Descriptive Manual - hbofinger.comhbofinger.com/docs/Data Manual Version 2 Rev 09.pdf ·...

AFRICA INFRASTRUCTURE

COUNTRY DIAGNOSTIC

Descriptive Manual

Air Transport Sector Performance Indicators

Heinrich C. Bofinger

3

Contents I. Data sources for the AICD study .........................................................................................5

A. Primary Data: Nature and format of the SRS data extract ..............................................6

1. Size and structure of the extract .................................................................................6

2. Data adjustments and reference (lookup) tables ....................................................... 10

B. Data to be obtained through distribution of a questionnaire ......................................... 13

C. Contacts and additional data and sources ..................................................................... 16

1. List of indicators by data providers .......................................................................... 16

2. Contacts and sources ............................................................................................... 24

II. Data Analysis .................................................................................................................... 26

A. Data analysis using the Seabury SRS data analyzer ..................................................... 26

1. A Note on the technology and skills needed for replicating and continuing the AICD Air Transport Study ........................................................................................................... 26

2. Specific points in the analysis .................................................................................. 29

B. Listing of indicators by category ................................................................................. 32

1. Usage and access ..................................................................................................... 32

2. Institutions............................................................................................................... 38

3. Technical indicators................................................................................................. 40

4. Pricing data ............................................................................................................. 41

Appendix A: List of airports included in the analysis................................................................. 42

Appendix B: Sample of SQL code used for the analysis of SRS sata ......................................... 54

Appendix C: Freedoms of the air ............................................................................................... 56

Appendix D: Regional communities .......................................................................................... 59

5

I. Data sources for the AICD study

The primary data source for research in the aviation sector should be ICAO’s statistical web site. However, in many developing countries the data collection system, and additionally the submission systems for reporting such data to ICAO, are in desperate need, and the resources for making theses submissions happen simply seem not to be there. This is the case for most sub-Saharan countries. Though often the data exists somewhere with the authorities, they rarely are reported to ICAO as required by the ICAO charter. This makes data collection of actual passenger figures on a continental level much more difficult.

There are other data collection centers in the private sector that receive scheduling reports from airlines. Two companies, Official Airlines Guides (OAG), and Seabury's Aviation Data Group, collect submissions from airlines on their schedules. These submissions then feed into reservation systems and other end applications, such as the flight scheduling screens found at airports. Though this data does not contain passenger numbers, it contains amongst other rich elements the actual seat capacity of the flights, so a supply of seats can be determined. Since not many airlines would fly aircraft that are empty or at load factors that are not sustainable for a very long time, the seat capacity can be used as an approximation of actual passenger travel, particularly when examining route trends.

The AICD Air Transport infrastructure study depends on several sources of data. The most comprehensive source, however, is the data acquired from Seabury Aviation Group’s SRS, an IATA certified data provider. This data, similar to that sold by the Official Airline Guide (OAG), covers about 99% of all airlines. However, it must be cautioned that the 1-2 percent of airlines not covered are actually the ones found mostly in the developing world, providing domestic services.

Since a majority of the indicators are derived from these SRS extracts, an explanation of this data is presented here before any additional details on the indicators will be explained.

6

A. Primary Data: Nature and format of the SRS data extract

1. Size and structure of the extract Since the data found in the SRS analyzer is voluminous and needed to be kept to a manageable size, and also due to cost constraints, targeted “full extracts” were made for the years 2001, 2004, and 2007 using the SRS web interface, filtering for all traffic involving Africa. In order to capture seasonality, the data was extracted for one week in the month of February of each year, with the same for May, August, and November, each extract creating a separate Excel file. The resulting dataset is a table of 107,100 records with 17 fields (columns), resulting in a total of over 1.8 million data points. If this exercise were to be repeated annually, it would be best to stick with those targeted months, again allowing for four extracts per year.

The columns of the retrieved raw data tables in Excel are structured as shown in Figure 1 below. For the report, the extracts were consolidated and then stored in MS Access, and processed and queried using the MS Access SQL interface. When the rows where combined, the month and year each row belonged to were added to in two columns (“Year” and “Month”) to exch of the Excel sheets, since the extracts themselves do not have such columns. This step would need to be repeated for any future exercise.

In the future, as the dataset grows, it might be advisable to use MS SQL as a data backbone, with perhaps MS Access as the user interface.

A general data diagram for the final structure of the database is shown in Figure 2 below, followed by a description of the process for obtaining the structure.

7

Text Box 1: Nomenclature used with regards to the SRS data The AICD study uses the phrase "estimated advertised scheduled seats" for most capacity analysis. This careful working was constructed in order to highlight some of the shortcomings of the data, and what it does not represent.

"Estimated": Seats are estimated because of two reasons: (1) The data is extrapolated from only four weeks of data per year. Seasonality is captured by the location of those weeks - each is in the second month of each quarter. (2) The data has been adjusted for multi-legged flights, where possible, with rough assumptions, as described earlier.

"Advertised": There are a number of airlines in domestic markets that do not advertise their flights through normal channels, such as on-line booking agencies, or are part of any reservation system. In addition, though these airlines may call themselves non-scheduled, they may be acting as a scheduled carrier to fill a market niche. The data only reflects airlines that partake in the most formal systems, and that appear in airport announcement panels.

"Scheduled": Charter operations are generally not included. Only formally declared scheduled services are included.

"Seats": The data does not represent actual passenger flows, just capacity, as measured in seats.

When possible, the data was compared to actual passenger flows, though this could only be done in a few instances. The comparison showed that the actual passenger figures were roughly 65 to 69 percent of the capacity figure - a realistic load figure that approximates the industry. However, these figures could only be verified in international flights. Due to the informal nature of many domestic service providers, it is very likely that the SRS data is underestimating passenger flows in some areas of Africa. In addition, the rise of fuel prices has led to higher load factors overall, which means that the actual growth rates of passenger flows may have been even higher than the growth rate in capacity.

8

Figure 1: A record (data line) in the Seabury SRS Data Analyzer extract used for the Air Transport Infrastructure portion of the AICD study

AF SKYT AF DLA NSI 129 840 0 313 167 1715 1800 45 .....6. 1 167 0 504 504 0

Origin Airport (IATA code)

Via Airport (if applicable) (IATA code)

Destination Airport (IATA code)

Aircraft Type

No. of seats

Departure time

Arrival time

Block minutes flying time

Airline (IATA code)

Alliance

Operating Airline (IATA code)

Miles

Flight Number

No. of stops

Day of week

Total flights in week

Total seats in week

Not used

9

Figure 2: Data Diagram of the database constructed from the SRS data after modifications had been made. Some fields that are not being used have been omitted.

Airline Master Table

Airline ID Country ID Other airline information unique for each airline

Equipment Master Table

Equipment Code Other aircraft information unique for each aircraft

City Pairs Master List

City Pair ID City 1 City 2

Adjusted and Complied SRS Extract Table Airline

ID Operating Airline ID

Origin Airport

ID

Via Airport

ID

Destination Airport ID Miles Flight

Number Equipment

Code Dep. time Arr.time Block

minutes

Adjusted Seats per

Week

City Pair ID

Country Pair ID Month Year

Airport Master Table

Airport ID Country ID Other airport information unique for each airport

Country Master Table

Country ID Region Other airline information unique for each country

Country Pairs Master List

Country Pair ID Country ID 1 Country ID 2

10

2. Data adjustments and reference (lookup) tables Once imported, several steps need to be taken to make necessary adjustments and complete the dataset.

a) Additional flags and descriptive data Two fields where added and filled to simplify processing, namely a yes/no flag for international flights, and a yes/no non-stop flag for one-legged flights.

b) Multi-legged flights Seven percent of seat capacity in 2007 (over 10% in 2001) was found to be in flights that had multiple stopovers. For example, a hypothetical flight that would go Addis - Nairobi - Kilimanjaro - Dar es Salaam would have multiple records:

1. Addis Ababa via Nairobi to Kilimanjaro 2. Nairobi to Kilimanjaro 3. Nairobi to Dar es Salaam via Kilimanjaro 4. Kilimanjaro to Dar es Salaam.

