Just how international is my Web site?
First Monday

Just how international is my Web site? Estimating reach through analysis of hourly demand by Dirk H.R. Spennemann


Abstract
The increased commercialisation of Internet domain sales created the unanticipated side effect that domain extensions no longer signify the residence of the domain user. As a result, the analysis of the domain attributes in the Web access logs no longer provides accurate information on the origin of the users and thus of the geographical ‘reach’ of a given site. This study provides an alternative method to assess the geographical ‘reach’ by calculating the average demand for Web pages in hourly intervals originating from each time zone. The resulting analysis tool, which relates to Greenwich Mean Time, is location independent and can be applied to Web sites world wide.

Contents

Introduction
Hourly demand as a solution?
Daily human life patterns and Web usage
Distribution of Web users in the world

 


 

++++++++++

Introduction

One of the major selling points of an on-line presence is that ‘business is conducted 24/7’, that is at all times of the day of any given day of the year. From a business perspective, it is worth considering though, whether this actually occurs, or whether it is a mere figment of managerial imagination.

Geographical analysis of the users is becoming increasingly problematic, because (i) many servers calling up a Web site provide unresolved URLs; and, (ii) the .com, .net and .org domains are also used outside the U.S. Unresolved URLs are calls made to a Web server where the DNS entry of the calling server does not extend beyond its numerical IP address. Locational information, through a coded domain name (such as .au, .de, .fr, etc.) is not provided. As result of the sale of domain names it is now possible to register domains in various countries without maintaining a server there. Furthermore, the increasing globalisation of the communications industry has seen an expansion of U.S. communication companies. Many of these now offer services in Germany, Canada, Australia and other countries. While these providers have resolved their DNS entries, they end in ‘.net’ (such as bellsouth.net, comcast.net, etc.) or ‘.com’ (such as rr.com, charter.com). While it is often possible to analyse the server logs and ‘break’ the location code of some of these providers, this is cumbersome. It is near impossible to do so for the unresolved IP addresses, even with a reasonable level of effort for each.

Standard, publicly available Web site statistics provide information on a wide range of data, both on absolute usage and on a temporal scale well as permit the tracking of users’ movements through a Web site (Petersen 2004; 2005). Many of the commercial programs are either too technical for the lay person, or the Web site maintained is not deemed to warrant expenditure in commercial applications or analysis contracts. There is a need for a rough–and–ready method that allows anyone with access to basic Web site statistics to estimate the internationality of a site.

The author, a university academic, maintains ‘The Marshall Islands — A Digital Library and Archive,’ a Web site providing a wide range of primary and secondary source material on that Pacific Island country (http://marshall.csu.edu.au). The subject matter, covering all aspects of history, culture, health, politics and economy of the island country, appeals to the Internet user population as a whole, but to ensure that the site meets the expectations of the audience, it is necessary to understand the origin and nature of the users, as well as what information they are seeking. An analysis of the user population and their needs has been carried out (Spennemann, 2004). One of the key questions to be answered asks how international the site usage actually is. Figure 1 sets out the frequency of domains requesting pages from the Marshall Islands site arranged by continent for the quarter November 2003 to January 2004. The heavy preponderance of .com. .net and unresolved domains is self–evident, distorting any meaningful analysis. Attributing all .com and .net domains to users in the U.S. is clearly untenable in the light of the international sales of Web domains.

 

Figure 1: Frequency of domains requesting pages from the Marshall Islands site arranged by continent.

 

Alternative options, such as assessing the frequency of links pointing to the site, or the Google rank (rank 3, search term ‘Marshall Islands’) only provide an estimate of the popularity of the site among other Web service providers, but do not provide information on the origin of the actual users. The same applies to the unsolicited comments and information queries that the site generates.

In view of this an alternative method had to be found that provides some measure to assess the ‘internationality’ of the Web site. This paper will provide a comparatively simple method that allows Web site authors to gain an indication on the range of utilisation, based on the hourly frequency of server calls. The method described below is also designed in such a way that it allows to assess any changes in internationality over time.

