Method and apparatus for estimating a geographic location of a networked entity

• US 6,684,250 B2
• Filed: 04/03/2001
• Issued: 01/27/2004
• Est. Priority Date: 04/03/2000
• Status: Expired due to Term

• Current Assignee: NeuStar IP Intelligence, Inc. (Neustar Incorporated)
• Original Assignee: Quova Inc. (Neustar Incorporated)
• Inventors: Anderson, Mark, Bansal, Ajay, Herringshaw, Christopher, Muniz, Derald, Doctor, Brad, Hadjiyiannis, George, Karplus, Eli E.
• Primary Examiner(s): Alam, Hosain T.
• Assistant Examiner(s): DINH, KHANH Q
• Application Number:
  US09/825,675
• Publication Number:
  US 20030074471A1
• Time in Patent Office: 1,029 Days
• Field of Search: 709/203, 709/206, 709/220, 709/223, 709/224, 709/225, 709/226, 709/227, 709/228, 709/238, 707/102, 370/392, 370/242, 370/312, 370/255, 370/401, 455/424, 379/242, 710/1
• US Class Current: 709/225
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: H04L 2101/30 Types of network names
• CPC Class Codes: H04L 61/00 Network arrangements, protocols or services for addressing or naming
• CPC Class Codes: H04L 61/10 of different types
• CPC Class Codes: H04L 61/30 Managing network names, e.g. use of aliases or nicknames name-to-address mapping H04L61/45
• CPC Class Codes: H04L 61/3015 Name registration, generation or assignment
• CPC Class Codes: H04L 61/35 involving non-standard use of addresses for implementing network functionalities, e.g. coding subscription information within the address or functional addressing, i.e. assigning an address to a function
• CPC Class Codes: H04L 63/107 wherein the security policies are location-dependent, e.g. entities privileges depend on current location or allowing specific operations only from locally connected terminals
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/52 specially adapted for the location of the user terminal
• CPC Class Codes: H04L 69/329 in the application layer [OSI layer 7]
• CPC Class Codes: H04L 9/40 Network security protocols
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface

Abstract

A method and an apparatus operates to associate a geographic location associated with a network address. At least one data collection operation is performed to obtain information pertaining to a network address. The retrieved information is processed to identify a plurality of geographic locations potentially associated with the network address, and to attach a confidence factor to each of the plurality of geographic locations. An estimated geographic location is selected from the plurality of geographic locations as being a best estimate of a true geographic location of the network address, where the selection of the estimated geographic location is based upon a degree of confidence-factor weighted agreement within the plurality of geographic locations.

Claims

1. A computer-implemented method to estimate a geographic location associated with a network address, the method including:

performing at least one data collection operation to obtain information pertaining to a network address;

processing of the retrieved information to identify a plurality of geographic locations potentially associated with the network address, and to attach a confidence factor to each of the plurality of geographic locations; and

selecting an estimated geographic location from the plurality of geographic locations as being a best estimate of a true geographic location of the network address, where the selection of the estimated geographic location is based upon a degree of confidence-factor weighted agreement within the plurality of geographic locations.

2. The computer-implemented method of claim 1 wherein the at least one data collection operation comprises a traceroute operation.

3. The computer-implemented method of claim 1 wherein the at least one data collection operation includes retrieving any one of a group of registry records, the group of registry records including a Net Whois records, a Domain Name Server (DNS) Whois record, an Autonomous System Network (ASN), and a DNS Location record.

4. The computer-implemented method of claim 1 wherein the processing of the retrieved information includes performing a plurality of geographic location operations, each of the plurality of geographic location operations implementing a unique process to generate at least one geographic location.

5. The computer-implemented method of claim 4 wherein each of the plurality of geographic location operations is to associate a confidence factor with the at least one geographic location generated thereby.

6. The computer-implemented method of claim 5 wherein the association of the confidence factor with the at least one geographic location by each of the plurality of geographic location operations comprises applying a confidence map that relates at least one parameter to a confidence factor.

7. The computer-implemented method of claim 6 wherein the confidence map relates multiple parameters derived from the retrieved information to a confidence factor.