Since the SRS dataset is limited to only one intermittent stop, there would be no record stating Addis to Nairobi to Kilimanjaro to Dar es Salaam (two stopovers).

If one simply tried to add the numbers for each flight and tried to assign them to the route the origin and destination indicated, one would ignore the fact that, for example, a large part of the passengers boarding in Addis Ababa could be bound to Dar es Salaam. Though not a perfect solutions by any means, the data was adjusted by dividing the original capacity of the aircraft by the number of destinations. This methodology presents an undercount, since it ignores that passengers are boarding at the intermediate stops, and assumes an even distribution. In addition, a flight with multiple legs was defined as being the same flight number, with the same operating carrier, the same equipment, and the same capacity, operating on the same day. This implied that flights operating over midnight were not included - small percentage of the flights.

In order to repeat the exercise, two new fields would be added to the data table: “AdjustedSeats” (for the total adjusted seats per week), and “Legs” for the total number of legs (or destinations) per record. These fields would then need to be populated, using a series of SQL queries identifying the multi-legged flights, and placing the result of dividing the total seats per week by the number of legs into the “Estimated Seats” field.

c) Airline master list Airlines in general are identified by their IATA indicator - a two letter (alphanumeric) code that has two disadvantages:

11

1. The code can be used for two entirely different airlines in different regions, and 2. The code can be re-assigned to a new airline when on old airline with the code becomes defunct.

In data technical terms, the IATA code does not represent a solid "key field" on which a relational table could be built. This meant that an airline master list had to be built, using various sources, where each airline, beyond the IATA code, also had a unique numeric identifier. When "linking" this table with the IATA code to the main data table, duplications of the main data record would occur wherever the IATA code could identify more than one airline. These where captured, and subsequently correct numeric airline identifiers where added to every flight record, making the numeric airline identifier the key field for linking flight records to airline analysis. Considerable manual scrubbing of the airline data had to be performed, especially with the inconsistency of the IATA code over time, and the use of the ICAO airline identifier (three alphanumeric letters) being used when the airline had no IATA code. The maintaining and scrubbing of an airline master list would need to be continues for repeating this exercise, though it can be assumed that this process will be much less daunting than it had been for the original study, since most of the historical data has already been cleaned up.

d) Airports master list Airports enjoy a three letter IATA identifier that is truly unique. However, many airports have very little traffic, so in constructing an airports master list, many identifiers had to be researched in order to get the actual location and related city name. Several lists using the IATA identifier were merged to create a master airports list, containing all pertinent information, including the geographic coordinates. This allowed for more data gathering beyond the data provided by the SRS Analyzer System. However, because of the consistency of the IATA airport identifier, no additional unique keys had to be created for airports. In the future, new airports not found in the airports master list created for this report would need to be identified and added.

e) City, country, and regional community pairs

(1) City pairs One complication in determining route data was the exponential nature of city pairs. The data extracts do not show city pairs in a consistent manner, since a particular pair can show up with airport A as either the origin or the destination, with airport B showing up as either the destination or the origin. In order to capture city pair information, two numeric tables were created. The first table contained three columns: One with a unique city pair id number, and the two others with the actual city id numbers (a city table, with a city id number, was created from the airports master list. The airports master list itself could not be used, because some cities have more than one airport, and cities needed to be uniquely identified). This city pair id table had one important feature: If cities A and B where a pair, it would appear only once, i.e. there might be an entry for B and A, but then, no entry with A and B.

12

However, since in the dataset cities (as identified through airports) could show up as either origins or destinations, a second table had to be created, where each city pair id appeared twice, with the original order and the reverse order of the cities under the same city pair id number. This then allowed for the assignment of a unique city pair id to each of the records, permitting analysis of city pair numbers, and their capacities.

Since the composition of pairs is mathematically an exponential problem, and the analysis had to follow a rigorous process, the city pair table created included all potential city pairs from the dataset, not just those actually found. This meant that a total of 6,217,696 unique city pairs were identified for Africa.

The report used city pairs to analyze traffic patterns within Africa, however, for intercontinental travel summarizing by country pairs, rather than by city pairs, seemed more applicable.

(2) Country pairs A similar process was used for creating country pairs. Two numeric tables were created in s similar fashion, though with fewer records (24,754). In a similar fashion as described above, this table, together with a second table of duplicate length, allowed for assigning unique country pair ids to each international flight. For domestic flights, the country pair id was simply left blank.

(3) Regional Community Pairs For traffic within regional communities, and traffic amongst regional communities, a lookup table was created giving each regional community an ID number and a name. Beyond the currently known regional communities, groupings for all of North Africa, all of Central Africa, and an expanded version of the EAC including Sudan and Ethiopia were created. Beyond an additional table which then listed all members of a community by their country ID, a table was created listing all possible regional community pairs, with their respective countries. Since some countries were members of both regional communities in a regional community pair, they were identified and then excluded from the regional community pair when summarizing traffic between the pairs.

f) Equipment master list The equipment codes found in the SRS data where used to create an aircraft type master table, with capacities, size categories, and age categories where applicable. For simplicity in processing, the age categories were "frozen" in time, i.e. an aircraft considered old in 2007 was also categorized as old in 2001.

13

B. Data to be obtained through distribution of a questionnaire Several important issues on infrastructure are not being collected centrally, and are therefore difficult to analyze on a continent-wide basis. However, the data should be readily available at the applicable CAAs or airport authorities.

The following questionnaire is suggested for collecting additional data. The questionnaire should be given to the CAAs of each country in the African continent, with the awareness that the airport investments are most likely determined by the respective airports authority.

Since gathering solid returns from questionnaires can be challenging, this questionnaire has been kept as simple as possible. However, some of the data points, such as needed land-side investments (terminals), cannot be gotten through any other sources, so though the questionnaire has been kept simple, the information gathered through this should give a more comprehensive view of mostly land-side infrastructure needs not found elsewhere.

14

Country:__________________________________________________ Date:__________________

Main International Airport Official Name: _________________________________________________

IATA Identifier: _________________

Number of Terminals:

_______ Domestic

_______ International

_______ Mixed use

Domestic Passengers for Year:: _______________ Intl. Passengers for Year:______________

Domestic Terminal Passenger Capacity per Year: ________________

International Terminal Passenger Capacity per Year: ________________ Planned terminal investments (specify currency):

Within five years:____________________________

Within ten years, starting after five years:___________________________ Are there any PPP’s planned for airport capacity expansion (circle one): YES / NO

Planned airside investments (specify currency):

Within five years:____________________________

Within ten years, starting after five years:___________________________

15

Charges & Revenues (please specify currency):

Domestic passenger charges per flight/ticket:_____________________________________

International passenger charges per flight/ticket: _________________________________

Aircraft Landing Charges and parking charges at main international airport:

Aircraft Type

Passenger Capacity

Landing

Parking grace period

Parking charges

Other

Total revenues from airport charges for year: _______________________ Currency: __________ Note: Please show total revenues charged to airlines, though they may be distributed between the airports authority, the CAA, or a ministry. Revenues charged, but not deemed collectible, should be excluded.

Total overflight revenues for year :__________________________ Currency: ______________

Current cost of Jet-A at main international airport in US$: ____________ kg / litre / gallon / lbs (circle measure)

Traffic:

Number of domestic passengers for year (country wide): _____________________ Year: ________

Number of international passengers: _____________________________________ Year: ________

16

C. Contacts and additional data and sources

1. List of indicators by data providers Indicators calculated using the Seabury SRS extracts are listed in Table 1 below.

Table 1: Indicators relying on data from Seabury

Policy Category

Series Code

Indicator Type Indicator Description Measurement

Usage Intercontinental Market Size

Defined as the sum of all traffic at individual African airports that includes airports outside North and Sub - Saharan Africa (such as Europe) as either an origin or destination.

Sum of Adjusted Seats per period

Usage Intercontinental

Market Growth Defined as the geometric growth rate of indicator above.

