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Natural Hazards 24: 147–156, 2001. © 2001 Kluwer Academic Publishers. Printed in the Netherlands.

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Uses and Limitations of Socioeconomic Indicators of Community Vulnerability to Natural Hazards: Data and Disasters in Northern Australia DAVID KING Director of the Centre for Disaster Studies, James Cook University, Townsville and Cairns, North Queensland, Australia (Received: 1 June 2000; in final form: 22 August 2000) Abstract. Advances in computer technology have made very large databases easily accessible to users and managers. The census and land databases are of enormous use to hazard managers and planners. An extensive literature has identified groups of social, economic and demographic indicators that may be combined with physical and land data to predict and categorise levels of community vulnerability. Impact scenario mapping can be very precise and impressive in its detail, but a range of constraints, such as ageing of the data, the arbitrary nature of boundaries, problems of weighting indicators, and categorisation of vulnerability, impose limitations on the use of socioeconomic indicators to predict community vulnerability.

1. Introduction Northern Australia is regularly impacted by tropical cyclones and terrestrial floods, often simultaneously in the same location. The regularity of these catastrophic events demands a response from the population, and those planners and administrators responsible for their safety, in preparing for hazards and putting in place appropriate mitigation strategies. Monitoring and warnings of floods and cyclones have advanced with knowledge and new technology. It is our communities, settlements, buildings and infrastructure that have increasingly become the objects of our efforts to reduce the impacts of disasters. Community vulnerability to predictable natural hazards such as cyclones, storm surges and floods, may be measured and mapped to a scale and level of detail that was barely possible before the 1990s. Large databases and complex Geographical Information Systems have become available to a range of managers and practitioners who would formerly have had to rely on the programming and advice of scarce experts. This easy availability of large databases and information systems has played a significant role in prompting analyses of community vulnerability. Herein lies a danger; that community vulnerability is defined and measured by and through the available large databases, such as the census, because they are there, rather than because these databases encapsulate vulnerability. In reality

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there are enormous problems associated with using large community databases – problems of scale, data decay, relevance and weighting of indicators, and the way in which our definitions of community and vulnerability affect our selection of indicators from such databases.

2. Indicators of Vulnerability and Resilience The vulnerability of a community to a natural hazard is ‘the degree of loss to a given element of risk or set of such elements’ (Granger et al., 1999, p. 3). While resilience is the reverse of vulnerability, it is more than just an opposite, because a different set of factors in a community strengthen it against natural hazards and contribute to its recovery. In the early 1990’s the basic risk equation was given substance through the definition of social, economic and demographic characteristics that contributed either to increased vulnerability to a hazard or by its opposite, to increased resilience. Papers by Granger (1995), Smith (1994), Blaikie et al. (1994) and Keys (1991), for example, listed generally agreed socioeconomic and demographic characteristics that could specifically be identified from the five yearly censuses. Granger et al. (1999) subsequently modified and extended both the risk equation and population indicators in tackling the real world issue of a multi-hazard risk assessment of Cairns. Their extension to the risk equation is to include “elements at risk” as well as the hazard and vulnerability. It is clear that the physical infrastructure and housing stock (some of the elements at risk) both increase and modify vulnerability. Aside from the danger to life and health of the population, and characteristics of that population that may help in assessing their vulnerability, there is the vulnerability of the city or town itself, as well as its economic, shelter, lifeline and recovery roles. These physical things can be measured and characterised separately from the population and are in many senses easier to identify and quantify than population characteristics. Granger’s initial Cairns City Council database (that forms part of the Geographical Information System of the multi hazard assessment of that city) recorded every building and structure in Cairns with some assessment of their cyclone vulnerability, as well as height above sea level. Granger also listed population census characteristics that contribute to vulnerability and resilience by using the Australian Bureau of Statistics SEIFA (Socioeconomic Indicators for Areas) indices of socioeconomic disadvantage and economic resources. These take many of the previously accepted population characteristics and add a crude but standardised weighting within broad categories of indicators. Percentages are used for comparison between areas, which loses the precision of actual numbers (information that is essential for evacuations), but is a better basis for attempting to categorise vulnerability on the basis of multiple factors.