8. The computer-implemented method of claim 5 wherein the association of the confidence factor with the at least one geographic location by each of the plurality of geographic location operations comprises applying a plurality of confidence maps, associated with the respective geographic location operation, that each relate at least one parameter to a respective confidence factor.

9. The computer-implemented method of claim 8 wherein each of the plurality of confidence maps has a confidence weight, the confidence weight indicative of a relative importance attributed to the at least one parameter by the respective geographic location operation.

10. The computer-implemented method of claim 8 including combining a plurality of confidence factors generated by the plurality of confidence maps into a combined confidence factor.

11. The computer-implemented method of claim 10 wherein the combining of the plurality of confidence factors is performed utilizing weights attributed to each of the plurality of confidence factors.

12. The computer-implemented method of claim 11 wherein the combining of the plurality of confidence factors is performed by a weighted arithmetic mean, and according to the following formula:

$C<br><br>\text{\hspace{1em}<br><br>}<br><br>C<br><br>\text{\hspace{1em}<br><br>}<br><br>F=\frac{\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br>{\mathrm{cf}}_i<br><br>w_i}{\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br>w_i}$

where cf_i is the i^th of n confidence factors generated by the i^th confidence map with associated weight w_i.

13. The computer-implemented method of claim 1 including designating at least one geographic location generated by a first geographic location operation as a filter geographic location, and filtering from the plurality of graphics locations those geographic locations that do not exhibit a predetermined degree of agreement with the filter geographic location.

14. The computer-implemented method of claim 13 wherein the filter geographic location is of a first geographic resolution, and wherein inconsistent geographic locations, of the plurality of geographic locations and having a lower geographic resolution than the first geographic resolution, are filtered on the basis of a failure to fall within the filter geographic location.

15. The computer-implemented method of claim 14 wherein the filter geographic location is a first country, and wherein the inconsistent geographic locations are filtered on the basis of a failure to be located within the first country.

16. The computer-implemented method of claim 14 wherein the filter geographic location is a first continent, and wherein the inconsistent geographic locations are filtered on the basis of a failure to be located within the first continent.

17. The computer-implemented method of claim 1 wherein the selecting of the estimated geographic location includes generating a separate confidence factor for each of a plurality of levels of geographic resolution associated with the estimated geographic location.

18. The computer-implemented method of claim 17 wherein the plurality of levels of geographic resolution include continent, country, state, and city geographic resolutions.

19. The computer-implemented method of claim 4 wherein the selecting of the estimated geographic location includes comparing each of the plurality of geographic locations potentially associated with the network address against further geographic locations of the plurality of geographic locations.

20. The computer-implemented method of claim 19 wherein at least one of the plurality of geographic location operations generates a set of geographic locations, and wherein geographic locations within the set are not compared against other geographic locations within the set.

21. The computer-implemented method of claim 1 wherein the selecting of the estimated geographic location includes collapsing at least some of the confidence factors associated with the geographic locations into a confirmation confidence factor.

22. The computer-implemented method of claim 21 wherein the collapsing includes combining the plurality of confidence factors for a geographic location that exhibit a correspondence into the confirmation confidence factor.

23. The computer-implemented method of claim 22 wherein the collapsing includes combining the plurality of confidence factors to generate the confirmation confidence factor according to the following equation:

$C<br><br>\text{\hspace{1em}<br><br>}<br><br>C<br><br>\text{\hspace{1em}<br><br>}<br><br>F=100\times <br><br>\left[1-\underset{i=1}{\overset{n}{\prod <br><br>}}<br><br>\left(1-\frac{{\mathrm{mcf}}_i}{100}\right)\right]$

where mcf_{i is the i}^th of n confidence factors for the geographic locations that exhibit the correspondence.

24. The computer-implemented method of claim 22 wherein the correspondence is detected at a plurality of levels of geographic location resolution, and wherein the combining of the confidence factors of the geographic locations is performed at each of the plurality of levels of geographic location resolution at which the correspondence is detected, to thereby generate a respective confirmation confidence factor for each of the plurality of geographic locations at each of the plurality of levels of geographic location resolution.