 

++++++++++

Hourly demand as a solution?

Figure 2 shows the average hourly demand for pages of the Marshall Islands site (in percentage of requests per day) for the period 2000 to May 2004. The data for 2000 and 2001 (until August inclusive) are based on portal page access only; for September 2001 to March 2002 they are based on all pages, with statistics provided courtesy of Netscape’s now defunct ‘hitometer service.’ Commencing April 2002 accurate server–level statistics (through logs) became available. To smooth monthly variations in load, the totals were expressed in percent. These monthly percentage values were then averaged for each year.

The hourly demand curve shows a gradual flattening out — nearing the theoretical ideal loading for a server. Does this mean that the demand for the service provided by the Marshall Islands site is nearing the ideal world–wide audience? Before we can answer this question, however, we briefly need to look at the patterns of human daily life.

 

Figure 2: Average hourly demand for pages of the Marshall Islands site (in percentage of requests per day) for 2000 to 2003, compared to the ideal load (time zone is Greenwich Mean Time)..

 

 

++++++++++

Daily human life patterns and Web usage

The normal day–to–day life pattern of a citizen of a Western, industrialised country follows the routines of works, leisure and sleep. Daily routines, such as working, using the public roads network, as well as using communications media such as the telephone, follow a diurnal pattern, whereby usage is at its lowest during the early hours of the morning, and at its highest in the middle of the day. Other activities, such as leisure (e.g., watching television) follow similar curves, taking into account time spent at work.

 

Figure 3: Diurnal variation in the demand for electricity, road traffic, telephone services and television (whole week).

 

Figure 3 shows the diurnal variation in the demands for electricity (Albury, Australia); roads (Seattle, Wash.; 2x Sydney, NSW; State of Vermont); telephone (six switches along the West Coast of the U.S.; seven exchanges along the eastern seaboard of Australia) and television (Australia). These data clearly demonstrate the different demands on services by work conditions: telephone usage is concentrated in the standard work hours 8:00 to 17:00, while high road usage starts and ends about one hour outside the standard work hours. Television usage, on the other hand is a widely used leisure activity, which, not surprisingly, peaks after the conclusion of the work day. The exception is the curve that for electricity. While it shows a small depression in the hours of early morning, the majority of the curve is flat. This is hardly surprising given that electricity supply is a regulated product, which is generated solely based on demand. Here the providers have the opportunity to level out the curve close to the theoretical ideal.

Web usage on the other hand, is a combination of work and leisure activity. Thus we can expect a combination of demand during and after work hours, with a low demand in the early hours of the morning. A Web usage standard was calculated based on the average number of dial up call made through ten exchanges, five on the western seaboard of the United States and five on the eastern seaboard of Australia. PacWest provided the data for the five U.S. exchanges (Phoenix, Ariz.; Seattle, Wash.; Stockton, Calif.; Los Angeles, Calif. [2 x]), while Telstra and Albury Internet provided the five Australian data sets (Albury, Brisbane, Melbourne, Sydney [2x]). Figure 4 shows just how similar the Australian and U.S. averages are, with the Australian Web users showing a slightly higher usage in the hours between 4 pm and 9 pm.

Web usage is high at the start of the work day and then peaks at the end of the work period, with another slight peak by 7 pm, presumably after the dinner period, representing those who come online in the evening (Figure 4). Table 1 provides the raw data set to permit the reader a comparison with their own Web site demand curves.

 

Table 1: Web usage — Standard for a single time zone.
Local time
Percentage
00:00–01:00
1.45
01:00–02:00
1.01
02:00–03:00
0.80
03:00–04:00
0.72
04:00–05:00
0.83
05:00–06:00
1.27
06:00–07:00
2.34
07:00–08:00
3.73
08:00–09:00
5.14
09:00–10:00
5.91
10:00–11:00
6.00
11:00–12:00
5.70
12:00–13:00
5.49
13:00–14:00
5.56
14:00–15:00
5.83
15:00–16:00
6.35
16:00–17:00
6.65
17:00–18:00
6.25
18:00–19:00
6.20
19:00–20:00
6.30
20:00–21:00
6.07
21:00–22:00
4.80
22:00–23:00
3.44
23:00–24:00
2.15

 

 

Figure 4: Diurnal standards of Internet usage in Australia and the U.S. West Coast compared to the ideal load.

