Sixty-four pedestrians were killed on roads across Kent and Medway between 2021 and 2025.
Another 840 were seriously injured.
And beneath those two figures sits the most troubling finding in The Shepway Vox Team analysis of five years of Department for Transport collision data: the total number of pedestrian casualties has barely changed, but the proportion suffering the worst injuries has risen every single year.
Across the five years, police recorded 3,207 pedestrian casualties arising from 3,063 separate injury collisions. Of those casualties, 64 were fatal, 840 serious and 2,303 slight.
That means 904 pedestrians were killed or seriously injured (KSI).
In 2021 there were 614 pedestrian casualties. In 2025 there were 642 — an increase of just 4.6%.
But the number killed or seriously injured rose from 156 to 205 — an increase of 31.4%.
More striking still, the proportion rises year after year: 25.4% in 2021, 26.2% in 2022, 27.7% in 2023, 29.7% in 2024 and 31.9% in 2025.
By 2025, almost one in three pedestrians recorded as casualties in Kent and Medway had been killed or seriously injured.
That is the story hidden by the headline total.
The figures come from the Government’s STATS19 road-collision system. We filtered out drivers, passengers, cyclists and motorcyclists and analysed pedestrians alone, then linked every casualty to its collision record, including date, time, speed limit, road type, lighting, crossing information and precise location.
Every one of the 3,207 pedestrian casualties in our dataset has latitude and longitude, allowing the collisions to be mapped year by year.
The geography matters, but raw totals need care.
Medway recorded 605 pedestrian casualties over the five years, followed by Thanet with 336, Maidstone 301, Canterbury 280 and Swale 241.
Folkestone & Hythe recorded 172.
Those figures do not prove where walking is “most dangerous”. Bigger and busier places generally have more people, more roads and more pedestrian activity. Without a reliable measure of how much walking actually takes place, raw totals are not risk rates.
Severity, however, gives another view.
In Folkestone & Hythe, four pedestrians were killed and 50 seriously injured. So 54 of the district’s 172 casualties — 31.4% — were KSI.
Across Kent and Medway as a whole the equivalent figure was 28.2%.
The highest district KSI proportions were Ashford at 33.0%, Thanet at 32.4%, Folkestone & Hythe at 31.4% and Dover at 31.0%.
Again, that is not a league table of danger. It tells us that, among pedestrians who were injured in those districts, a larger share suffered fatal or serious outcomes.
Children are another major part of the story.
There were 825 casualties aged under 16 — more than one in four of all pedestrian casualties in the five-year dataset.
Children aged 11 to 15 alone accounted for 518 casualties.
The timing is striking. Of those 825 child casualties, 427 — 51.8% — occurred on a weekday during the 8am, 3pm or 4pm hourly bands.
Those hours clearly overlap with travel to and from school, but STATS19 does not record the purpose of the journey. They cannot simply be labelled “school-run collisions”.
What the data does justify is a much harder question: why are more than half of child pedestrian casualties concentrated into those three weekday hours?
At the other end of the age range, the severity becomes even starker.
There were 448 pedestrian casualties aged 65 or over. Of those, 201 — 44.9% — were KSI.
Among pedestrians aged 75 and over, almost half of recorded casualties were killed or seriously injured.
That does not mean older people are more likely to be struck. The data does not tell us how far each age group walks or how often they cross roads.
It does show that when older pedestrians appear in the casualty data, the consequences are much more severe.
Speed-limit data produces another important contrast.
The speed limit was known for all 3,207 casualties.
Some 2,522 occurred on 30mph roads and another 318 on 20mph roads. Together, 88.6% of all recorded pedestrian casualties happened on roads limited to 30mph or below.
But the relatively small number on higher-speed roads carried a much greater severity burden.
Only 176 casualties — 5.5% of the total — occurred on roads limited to 50, 60 or 70mph.
Yet those roads accounted for 21 of the 64 pedestrian deaths — almost one-third of all fatalities — and 84 of the 176 casualties were KSI.
That is 47.7%.
The same pattern appears when the lights go down.
Only 716 casualties — 22.3% — occurred in darkness.
But 37 of the 64 deaths — 57.8% — happened in darkness.
Around 39% of pedestrian casualties recorded in darkness were KSI, compared with about 25% in daylight.
That does not prove darkness caused those injuries. Nor do the speed-limit figures prove speed alone was responsible. Collision data can identify patterns; it cannot establish causation by itself.
Crossing information raises further questions.
Of 1,552 casualties where the pedestrian was recorded as crossing the road, 882 — 56.8% — were at locations where police recorded no physical pedestrian crossing facility within 50 metres.
Separately, 996 casualties were coded as pedestrians crossing elsewhere in the carriageway, and 858 of those had no physical crossing recorded within 50 metres.
That is not evidence that the absence of a crossing caused the collision.
But it gives highways authorities a very obvious list of places to examine.
So does the mapping.
When we grouped collision coordinates into 100-metre areas, one Medway cluster contained seven separate pedestrian-injury collisions across 2021, 2022 and 2025. Two resulted in KSI casualties.
A repeated cluster does not automatically make a junction unsafe. Traffic volumes, pedestrian flows, road design and the circumstances of each collision all matter.
But repeated dots in the same small area are precisely where the next questions should begin.
That is why the map matters.
A spreadsheet can tell us that 3,207 pedestrians were hurt.
A map can show us where the same roads and junctions keep appearing.
And neither should be the end of the investigation.
Because these are not 3,207 rows in a Government dataset.
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