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Regional Development and Environmental
Information System: EU ranking and benchmarking
(application example: Lower Austria)

System functionality

Startup page:
To enter the system, select one of the main menu icons:

Currently supported are:

  • Analysis function (ranking and benchmarking) for Europe (NUTS0-3);
  • Analysis function (ranking and benchmarking) for Austria (NUTS1 - Communities);
  • Analysis function (ranking and benchmarking) for Lower Austria (NUTS3 - Communities);
  • Object database (monitoring data for air quality);
  • Data management (ORACLE);
  • Simulation (air quality for major point sources);
  • Geographical background data (GIS);
  • Hypertext help- and explain function;
  • Exit.
The object ranking is based on:
  • The (sub)set of criterias selected, as well as any constraints defined for any of these criteria;
  • The value of primary indicators from the data base;
  • The value of any derived indicators, defined by rules of the expert system;
  • A multi-criteria method, that combines several primary or derived indicators with the possibility of weight factors, summarized as a % of "achievement" toward a (theoretical) optimum.
    The default ranking is based on the (multi-dimensional) distance (in hyperspace) of any object from the UTOPIA point in the current data set and criteria selected.

Geographical Information System

  • Selection and display of maps (overlays) in vector and raster formats including satellite imagery and orthophotos
  • Interactive selection of overlays and stacking sequence,
  • Arbitrary multi-level zooming
  • Parallel display of maps in four windows
  • Color editing of attributes/legend entries
  • 3D display of digital terrain model, shading draping with interactively selected overlays
  • Read-back of object classes and attributes, positions
  • Selection of objects from their map position (symbol display), leading to the data base and diaply page for that object.
Beyond the integration of maps as background or part of thematic, topical map display of distributed data, the GIS provides input data to spatially distributed models.

Analysis: selection of geographical scope

Display and analysis of indicators is possible for different geographical scope with different resolution for the underlying administrative entities:

  • Europe (NUTS0, NUTS1, NUTS2, NUTS3)
  • Austria (NUTS2, NUTS3, districts, communities)
  • Lower Austria (NUTS3, district, communities)
  • Any user defined symbolic geograhical or administrative classification.
  • Any grouping (user defined) of these objects.
In addition one can zoom into the geographical display for a more detailed view of any sub-region.

Analysis (ranking and benchmarking)

  • Selection of geographical scope and object class;
  • Selection of indicators;
  • Display of objects (e.g., districts) sorted by the indicator selected;
  • Display of the indicator as color coded thematic map.
Objects on the list-selector can be sorted ascending or descending, by
  • Indicator (or basic criteria) value
  • Object name
  • Object code

Benchmarking: TOP 10

Display of an indicator (primary or derived) against a reference object:

  • Display (ranking) of the TOP 10;
  • Display of the reference object;
  • Display of the spatial distribution of the indicator;
  • Histogram indicating the position of the reference object;
  • Ranking display with the position of the reference object
  • Statistical summary of the indicator.
Sensitivity analysis:

By toggling on and off individual criteria and indicator or changing the weights of their contribution, their effect on the ranking can be explored.

Statistical analysis

  • Selection of a transformation
    • truncated (without zero class)
    • logarithmic (log (x+1))
    • square root function
    • Power-Law (interactively selected exponent)
  • Definition of the number of classes
  • Selection of display style:
    • Histogram (equal distance)
    • Histogram (equal number)
    • Histogram cumulative
    • Ranking
    • Scattergrams (two criteria), coincidence and regression (paramettric and non-parametric methods).

Statistical analysis

Different histogram types

  • Frequency distribution
  • Cumulative frequency distribution
  • Frequency distribution with equal number per class
  • Position (rank) versus indicator value

For the display of rank versus descriptor value, any object can be selected from the display to show the number of objects above and below in the ranking. The reference object can be selected from the graph, or from a list of all objects.


Correlation and Coincidence

Two indicators can be compared directly with each other:

  • Display in a scattergram
  • Tabular comparison of the statistical data
  • Computation of the correlation coefficient (linear, Spearman
  • Parallel display of the spatial distribution

For ordinal and nominal variables, a coincidence matrix is constructed.

Both parametreic and non-parametric statistical methods are supported.

Dynamically generated indicators

In addition to the primary indicators in the data bases, indicators can be constructed dynamically by rules. the values are computed dynamically with the expert system, and can be displayed and analysed in the same style as primary data. The underlying rules of the expert system and their input values can be displayed for each derived indicator and object.

For the multi-criteria analysis, several indicators are combined, optionally using a weight factor for each. The ranking is then based on one of a set of distance functions measuring the multi-dimensional distance from Utopia as a reference point.


Object display (district, community)

Display of a district object (same for all NUTS classes); only available for Lower Austria (data constraint). Implemented with

  • list of indicators and their values
  • local map display
  • hypertext page with text and images

In the same style, other objects such as measurement stations, power plants, waste disposal and processing sites, industrial enterprises, schools, hospitals, etc. can be displayed.

Models and Analysis for Objects

For individual object classes specific functions are available such as simulation models and special analytical tools for object-specific data sets.

Examples are:

  • Soil data with the display of vertical profiles and spatial analysis;
  • A reachability matrix to calculate and display distances and travel times;
  • Simulation of population development (by age classes and sex).

Object display (measurement stations)

A special object class are monitoring stations of the air quality measurement network.

The stations are displayed as a list and in parallel, with symbols on the map.

Selection of a station will display its basic description (multi-media with text, imagery, optional video) and measurement data (one or more time series of monitoring data including the possibility of a real-time data link to the sensors) and provide tools for their analysis including spatial interpolation of all measurement values, complaince analysis, comparison between stations and parameters (see below).

Object display (measurement stations -
time series analysis)

The monitoring data (air quality) are displayed in two windows on top of each other:

  • The lower window shows the entire data set in the best possible resolution;
  • The upper window shows a (movable) subset corresponding to the window shown in the lower one, and according to the temporal aggregation level selected.
The raw data values can be aggregated and transformed based on the underlying distribution.

Object display (measurement stations -
time series analysis)

For the statistical analysis of the measurement data, a set of statistical analysis functions is supported:

  • Spatial interpolation
  • Tests for spatial homogeneity and patterns
  • Comparison of adjacent stations (spatial homogeneity)
  • Comparison with standards (regulatory compliance)
  • Temporal autocorrelation
  • Trend analysis (stability of mean and variance)
  • Comparison with model results (model validation, error statistics)

Simulation modeling and forecasting

For selected application domains, the system includes simulation models for scenario analysis and forecasting. Examples include:

  • Traffic-generated emissions/immissions from arbitrary street networks;
  • Air quality modeling for large point sources such as major industries, thermal power stations, and waste incinerators.

The results of the simulation modeling can then be used as additional dynamically generated attributes for the spatial objects such as communities and districts.



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