 

 

++++++++++

Distribution of Web users in the world

Given the observed diurnal variations in human activity patterns, it follows that a Web site which possesses a purely national audience will have a demand curve that resembles the standard diurnal pattern. A site with a truly international audience, therefore, should have a flat curve, caused by superimposing the diurnal curves for all individual world time zones. Such a totally horizontal curve is hypothetical only, however, because the World’s population is not evenly spread across the time zones, with the Pacific Ocean being the most obvious and prominent deviation (Figure 5).

 

Figure 5: Map of the world showing time zones (Hong Kong Observatory, at http://www.hko.gov.hk/gts/time/clock/clockA.htm).

 

To arrive at an accurate picture, the distribution of Web users per time zone was calculated drawing on country–specific demographic data and Web user estimates. The total world population estimate as of July 2003 has been culled from the CIA World Factbook 2003 (CIA, 2003). The latter source also served for the estimate of the population of Web users. The CIA Handbook, however, does not provide estimates of Web users for some countries/domains, which are represented in the logs of the Marshall Islands site. For some of these, such as Guernsey or the Isle of Man, the United Kingdom percentage was applied, while for others small ‘token’ numbers of 1,000 or less were estimated. Table 3 (see end of paper) sets out these data. The countries of the world were regrouped into the world’s time zones, using Coordinated Universal Time (UTC). The time zone information was culled from SwissInfo (2003).

The estimated number of Web users in countries which stretch multiple time zones, i.e. Australia (3), Brazil (3), Canada (6), Indonesia (3), Mexico (3), Russia (10), and the U.S. (6), was proportionally allocated to the time zones they span, based on the actual total human population distribution per time zone within the country. It is acknowledged that the distribution of Internet access in countries such as Russia, Indonesia or Brazil, may not be uniformly distributed, but be concentrated in population centres. It is impossible at this time to develop a more detailed approach that would remove any ambiguities, not matter how small they are deemed to be at present.

What becomes obvious from a perusal of Figure 6 is that the relative proportion of Web users across the globe is distributed quite differently from the population. This is not surprising, given the socio–economic differential between the developed countries in North America, Europe and Australia on the one hand, and the populous regions of South–East Asia and China on the other.

 

Figure 6: World–wide distribution of Internet users compared to the total population arranged by time zones.

 

The matter is complicated by that fact that not all Web users are English–speaking. While in the early days of the World Wide Web the fear had been expressed that English would dominate the Web at the expense of small linguistic groups (cf. Spennemann, et al., 1996), the development of the Web and the concomitant development of graphics–based personal computers has seen a multitude of languages being used on the Internet, some of them using different character sets such as Japanese, Chinese and Arabic or Russian. While English is one of the most widely understood languages, one cannot readily extrapolate from the number of Web users in the world to the number of English–speaking Web users. While there are tables detailing the languages spoken in the countries of the world, there is no compilation that sets out the percentage of people in a given country that either speaks or at least understands English.

For the Web user community limited estimates were published by Global Reach (2003, see for methodology and references; see also OCOL, 2002). In the absence of reliable and accessible data, a new estimate was compiled for this study based on the languages spoken in each country. Where English is spoken as an official language it was assumed that 95 percent of the population had the language ability to read or use English–language Web sites, taking into account a small minority of non–English speaking immigrants. Where English was listed as a second language or as a language widely spoken, a value of 50 percent was used, while a value of 33.3 percent was used for countries where English is deemed as widely understood. Those countries where English is learned as a foreign language, were split into countries with a well–developed Western–oriented education system (with English–language proficiency set at 15 percent) and the remaining countries for which a proficiency of five percent was assumed. Japan and China were set at five percent while South Korea was set at 15 percent.

