SIENA data

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The text on this page is taken from an equivalent page of the IEHIAS-project.

SIENA provides the spatial data infrastructure to support health risk assessment in an urban setting. It consists of a collection of geographical data that simulate the physical feature and spatial interactions of an urban area.

Two different data types are the basis of the simulation. The first data type, the core data includes topography, transportation network, land cover and the urban population. Core data build the foundation of the urban system on which all other urban data rest. The second data type is contextual data which is added to provide context when carrying out applications with SIENA. Some contextual data is already included in SIENA, other application-specific data can be generated or added as needed. Additional data, so called derived data, can be generated within SIENA by running existing models, e.g. air pollution disperion model, using core and contextual data as input.

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SIENA can also be used to test models using the readily available data structure. It can further be used to run scenarios or to analysis processes that are difficult or impossible to analyse in a real-world setting. Examples of analysis carried out include the assessment of representativeness of different monitoring networks (in "See also".

The data in SIENA consists of point, line and polygon ESRI shapefiles which can be loaded into any GIS. Metadata for each folder and shapefile can be viewed in ArcCatalog or accessed here.


SIENA is modelled using a probabilistic modelling approach. Real-world cities around Great Britain are statistically explored in terms of their spatial distribution and interactions between the core data: topography, transportation network, land cover and population. Based on the results of this statistical analysis design rules are established which guide the construction of the core data.

The model used here is based on the assumption that urban features have a certain probability to occur at a certain location within an urban area based on their spatial pattern and their associations with other urban features. Probabilistic models are built that quantify the probability of a feature (e.g. a certain land cover class) to appear at a certain location in space based on probabilities observed in the real-world. Contextual data is modelled in a similar manner by establishing the probability for an event (e.g. a mobile phone mast) to occur at a certain location based on the spatial patterns of the core data.

SIENA uses various model parameters and approach as well as different sources of input data to simulate the urban data structure. For a detailed report of the model process please see document Building SIENA.

See also

Integrated Environmental Health Impact Assessment System
IEHIAS is a website developed by two large EU-funded projects Intarese and Heimtsa. The content from the original website was moved to Opasnet.
Topic Pages

Boundaries · Population: age+sex 100m LAU2 Totals Age and gender · ExpoPlatform · Agriculture emissions · Climate · Soil: Degredation · Atlases: Geochemical Urban · SoDa · PVGIS · CORINE 2000 · Biomarkers: AP As BPA BFRs Cd Dioxins DBPs Fluorinated surfactants Pb Organochlorine insecticides OPs Parabens Phthalates PAHs PCBs · Health: Effects Statistics · CARE · IRTAD · Functions: Impact Exposure-response · Monetary values · Morbidity · Mortality: Database

Examples and case studies Defining question: Agriculture Waste Water · Defining stakeholders: Agriculture Waste Water · Engaging stakeholders: Water · Scenarios: Agriculture Crop CAP Crop allocation Energy crop · Scenario examples: Transport Waste SRES-population UVR and Cancer
Models and methods Ind. select · Mindmap · Diagr. tools · Scen. constr. · Focal sum · Land use · Visual. toolbox · SIENA: Simulator Data Description · Mass balance · Matrix · Princ. comp. · ADMS · CAR · CHIMERE · EcoSenseWeb · H2O Quality · EMF loss · Geomorf · UVR models · INDEX · RISK IAQ · CalTOX · PANGEA · dynamiCROP · IndusChemFate · Transport · PBPK Cd · PBTK dioxin · Exp. Response · Impact calc. · Aguila · Protocol elic. · Info value · DST metadata · E & H: Monitoring Frameworks · Integrated monitoring: Concepts Framework Methods Needs
Listings Health impacts of agricultural land use change · Health impacts of regulative policies on use of DBP in consumer products
Guidance System
The concept
Issue framing Formulating scenarios · Scenarios: Prescriptive Descriptive Predictive Probabilistic · Scoping · Building a conceptual model · Causal chain · Other frameworks · Selecting indicators
Design Learning · Accuracy · Complex exposures · Matching exposure and health · Info needs · Vulnerable groups · Values · Variation · Location · Resolution · Zone design · Timeframes · Justice · Screening · Estimation · Elicitation · Delphi · Extrapolation · Transferring results · Temporal extrapolation · Spatial extrapolation · Triangulation · Rapid modelling · Intake fraction · iF reading · Piloting · Example · Piloting data · Protocol development
Execution Causal chain · Contaminant sources · Disaggregation · Contaminant release · Transport and fate · Source attribution · Multimedia models · Exposure · Exposure modelling · Intake fraction · Exposure-to-intake · Internal dose · Exposure-response · Impact analysis · Monetisation · Monetary values · Uncertainty