42 Common Mistakes Novelists Make (Paula Bs Writing Fiction Secrets)

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Paradise City by Elizabeth Day. Howard Pink is a wildly successful businessman still struggling to cope fifteen years after his nineteen-year-old daughter disappeared. Beatrice Kizza fled persecution from Uganda where homosexuality is illegal.

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She now works as a maid at a hotel Howard frequents. Esme Reade, an ambitious staff reporter on a Sunday tabloid, is desperate to get the Howard Pink interview for which all London reporters froth at the mouths. Pigeon English by Stephen Kelman. Newly-arrived from Ghana with his mother and older sister Lydia, Harri absorbs the many strange elements of city life, from the bewildering array of Haribo sweets, to the frightening, fascinating gang of older boys from his school.

But his life is changed forever when one of his friends is murdered. Rivers of London by Ben Aaronovitch. In one he must find what is possessing ordinary people and turning them into vicious killers, and in the second he must broker a peace between the two warring gods of the River Thames. Second Class Citizen by Buchi Emechata. Seeking an independent life for herself and her children she encounters racism and hard truths about being a new citizen.

Shadow of the Hangman by Edward Marston. Peter and Paul Skillen, identical twins and fearless thief-takers, stalk all who dare to walk in the shadow of the hangman. When they catch a notorious burglar, they claim a handsome reward and infuriate the Bow Street Runners who believe they have a monopoly on policing in the capital. The Home Secretary, Viscount Sidmouth, faces a crisis. During a massacre of American prisoners of war at Dartmoor, two escape and come to London in search of retribution. If their demands are not met, Sidmouth will be killed.

The Skillen brothers are hired to catch the fugitives and must compete with the Runners to bring the villains to justice in a compelling tale of murder, kidnap, revenge, intrigue and political machination. Small Island by Andrea Levy. Her husband, Gilbert Joseph, returns from the war expecting to be received as a hero, but finds his status as a black man in Britain to be second class. Single in the City by Michele Gorman. Armed with little more than her enthusiasm, she charges headlong into London, baffling the locals in her pursuit of a new life, new love and sense of herself.

Must-Read Novels Set in London

Sorcerer to the Crown by Zen Cho. It was first published on 14 October ; the individual stories had been serialised in The Strand Magazine between June and July Tom is now a world-famous artist, Alice is much-changed too — bruised from the events of the last decade. Perhaps they can lose themselves in the love story that ignited by a moonlit lake all those years ago? The Buddha of Suburbia by Hanif Kureishi.

When the unlikely opportunity of a life in the theatre announces itself, Karim starts to win the sort of attention he has been craving — albeit with some rude and raucous results. The Colour of Memory by Geoff Dyer. They while away their hours drinking cheap beer, landing jobs and quickly squandering them, smoking weed, dodging muggings, listening to Coltrane, finding and losing a facsimile of love, collecting unemployment, and discussing politics in the way of the besotted young—as if they were employed only by the lives they chose.

From brutal brothel-keeper Mrs Castaway, she ascends in society. Affections of self-involved perfume magnate William Rackham soon smells like love. Her social rise attracts preening socialites, drunken journalists, untrustworthy servants, vile guttersnipes, and whores of all kinds. The End of the Affair by Graham Greene. After a chance meeting rekindles his love and jealousy two years later, Bendrix hires a private detective to follow Sarah, and slowly his love for her turns into an obsession.

The Girl on the Train by Paula Hawkins. She knows it will wait at the same signal each time, overlooking a row of back gardens. Their life — as she sees it — is perfect. If only Rachel could be that happy. And then she sees something shocking. There, at the heart of the s Soho art scene, she carves out a new life. In the present day, Elina and Ted are reeling from the difficult birth of their first child. As Ted begins to search for answers, an extraordinary portrait of two women is revealed, separated by fifty years, but connected in ways that neither could ever have expected.

The Impressionist by Hari Kunzru. Chasing his fortune, he will travel from the red light district of Bombay to the green lawns of England to the unmapped African wilderness. He will play many different roles — a young prize in a brothel, the adopted son of Scottish missionaries, the impeccably educated young Englishman headed for Oxford — in order to find the role that will finally fit.

