education-data-source-campus-safety

name	education-data-source-campus-safety
description	CSS — annual Clery Act crime/fire safety for Title IV institutions. Portal: hate crimes only (2005-2021); primary offenses, VAWA, arrests, fire safety need ope.ed.gov directly. Use for campus crime analysis. Identified by IPEDS unitid.
metadata	{"audience":"any-agent","domain":"data-source","skill-authored":"2026-02-09","skill-last-updated":"2026-02-09"}

Campus Safety Data Source Reference

Campus Safety and Security (CSS) — annual Clery Act crime and fire safety reporting for all Title IV postsecondary institutions. Full dataset covers primary offenses, VAWA offenses, hate crimes, arrests/referrals, and fire safety. Portal mirror provides only hate crimes data (2005-2021); primary offenses, VAWA, arrests, and fire safety require direct access to ope.ed.gov/campussafety. Use when analyzing campus hate crimes via Portal or understanding Clery Act reporting structure. Identified by IPEDS unitid.

Guide to understanding and using campus crime and fire safety data collected under the Clery Act.

CRITICAL: Value Encoding

The Education Data Portal mirrors use integer codes for categorical variables, not string labels:

Variable	Example Code	Meaning
bias	1	Race-based bias
crime_type	14	Intimidation
fips	6	California

See ./references/variable-definitions.md for complete code mappings.

Truth Hierarchy: When interpreting variable values, apply this priority:

1. Actual data file (what you observe in the parquet/CSV) -- this IS the truth
2. Live codebook (.xls in mirror) -- authoritative documentation, may lag
3. This skill documentation -- convenient summary, may drift from codebook

If this documentation contradicts the codebook, trust the codebook. If the codebook contradicts observed data, trust the data and investigate. Use get_codebook_url() from fetch-patterns.md to download codebooks.

Codebook coverage gap (verified 2026-08-07): the mirrored csafety .xls codebook documents only 7 of the 13 data columns — the bias category and the 6 geographic offense counts — and omits crime_type, year, unitid, inst_name, opeid, and fips. For those 6 omitted columns the codebook is silent, so this skill's own data-verified variable documentation (see ./references/variable-definitions.md) is the better authority; the "trust the codebook" step above applies only to columns the codebook actually covers.

What is Campus Safety and Security Data?

The Campus Safety and Security (CSS) data comes from the annual survey required by the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act:

• Federal mandate: All Title IV institutions must report crime and fire statistics annually
• Consumer protection: Designed to inform students, parents, and employees about campus safety
• Primary source: U.S. Department of Education Office of Postsecondary Education
• Coverage: All postsecondary institutions receiving federal financial aid
• Reporting period: Calendar year (January 1 - December 31)
• Available years: 2005-2021 (hate crimes in Portal mirrors)
• Primary identifier: unitid (IPEDS institution ID, 6-digit integer)
• Official portal: https://ope.ed.gov/campussafety/
• Portal mirror coverage: Hate crimes only. For primary offenses, VAWA, arrests, and fire safety, access the Department of Education portal directly.

Reference File Structure

File	Purpose	When to Read
clery-act.md	Legal framework, history, reporting requirements	Understanding compliance context
crime-categories.md	Criminal offenses, definitions, classification rules	Interpreting crime statistics
vawa-offenses.md	Dating violence, domestic violence, stalking	Analyzing gender-based violence data
hate-crimes.md	Bias categories, additional offenses, classification	Working with hate crime statistics
campus-geography.md	On-campus, residence halls, public property, noncampus	Understanding location categories
fire-safety.md	Fire statistics, safety systems, HEOA requirements	Analyzing residential fire data
arrests-referrals.md	Drug, alcohol, weapons violations	Working with disciplinary data
variable-definitions.md	Key variables, data structure, identifiers	Building queries
limitations.md	Underreporting, comparability issues, interpretation	Critical data analysis

Decision Trees

What type of crime data do I need?