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3. Problems with Using Population Characteristics A number of problems then emerged – the size of the Collection Districts, standardising data for comparison, the ABS weighting of the SEIFA indices, inclusion or exclusion of indicators, and data decay. Collection Districts are the smallest unit of analysis of the census. Being the workload of one census collector, they contain about 200 households with an average of 600 people. However, it is impossible to achieve homogeneity because boundaries between Collection Districts have to be recognisable and constant over time, to enable comparison between censuses. Thus they vary in size from zero to over 1,000 people, but with the greatest diversity occurring in non-urban areas. Most of the variability results from migration in or out, but the necessity of defining recognisable boundaries also contributes to population inequality. The population data has to be aggregated to Collection District level by the census to avoid identification of individual people. Therefore where linkages are attempted between structural data, such as individual houses, and population characteristics, the detail of the housing database has to be sacrificed to the Collection District aggregation. Detail and precision are lost. An example of such an issue is the indication from the census and structure databases that many older, wooden and fibro (asbestos sheet) houses in storm surge prone areas, are rented properties occupied by people from lower socioeconomic backgrounds, or by the elderly, or single parent families or indigenous people. Because each of these groups may only be a minority in each Collection District it is not possible to link socioeconomic characteristics to building type any more precisely than at an aggregated level. Standardising both census and structural data, to an overall percentage for each Collection District makes socioeconomic/demographic/building vulnerability characteristics directly comparable and allows us to use multivariate methods, such as factor or principal components analysis, to group and summarise vulnerability characteristics. But because the Collection Districts are not equal in size, the raw figures of numbers of people in vulnerable communities are lost when undertaking statistical analysis. Unfortunately if one used total numbers, the statistical analysis would simply measure the population in Collection Districts rather than their proportional characteristics. Thus a high proportion of the elderly, or single parent families may indicate high vulnerability for particular Collection Districts, whereas total numbers of these vulnerable groups may be much higher in Collection Districts with larger populations. For emergency managers the latter figure, total and accurate numbers of ‘at risk’ groups, may be of greater importance than a measure of relative vulnerability. The ABS weighting used in the SEIFA indexes, or other kinds of weighting used by researchers or institutions exacerbate this problem, primarily because we do not know how to quantify and weight one census characteristic against another. For example the SEIFA index weights “persons aged 15 years and over with no qualifications” as a greater socioeconomic disadvantage than “dwellings with no

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motor car”. The ABS have their methodology for the purposes of measuring relative socioeconomic advantage or disadvantage. In attempting to modify such a weighting one immediately faces a dilemma of justification and extent. Thus while it may be wiser to stay with the ABS classification, it is obvious that it is fundamentally imperfect for the needs of either a disaster planner or an emergency manager, both of whose information needs are different. Decisions as to which characteristics to include and which to exclude further exacerbate the problems of using census characteristics to assess vulnerability. A standardised set, such as SEIFA, is statistically reliable, but it excludes many variables that are very relevant to vulnerability and resilience. When analysing census characteristics in Cairns by factor analysis (Melick, 1996), it was found that a selection of opposites to classify vulnerability or resilience did not present a spatial pattern of opposite classifications. For example single parents, elderly and low income earners, among others, were included in the vulnerable group, while family households, high proportions in the 20 to 40 year age range and higher income categories were included with other characteristics to indicate resilience. Low vulnerability should have corresponded to high resilience and vice versa, but it did not. Some Collection Districts came out high on both vulnerability and resilience, and others were low on both. The reasons are because of the problems of weighting, selection of variables and the heterogeneity of the Collection Districts. Furthermore any census or survey data decays with the passage of time. Mobility, migration and social change all alter the usefulness of the database. Between 1991 and 1996 the population of 35% of the Collection Districts in Cairns had changed by more than 20% and 55% had changed by more than 10%. Figure 1 shows that most of this had been growth rather than decline, and that most of that growth had been in coastal Northern Beaches suburbs and in inner city transition zones, as well as new suburbs inland, immediately west of the city centre. Figure 2 illustrates how some of the specific socioeconomic indicators had changed. As examples, single parent families and the elderly are mapped at the Statistical Local Area level. These show significant changes in the numbers of the vulnerable. The elderly are represented by total numbers, showing strong growth in newer suburbs and decline in the inner city area. The same is true for the pattern of change in single parent families. As they and the elderly are particularly vulnerable groups, knowledge of their changing patterns of residence is crucial to emergency planners. As part of the studies of vulnerability in Cairns Northern Beaches, Berry (1996) carried out extensive household surveys on peoples’ awareness and preparedness for cyclones and storm surges both before and after cyclone Justin in 1997. Apart from showing a general lack of adequate awareness and preparedness, the surveys suggested a lack of any relationship between defined vulnerability characteristics of the population and awareness and preparedness. For example many elderly people are extremely aware of the hazard and are capable of looking after themselves, yet many younger people may be new migrants to the area and do not know what preparations to make.


