|Urban||% Total population||47.943|
|Rural||% Total population||52.057|
|Urban population growth||% Annual||3.002|
|Population density||People / km2||131.2|
|GDP (Gross Domestic Product)||Million US$||387,252.164|
|GDP per capita||Per capita US$||5,778.98|
|Capital stock||Million US$||1,378,999|
|GFCF (Gross Fixed Capital Formation)||Million US$||103,523.563|
|Social Expenditure||Million US$||57,352|
|Gross Savings||Million US$||110,630.112|
|Total reserves||Million US$||161,328.175|
For a disaster to be entered into the database at least one of the following criteria must be fulfilled:
SourceEM-DAT (Feb. 2015) - The OFDA/CRED - International Disaster Database http://www.emdat.be - Université catholique de Louvain Brussels - Belgium
CRED EM-DAT (Feb. 2015) : The OFDA/CRED - International Disaster Database www.emdat.be Université catholique de Louvain Brussels - Belgium.
|10-year moving average 2005-2014|
|Economic loss (,000 US$)||4,202,253|
Probabilistic risk assessment uses mathematical models to combine any possible future hazard scenarios, information about the exposed assets and the vulnerability, to provide results of an estimate of probable loss levels in a region of interest. Unlike historical estimates, probabilistic risk assessment takes into account all disasters that can occur in the future, including very intensive losses with long return periods, and does overcomes the limitations associated with estimated derived from historical disaster loss data.
Why does it matterProbabilistic risk assessment gives an overview of estimated losses, which can provide guidance to predict and plan for future losses. This information can be used to plan and prioritize investments and strategies for managing disaster risk.
Source UNISDR (GAR) - https://www.preventionweb.net/english/hyogo/gar/2015/en/home/
View morePreventionweb - Understanding Disaster Risk - Deterministic and probabilistic risk - https://www.preventionweb.net/risk/deterministic-probabilistic-risk
The Average Annual Loss is the expected loss per annum associated to the occurrence of future perils assuming a very long observation timeframe.
Why does it matterIt considers the damage caused on the exposed elements by small, moderate and extreme events and results a useful and robust metric for risk ranking and comparisons.
AAL Flood results are provisional. These results give an overview of the risk associated with river flooding. Factors other than the depth of the water also have a considerable influence on loss, which means that there is greater uncertainty compared with other hazards.
The Probable Maximum Loss is a risk metric that represents the maximum loss that could be expected, on average, within a given number of years.
Why does it matterPML is widely used to establish limits related to the size of reserves that, for example, insurance companies or a government should have available to buffer losses: the higher the return period, the higher the expected loss. PML always have associated a mean return period.
Mean return period of 100, 250, 500, 1000 and 1500 years means the 5%, 2%, 1%, 0.5% and 0.3% probability respectively of exceeding those losses in 5 years.
The INFORM model adopts the three aspects of vulnerability reflected in the UNISDR definition. The aspects of physical exposure and physical vulnerability are integrated in the hazard & exposure dimension, the aspect of fragility of the socio-economic system becomes INFORM's vulnerability dimension while lack of resilience to cope and recover is treated under the lack of coping capacity dimension.
SourceIndex for Risk Management 2019 (INFORM 2019) - Inter-Agency Standing Committee Task Team for Preparedness and Resilience and the European Commission- http://www.inform-index.org