Calculated on the sum of Estimated Seats

Usage International Market Size - Africa

Defined as the sum of all traffic at individual airports in Africa that includes airports outside the individual airport’s country, but within Sub-Saharan and North Africa.


Usage International Market

Growth –Africa Defined as the geometric growth rate of indicator above.


Usage Intercontinental Market Size - North Africa

Defined as the sum of all traffic at individual African airports in North Africa that includes airports outside North and Sub - Saharan Africa, such as Europe, as either an origin or destination


Usage

Intercontinental Market Growth - North Africa

Defined as the geometric growth rate of indicator above.


Usage International Market Size - North Africa

Defined as the sum of all traffic at individual airports in North Africa that includes airports outside the individual airport’s country, but within (and only within) North Africa.

Sum of per period

Usage

International Market Growth - North Africa



17

Usage

International Market size - between Sub Saharan Africa and North Africa

Defined as the sum of all traffic at individual airports in Sub-Saharan Africa that includes airports outside the individual airport’s country, but within (and only within) North Africa, or vice versa.


Usage

International Market growth - between Sub Saharan Africa and North Africa



Usage

Regional Economic Communities - international traffic market sizes within

Defined as the sum of all traffic at individual airports in Africa that includes airports outside the individual airport’s country, but within African regional economic community(ies) the country is located in.


Usage

Regional Communities - international Market growth within

Defined as the geometric growth rate of indicator (12) above.


Usage

Regional Communities - type of service (5th, 6th, and 7th freedom)

As shown in table 1.8 of the main report, defined as the percentage of estimated seats for international country pairs between African countries within the same regional economic community, where the airline providing the capacity is based in neither country

Calculated on the sum of Adjusted Seats

Usage Per Country - type of service (5th, 6th, and 7th freedom)

Defined as the percentage of estimated seats for international country pairs between African countries, where the airline providing the capacity is based in neither country


Usage Airport Specific

Traffic Summation of estimated seats per airport

Sum of Adjusted Seats

Usage Airport Traffic by Time Slot

Summation of estimated seats according to time slots (time slots being defined as hourly with 25 intervals. Since the SRS data set has both the departing and arriving time per flight in local time, the count of activity per airport per timeslot can be calculated by summing both the arriving and departing seats for the same hour per airport.)


18

Access Airlines serving country (Country Annex) in year

Defined as the number of airlines that have any capacity in a country at a given date or during a given period.

Count of Airline IDs

Access

Airline overall market share in a given country in year

Defined as the airline’s total number of estimated arriving and departing seats divided by the country’s total number of arriving and departing estimated seats


Access

Airline domestic market share in a given country in year

Defined as the airline’s total number of estimated arriving and departing seats divided by the country’s total number of arriving and departing estimated domestic seats


Access Airline international market share in a given country

Defined as the airline’s total number of estimated arriving and departing seats divided by the country’s total number of arriving and departing estimated domestic seats, where either the origin or the destination is another country


Access Herfindahl Index for domestic air transport

Calculated using individual airline’s total estimated seats for a given year divided by the country’s total domestic estimated seats to obtain market share.


Access Connectivity - City Pairs, end of year

Calculated by counting the unique number of city pairs in the last extract for the year

Origin and destination pairs, single and multi-legged flights

Access Connectivity - Country Pairs, end of year

Calculated by counting the unique number of country pairs in the last extract for the year


Access Connectivity -

Country Matrix

Complex crosstab query and spreadsheet manipulations based on country pair IDs

Average Speed (km/h)

Access Connectivity -

Country Matrix

Complex crosstab query and spreadsheet manipulations based on country pair IDs

Number of Flights

Access Total number of airports (estimated) in Africa

Count of airports found by extracting web site tables and placing them into a combined database table

Number of airports (records)

Technical

Capacity in specified Aircraft size categories for year

Calculated by summing the estimated seats grouped by equipment codes. Note: A size grouping parameter needs to be added or updated to the table containing the equipment codes.


19

Technical

Capacity in specified Aircraft age category for year

Calculated by summing the estimated seats grouped by equipment codes. Note: An age grouping parameter needs to be added or updated to the table containing the equipment codes.


20

Indicators from the new questionnaire are listed below in Table 2:

Table 2: Indicators depending on the new questionnaire included in this manual

Policy Category

Series Code

Indicator Type Indicator Description Measurement

Usage Number of domestic passengers for year

Collected from the country’s CAA or airports authority

Passenger count

Usage

Number of international passengers for year

Collected from the country’s CAA or airports authority

Passenger count

Usage

For main country airport: Overall domestic passengers for year

Overall domestic passenger traffic for year

Sum of the total number of domestic passenger for the year at the main airport

Usage

For main country airport: Overall international passengers for year

Overall international passenger traffic for year

Sum of the total number of international passenger for the year at the main airport

Access

Per Country: for main country airport – Number of terminals, domestic

Count of the number of domestic terminals at the main airport Count

Access Per Country: for main country airport – Number of terminals, international

Count of the number of international terminals at the main airport Count

Access

For main country airport – Number of terminals, mixed use

Count of the number of mixed use terminals at the main airport Count

Access For main country airport: Overall Domestic Terminal capacity per year

Overall annual capacity of domestic terminal (s) (not peak hour capacity)

Annual passenger capacity

Access For main country airport: Overall International Terminal capacity per year

Overall annual capacity of international terminal (s) (not peak hour capacity)


Institutions

Total revenues from overflight charges for year

Total for fees collected for overflights for year, either collected through IATA or otherwise

Number in U.S. $

Institutions Total revenues from airport charges for year

Total for parking and landing charges, or any other charges to the airline that goes in part to the airports authority

Number in U.S. $

Institutions Planned terminal investment for the next five years

Total amount of planned improvements in country’s main airport terminals for the next five years.

Sum of US$

Institutions Planned terminal investment for the subsequent five years

Total amount of planned improvements in country’s main airport terminal for subsequent five years.

Sum of US$

Institutions

Are there any PPP’s being planned for terminal capacity

This is a binary question showing if the airports authority feels it has the option of involving the private sector

Yes/No

21

expansion? in airport terminal investments.

Institutions Planned airside investments in the next five years

Total amount of planned improvements in country’s main airport runways, surveillance equipment, navaids, taxiways, and aprons for the next five years.

Sum of US$

Institutions Planned airside investment for the subsequent five years

Total amount of planned improvements in country’s main airport runways, surveillance equipment, navaids, taxiways, and aprons for the subsequent five years.

Sum of US$

Pricing Domestic passenger

chargers per ticket

Fee added to every passenger traveling domestically, be it on the ticket itself, or at the airport

Number in U.S. $

Pricing

International passenger chargers per ticket

Fee added to every passenger traveling internationally,, be it on the ticket itself, or at the airport

Number in U.S. $

Pricing Fuel Price at country

main airports

Fuel costs per gallon, in cents, to for global comparison with IATA’s fuel statistics

US Cents per Gallon

Pricing

Par main airport for each country, the average parking charges per seat

Calculated by dividing the parking charges per aircraft by the number of seats per aircraft and finding a weighted average per seat over all aircraft types listed,

US$

Pricing Average parking grace period

Average of grace periods for parking charges for commercial aircraft. Hours

22

Indicators involving other institutions are found in Table 3.

Table 3: Indicators involving data from other Governmental and Non-Governmental institutions

Policy Category

Series Code

Indicator Type Indicator Description Measurement Source

Institutions Continental Accident Rate

Western-built aircraft hull loss rate per million flights of airlines that are IATA members.