Crime data type?
├─ Violent crimes (murder, assault, robbery, sex offenses)
│   └─ See ./references/crime-categories.md
├─ Property crimes (burglary, motor vehicle theft, arson)
│   └─ See ./references/crime-categories.md
├─ Sex offenses (rape, fondling, incest, statutory rape)
│   └─ See ./references/crime-categories.md
├─ Dating/domestic violence, stalking
│   └─ See ./references/vawa-offenses.md
├─ Hate crimes (bias-motivated)
│   └─ See ./references/hate-crimes.md
├─ Drug/alcohol/weapons violations
│   └─ See ./references/arrests-referrals.md
└─ Fire incidents in residence halls
    └─ See ./references/fire-safety.md

Where did the crime occur?

Understanding crime location?
├─ On-campus (main campus buildings/grounds)
│   └─ See ./references/campus-geography.md
├─ On-campus student housing (subset of on-campus)
│   └─ See ./references/campus-geography.md
├─ Public property (streets, sidewalks adjacent to campus)
│   └─ See ./references/campus-geography.md
├─ Noncampus (off-site but institution-controlled)
│   └─ See ./references/campus-geography.md
└─ Not sure which category
    └─ See ./references/campus-geography.md (definitions section)

Can I compare schools?

Planning institutional comparisons?
├─ Want to rank schools by safety
│   └─ See ./references/limitations.md (CRITICAL: comparability issues)
├─ Comparing similar institution types
│   └─ See ./references/limitations.md (controlling for factors)
├─ Analyzing trends over time
│   └─ See ./references/limitations.md (reporting changes)
└─ Understanding what statistics mean
    └─ See ./references/limitations.md (interpretation guidance)

Quick Reference: Crime Categories

Crime Type Codes (Portal Integer Encoding)

Code	Crime Type	Category
1	Murder/Non-negligent Manslaughter	Primary Offense
2	Manslaughter by Negligence	Primary Offense
3	Rape	Sex Offense
4	Fondling	Sex Offense
5	Incest	Sex Offense
6	Statutory Rape	Sex Offense
7	Robbery	Primary Offense
8	Aggravated Assault	Primary Offense
9	Burglary	Property Crime
10	Motor Vehicle Theft	Property Crime
11	Arson	Property Crime
12	Larceny-Theft	Hate Crime Only
13	Simple Assault	Hate Crime Only
14	Intimidation	Hate Crime Only
15	Destruction/Damage/Vandalism	Hate Crime Only
16	Domestic Violence	VAWA (2014+)
17	Dating Violence	VAWA (2014+)
18	Stalking	VAWA (2014+)

Note: Crime type code 99 (Total) is documented in some references but does NOT appear in the actual Portal mirror data. The data contains individual crime types 1-18 only.

Bias Category Codes (Portal Integer Encoding)

Code	Bias Category	Notes
1	Race	Most common category
2	Religion	Anti-Jewish, Anti-Islamic, etc.
3	Sexual Orientation	Anti-Gay, Anti-Lesbian, etc.
4	Gender	Based on perceived gender
5	Gender Identity	Added 2014
6	Ethnicity	Separated from National Origin in 2014
7	National Origin	Based on country of birth
8	Disability	Physical or mental
9	Unknown/Other
99	Total	All bias categories combined

VAWA Offenses (Added 2013)

Code	Offense	Definition
16	Domestic Violence	Violence by current/former spouse, cohabitant, or similar
17	Dating Violence	Violence by person in romantic/intimate relationship
18	Stalking	Course of conduct causing fear for safety

Hate Crime-Only Offenses

Codes 12-15 are only reported as hate crimes. They are not standalone Clery crimes unless bias-motivated.

Arrests and Disciplinary Referrals

Category	What's Counted
Liquor Law Violations	Arrests + Disciplinary referrals
Drug Law Violations	Arrests + Disciplinary referrals
Weapons Violations	Arrests + Disciplinary referrals

Key Identifiers (Hate Crimes Dataset)

ID	Format	Level	Example	Notes
unitid	6-digit integer	Institution	100654	IPEDS institution ID
opeid	8-character String	Institution	00100200	OPE institution ID (126 nulls in data)
inst_name	String	Institution	University of Alabama	Institution name
fips	Integer	State	6	State FIPS code

Note: The hate crimes dataset uses inst_name (not instnm). Columns like branch, address, city, zip, state, sector, enrollment are NOT present in the Portal mirror data. Join with IPEDS directory on unitid to obtain institutional characteristics.