It is obvious that such arbitrary figures are not a reflection of the true distribution, but only an approximation. It is probable that the five percent estimates for many of the developing countries are an underestimate. The distribution of English–proficient Web users against populations proficient in English (Figure 7) is even more skewed than the previous comparison on the basis of total population.

 

Figure 7: World–wide distribution of people proficient in English compared to English–proficient Web users (see text).

 

These graphs, however, cannot be taken at face value for comparison with Web demand of a given site. As the Internet usage for each country follows the diurnal pattern set out earlier, these figures need to be modulated. For this reason, the potential Web users for each time zone were distributed in percent according to the diurnal standard set out in Figure 4. This results in the modulated demand curves shown in Figure 8. Table 2 provides the raw data set to permit the reader a comparison with their own Web site demand curves.

 

Figure 8: World–wide distribution of Internet users compared to English–proficient Web users modulated for diurnal variations of usage.

 

 

Table 2: Web usage — Web usage Standard for all time zones combined.
GMT
Total Web users
English–proficient
Web users
-11
3.57
2.97
-10
3.75
3.60
-9
3.95
4.24
-8
4.18
4.71
-7
4.27
4.89
-6
4.14
4.83
-5
3.92
4.77
-4
3.88
4.86
-3
4.05
5.11
-2
4.33
5.33
-1
4.56
5.52
GMT
4.61
5.54
1
4.52
5.38
2
4.48
5.24
3
4.54
5.08
4
4.65
4.75
5
4.71
4.18
6
4.63
3.55
7
4.35
3.01
8
4.12
2.70
9
3.94
2.51
10
3.78
2.36
11
3.58
2.34
12
3.49
2.52

 

 

Figure 9: Comparison of the usage of the Marshall Islands site (2003–May 2004) with the Australian Web standard and the world standard English–speaking Web users.

 

Finally, how does the demand curve for the Marshall Islands site, which started this investigation, stack up? Figure 9 shows the demand curve for the Marshall Islands site (average January 2003 to May 2004) compared to the Australian Web standard and compared to the international Web standard of English–speaking Web users. Clearly, the demand curve for the Marshall Islands site does not conform at all to the national Australian curve, demonstrating a much more international appeal of the site. Yet while the demand curve follows the international Web standard in general terms, it does not follow it closely: it is too flat. Looking at the time zone distribution in relation to GMT, this suggests that the site has a higher than standard appeal in the Australia–Pacific region, a slightly lower than standard appeal in the U.S. and a lower that standard appeal in the South–East Asian region. Given the regional thematic focus of the site, dealing with a small Pacific Island nation, this distribution pattern is to be expected. End of article

Table 3 : Population, number of Web users and estimated English–language proficiency per country

Land

Time Zone

UTC

Population

Estimated Web users

Percentage English–Speaking

Estimated English–proficient Web users

Afghanistan *

4.5

28717213

200

5

10

Albania

1

3582205

12000

5

600

Algeria

1

32818500

180000

5

9000

American Samoa *

-11

70260

1000

95

950

Andorra

1

69150

24500

5

1225

           

Angola

1

10766471

60000

5

3000

Anguilla

-4

12738

919

33

303

Antigua and Barbuda

-4

67897

5000

95

4750

Argentina

-3

38740807

3880000

50

1940000

Armenia

4

3326448

30000

5

1500

           

Aruba

-4

70844

24000

5

1200

Australia EST

10

1745365

940262

95

893249

Australia CST

9.5

16060997

8652369

95

8219751

Australia WST

8

1925622

1037370

95

985502

Austria

1

8188207

3700000

15

555000

           

Azerbaijan

4

7830764

25000

5

1250

Bahamas

-5

297477

16900

95

16055

Bahrain

3

667238

140200

50

70100

Bangladesh

6

138448210

150000

50

75000

Barbados

-4

277264

6000

95

5700

           