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Figure 1. Cairns: population change.

Thus though vulnerability and resilience indicators are complex measures of society, they are only measures of a part of what constitutes society or the community. Young (1998) suggests a much broader nature of risk and vulnerability. She classifies socioeconomic characteristics as well as knowledge of the environment and local hazards, ignorance of these things, the ability to cope and the ability

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Figure 2. Cairns: demographic change.

to access help from outside. However, she does acknowledge that it is the people living at the margins of society who are most likely to be at greater risk. We make extensive use of statistically derived indicators because they are easily and relatively cheaply available, and because we can easily aggregate, manipulate and analyse them. But they are only indicators of some aspects of the public and


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the community. When using census data we use census defined boundaries and definitions to identify communities. The Collection District is not necessarily a real community, and even if it is, it is still primarily a spatial territory. Buckle (1999) has further identified the complexity of community, vulnerability and resilience. He defines community in a series of overlapping groups and units. One may be a member of many communities at any moment in time with different needs according to the community and one’s position in it, and the agency that supplies those needs. Buckle consequently suggests a continuum of vulnerability and resilience characteristics that are specific to issues, communities and hazards. He suggests overall categories of vulnerability that include such factors as management capacity, resource availability, cultural attitudes and values, access to services (including culture, education, geographical barriers, social isolation and social change), and pre-existing stress factors such as another recent disaster. Thus there emerges a much more complex idea of vulnerability than the initial groupings made of census characteristics. One becomes aware of this complexity when, after analysing census characteristics in Cairns and concluding a lack of reliability in attempting broad community classifications of vulnerability and resilience, the elements of awareness and preparedness are added. Finally, analysis of target vulnerable communities in Cairns (Machans Beach and Portsmith), showed even greater social and community variability. These two locations were equally vulnerable on the census based scale, plus their physical locations, but qualitative research in the communities showed that people would behave in radically different ways. Communities and their populations are too complex to be reduced meaningfully to indicators or generalised and absolute classifications of vulnerability.

4. Conclusion: The Big Picture Applications of Geographical Information Systems and Social Databases This critique of vulnerability measurement supports the concept of local grassroots knowledge and the importance of the local State Emergency Service (SES) in knowing its own community. But here there may also be flaws, as SES volunteers are probably an unrepresentative part of the community, and though their local knowledge of the place and its hazards and geographical peculiarities may be of utmost value, their knowledge of other community members, their needs and their awareness may be much more limited. The local SES and all other emergency managers need data on how many people in different sorts of categories may need help, special intervention, assistance in a crisis or additional information and so on. This paper primarily argues a need to be wary of making absolute and finite classifications of communities on the basis of quantifiable vulnerability alone. The large databases and Geographical Information Systems provide enormous amounts of data that can be applied as needed to specific hazards, crises and communities.

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Figure 3. Townsville/Thuringowa: houses affected in January 1998 floods.

