Vulnerability metadata

The vulnerability component is described as:

Metadata that is specific to datasets that describe the vulnerability relationships in relation to specific hazards, or geospatial indices associated with the chance of suffering losses from hazard events.

The vulnerability component describes metadata for datasets that detail relationships between hazard intensity and expected losses over exposed items, or spatial indexes associated with socio-economic vulnerability.

The function component uses hazard type, process type and intensity measure consistent with the hazard and loss components, exposure information consistent with the exposure and loss components. It contains key information including the type of function, intensity and impact metrics used, which asset types or population groups it applies to, how it was developed and for what locations. The spatial indices component uses indicator id, description and thresholds. Spatial reference and location information are described using existing external standards.

Overview 

            erDiagram
        Direction LR

        "Vulnerability metadata" {
        }

        Dataset ||--o{ "Vulnerability metadata": ""
        "Vulnerability metadata" o|--o{ "Vulnerability function": "Used to calculate impact"
        "Vulnerability metadata" o|--o{ "Fragility function": "Used to calculate impact"
        "Vulnerability metadata" o|--o{ "Damage-to-loss function": "Used to calculate impact"
        "Vulnerability metadata" o|--o{ "Engineering demand function": "Used to calculate impact"
        "Vulnerability metadata" o|--o{ "Socio-economic indicator": "Used to calculate impact"

Examples 

Example: Global flood depth-damage functions

The following example shows RDLS metadata for the Global flood depth-damage functions in tabular format and JSON format.

Figure

Metadata (JSON)

{
    "id": "rdls_vln_dataset",
    "title": "Global flood depth-damage functions ",
    "description": "This dataset contains damage curves depicting fractional damage function of water depth as well as maximum damage values for a variety of assets and land use classes.",
    "risk_data_type": [
        "vulnerability"
    ],
    "publisher": {
        "name": "EU Joint Research Centre (JRC)",
        "url": "https://joint-research-centre.ec.europa.eu/index_en"
    },
    "version": "2.0",
    "purpose": "Assessing potential damage of flood events is an important component in flood risk management. Determining direct flood damage is commonly done using depth-damage curves, which denote the flood damage that would occur at specific water depths per asset or per land-use class. Many countries have developed flood damage models using depth-damage curves based on analysis of past flood events and on expert judgement. However, the fact that such damage curves are not available for all regions hampers damage assessments in some areas. Moreover, due to different methodologies employed for various damage models in different countries, damage assessments cannot be directly compared with each other, obstructing also supra-national flood damage assessments.",
    "details": "Based on an extensive literature survey concave damage curves have been developed for each continent, while differentiation in flood damage between countries is established by determining maximum damage values at the country scale. These maximum damage values are based on construction cost surveys from multinational construction companies, which provide a coherent set of detailed building cost data across dozens of countries. A consistent set of maximum flood damage values for all countries was computed using statistical regressions with socio-economic World Development Indicators. Further, based on insights from the literature survey, guidance is also given on how the damage curves and maximum damage values can be adjusted for specific local circumstances, such as urban vs. rural locations or use of specific building material. This dataset can be used for consistent supra-national scale flood damage assessments, and guide assessment in countries where no damage model is currently available.",
    "contact_point": {
        "name": "Jan Huizinga",
        "url": "https://www.linkedin.com/in/jan-huizinga1966/"
    },
    "creator": {
        "name": "Jan Huizinga",
        "url": "https://www.linkedin.com/in/jan-huizinga1966/"
    },
    "spatial": {
        "scale": "global"
    },
    "license": "https://creativecommons.org/licenses/by/4.0/",
    "referenced_by": [
        {
            "author_names": [
                "Jan Huizinga",
                "Hans de Moel",
                "Wojciech Szewczyk"
            ],
            "name": "JRC Technical report - Global flood depth-damage functions",
            "date_published": "2017-12-04",
            "url": "https://publications.jrc.ec.europa.eu/repository/bitstream/JRC105688/global_flood_depth-damage_functions__10042017.pdf",
            "doi": "10.2760/16510",
            "id": "reference_0PctqRZG"
        }
    ],
    "resources": [
        {
            "title": "Global flood depth-damage functions database",
            "description": "This spreadsheet contains two components required for flood damage assessment: fractional depth-damage functions and maximum damage values. The damage functions provide the share of asset that is damaged at a given flood depth, while the maximum damage values provide the associated maximum damage value for the given asset and, when combined together, they yield the monetary value of the damage.",
            "download_url": "https://publications.jrc.ec.europa.eu/repository/bitstream/JRC105688/copy_of_global_flood_depth-damage_functions__30102017.xlsx",
            "media_type": "text/csv",
            "id": "resource_9ohueIZf"
        }
    ],
    "vulnerability": {
        "functions": {
            "vulnerability": [
                {
                    "approach": "empirical",
                    "relationship": "math_parametric",
                    "hazard_primary": {
                        "type": "flood",
                        "process": "fluvial_flood",
                        "intensity_measure": "flow_depth_ground:m"
                    },
                    "hazard_analysis_type": "deterministic",
                    "category": "buildings",
                    "impact": {
                        "type": "direct",
                        "modelling": "simulated",
                        "metric": "mean_damage_ratio",
                        "measurement": {
                            "quantity_kind": "dimensionless_ratio",
                            "unit": "percent"
                        }
                    },
                    "id": "item_DHnrdjtv"
                }
            ]
        }
    },
    "links": [
        {
            "href": "https://docs.riskdatalibrary.org/en/1__0__0/rdls_schema.json",
            "rel": "describedby"
        }
    ]
}