Belarus

3

10322151

422000

5

21100

Belgium

1

10289088

3760000

15

564000

Belize

-6

266440

18000

95

17100

Benin

1

7041490

25000

5

1250

Bermuda

-2

64482

25000

95

23750

           

Bhutan

6

2139549

2500

5

125

Bolivia

-4

8586443

78000

5

3900

Bosnia–Herzegovina

1

3989018

45000

5

2250

Botswana

2

1573267

33000

95

31350

Brazil — Andes

-5

589810

45297

15

6795

           

Brazil — Western

-4

16176261

1242328

15

186349

Brazil — Eastern

-3

165266534

12692375

15

1903856

British Virgin Islands *

-4

21730

1000

95

950

Brunei

8

358098

35000

50

17500

Bulgaria

2

7537929

585000

5

29250

           

Burkina

0

13228460

25000

5

1250

Burma

6.5

42510537

10000

5

500

Burundi

2

6096156

6000

5

300

Cambodia

7

13124764

10000

50

5000

Cameroon

1

15746179

45000

95

42750

           

Canada — Pacific

-8

4228780

2211084

95

2100530

Canada — Mountain

-7

3235934

1691959

95

1607361

Canada — Central

-6

2254368

1178732

95

1119795

Canada — Eastern

-5

20032559

10474341

95

9950624

Canada — Atlantic

-4

1904476

995786

95

945997

           

Canada — Newfoundland

-3.5

550996

288097

95

273692

Cape Verde

-1

412137

12000

5

600

Cayman Islands *

-5

41934

5000

95

4750

Central African Republic

1

3683538

2000

5

100

Chad

1

9253493

4000

5

200

           

Chile

-4

15665216

3100000

15

465000

China

8

1286975468

45800000

5

2290000

Christmas Island (Indian Ocean) *

7

433

0

95

0

Cocos Islands (Indian Ocean) *

6.5

630

0

95

0

Colombia

-5

41662073

1150000

5

57500

           

Comoro Islands

3

632948

2500

5

125

Congo, Dem. Rep. of the

1

56625039

6000

5

300

Congo, Rep. of the

1

2954258

500

5

25

Cook Islands *

-10

21008

1000

50

500

Costa Rica

-6

3896092

384000

5

19200

           

Cote d’Ivoire

0

16962491

70000

5

3500

Croatia

1

4422248

480000

15

72000

Cuba

-5

11263429

120000

5

6000

Cyprus

2

771657

150000

33.3

49950

Czech Republic

1

10249216

2690000

5

134500

           

Denmark

1

5384384

3370000

15

505500

Djibouti

3

457130

3300

5

165

Dominica

-4

69655

2000

95

1900

Dominican Republic

-4

8715602

186000

33.3

61938

East Timor *

9

997853

200

50

100

           

Ecuador

-5

13710234

328000

5

16400

Egypt

2

74718797

600000

15

90000

El Salvador

-6

6470379

40000

5

2000

Equatorial Guinea

1

510473

900

5

45

Eritrea

3

4362254

10000

5

500

           

Estonia

2

1408556

429700

50

214850

Ethiopia

3

66557553

20000

50

10000

Falkland Islands *

-4

2967

100

50

50

Faroe Islands

0

46345

3000

5

150

Fiji

12

868531

15000

95

14250

           

Finland

2

5190785

2690000

15

403500

France

1

60180529

16970000

15

2545500

French Guyana

-3

186917

2000

5

100

French Polynesia

-10

262125

16000

5

800

Gabon

1

1321560

18000

5

900

           

Gambia

0

1501050

5000

95

4750

Georgia

4

4934413

25000

5

1250

Germany

1

82398326

32100000

15

4815000

Ghana

0

20467747

200000

95

190000

Gibraltar *

1

27776

5000

95

4750

           

Greece

2

10665989

1400000

5

70000

Greenland

-2

56385

20000

5

1000

Grenada

-4

89258

5200

95

4940

Guadeloupe

-4

440189

4000

5

200

Guam

10

163941

5000

95

4750

           

