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58 changes: 29 additions & 29 deletions docs/about/governance.md
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Expand Up @@ -30,64 +30,64 @@ Additional documentation and source code produced by this initiative will also b

## Roles and responsibilities

This project is currently managed by the Risk Data Library team working within the
This project is currently managed by the Risk Data Library team working within the
[Global Facility for Disaster Risk and Reduction](https://www.gfdrr.org/en).

To guide development and adoption of the standards, in addition to open working through this github project, the team invites expert input via an Advisory Group and, as
To guide development and adoption of the standards, in addition to open working through this github project, the team invites expert input via an Advisory Group and, as
required, specific Technical Working Groups.

### Advisory Group

The RDL Advisory Group consists of experts in risk data management and analysis from
The RDL Advisory Group consists of experts in risk data management and analysis from
the humanitarian sector, insurance industry and other related domains.

The Advisory Group will help to:

1. ensure the development of the standard is informed by the needs of a broad selection
of stakeholders across a range of domains
2. steer the development of the RDL standards, providing expert review on priorities, activities and major technical decisions.
3. ensure that our work aligns with other relevant initiatives, to avoid
duplication of work and to build momentum around adoption of common standards
4. identify opportunities for joint projects that can demonstrate the value of adopting the
RDL standards
5. disseminate the impacts and outputs of the RDL project to other stakeholders

Participation in the Advisory Group is by invitation only. The initial membership will
consist of representatives from organisations who have contributed to the early
1. ensure the development of the standard is informed by the needs of a broad selection
of stakeholders across a range of domains
1. steer the development of the RDL standards, providing expert review on priorities, activities and major technical decisions.
1. ensure that our work aligns with other relevant initiatives, to avoid
duplication of work and to build momentum around adoption of common standards
1. identify opportunities for joint projects that can demonstrate the value of adopting the
RDL standards
1. disseminate the impacts and outputs of the RDL project to other stakeholders

Participation in the Advisory Group is by invitation only. The initial membership will
consist of representatives from organisations who have contributed to the early
development of the standard.

Membership will be regularly reviewed. Please contact the team to discuss participation if
Membership will be regularly reviewed. Please contact the team to discuss participation if
you believe that you, or your organisation, are able to contribute.

The Advisory Group will meet every two months. The agenda, slides and minutes from these
The Advisory Group will meet every two months. The agenda, slides and minutes from these
meetings will be publicly shared via this Github forum.

### Technical Working Groups

At times we may create specific Technical Working Groups that will collaborate on
development of specific aspects of the RDL standard. For example to develop taxonomies,
At times we may create specific Technical Working Groups that will collaborate on
development of specific aspects of the RDL standard. For example to develop taxonomies,
or revise file formats or schemas.

These Working Groups will have a clear remit and will coordinate their work through this
github.
These Working Groups will have a clear remit and will coordinate their work through this
github.

Membership of these groups and participation in calls and workshops will be open to
all participants. Depending on the scope of work, they may need to draw on expertise from a wider
group of stakeholders.
Membership of these groups and participation in calls and workshops will be open to
all participants. Depending on the scope of work, they may need to draw on expertise from a wider
group of stakeholders.

This model reflects the process of development of early versions of the standard, which involves
This model reflects the process of development of early versions of the standard, which involves
convening a range of organisations around developing models for hazard, exposure and other types
of data. This approach will help us to drawing on expertise, and seek focused input, from
of data. This approach will help us to drawing on expertise, and seek focused input, from
across a wide range of organisations.

All working documents, issue reporting and decisions made within these groups will be
All working documents, issue reporting and decisions made within these groups will be
reviewed and communicated via this repository.

<hr>

## How we collaborate

At present our primary means of collaborating on the standard is via a set of open processes
At present our primary means of collaborating on the standard is via a set of open processes
coordinated through this Github repository.

We will use the:
Expand All @@ -100,7 +100,7 @@ We will use the:

## Managing and proposing changes

Anyone can propose changes to the specifications.
Anyone can propose changes to the specifications.

The process for doing this is by [submitting an issue](https://github.com/GFDRR/rdl-standard/issues) using the appropriate template.
We ask that you provide sufficient context to the proposed changes so that it can be discussed and reviewed by others.
Expand All @@ -125,4 +125,4 @@ Releases of the standard will follow the [semantic versioning](https://semver.or

To support gradual migration to the latest specification, we will maintain historical versions of the specifications in named folders in this repository.