The Innocents by Francesca Segal. Childhood sweethearts whose lives and families have been intertwined for years; theirs is set to be the wedding of the year. Beautiful, reckless and troubled, Ellie represents everything that Adam has tried all his life to avoid — and everything that is missing from his world. As the long-awaited wedding approaches, Adam is torn between duty and temptation, security and freedom, and must make a choice that will break either one heart, or many.

Jane narrates the story as we follow her through her pregnancy and her encounters with the other misfits and outsiders who reside at the boarding house. The Lonely Londoners by Sam Selvon. Instead, they have to face the harsh realities of living hand to mouth, of racism, of bone-chilling weather and bleak prospects. Yet friendships flourish among these Lonely Londoners and, in time, they learn to survive.

The Long Firm by Jake Arnott. The Mimic Men by V. Now in exile from his native country, he has taken up residence at a quaint hotel in a London suburb, where he is writing his memoirs in an attempt to impose order on a chaotic existence. His memories lead him to recognize the cultural paradoxes and tainted fantasies of his colonial childhood and later life: his attempts to fit in at school, his short-lived marriage to an ostentatious white woman.

But it is the return to Isabella and his subsequent immersion in the roiling political atmosphere of a newly self-governing nation — every kind of racial fantasy taking wing — that ultimately provide Singh with the necessary insight to discover the crux of his disillusionment. The Name of the Star by Maureen Johnson. The police are left with few leads and no witnesses. Except one. Rory spotted the man believed to be the prime suspect. And now Rory has become his next target…unless she can tap her previously unknown abilities to turn the tables. When a pint-sized clerk named Auberon Quinn is randomly selected as head of state, he decides to turn London into a medieval carnival for his own amusement.

One man, Adam Wayne, takes the new order of things seriously, organizing a Notting Hill army to fight invaders from other neighbourhoods. At first his project baffles everyone, but eventually his dedication proves infectious, with delightful results. The Night Watch by Sarah Waters. Kay, who drove an ambulance during the war and lived life at full throttle, now dresses in mannish clothes and wanders the streets with a restless hunger, searching. Helen, clever, sweet, much-loved, harbours a painful secret.

Viv, glamour girl, is stubbornly, even foolishly loyal, to her soldier lover. Duncan, an apparent innocent, has had his own demons to fight during the war. Their lives, and their secrets connect in sometimes startling ways. The Opposite House by Helen Oyeyemi. Henry VIII rules over a fashionable court alive with pageant and celebration, the lack of a son his only threat. When young Mary Boleyn arrives at court, she becomes his new mistress, an unwitting pawn in the ambitions of the powerful Boleyn and Howard families.

The Report by Jessica Francis Kane. From every corner of Bethnal Green, people emerge from pubs, cinemas and houses and set off for the shelter of the tube station. But at the entrance steps, something goes badly wrong, the crowd panics, and people are crushed to death. When an enquiry is called for, it falls to the local magistrate, Laurence Dunne, to find out what happened during those few, fatally confused minutes.

But as Dunne gathers testimony from the guilt-stricken warden of the shelter, the priest struggling to bring comfort to his congregation, and the grieving mother who has lost her youngest daughter, the picture grows ever murkier. The more questions Dunne asks, the more difficult it becomes to disentangle truth from rumour — and to decide just how much truth the damaged community can actually bear. The Road Home by Rose Tremain. After a spell of homelessness, he finds a job in the kitchen of a posh restaurant and a room in the house of an appealing Irishman who has already lost his family.

Never mind that Lev must sleep in a bunk bed surrounded by plastic toys—he has found a friend and shelter. However constricted his life in England remains, he compensates by daydreaming of home, by having an affair with a younger restaurant worker, and by trading gossip and ambitions via cell phone with his hilarious friend Rudi, who, dreaming of the wealthy West, lives largely for his battered Chevrolet.