Guatemala

-6

13909384

200000

5

10000

Guernsey *

0

64818

36946

95

35099

Guinea

0

9030220

15000

5

750

Guinea Bissau

0

1360827

4000

5

200

Guyana

-3

702100

95000

95

90250

           

Haiti

-5

7527817

30000

5

1500

Honduras *

-6

6669789

1000

33.3

333

Hong Kong

8

7394170

4350000

66

2871000

Hungary

1

10045407

1200000

50

600000

Iceland

0

280798

220000

15

33000

           

India

5.5

1049700118

7000000

95

6650000

Indonesia — Eastern

9

101085273

1893519

50

946760

Indonesia — Central

8

76342801

1430045

50

715023

Indonesia — Western

7

57465379

1076436

50

538218

Iran

3.5

68278826

1326000

5

66300

           

Iraq

3

24683313

12500

5

625

Ireland

0

3924140

1310000

95

1244500

Israel

2

6116533

1940000

33

640200

Italy

1

57998353

19250000

15

2887500

Jamaica

-5

2695867

100000

95

95000

           

Jan Mayen Island *

1

90156

1000

5

50

Japan

9

127214499

56000000

5

2800000

Jordan

2

5460265

212000

33

69960

Kazakhstan

5

16763795

100000

5

5000

Kenya

3

31639091

500000

95

475000

           

Kiribati

12

98549

1000

95

950

Korea, North *

9

22466481

500

5

25

Korea, South

9

48289037

25600000

5

1280000

Kuwait

3

2183161

200000

50

100000

Kyrgystan

6

4892808

51600

5

2580

           

Laos

7

5921545

10000

50

5000

Latvia

2

2348784

312000

5

15600

Lebanon

2

3727703

300000

50

150000

Lesotho

2

1861959

5000

95

4750

Liberia

0

3317176

500

95

475

           

Libya

1

5499074

20000

33.3

6660

Liechtenstein FL *

1

33145

5000

15

750

Lithuania

1

3592561

341000

5

17050

Luxembourg

1

454157

100000

15

15000

Macao

8

2063122

100000

33.3

33300

           

Macedonia

1

469903

101000

5

5050

Madagascar

3

16979744

35000

5

1750

Malawi

2

11651239

35000

95

33250

Malaysia

8

23092940

5700000

95

5415000

Maldives

4

329684

6000

50

3000

           

Mali

0

11626219

30000

5

1500

Malta

1

400420

59000

95

56050

Man, Isle of *

0

74261

42329

95

40212

Mariana Islands *

10

80006

1000

95

950

Marshall Islands

12

56429

900

95

855

           

Martinique

-4

425966

5000

5

250

Mauritania

0

2912584

7500

5

375

Mauritius

4

1210447

158000

50

79000

Mayotte (France) *

3

178437

2000

5

100

Mexico

-8

2680513

89429

15

13414

           

Mexico

-7

9847657

328543

15

49281

Mexico

-6

92379821

3082028

15

462304

Micronesia, Federated States of

-11

108143

2000

95

1900

Moldova

2

4439502

15000

5

750

Monaco *

1

32130

15000

50

7500

           

Mongolia

8

2712315

40000

5

2000

Montserrat *

-4

8995

500

95

475

Morocco

0

31689265

400000

5

20000

Mozambique

2

17479266

22500

5

1125

Namibia

2

1927447

45000

95

42750

           

Nauru *

12

12570

200

95

190

Nepal

5.75

26469569

60000

5

3000

Netherlands

1

16150511

9730000

15

1459500

Netherlands Antilles

-4

216226

2000

5

100

New Caledonia

11

210798

24000

5

1200

           

New Zealand

12

3951307

2060000

95

1957000

Nicaragua

-6

5128517

20000

5

1000

Niger

1

11058590

12000

5

600

Nigeria

1

133881703

100000

95

95000

Niue Island *

-11

2145

100

95

95

           