Example: Relative Wealth Index

The following example shows RDLS metadata for the Relative Wealth Index in JSON format.

Figure

Metadata (JSON)

{
    "id": "rdls_vln_ai_for_good",
    "title": "Relative Wealth Index",
    "description": "The Relative Wealth Index predicts the relative standard of living within countries using de-identified connectivity data, satellite imagery and other nontraditional data sources. The data is provided for 99 low and middle-income countries at 2.4km resolution.",
    "risk_data_type": [
        "vulnerability"
    ],
    "publisher": {
        "name": "HDX",
        "url": "https://data.humdata.org"
    },
    "version": "2021",
    "project": {
        "name": "AI for good",
        "url": "https://ai4good.org/"
    },
    "details": "Researchers at the University of California - Berkeley and Meta developed micro-estimates of wealth and poverty that cover the populated surface of 99 low and middle-income countries (LMICs) at 2.4km resolution. The estimates are built by applying machine learning algorithms to vast and heterogeneous data from satellites, mobile phone networks, topographic maps, as well as aggregated and de-identified connectivity data from Meta. They train and calibrate the estimates using nationally-representative household survey data from 56 LMICs, then validate their accuracy using four independent sources of household survey data from 18 countries. They also provide confidence intervals for each micro-estimate to facilitate responsible downstream use.",
    "contact_point": {
        "name": "Meta",
        "url": "https://ai.meta.com/"
    },
    "creator": {
        "name": "Meta",
        "url": "https://ai.meta.com/"
    },
    "spatial": {
        "scale": "regional",
        "countries": [
            "ALB",
            "DZA",
            "AGO",
            "AIA",
            "ATG",
            "ARG",
            "ARM",
            "ABW",
            "AZE",
            "BHS",
            "BGD",
            "BRB",
            "BLZ",
            "BEN",
            "BTN",
            "BOL",
            "BIH",
            "BWA",
            "BRA",
            "VGB",
            "BRN",
            "BGR",
            "BFA",
            "BDI",
            "CPV",
            "KHM",
            "CMR",
            "CYM",
            "CAF",
            "TCD",
            "CHL",
            "HKG",
            "MAC",
            "COL",
            "COM",
            "COG",
            "CRI",
            "CIV",
            "COD",
            "DJI",
            "DMA",
            "DOM",
            "ECU",
            "EGY",
            "SLV",
            "GNQ",
            "ERI",
            "SWZ",
            "ETH",
            "GUF",
            "GAB",
            "GMB",
            "GEO",
            "GHA",
            "GRD",
            "GLP",
            "GTM",
            "GIN",
            "GNB",
            "GUY",
            "HTI",
            "HND",
            "IND",
            "IDN",
            "JAM",
            "JPN",
            "JOR",
            "KAZ",
            "KEN",
            "KGZ",
            "LAO",
            "LSO",
            "LBR",
            "LBY",
            "MDG",
            "MWI",
            "MYS",
            "MDV",
            "MLI",
            "MTQ",
            "MRT",
            "MUS",
            "MYT",
            "MEX",
            "MDA",
            "MNG",
            "MNE",
            "MSR",
            "MAR",
            "MOZ",
            "NPL",
