Statistical and Decision-Making Support Model

1. Statistical and Decision-MakingSupportModel Concept and Methodology Presentedby Mr. Gábor Hámori airLED - Master class event in Bologna, 12-13 and 14 February 2014

2. The statistical decision support model as a part of airLED project, conducted by the responsible Partner, namely the municipality of Budapest district 18. The conception of the statistical model was approved by the responsible Partner. Current status: construction of the database.

3. Milestones of the conception

4. Milestone I • I. Statistical definition of the Impact zones. • Direct and indirect impact zones are in the scope of the model according to the Status Quo analysis. In the area of BUD Airport the direct impact zone consist of 6 Budapest districts 7 towns, and further 17 Budapest districts and 39 towns are related to the indirect impact zone. All together these 69 districts and towns are the observation units (statistical population) of the cross sectional database (2011 now). • In the database all units are marked by the impact zone status (direct or indirect).

5. Milestone II • II. Collecting the socio-economic dimensions (latent factors) and the related manifest variables of the impact zone of the BUD-Airport. • Issue of data collection: • Completness: Those data are appropriate which are available for all units, and all of the relevant data need to be collected. • Non-Redundancy : Avoid the redundancy among the information • Try to collect time series data for all towns included in the analysis.

6. Initial latent socio-economic variables

7. Initial manifest socio-economic variables

8. Initial manifest socio-economic variables

9. Initial manifest socio-economic variables

10. Initial manifest socio-economic variables

11. Milestones III-VI • III. Testing thedatabase, exploration of thedirection and theintensity of causualrelationshipbetweentheindicatorswith Structural Equation Modeling (SEM) metodology, filtering out the non relevantvariables, furtherdatacollectioninthecase of necessity, testreport . • IV. Definition of themodel, recalculation (refinement), documentationofthefinalstructureoftheindicators. • V. Time series analysis of statistical and ecomomicindicators of theairport. • VI. Time series analysis of thefinalsocio-economicindicatorsderivedfromtown(district)-database. • The finalsocio-economicindicators of theairportdata and thetown (district) databasemayrelateforthesametimeperiod.

12. Milestone VII • VII. The predictive model • Analysis of the time series of statistical and economic indicators of the airport and socio-economic indicator set of the towns (districts) in a joint modell • Forecasting of predictors • Estimation of the target variables of a certain town (local taxes, employment) by the forecasted predictors.

13. Transnational application of the model • The definition of the relevant socio-economic indicators of the impact zones and the related modeling activity based on different available hungarian database. • Collection of analogous indicators recommended for the participant countries in the project. These indicators may have local characteristics. • Following tables show the variables (indicators)proposed by the Status Quo evaluation.

16. Preliminary data collection concept for the partner region • The set of indicatorswill be finalizedonHungariandatabases. Inthisstepthe partner regionsdonothavetoprovideanykind of data. • The finalizedset of indicatorsrelatedtotheimpactzonewill be senttothe partner regions. Theseindicatorsshould be sentfortheavailabletimeperiodsthen. The indicatorsarenotcountry-specific, sotheylikelyto be availableineach partner region. • The forecastingmodelrequiresturnovertime series data of the project relatedpartner-airportsfortheavailabletimeperiod.