Hull losses per 1 million flights

IATA

Technical African Airlines passing IOSA audit

Manual match of airlines in the SRS system with airlines in IATA’s registry

Count of african airlines existing in the IATA IOSA registry

Usage Passenger numbers

For limited specific airports, for specific periods, for cross-checking SRS data

Passenger count

ICAO

Institutions Overall Safety Grading

Overall compliance to ICAO’s Standards and Recommended Practices as found in the latest ICAO USOAP audit for the country

Compliance ration

Institutions EU Blacklist Airline of country listed in Blacklist Yes/no EU

Institutions

FAA rating of country safety oversight

Can be either (a) not rated, (b) category 2 (fail), or (c) category 1 (pass)

FAA IASA audit grade FAA

Institutions

Worldwide investments occurring as of December 2007, in excess of US$ 500 million

May require a subscription for the survey US $

ACI Airport Economics Survey 2007

Access Country per capita capacity

Calculated using WDI’s population with the same year’s matching total estimated seats

Country population estimate

World Bank (WDI)

Institutions

Private Sector Participation in Airports/Airlines

Found in the PPIAF database. US$, date World Bank (PPIAF)

23

Indicators from other web sites and sources are found below:

Table 4: Indicators from other web sites and sources

Policy Category

Series Code

Indicator Type Indicator Description Measurement Source

Usage Overall Market Growth

Since 1997 (see Report Figure 1.1)

Revenue Passenger Kilometer (RPK)

Boeing

Access Total number of airports (estimated) in Africa

Count of airports found by extracting web site tables and placing them into a combined database table


www.aircraft-charter-world.com

Access Terminal Capacity

This database has some figures on terminal capacity. The questionnaire found in the indexes below should provide a significant augmentation of the data.

Passengers per annum for terminal

azworldairports.com

Technical Airports - number of runways

Manual count of runways of all airports in the SRS dataset. Count

Jeppensens, Google, Wikipedia, GoogleEarth, azworldairports.com, worldaerodata.com

Pricing Route tariffs Sample tariffs for routes US $, Euros www.opodo.com, www.expedia.com

24

2. Contacts and sources

Seabury SRS:

The Advanced Data Group of Seabury can be found at the following web site:

www.airlineplanning.com

The sales contact for the World Bank has been

Jordan Kayloe +1 703-748-5307

Boeing: Boeing issues an recurring analysis and forecast for the commercial aircraft market, which also looks at passenger flows. This report can be found at

http://www.boeing.com/commercial/cmo/highlights.html

However, for the AICD study back issues were needed that were not available electronically. In addition, the segmenting of passenger figures over regions uses different definitions for the regions over time. Contacting Boeing directly regarding back issues was very productive. IN addition, raw data from Boeing is also available at the site.

ICAO: ICAO has an extensive statistics department, with a web interface for their data found at http://icaosec/icao.int

A user name and password needs to be obtained by contacting ICAO. The statistics available via ICAO are the most official, however, they are very limited for the Africa region, since most countries do not report on a regular basis. IATA: For safety audit results of airlines, see the complete IATA IOSA registry found at

http://www.iata.org/ps/certification/iosa/Registry?Query=all

Additional safety information can be found at their web site at

http://www.iata.org/pressroom/facts_figures/fact_sheets/safety.htm

25

For travel pricing: Several web sites were used for finding pricing on specified routes. Two in particular were used:

www.opodo.com

www.expedia.com

For airport information:

To help develop a comprehensive list of airports www.aircraft-charter-world.com was a very helpful site.

For additional infrastructure, Jeppensens, azworldairports.com, and worldaerodata.com are usable sources. azworldairports.com has some terminal capacity information for some airports.

For airline information:

www.iata.com

Wikipedia

Other information, including economics:

World Bank’s WDI www.aircraft-charter-world.com PPIAF ACI Airport Economics Survey 2007

Possible future sources of data: Airport Authority Questionnaire Official Airline Guide OAG (competitor to Seabury SRS) ICAO (particularly the USOAP safety audits)

26

II. Data Analysis

A. Data analysis using the Seabury SRS data analyzer

1. A Note on the technology and skills needed for replicating and continuing the AICD Air Transport Study The Air Transport component of the AICD study is based to a large extent on highly granular data retrieved from a professional data provider. Though ICAO is considered the mainstay of official data collection in the industry, many countries in client regions report their passenger data to ICAO on a regular basis, making time series analysis virtually impossible.

Since the majority of the data for the ACID study came from very large data tables, structure query language (SQL) became of essence for mining and research.

A replication or continuation of the exercise requires knowledge in relational theory and writing SQL queries. For the analysis, MS Access was used, which is quite robust and can easily handle the applicable datasets of 100,000 – 200,000 records. The query interface in MS Access speeds up the analysis process considerably, however, especially when considering union queries, the capability of hand-typing SQL queries, and a robust knowledge of relational theory (normalization for clarity and denormalization for performance), is required. In many of the analysis temporary sub-tables needed to be created in order to reduce calculation time, and the initial structuring and cleaning up of relational tables require a comprehensive understanding of key fields, the effect of (and identification of) duplications in records, and the vital functions of lookup tables.

Appendix B: Sample of SQL code used for the analysis of SRS sata of this manual presents a set of queries looking for a simple time series in the number of airports being served in Africa. The understanding of the design of these queries is a litmus test for the skills needed to be able to replicate and continue the study.

In Section C of this report a description of the indicators derived from the SQL dataset is listed. When reading the descriptions, it is best to think as shown in the example below:

27

Indicator Measurement

(1) Intercontinental Market Size, defined as the sum of all traffic at individual African airports that includes airports outside North and Sub - Saharan Africa1 (such as Europe) as either an origin or destination.


The field to sum on using a summation query is “[EstimatedSeats]” in the main data table, categorized by year. Linked to the table would be the Airports master tabled (twice, once to the origin field of the main table, and once to the destination field), and linked to the two occurrences of the Airports master table would be the Country master table. Two search conditions would be attached to each of the Country Master table:

(1) The first condition would specify that in the first occurrence of the Country table the region MUST BE either North Africa or Sub Saharan Africa, and that in the second occurrence of the Country table the region CANNOT BE neither North Africa nor Sub Saharan Africa. If the first occurrence of the country table is linked to the Origin airport, this implies that the flight originates out of Africa and terminates outside Africa.

(2) The second condition would specify the exact reverse. In the first occurrence of the country table it the region CANNOT BE neither North Africa nor Sub Saharan Africa, while in the second occurrence of the country table (presumably linked to the destination airport) the region MUST BE either North Africa or Sub Saharan Africa. This would imply that the origin of the flight is outside Africa, terminating within Africa.

The sum of the estimated seats per year would then be all capacity for intercontinental flights with Africa.

The understanding of these mechanisms is vital for the accurate understanding of the definitions of the described indicators.

The query would read as below:

SELECT tblFlightsAllYears.Year, Sum([AdjustedSeats])*13 AS TotalSeats

FROM ((tblFlightsAllYears INNER JOIN (tblAirportsMasterList INNER JOIN tblCountries ON tblAirportsMasterList.CountryCode = tblCountries.CountryCode) ON tblFlightsAllYears.Org = tblAirportsMasterList.Code) INNER JOIN tblAirportsMasterList AS tblAirportsMasterList_1 ON tblFlightsAllYears.Dst = tblAirportsMasterList_1.Code) INNER JOIN tblCountries AS tblCountries_1 ON tblAirportsMasterList_1.CountryCode = tblCountries_1.CountryCode

1 Intercontinental travel is travel between all of Africa and other continents. Though the World Bank categorizes Africa into two regions, (1) Sub-Saharan Africa and (2) Middle East & North Africa, the categorization here defines traffic between a country, be it in North Africa or Sub-Saharan Africa, and any country in the Middle East as Intercontinental.

28

WHERE (((tblCountries.WorldBankRegion)="NA" Or (tblCountries.WorldBankRegion)="SSA") AND ((tblCountries_1.WorldBankRegion)<>"NA" And (tblCountries_1.WorldBankRegion)<>"SSA")) OR (((tblCountries.WorldBankRegion)<>"NA" And (tblCountries.WorldBankRegion)<>"SSA") AND ((tblCountries_1.WorldBankRegion)="NA" Or (tblCountries_1.WorldBankRegion)="SSA"))

GROUP BY tblFlightsAllYears.Year;

In the Access query designer, the query would be graphically shown as below:

29

This example provides a simple solution involving just one query, however, many indicators require the use of multiple queries building on one another, as shown in the example in Appendix B.

