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GISRUK and AAG 2013

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April 17, 2013
24

GISRUK and AAG 2013

Presentations from GISRUK 2013 and AAG Annual Conference 2013

Avatar for lblade

lblade

April 17, 2013

Transcript

  1. Space  Time  Data  Mining  of   RIDDOR  data   Linda

     Blade   Prof  Chris  Brunsdon  
  2. Health  and  Safety  Execu<ve     ‘HSE  is  the  na<onal

     independent  watchdog  for   work-­‐related  health,  safety  and  illness.’     The  HSE  enforces  health  and  safety  in  Great   Britain’s  workplaces  by  tackling  issues  which   result  in  death,  serious  injury  and  illness.  
  3. •  Employers  must  report  work-­‐related   deaths,  injuries,  diseases  and

     near   misses  at  work  to  the  HSE.     •  A  form  is  filled  out  by  the  employer,   accessible  via  the  HSE  website,  or  the   accident  can  be  reported  by  telephone.     •  The  form  consists  of  several  ques<ons   regarding  the  workplace,  where  the   accident  occurred  and  the  severity  of   the  injury.     •  This  informa<on  is  then  stored  by  the   HSE  in  a  database  called  RIDDOR-­‐   Repor4ng  of  Injuries,  Diseases  and   Dangerous  Occurrences  Regula4ons.   Repor<ng  of  Accidents  
  4. •  There  has  been  liNle  research  into  where   work-­‐related

     accidents  occur  and  the  causes   of  such  accidents.     •  The  RIDDOR  dataset  provided  by  HSE  contains   6  years  of  data.  The  dataset  is  geographically   referenced  and  provides  a  rich  source  of   informa<on  on  the  type  of  job  sector  and   work-­‐related  injuries.       RIDDOR  
  5. Daylight  hours     •  Workers  have  reduced   visibility

     through  evening/ night  (Ardi<  et  al,  2007  and   Mustard  et  al,  2012).     •  Workers  with  long  shiXs   through  the  evening/night   suffer  from  fa<gue  (Shields,   2002  and  Åkerstedt,  2003).    
  6. Seasonal  Impacts     •  Cold  winter  months  result  in

      rain,  snow  and  ice  and  low   levels  of  daylight  which  can   be  the  cause  of  many  physical   accidents  at  work  (Hanbali,   1992  and  Johannson  et  al,   2009).     •  Hot  summer  months  can   cause  <redness  and  stress  at   work  (Morabito  et  al,  2006).      
  7. Research  Ques<ons   •  Is  there  a  rela<onship  between  hours

     of   daylight  and  accidents  at  work?     •  Are  the  number  of  accidents  at  work  related   to  seasonal  changes?     •  Do  the  paNerns  change  across  Great  Britain?  
  8. Methodology   Hours  of  daylight  and  accidents  at  work  

    The  frequency  of  accidents  are  calculated  and  weighted  against   the  working  popula<on  categorically  by  Local  Authority  district.     The  hours  of  daylight  are  computed  through  R  using  the  package   ‘lubridate’  and  mapped  geographically  across  Great  Britain.       Seasonal  change  and  accidents  at  work   A  mosaic  plot  is  computed  in  R  showing  the  standardised   residuals  of  accidents  corresponding  to  geographic  region  and   month  of  the  year.  These  visualisa<on  techniques  are  used  to   facilitate  the  ini<al  analysis  of  space-­‐<me  data  mining  of  the   RIDDOR  data.    
  9. Percentage  of  Accidents  out  of  Working  Popula<on   Percentage (%)

    under 1.9 1.9 to 2.5 2.5 to 3 3 to 3.8 over 3.8 NORTH 0 50 100 Km
  10. Places  with  highest  rates  of  accidents   (2006-­‐2012)   Place

      Accidents  per  1000   workers   Standard  Error  (per   1000  workers)   Edinburgh   222   2.58   Cornwall   197   2.30   County  Durham   185   1.99   Glasgow   170   1.61   Doncaster   165   2.14  
  11. Standardized Residuals: <−4 −4:−2 −2:0 0:2 2:4 >4 Workplace Accidents

    Regions Months East Midlands East of England London North East North West South West West Midlands Yorkshire and The Humber Scotland Wales Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Accidents  by  Region  and  Month  
  12. Conclusions   •  Daylight  hours  appear  to  have  an  effect

     on  work-­‐ related  accidents.  Generally  more  accidents  in   the  North  of  England  and  Scotland,  which  have   fewest  hours  of  daylight.       •  In  the  UK  the  issue  of  whether  to  alter  daylight   saving  <me,  is  regularly  debated,  and  these   findings  are  directly  relevant  to  this  debate.   Increasing  the  number  of  hours  of  daylight  in  the   working  day  may  prevent  such  high  frequencies   of  accidents  occurring  in  par<cular  outdoor   industries.  
  13. What  next…?   •  Explore  regression  analysis  for  hours  in

     the   day  with  accident  rate.     •  Text  mining-­‐  type  of  injuries  with  job  sectors.     •  Time  series  analysis-­‐  RIDDOR  data  da<ng  back   to  the  1980s  is  accessible  via  HSE.  
  14. References   Åkerstedt,  T.  (2003)  ShiX  work  an  disturbed  sleep/wakefulness

     Occup  Med    2003(53)  pp.  89-­‐94.   Ardi<,  D.,  Lee,  D-­‐E.  and  Polat,  G.  (2007)  Fatal  accidents  in  nighkme  vs.    day<me  highway  construc<on  work  zones,  Journal  of  Safety  Research,    38:4  pp.  399-­‐405   Hanbali,  R.  M.  (1992)  Influence  of  Winter  Road  Maintenance  on  Traffic    Accident  Rates,  Doctoral  Disserta<on,  Department  of  Civil  and    Environmental  Engineering,  MarqueNe  University,  Milwaukee,  WI,    1992.     Johansson,  O.,  Wanvik,  P.  O.  and  Elvik,  R.  (2009)  A  new  method  for  assessing    the  risk  of  accident  associated  with  darkness,  Accident  Analysis  and    Preven4on  41:4  pp.  809-­‐815   Morabito,  M.,  Cecchi,  L.,  Crisci,  A.,  Modes<,  P.  A.  and  Orlandini,  S.  (2006)    Rela<onship  between  work-­‐related  accidents  and  hot  weather  condi<ons    in  Tuscany  (Central  Italy)  Industrial  Health  2006(44:3)  pp.  458-­‐464   Mustard,  C.  A.,  Chambers,  A.,  McLeod,  C.,  Bielecky,  A.  and  Smith,  P.  M.  (2012)    Work  injury  by  <me  of  day  in  town  popula<on-­‐based  data    sources    Occup  Environ  Med  2012(00)  pp.  1-­‐8   Shields  M.  (2002)  ShiX  work  and  health  Health  Rep  2002(9)  pp.  11-­‐33