Fire and geodemographics - Tessa Anderson

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Fire and geodemographics - Tessa Anderson

  1. 1. The application of a geodemographic classification to explore variations in the incidence of fire events in an urban area<br />By Tessa Anderson, Gary Higgs and Jonathan Corcoran<br />GISRUK 2010<br />
  2. 2. Fire analysis – the urban fire problem<br />Fire (malicious or unintentional) as has significant effects on our urban life. <br />Financial,psychological,physical injuries and death.<br />Very little research that has investigated the geography of the urban fire problem in comparison to crime.<br />Majority of previous work looked at just household fires and relation to socio-economic variables<br />
  3. 3. Location is important as well as time...<br />Fire equipment and personnel location needs to match fire location<br />Different places are likely pose as problems at different times of day...<br />
  4. 4. Space-Time Patterns of Fire<br />Much published on crime theories concerning place;<br />Routine Activity Theory (Cohen & Felson, 1979)<br />“Criminal offences are related to the nature of everyday patterns of social interaction”<br />Time Geography (Hagerstrand 1970)<br />“... concerned not so much with the long time spans of historical research but with daily, weekly and seasonal rhythms within human behaviour over space”<br />
  5. 5. Are all the patterns obvious?<br />Using crime theory…<br />No! – theory predicts that such patterns exist, however, the when and where components of hotspots are not discussed.<br />Work by Felson (2001) highlights that there is geographical fluctuations in daily incidents – therefore one areas fluctuations can vary from another.<br />Can these be applied to the incidence of fire (malicious calls)?<br />
  6. 6. Project scope<br /><ul><li> Part of an ongoing project comparing urban fires in Cardiff and Brisbane
  7. 7. Geodemographics is just one example of methods used to compare fires
  8. 8. Previous research has looked at weather patterns and fire, calendar events and geovisualisation techniques</li></li></ul><li>Introduction to research <br />Questions:<br />How can we advance previous research which looked at fire occurrence and standard deprivation indices?<br />Can geodemographics add any explanatory information to identify neighbourhood patterns of involvement?<br />What are the patterns of involvement in fires in Cardiff, South Wales?<br />Does geodemographics offer a suitable methodology to urban fire analysis?<br />
  9. 9. Data<br />Fire data<br />4 fire types:<br />Hoax (FAM) - 6112<br />Secondary (FDR3) - 62895<br />Vehicle (FRD1V) - 16872<br />Buildings (FRD1) – 13286<br />Total: 99165<br />4 year dataset from 1st January 2000 – 31st December 2004)<br />Study area<br />Cardiff, capital of Wales, estimated population of 324,800<br />Geodemographic data<br />MOSAIC geodemographic classification<br />61 types, 11 groups, 1.4 million postcodes<br />
  10. 10. Methodology<br />A Mosaic type was allocated to each fire type (using point in polygon operation)<br />Total fires for each Mosaic type were counted<br />Using the Household population count of Cardiff per Mosaic Type, and the count of the fire events, an index score was computed.<br />This was achieved by dividing the total fire count by the total household population count and multiplying this by individual MOSAICTM type total. <br />This produces what is known as the ‘expected value’. <br />To determine the index score, the fire event today for each type is divided by the expected value and multiplied by 100. <br />A value above 100 indicates higher than average risk of fire for that type and a value of below 100 suggests that the area is less likely to experience that type of fire incident.<br />Index values are then joined to each postcode and then mapped to identify spatial variation across the study region. <br />
  11. 11. Results<br />
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  14. 14. <ul><li>Spatial distribution of Mosaic types in Cardiff reveal patterns of neighbourhood involvement
  15. 15. Groups F (Welfare Borderline) and G (Municipal Dependency) have over represented indexes for fire types
  16. 16. Different fire types reveal interesting patterns across time and space, for example, the different fire types reveal very different patterns, as you would hope to expect.
  17. 17. Hoax fires are much more prevalent in lower socio economic areas, vehicle fires tend to be spread more between the Mosaic types, secondary and building fires seem to again concentrate in areas with lower socio economic Mosaic types</li></li></ul><li>Conclusions<br />Although commercial geodemographics is largely distrusted by the academic community, there is merit for working alongside them and with open source classification systems to provide the most well rounded and suitable approach for these complex problems. <br />More work still to do in terms of elaborating on the methodological techniques, this is just one of a number of spatial techniques which will be looking at in terms of analysis<br />Perhaps we need to think about the use of geodemographics in such applications, we all know there is more need for accountability of method, but there is little academic concentration on the communication and policy implications. <br />
  18. 18. Future work<br /><ul><li>More research is needed into the emergency services application
  19. 19. It is proven to work in a strategic framework, evidence from Experian supports this
  20. 20. There is a need to use other geodemographic systems (both commercial and public) to support findings and make the process of methodology transparent
  21. 21. Potential comparative work in the UK and internationally
  22. 22. Use Census OAC for public geodemographic analysis
  23. 23. Multi level analysis of urban fires in a spatial context
  24. 24. Geographically weighted regression</li></li></ul><li>Acknowledgements<br />With special thanks to:<br />Jonathan Corcoran<br />Gary Higgs<br />Andy Ward<br />Richard Webber<br />

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