Yearly Traffic Safety Analysis

582 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In Jasper County, total traffic crashes remained stable, with 582 incidents in 2017 compared to 586 in 2016, a decrease of less than 1%. While total fatalities were unchanged at 4, the most notable year-over-year shift was a significant 57.7% reduction in crashes resulting in serious injuries, which fell from 26 to 11.

582

▼ -0.7%was 586

Total Crash Events

4

Persons Killed

202

▼ -1.0%was 204

Persons Injured

4

Fatal Crash Events

Note: "Persons Killed" (4) 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 crash trends in Jasper County were stable year-over-year. The total number of crashes decreased minimally by 4 incidents, from 586 in 2016 to 582 in 2017. Key metrics such as total fatalities (4 in both years) and total injuries (204 vs. 202) also saw little to no change, indicating a steady safety environment.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 0→0.0%

4

Motorists Killed

Prior: 4→0.0%

3

Pedestrians Injured

Prior: 3→0.0%

3

Cyclists Injured

Prior: 4↓-25.0%

196

Motorists Injured

Prior: 197↓-0.5%

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 minor shifts between the two years. The peak day for crashes moved from Saturday (98 crashes) in 2016 to Friday (104 crashes) in 2017. The daily peak hour for collisions also shifted slightly later, from the 4 PM hour in 2016 (42 crashes) to the 5 PM hour in 2017, which saw a higher concentration of 50 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

While the number of fatal crashes remained constant at 4 for both periods, the distribution of injury severity changed notably. The count of serious injury crashes dropped by 57.7%, from 26 in 2016 to 11 in 2017, lowering their share of total crashes from 4.4% to 1.9%. This decrease was partly offset by an increase in crashes classified with 'possible injury,' which rose from 62 to 79.

Outcome by Severity (Crash Events)

Fatal4fatal crashes0.7%
→0.0%prior 4
Serious Injury11serious injury crashes1.9%
↓-57.7%prior 26
Minor Injury65minor injury crashes11.2%
↓-11.0%prior 73
Possible Injury79possible injury crashes13.6%
↑27.4%prior 62
No Injury423no injury crashes72.7%
↑0.5%prior 421

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 an animal remained the top contributing factor in both years, with the count of such incidents increasing by 18.6% from 113 in 2016 to 134 in 2017. In contrast, several other leading factors saw their counts decrease, including 'Lost Control' (from 71 to 60) and 'Driving too fast for conditions' (from 48 to 41). 'Failure to yield from a stop sign' incidents increased from 28 to 32, entering the top five contributing factors in 2017.

Officer-Reported Primary Contributing Cause

Animal134 (23%)↑18.6%prior 113
Lost Control60 (10.3%)↓-15.5%prior 71
Ran off road - straight53 (9.1%)↓-13.1%prior 61
Driving too fast for conditions41 (7%)↓-14.6%prior 48
FTYROW: From stop sign32 (5.5%)↑14.3%prior 28
Followed too close30 (5.2%)↓-9.1%prior 33
Ran off road - left28 (4.8%)↓-22.2%prior 36
Other (explain in narrative): Other27 (4.6%)↑68.8%prior 16
Ran Stop Sign22 (3.8%)↑15.8%prior 19
Ran Traffic Signal13 (2.2%)

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

Road & Environmental Conditions

A greater proportion of crashes in 2017 occurred under favorable conditions compared to the prior year. Crashes on dry road surfaces increased from 326 to 365, while incidents on snowy or icy roads declined from 86 to 58. Similarly, crashes in clear weather rose from 298 to 335. The distribution of crashes by lighting conditions remained consistent, with daylight hours accounting for the majority of incidents in both periods.

Weather

Clear335 (68.0%)
↑12.4%prior 298
Cloudy87 (17.6%)
↓-13.0%prior 100
Snow28 (5.7%)
↓-34.9%prior 43
Rain15 (3.0%)
→0.0%prior 15
Fog, smoke, smog9 (1.8%)
Freezing rain/drizzle9 (1.8%)
↓-57.1%prior 21
Blowing Snow5 (1.0%)
↓-16.7%prior 6
Other (explain in narrative)2 (0.4%)
Severe Winds1 (0.2%)
Sleet, hail1 (0.2%)

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

Lighting

Daylight319 (64.7%)
↑6.0%prior 301
Dark - roadway not lighted115 (23.3%)
↓-8.0%prior 125
Dark - roadway lighted31 (6.3%)
↓-8.8%prior 34
Dusk13 (2.6%)
↑8.3%prior 12
Dawn11 (2.2%)
↓-42.1%prior 19
Dark - unknown roadway lighting4 (0.8%)

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

Road Surface

Dry365 (74.0%)
↑12.0%prior 326
Gravel35 (7.1%)
↑40.0%prior 25
Wet31 (6.3%)
↓-27.9%prior 43
Ice/frost30 (6.1%)
↓-34.8%prior 46
Snow28 (5.7%)
↓-30.0%prior 40
Mud, dirt2 (0.4%)
↓-60.0%prior 5
Other (explain in narrative)1 (0.2%)
Slush1 (0.2%)
↓-87.5%prior 8

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

Vehicles & Demographics

The makes of vehicles involved in crashes remained consistent, with Ford and Chevrolet being the most frequent in both years, though their counts decreased. The number of Dodge vehicles in crashes increased from 68 to 88. An analysis of persons involved shows a demographic shift; the count of females involved rose from 214 to 257, while males decreased from 428 to 386. By age, there was a notable increase in persons aged 16-20 (from 110 to 129) and a decrease in those aged 21-25 (from 108 to 87).

Top Vehicle Makes (849 vehicles)

1
FORD143 (16.8%)
↓-5.3%prior 151
2
CHEV91 (10.7%)
↑19.7%prior 76
3
CHEVROLET55 (6.5%)
↓-43.3%prior 97
4
DODGE44 (5.2%)
↑12.8%prior 39
5
DODG44 (5.2%)
↑51.7%prior 29
6
PONT32 (3.8%)
↑113.3%prior 15
7
JEEP30 (3.5%)
↑20.0%prior 25
8
TOYOTA23 (2.7%)
↑27.8%prior 18
9
HOND21 (2.5%)
↑40.0%prior 15
10
GMC20 (2.4%)
↓-16.7%prior 24

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

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

Sex Distribution (643 persons with recorded sex)

Male386 (60.0%)
↓-9.8%prior 428
Female257 (40.0%)
↑20.1%prior 214

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: 582
  • Total persons involved: 998
  • Total vehicles involved: 849

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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