Geographic Categories

Location	Definition	Reported Separately?
On-Campus	Buildings/property owned/controlled by institution, reasonably contiguous	Yes
On-Campus Student Housing	Residence halls (subset of on-campus)	Yes (as subset)
Noncampus	Institution-owned/controlled property not contiguous; student org properties	Yes
Public Property	Streets, sidewalks, parking within or immediately adjacent to campus	Yes

Fire Safety Data

Fire data is reported only for on-campus student housing facilities:

Data Element	Description
Number of fires	Total fires per building per year
Cause of fire	Intentional, unintentional, undetermined
Injuries	Fire-related injuries requiring treatment
Deaths	Fire-related fatalities
Property damage	Estimated dollar value
Fire safety systems	Sprinklers, alarms, smoke detectors, etc.

Missing Data Codes

Code	Meaning	When Used
null	Missing/not reported	Data not submitted or not applicable (e.g., 352k null values in bias column)
0	Zero incidents	Explicitly reported as zero (distinct from missing)

Empirically verified: The hate crimes dataset in the Portal mirror does NOT use -1, -2, or -3 coded values. All missing data appears as null. This differs from some other Portal datasets (e.g., CCD) that use negative integer codes. Always verify against the actual data.

Data Access

Datasets for Campus Safety are available via the mirror system. See datasets-reference.md for canonical paths, mirrors.yaml for mirror configuration, and fetch-patterns.md for fetch code patterns.

Dataset	Type	Years	Path	Codebook
Hate Crimes	Single	2005-2021	csafety/colleges_csafety_hate_crimes	csafety/codebook_colleges_csafety_hate_crimes

CRITICAL: Limited Data in Portal Mirrors -- Only hate crimes data is available in the Portal mirrors (1 dataset). For other campus safety data (primary offenses, VAWA, arrests/referrals, fire safety), access the Department of Education directly at https://ope.ed.gov/campussafety/. Consult datasets-reference.md for the authoritative list of available mirror datasets.

Codebooks are .xls files co-located with data in all mirrors. Use get_codebook_url() from fetch-patterns.md to construct download URLs:

url = get_codebook_url("csafety/codebook_colleges_csafety_hate_crimes")

# get_codebook_url() is a Python helper; in R, build the URL from the mirror root.
# Mirror failover: see `education-data-query/references/fetch-patterns.md` (R pattern)
mirror <- yaml::read_yaml("mirrors.yaml")$mirrors[[1]]
url <- paste0(mirror$root_url, "/", "csafety/codebook_colleges_csafety_hate_crimes", ".xls")

Fetching Data

Use the fetch_from_mirrors() pattern from fetch-patterns.md:

import polars as pl

# Fetch hate crimes data (all years, single-file dataset)
df = fetch_from_mirrors("csafety/colleges_csafety_hate_crimes")

# Filter by year
df_2021 = df.filter(pl.col("year") == 2021)

# Filter by bias category (1 = Race)
race_crimes = df.filter(pl.col("bias") == 1)

# Filter by crime type (14 = Intimidation)
intimidation = df.filter(pl.col("crime_type") == 14)

# Filter by institution
institution = df.filter(pl.col("unitid") == 100654)

# Filter by state FIPS code (6 = California)
california = df.filter(pl.col("fips") == 6)

# R equivalent
library(arrow)
library(dplyr)

# fetch_from_mirrors() is a Python helper; in R, build the URL from the mirror
# root — the canonical path is NOT a local file. Mirror failover: see
# `education-data-query/references/fetch-patterns.md` (R pattern)
mirror <- yaml::read_yaml("mirrors.yaml")$mirrors[[1]]
# NOTE: illustrative only — mirror parquet files are Polars-written and may
# declare string_view columns, so a plain read can fail under R arrow
# ("cannot handle Array of type <utf8_view>"). Real fetch scripts must use the
# view-safe parquet read from `education-data-query/references/fetch-patterns.md`.
df <- read_parquet(paste0(mirror$root_url, "/",
                          "csafety/colleges_csafety_hate_crimes", ".", mirror$format))