<br><hr>
<br><hr>
3 changes: 1 addition & 2 deletions docs/about/license.md
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The content of this guide was prepared by GFDRR under the Risk Data Library Project and is licensed under [Creative Commons Attribution-ShareAlike 4.0 International (CC-BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/legalcode).


<br><hr>
<br><hr>
2 changes: 1 addition & 1 deletion docs/about/roadmap.md
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A summary of any planned developments or improvements to the data model and standard.

<br><hr>
<br><hr>
8 changes: 4 additions & 4 deletions docs/data_model/exposure.md
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## Schema attributes

The exposure schema covers a wide variety of data describing structural, infrastructural and environmental asset, population, and socio-economic descriptors, each with relevant attributes for assessing risk from multiple hazards. The schema was developed based on [GEM Taxonomy 2.0](https://wiki.openstreetmap.org/wiki/GED4ALL) to accommodate the most important spatial features commonly employed in risk analysis to identify and estimate exposed value.
The exposure schema covers a wide variety of data describing structural, infrastructural and environmental asset, population, and socio-economic descriptors, each with relevant attributes for assessing risk from multiple hazards. The schema was developed based on [GEM Taxonomy 2.0](https://wiki.openstreetmap.org/wiki/GED4ALL) to accommodate the most important spatial features commonly employed in risk analysis to identify and estimate exposed value.

```{eval-rst}
.. mermaid::
Expand All @@ -25,7 +25,7 @@ The exposure schema covers a wide variety of data describing structural, infrast
```

The main features of an exposure dataset are specified by the **exposure model** attributes.
Each exposure model includes one or more **assets**. Each asset could represent a single asset (e.g. one building) or a collection of assets (e.g aggregated buildings in an area).
Each exposure model includes one or more **assets**. Each asset could represent a single asset (e.g. one building) or a collection of assets (e.g aggregated buildings in an area).
The exposure schema covers 4 categories and 11 occupancy types for consistent classification of assets across schema. The taxonomy source specifies the taxonomy string used to identify individual asset features within a dataset. Occupancy can be optionally assigned for night-time or day-time, e.g. to discern resident population from daily commuters.

|**Required**| **Attribute** | **Description** | **Type** |
Expand Down Expand Up @@ -60,7 +60,7 @@ Two exposure datasets are shown together in the example: building footprints pol
|*| Occupancy | Mixed |
|| Taxonomy | OSM |
|*| Value type | Structure |
|*| Unit of measure | USD |
|\*| Unit of measure | USD |

<br>

Expand All @@ -71,6 +71,6 @@ Two exposure datasets are shown together in the example: building footprints pol
|*| Occupancy | Residential |
|| Period of occupancy | Night |
|*| Value type | Other |
|*| Unit of measure | Count |
|\*| Unit of measure | Count |

<br><hr>
8 changes: 5 additions & 3 deletions docs/data_model/hazard.md
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Expand Up @@ -47,6 +47,7 @@ The hazard schema stores data about the intensity and occurrence probability of
Uncertainty
}
```

<br>

The schema specifies which type of analysis and data methodology that has generated the dataset. It supports either simulated probabilistic scenarios and empirical observations. If the dataset has been produced for a specific location, such a city, the name of the location can be included.
Expand All @@ -55,7 +56,7 @@ The schema specifies which type of analysis and data methodology that has genera
|:---:| --- | --- | --- |
|*| Hazard type | Main hazard type from list of options | <ul><li>Coastal Flood<li>Convective Storm<li>Drought<li>Earthquake<li>Extreme Temperature<li>Flood<li>Landslide<li>Tsunami<li>Volcanic<li>Wildfire<li>Strong Wind<li>Multi-Hazard</ul> |
|*| Analysis type | Type of analysis that generated the data | <ul><li>Deterministic<li>Probabilistic |
|*| Calculation method | The methodology used for the modelling of hazard | <ul><li>Simulated<li>Observed<li>Inferred |
|\*| Calculation method | The methodology used for the modelling of hazard | <ul><li>Simulated<li>Observed<li>Inferred |
|| Geographic area | Specific location for which the dataset has been developed | Name of location |

<br>When the scenario modelled refers to a specific period of time, this can be specified in terms of dates, period span and reference year. For example, an observed flood event that occurred from 1.10.2009 (time start) to 3.10.2009 (time end), spanning over 3 days (time span). When precise time collocation is unknow or inapplicabile, a general reference date such as "2009" is used to identify events (time year). This is also useful to specify future scenario, e.g. time year: 2050.
Expand Down Expand Up @@ -153,11 +154,12 @@ Schema attributes for flood hazard map related to occurrence probability of a ri
|| Occurence time (end) | 2001 |
|| Occurence time (span) | 44 years |
|*| Hazard process | River flood |
|*| Unit of measure | Water depth (m) |
|\*| Unit of measure | Water depth (m) |

<br>

### Earthquake hazard maps for Afghanistan

Schema attributes for earthquake hazard map related to occurrence probability of an event with return period of once in 1000 years over Afghanistan. The seismic data catalogue behind the calculation of occurrence probability start from year 800, covering a period of 1200 years. The hazard intensity is measured as Peak Ground Acceleration, expressed in (g).

![Screenshot](../img/hzd_eq_afg.jpg)
Expand All @@ -173,6 +175,6 @@ Schema attributes for earthquake hazard map related to occurrence probability of
|| Occurence time (end) | 2001 |
|| Occurence time (span) | 1200 years |
|*| Hazard process | Ground motion |
|*| Unit of measure | PGA (g) |
|\*| Unit of measure | PGA (g) |

<br><hr>
7 changes: 4 additions & 3 deletions docs/data_model/index.md
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Expand Up @@ -6,6 +6,7 @@ Following [key concepts](../keyconcepts.md), the Risk Data Library Standard sche
- [**Exposure**](exposure.md): asset category, occupancy and specific taxonomy, cost type and value.
- [**Vulnerability**](vulnerability.md): model that links hazard intensity and exposure classification to measure of impact over the total exposed value.
- [**Loss**](loss.md): modelled damage and losses produced in a risk assessment as a function of hazard, exposure and vulnerability components.

</ul>

The diagram below shows the core relationships between schema components, rather than listing all of their attributes.
Expand Down Expand Up @@ -48,8 +49,9 @@ The diagram below shows the core relationships between schema components, rather
-Loss metric
}
```