2. Specific points in the analysis Though many of the tables used in the report were created using Excel in their final format, in nearly all cases the actual analysis creating the tables were exports of SQL query results completed in MS Access. For the future repetition of the exercise with new data two items must be highlighted:

a) The use of union queries Since the dataset has airports identified in two locations, wither as the point of origin or the final destination, total capacity at a particular airport meant summing all adjusted capacity for the same airport twice: Once by identifying the airport in the origin column, and summing the passenger capacity numbers, and once by identifying the airport in the destination column. Using union queries allowed a quick, clean way of collapsing the origin and destination field into one, and then simply summing up the capacities. Graphically, the process can be describes as shown in Text Box 2 below.

b) A note on summation In essence, a week from the second month of each quarter of the calendar year was extracted, making all monthly and annual figures extrapolations. One could easily assume that since each month has four weeks, and there are three months per quarter, the correct annual extrapolation from the weekly data would be Weekly Seats * 4 (to make four weeks) * 3 (to make three months), for each quarter, and then summed. Or, more abbreviated, summing all seats from all quarters and multiplying the sum by 12 (4 weeks * 3 months). However, 4 (no of weeks in month) * 3 (no of months in quarter) * 4 (number of quarters in year) = 48, i.e. one would only extrapolate to 48 weeks. The year has 52 weeks. Rather than using the intuitively correct seeming multiplier of 12, the correct multiplier for annualizing the sum of one-week data from each of the quarters is 13, providing the four extra weeks to complete the year. It is important to remember that for all annual capacity summations the correct calculation involves the sum of all adjusted seats for the year multiplied by 13.

c) Important items that could be summarized Because of the richness of the SRS Analyzer data, after adjustments as described above the following could be directly summarized:

30

a. All flights and passenger capacities based on airports (using the union queries) b. All flights and passenger capacities based on airlines c. Distribution of flight arrivals and departures at airports (used in country annexes)

In all instances, the operating airline, rather than the advertising airline, was used.

However, by augmenting the data with lookup tables that held additional information, such as the runway lengths at specific airports, or the status of IATA registry entry for a particular airline, of the status of an FAA category assignment for a specific country, much further analysis could be made. These summations are listed below:

1. City pair summations (by adding a city pair ID as described above) 2. Country pair summations (by adding a country pair ID) 3. Since routes (city pairs and country pairs) are identified, concentration of airlines (Herfindahl Index) 4. Economic regions and region pair summations 5. Since estimated runway conditions were added to the airports master list, summations of traffic by runway conditions 6.With the classification of aircraft, summations could be made on the size and age of aircraft, with the age summations being rough estimates. 7. With a country lookup table that contains population and per capita income levels, summations could be made by income group, and comparisons of countries with such things as per capita service provision. 8. With the knowledge of the number of legs belonging to the same flight, fifth-freedom and sixth-freedom flight could be identified, and their increase in use as a trend be obeserved

31

Text Box 2: The Use of Union Queries The data for an airport in the SRS Analyzer could be represented in the table below: In a normal SQL query, you would ask all data to be grouped, and then summed, by a value in a specific column. However, using two columns this becomes tricky. Using a union query as shown below before summing resolved this issue. In this case the query would look as follows: SELECT [Departing Airport] AS Airport, [Adjusted Seats] FROM TableAllData UNION ALL SELECT [Arriving Airport] AS Airport, [Adjusted Seats] FROM TableAllData; The following would result (not always in that order, but for clarity shown in order): A simple summation query would the collapse the table as follows, providing totals:

Departing Airport Arriving Airport Adjusted Seats A B 128 B A 128 C A 210 A C 210

Airport Adjusted Seats A 128 A 128 A 210 A 210 B 128 B 128 C 120 C 120

Airport Adjusted Seats A 676 B 256 C 240

32

B. Listing of indicators by category

1. Usage and access

a) Usage: Markets and routes The most measurable data on airports and routes are flight and passenger numbers. These are in general collected by airport and civil aviation authorities. All contract states that are members of ICAO are then, as part of their agreement, requested to report such data to ICAO. However, in many developing countries the reporting happens on a highly irregular schedule, if at all.

Due to the limitations of ICAO data, the market analysis relies heavily on the SRS analyzer data. In addition to the SRS analyzer data, several editions of Boeing's Global Market Forecast were obtained, some provided by Boeing's economic unit in photocopied versions of printed documents not available electronically. In addition, ICAO's statistical web site was consulted for data on specific airports, if available.

A listing of all overall market indicators, and their sources, is found in Table 5 below.

33

Table 5: Usage and markets in the Air Transport sector

2 Intercontinental travel is travel between all of Africa and other continents. Though the World Bank categorizes Africa into two regions, (1) Sub-Saharan Africa and (2) Middle East & North Africa, the categorization here defines traffic between a country, be it in North Africa or Sub-Saharan Africa, and any country in the Middle East as Intercontinental. 3 The geometric growth rate is defined as r = - 1, where t = the number of periods (years, since the growth rate is

annual, for example 6 for the period between 2001 and 2007), x = the beginning observation value (total estimated seats in 2001 for example), and y = the period end observation value (in for example, total estimated seats for 2007) 4 International traffic is all traffic between countries in Africa.

Policy Category Series Code Indicator Description Measurement Source

Overall Market Growth Since 1997 (see Report Figure 1.1).

Revenue Passenger Kilometer (RPK)

Boeing

Intercontinental Market Size

Defined as the sum of all traffic at individual African airports that includes airports outside North and Sub - Saharan Africa2 (such as Europe) as either an origin or destination.


SRS Data Analyzer

Intercontinental Market Growth

Defined as the geometric growth rate of indicator above.3


International Market Size - Africa4

Defined as the sum of all traffic at individual airports in Africa that includes airports outside the individual airport’s country, but within Sub-Saharan and North Africa.


International Market Growth –Africa



Intercontinental Market Size - North Africa

Defined as the sum of all traffic at individual African airports in North Africa that includes airports outside North and Sub - Saharan Africa, such as Europe, as either an origin or destination


Intercontinental Market Growth - North Africa



International Market Size - North Africa

Defined as the sum of all traffic at individual airports in North Africa that includes airports outside the individual airport’s country, but within (and only within) North Africa.

Sum of per period

34

5 A map outlining the regional economic communities and their member countries can be found in Appendix D. 6 A description of the freedoms of the air can be found in Appendix C.

International Market Growth - North Africa



International Market size - between Sub Saharan Africa and North Africa

Defined as the sum of all traffic at individual airports in Sub-Saharan Africa that includes airports outside the individual airport’s country, but within (and only within) North Africa, or vice versa.


International Market growth - between Sub Saharan Africa and North Africa



Regional Economic Communities - international traffic market sizes within

Defined as the sum of all traffic at individual airports in Africa that includes airports outside the individual airport’s country, but within African regional economic community(ies) the country is located in.5


Regional Communities - international Market growth within



Regional Communities - type of service (5th, 6th, and 7th freedom)6

As shown in table 1.8 of the main report, defined as the percentage of estimated seats for international country pairs between African countries within the same regional economic community, where the airline providing the capacity is based in neither country.


Per Country - type of service (5th, 6th, and 7th freedom)

Defined as the percentage of estimated seats for international country pairs between African countries, where the airline providing the capacity is based in neither country.


Airport Specific Traffic

Summation of estimated seats per airport.


Airport Traffic by Time Slot

Summation of estimated seats according to time slots (time slots being defined as hourly with 25 intervals. Since the SRS data set has both the departing and arriving time per flight in local time, the count of activity per airport per timeslot can be calculated by summing both the arriving and departing seats for the same hour per airport.)


Passenger numbers

For limited specific airports, for specific periods, for cross-checking SRS data.

Passenger count ICAO

35

Number of domestic passengers for year

Collected from the country’s CAA or airports authority.

Passenger count

Questionnaire

Number of international passengers for year

Collected from the country’s CAA or airports authority.

Passenger count

For main country airport: Overall domestic passengers for year

Overall domestic passenger traffic for year.

Sum of the total number of domestic passenger for the year at the main airport

For main country airport: Overall international passengers for year

Overall international passenger traffic for year.