The Satanic Verses by Salman Rushdie. Two Indian actors of opposing sensibilities fall to earth, transformed into living symbols of what is angelic and evil. The Secret Agent by Joseph Conrad. When he saves the life of an elderly man in a public convenience an unlikely job opportunity presents itself — the man, Lord Nantwich, is seeking a biographer.

One of his longest novels it contains a hundred chapters , The Way We Live Now is particularly rich in sub-plot. It was inspired by the financial scandals of the early s, and lashes at the pervading dishonesty of the age, commercial, political, moral, and intellectual. It is one of the last significant Victorian novels to have been published in monthly parts. Catrin is conscripted into the world of propaganda films. His treachery has already blown some of its most vital operations and its best networks. It is clear that the double agent is one of its own kind.

Table 1 shows the geographic level at which each methodology has been applied. Those at the national level have studied nations or been completed on a global basis. Sub-national administrative units are studies completed in well-defined sub-national areas. Community level refers to studies in locations where the geographic boundary is not well defined or doesn't correspond to an administrative area.

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Multiple levels refer to studies which have applied the same methodology at both national and sub-national levels. Despite the high profile given to many indices that compare nations, they only comprise a fifth of the total. Three quarters are focussed on sub-national settings, with the majority of these based on well-defined administrative units. This is consistent with the large number of data driven methodologies, as statistical data is typically provided on the basis of these territorial units. Only a small number of methodologies have been developed for application at multiple levels.

This suggests that most authors are attempting to tailor their approach to a particular level and at least in this sense are not in danger of committing the ecological fallacy, a concern that has been raised in the literature. Of the 25 national or multiple level methodologies only 23 directly compare nations, with two of the multiple level methodologies taking a gridded approach to mapping the index value.

Of these 23 methodologies that compared nations only eight were global in scope, with the remainder prepared for particular regions or some other sub-set of countries such as developing nations or Small Island Developing States. The majority of sub-national methodologies have been applied in single countries with only nine methodologies applied in a cross-national context.

Only five of these are applied in more than two countries. The small number of methodologies that are applied across multiple countries is consistent with the literature, which has identified this approach as both difficult and a key gap in the field. These are displayed in Table 2 and Figure 4. In the vast majority of methodologies 90 the variables were chosen by expert judgement relying on the literature, theory models and stakeholder knowledge.

A smaller number 9 utilised statistical analysis such as examining correlations to exclude redundant variables to assist in variable selection. Three methods directly used stakeholders through workshops to select variables, whilst the remaining four used other means or a mix of approaches.

In many cases developers cited variable inclusion in other indices in the literature as justification for inclusion, rather than empirical evidence or theoretical vulnerability and resilience frameworks. A variety of methods were used to collect data which are summarised in Table 3. The majority 76 utilised existing data collected by national statistical agencies and other government or non-government organisations that gather sociological and economic data.

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For sub-national methods the data in many cases came from national censuses to enable the use of small areas. Due to the use of census data which is collected relatively infrequently, the data used in a number of the indices was up to 10 years old at time of publication. A smaller number of methodologies 10 used household surveys to gather relevant data.

The more explicitly stakeholder focussed methodologies utilised either workshops 4 or surveys of relevant stakeholders 4 to collect information. Expert advice was relied on in only two methods. A further ten studies adopted a mixed approach to data collection utilising data from statistical agencies, expert advice and stakeholder responses. The majority of methods 96 did not perform any imputation with a small number using either case deletion 2 or some form of single imputation 8 to deal with missing data.

A more important factor in dealing with missing data is that, as discussed in the literature, a number of authors acknowledged that data availability influenced variable selection. Many methodologies applied no normalisation to the data, either because it was not relevant to the aggregation method or because the data types were already consistent. Where normalisation was applied Min-Max methods were the most popular followed by standardisation, categorical scales and ranking. A range of other methods or mixed methods were used in other cases. The results are summarised in Table 4.

A broad variety of methods for weighting variables in index construction have been deployed, including a number of bespoke methods. The results are summarised in Table 5. Equal weighting of variables and indicators appears to be the default setting in index construction, having been used by 44 index methodologies. Where equal weights were used indices were constructed to either weight each variable equally or in some multi-level hierarchies to weight each branch equally.