25. The computer-implemented method of claim 24 wherein the plurality of levels of geographic location resolution include continent, country, state, province, city, region, MSA, PMSA, and DMA geographic resolutions.

26. The computer-implemented method of claim 24 wherein the selecting of the estimated geographic location includes combining the respective confirmation confidence factors for each of the geographic locations at each of the plurality of levels of geographic location resolution, to thereby generate a combined confirmation confidence factor.

27. The computer-implemented method of claim 26 wherein the combining of the respective confirmation confidence factors comprises assigning each of the plurality of levels of geographic location resolution a respective weighting, and calculating the combined confirmation confidence factor by weighing each of the confirmation confidence factors with the respective weighting assigned to a corresponding level of geographic location resolution.

28. The computer-implemented method of claim 26 wherein the selecting of the estimated geographic location comprises identifying a geographic location with a highest combined confirmation confidence factor as the estimated geographic location.

29. The computer-implemented method of claim 4 wherein a first geographic location operation of the plurality of geographic location operations utilizes a string pattern within a host name associated with the at least one network address to generate the at least one geographic location.

30. The computer-implemented method of claim 29 wherein the string pattern includes any one of a group including a full city name, a full state name, a full country name, a city name abbreviation, a state name abbreviation, a country name abbreviation, initial characters of a city name, an airport code, day, abbreviation for a city name, and an alternative spelling for a city name.

31. The computer-implemented method of claim 4 wherein a first geographic location operation of the plurality of geographic location operations utilizes a record obtained from a network registry to generate the at least one geographic location.

32. The computer-implemented method of claim 31 wherein the network registry includes any one of a group of registries including an Internet Protocol (IP) registry, a Domain Name Server (DNS) registry, an Autonomous System Registry, and a DNS Location Record registry.

33. The computer-implemented method of claim 4 wherein a first geographic location operation of the plurality of geographic location operations utilizes a traceroute generated against the at least one network address to generate the at least one geographic location.

34. The computer-implemented method of claim 33 wherein the first geographic location operation utilizes a Last Known Host determined from the traceroute to generate the at least one geographic location.

35. The computer-implemented method of claim 33 wherein the first geographic location operation utilizes a Next Known Host determined from the traceroute to generate the at least one geographic location.

36. The computer implemented method of claim 33 wherein the first geographic location operation utilizes a combination of a Next Known Host and a Last Known Host from the traceroute to generate the at least one geographic location.

37. The computer-implemented method of claim 33 wherein the first geographic location operation utilizes at least one suffix of a host name to generate the at least one geographic location.

38. The computer-implemented method of claim 6 wherein the at least one parameter of the confidence map is a connectivity index indicating a degree of connectivity for the at least one geographic location.

39. The computer-implemented method of claim 6 wherein the at least one parameter of the confidence map is a hop ratio indicating a relative position of a hop relative to an end node within a traceroute against the network address.

40. The computer-implemented method of claim 6 wherein the at least one parameter of the confidence map is a string length indicating the number of characters within a string interpreted as indicating the at least one geographic location.

41. The computer-implemented method of claim 6 wherein the at least one parameter of the confidence map is a number of geographic locations generated by the at least one geographic location operation.

42. The computer-implemented method of claim 6 wherein the at least one parameter of the confidence map is a population value for the at least one geographic location.

43. The computer-implemented method of claim 6 wherein the at least one parameter of the confidence map is a distance to a Last Known Host from the at least one geographic location.

44. The computer-implemented method of claim 6 wherein the at least one parameter of the confidence map is a number of hops within a trace route between a Last Known Host and the at least one geographic location.

45. The computer-implemented method of claim 6 wherein the at least one parameter of the confidence map is a minimum population of the at least one geographic location and a Last Known Host.

46. The computer-implemented method of claim 6 wherein the at least one parameter of the confidence map is a minimum connectivity index of the at least one geographic location and a Last Known Host.

47. The computer-implemented method of claim 6 wherein the at least one parameter of the confidence map is a distance to a Next Known Host from the at least one geographic location.

48. The computer-implemented method of claim 6 wherein the at least one parameter of the confidence map is a hop ratio indicating a relative position of a Next Known Host within a traceroute against the network address.

