UK Crime — Where, What & How it's resolved

1 · Introduction (Ask)

The questions

Crime headlines usually quote raw counts — which just tells you where the most people live. The useful questions are sharper: where is crime highest per head of population, what kinds of crime drive the totals, which way is the trend moving, and what happens to all those reports?

Scope & honesty (this matters):

Recorded crime ≠ actual crime. These are offences recorded by police; they depend on what's reported and how it's logged. The Crime Survey (CSEW) measures victimisation differently, and part of the 2013–2019 rise reflects improved recording practices, not just more crime.
England & Wales only (43 territorial forces). Scotland and Northern Ireland use separate systems.
Fraud is recorded centrally (Action Fraud), not by local forces, so it's excluded from the per-area rates and reported separately.
Rates use ONS mid-2024 resident population. That distorts commuter/tourist hubs — most visibly the City of London (≈9k residents, huge daytime population), which is flagged, not headlined.
No offender demographics. UK open crime data contains no nationality or ethnicity of offenders, so this study makes no such claims — it would not be supportable from the data.

Headline numbers · year ending March 2025

England & Wales at a glance

6.59M

Total recorded crimes

5.23M

Territorial forces (excl. fraud)

84.5

Crimes per 1,000 residents

1.28M

Fraud (recorded centrally)

8.6%

Charged / summonsed

39.8%

Closed: no suspect identified

Police force areas

61.8M

Population (mid-2024)

Reconciles to source: 5.23M (territorial, excl. fraud) + 1.28M (fraud) + ~0.08M (British Transport Police) = 6.59M total recorded. Full method & code →

2 · Problems → 3 · Analyze & Share

The story in the data

Each view answers one question and states the fact it supports. Hover the map or charts for detail.

Where — crime rate per 1,000 residents

Per-capita rate by police force area (excl. fraud). Darker = higher. Raw counts peak in London, but per head the highest rates sit in northern metro forces — Cleveland, West Yorkshire, Greater Manchester.

Highest-rate forces

Top 12 by crimes per 1,000. City of London (red) is a denominator artifact — tiny resident population vs. a huge daytime/commuter one.

What — offences by type

Violence (37%) and theft (33%) make up two-thirds of recorded crime (fraud shown separately).

When — the trend (territorial forces, excl. fraud)

Recorded crime climbed from 3.5M (2012/13) to ~5.2M by 2019/20 — partly real, partly improved recording — dipped in the COVID year, peaked in 2022/23, and has eased slightly since.

Shoplifting — a sharp recent rise

Recorded shoplifting jumped from 340k (2022/23) to 526k (2024/25) — up ~54% in two years, one of the clearest recent shifts.

How it's resolved — outcomes

Only 8.6% of recorded crimes end in a charge or summons; ~40% are closed with no suspect identified.

Charge rate by offence type

Charge rates are highest for offences uncovered by policing (weapons, drugs) and lowest for volume crimes like criminal damage, theft and violence — where suspects are often never identified.

Deep-dive · Crime × house prices

Do cheaper areas have more crime?

Joining crime to median house price across 287 local authorities (92% of recorded crime). The honest answer needs care — a few central-London boroughs flip the headline.

Read it carefully: the simple (Pearson) correlation is a misleading +0.18, because the City of London & Westminster have extreme prices and extreme (daytime-driven) crime rates. Rank-based and with London removed, the real pattern is negative — Spearman −0.28 overall, −0.51 excluding London: more crime tends to go with cheaper housing. Correlation isn't causation — deprivation, urban density and footfall plausibly drive both.

Crime rate vs median house price

Each dot is one local authority. London (red) is the high-price, high-rate cluster that flips the naive correlation positive.

Crime hotspots — rate per 1,000

Darker = more crime per resident. Central London and northern cities stand out.

Median house price

Darker = pricier. Compare with the crime map — outside London the overlap is weak and mostly inverse.

Crime: Home Office CSP recorded crime 2024/25 (excl. fraud). Prices: ONS HPSSA median price paid (to Mar 2023). Population: ONS mid-2022. ~70 areas excluded due to local-government reorganisation between data vintages; the 287 shown cover 92% of recorded crime.

4 · Solutions (how to use this)

What the data supports — options & trade-offs

This is descriptive open data, not a causal model — so the honest output is prioritisation, not blame. Three evidence-based directions:

Best supported

A · Target the volume crimes

Violence + theft are two-thirds of all crime. Shoplifting is surging. These are where effort moves the total.

+ Largest share of the problem; clear recent trend
+ Measurable (rate per 1,000, charge rate)
– Needs retail/business cooperation and recording consistency

B · Allocate by rate, not raw count

Compare forces per 1,000 residents; investigate why northern metros run high and rural forces low.

+ Fairer cross-area comparison than headlines
– Resident population mis-states commuter hubs (City of London)

C · Tackle the charge-rate gap

An 8.6% charge rate and ~40% “no suspect” point to investigative capacity and victim-support questions.

+ Directly tied to public confidence
– Outcome data shows what, not why; needs case-level work

5 · Conclusion & 6 · Next steps

Key takeaways

Raw counts mislead. London records the most crime because it has the most people; per 1,000 residents the highest rates are in Cleveland, West Yorkshire and Greater Manchester.
Two crime types dominate — violence (37%) and theft (33%) — and shoplifting is rising fast (+54% in two years).
Most crimes go unresolved: only 8.6% end in a charge; ~40% close with no suspect identified.
Lesson: the numbers are only as honest as their caveats — population denominators, central fraud recording, and "recorded ≠ actual" all change the read.

Recommended next steps — what · who · when

1
Add a daytime-population denominator so commuter hubs (City of London, central London) are comparable. Who: Analyst · When: next iteration.
2
Drill into street-level hotspots using data.police.uk lat/long for the top forces. Who: Analyst · When: follow-up notebook.
3
Join to deprivation (IMD) to test how much of the area variation socio-economics explains. Who: Analyst · When: follow-up.
4
Track shoplifting & charge rates quarterly as the headline metrics to watch. Who: Force/PCC analysts · When: ongoing.