Two of the scorecard methods did not use aggregation of the variables and don't have any weighting. In some other cases 13 the study authors chose weights based on expert experience and the literature, but did not employ any rigorous participatory or statistical method for selecting weights. Nineteen indices used a participatory method of selecting weights. The most popular participatory method was the Analytic Hierarchy Process, used in 8 indices. Eleven studies used other participatory methods included ranking, 30 , 31 DEMATEL analysis, 32 , 33 , 34 the Delphi method, 35 the Budget Allocation Process, 36 and workshop or other interview based assignment.

Principal Components Analysis is the most popular statistical weighting method, used by 17 methods and typically implemented using the procedure developed for Cutter's Social Vulnerability Index. In most cases 10 PCA factors were aggregated with equal factor weights. Three methods used the factor score which indicates the percent of variance explained as the factor weightings, three did not aggregate the factors and one was unclear. Other statistical methods employed include triangular fuzzy numbers and stochastic simulation, 28 data envelopment analysis, 28 , 41 , 42 multivariate regression against other indices, 28 , 43 multivariate regression against outcome measures, 44 , 45 , 46 microsimulation and decision tree analysis, 27 the projection pursuit cluster model, 29 the procedure developed for the Disaster Deficit Index, 47 and the procedure developed for the Local Disaster Index.

A variety of inductive and deductive approaches were used to construct the indices. Three approaches used no aggregation, three of the principal components approaches just provided the factors and used no further aggregation, 74 used a hierarchical approach, 14 combined factors from principal components analysis, whilst the remainder employed other mostly relational approaches.

Of the hierarchical indices the majority 59 used a simple structure with aggregation at each level by arithmetic or geometric weighted mean. In the remaining 15, the theoretical models incorporated a risk, vulnerability or adaptive capacity equation which was typically at the top of the hierarchy, the equation's components being constructed as the weighted mean of the indicator variables.

Some of the methodologies based on PCA also employed a hierarchical approach in the combination of the factors, once they had been calculated. Numbers of variables in deductive methodologies with different numbers of intermediate levels between the variables and the reported index.

Almost all methodologies provided some display of the results with maps and tables being the most popular as summarised in Table 7. A small number of methods published no output whatsoever. Although many papers discussed the potential limitations of the methodology developed, only twenty have any explicit analysis of uncertainty or sensitivity. Three methodologies explored the sensitivity to alternative aggregation methods.

Two assessed the use of different groups of variables. Only two methodologies provided estimated errors on the resultant vulnerability scores. Of the methodologies included in this study only one comprehensive sensitivity analysis was undertaken, for Cutter's SoVI 50 which incorporated an investigation of different geographic levels and 54 unique variations on the index construction. However other work that failed to meet the inclusion criteria has also examined global approaches to sensitivity and uncertainty analysis. One other compared the results against ethnographic assessment and one community survey methodology validated the resulting index against a broader set of survey questions.

There was some variation in the findings of the sensitivity analyses that were undertaken, with some finding low sensitivity to changes to the methodology and others finding high sensitivity to change. There was a large variation in the number of variables each methodology used with the minimum being 2 and the maximum being , however most methodologies used relatively few with two thirds using less than The distribution of variables is illustrated in Figure 5.

Most of the methodologies employed mining of national and international statistical databases. Methodologies using this data collection technique used the fewest variables on average median 18, minimum 2, maximum Some of these techniques included a stage of variable exclusion based on correlation or other statistical analysis. Those using community surveys collected more variables median However the stakeholder focussed methods using surveys or workshops relied on substantially more variables median The methodologies used variables of which were unique.

An analysis of their frequency of occurrence found that the most used variables were dominated by common statistical indicators. This is consistent with the dominant data collection methodology. The 11 most common variables are shown in Table 8. As per the classification hierarchy shown in Figure 2 , the variables were grouped under Sub-Indicators, an average of 6. The most common Sub-Indicators were strongly influenced by the most common variables with some Sub-Indicators, such as Household Water Access appearing despite not having component variables in the top This appears to be due to the larger number of ways of measuring certain sub-indicators.