49. The computer-implemented method of claim 6 wherein the at least one parameter of the confidence map is a distance between a Next Known Host and the at least one geographic location.

50. The computer-implemented method of claim 6 wherein the at least one parameter of the confidence map is a number of hops between a Next Known Host and the at least one geographic location within a trace route against the network address.

51. The computer-implemented method of claim 6 wherein the at least one parameter of the confidence map is a minimum population of a Next Known Host and the at least one geographic location.

52. The computer-implemented method of claim 6 wherein the at least one parameter of the confidence map is a minimum connectivity index between the at least one geographic location and a Next Known Host.

53. The computer-implemented method of claim 6 wherein the at least one parameter of the confidence map is a mean of connectivity indices for a Last Known Host and a Next Known Host within a traceroute against the network address.

54. The computer-implemented method of claim 6 wherein the at least one parameter of the confidence map is a position of a first character of a word indicative of the at least one geographic location within a host name.

55. The computer-implemented method of claim 6 wherein the at least one parameter of the confidence map is a number of network addresses within a registered block of network addresses.

56. The computer-implemented method of claim 1 including identifying a block of network addresses, identifying a first geographic location for at least one network address within the block of network addresses, and recording the first geographic location as being associated with the block of network addresses.

57. The computer-implemented method of claim 56 wherein the recording of the geographic location as being associated with the block of network addresses is performed within a record within a database for the block of network addresses.

58. The computer-implemented method of claim 56 including:

performing a plurality of data collection operations to obtain block information pertaining to a plurality of network addresses within the block of network addresses;

processing the retrieved block information to identify a plurality of geographic locations potentially associated with the plurality of network addresses within the block of network addresses, and attaching a confidence factor to each of the plurality of geographic locations; and

selecting an estimated block location from the plurality of geographic locations, wherein the selection of the estimated block geographic location is based upon a confidence-factor weighted agreement within the plurality of geographic locations.

59. The computer-implemented method of claim 58 wherein the identification of the block of network addresses is performed utilizing a divide-and-conquer blocking algorithm that identifies common information between a subject network address and a test network address to determine whether the subject and test network addresses are within a common network block of network addresses.

60. The computer-implemented method of claim 59 wherein the identification of the common information between the subject network address and the test network address comprises identifying a common geographic location associated with each of the subject and the test network addresses.

61. The computer-implemented method of claim 59 wherein the identification of the common information between the subject network address and the test network address comprises identifying a substantially common traceroute generated responsive to traceroute operations performed against each of the subject and test network addresses.

62. The computer-implemented method of claim 60 wherein the identification of the common information between the subject network address and the test network address comprises determining whether the subject and test network addresses utilizing a common DNS server.

63. The computer-implemented method of claim 58 wherein the identification of the block of network addresses is performed utilizing a netmask blocking algorithm that utilizes a netmask associated with a subject network address.

64. The computer-implemented method of claim 58 wherein the identification of the block of network addresses is performed utilizing a topology map.

65. The computer-implemented method of claim 56 wherein the block of network addresses is identified as being a subnet, and wherein the recording of the first geographic location as being associated with the block of network addresses is recorded in a record within the database for the subnet.

66. The computer implemented method of claim 56 wherein the block of network addresses is identified by respective start and end network addresses.

67. A system to estimate a geographic location associated with a network address, the system including:

a plurality of data collection agents to perform at least one data collection operation to obtain information pertaining to a network address;

a plurality of geographic location processes to process the retrieved information to identify a plurality of geographic locations potentially associated with the network address, and to attach a confidence factor to each of the plurality of geographic locations; and

a selection process to select an estimated geographic location from the plurality of geographic locations as being a best estimate of a true geographic location of the network address, where the selection process utilizes a degree of confidence-factor weighted agreement within the plurality of geographic locations to select the estimated geographic location.

68. The system of claim 67 wherein the plurality of data collection agents include a traceroute process.

69. The system of claim 67 wherein the plurality of data collection agents include registry retrieval agents to retrieved any one of a group of registry records, the group of registry records including a Net whois records, a Domain Name Server (DNS) Whois record, an Autonomous System Network (ASN), and a DNS Location record.