<br>

## Dataset general attributes

In addition to schema-specific attributes, each dataset is identified by a list of attributes based on <a href="https://www.dublincore.org/specifications/dublin-core/dcmi-terms">DublinCore metadata standard</a>.
Expand All @@ -65,7 +67,7 @@ In addition to schema-specific attributes, each dataset is identified by a list
| | Bibliography | Author, titles and publication year of documents containing relevant information on the dataset | Authors (Year) - Title; URL |
| | Version | Version of the dataset | Number |
|*| Geo coverage | ISO code(s) of countries covered | ISOa3 country code |
|*| License code | Type of license | Licensing options |
|\*| License code | Type of license | Licensing options |

<br>

Expand All @@ -81,7 +83,6 @@ Other attributes are specific to individual resources, covering level of aggrega
| | Format | ext |
| | Download Url | url |


<br><hr>

```{eval-rst}
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|| Occurence time (end) | 2001 |
|| Occurence time (span) | 1200 years |
|*| Hazard process | Ground motion |
|*| Unit of measure | PGA (g) |
|\*| Unit of measure | PGA (g) |

<br><hr>
82 changes: 41 additions & 41 deletions docs/data_model/vulnerability.md
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Expand Up @@ -65,34 +65,34 @@ The **specifics** attributes add more optional details.
<div class="scrollbar table-scroll" markdown="1">

| | **Field name** | **Description** | **Example** |
|:---:| --- | --- | --- |
| | par\_names | Parameters values names | MIDR , Ash depth | |
| | ub\_par\_value | Upper bound parameters value (Value1; Value2) | |
| | ub\_par\_perc | Upper bound parameters percentiles (Perc1; Perc2) | |
| | med\_par\_value | Median parameter values (Med1; Med2) | |
| | lb\_par\_value | Lower bound parameters value (Value1; Value2) | |
| | lb\_par\_perc | Lower bound parameters percentiles (Perc 1;Perc 2) | |
| | damage\_scale\_code | Code that identifies the damage scale | |
| | dm\_state\_name | Damage states studied in the reference study of the function | |
| | n\_dm\_states | Number of damage states studied in the reference study of the function | |
| | f\_disc\_im | Intensity measure values for the characterization of discrete functions | |
| | f\_disc\_ep | This field lists the associated exceeded probability values to the IM values of the previous field | |
| | lp\_code | | |
| | lp\_loss\_value | | |
| | edp\_code | Code related to specific engineering demand parameter (EDP) used to the DS thresholds | |
| | edp\_name | Specific engineering demand parameter (EDP) used to the DS thresholds | |
| | edp\_dmstate\_thre | Specific damage state EDP threshold | |
| | im\_code | Code of intensity measure | |
| | im\_name | Name of intensity measure | |
| | im\_range | Range of intensity measures as min;max (e.g. 0;500) | |
| | im\_units | Unit of intensity measure | |
| | im\_method | Type of source of the im data | |
| | im\_sim\_type | Type of simulation, Physics-based or IMPE | |
| | impe\_referenec | Reference study of the IMPE simulation | |
| | data\_countries | ISO code(s) of countries to which data refer | |
| | im\_data\_source | Reference studies for the IM data sources | |
| | n\_events | Number of events the function has been built on | |
| | n\_assets | INumber of assets the function has been built on | |
|:---:| --- | --- | --- |
| | par_names | Parameters values names | MIDR , Ash depth | |
| | ub_par_value | Upper bound parameters value (Value1; Value2) | |
| | ub_par_perc | Upper bound parameters percentiles (Perc1; Perc2) | |
| | med_par_value | Median parameter values (Med1; Med2) | |
| | lb_par_value | Lower bound parameters value (Value1; Value2) | |
