Security & Alarm Services Data: Street-Level Crime, Victimization Interviews, Installer Registers and Market Sizing · Head-to-head
data.police.uk vs FBI Crime Data Explorer (CDE)
Which security & alarm services data: street-level crime, victimization interviews, installer registers and market sizing data fits your job: data.police.uk, or FBI Crime Data Explorer. API, files, or your warehouse. Daily, weekly, or hourly.
data.police.uk
FBI Crime Data Explorer (CDE)
Where the fields line up
No shared field names. These two answer different questions.
| Field | data.police.uk | FBI Crime Data Explorer |
|---|---|---|
Crime category | documented | not in this set |
Incident month | documented | not in this set |
Anonymised latitude | documented | not in this set |
Anonymised longitude | documented | not in this set |
Anonymised street identifier and name | documented | not in this set |
Reporting force type | documented | not in this set |
Latest outcome category | documented | not in this set |
Latest outcome date | documented | not in this set |
2021 LSOA code and name | documented | not in this set |
ori | not in this set | Originating Agency Identifier for the reporting law-enforcement agency whose statistics ride on the row - the stable join key for anything agency-shaped. |
data_year | not in this set | Calendar year of the reported crime statistics; the time axis the whole archive hangs on. |
offense | not in this set | UCR offense category - burglary-breaking-or-entering, larceny, motor-vehicle-theft and robbery among the property offenses. |
What each contains
Pick by fit, not by loyalty.
| data.police.uk | FBI Crime Data Explorer | |
|---|---|---|
| Offense classification | `category` slugs - burglary, anti-social-behaviour, criminal-damage-arson, bicycle-theft, drugs | `offense` UCR codes - burglary-breaking-or-entering, larceny, motor-vehicle-theft, robbery |
| Period stamp | `month` (YYYY-MM) | `year` / `data_year` (calendar year) |
| Reporting agency | `Reported by` naming the contributing force | `agency` / `ori` - the Origin Agency Identifier of the reporting agency |
| Location | `location.latitude` / `location.longitude` anonymised points, `location.street.name` ("On or near Thorpe Street"), `LSOA code` / `LSOA name` | `state_abbr` plus covered `population`, with county and agency frames above it |
| Measured value | Each row is the incident - totals are yours to aggregate | `reported` / `actual` - offenses known to police, pre-totalled |
| Case progress | `outcome_status.category` and `outcome_status.date` per crime | Not carried in the offense-count core; arrest and clearance detail lives in companion collections |
| Person-search records | Stop-and-search fields: age range, gender, self-defined and officer-defined ethnicity, legislation, object of search | Not present |
What each does better
data.police.uk
Incident rows with geography attached. Every crime arrives as its own record: category, month, anonymised approximate coordinates, the street it was snapped to ("On or near Thorpe Street") and the 2021 Lower Layer Super Output Area containing it. Burglary density by neighbourhood, town centre or output area starts here; see LSOA geography.
Outcomes ride along. Each crime carries its latest investigative or court result - "Investigation complete; no suspect identified", "Offender fined", "Suspect charged" - dated in its own right, so detection performance is computable rather than guessed.
Stop-and-search at individual level. Age band, gender, self-defined and officer-defined ethnicity, the legislation cited and the object of the search, one record per search - context no aggregate table preserves; see stop-and-search records.
Volume with a privacy floor. Millions of incident rows a year, anonymised before publication, enough for hotspot modeling at street grain across 44 forces.
FBI Crime Data Explorer
A continent-scale frame. Tens of thousands of reporting agencies across roughly 50 states and territories, browsable national, state, county, tribal, university or single-agency - the whole American market inside one statistical system.
Depth the UK record cannot offer. Summary offense tables reach to the 1960s depending on the table and NIBRS participation expands from 1991 onward, handing models a multi-decade baseline for burglary, larceny-theft and motor-vehicle theft.
The canonical property-crime baseline. Burglary-breaking-or-entering sits alongside larceny, robbery and motor-vehicle theft as counted offenses with covered population attached, so rates per head are one division away - the demand math beneath every market-entry model.
More than one program. Hate crime statistics, arrests, officers assaulted and use-of-force collections ride in the same estate, widening what a single feed can answer.
The verdict
Verdict: sample both, pick by fit. Let the unit of analysis decide. If your question is territorial - which streets, estates and town centres concentrate burglaries, where installation and monitoring capacity should sit, how outcomes vary by force - data.police.uk is the instrument shaped for it. If your question is a market - how large is American demand for protection, which counties and agencies over-index on breaking-and-entering, is property crime trending with the cycle - FBI Crime Data Explorer (CDE) answers it.
Three quick tests settle most cases. Need a street-level UK picture? Only the UK side draws it. Need US counts with population denominators for rates and sizing? Only the CDE totals them. Selling across both markets? That is the both-of-them case.
Sample both, pick by fit. See data.police.uk · See FBI Crime Data Explorer
Fair questions
Is data.police.uk better than FBI Crime Data Explorer (CDE)?
Neither outranks the other - both hold 9/10 on the Datadory rubric, and they measure different countries at different grains. data.police.uk wins every question asked at street level: individual crimes, their outcomes and stop-and-search records across England, Wales and Northern Ireland. The CDE wins every question asked about the United States: offense counts by year, jurisdiction and agency reaching back to the 1960s. Pick by geography and granularity, not by leaderboard.
Which dataset reaches further back in time?
The FBI Crime Data Explorer, by decades. Its Summary offense tables run back to the 1960s depending on the table, and the incident-based NIBRS series expands from 1991 onward through the latest published year. data.police.uk's window at research time ran July 2023 to June 2026, with earlier months retained in its historical archive - deep enough for seasonal and yearly patterns, shallow beside half a century of American annual series.
Do data.police.uk and FBI Crime Data Explorer (CDE) overlap anywhere?
On vocabulary more than on values. Both classify burglary and its property-crime cousins, both stamp a period on every figure, and both attribute figures to a reporting agency. They share no rows: different countries, no common identifier, and a fundamental grain gap - anonymised incident points on one side, agency-year tallies on the other. Treat the overlap as a translation exercise, not a join.
Which one should a security-alarm-services analyst sample first?
Sample both, pick by fit - the geography of your book decides. Analysts sizing or benchmarking the American market start with [FBI Crime Data Explorer (CDE)](/datasets/security-alarm-services/fbi-crime-data-explorer-cde) for offense counts, jurisdiction frames and population denominators. Operators planning UK territories start with [data.police.uk](/datasets/security-alarm-services/data-police-uk) for street-snapped incidents, outcomes and output-area coding. Firms selling on both sides of the Atlantic keep both in rotation.
Can Datadory deliver both datasets together?
Yes. Either record arrives alone or merged onto one delivery calendar, aligned on grain and geography so the comparison is done before it reaches you - delivered daily, weekly, or hourly, your call. Name the forces, counties and windows when you request the sample and it lands pre-cut, with field definitions and coverage profiles attached.