17-daaf-contribution-community-daaf-dot-claude-skills-education-data-source-campus-safety

brycewang-stanford/Auto-Empirical-Research-SkillsEducationAudit pendingSnapshot 2d0d8a7bf05f

Summary

This source did not publish a separate summary. Review SKILL.md before using the skill.

SKILL.md

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.

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

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

Actual data file (what you observe in the parquet/CSV) -- this IS the truth

Live codebook (.xls in mirror) -- authoritative documentation, may lag

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 to download codebooks.

fetch-patterns.md

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

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

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)

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)

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+)

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

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

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

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

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

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.

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)

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

url = get_codebook_url("csafety/codebook_colleges_csafety_hate_crimes")

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)

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

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

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	Finding available endpoints
`education-data-query`	Data fetching	Downloading parquet/CSV files

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	`./references/limitations.md`
Data interpretation	`./references/limitations.md`

17-daaf-contribution-community-daaf-dot-claude-skills-education-data-source-campus-safety

Summary

SKILL.md

Campus Safety Data Source Reference

What is Campus Safety and Security Data?

Reference File Structure

Decision Trees

What type of crime data do I need?

Where did the crime occur?

Can I compare schools?

Quick Reference: Crime Categories

Crime Type Codes (Portal Integer Encoding)

Bias Category Codes (Portal Integer Encoding)

VAWA Offenses (Added 2013)

Hate Crime-Only Offenses

Arrests and Disciplinary Referrals

Key Identifiers (Hate Crimes Dataset)

Geographic Categories

Fire Safety Data

Missing Data Codes

Data Access

Fetching Data

Direct from Department of Education (Non-Mirror Data)

Common Pitfalls

Reporting Timeline

Related Data Sources

Topic Index

Related skills