| | lb_par_perc | Lower bound parameters percentiles (Perc 1;Perc 2) | |
| | damage_scale_code | Code that identifies the damage scale | |
| | dm_state_name | Damage states studied in the reference study of the function | |
| | n_dm_states | Number of damage states studied in the reference study of the function | |
| | f_disc_im | Intensity measure values for the characterization of discrete functions | |
| | f_disc_ep | This field lists the associated exceeded probability values to the IM values of the previous field | |
| | lp_code | | |
| | lp_loss_value | | |
| | edp_code | Code related to specific engineering demand parameter (EDP) used to the DS thresholds | |
| | edp_name | Specific engineering demand parameter (EDP) used to the DS thresholds | |
| | edp_dmstate_thre | Specific damage state EDP threshold | |
| | im_code | Code of intensity measure | |
| | im_name | Name of intensity measure | |
| | im_range | Range of intensity measures as min;max (e.g. 0;500) | |
| | im_units | Unit of intensity measure | |
| | im_method | Type of source of the im data | |
| | im_sim_type | Type of simulation, Physics-based or IMPE | |
| | impe_referenec | Reference study of the IMPE simulation | |
| | data_countries | ISO code(s) of countries to which data refer | |
| | im_data_source | Reference studies for the IM data sources | |
| | n_events | Number of events the function has been built on | |
| | n_assets | INumber of assets the function has been built on | |

</div>

Expand All @@ -104,19 +104,19 @@ The **additional** attributes cover more specific information that helps to unde

| | **Field name** | **Description** | **Example** |
|:---:| --- | --- | --- |
| * | nonsampling\_err | Is there sampling error? | NO |
| | type\_nonsampling\_err | Type of non sampling error | |
| | is\_fix\_nonsam\_err | Has non sampling error being fixed? | TRUE |
| | is\_data\_aggregated | Has data been aggregated? | FALSE |
| | is\_data\_disaggr | Has data been disaggregated? | TRUE |
| | n\_data\_points\_aggr | Number of aggregated data points used for the evaluation of data quality | 600 |
| | an\_analysis\_type | Type of analysis for Analytical functions | |
| | em\_analysis\_type | Type of analysis for Empirical functions | |
| | jd\_analysis\_type | Type of analysis for Judgement functions | |
| | is\_fit\_good | Is the model fitness to data good overall? | TRUE |
| | fit\_ref | Reference model for fitting |
| | val\_data\_source | If validation has been done, source of the independent data | |
| | val\_study\_reference | Reference of the Validation study | |
| * | nonsampling_err | Is there sampling error? | NO |
| | type_nonsampling_err | Type of non sampling error | |
| | is_fix_nonsam_err | Has non sampling error being fixed? | TRUE |
| | is_data_aggregated | Has data been aggregated? | FALSE |
| | is_data_disaggr | Has data been disaggregated? | TRUE |
| | n_data_points_aggr | Number of aggregated data points used for the evaluation of data quality | 600 |
| | an_analysis_type | Type of analysis for Analytical functions | |
| | em_analysis_type | Type of analysis for Empirical functions | |
| | jd_analysis_type | Type of analysis for Judgement functions | |
| | is_fit_good | Is the model fitness to data good overall? | TRUE |
| | fit_ref | Reference model for fitting |
| | val_data_source | If validation has been done, source of the independent data | |
| | val_study_reference | Reference of the Validation study | |
| | sample | Type of sampling | |

</div>
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Live istance of RDL JKAN catalogue available at [jkan.riskdatalibrary.org](https://jkan.riskdatalibrary.org).






<br><hr>
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