The 10 most common sub-indicators are shown in Table 9. The Sub-Indicators were grouped under Indicators, an average of 3. At the Indicator level in the classification hierarchy the significant use of various variables describing age distribution in communities and properties of housing stock was demonstrated. The 10 most common indicators are shown in Table The Indicators were grouped under 15 categories.

The number of methodologies that included variables from each of the categories is shown in Table This demonstrates that a majority of the methodologies included some measure of demographics, education and health, with existing indices and measurement of aspects of government and the environment being used the least. Looking at the number of Indicators, Sub-indicators and Variables in each category and the proportion of variables that are used more than once it is possible to better understand how much variety within the literature there is in terms variables to represent these different concepts.

The proportion of variables used more than once is indicative of the level of agreement in the literature on what variables to measure to understand the properties of that category. This is shown in Table Number of Indicators, Sub-Indicators and Variables in each category and the proportion of variables in each category that are used in more than one methodology.

The 15 categories were grouped into 6 environments, to better enable visual analysis of the composition of each index. The use of variables in these 6 different environments in the different methodologies is summarised in Table The most common variables are related to various social aspects of communities especially demographics, education and health. Respectively population density, number of doctors and literacy rate were the three most common variables in these categories. Variables representing various economic aspects of communities: livelihoods, labour market and economy were the next most common.

The number of renters and access to clean water were, respectively, the most common variables in these two categories. Existing indices were used in only 21 of the methodologies, with most relying instead on directly collected data. However despite the risk of double-weighting, by also including variables that are already present in an included index this appears to have only occurred in two methodologies. Although the prevalence of different variables provides some insight into their popularity in disaster risk, vulnerability and resilience indices it does not reveal the make-up of the individual indices.

To better understand their composition the proportion of the variables classified into each environment was calculated for each index. The results of this classification are displayed in Figure 6 and the averages across all the indices displayed in Table These show that most indices are dominated by variables related to the social environment, with a much smaller number using high proportions of variables from the disaster environment. However when disaster resilience variables are specifically examined i. Variables that compose each index classified into one of six environments as a proportion of the number of variables in each index.

Proportion of variables from each environment that comprise each index, on average, for all methodologies and for methodologies that only include variables in that environment. Examining the correlations between the proportions of variables included Table 15 shows the strongest relationship between the social and disaster environments; the more social variables that are included, the fewer disaster variables that are included. It is also possible to compare how the proportion of variables in each index varies according to the type of methodology which is shown in Table Only the Deductive, PCA and Stakeholder-based methodologies have been included due to the small number of methodologies in the Relational and Novel techniques categories.

This demonstrates that there is some variation in the type of variables used in these methodologies depending on the approach used. Although Deductive and PCA approaches appear to be broadly similar in the proportions of variables included, PCA approaches feature far fewer disaster related variables. Stakeholder based approaches include many more disaster related variables, making up approximately half of the variables in these methods with a much lower focus on economic variables.

Proportion of variables from each environment present in methods using the three most common construction approaches. It is desirable to know whether the large number of composite indicator methodologies is actually adding new explanatory power to understanding of vulnerability, risk or resilience or whether they are repeatedly using the similar sets of variables and only varying the construction method.

Ideally this would be tested by comparing index values for the methodologies in the same area, but with little geographic overlap and data unavailability this would not be practical. To gauge the amount of variation in the choice of variables across all the methodologies a custom measure, the 'Overlapping Score' was created to measure the proportion of elements in common at each level of the classification hierarchy variable, sub-indicator, indicator, category, environment. This index has been constructed such that a set of methods using identical variables, sub-indicators etc.

These results show that there is relatively low internal consistency between the methodologies when measured at the variable level this increases substantially when considering the indicator level and above. This suggests that many of these methodologies may not offer substantially different results in presenting an understanding of risk, vulnerability or resilience.

By examining the average of the absolute values of the correlation of each method against all others it is possible to uncover those methods that include a more unique set of variables — this was applied at the indicator level. The four most unique methodologies are:.

