irport atchment rea atabase

Transcription

1 irport atchment rea atabase

2 Examples 9 Airports Eight range sizes: - By distance (, 7, 1 and 1 km) - By travel time (, 6, 9 and 1 minutes) Time series variables About ACAD The database contains catchment area information for 9 European airports with ranges defined by both distance and travel times. The information covers more than relevant variables over the course of 1 years. With each new release the amount of data grows as we include the most recent figures, expand the region covered and include more variables.

3 Why - Importance - Challenges - Benefits Examples The importance of catchment area data In the increasingly competitive airport industry, having good catchment area data has never been more important. SEO s ACAD offers its users solutions for: Benchmarking the most important drivers of air travel demand with peer airports Input for traffic forecasting studies Input for route feasibility analysis Important information in airline marketing initiatives

4 Why - Importance - Challenges - Benefits Examples Challenges of collecting high-quality catchment area data Data spread over various databases Modifiable Area Unit Problem: Data is only available at different spatial and administrative scales/sizes Somehow, regional statistical data (e.g. Eurostat NUTS) needs to be assigned to airports, but airports may be at the border of a certain statistical region, with a catchment area stretching over different statistical regions Time series may not be available Collection and crunching of data for + European airports is time consuming

5 Why - Importance - Challenges - Benefits Benefits of SEO s ACAD Examples SEO s ACAD provides a comprehensive, ready to use database that will release its users from the difficulties and costs paired with the time consuming processes of data collection, GIS analyses and data manipulation. The database is very intuitive and does not require any additional software, being compatible with most spreadsheet programs including Microsoft Excel, Stata, SPSS, etc.

6 Contents - Coverage - Variables - Methodology - Future updates Examples France United Kingdom Norway Spain Turkey Sweden Italy Greece Germany Finland Romania Portugal Poland Denmark Croatia Ireland Switzerland Austria Slovakia Netherlands Iceland Czech Republic Belgium Estonia Bulgaria Lithuania Hungary Slovenia Montenegro Macedonia Cyprus Malta Luxembourg Latvia Airport distribution The latest version of SEO s ACAD provides catchment area information for 9 European airports, distributed over countries*. In future versions, both the amount of airports and countries covered will be expanded. * Airports and countries included may vary with version of the database.

7 Variables* Contents - Coverage - Variables - Methodology - Future updates Examples Socioeconomic Population Total Active population Share of active population GDP Total Per capita Employment Total Rate in knowledge intensive sectors Tourism data Hotel beds total Hotel beds per capita Establishments total Hotel rooms total Occupancy of hotels Education Number of students Business development Number of enterprises Innovation R&D expenditure Number of patent requests Etc. Aviation Direct connectivity Total Per capita Indirect connectivity Total Per capita Hub connectivity Total Per capita Airport competition Total amount of competing airports Direct connectivity from other airports Indirect connectivity from other airports Seat capacity from other airports Propensity to fly in region Yearly OD trips per capita * Variables included may vary with version of the database.

8 Contents - Coverage - Variables - Methodology - Future updates Examples Methodology For the provision of regional data Eurostat uses the NUTS (Nomenclature of Territorial Units for Statistics) classification, which goes from NUTS- (country level) to NUTS- (most detailed). The data assigned to each airport s catchment area in SEO s ACAD database is calculated through GIS analysis. The adjacent figures display 1 population data on a NUTS- and NUTS- level around Amsterdam Schiphol airport as an example. Through GIS analysis, the level of population is assigned to the catchment area (in this example 1km) in a manner proportional to the area of each NUTS-region within said catchment area. Of course, the accuracy of the data assigned to each catchment area improves as the level of detail increases in the source of regional data (higher NUTS-level). Population 1 AMS (1 km.): NUTS-: 11,6,1

9 Contents - Coverage - Variables - Methodology - Future updates Examples Future updates We are looking into expanding the ACAD in the following areas: North America Large Cities Worldwide population coverage Data completion non-eu countries Additional variables (ethnicities, international corporate headquarters, etc.)

10 Travel time areas Contents - Coverage - Variables - Methodology - Future updates Examples Next to catchment areas defined by distances of, 7, 1 and 1 km, it is also possible to define catchment areas based on travel times by road. These new catchment areas provide information that is often more valuable for airlines and airports. The image on the right shows the travel time catchment areas for Luxembourg airport (LUX) for, 6, 9 and 1 minutes.

11 Population over time Examples - (1/8) Total (millions) %.7%.6%.%.%.%.%.1%.% With the ACAD users can easily explore catchment area developments over time. The above example shows the growth in population within 1 km of Amsterdam Schiphol airport over the course of 1 years. Yearly growth Population Amsterdam Schiphol Airport 1 km radius

12 Population development around airport 16% km 7 km 1 km 1 km ( = 1%) 1% 1% (millions) Population Amsterdam Schiphol Airport Examples - (/8) 1% Because all information within the ACAD is provided for four different catchment area sizes, it is possible to monitor developments in the airport surroundings at different levels. The figure above shows the population developments over 1 years for ranges of, 7, 1 and 1 kilometers.