            "NIC",
            "NER",
            "NGA",
            "MKD",
            "PAK",
            "PAN",
            "PRY",
            "PER",
            "PHL",
            "PRI",
            "KOR",
            "REU",
            "ROU",
            "RWA",
            "SHN",
            "KNA",
            "LCA",
            "VCT",
            "STP",
            "SEN",
            "SRB",
            "SYC",
            "SLE",
            "SGP",
            "ZAF",
            "SSD",
            "LKA",
            "SUR",
            "TWN",
            "TJK",
            "THA",
            "TLS",
            "TGO",
            "TTO",
            "TUN",
            "TUR",
            "TKM",
            "TCA",
            "UGA",
            "TZA",
            "VIR",
            "URY",
            "UZB",
            "VNM",
            "ZMB",
            "ZWE"
        ]
    },
    "spatial_resolution": 2400,
    "temporal_resolution": "P1D",
    "license": "https://creativecommons.org/licenses/by-nc/4.0/",
    "resources": [
        {
            "title": "Relative Wealth Index download page",
            "description": "HDX page hosting the collection of RWI country datasets for download.",
            "access_url": "https://data.humdata.org/dataset/relative-wealth-index",
            "media_type": "text/csv",
            "id": "resource_x3ltVbFU"
        }
    ],
    "vulnerability": {
        "socio_economic": [
            {
                "indicator_name": "Relative Wealth Index",
                "indicator_code": "RWI",
                "description": "The Relative Wealth Index (RWI) estimates the relative standard of living of a population within a country, based on predicted consumption and asset ownership. It is a relative measure \u2014 it ranks areas against each other within a country, not against an absolute poverty line or across countries. It is expressed as standard deviation from the national mean: negative values identify below-average wealth conditions, and positive values identify above-average wealth condition for that country.\n",
                "reference_year": 2021,
                "uri": "https://data.humdata.org/dataset/relative-wealth-index",
                "id": "item_VOdRI7Ox"
            }
        ]
    },
    "links": [
        {
            "href": "https://docs.riskdatalibrary.org/en/1__0__0/rdls_schema.json",
            "rel": "describedby"
        }
    ]
}

Properties 

Title	Description	Type
`functions`		object
Functions	Details of the functions used to calculate the vulnerability dataset.
`functions/vulnerability`		array[Vulnerability function]
Vulnerability functions	The vulnerability functions used to calculate the impact of the hazard. See Vulnerability function
`functions/fragility`		array[Fragility function]
Fragility functions	The fragility functions used to calculate the impact of the hazard. See Fragility function
`functions/damage_to_loss`		array[Damage-to-loss function]
Damage-to-loss functions	The damage-to-loss functions used to calculate the impact of the hazard in conjunction with fragility functions. See Damage-to-loss function
`functions/engineering_demand`		array[Engineering demand function]
Engineering demand functions	The engineering demand functions used to calculate the impact of the hazard. See Engineering demand function
`socio_economic`		array[Socio-economic Indicator]
Socio-economic indices	Array of socio-economic indices vulnerable to the hazard. See Socio-economic indicator