Predictive Indicators of Vulnerability Communities Advancing Resilience Toolkit Local Disaster Index Disaster Deficit Index These methodologies had fewer indicators in common with the rest of the set, partly due to a focus on more unique concepts and partly because they use a relatively small number of variables. Similarly the least unique methodologies with the highest proportion of elements in common were the two versions of Joerin's Climate Disaster Resilience Index.

This review has revealed a broad range of practice in the development of composite indicators for the measurement of disaster risk, vulnerability and resilience. There is substantial diversity in the literature, with a range of variable selection approaches, data collection methods, normalisation methods, weighting methods, aggregation approaches and variables being used. However this review has also identified a number of trends which may limit the utility of composite indices in improving the understanding of these concepts.

Although the review found considerable diversity in the methodologies of index construction the majority take a fairly standard deductive or hierarchical approach with a weighted sum of the variables included in the index. In most cases the main point of difference in index construction was the choice of variables for inclusion. Hierarchical approaches are easy to construct and are relatively simple to understand which may largely explain their prevalence. Principal Components Analysis was also commonly employed, with many cases being strongly influenced by the publication of Cutter's Social Vulnerability Index SoVI.

In a number of instances its use has extended beyond addressing some of the problems associated with collinearity in deductive indices to more detailed analysis of the principal components and their spatial variation, thus taking advantage of PCA as a data reduction tool. PCA has only been applied at a sub-national level, however it is likely that comparing nations would not offer significant advantages over other methods as for it to be statistically valid only a small number of variables could be included.

Stakeholder based methods were less popular than the deductive methods or PCA, however appeared to be more targeted with more variables directly related to disaster resilience. The large number of variables gathered in the stakeholder focussed methodologies could be problematic. Although a number of these are checklist-based self assessments, the volume of questions could lead to little attention being paid to responses and overall disengagement from the process. The literature generally agrees that resilience is not a 'check the box' approach but is related to systemic performance, 55 which these methods may not focus on.

Because many stakeholder based approaches are self assessment and others are being driven by a single small research group it is difficult to ascertain their full geographical coverage. Aside from pilot locations, their implementation is often not reported. This makes it difficult to assess implementation difficulties or conduct reliability analysis to identify a shorter list of questions. Relational techniques, such as linear regression and data envelopment analysis have received some attention for both the comparison of nations and sub-national areas.

Although the indices that use relational methods are internally validated they have a number of potential problems that could limit their application. Data on disaster impacts may be of poor quality and have limited time coverage to adequately reveal the relationship with variables. Indices produced with these methods should be accompanied by robust uncertainty estimates that incorporate both aleatoric and epistemic uncertainty to better enable assessment of their reliability.

A small number of composite indices have taken more advanced approaches, some of which are closer to more model based assessments of disaster risk. Greater sophistication in this area may produce indices that better reflect theory models. However these 'black box' methods may be more difficult to understand for end-users and promote a less critical acceptance of the results. The review of the literature found 24 methodologies that were not sufficiently well described to include in the analysis. Although these included a number of proprietary rankings, many methods published in the academic literature also failed to provide sufficient detail to enable analysis, let alone replication.

Even methods included in the review failed to include sufficient detail that would enable reconstruction of the index. Where information was sought from statistical databases, some methodologies are unclear about the agency from which the data was sourced and the years to which variables refer to. Some studies did note the year from which the data was sourced, which was up to 10 years prior to publication potentially making the index out-of-date.

This has implications for decision makers using the index results.

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Although demographic variables may change relatively slowly this lack of currency was often not well communicated in the results of these studies, nor were attempts made to 'nowcast' the index values. Those based on community or stakeholder survey often did not include the questions asked, making comparison with other surveys difficult.

Numerous researchers have been pointing out flaws in index construction and calling for greater use of sensitivity and uncertainty analysis for quite some time as outlined in the introduction. This study has found that these calls have been largely unheeded. Despite the large increase in number of disaster risk, resilience and vulnerability composite indicators being developed, there has not been an increase in the use of sensitivity and uncertainty analysis. Few methodologies are undertaking explicit sensitivity and uncertainty analyses and where it is undertaken it is typically limited to one or two aspects of the methodology.