13 Population around airports AMS CDG FRA EIN IST LHR MAD (millions) 1 Population 1 1 Examples - (/8) km 7 km 1 km 1 km With more than European airports, the ACAD allows its users to compare the catchment areas of different airports. The figure above shows the 1 population in each catchment area range for seven European airports.

14 Examples - (/8) Compare airports Munich MUC Hamburg HAM Hannover HAJ Frankfurt FRA Düsseldorf DUS Cologne Bonn CGN Population x Hotel beds X Enterprises X1. Because of its simplicity, ACAD users can quickly and easily gather all the necessary airport information to help make the right decisions. The figure above shows a quick comparison between the 1 km catchment areas of 6 German airports. With an increasing number of variables included in the database ( in the last version), the analysis possibilities and applications are extensive GDP Per capita,, 1,8 6,9,,9 6 airports (Germany) 11 1 km

15 Top European airports Examples - (/8) Population (1) 1 - Düsseldorf Weeze (NRN), DE - Eindhoven (EIN), NL - Maastricht (MST), NL - London Luton (LTN), UK - Birmingham (BHX), UK 6 - Düsseldorf (DUS), DE 7 - London Heathrow (LHR), UK 8 - Antwerp (ANR), BE 9 - Liege (LGG), BE 1 - Cologne-Bonn (CGN), DE 11 - East Midlands (EMA), UK 1 - Dortmund (DTM), DE 1 - London Stansted (STN), UK 1 - Brussels (BRU), BE 1 - London City (LCY), UK 16 - Münster-Osnabrück (FMO), DE 17 - London Gatwick (LGW), UK 18 - Southampthon (SOU), UK 19 - Doncaster-Sheffield (DSA), UK - Manchester (MAN), UK millions 1 1 th 6th rd 6th th 8th nd 19th 8th 1th 7th 1th 9th 16th 1st 6th th 7th th 11th km 7km 1km 1km Rank by population within km

16 Other examples Top airports with fastest growing GDP on European continent and daily flights GDP development Brno airport for periods until 1 % Zurich Airport 18% Brno Examples - (6/8) Daily flights 1 1 Dublin Malta Geneva Basel Suceava Shannon Cork Baia Pardubice Airport Bacau Ostrava Sibiu Craiova Brno Mare Waterford Kerry Knock Berne Belp Donegal % % % 6% 8% 1% 1% 1% GDP growth (1) GDP development ( = 1%) 16% 1% 1% 1% Average continental Europe 8%

17 irport atchment rea atabase Dashboards AMS 6 Schiphol (AMS) Netherlands (6) km Examples - (7/8) Demographic GDP Development Population Population,76,9, Per catchment area radius (selection marked in red), 6 6, Work force,71,7 68.1% Households,1,6 People w ith terciary education 91,7 16.7% Economic GDP (mln & per capita), 1,19 Employed,,6 6.8% Employed in high tech industry.% Unemployed 1,8.9% Tourism Hotel beds 67,.1 Hotel rooms,9.71 Hotel nights (per year) 1,761,1.178 Hotel nights by non-residents 8,7, % Business Development Labour market Airport Competing airports local business enterprise units - Employed (millions) Unemployment rate Connectivity (CNUs) Feeder value R&D expenditure - Patent applications 861 Households w ith broadband internet acces 1,,7 68.6% Aviation Direct connectivity, Indirect connectivity 9, Hub connectivity,.919 Direct connectivity (competing airports) 11 Competing airports (incl. airforce bases) 1 1, 1,, % 6% % % % Millions Direct Share of total flights provided 96.%

18 irport Contents Year 8 BRU GDP (growth wrt previous year) Population 6 Radius 1.% 1.8%.%.7%.% 1.% 1 - (8/8) BRU OST 6.% 1.% LGG.7% 6.8% ANR 66.%. 1. OST Employed.% 6.% 7.7%. 7.% % 1.% BRU CRL ANR LGG OST.1%.% 1.% -.% 1.% 1 BRU CRL ANR LGG OST Number of enterprises (growth wrt to previous year) % 1.1% Hotel beds and occupacy rate (growth wrt previous year) Unemployed.9% 18.% 1.1%.% 17.% 66.% LGG 1.% 18.6% 66.% BRU Contact ANR Terciary educ Work force CRL CRL OST 1 Examples LGG GDP per capita (growth wrt previous year) Airports ANR CRL Thousands Brussels Airport (BRU) Brussels S. Charleroi (CRL) Antw erp International (ANR) Liege Airport (LGG) Oostende/Brugge (OST) Thounsands Why Thousands rea Quick comparisons Millions About Millions 1 atchment.%.%.% 1 1.%.% LGG OST BRU CRL ANR atabase

19 Examples Please contact: Thijs Boonekamp SEO Amsterdam Economics +1 () More information? For a quotation or further questions, please do not hesitate to contact us.