Norfolk Island (Pacific) *

11.5

1853

999

50

499

Norway

1

4546123

2680000

15

402000

Occupied Palestinian Territory

2

3512062

120000

5

6000

Oman

4

2807125

120000

50

60000

Pakistan

5

150694740

1200000

50

600000

           

Palau *

9

19717

500

95

475

Panama

-5

2960784

45000

33.3

14985

Papua New Guinea

10

5295816

135000

50

67500

Paraguay

-4

6036900

20000

5

1000

Peru

-5

28409897

3000000

5

150000

           

Philippines

8

84619974

4500000

50

2250000

Pitcairn Island *

-9

47

0

95

0

Poland

2

38622660

6400000

5

320000

Portugal

0

10102022

4400000

5

220000

Puerto Rico

-4

3885877

600000

95

570000

           

Qatar

3

817052

75000

33.3

24975

Reunion (France)

4

755171

10000

5

500

Romania

2

22271839

1000000

5

50000

Russia Zone 1

2

918783

114430

5

5722

Russia Zone 2

3

88512805

11023812

5

551191

           

Russia Zone 3

4

4758516

592649

5

29632

Russia Zone 4

5

21176372

2637408

5

131870

Russia Zone 5

6

7540003

939068

5

46953

Russia Zone 6

7

8604363

1071629

5

53581

           

Russia Zone 7

8

3644369

453887

5

22694

Russia Zone 8

9

3434447

427743

5

21387

Russia Zone 9

10

4307228

536443

5

26822

Russia Zone 10

11

1183539

147404

5

7370

Russia Zone 11

12

445853

55529

5

2776

           

Rwanda

2

7810056

20000

95

19000

Samoa (Western)

-11

178173

3000

95

2850

San Marino

1

28119

9000

5

450

Sao Tome and Principe

0

175883

1453000

5

72650

Saudi Arabia

3

24293844

100000

33.3

33300

           

Senegal

0

10580307

400000

5

20000

Serbia and Montenegro *

1

10655774

20000

15

3000

Seychelles

4

80469

9000

95

8550

Sierra Leone

0

5732681

20000

95

19000

Singapore

8

4608595

2310000

95

2194500

           

Slovakia

1

5430033

700000

5

35000

Slovenia

1

1935677

600000

5

30000

Solomon Islands

11

509190

8400

50

4200

Somalia

3

8025190

200

50

100

South Africa

2

42768678

3068000

50

1534000

           

Spain

1

40217413

7890000

15

1183500

Sri Lanka

5.5

19742439

121500

50

60750

St Helena Island *

0

7367

500

95

475

St Kitts–Nevis

-4

38763

2000

95

1900

St Lucia

-4

162157

3000

95

2850

           

St Pierre/Miquelon *

-3

6976

200

5

10

St Vincent

-4

116812

3500

95

3325

Sudan

2

38114160

56000

50

28000

Surinam

-3

435449

14500

33.3

4829

Swaziland

2

1161219

7000

95

6650

           

Sweden

1

8878085

6020000

15

903000

Switzerland

1

7318638

3850000

15

577500

Syria

2

17585540

60000

33.3

19980

Taiwan

8

22603001

11600000

15

1740000

Tajikistan

6

6863752

5000

5

250

           

Tanzania

3

35922454

300000

95

285000

Thailand

7

64265276

1200000

50

600000

Togo

0

5429299

50000

5

2500

Tokelau *

-11

1418

0

50

0

Tonga

12

108141

1000

50

500

           

Trinidad and Tobago

-4

1104209

120000

95

114000

Tunisia

1

9924742

400000

5

20000

Turkey

2

68109469

2500000

5

125000

Turkmenistan

5

4775544

2000

5

100

Turks and Caicos Islands *

-5

19350

1000

95

950

           

Tuvalu *

12

11305

200

50

100

Uganda

3

25632794

60000

95

57000

Ukraine

2

48055439

750000

5

37500

United Arab Emirates

4

2484818

900000

33.3

299700

United Kingdom

0

60094648

34300000

95

32585000

           