This makes it difficult to assess quality, especially when many choices in index construction appear to have been made arbitrarily or with limited justification. Some sensitivity analyses have found significant impact of methodological choices on the resulting index values which has implications on the broader use of these indices by policy-makers. Without appropriate caveats or the provision of uncertainty estimates, decision makers may believe the index results to be much more reliable than they actually are.

Most studies communicated results for example by using maps and summary tables. However many did not provide full numerical results. Although full reporting on index results for large numbers of study units is difficult in the academic literature more effort needs to be made to make these available to enable improved review of these studies, for example by comparing different index results for the same set of territories.

Furthermore only 4 methodologies provide interactive portals to access and visualise the data in graphical, map or table form. Interactive options may be more preferred by policy makers, the community and others interested in the results of these studies which suggests that many authors may not be making them useful for end-users. This study aimed to review composite indices that claim to measure disaster risk, vulnerability and resilience.

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The limited use of disaster related variables is likely to be due to limited availability. This is supported by the fact that those methodologies that directly elicited information from stakeholders included substantially more of these variables than those that depended more on data gathered from statistical databases. However without access to the results of all the indices in this review and a social disadvantage index for comparison, this is difficult to assess.

Analysis of the inclusion of the variables found a strong negative correlation between use of social variables and use of disaster variables. This suggests that social variables are the proxy of choice when access to more disaster related data is difficult, with demographic variables being very common. A large number of these variables are collected through workshops and stakeholder and community surveys.

Alternative formulations of questions on disaster resilience, for example customised to the terminology and context of a particular jurisdiction, may limit the broader applicability of these methodologies. Work surrounding the implementation of the post development agenda, such as the development of a new "10 Essentials" may assist in addressing this gap.

This highlights the difficulty in adapting tools across national contexts, particularly where those tools utilise data from statistical agencies which may not be available for similar scales, forms and time periods across multiple nations. An updated tool, consistent with the new 10 Essentials may be beneficial in improving cross-national coverage although the differing contexts of nations may prove too difficult for this tool to enable meaningful comparison. Two key motivations have emerged from this analysis of composite index and dashboard methodologies of disaster risk, vulnerability and resilience.

Many methods use primarily statistical data to compare large numbers of study units. Consistent comparison of different jurisdictions may be desired by national and international organisations seeking to inform decisions on resource allocation. Another group of methods seek to provide a tool, primarily to sub-national authorities, for self-assessment of disaster vulnerability through the asking of targeted questions. Self-assessment may be desired by national and local governments and communities seeking to improve their performance.

These two groupings are consistent with the three key motivations found by da Silva and Morera of ranking relative performance and diagnosing performance and influencing change. There does not appear to have been any efforts to make the results of self-assessments more comparable. This could be achieved by establishing quantitative measures of performance where each assessing authority would choose their own benchmarks.

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However this may not produce desirable results; the use of self-assessments for resource allocation could potentially bias them towards assessing their performance as lower than reality to enable greater access to resources. The number and variety of composite indicator methodologies that have been developed clearly indicate their potential end use for decision makers working in disaster risk reduction, humanitarian and emergency response, civil protection or other fields related to disaster resilience.

However the limitations of the present literature have a number of implications for end users and there is a risk that biases and uncertainty may lead to inappropriate decisions. To counter this risk end users should consider multiple techniques when attempting to understand community vulnerability and resilience. To gain the broadest understanding this should include qualitative and quantitative techniques that go beyond composite indicators, for example using tools that are part of the Vulnerability and Capacity Assessment process developed by the International Federation of Red Cross and Red Crescent Societies.

End users can also take steps to ensure that composite indicator frameworks they are using are of high quality and reliability when they are selecting an existing index for use or commissioning the development of one either internally or by an external team of experts. Composite indicator frameworks with high quality and reliability are likely to have:. Consideration of the purpose of the composite indicator framework, in particular whether it is needed for comparison of many areas or for local self-assessment. Demonstration that the disaster specific index adds value to the discussion of risk, vulnerability or resilience.

