Presentation is loading. Please wait.

Presentation is loading. Please wait.

Approaches to Comparative Analysis Bruce Walton All reports in MAST Log in as MASTComparative.

Similar presentations


Presentation on theme: "Approaches to Comparative Analysis Bruce Walton All reports in MAST Log in as MASTComparative."— Presentation transcript:

1 Approaches to Comparative Analysis Bruce Walton All reports in MAST Log in as MASTComparative

2 Why compare performance? Importance of context Identifying potential for improvement Citizens are encouraged to compare – “This will allow local citizens to easily compare the performance of their area, on road safety, against other similar areas and to compare improvement rates” DfT strategy

3 How not to compare? Collisions by road type – “Collisions on urban or rural roads within the local authority” – No allowance for network variation Casualties by population – “Total number of casualties per 10,000 residents of the local authority” – Typically only 67% of casualties live in HA of crash 20 HAs have less than half, only 30 have over 80% – HAs have widely varying population sizes Districts and/or constituencies are a better basis for comparison

4 How to compare? Vehicle accident rate – “Accident rate for each type of vehicle, expressed as the number of collisions per 100 million vehicle miles of that vehicle type” – Car involved in reported injury crash every 1.22 million miles – Possible data issues identifying other vehicle types Bus once every 430 thousand miles: nearly three times more often Van once every 3.28 million – nearly three times less often. Collisions by road length – “Total number of collisions per 100 miles of road within the authority, where the road owner is the local authority” – What about dramatically different networks? City of London: 54 per thousand miles Herefordshire: 2 per thousand miles – 27 times safer??

5 So is it possible to compare? Comparison is not straightforward Distinguish network or resident issues What are the best comparators? – Proximity? – Comparable size? – Rurality / population density? – Socio demographics? – Network characteristics?

6 Local comparisons with MAST Highway Authority Network Classification Most Similar Authority Others may become available – Adjacency, working like Most Similar Authority – Demographic classification also possible Could use in conjunction with HANCS Absolute population? Population density?

7 A network example Characteristics of crashes involving senior drivers Single or multiple vehicle incidents – Rows: Crash Number of Vehicles Built up areas (by speed limit) – Columns: Speed Limit – Filter out handful of unknowns Filter to establish baseline – Crash Year: last five years – Crash Involved Senior Driver: Yes 100% stacked column chart

8 The need for comparison Useful to have a national context Regional context also useful Easy to add geographical filter for one area – Crash Location Small Area – Driver Home Hard to know which other areas to choose

9 HANCS Highway Authority Network Classification System

10 Signpost Series Highway authority road risk (crashes) and local authority resident risk (casualties). Road risk – Annual average crashes ( ) / Annual average million vehicle miles ( ). Variation between authorities. – Resident casualty risk range from 43 to 143. – Crash risk index range from 33 to 613.

11 Signpost Series London Boroughs - generally very high road risk. Rural Boroughs tend to be lower risk. Highway AuthorityCrash Index City of London 613 Islington London Borough 519 Westminster London Borough 504 Camden London Borough 503 Hackney London Borough 493 Lambeth London Borough 425 Kensington and Chelsea London Borough 407 Highway AuthorityCrash Index Monmouthshire County 33 West Berkshire 41 East Renfrewshire 45 Perth and Kinross 47 South Gloucestershire 49 Shetland Islands 49 Rutland County 50

12 How to Group? Differences – Strategic road network West Berkshire (5%) Monmouthshire (7%) City of London (0%) GB (3%) – Rurality (Urban Roads) West Berkshire (22%) Monmouthshire (10%) City of London (100%) GB (36%)

13 How to group? Network Proportion of urban roads Density

14 Methodology Percentage of Urban Roads – Length of urban road (miles) / Length of all road (miles) Network Density – Length of road (miles) / Area of highway authority (sq. miles)