70. The system of claim 67 wherein the each of the plurality of geographic location processes implements a unique process to generate at least one geographic location.

71. The system of claim 67 wherein a first geographic location process of the plurality of geographic location processes is to associate a confidence factor with the at least one geographic location generated thereby.

72. The system of claim 71 wherein the first geographic location process is to apply a confidence map that relates at least one parameter to a confidence factor to thereby generate the confidence factor associated with the at least one geographic location generated thereby.

73. The system of claim 72 wherein the confidence map relates multiple parameters derived to a confidence factor.

74. The system of claim 71 wherein the first geographic location process is to apply a plurality of confidence maps, associated with the first geographic location process, that each relate at least one parameter to a respective confidence factor.

75. The system of claim 74 wherein each of the plurality of confidence maps has a confidence weight, the confidence weight indicative of a relative importance attributed to the at least one parameter by the first geographic location process.

76. The system of claim 75 wherein the first geographic location process is to combine a plurality of confidence factors generated by the plurality of confidence maps into a combined confidence factor.

77. The system of claim 76 wherein first geographic location process utilizes weights attributed to each of the plurality of confidence factors to combine the plurality of confidence factors.

78. The system of claim 77 wherein the first geographic location process is to combine the plurality of confidence factors utilizing a weighted arithmetic mean, and according to the following formula:

$C<br><br>\text{\hspace{1em}<br><br>}<br><br>C<br><br>\text{\hspace{1em}<br><br>}<br><br>F=\frac{\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br>{\mathrm{cf}}_i<br><br>w_i}{\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br>w_i}$

where cf_i is the i^th of n confidence factors generated by the i^th confidence map with associated weight w_i.

79. The system of claim 67 including a filter process to designate at least one geographic location generated by a selected geographic location process as a filter geographic location, and to filter from the plurality of graphics locations those geographic locations that do not exhibit a predetermined degree of agreement with the filter geographic location.

80. The system of claim 79 wherein the filter geographic location is of a first geographic resolution, and wherein the filter process is to filter inconsistent geographic locations, of the plurality of geographic locations and having a lower geographic resolution than the first geographic resolution, on the basis of a failure to fall within the filter geographic location.

81. The system of claim 80 wherein the filter geographic location is a first country, and wherein the filter process is to filter the inconsistent geographic locations on the basis of a failure to be located within the first country.

82. The system of claim 80 wherein the filter geographic location is a first continent, and wherein the filter process is to filter the inconsistent geographic locations on the basis of a failure to be located within the first continent.

83. The system of claim 67 wherein the selection process is to generate a separate confidence factor for each of a plurality of levels of geographic resolution associated with the estimated geographic location.

84. The system of claim 83 wherein the plurality of levels of geographic resolution include continent, country, state, and city geographic resolutions.

85. The system of claim 67 wherein the selection process is to compare each of the plurality of geographic locations potentially associated with the network address against further geographic locations of the plurality of geographic locations.

86. The system of claim 85 wherein at least one of the geographic location processes is to generate a set of geographic locations, and wherein geographic locations within the set are not compared against other geographic locations within the set.

87. The system of claim 67 wherein the selection process is to collapse at least some of the confidence factors associated with the geographic locations into a confirmation confidence factor.

88. The system of claim 87 wherein the selection process is to combine the plurality of confidence factors for a geographic location that exhibit a correspondence into the confirmation confidence factor.

89. The system of claim 88 wherein the selection process is to combine the plurality of confidence factors to generate the confirmation confidence factor (CCF) according to the following equation:

$C<br><br>\text{\hspace{1em}<br><br>}<br><br>C<br><br>\text{\hspace{1em}<br><br>}<br><br>F=100\times <br><br>\left[1-\underset{i=1}{\overset{n}{\prod <br><br>}}<br><br>\left(1-\frac{{\mathrm{mcf}}_i}{100}\right)\right]$

where mcf_i is the i^th of n confidence factors for the geographic locations that exhibit the correspondence.

