As of August 2026, the annual crime rate in South Yorkshire county stands at 11.6 crimes per 1000 people. South Yorkshire's current crime levels are at 134% of the national crime rate. Shoplifting crime makes up 9.3% of all crimes reported in the county. Shoplifting crime totalled 15.3k representing an increase 12.0% year-over-year.
| South Yorkshire county | crime statistic | England & Wales |
|---|---|---|
| 11.6 | annual crime rate per 1k workday people | 8.6 |
| 134% | ratio of national crime rate | 100% |
| 9.3% | share of all crimes | 8.6% |
| 15.3k | crimes reported | 484k |
| 12.0% | annual change | -0.6% |
| Postcode sector | Crime rate vs. ENG & WLS rate | Crime rate per 1000 workday people | Total number of crimes | |
|---|---|---|---|---|
| DN1 1 | 839% | 73 | 479 | |
| DN1 2 | 69% | 6 | 69 | |
| DN1 3 | 42% | 4 | 26 | |
| DN11 0 | 49% | 4 | 43 | |
| DN11 9 | 24% | 2 | 14 | |
| DN12 1 | 213% | 18 | 129 | |
| DN12 2 | 6% | 0 | 2 | |
| DN12 3 | 156% | 14 | 62 | |
| DN12 4 | 145% | 13 | 59 | |
| DN14 9 | 10% | 1 | 4 | |
| DN2 4 | 487% | 42 | 388 | |
| DN2 5 | 557% | 48 | 568 | |
| DN2 6 | 76% | 7 | 43 | |
| DN3 1 | 55% | 5 | 33 | |
| DN3 2 | 68% | 6 | 51 | |
| DN3 3 | 101% | 9 | 86 | |
| DN4 0 | 583% | 50 | 473 | |
| DN4 5 | 132% | 11 | 144 | |
| DN4 6 | 65% | 6 | 49 | |
| DN4 7 | 94% | 8 | 46 | |
| DN4 8 | 148% | 13 | 182 | |
| DN4 9 | 45% | 4 | 30 | |
| DN5 0 | 75% | 6 | 55 | |
| DN5 8 | 453% | 39 | 327 | |
| DN5 9 | 142% | 12 | 106 | |
| DN6 0 | 72% | 6 | 33 | |
| DN6 7 | 199% | 17 | 155 | |
| DN6 8 | 9% | 1 | 6 | |
| DN7 4 | 85% | 7 | 46 | |
| DN7 5 | 126% | 11 | 70 | |
| DN7 6 | 5% | 0 | 4 | |
| DN8 4 | 28% | 2 | 20 | |
| DN8 5 | 139% | 12 | 99 | |
| DN9 3 | 81% | 7 | 39 | |
| S1 2 | 281% | 24 | 431 | |
| S1 4 | 311% | 27 | 505 | |
| S10 1 | 64% | 6 | 63 | |
| S10 2 | 177% | 15 | 285 | |
| S10 3 | 16% | 1 | 15 | |
| S10 4 | 5% | 0 | 2 | |
| S10 5 | 52% | 5 | 35 | |
| S11 7 | 108% | 9 | 65 | |
| S11 8 | 155% | 13 | 182 | |
| S11 9 | 1% | 0 | 1 | |
| S12 2 | 419% | 36 | 336 | |
| S12 4 | 127% | 11 | 113 | |
| S13 7 | 262% | 23 | 185 | |
| S13 8 | 42% | 4 | 35 | |
| S13 9 | 70% | 6 | 53 | |
| S14 1 | 161% | 14 | 96 | |
| S17 4 | 23% | 2 | 12 | |
| S2 1 | 105% | 9 | 88 | |
| S2 2 | 107% | 9 | 90 | |
| S2 3 | 53% | 5 | 39 | |
| S2 4 | 97% | 8 | 79 | |
| S2 5 | 13% | 1 | 16 | |
| S20 1 | 267% | 23 | 134 | |
| S20 3 | 617% | 53 | 93 | |
| S20 4 | 15% | 1 | 3 | |
| S20 5 | 134% | 12 | 40 | |
| S20 7 | 985% | 85 | 282 | |
| S20 8 | 52% | 5 | 14 | |
| S25 2 | 64% | 6 | 48 | |
| S25 3 | 29% | 2 | 5 | |
| S25 4 | 181% | 16 | 80 | |
| S25 5 | 13% | 1 | 2 | |
| S26 2 | 24% | 2 | 9 | |
| S26 3 | 6% | 0 | 1 | |
| S26 4 | 75% | 7 | 29 | |
| S26 5 | 8% | 1 | 3 | |
| S26 6 | 183% | 16 | 48 | |
| S26 7 | 219% | 19 | 29 | |
| S3 7 | 42% | 4 | 60 | |
| S3 8 | 47% | 4 | 35 | |
| S35 0 | 25% | 2 | 11 | |
| S35 1 | 226% | 20 | 106 | |
| S35 2 | 22% | 2 | 9 | |
| S35 3 | 113% | 10 | 48 | |
| S35 4 | 132% | 11 | 50 | |
| S35 9 | 195% | 17 | 93 | |
| S36 1 | 15% | 1 | 7 | |
| S36 2 | 45% | 4 | 20 | |
| S36 6 | 126% | 11 | 58 | |
| S4 7 | 514% | 44 | 474 | |
| S4 8 | 25% | 2 | 18 | |
| S5 0 | 22% | 2 | 17 | |