Vulnerability function 

Title	Description	Type	Required
`id`		string	Required
Identifier	A unique identifier for the function. Use of an HTTP URI is recommended.
`approach`		string	Required
Approach	The approach the function is based upon, taken from the closed function_approach codelist.
`relationship`		string	Required
Impact relationship type	The type of function relationships used to calculate the impact values, taken from the closed relationship_type codelist.
`hazard_primary`		object
Primary hazard type	The primary hazard involved in the modelled scenario(s). See Hazard
`hazard_secondary`		object
Secondary hazard type	The secondary hazard involved in the modelled scenario(s). See Hazard
`hazard_analysis_type`		string
Hazard analysis type	The type of analysis applied to the hazard data used in the modelled scenario(s), from the closed analysis type codelist.
`category`		string
Exposure category	The category of the exposed assets, from the closed exposure_category codelist.
`impact`		object
Impact	Information about how the impacts of hazards are calculated.
`impact/type`		string
Impact type	The type of impact calculated, taken from the closed impact_type codelist.
`impact/modelling`		string
Impact modelling	The type of data used to calculate the impact values, taken from the closed data_calculation_type codelist.
`impact/metric`		string
Impact metric	The metric used to describe the impact, taken from the open impact_metric codelist.
`impact/loss_statistic`		string
Loss statistic	The type of statistical summary applied to the loss values, from the closed loss_statistic codelist.
`impact/measurement`		object
Impact measurement	How the impact is measured.
`impact/measurement/quantity_kind`		string
Quantity kind	The kind of quantity by which it is quantified, from the open quantity_kind codelist.
`impact/measurement/unit`		string
Unit	The unit by which it is measured, taken from the unit codelist for the quantity kind.
`impact/measurement/valuation_year`		string
Valuation year	The year of the monetary valuation, expressed as a 4-digit year (YYYY). Applicable when quantity_kind is ‘currency’.
`taxonomy`		string
Exposure taxonomy scheme	The name of the taxonomy scheme used to create descriptive individual asset feature strings within the dataset, from the open classification_scheme codelist. Use of GED4ALL is recommended.
`analysis_details`		string
Analysis details	Additional details about the analysis used to produce the function used in the modelled scenario(s).

Fragility function 

Title	Description	Type	Required
`id`		string	Required
Identifier	A unique identifier for the function. Use of an HTTP URI is recommended.
`approach`		string	Required
Approach	The approach the function is based upon, taken from the closed function_approach codelist.
`relationship`		string	Required
Impact relationship type	The type of function relationships used to calculate the impact values, taken from the closed relationship_type codelist.
`hazard_primary`		object
Primary hazard type	The primary hazard involved in the modelled scenario(s). See Hazard
`hazard_secondary`		object
Secondary hazard type	The secondary hazard involved in the modelled scenario(s). See Hazard
`hazard_analysis_type`		string
Hazard analysis type	The type of analysis applied to the hazard data used in the modelled scenario(s), from the closed analysis type codelist.
`category`		string
Exposure category	The category of the exposed assets, from the closed exposure_category codelist.
`impact`		object
Impact	Information about how the impacts of hazards are calculated.
`impact/type`		string
Impact type	The type of impact calculated, taken from the closed impact_type codelist.
`impact/modelling`		string
Impact modelling	The type of data used to calculate the impact values, taken from the closed data_calculation_type codelist.
`impact/metric`		string
Impact metric	The metric used to describe the impact, taken from the open impact_metric codelist.
`impact/loss_statistic`		string
Loss statistic	The type of statistical summary applied to the loss values, from the closed loss_statistic codelist.
`impact/measurement`		object
Impact measurement	How the impact is measured.
`impact/measurement/quantity_kind`		string
Quantity kind	The kind of quantity by which it is quantified, from the open quantity_kind codelist.
`impact/measurement/unit`		string
Unit	The unit by which it is measured, taken from the unit codelist for the quantity kind.
`impact/measurement/valuation_year`		string
Valuation year	The year of the monetary valuation, expressed as a 4-digit year (YYYY). Applicable when quantity_kind is ‘currency’.
`taxonomy`		string
Exposure taxonomy scheme	The name of the taxonomy scheme used to create descriptive individual asset feature strings within the dataset, from the open classification_scheme codelist. Use of GED4ALL is recommended.
`analysis_details`		string
Analysis details	Additional details about the analysis used to produce the function used in the modelled scenario(s).
`damage_scale_name`		string
Damage scale name	The name of the damage scale used in the function, taken from the open damage_scale_name codelist.
`damage_states_names`		array[string]
Damage states names	The names of the damage states listed in the function.

