.3%prior 15
No Injury111no injury crashes77.6%
↓-14.6%prior 130

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

Crashes involving an animal were the leading contributing factor in both periods, and their count increased by 23.5% from 34 in 2016 to 42 in 2017. 'Lost Control' remained the second-most cited factor despite its count decreasing from 19 to 14. 'Failure to yield from a stop sign' incidents increased from 8 to 11, replacing 'Ran off road - straight' in the top three contributing factors for 2017.

Officer-Reported Primary Contributing Cause

Animal42 (29.4%)↑23.5%prior 34
Lost Control14 (9.8%)↓-26.3%prior 19
FTYROW: From stop sign11 (7.7%)↑37.5%prior 8
FTYROW: At uncontrolled intersection8 (5.6%)↓-33.3%prior 12
Ran off road - straight7 (4.9%)↓-41.7%prior 12
Driver Distraction: Other interior distraction6 (4.2%)↓-33.3%prior 9
FTYROW: From parked position4 (2.8%)
Followed too close3 (2.1%)
Driver Distraction: Reaching for object(s)/fallen object(s)3 (2.1%)
Driving too fast for conditions3 (2.1%)↓-62.5%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

Crashes under adverse weather conditions were less frequent in 2017, accounting for 4.2% of all incidents compared to 11.8% in 2016. Clear weather and daylight conditions remained the most common circumstances for crashes in both years, though their respective shares of total crashes slightly decreased in 2017. The proportions of crashes occurring in different lighting and road surface conditions were otherwise largely stable year-over-year.

Weather

Clear73 (60.8%)
↓-26.3%prior 99
Cloudy41 (34.2%)
↑13.9%prior 36
Freezing rain/drizzle2 (1.7%)
Snow2 (1.7%)
↓-75.0%prior 8
Fog, smoke, smog1 (0.8%)
Rain1 (0.8%)
↓-80.0%prior 5

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

Lighting

Daylight72 (60.0%)
↓-27.3%prior 99
Dark - roadway not lighted30 (25.0%)
↓-16.7%prior 36
Dark - roadway lighted8 (6.7%)
↓-27.3%prior 11
Dawn5 (4.2%)
→0.0%prior 5
Dusk3 (2.5%)
↓-50.0%prior 6
Dark - unknown roadway lighting2 (1.7%)

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

Road Surface

Dry85 (69.7%)
↓-24.1%prior 112
Wet15 (12.3%)
↑50.0%prior 10
Snow8 (6.6%)
↓-27.3%prior 11
Gravel8 (6.6%)
→0.0%prior 8
Ice/frost4 (3.3%)
↓-71.4%prior 14
Slush1 (0.8%)
Mud, dirt1 (0.8%)

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

Vehicles & Demographics

Ford and Chevrolet were the top two vehicle makes involved in crashes in both periods, with involvement counts for both decreasing in line with the overall trend. People in the 16-20 age group were the most frequently involved demographic in both years, with their count falling from 44 in 2016 to 36 in 2017. The number of individuals aged 65 and older involved in crashes also decreased from 39 to 30.

Top Vehicle Makes (203 vehicles)

1
FORD38 (18.7%)
↓-15.6%prior 45
2
CHEV27 (13.3%)
↑17.4%prior 23
3
CHEVROLET18 (8.9%)
↓-52.6%prior 38
4
TOYT9 (4.4%)
↓-18.2%prior 11
5
BUIC9 (4.4%)
↑50.0%prior 6
6
DODG8 (3.9%)
↓-27.3%prior 11
7
HOND7 (3.4%)
↑16.7%prior 6
8
CHRY7 (3.4%)
9
PONT6 (3%)
↓-25.0%prior 8
10
GMC6 (3%)
↓-45.5%prior 11

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

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

Sex Distribution (147 persons with recorded sex)

Male80 (54.4%)
↓-27.9%prior 111
Female67 (45.6%)
↓-14.1%prior 78

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: 143
  • Total persons involved: 228
  • Total vehicles involved: 203

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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