90. The system of claim 88 wherein the selection process is to detect the correspondence at a plurality of levels of geographic location resolution, and to combine the confidence factors of the geographic locations at each of the plurality of levels of geographic location resolution at which the correspondence is detected, to thereby generate a respective confirmation confidence factor for each of the plurality of geographic locations at each of the plurality of levels of geographic location resolution.

91. The system of claim 90 wherein the plurality of levels of geographic location resolution include continent, country, state, province, city, region, MSA, PMSA, and DMA levels of geographic resolution.

92. The system of claim 90 wherein the selection process is to combine the respective confirmation confidence factors for each of the geographic locations at each of the plurality of levels of geographic location resolution, to thereby generate a combined confirmation confidence factor.

93. The system of claim 92 wherein the selection process is to assign each of the plurality of levels of geographic location resolution a respective weighting, and to calculate the combined confirmation confidence factor by weighing each of the confirmation confidence factors with the respective weighting assigned to a corresponding level of geographic resolution.

94. The system of claim 93 wherein the selection process is to identify a geographic location with a highest combined confirmation confidence factor as the estimated geographic location.

95. The system of claim 67 wherein a first geographic location process of the plurality of geographic location processes is to utilize a string pattern within a host name associated with the at least one network address to generate the at least one geographic location.

96. The system of claim 95 wherein the string pattern includes any one of a group including a full city name, a full state name, a full country name, a city name abbreviation, a state name abbreviation, a country name abbreviation, initial characters of a city name, an airport code, day, abbreviation for a city name, and an alternative spelling for a city name.

97. The system of claim 67 wherein a first geographic location process of the plurality of geographic location processes is to utilize a record obtained from a network registry to generate the at least one geographic location.

98. The system of claim 97 wherein the network registry includes any one of a group of registries including an Internet Protocol (IP) registry, a Domain Name Server (DNS) registry, an Autonomous System Registry, and a DNS Location Record registry.

99. The system of claim 67 wherein a first geographic location process of the plurality of geographic location processes is to utilize a traceroute generated against the at least one network address to generate the at least one geographic location.

100. The system of claim 67 wherein the first geographic location process is to utilize a Last Known Host determined from the traceroute to generate the at least one geographic location.

101. The system of claim 67 wherein the first geographic location process is to utilize a Next Known Host determined from the traceroute to generate the at least one geographic location.

102. The system of claim 67 wherein the first geographic location process is to utilize a combination of a Next Known Host and a Last Known Host from the traceroute to generate the at least one geographic location.

103. The system of claim 67 wherein the first geographic location process is to utilize at least one suffix of a host name to generate the at least one geographic location.

104. The system of claim 72 wherein the at least one parameter of the confidence map is a connectivity index indicating a degree of connectivity for the at least one geographic location.

105. The system of claim 72 wherein the at least one parameter of the confidence map is a hop ratio indicating a relative position of the at least one geographic location within a traceroute against the network address.

106. The system of claim 72 wherein the hop ratio indicates the at least one geographic location as being at a beginning or at an end of the traceroute.

107. The system of claim 72 wherein the at least one parameter of the confidence map is a string length indicating the number of characters within a string interpreted as indicating the at least one geographic location.

108. The system of claim 72 wherein the at least one parameter of the confidence map is a number of geographic locations generated by the at least one geographic location process.

109. The system of claim 72 wherein the at least one parameter of the confidence map is a population value for the at least one geographic location.

110. The system of claim 72 wherein the at least one parameter of the confidence map is a distance to a Last Known Host from the at least one geographic location.

111. The system of claim 72 wherein the at least one parameter of the confidence map is a number of hops within a trace route between a Last Known Host and the at least one geographic location.

112. The system of claim 72 wherein the at least one parameter of the confidence map is a minimum population of the at least one geographic location and a Last Known Host.

113. The system of claim 72 wherein the at least one parameter of the confidence map is a minimum connectivity index of the at least one geographic location and a Last Known Host.

114. The system of claim 72 wherein the at least one parameter of the confidence map is a distance to a Next Known Host from the at least one geographic location.