Damage-to-loss function 

Title	Description	Type	Required
`id`		string	Required
Identifier	A unique identifier for the function. Use of an HTTP URI is recommended.
`approach`		string	Required
Approach	The approach the function is based upon, taken from the closed function_approach codelist.
`relationship`		string	Required
Impact relationship type	The type of function relationships used to calculate the impact values, taken from the closed relationship_type codelist.
`hazard_primary`		object
Primary hazard type	The primary hazard involved in the modelled scenario(s). See Hazard
`hazard_secondary`		object
Secondary hazard type	The secondary hazard involved in the modelled scenario(s). See Hazard
`hazard_analysis_type`		string
Hazard analysis type	The type of analysis applied to the hazard data used in the modelled scenario(s), from the closed analysis type codelist.
`category`		string
Exposure category	The category of the exposed assets, from the closed exposure_category codelist.
`impact`		object
Impact	Information about how the impacts of hazards are calculated.
`impact/type`		string
Impact type	The type of impact calculated, taken from the closed impact_type codelist.
`impact/modelling`		string
Impact modelling	The type of data used to calculate the impact values, taken from the closed data_calculation_type codelist.
`impact/metric`		string
Impact metric	The metric used to describe the impact, taken from the open impact_metric codelist.
`impact/loss_statistic`		string
Loss statistic	The type of statistical summary applied to the loss values, from the closed loss_statistic codelist.
`impact/measurement`		object
Impact measurement	How the impact is measured.
`impact/measurement/quantity_kind`		string
Quantity kind	The kind of quantity by which it is quantified, from the open quantity_kind codelist.
`impact/measurement/unit`		string
Unit	The unit by which it is measured, taken from the unit codelist for the quantity kind.
`impact/measurement/valuation_year`		string
Valuation year	The year of the monetary valuation, expressed as a 4-digit year (YYYY). Applicable when quantity_kind is ‘currency’.
`taxonomy`		string
Exposure taxonomy scheme	The name of the taxonomy scheme used to create descriptive individual asset feature strings within the dataset, from the open classification_scheme codelist. Use of GED4ALL is recommended.
`analysis_details`		string
Analysis details	Additional details about the analysis used to produce the function used in the modelled scenario(s).
`damage_scale_name`		string
Damage scale name	The name of the damage scale used in the function, taken from the open damage_scale_name codelist.
`damage_states_names`		array[string]
Damage states names	The names of the damage states listed in the function.

