Data Guide 8 min read

How to Read Clery Data on PlainCampus: A Practical Walkthrough

Step-by-step guide to interpreting the Clery Act crime and fire safety data on PlainCampus school profiles. Avoid common pitfalls and make meaningful comparisons.


What You See on a PlainCampus School Profile


Every school profile on PlainCampus presents data from the US Department of Education's OPE Campus Safety and Security Survey. This data is submitted annually by every institution that participates in federal student aid programs — over 6,000 schools.


When you pull up a school profile, you will see several data sections. This guide walks through each one and explains how to interpret the numbers correctly.


The Safety Score


The safety score is the single most useful number on each profile. It is calculated as:


Total on-campus Clery Act incidents divided by enrollment, multiplied by 1,000.


A safety score of 2.0 means 2 incidents per 1,000 enrolled students. Lower is safer. The national rank orders all institutions by this score, with rank #1 being the safest.


# What to watch for:

  • **Compare schools of similar size and type.** A community college with 2,000 commuter students is fundamentally different from a residential university with 40,000 students. Their safety scores reflect different risk environments
  • **Look at the 3-year trend.** A school with a rising score may be experiencing worsening conditions — or improving its reporting. Context matters
  • **Zero is suspicious in large schools.** A university with 20,000+ students reporting zero incidents across all categories may have reporting issues rather than perfect safety

  • Crime Categories Explained


    PlainCampus breaks crime data into the same categories required by the Clery Act:


    # Criminal Offenses

    These are the most serious crimes tracked under the Clery Act. They include murder, sexual assault (rape, fondling, incest, statutory rape), robbery, aggravated assault, burglary, motor vehicle theft, and arson.


  • **Burglary** is typically the most common criminal offense on campus — it includes unlawful entry into residence halls and offices
  • **Sexual assault** numbers should always be read as a floor, not a ceiling. The Department of Justice estimates that only 20-25% of campus sexual assaults are reported
  • **Motor vehicle theft** may spike at institutions with large, open parking areas

  • # VAWA Offenses

    The Violence Against Women Act (VAWA) additions to the Clery Act require reporting of domestic violence, dating violence, and stalking. These were added in 2013.


  • VAWA numbers are the newest data categories and are still subject to significant underreporting
  • A school with zero VAWA offenses in all three categories may not have adequate reporting mechanisms
  • Year-over-year increases in VAWA reporting often indicate improving victim services, not worsening conditions

  • # Arrests and Disciplinary Referrals

    These track liquor law violations, drug law violations, and illegal weapons possession. The data distinguishes between arrests (referred to law enforcement) and disciplinary referrals (handled internally by the institution).


  • **High disciplinary referral numbers** often indicate active enforcement — the school is catching and addressing violations
  • **Low arrest numbers with high referral numbers** suggest the school handles most violations internally rather than involving police
  • These numbers are heavily influenced by institutional policies on substance use

  • Geographic Categories


    Clery Act data is reported across four geographic categories:


  • **On Campus** — All properties owned or controlled by the institution within a reasonable contiguous area
  • **On-Campus Residential** — A subset of on-campus, limited to student housing facilities
  • **Non-Campus** — Buildings owned or controlled by the institution but not on the main campus (satellite campuses, research facilities, fraternity/sorority houses owned by the school)
  • **Public Property** — Sidewalks, streets, and public areas immediately adjacent to campus

  • PlainCampus uses on-campus data for the safety score because it best represents the campus environment. Non-campus and public property data provide additional context but measure different geographies.


    Fire Safety Data


    For institutions with on-campus student housing, the Clery Act also requires fire safety reporting. PlainCampus displays:


  • **Total fires** in on-campus housing
  • **Injuries and deaths** from those fires
  • **Property damage categories** (Category I: under $100, Category II: $100-$999, Category III: $1,000+)

  • A high number of Category I fires with zero Category II/III fires typically indicates good detection systems catching small incidents before they escalate. See our fire safety guide for a deeper dive.


    Common Mistakes When Reading the Data


    # Comparing raw totals instead of per-student rates

    A university with 50,000 students will almost always have more total incidents than a college with 2,000 students. The safety score normalizes for this. Always use the score, not the total.


    # Assuming low numbers mean safety

    Very low or zero numbers in certain categories — particularly sexual assault and VAWA offenses — may indicate underreporting rather than safety. Research consistently shows that schools with robust victim services report more incidents, not fewer.