115. The system of claim 72 wherein the at least one parameter of the confidence map is a hop ratio indicating a relative position of a Next Known Host within a traceroute against the network address.

116. The system of claim 72 wherein the at least one parameter of the confidence map is a distance between a Next Known Host and the at least one geographic location.

117. The system of claim 72 wherein the at least one parameter of the confidence map is a number of hops between a Next Known Host and the at least one geographic location within a trace route against the network address.

118. The system of claim 72 wherein the at least one parameter of the confidence map is a minimum population of a Next Known Host and the at least one geographic location.

119. The system of claim 72 wherein the at least one parameter of the confidence map is a minimum connectivity index between the at least one geographic location and a Next Known Host.

120. The system of claim 72 wherein the at least one parameter of the confidence map is a mean of connectivity indices for a Last Known Host and a Next Known Host within a traceroute against the network address.

121. The system of claim 72 wherein the at least one parameter of the confidence map is a position of a first character of a word indicative of the at least one geographic location within a host name.

122. The system of claim 72 wherein the at least one parameter of the confidence map is a number of network addresses within a registered block of network addresses.

123. The system of claim 67 including a blocking process to identify a block of network addresses, to identify a first geographic location for at least one network address within the block of network addresses, and to record the first geographic location as being associated with the block of network addresses.

124. The system of claim 123 wherein the recording of the geographic location as being associated with the block of network addresses is performed within a record within a database for the block of network addresses.

125. The system of claim 124 wherein:

the plurality of data collection agents is to obtain block information pertaining to a plurality of network addresses within the block of network addresses;

the plurality of geographic location processes is to process the retrieved block information to identify a plurality of geographic locations potentially associated with the plurality of network addresses within the block of network addresses, and to attach a confidence factor to each of the plurality of geographic locations; and

the selection process is to select an estimated block location from the plurality of geographic locations, wherein the selection of the estimated block geographic location is based upon a confidence-factor weighted agreement within the plurality of geographic locations.

126. The system of claim 123 wherein the blocking process comprises a divide-and-conquer blocking algorithm to identify common information between a subject network address and a test network address and to determine whether the subject and test network addresses are within a common network block of network addresses.

127. The system of claim 126 wherein the divide-and-conquer blocking algorithm is to identify a common geographic location associated with each of the subject and the test network addresses.

128. The system of claim 126 wherein the divide-and-conquer blocking algorithm is to identify a substantially common traceroute generated responsive to traceroute operations performed against each of the subject and test network addresses.

129. The system of claim 126 wherein the divide-and-conquer blocking algorithm is to identify whether the subject and test network addresses utilizing a common DNS server.

130. The system of claim 126 wherein the blocking process comprises a netmask blocking algorithm that utilizes a netmask associated with a subject network address.

131. The system of claim 123 wherein the blocking process is to utilize a topology map to identify the block of network addresses.

132. The system of claim 123 wherein the blocking process is to identify the block of network addresses has been a subnet, and to record the first geographic location as being associated with the block of network addresses within a record within the database for the subnet.

133. The system of claim 123 wherein the blocking process is to identify the block of network addresses by respective start and end network addresses.

134. A machine-readable medium storing a sequence of instructions that, when executed by machine, caused machine to:

perform at least one data collection operation to obtain information pertaining to a network address;

process the retrieved information to identify a plurality of geographic locations potentially associated with the network address, and attach a confidence factor to each of the plurality of geographic locations; and

select an estimated geographic location from the plurality of geographic locations as being a best estimate of a true geographic location of the network address, where the selection of the estimated geographic location is based upon a degree of confidence-factor weighted agreement within the plurality of geographic locations.

135. A system to estimate a geographic location associated with a network address, the system including:

first means for performing at least one data collection operation to obtain information pertaining to a network address;

second means for processing the retrieved information to identify a plurality of geographic locations potentially associated with the network address, and for attaching a confidence factor to each of the plurality of geographic locations; and

third means for selecting an estimated geographic location from the plurality of geographic locations as being a best estimate of a true geographic location of the network address, where the third means utilizes a degree of confidence-factor weighted agreement within the plurality of geographic locations to select the estimated geographic location.