| S5 6 | 68% | 6 | 68 | |
| S5 7 | 76% | 7 | 77 | |
| S5 8 | 137% | 12 | 131 | |
| S5 9 | 166% | 14 | 103 | |
| S6 1 | 38% | 3 | 34 | |
| S6 2 | 65% | 6 | 56 | |
| S6 3 | 61% | 5 | 40 | |
| S6 4 | 143% | 12 | 117 | |
| S6 5 | 19% | 2 | 11 | |
| S6 6 | 16% | 1 | 8 | |
| S60 1 | 208% | 18 | 118 | |
| S60 2 | 133% | 11 | 179 | |
| S60 3 | 2% | 0 | 1 | |
| S60 4 | 153% | 13 | 62 | |
| S60 5 | 48% | 4 | 57 | |
| S61 1 | 115% | 10 | 75 | |
| S61 2 | 45% | 4 | 39 | |
| S61 3 | 166% | 14 | 80 | |
| S61 4 | 16% | 1 | 10 | |
| S62 5 | 23% | 2 | 10 | |
| S62 6 | 701% | 61 | 383 | |
| S62 7 | 418% | 36 | 226 | |
| S63 0 | 116% | 10 | 74 | |
| S63 5 | 33% | 3 | 4 | |
| S63 6 | 8% | 1 | 5 | |
| S63 7 | 39% | 3 | 47 | |
| S63 8 | 14% | 1 | 6 | |
| S63 9 | 67% | 6 | 37 | |
| S64 0 | 47% | 4 | 27 | |
| S64 8 | 61% | 5 | 55 | |
| S64 9 | 68% | 6 | 41 | |
| S65 1 | 732% | 63 | 720 | |
| S65 2 | 60% | 5 | 54 | |
| S65 3 | 174% | 15 | 111 | |
| S65 4 | 133% | 11 | 69 | |
| S66 1 | 258% | 22 | 95 | |
| S66 2 | 73% | 6 | 49 | |
| S66 3 | 244% | 21 | 77 | |
| S66 7 | 106% | 9 | 77 | |
| S66 8 | 77% | 7 | 70 | |
| S66 9 | 104% | 9 | 43 | |
| S7 1 | 207% | 18 | 132 | |
| S7 2 | 18% | 2 | 11 | |
| S70 1 | 383% | 33 | 282 | |
| S70 2 | 125% | 11 | 71 | |
| S70 3 | 139% | 12 | 95 | |
| S70 4 | 19% | 2 | 10 | |
| S70 5 | 157% | 14 | 83 | |
| S70 6 | 182% | 16 | 108 | |
| S71 1 | 113% | 10 | 99 | |
| S71 2 | 87% | 8 | 53 | |
| S71 3 | 380% | 33 | 333 | |
| S71 4 | 67% | 6 | 43 | |
| S71 5 | 16% | 1 | 10 | |
| S72 0 | 73% | 6 | 15 | |
| S72 7 | 65% | 6 | 26 | |
| S72 8 | 36% | 3 | 32 | |
| S73 0 | 116% | 10 | 113 | |
| S73 8 | 41% | 4 | 23 | |
| S73 9 | 28% | 2 | 14 | |
| S74 0 | 20% | 2 | 11 | |
| S74 8 | 4% | 0 | 1 | |
| S74 9 | 256% | 22 | 119 | |
| S75 1 | 43% | 4 | 26 | |
| S75 2 | 145% | 13 | 122 | |
| S75 3 | 25% | 2 | 17 | |
| S75 4 | 23% | 2 | 8 | |
| S75 5 | 7% | 1 | 4 | |
| S75 6 | 37% | 3 | 17 | |
| S8 0 | 142% | 12 | 125 | |
| S8 7 | 257% | 22 | 220 | |
| S8 8 | 157% | 14 | 135 | |
| S8 9 | 9% | 1 | 5 | |
| S81 8 | 13% | 1 | 9 | |
| S9 1 | 387% | 33 | 721 | |
| S9 2 | 224% | 19 | 109 | |
| S9 4 | 46% | 4 | 33 | |
| S9 5 | 20% | 2 | 15 | |
Crime rate ranking : South Yorkshire county has the 8. highest crime rate out of 54 counties
Shoplifting crime totalled 15.3k representing an increase 12.0% year-over-year. In the graph below, county are compared by crime rate and crime rate percentage change. Crime rate percentage change compares the county crime rate between August 2025 - July 2026 to the county crime rate in the previous 12 months. The size of the circle reflects the number of total crimes reported. The bigger the circle, the higher the number of crimes reported in the county.
+12.0% annual change, crime is increasing
15.3k crimes reported
Crime rate percentage changeNumber of crime incidents by month from January 2014 to July 2026.
Crime rate per 1000 workday people compared to the national crime rate by year.
Total number of crimes reported in a given calendar month (1 = January … 12 = December) in the previous five years, i.e. from 2020 till 2024.
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