Yearly Traffic Safety Analysis

708 CRASHES IN
IOWA, IA
2019

All metrics benchmarked against2018

In Jasper County, total traffic crashes remained stable, with 708 incidents in 2019 compared to 704 in 2018, an increase of less than one percent. While total injuries decreased by 11.6% from 207 to 183, the number of fatalities rose from 4 to 6. The most notable shift in crash circumstances was a significant increase in collisions occurring on roads with snow or ice, which grew from 100 incidents in 2018 to 164 in 2019.

708

▲ 0.6%was 704

Total Crash Events

6

▲ 50.0%was 4

Persons Killed

183

▼ -11.6%was 207

Persons Injured

5

▲ 25.0%was 4

Fatal Crash Events

Note: "Persons Killed" (6) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall crash volume in Jasper County was nearly flat year-over-year, rising by only 4 incidents from 704 in 2018 to 708 in 2019. However, the outcomes of these crashes changed, as total injuries reported fell by 11.6% (from 207 to 183). Conversely, the number of fatalities increased by 50%, from 4 in 2018 to 6 in 2019.

Vulnerable Road User Casualties

0

Cyclists Killed

Prior: 0→0.0%

6

Motorists Killed

Prior: 4↑50.0%

0

Other Killed

Prior: 0→0.0%

5

Cyclists Injured

Prior: 4↑25.0%

177

Motorists Injured

Prior: 201↓-11.9%

1

Other Injured

Prior: 0→%

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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 between the two periods. The peak day for crashes moved from Saturday (124 crashes) in 2018 to Monday (131 crashes) in 2019. Similarly, the peak hour for collisions shifted earlier in the day, from 5 p.m. (52 crashes) in the prior year to 3 p.m. (48 crashes) in the current year.

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes saw mixed changes year-over-year. The number of fatal crashes increased from 4 to 5, and the fatal crash rate rose from 0.57% to 0.71%. Crashes resulting in serious injuries decreased from 22 to 16, and their share of all crashes fell from 3.1% to 2.3%. The proportion of crashes with no injuries increased from 75.4% in 2018 to 77.8% in 2019.

Severity is per crash event (most severe injury). 5 fatal crash events resulted in 6 persons killed.

Outcome by Severity (Crash Events)

Fatal5fatal crashes0.7%
↑25.0%prior 4
Serious Injury16serious injury crashes2.3%
↓-27.3%prior 22
Minor Injury67minor injury crashes9.5%
↑8.1%prior 62
Possible Injury69possible injury crashes9.7%
↓-18.8%prior 85
No Injury551no injury crashes77.8%
↑3.8%prior 531

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors for crashes showed some changes between 2018 and 2019. Collisions involving an animal remained the top factor in both years, though the count decreased from 156 incidents in 2018 to 122 in 2019. The second-leading factor in 2019 was 'Driving too fast for conditions,' which increased by 25% from 56 to 70 incidents. 'Followed too close' also saw a notable increase, rising from 33 to 47 incidents year-over-year.

Officer-Reported Primary Contributing Cause

Animal122 (17.2%)↓-21.8%prior 156
Driving too fast for conditions70 (9.9%)↑25.0%prior 56
Lost Control69 (9.7%)↑11.3%prior 62
Other (explain in narrative): Other68 (9.6%)↑44.7%prior 47
Ran off road - straight67 (9.5%)↓-5.6%prior 71
Followed too close47 (6.6%)↑42.4%prior 33
Ran off road - left36 (5.1%)↑12.5%prior 32
FTYROW: From stop sign22 (3.1%)↓-40.5%prior 37
Ran Stop Sign21 (3%)↑16.7%prior 18
Driver Distraction: Other interior distraction18 (2.5%)↑63.6%prior 11

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crash conditions varied year-over-year, particularly regarding road surface. While crashes on dry roads were stable (389 in 2019 vs. 380 in 2018), incidents on snow or ice-covered surfaces increased by 64%, from 100 in 2018 to 164 in 2019. Crashes in clear weather and daylight conditions remained the most common scenarios in both periods, with their proportions staying consistent.

Weather

Clear355 (56.2%)
↑0.6%prior 353
Cloudy121 (19.1%)
↑10.0%prior 110
Snow73 (11.6%)
↑28.1%prior 57
Rain28 (4.4%)
↓-31.7%prior 41
Blowing Snow26 (4.1%)
↑136.4%prior 11
Freezing rain/drizzle23 (3.6%)
↑109.1%prior 11
Other (explain in narrative)3 (0.5%)
Severe Winds1 (0.2%)
Sleet, hail1 (0.2%)
↓-80.0%prior 5
Fog, smoke, smog1 (0.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Weather condition at time of crash

Lighting

Daylight408 (64.5%)
↑7.1%prior 381
Dark - roadway not lighted149 (23.5%)
↑0.7%prior 148
Dark - roadway lighted35 (5.5%)
↑40.0%prior 25
Dawn20 (3.2%)
→0.0%prior 20
Dusk14 (2.2%)
↑7.7%prior 13
Dark - unknown roadway lighting7 (1.1%)
↓-12.5%prior 8

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Lighting condition field

Road Surface

Dry389 (61.6%)
↑2.4%prior 380
Snow84 (13.3%)
↑31.3%prior 64
Ice/frost80 (12.7%)
↑122.2%prior 36
Wet60 (9.5%)
↓-27.7%prior 83
Gravel13 (2.1%)
↓-27.8%prior 18
Mud, dirt3 (0.5%)
Slush2 (0.3%)
↓-80.0%prior 10
Other (explain in narrative)1 (0.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Road surface condition field

Vehicles & Demographics

The makes of vehicles involved in crashes shifted between periods. The number of Chevrolet vehicles (combining 'CHEV' and 'CHEVROLET' entries) increased from 170 in 2018 to 240 in 2019, while Ford vehicles decreased from 183 to 160. Looking at the age of persons involved, the 16-20 age group saw its representation increase from 137 individuals in 2018 to 182 in 2019.

Top Vehicle Makes (1,065 vehicles)

1
FORD160 (15%)
↓-12.6%prior 183
2
CHEV160 (15%)
↑36.8%prior 117
3
CHEVROLET80 (7.5%)
↑50.9%prior 53
4
DODG47 (4.4%)
↓-16.1%prior 56
5
FREIGHTLINER42 (3.9%)
↑16.7%prior 36
6
JEEP38 (3.6%)
↑35.7%prior 28
7
GMC33 (3.1%)
→0.0%prior 33
8
TOYT32 (3%)
↑10.3%prior 29
9
CHRY29 (2.7%)
↑11.5%prior 26
10
DODGE26 (2.4%)
↑4.0%prior 25

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · Vehicle unit records

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

Sex Distribution (987 persons with recorded sex)

Male617 (62.5%)
↑25.2%prior 493
Female370 (37.5%)
↑19.4%prior 310

Source: Iowa Crash Data · ArcGIS Open Data · 2019-01-01 to 2019-12-31 · 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: 2019-01-01 through 2019-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2019-01-01 through 2019-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 708
  • Total persons involved: 1,384
  • Total vehicles involved: 1,065

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: 2019." Published September 9, 2026. Reporting period: 2019-01-01 to 2019-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2019-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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