Sum of the total number of international passenger for the year at the main airport

36

b) Access The analysis on airlines also relied heavily on the SRS data analyzer extracts for market share and connectivity. Additional data came from two online databases as listed. Because of the lack of coverage of those databases, questions have been added into the questionnaire addressing some of the terminal capacity issues.

The indicators used to analyze over service access are listed in Table 6 below.

Table 6: Indicators on air transport access

Policy Category

Series Code Indicator Description Measurement Source

Airlines serving country (Country Annex) in year

Defined as the number of airlines that have any capacity in a country at a given date or during a given period.

Count of Airline IDs

SRS Data Analyzer

Airline overall market share in a given country in year

Defined as the airline’s total number of estimated arriving and departing seats divided by the country’s total number of arriving and departing estimated seats.


Airline domestic market share in a given country in year

Defined as the airline’s total number of estimated arriving and departing seats divided by the country’s total number of arriving and departing estimated domestic seats.


Airline international market share in a given country

Defined as the airline’s total number of estimated arriving and departing seats divided by the country’s total number of arriving and departing estimated domestic seats, where either the origin or the destination is another country.


Herfindahl Index for domestic air transport

Calculated using individual airline’s total estimated seats for a given year divided by the country’s total domestic estimated seats to obtain market share7.


Connectivity - City Pairs, end of year

Calculated by counting the unique number of city pairs in the last extract for the year.


Connectivity - Country Pairs, end of year

Calculated by counting the unique number of country pairs in the last extract for the year.


Connectivity - Country Matrix

Complex crosstab query and spreadsheet manipulations based on

Average Speed (km/h)

7 The Herfindahl index is defined as the sum of squares of market shares, or

=

In this study, N = number of airlines, and si is the market share of a particular airlines, as measured in percentage of the airline’s estimated seats over the market’s overall total of estimated seats.

37

country pair IDs. Connectivity - Country

Matrix

Complex crosstab query and spreadsheet manipulations based on country pair IDs.

Number of Flights

Total number of airports (estimated) in Africa

Count of airports found by extracting web site tables and placing them into a combined database table.


www.aircraft-charter-world.com

Country per capita capacity

Calculated using WDI’s population with the same year’s matching total estimated seats.

Sum of Adjusted Seats divided by WDI country population figure

SRS Data Analyzer, WDI for population

Terminal Capacity

This database has some figures on terminal capacity. The questionnaire found in the indexes below should provide a significant augmentation of the data.

Passengers per annum for terminal

azworldairports.com

Per Country: for main country airport – Number of terminals, domestic

Count of the number of domestic terminals at the main airport. Count

Questionnaire

Per Country: for main country airport – Number of terminals, international

Count of the number of international terminals at the main airport. Count

For main country airport – Number of terminals, mixed use

Count of the number of mixed use terminals at the main airport. Count

For main country airport: Overall Domestic Terminal capacity per year

Overall annual capacity of domestic terminal (s) (not peak hour capacity).


For main country airport: Overall International Terminal capacity per year

Overall annual capacity of international terminal (s) (not peak hour capacity).


38

2. Institutions The section on the legal framework and oversight was able to from measurable data from ICAO, IATA, and the EU, while the overall assessment of oversight is a direct citation taken from The Implementation of the Yamoussoukro Decision, Charles E. Schlumberger, McGill University, 2009. Indicators and their sources are shown in Table 7 below. New data is expected to be collected by the questionnaire.

Table 7: Institutional indicators

8In the report the new ICAO audit standards had not taken full effect yet, and the safety grading was taken from Schlumberger: The Implementation of the Yamoussoukro Decision. It is recommend to now use the overall compliance with ICAO standards and recommended practices as based on ICAO USAOP audits. The results, by country, are located in the ICAO web site at www.icao.int/soa. A password is required and should be obtained from ICAO. The AICD study presents evidence showing the predictability in accidents records based on audit results. This number should replace the overall safety grading presented in the report.

Policy Category


Continental Accident Rate

Western-built aircraft hull loss rate per million flights of airlines that are IATA members.

Hull losses per 1 million flights

IATA

FAA rating of country safety oversight

Can be either (a) not rated, (b) category 2 (fail), or (c) category 1 (pass).

FAA IASA audit grade FAA

EU Blacklist Airline of country listed in Blacklist Yes/no EU Overall Safety

Grading8 Overall compliance to ICAO’s Standards and Recommended Practices as found in the latest ICAO USOAP audit for the country.

Compliance ration ICAO

Private Sector Participation in Airports/Airlines

Found in the PPIAF database. US$, date PPIAF

Worldwide investments occurring as of December 2007, in excess of US$ 500 million

May require a subscription for the survey. US $

ACI Airport Economics Survey 2007

Total revenues from overflight charges for year

Total for fees collected for overflights for year, either collected through IATA or otherwise.

Number in U.S. $

Questionnaire

Total revenues from airport charges for year

Total for parking and landing charges, or any other charges to the airline that goes in part to the airports authority .

Number in U.S. $

Planned terminal investment for the next five years

Total amount of planned improvements in country’s main airport terminals for the next five years.

Sum of US$

Planned terminal investment for the subsequent five years

Total amount of planned improvements in country’s main airport terminal for subsequent five .years.

Sum of US$

39

The original approach using the questionnaires covered only roughly 60 airports, or one or two major airports per country. Using the SRS data, however, the list of airports expanded to 451. The list of airports can be found in Appendix A below.

Are there any PPP’s being planned for terminal capacity expansion?

This is a binary question showing if the airports authority feels it has the option of involving the private sector in airport terminal investments.

Yes/No

Planned airside investments in the next five years

Total amount of planned improvements in country’s main airport runways, surveillance equipment, navaids, taxiways, and aprons for the next five years.

Sum of US$

Planned airside investment for the subsequent five years

Total amount of planned improvements in country’s main airport runways, surveillance equipment, navaids, taxiways, and aprons for the subsequent five years.

Sum of US$

40

3. Technical indicators Aircraft statistics were compiled using the data of the SRS analyzer. Table 8 below lists the data sources for airport information, as well as ground based navigational aids.

Table 8: Sources Technical indicators

Policy Category


Capacity in specified Aircraft size categories for year

Calculated by summing the estimated seats grouped by equipment codes. Note: A size grouping parameter needs to be added or updated to the table containing the equipment codes.


SRS Data Analyzer

Capacity in specified Aircraft age category for year

Calculated by summing the estimated seats grouped by equipment codes. Note: An age grouping parameter needs to be added or updated to the table containing the equipment codes.


African Airlines passing IOSA audit

Manual match of airlines in the SRS system with airlines in IATA’s registry.

Count of African airlines existing in the IATA IOSA registry

IATA

Airports - number of runways

Manual count of runways of all airports in the SRS dataset. Count

Jeppensens, Google, Wikipedia, GoogleEarth, azworldairports.com, worldaerodata.com

41

4. Pricing data Route tariffs were simple queries completed on several travel web sites, specifically www.opodo.com. The specific routes chosen can be found in the Appendix 4 of the main report. The questionnaire should provide a better picture of institutional pricing, as well as fuel costs. Table 9 below shows the sources of data for pricing.

Table 9: Indicators and their sources on services pricing

Policy Category


Route tariffs Sample tariffs for routes9. US $, Euros

www.opodo.com, www.expedia.com

Domestic passenger chargers per ticket

Fee added to every passenger traveling domestically, be it on the ticket itself, or at the airport.

Number in U.S. $

Questionnaire

International passenger chargers per ticket

Fee added to every passenger traveling internationally,, be it on the ticket itself, or at the airport.

Number in U.S. $

Fuel Price at country main airports

Fuel costs per gallon, in cents, to for global comparison with IATA’s fuel statistics.

US Cents per Gallon

Par main airport for each country, the average parking charges per seat

Calculated by dividing the parking charges per aircraft by the number of seats per aircraft and finding a weighted average per seat over all aircraft types listed.

