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What is risk?

Risk is the likelihood of a hazard occurring, where a hazard is something that has the potential to cause harm, based on exposure and vulnerability to that hazard. Further information regarding risk and its components are provided in Appendix A.

Risk and fire data

In assessing fire risk, the fire represents the hazard, while the likelihood of fire is examined as a function of past fire history (using incident data from FRV and the CFA). Response time affects the level of exposure to the hazard, while casualties result from both exposure and vulnerability to the fire and may be viewed as an impact or consequence of the fire. Our analysis is dependent on the availability of data and its effectiveness in representing risk (Table 1).

Table 1 Risk components and their relationship to available datasets
Risk component Available dataset
Likelihood of fire
  • Fire and explosion data 2010-2019
Impact of fire
Exposure
  • Residential
    • Residential dwelling density (NEXIS)
    • Residential structural characteristics (NEXIS)
  • Industrial (VicGov)
  • Commercial (VicGov)
  • Recreational areas (VicGov)
  • Wildland-Urban Interface (VicGov)
  • Infrastructure (VicGov)
Hazard
  • Casualties 2010–2019
    • Deaths by fire (fire personnel)
    • Deaths by fire (public)
    • Injuries by fire (fire personnel)
    • Injuries by fire (public)
Vulnerability
  • Disability data (ABS – Census 2016)
  • People requiring care (children and elderly) (ABS – Census 2016)
  • Families with no vehicle (ABS – Census 2016)
  • Families with single parent, elderly and children (ABS – Census 2016)

Methodology

The Panel have explored many techniques to assess the distribution of fire risk across Victoria. The core methodology is presented in Figure 1B (Appendix B). This methodology explores the temporal and spatial distribution of the incidences of fire, the casualties and associated response times.

Probabilistic and deterministic modelling have also been undertaken to understand the drivers behind fire.

The unit of analysis is the Statistical Areas Level 1 (SA1), which is an ABS geographical unit. It has been designed as the smallest unit for the release of Census data and typically have a population between 200 and 800 people with an average of 400.

Additional analysis to the core methodology includes:

  • False alarms and hoax calls
  • Capacity and capability mapping for each fire station

The assumptions and limitations associated with the methodology will be provided in the final risk assessment methodology report. It is anticipated that the methodology will be refined and (if necessary) modified in response to learnings from the inaugural review.

Temporal risk pattern

Temporal analysis investigates data over a time period. The Panel’s analysis uses a decade of data from 2010–2019. Fire and explosion data, the casualties that result from these incidents and response time by the fire agencies to these incidents, will be analysed over time. This analysis will identify patterns over a year, month, week, and for each hour of the day. The total number of incidents for each time period, as well as the number of incidents, casualties and responses for each fire agency (FRV and CFA) will be explored.

Spatial risk pattern

Spatial analysis investigates data over an area. Fire and explosion data, casualties resulting from them and response time by the fire agencies to these incidents will be analysed over the spatial extent of Victoria. The Panel has used of the following methods to explore data spatially:

  • Fire rate
  • Hot spot
  • Kernel density estimation

Markov chain estimation

The utility of Markov chain estimation is in its capacity to estimate the future fire risk using the fire events history. The Markov chain process is a probabilistic approach to risk estimation, resulting in a time-based estimate of the likelihood of fire. However, it is developed to estimate the likelihood of fire across a spatial extent.

The probability of having a fire at time (t) in a certain location given there was no fire within the neighbourhood in past (t-1) is used to inform the work of the Panel. The probability is categorised into 5 groups. Of all categories, the areas having more than 50 per cent of probability of fire risk is of greater concern.

Regression modelling

Regression modelling reveals the underlying drivers for the variations in fire risk across an area. Independent variables from the ABS 2016 Census[1], include population, household structure and socioeconomic indices, land use and landcover. These independent variables, which represent the various drivers of fire, are inputs to regression modelling techniques; Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR).

Decision Support Analysis

The outputs described in sections Spatial risk pattern to Regression modelling of this document, produced at the SA1 scale are subsequently input into the Decision Support Analysis (DSA).

The DSA supports the development of a single, state-wide, fire risk map. Those areas identified by the DSA at high risk of fire will be published in the Victorian Government Gazette.

Overlay

They have been explained in detail below.

A bushfire risk spatial layer will be obtained from the DELWP Forest Fire and Regions Group. It will provide an up-to-date, state-wide representation of the bushfire risk posed to address points, derived using their bushfire simulation software.

The location (coordinates) of operable hydrants has been provided by the water companies. Firefighters use water as a prime tool of attack for fire. Reticulated water pipes have hydrants (above ground or below) that enable firefighters to tap into the reticulated system and control the flow. The water is pressurised by pumps in the fire truck and delivered via hoses to the fire. If this infrastructure is not in place, firefighters are reliant on static water supplies and/or the water on board tanker trucks.

Population and land use projections

Victoria in FutureExternal Link is the official Victorian Government projection of population and households. Projections are based on trends and assumptions for births, life expectancy, migration, and living arrangements across all of Victoria. These projections are available at the SA2 scale and cover the period to 2036. Additional land use planning datasets for Melbourne metro and regional Victoria relating to future residential, commercial and industry zones will be explored.

Prevention and preparedness

Prevention is aimed at stopping the fire from occurring. It involves building codes, AUS/NZ Standards, building materials, certification and compliance systems, statutory land-use planning, active and passive fire safety systems, fire safety inspections. Many of these preventions by CFA are represented by spatial data (for example, land use planning and bushfire overlays) that can be integrated into the Panel’s methodology:

  • Bushfire Management Overlay
  • Bushfire Attack Level
  • Water supply requirements
  • Road access requirements
  • Integrated Bushfire Management Planning
  • Fuel Management Activities
  • Electric Line Vegetation Clearance
  • Declared Fire danger period and total fire bans

Preparedness is readying people for a fire event and mitigating its likelihood/consequence. For example, installation of smoke alarms and sprinkler systems, advice and education programs and workshops. Fuel management by CFA is also considered under preparedness.

Lastly, the FRV and CFA district boundaries will be viewed in relation to these supplementary layers and in consideration of CFA’s advice on capacity and capability for high risk areas. Any requirement for changes to the FRV boundaries will then be determined.

Phasing

Due to changes in the methodology, phasing has also changed. The final phasing of the review is outlined below.

  • Phase 1 – Data collection and preparation
  • Phase 2 – Data analysis
  • Phase 3 – Decision Support Analysis
  • Phase 4 – Overlay and recommendations for boundary adjustment

Following completion of Phase 3, the output will be published in the Victorian Government Gazette.


Footnote

[1] Datasets from the 2021 Census are currently not available

Reviewed 10 October 2022

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