# Filter by year
df_2021 <- df |> filter(year == 2021)

# Filter by bias category (1 = Race)
race_crimes <- df |> filterbias  


intimidation  df  filtercrime_type  


institution  df  filterunitid  


california  df  filterfips  

Direct from Department of Education (Non-Mirror Data)

For data NOT available in Portal mirrors (primary offenses, VAWA, arrests, fire safety):

• Web tool: https://ope.ed.gov/campussafety/
• Bulk download: Available through Campus Safety website
• Custom reports: Generate by institution, state, or sector

Common Pitfalls

Pitfall	Issue	Solution
Using string codes	Portal uses integer codes for bias, crime_type, etc.	Always map integers via ./references/variable-definitions.md
Ranking schools by crime counts	Higher numbers may indicate better reporting, not worse safety	Normalize by enrollment; note reporting culture differences in ./references/limitations.md
Comparing across years naively	Crime definitions changed over time (especially sex offenses in 2014, VAWA added 2013)	Restrict comparisons to consistent-definition periods; document breaks
Treating zero as missing	Zero means explicitly reported zero incidents; missing means not reported	Check for null (missing) vs 0 (zero incidents) before analysis
Comparing CSS to FBI UCR data	Different definitions, scope, and reporting requirements	Never equate CSS and FBI statistics; see ./references/limitations.md
Assuming all crime data in mirror	Mirror only has hate crimes	Use Department of Education portal for primary offenses, VAWA, arrests, fire safety
Using instnm as column name	Hate crimes data uses inst_name, not instnm (which is the IPEDS convention)	Check actual column names with df.columns
Expecting -1/-2 missing codes	Hate crimes data uses null for missing, not negative integer codes	Check for nulls with pl.col("bias").is_null(), not pl.col("bias") == -1
Ignoring multi-campus institutions	Branch campuses report separately; aggregating incorrectly inflates/deflates	Use branch field to distinguish campuses; see ./references/campus-geography.md
Ignoring underreporting	Many crimes go unreported; CSS captures only reported incidents	Acknowledge underreporting as a limitation in all analyses

Reporting Timeline

Date	Action
Calendar year	Data collection period
October 1	Annual Security Report (ASR) due
October 15	CSS survey submission deadline
Following spring	Data available publicly

Related Data Sources

Source	Relationship	When to Use
education-data-source-ipeds	Institutional characteristics	Joining with IPEDS directory for enrollment, sector, control
education-data-explorer	Parent discovery skill	Routing questions to mirror dataset files
education-data-query	Data fetching	Downloading parquet/CSV files

Topic Index

Topic	Reference File
Clery Act history and requirements	./references/clery-act.md
Criminal offense definitions	./references/crime-categories.md
Sex offense classification	./references/crime-categories.md
VAWA amendments	./references/vawa-offenses.md
Dating violence definition	./references/vawa-offenses.md
Domestic violence definition	./references/vawa-offenses.md
Stalking definition	./references/vawa-offenses.md
Hate crime bias categories	./references/hate-crimes.md
Hate crime offenses	./references/hate-crimes.md
On-campus definition	./references/campus-geography.md
Residence hall reporting	./references/campus-geography.md
Public property definition	./references/campus-geography.md
Noncampus property	./references/campus-geography.md
Fire statistics	./references/fire-safety.md
Fire safety systems	./references/fire-safety.md
Arrests vs referrals	./references/arrests-referrals.md
Drug/alcohol violations	./references/arrests-referrals.md
Weapons violations	./references/arrests-referrals.md
Variable names and codes	./references/variable-definitions.md
Underreporting issues	./references/limitations.md
Institutional comparisons	./references/limitations.md
Trend analysis caveats