US$

Average parking grace period

Average of grace periods for parking charges for commercial aircraft. Hours

9 Routes chosen for the AICD study can be found in Appendix 4 of the study.

42

Appendix A: List of airports included in the analysis Country Name City Code Algeria Adrar AZR

Algiers ALG Annaba AAE Batna BLJ Bechar CBH Bejaia BJA Biskra BSK Bordj Badji Mokhtar BMW Chlef CFK Constantine CZL Djanet DJG El Golea ELG El Oved ELU Ghardala GHA Ghriss MUW Hassi Messaoud HME Illizi VVZ In Amenas IAM In Salah INZ Jijel GJL L'Mekrareg LOO Mechria MZW Oran ORN Ouargla OGX Setif QSF Tamanrasset TMR Tbessa TEE Tiaret TID Timimoun TMX Tindouf TIN Tlemcen TLM Touggourt (Sidi Mahdi), TGR

Angola Cabinda CAB Catumbela CBT Dundo DUE Huambo (Nova Lisboa) NOV Luanda LAD Lubango SDD Malange MEG M'banza Congo SSY Menogue SPP Namibe (Mocamedes New Airport) MSZ Negage GXG Ongiva VPE Soyo SZA

43

Country Name City Code Benin Cotonou COO Botswana Francistown FRW

Gaborone GBE Kasane BBK Maun MUB Tuli Lodge TLD

Burkina Faso Bobo-Dioulasso BOY Ouagadougou OUA

Burundi Bujumbura BJM Cameroon Bafoussam BFX

Bamenda BPC Bertoua BTA Douala DLA Garoua GOU Marova MVR Ngaoundere NGE Yaounde NSI Yaounde YAO

Cape Verde Boa Vista BVC Fogo SFL Maio MMO Praia RAI Sal Island SID Santo Antao NTO Sao Nicolau SNE Sao Vicente VXE

Central African Republic Bangui BGF Chad Ndjamena NDJ Comoros Anjouan AJN

Dzaoudzi DZA Moheli NWA Moroni HAH Moroni (Hahaya/Iconi) YVA

Congo Brazzaville BZV Impfondo ION Loubomo DIS Nkayi NKY Ouesso OUE Pointe Noire PNR

44

Country Name City Code Congo DRC Boma BOA

Gbatolite BDT Gemana GMA Goma GOM Kalemie FMI Kananga KGA Kindu KND Kinshasa FIH Kisangani FKI Kolwezi KWZ Lubumbashi FBM Matadi MAT Matari IRP Mbandaka MDK Mbuji Mayi MJM Moanda MNB

Cote D'Ivoire Abidjan ABJ Cote D'Ivoire Tingrela TGX Djibouti Djibouti JIB Egypt Abu Simbel ABS

Al Arish AAC Alexandria ALY Assiut ATZ Aswan ASW Borg El Arab (Alexandria) HBE Cairo CAI Dakhla DAK Hurghada HRG Kharga UVL Luxor LXR Marsa Alam RMF Mersa Matruh MUH Sharm El Sheikh SSH Taba TCP

Equatorial Guinea Bata BSG Malabo SSG

Eritrea Asmara ASM Assab International Airport ASA Massawa MSW

45

Country Name City Code Ethiopia Addis Ababa ADD

Arba Mintch AMH Asosa ASO Axum AXU Bahir Dar BJR Beica BEI Beles PWI Debra Marcos DBM Debra Tabor DBT Dembidollo DEM Dessie DSE Dire Dawa DIR Gambela GMB Goba GOB Gode/Iddidole GDE Gondari GDQ Gore GOR Humera HUE Indaselassie SHC Jijiga JIJ Jimma JIM Jinka BCO Kabri Dar ABK Lalibela LLI Makale MQX Mekane Selam MKS Mizan Teferi MTF Neghelli EGL Shillavo HIL Tippi TIE Tum TUJ

Gabon Franceville MVB Gamba GAX Koulamoutou KOU Libreville LBV Makokou MKU Mayoumba MYB Moanda MFF Mouila MJL Ndjole KDJ Omboue OMB Oyem OYE Port Gentil POG Tchibanga TCH

46

Country Name City Code Gambia Banjul BJL Ghana Accra ACC

Kumasi KMS Sunyani NYI Tamale TML

Guinea Conakry CKY Guinea-Bissau Bissau OXB Kenya Amboseli ASV

Eldoret EDL Kisumu KIS Kiwayu KWY Lamu LAU Lokichoggio LKG Malindi MYD Mara Lodges MRE Mombasa MBA Nairobi, Jomo Kenyatta Intl NBO Nairobi, Wilson WIL Nanyuki NYK Samburu UAS

Lesotho Maseru MSU Liberia Monrovia ROB

Monrovia (Spriggs Payne) MLW Al Bayda, La Abraq LAQ Benghazi BEN Brack BCQ Ghadames LTD Ghat GHT Houn HUQ Kufrah AKF Misurata MRA Mitiga MJI Sebha SEB Sert SRX Tobruk TOB Tripoli TIP

47

Country Name City Code Madagascar Ambanja IVA

Ambatomainty AMY Ambatondrazaka WAM Analalava HVA Ankavandra JVA Antalaha/Antsirabato ANM Antaninvarivo TNR Antsalova WAQ Antsiranana DIE Antsohihy WAI Belo BMD Besalampy BPY Farafangana RVA Fianarantsoa WFI Fort Dauphin FTU Mahanoro VVB Maintirano MXT Majunga MJN Mampikony WMP Manakara WVK Mananara WMR Mananjary MNJ Mandritsara WMA Manja MJA Maroantsetra WMN Miandrivazo ZVA Morafenobe TVA Morambe MXM Morondava MOQ Nossi-be NOS Port Berge WPB Sambava SVB Sante Marie SMS Soalala DWB Tamatave TMM Tambohorano WTA Tsaratanana TTS Tsiroanomandidy WTS Tulear TLE Vatomatry VAT Vohemar VOH

48

Country Name City Code Malawi Blantyre BLZ

Chelinda CEH Club Makokola CMK Karonga KGJ Lilongwe LLW Mvuu Camp VUU Mzuzu ZZU Salima LMB

Mali Bamako BKO Gao GAQ Goundam GUD Kayes KYS Kenieba KNZ Mopti MZI Nioro NIX Tombouctou TOM Yelimane EYL

Mauritania Aioun El Atrouss AEO Kiffa KFA Nema EMN Nouadhiba NDB Nouakchott NKC Selibaby SEY Tioljikja TIY Zouerate OUZ

Mauritius Mauritius MRU Rodrigues Island RRG

Morocco Agadir AGA Al Hoceima AHU Casablanca, Anfa CAS Casablanca, Mohamed V CMN Dakhla VIL Er Rachidia ERH Essaouira ESU Fez FEZ Goulimime GLN Laayoune EUN Marrakech RAK Nador NDR Ouarzazate OZZ Oujda OUD Rabat RBA Tan Tan (Plage Blanche) TTA Tangier TNG Tetuan TTU

49

Country Name City Code Mozambique Bazaruto Island BZB

Beira BEW Benguera Island BCW Chimoio VPY Cuamba FXO Indigo Bay IBL Inhambane INH Lichinga VXC Lumbo LFB Maputo MPM Nacala MNC Nampula APL Pemba POL Quelimane UEL Tete TCV Tete TET Vilanculos VNX

Namibia Lianshulu LHU Luderitz LUD Mokuti Lodge OKU Mpacha MPA Ondangwa OND Oranjemund OMD Rosh Pina RHN Sesriem SZM Swakopmund SWP Tsumeb TSB Walvis Bay WVB Windhoek WDH Windhoek (Eros) ERS

Niger Agades AJY Niamey NIM

50

Country Name City Code Nigeria Abuja ABV

Benin City BNI Calabar CBQ Enugu ENU Ibadan IBA Ilorin ILR Jos JOS Kaduna KAD Kano KAN Lagos LOS Maiduguri MIU Port Harcourt PHC Port Harcourt PHG Qwerri QOW Sokoto SKO Warri QRW Yola YOL