Engineering demand function 

Title	Description	Type	Required
`id`		string	Required
Identifier	A unique identifier for the function. Use of an HTTP URI is recommended.
`approach`		string	Required
Approach	The approach the function is based upon, taken from the closed function_approach codelist.
`relationship`		string	Required
Impact relationship type	The type of function relationships used to calculate the impact values, taken from the closed relationship_type codelist.
`hazard_primary`		object
Primary hazard type	The primary hazard involved in the modelled scenario(s). See Hazard
`hazard_secondary`		object
Secondary hazard type	The secondary hazard involved in the modelled scenario(s). See Hazard
`hazard_analysis_type`		string
Hazard analysis type	The type of analysis applied to the hazard data used in the modelled scenario(s), from the closed analysis type codelist.
`category`		string
Exposure category	The category of the exposed assets, from the closed exposure_category codelist.
`impact`		object
Impact	Information about how the impacts of hazards are calculated.
`impact/type`		string
Impact type	The type of impact calculated, taken from the closed impact_type codelist.
`impact/modelling`		string
Impact modelling	The type of data used to calculate the impact values, taken from the closed data_calculation_type codelist.
`impact/metric`		string
Impact metric	The metric used to describe the impact, taken from the open impact_metric codelist.
`impact/loss_statistic`		string
Loss statistic	The type of statistical summary applied to the loss values, from the closed loss_statistic codelist.
`impact/measurement`		object
Impact measurement	How the impact is measured.
`impact/measurement/quantity_kind`		string
Quantity kind	The kind of quantity by which it is quantified, from the open quantity_kind codelist.
`impact/measurement/unit`		string
Unit	The unit by which it is measured, taken from the unit codelist for the quantity kind.
`impact/measurement/valuation_year`		string
Valuation year	The year of the monetary valuation, expressed as a 4-digit year (YYYY). Applicable when quantity_kind is ‘currency’.
`taxonomy`		string
Exposure taxonomy scheme	The name of the taxonomy scheme used to create descriptive individual asset feature strings within the dataset, from the open classification_scheme codelist. Use of GED4ALL is recommended.
`analysis_details`		string
Analysis details	Additional details about the analysis used to produce the function used in the modelled scenario(s).
`damage_scale_name`		string
Damage scale name	The name of the damage scale used in the function, taken from the open damage_scale_name codelist.
`damage_states_names`		array[string]
Damage states names	The names of the damage states listed in the function.
`parameter`		string
Engineering demand parameter	The name of the engineering demand parameter, taken from the open engineering_demand_parameter codelist.

Socio-economic indicator 

Title	Description	Type	Format	Required
`id`		string		Required
Socio-economic indicator identifier	A locally unique identifier for this socio-economic indicator
`scheme`		string
Classification scheme	The classification scheme or framework from which the indicator is taken, from the open classification_scheme codelist.
`indicator_name`		string		Required
Indicator name	The name of the specific indicator or index used to characterize vulnerability.
`indicator_code`		string		Required
Indicator code	The code or identifier for the indicator within the scheme.
`description`		string		Required
Description	Description of what this indicator measures and how it relates to vulnerability to the hazard.
`threshold`		string
Threshold or value	Specific threshold or value range used to define vulnerability level (if applicable).
`reference_year`		integer		Required
Reference year	The year the indicator data applies to.
`uri`		string	iri
URI	A URI to the indicator definition or data source.
`analysis_details`		string
Analysis details	Additional details about the analysis used to produce the vulnerability function used in the modelled scenario(s).

Hazard 

Title	Description	Type	Format	Required
`type`		string		Required
Type	The type of the hazard, from the closed hazard type codelist.
`process`		string
Process	The process that resulted in the hazard, from the closed hazard process type codelist.
`intensity_measure`		string		Required
Intensity measure	The metric and unit in which the intensity of this hazard is measured, from the open intensity measure codelist for the hazard type.
`classification`		object
Classification	A classification of the hazard type against an external taxonomy such as the UNDRR Hazard Information Profiles.
`classification/scheme`		string
Scheme	The scheme or codelist from which the classification code is taken, using the open classification_scheme codelist.
`classification/id`		string		Required
Classification identifier	The classification code taken from the scheme.
`classification/title`		string
Title	A title for the classification code.
`classification/description`		string
Description	A description for the classification code.
`classification/uri`		string	iri
URI	A URI to uniquely identify the classification code.