This may come from the inclusion of multiple variables that directly relate to the phenomenon of interest or a comparison of the index with a generic socio-economic status index. Full publication of the methodology and results. Interested third parties should be able to replicate, evaluate and build-upon the results of composite indicators. Particular attention should have been paid to clearly specifying data sources, including agency, year and the wording of any survey questions used. This may be particularly important for increasing transparency in government decision making.

The results in a range of formats. Results should be published as tables, graphs and maps to enhance understanding and available in downloadable machine readable formats. Interactive displays and dashboards may also be highly useful to end users. Adequate sensitivity and uncertainty analysis. This should incorporate, as far as possible, global analysis of sensitivity to understand which construction choices contribute most to possible variance in index values and uncertainty estimates for all index values.

Attempts to validate the index values. Although relational indices are internally validated, efforts should be made to relate other indices to outputs or outcomes relevant to the phenomena of concern. This may include disaster impacts or surveys of experts or community members on their opinion of overall community disaster risk, vulnerability or resilience. An extensive review of disaster risk, vulnerability and resilience composite indicator methodologies has been conducted drawing on a range of sources in both the academic and grey literature.

The review has revealed a broad diversity of practice with implementations at both the global and local level and within many different countries. The significant increase in the number of methodologies being implemented over recent years demonstrates greater availability of composite indicators for use by researchers and policy makers. However present practice has two key limitations that may restrict their use or potentially lead to poor decisions being made in their implementation - low use of direct measures of disaster resilience and low use of sensitivity and uncertainty analysis.

Very few studies are implementing comprehensive sensitivity and uncertainty analysis, nor communicating it to end users. This may lead policy-makers to believe that index results are more precise and accurate than is actually the case. Were a comparative index to be used by a government to allocate resources for disaster risk reduction, without consideration of its reliability, it could lead to waste of government resources or possibly even increased risks if existing resources are shifted away from high risk areas.

The lack of sensitivity and uncertainty analysis may be compounded by the low use of variables directly related to disaster risk reduction, preparedness and resilience. This low use of more direct variables may limit the explanatory power of these tools. Indices lacking direct measures of disaster resilience may be indistinguishable from more general measures of socioeconomic status, such as the Human Development Index, and thus may not offer increased value to researchers investigating disaster vulnerability.

Lack of sensitivity analysis means that the exclusion of disaster related variables may go unquestioned by policy makers or researchers using such an index, increasing the risk of inappropriate use. Policy makers and others who wish to use composite indices to inform decision making need to critically evaluate their quality and reliability before their use.

Consideration of the features of high quality and reliable indices, as outlined in the discussion, would assist decision makers to commission or select an appropriate index for their needs. Similarly, researchers developing these indices need to make greater efforts to ensure that they are relevant to the needs of decision makers, are of high quality, and add value to the understanding of vulnerability and resilience.

In particular they should demonstrate that their index has greater explanatory power of disaster risk, vulnerability or resilience than generic socioeconomic status indicators and incorporate robust sensitivity and uncertainty analysis. Furthermore, the low use of direct measures of disaster resilience may be related to the limited agreement between the methodologies of which direct measures to use.

This limited agreement appears to reflect a broader gap in disaster research on the drivers of disaster resilience. It is unclear which variables, in which situations, matter most to disaster resilience. Current approaches appear to be largely tailored to individual contexts and broadly incompatible with one and other. This could be a significant barrier for achievement of the Sendai Framework for Disaster Risk Reduction, disaster related targets of the Sustainable Development Goals and other elements of the post development agenda as parties seek to agree indicators to measure performance towards targets in these agreements.

Further research is needed to better identify which variables are most predictive of disaster risk, vulnerability and resilience and in which contexts they apply. This would enable the construction of more relevant and targeted composite indicators, which combined with improvements in practices related to their construction would lead to indices that are robust, fit for purpose and comparable improving the understanding of disaster risk, vulnerability and resilience and providing decision makers with tools to better monitor progress towards a disaster resilient society.

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Link to external file. The author would like to thank Dr. Christopher Burton and Prof. Alberto Monti for their feedback on drafts of this paper.

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