15 Methodology

16 Highway AuthorityNetwork DensityPercentage Urban RoadsRoad Risk Index Value City of London Kensington and Chelsea London Borough Islington London Borough Westminster London Borough Tower Hamlets London Borough Hackney London Borough Lambeth London Borough Highway AuthorityNetwork DensityPercentage Urban RoadsRoad Risk Index Value Highland Argyll and Bute Western Isles Stirling Perth and Kinross Scottish Borders Shetland Islands Authorities with the Highest Network Density: Authorities with the Lowest Network Density:

17 Methodology Groupings – London Top 10 most densely networked highway authorities are in London 15 of the top 20 are also in London – Other urban areas – Mixed Areas – Rural areas 5 Super-Groups and 11 Sub-Groups

18 Intelligent Comparator selection ONS CodeAuthorityReasons for inclusion as a Comparator Authority E Derby City Most socio-demographically similar authority in Britain (76%); network in the same HANCS subgroup; similar population and traffic densities E Leicester City Very socio-demographically similar (74%); similar population density and proportions of urban, A and strategic roads E Salford City Very socio-demographically similar (74%); network in the same HANCS subgroup; similar proportion of urban roads E Coventry City Network in the same HANCS subgroup; similar proportions of A and strategic roads; in West Midlands; fairly socio- demographically similar (66%) E Dudley M.B. Adjacent in West Midlands; similar population density and proportion of urban, A and strategic roads; fairly socio- demographically similar (60%) E Sandwell M.B. Adjacent in West Midlands; similar population and traffic densities; fairly socio-demographically similar (61%) E Walsall Adjacent in West Midlands; network in same HANCS subgroup with similar traffic density and proportions of A and strategic roads; fairly socio-demographically similar (59%) E Leeds CityVery socio-demographically similar (74%); closest city unitary to Birmingham in terms of absolute population; similar traffic density

19 National comparison - Signpost series Insight into local risk needs a national context A consistent overall approach is necessary – The public will be confused by ‘league tables’ which do not compare like with like RSA Signpost Series is designed to fill this gap – Rates preferable to absolute figures – Personal / network risk clearly distinguished

20 Signpost - Network comparison Robust comparison of highway authority areas – Overall comparisons – HANCS grouping comparisons Overall crash risk measured relative to traffic – Progress based on rolling baseline Road user groups by road length – Mileage by mode not reliable at authority level – Urban road risk for pedestrians and cyclists included – Young driver involvement assessed and compared

21 Signpost - Community comparison Robust comparison of personal risk – Both district and constituency geographies Overall casualty risk relative to population – Progress based on rolling baseline Age groups relative to same age population – Children as well as young, mid and senior adults Risk to vulnerable road user groups assessed Young adult driver involvement quantified

22 Correction Factors Measuring community risk relies on residency STATS19 measures residency by postcode Postcode reporting is getting better – In last 5 years, 86% casualty postcodes collected Reporting not consistent between forces – Full details available in MAST National comparisons must allow for this

23 How does missing postcode correction work? Casualties with known residency Assumed Casualties with unknown residency Known All casualties reported on the roads of a police force area Casualties with postcodes Casualties without postcodes All casualties resident in an external authority area Identified authority residents Unidentified authority residents

24 Geographical units Network comparison – Highway authorities – Strategic roads can be separated Community comparison best with similar sizes – Local authority districts Relevant in two tier counties Congruent with HA areas – Constituencies Now available as MAST elements, based on ONS definitions – Crash Location Small Area – Driver and Casualty Home Useful as divisions within larger unitary authorities National significance Not always congruent with any local government boundaries

25 Signpost Dashboards Working on comparative analysis for 5 years Presented in a number of different ways – Internal reports – Area Profiles – eSpatial Maps – Signpost Series – Research Documents Powerful, meaningful analysis to be made available to MAST members free of charge MAST Dashboards to roll-out in 2014

26


Download ppt "Approaches to Comparative Analysis Bruce Walton All reports in MAST Log in as MASTComparative."

Similar presentations


Ads by Google