Uruguay

-3

3413329

400000

5

20000

U.S. — Hawaii

-10

1249902

713541

95

677864

U.S. — Alaska

-9

646771

369227

95

350766

U.S. — Pacific

-8

47952694

27375109

95

26006353

U.S. — Mountain

-7

18558592

10594681

95

10064947

           

U.S. — Central

-6

91264707

52100958

95

49495910

U.S. — Eastern

-5

130669888

74596485

95

70866661

Uzbekistan

6

25981647

100000

5

5000

Vanuatu

11

199414

3000

80

2400

Vatican City

1

911

40000

5

2000

           

Venezuela

-4

24654694

1300000

5

65000

Vietnam

7

81624716

400000

50

200000

Virgin Islands

-4

124778

12000

50

6000

Wallis Island *

12

15734

200

5

10

Western Sahara *

0

261794

500

5

25

           

Yemen

3

19349881

17000

5

850

Zambia

2

10307333

25000

95

23750

Zimbabwe

2

12576742

100000

95

95000

 

About the author

Dirk H.R Spennemann is Associate Professor in Cultural Heritage Management at Charles Sturt University (http://www.csu.edu.au/) in Australia. His main research interests are German colonial heritage in Oceania, in particular Micronesia, and historic preservation issues in Micronesia in general. The second plank of his research deals with the utilization of information technology for the benefit of tertiary and public education. He is also the general editor of Digital Micronesia (http://marshall.csu.edu.au/).
E–mail: dspennemann [at] csu [dot] edu [dot] au

 

Acknowledgments

In am indebted to a number of individuals who provided data for the study: Robert Ayre (Team Telstra, Australia); Graham Browne (Country Energy, Albury); Robert Hay (Division of Information Technology, Charles Sturt University); Randol Tigrett (Pacwest, U.S.); and, Ross Wheeler (Albury Internet, Albury).

I am further indebted to a number of anonymous referees for First Monday who made constructive comments on the paper.

 

References

CIA, 2003. CIA Factbook, at http://www.cia.gov/cia/publications/factbook/, accessed 6 December 2006.

Global Reach, 2003. “Details of country/language analysis,” at http://www.global-reach.biz/globstats/details.html, accessed 6 December 2006.

OCOL (Canada. Office of the Commissioner of Official Languages), 2002. “Special Study. Official Languages On The Internet,” at http://www.ocol-clo.gc.ca/archives/sst_es/2002/lang_internet/lang_internet_2002_e.htm, accessed 6 December 2006.

Eric T. Peterson, 2005. Web Site Measurement Hacks: Tips and Tools to Help Optimize Your Online Business. Sebastopol, Calif.: O’Reilly.

Eric T. Peterson, 2004. Web Analytics Demystified: A Marketer’s Guide to Understanding How Your Web Site Affects Your Business.Portland, Ore.: Celilo Group Media.

Dirk H.R. Spennemann, 2004. “A Digital library and archive about the Marshall Islands: Experiences and challenges,” Australian Library Journal, volume 53, number 3. at http://alia.org.au/publishing/alj/53.3/full.text/spennemann.html, accessed 6 December 2006.

Dirk H.R. Spennemann, Jim Birckhead, David G. Green, and John S. Atkinson, 1996. “The electronic colonisation of the Pacific,” Computer Mediated Communications Magazine, volume 3, number 2, at http://www.december.com/cmc/mag/1996/feb/spen.html, accessed 6 December 2006.

SwissInfo, 2003, “Local times around the world,” at http://swissinfo.net/cgi/worldtime/, accessed 6 December 2006.


Editorial history

Paper received 2 October 2005; accepted 4 November 2005.


Contents Index

CC Developing Nations
This work is licensed under a Creative Commons Developing Nations license.

Just how international is my Web site? Estimating reach through analysis of hourly demand by Dirk H.R. Spennemann
First Monday, volume 10, number 12 (December 2005),
URL: http://firstmonday.org/issues/issue10_12/spennemann/index.html





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