Monthly Traffic Safety Analysis

19 CRASHES IN
SPENCER, MA
NOVEMBER 2023

All metrics benchmarked againstNovember 2022

In November 2023, SPENCER, MA experienced 19 total crashes, a significant decrease from the 32 crashes reported in November 2022, representing a 40.6% reduction. Total injuries also declined from 7 to 4 year-over-year. Notably, speeding-related crashes, which accounted for 4 incidents in the prior period, were entirely absent in the current period.

19

▼ -40.6%was 32

Total Crash Events

0

Persons Killed

4

▼ -42.9%was 7

Persons Injured

1

Hit-and-Run Crashes

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates a notable decrease in crash incidents year-over-year. Total crashes fell by 40.6%, from 32 in November 2022 to 19 in November 2023, while total injuries also decreased from 7 to 4.

1

Hit-and-Run Crashes — November 2023

5.3% hit-and-run rate this period vs 0.0% prior. Prior period: 0.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 0→0.0%

4

Motorists Injured

Prior: 7↓-42.9%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes shifted from Wednesday with 8 crashes in November 2022 to Tuesday and Thursday, each with 6 crashes, in November 2023. The peak crash hour also changed, with 4 crashes occurring at 5 PM in November 2022, while November 2023 saw 4 crashes at 4 PM.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Both periods reported zero fatalities. Total injuries decreased from 7 in November 2022 to 4 in November 2023. The proportion of 'No Injury' crashes increased slightly from 81.3% in the prior period to 84.2% in the current period, while minor and possible injury crashes saw a decrease in count.

Outcome by Severity (Crash Events)

Minor Injury2minor injury crashes10.5%
↓-33.3%prior 3
Possible Injury1possible injury crashes5.3%
↓-66.7%prior 3
No Injury16no injury crashes84.2%
↓-38.5%prior 26

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Most severe injury per crash record

Top Contributing Factors

Crashes attributed to 'No improper driving' decreased by 4 incidents, from 8 in November 2022 to 4 in November 2023, a 50% reduction in count. 'Failed to yield right of way' remained constant at 6 crashes in both periods, though its share of total crashes increased from 18.8% to 31.6% due to fewer overall incidents. Speeding-related factors, such as 'Exceeded authorized speed limit', which accounted for 3 crashes in the prior period, were not reported in the current period.

Officer-Reported Primary Contributing Cause

Failed to yield right of way6 (31.6%)→0.0%prior 6
No improper driving4 (21.1%)↓-50.0%prior 8
Fatigued/asleep2 (10.5%)
Disregarded traffic signs, signals, road markings2 (10.5%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner1 (5.3%)
Other improper action1 (5.3%)
Failure to keep in proper lane or running off road1 (5.3%)
Followed too closely1 (5.3%)
Inattention1 (5.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The number of crashes occurring in 'Clear/Clear' weather conditions decreased from 26 in November 2022 to 15 in November 2023. Crashes in 'Dark - lighted roadway' conditions decreased from 11 to 4, while 'Daylight' crashes remained stable at 13 incidents in both periods. Road surface conditions showed a decrease in 'Dry' crashes from 27 to 15, and 'Wet' crashes from 4 to 2.

Weather

Clear/Clear15 (78.9%)
↓-42.3%prior 26
Cloudy/Cloudy2 (10.5%)
Clear1 (5.3%)
Rain/Rain1 (5.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Weather condition at time of crash

Lighting

Daylight13 (68.4%)
→0.0%prior 13
Dark - lighted roadway4 (21.1%)
↓-63.6%prior 11
Dark - roadway not lighted1 (5.3%)
↓-83.3%prior 6
Dawn1 (5.3%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Lighting condition field

Road Surface

Dry15 (78.9%)
↓-44.4%prior 27
Ice2 (10.5%)
Wet2 (10.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Road surface condition field

Vehicles & Demographics

Top Vehicle Makes (32 vehicles)

1
TOYOTA7 (21.9%)
→0.0%prior 7
2
CHEVROLET7 (21.9%)
3
FORD4 (12.5%)
↓-60.0%prior 10
4
INFI2 (6.3%)
5
JEEP2 (6.3%)
↓-66.7%prior 6
6
GMC2 (6.3%)
7
NISSAN2 (6.3%)
8
VOLVO1 (3.1%)
9
HYUNDAI1 (3.1%)
10
MAZDA1 (3.1%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Vehicle unit records

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

Sex Distribution (34 persons with recorded sex)

Female20 (58.8%)
↓-35.5%prior 31
Male14 (41.2%)
↓-48.1%prior 27

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Person-level records linked to crash events

Speed Limit Zones

Crashes in 30 mph zones decreased from 12 in November 2022 to 10 in November 2023, a 16.7% reduction. Incidents in 40 mph zones also fell from 12 to 8, representing a 33.3% decrease. No crashes were reported in speed zones of 5, 10, 15, or 25 mph in the current period, unlike the prior period which recorded 6 crashes across these lower speed limits.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-11-01 to 2023-11-30 · Posted speed limit at crash location

Data Sources & Methodology

Primary Data Source

All crash data in this report is sourced from Massachusetts Crash Data (MassDOT CDV), accessed programmatically via the Arcgis_yearly 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_yearly 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: 2023-11-01 through 2023-11-30
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-11-01 through 2023-11-30 (30 days)
  • Geographic scope: SPENCER, MA
  • Total crash records analyzed: 19
  • Total persons involved: 36
  • Total vehicles involved: 32

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). "SPENCER, MA Crash Intelligence Report: November 2023." Published June 21, 2026. Reporting period: 2023-11-01 to 2023-11-30. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/spencer/november-2023-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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Spencer, MA Crash Report — November 2023 | ThatCarHitMe.com