    # Ignoring the time lag

    Clery data is reported on a calendar-year basis with a publication lag. The newest data available is typically 1-2 years old. A school that experienced a major incident last semester may not have it reflected in the current data.


    # Treating the data as a complete picture

    Clery data covers a narrow geographic footprint (campus property and immediately adjacent areas) and a limited set of crime categories. It does not capture off-campus crime, many types of theft, fraud, cybercrime, or harassment that does not rise to a Clery-reportable level.


    Making Meaningful Comparisons


    For the most useful comparisons:

  • Use our [comparison tool](/compare) to view schools side-by-side
  • Filter [rankings](/rankings) by institution type (public, private, community college) and enrollment size
  • Look at 3-year averages rather than single-year snapshots
  • Read the school's Annual Security Report for context that numbers alone cannot provide

  • Related

    Data sourced from official U.S. government datasets. See our methodology for details. Retrieved and formatted by PlainCampus Editorial

    Understanding the Data

    The information presented throughout this guide is informed by publicly available public records published by federal and state government agencies. Our database aggregates and standardizes these records to make them more accessible and easier to interpret for general audiences. When we reference specific statistics or trends, they are drawn directly from these authoritative sources unless explicitly noted otherwise.

    It is important to understand the limitations of any large-scale data dataset. Records may contain errors from the original data collection process, some fields may be incomplete for older entries, and classification systems may have changed over time. Our analysis accounts for these factors by clearly labeling data vintage, flagging records with missing critical fields, and noting when temporal comparisons span methodology changes in the source data.

    For readers who want to conduct their own research, we recommend going directly to the source whenever possible. federal and state government agencies provides detailed documentation on collection methodology, sampling frames, and known data quality issues. Our goal is not to replace primary sources but to make them more approachable and to highlight patterns that may not be immediately obvious when browsing raw records.

    How We Analyze Data Records

    Our analytical approach involves several steps designed to surface meaningful insights from large datasets. First, we clean and standardize the raw data, handling variations in naming conventions, date formats, and categorical labels. Then we compute summary statistics, distributions, and comparative benchmarks across relevant dimensions such as geography, time period, and category type.

    Key metrics we examine include statistical records, geographic distributions, temporal trends. These indicators provide a multi-dimensional view of each entity in our database, allowing users to understand not just individual records but how they compare to peers, regional averages, and national benchmarks. We believe this contextual approach is far more valuable than presenting raw numbers in isolation.

    Sources: FBI UCR program · U.S. Department of Education Clery Act database · HUD affordable housing

    A worked example

    Consider a household earning $75,000 per year facing an annual cost of $18,000 for the service this guide covers. Their cost-to-income ratio is 24% — below the 30% red-line that federal affordability frameworks use to flag burden. By comparison, a household at $45,000 facing the same $18,000 cost lands at 40% — well into severely-burdened territory under the same definitions.

    Where to dig deeper

    The methodology page documents exactly which federal series we draw from, how we weight regional differences, and the reference period for each metric. The research section publishes original analyses derived from the same underlying database — useful when you want to see year-over-year shifts or peer-jurisdiction comparisons that the per-page detail views don't surface.

    ThresholdFederal definitionPractical meaning
    Below 7%AffordableComfortable margin for unexpected expenses
    7-30%Moderate burdenManageable but constrains discretionary spending
    Above 30%BurdenedHUD definition — qualifies for federal subsidy programs
    Above 50%Severely burdenedTrade-offs with food, healthcare, savings

    Frequently asked questions

    Where does this data come from?

    All figures on this page derive from official federal data — primarily the U.S. Bureau of Labor Statistics, U.S. Census Bureau, U.S. Department of Health and Human Services, and U.S. Department of Labor. We cite the underlying agency and series in the methodology section. No proprietary aggregators are used.

    How often are figures updated?

    Each series follows its own publication cadence. We refresh our database within 30 days of each upstream release. Specific update timestamps appear in the page footer where available; the methodology page documents the cadence per data series.

    Can I use this data for my own analysis?

    Yes. The underlying federal data is public domain. Our presentation, calculations, and editorial commentary are licensed for individual reference. For commercial republication or large-scale data extraction, contact us at the email listed on the contact page.

    What if the figures here disagree with another source?

    Different sources use different methodologies, definitions, geographic boundaries, and reference periods — disagreement is normal and informative. Our methodology page documents exactly which series and reference period we use for each metric, so you can reproduce or audit the figures against the upstream agency directly.