Rwanda Gisenyi GYI Kamembe KME Kigali KGL

Sao Tome and Principe Principe PCP Sao Tome Is. TMS

Senegal Cap Skirring CSK Dakar DKR Saint Louis XLS Tambacounda TUD Ziguinchor ZIG

Seychelles Mahe Island SEZ Praslin Island PRI

Sierra Leone Freetown, Lungi Intl FNA Somalia Berbera BBO

Borama BXX Bosaso BSA Burao BUO Galcaio GLK Hargeisa HGA Mogadishu MGQ

51

Country Name City Code South Africa Alexander Bay ALJ

Bloemfontein BFN Cape Town CPT Durban DUR East London ELS George GRJ Hoedspruit HDS Johannesburg JNB Kimberley KIM Kleinzee KLZ Lanseria HLA Mala Mala AAM Malelane LLE Margate MGH Mmabatho, Bophuthatswana (Int'l) MBD Nelspruit MQP Nelspruit NLP Phalaborwa PHW Pietermaritzburg PZB Pietersburb PTG Plettenburg Bay PBZ Port Elizabeth PLZ Richards Bay RCB Skukuza SZK Springbok SBU Sun City NTY Ulundi ULD Umtata UTT Upington UTN

Sudan Dongola DOG El Fasher ELF El Obeid EBD Geneina EGN Juba JUB Khartoum KRT Malakal MAK Merave MWE Nyala UYL Port Sudan PZU Rumbek RBX Wadi Halfa WHF Wau WUU

52

Country Name City Code Swaziland Manzini MTS Tanzania Arusha ARK

Bukoba BKZ Dar Es Salaam DAR Dodoma DOD Kigoma TKQ Kilimanjaro JRO Kilwa KIY Lake Manyara LKY Lindi LDI Mafia MFA Mtwara MYW Musoma MUZ Mwanza MWZ Nachingwea NCH Pemba, Wawi PMA Seronera SEU Shunyanga SHY Tabora TBO Zanzibar, Kisauni ZNZ

Togo Lome LFW Tunisia Djerba DJE

Gafsa GAF Monastir MIR Sfax (Thyna) SFA Tabarka TBJ Tozeur TOE Tunis TUN

Uganda Arua RUA Entebbe EBB Gulu ULU Jinja JIN Kasese KSE Moyo OYG Pakuba PAF

53

Country Name City Code Zambia Chipata CIP

Kasaba Bay ZKB Kasama KAA Kasompe ZKP Kitwe KIW Livingstone LVI Lusaka LUN Mangu MNR Mansa MNS Mfume MFU Ndola NLA Solwesi SLI

Zimbabwe Bulawayo BUQ Harare HRE Hwange Nat Park HWN Kariba KAB Victoria Falls VFA

54

Appendix B: Sample of SQL code used for the analysis of SRS sata The data description of each of the indicators above need to be translated into SQL queries for the actual calculations. Below is a sample on what a series of SQL queries might look like for determining the number of airports being served in North Africa and Sub-Saharan Africa. The bases of the queries is the data structure shown in Figure 2 earlier in this manual. The main data table is named “tblFlightsAllYears”. The result of the queries is a table appearing at the end of the query samples.

Step 1, Primary query (saved as qry_410a_No_of_Airports_Served_01):

SELECT tblFlightsAllYears.Org AS Airport, tblFlightsAllYears.Year, tblCountries.WorldBankRegion

FROM tblFlightsAllYears INNER JOIN (tblCountries INNER JOIN tblAirportsMasterList ON tblCountries.CountryCode = tblAirportsMasterList.CountryCode) ON tblFlightsAllYears.Org = tblAirportsMasterList.Code

GROUP BY tblFlightsAllYears.Org, tblFlightsAllYears.Year, tblCountries.WorldBankRegion

HAVING (((tblCountries.WorldBankRegion)="SSA" Or (tblCountries.WorldBankRegion)="NA"))

UNION ALL SELECT tblFlightsAllYears.Dst AS Airport, tblFlightsAllYears.Year, tblCountries.WorldBankRegion

FROM (tblCountries INNER JOIN tblAirportsMasterList ON tblCountries.CountryCode = tblAirportsMasterList.CountryCode) INNER JOIN tblFlightsAllYears ON tblAirportsMasterList.Code = tblFlightsAllYears.Dst

GROUP BY tblFlightsAllYears.Dst, tblFlightsAllYears.Year, tblCountries.WorldBankRegion

HAVING (((tblCountries.WorldBankRegion)="SSA" Or (tblCountries.WorldBankRegion)="NA"));

55

Step 2, where above is used then as follows (saved as qry_410b_No_of_Airports_Served_02):

SELECT qry_410a_No_of_Airports_Served_01.Airport, qry_410a_No_of_Airports_Served_01.Year, qry_410a_No_of_Airports_Served_01.WorldBankRegion

FROM qry_410a_No_of_Airports_Served_01

GROUP BY qry_410a_No_of_Airports_Served_01.Airport, qry_410a_No_of_Airports_Served_01.Year, qry_410a_No_of_Airports_Served_01.WorldBankRegion;

Step 3, which uses the previous as follows (saved as qry_410b_No_of_Airports_Served_02_Crosstab):

TRANSFORM Count(qry_410b_No_of_Airports_Served_02.Airport) AS CountOfAirport

SELECT qry_410b_No_of_Airports_Served_02.WorldBankRegion

FROM qry_410b_No_of_Airports_Served_02

GROUP BY qry_410b_No_of_Airports_Served_02.WorldBankRegion

PIVOT qry_410b_No_of_Airports_Served_02.Year;

The final result appears as below:

qry_410b_No_of_Airports_Served_02_Crosstab WorldBankRegion 2001 2004 2007

NA 77 73 71 SSA 318 276 261

56

Appendix C: Freedoms of the air ICAO defines nine “freedoms of the air,” which are one of the components found in (usually bilateral) air services agreements forged between countries. The first five are internationally recognized by treaty, whereas ICAO calls the last four concept “so-called freedoms of the air.” (1) First freedom of the air: Airline of home country can overfly another country (country A)

(2) Second freedom of the air: Airline of home country can do a technical stop for fuel, maintenance, supplies, etc. in another country (country A)

(3) Third freedom of the air: Airline of home country can land in another country (country A) to drop off passengers from home country. (4) Fourth freedom of the air: Airline of home country can land in another country (country A) to drop off passengers from home country and pick up passengers from country A going to home country.

Airline home Country A

No passengers embark or disembark

Airline home country Country A

All passengers disembark.


(1) All passengers from home country disembark. Passengers from Country A board.

(2) All passengers from home country A disembark.


57

(5) Fifth freedom of the air: Airline of home country can pick up and drop off passengers in Country A, with some passengers boarding in country A going to a third country C. The caveat is that this is an ongoing operation originating (or terminating) in the home country.

(6) So-called sixth freedom of the air: Traffic originates (or terminates) outside home country (say country A), and goes to (or comes from) a second country (say Country B) via a stop at the home country of the airline.

(7) So-called seventh freedom of the air: Airline from home country can travel between country A and country B without the home country being in the path (i.e. no stop at the home country in any leg).


Besides dropping off passengers heading to country A, airline is also picking up passengers for heading to country B.

Country B

Country A Airline home country

Home country of the airline is in the middle of the path.

Country B


Home country of the airline is not involved in any stop.

Country B

58

(8) So-called eighth freedom of the air: Airline from home country can serve several destinations in other country A in one flight, both pickup up and dropping off passengers, as long as the flight originates or terminates in home country.

(9) So-called ninth freedom of the air, also referred to as “cabotage”: Airline from home country servers domestic stops within other country, without the home country being part of the flight.


Airline serving multiple stops within other country. Flight originates in home country.

Destination 1

Destination 2


The home country of the airline is not a stop in any way.

Destination 1

Destination 2

59

Appendix D: Regional communities

Descriptive Manual - hbofinger.comhbofinger.com/docs/Data Manual Version 2 Rev 09.pdf ·...

Documents

Transcript of Descriptive Manual - hbofinger.comhbofinger.com/docs/Data Manual Version 2 Rev 09.pdf ·...