Granger has demonstrated very effectively in numerous meetings and in the media, the power of information systems to summarise, predict impacts under various scenarios, and to shock. Bringing these databases together to apply to specific crises and communities with identified needs, creates a powerful picture of the scale of the problem and its precise geographical concentration and bottlenecks. The Cairns multi-hazard database was used effectively to map storm surge scenarios in Cairns, identifying individual buildings at different levels of impact, the roads that are cut (Goudie and King, 1999), numbers of people needing to evacuate, and in each community the likely numbers of any selected population characteristic or community of need. When we consider actual hazard events, Figure 3 shows an example from a postdisaster survey of Townsville in January 1998. In a 24 hour period on January 10th 1998, over 700 millimetres of rain fell on Townsville, causing extensive run-off flooding and locally concentrated river flooding. A telephone survey of 1,000 randomly selected households was taken from Telstra’s white pages database, which records each street address as well as the name and number. Using Streetinfo, a commercially available mapping software (ERSIS), it was relatively easy to map the survey database. For the sake of clarity Figure 3 only shows about half of the houses, as a clumping of households from the same street section hides some of the locations (these become visible when zooming in to the small scale). Even at this level the geographical impact of the flood is clearly apparent and easily quantifiable by community. In Figure 3 one feature from the survey has been mapped – the


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height of water over a living area floor – but many other characteristics were as easily mapped, such as damage, road and property flooding, types of dwelling, ownership etc. Thus because the basic Geographical Information System already existed, a large survey was able to be carried out, entered into a database and mapped into preliminary reports within a short period after the disaster. Similarly the city council was able to record inundation levels and map the contours of the flood very rapidly. Although this information came too late to assist in the immediate SES response and clean up, mapping of the patterns of inundation was useful to the councils and emergency planners in identifying the most vulnerable locations and streets. The actual impact of the flood was not as simple as physical flood contours and return levels would have suggested. The inundation impact was a combination of physical location, local blockages, housing type and height of building base-pads. The combination of physical and structural information with social and population data provides a very powerful tool to measure community vulnerability to natural hazards. However, this paper has pointed out some of the constraints of census data, and yet it is this information that is the most easily and cheaply available. Physical and structural information has to be modified or very expensively gathered from surveys. Examples are digital elevation model data for small areas and structural characteristics of individual buildings. Less developed or more rapidly changing places or countries are less likely to be able to gather such data, and will frequently have to rely on just the census. On the other hand, small wealthy countries are in a good position to assemble a wide range of physical and social data that may be used to predict vulnerability at the small or local community level. In Northern Australia an increasing population is also steadily increasing in vulnerability as people move into flood and cyclone prone areas. The size of the vulnerable population also underscores the one key population characteristic that emerges as the dominant independent variable – total population. From the building structure database the key independent variable is location – a combination of height above sea level and proximity to the sea or river. A large population in a hazardous location alone defines maximum vulnerability. All other measures modify that basic classification.

References Berry L.: 1996, Community Vulnerability to Tropical Coastal Cyclones and Associated Storm Surges: Case Study of the Cairns Northern Beaches Townships, Centre for Disaster Studies and Centre for Tropical Urban and Regional Planning, James Cook University. Blaikie P., Cannon T., Davis I., and Wisner B.: 1994, At Risk: Natural Hazards, People’s Vulnerability and Disasters, Routledge, London and New York. Buckle, P.: 1999, Redefining community and vulnerability in the context of emergency management, The Australian Journal of Emergency Management 13(4), 21–26. Goudie, G. and King, D.: 1999, Cyclone surge and community preparedness, The Australian Journal of Emergency Management 13(4), 54–60.

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Granger K.: 1995, Community vulnerability: the human dimensions of disaster, Presented at AURISA/SIRC 95 – The 7th Colloquium of the Spatial Information Research Centre. Granger K., Jones T., Leiba M., and Scott G.: 1999, Community Risk in Cairns: A Provisional Multi Hazard Risk Assessment, AGSO Cities Project Report No. 1. Australian Geological Survey Organisation, Canberra. Keys C.: (1991). Community analysis: some considerations for disaster preparedness and response, The Macedon Digest 6(2), 13–16. Melick R.: 1996, Risk Assessment of the Cairns Northern Beaches in a Storm Surge: Testing the Use of Australian Bureau of Statistics Data to Identify Vulnerable Communities, Centre for Disaster Studies and Centre for Tropical Urban and Regional Planning, James Cook University. Smith D. I.: 1994, Storm tide and emergency management, The Macedon Digest 9(3), 22–26. Young, E.: 1998, Dealing with hazards and disasters: risk perception and community participation in management, The Australian Journal of Emergency Management 13(2), 14–16.