MAST bite-size Small Area Analysis

1. MAST bite-sizeSmall Area Analysis Bruce Walton All reports in MAST Log in as MASTBitesize

2. What is a small area? • Statistical unit of geography based on census • England and Wales: Middle layer SOAs • Scotland: Intermediate Zones • Standard population range • MSOAs average about 7,700 residents (5k to 15k) • IZs average about 4,300 residents • Revised after each census • About 2% of MSOAs changed after 2011 census • New MSOAs will be included in MAST this summer • Residents often share certain characteristics

3. Information about small areas • Always congruent with authority boundaries • Often conform to wards and fit in constituencies • Complete information published online • http://www.neighbourhood.statistics.gov.uk/dissemination/ • http://www.sns.gov.uk/ • Mapping layers available for download • All official stats published by small area • Key statistics and classifications • Detailed census data • Example: Leeds 042 (Potternewton)

4. Small area dimensions in MAST • Filter by authority level geography dimensions first • Crash Location to specify area scope • Driver / Casualty Home to remove Unknown postcodes • Select the right element for the job • Small Area Middle Desc • Standard ONS description (authority name then MSOA number) • Small Area Middle ONS code • unique 9 digit ID, ideal for export to GIS applications • MAST is capable of storing local names • Would be exposed as a separate element • If drilling down, you can turn the totals off • Be patient – there are over 8,800 small areas in MAST!

5. Crash Location Small Area • Uses STATS19 OSGR to locate crash • Does not always conform to STATS19 LAD code • Example: 10,836 with Leeds area code and/or MSOA • 21 have Leeds area code but not in Leeds MSOACrashes in Leeds by Small Area • 6 in Leeds MSOA but not with Leeds area code Crashes wrongly plotted in Leeds • 10,809 have both Leeds area code and MSOA (99.75%) • Valuable for identifying local issues • Potternewton cycle crashes

6. Driver Home Small Area • Driver and rider residency by postcode centre • Indentify areas with ‘rat run’ issues • Home MSOA of drivers crashing in Potternewton • Use authority level Driver Home to filter NK • Only 18% of crashing drivers/riders live there • Ideal for profiling road users in communities • Potternewton resident driver profile • Licensing prevents MSOA Mosaic profiles in MAST • Possible for those authorities with Mosaic licenses

7. Casualty Home Small Area • Casualty residency by postcode centre • Ideal for local demographic profiling • Potternewton pedestrian profile • IMD dimension particularly useful • Licensing prevents MSOA Mosaic profiles in MAST • Possible for those authorities with Mosaic licenses • Coming soon – comparisons with Small Area population by age band

8. MAST bite-sizeAnalysing road user groups - Cyclists Bruce Walton

9. Defining a user group • Crashes involving a particular road user group • Each crash may will fall into multiple groups • Example: crashes which result in pedestrian casualties • Vehicle drivers and riders involved in crashes • Includes those who were not injured • Example: young car drivers (measure for DfT KOI) • Casualties associated with a particular vehicle type • Not necessarily occupants of the vehicle • Example: pedestrians injured when hit by a bus • Vehicle user casualties resulting from crashes • Not necessarily driving the vehicle • Example: motorcycle user casualties including pillion passengers

10. The crashes approach • Identify user group with Crash Involved … • Ideal for examining day and time trends • Small areas can identify local variation • Leeds crashes involving pedal cycles

11. The Riders approach • Identify the user group by Vehicle Type • Filter by Crash Location or by Driver Home • Examine cyclists in conflict with other vehicle • Filter by two vehicle crashes • Use Crash Involved dimensions for other vehicles • Consider cyclists by age group • Use age bands for equal columns • Try examining average distance from home • Leeds cyclists by age in collision with other vehicles

12. The Casualties approach • Identify the user group by combining filters • Type of Related Vehicle • Casualty Class (exclude pedestrians) • Filter by Casualty Home and Crash Location • Mosaic best used as Row heading • Try out different Column headings • Remove Column headings for Mosaic Profile • Use Most Similar Authorities • Leeds cyclist casualties

13. MAST bite-sizeRoute Analysis Bruce Walton

14. Local route analysis by number • Use Filter by Road Number action link • Adding dimension manually can be slow • Multiple road numbers allowed • Specify length using Crash Location • Crash Location Small Area can be applied • Same filters apply in Vehicles and Casualties • Allows socio demographic analysis of route users • A61 through Potternewton

15. A61 through Potternewton

16. Strategic route analysis from HA • MAST has the unique ability to combine local authority and Highways Agency geographies • Filter by authority or police force • Add the Strategic Road Location dimension • Link by link analysis • M1 crashes in Leeds by time of day