1.0 Introduction: the business and regulatory imperative for geolocation
The enforcement of region-specific regulations and content policies is a primary operational challenge for global online services. These services must reliably determine a user’s geographical location to tailor content and ensure compliance, but must do so without resorting to intrusive methods like GPS or Wi-Fi scanning, which require explicit user consent and are often declined. This necessitates a dependable, passive method for location inference.
The core problem is one of navigating a complex and fragmented regulatory landscape. In Europe, the General Data Protection Regulation (GDPR) mandates obtaining user consent for certain cookies. In a hypothetical nation like “Ruritania,” a law might require all websites to display a specific national message. For a global entity like Google or Amazon, the ability to distinguish a user in Germany from one in Ruritania is not merely a feature—it is a critical requirement for market access, with non-compliance risking substantial fines or outright bans.
The purpose of this report is to provide a technical analysis of the primary method used to solve this problem: IP address geolocation. We will examine how an emergent property of the internet’s core architecture enables location inference, analyze the data chain that supports it, evaluate its practical accuracy and inherent limitations, and discuss common methods of circumvention. The analysis begins with the foundational component of this system: the IP address itself.
2.0 The Accidental Locator: deconstructing the IPv4 address
The ability to geolocate an IP address is not a designed feature of the Internet Protocol but rather an emergent property—a foundational accident born from the technical requirements of scalable routing. Understanding the causal chain that led to this capability requires a structural analysis of the IPv4 address.
An IPv4 address is a 32-bit number, conventionally written in “dotted-decimal notation” as four decimal numbers separated by periods (e.g., 128.64.x.x). To make global internet routing computationally feasible, this address is divided into two parts:
- The Network Part: The initial bits of the address (the “prefix”) that identify the specific network to which the address belongs.
- The Host Part: The remaining bits that identify a unique device, or “host,” within that network.
This division is fundamental. Internet routers cannot maintain a routing table entry for every individual IP address on the planet. Instead, they use a technique called “longest prefix matching” to make forwarding decisions for massive blocks of addresses based on a single rule for their shared network part. This need for address aggregation, driven by the imperative for routing efficiency, is the first link in the causal chain. It forced the creation of an administrative overlay to manage the allocation of these address blocks, which in turn was repurposed for the unintended application of geolocation. This is the foundational accident that enables the entire system.
3.0 the geolocation data chain: from regional registries to commercial databases
The link between IP address blocks and physical locations is formalized and maintained through a multi-layered administrative and commercial ecosystem. This system, which governs the allocation of IPv4 addresses as a valuable and tradable commodity, serves as the primary source of geolocation data.
The key entities in this data chain include:
- ICANN (Internet Corporation for Assigned Names and Numbers): The global non-profit organization that provides high-level oversight for the IP address system.
- Regional Internet Registries (RIRs): Approximately five regional bodies that manage and allocate blocks of IP addresses to organizations within their specific territories (e.g., Africa, North America, Europe).
When an organization requires public IP addresses, it requests a block—often specified using slash notation (e.g., a /16block containing 65,536 addresses)—from its governing RIR. As a mandatory part of the registration process, the organization must provide its location. This administrative requirement creates the authoritative link between a block of digital addresses and a physical geography.
This raw registration data is then leveraged by third-party geolocation service providers, such as MaxMind. These companies act as data aggregators, compiling the public data from all RIRs and supplementing it with other data sources to create enhanced, queryable databases. When a service needs to determine a user’s location, it queries one of these providers with the user’s IP address and receives the geographical information associated with that address’s registered block. The accuracy of this entire system, therefore, depends directly on the quality of the initial registration data.
4.0 practical application and analysis of precision
Moving from theory to practice, this section evaluates the real-world accuracy of IP geolocation. Using a tracerouteexample that maps the network path from London, UK, to Beijing, China, we can analyze the round trip time and geolocation data for various “hops” along the route. This analysis reveals that the precision of IP geolocation can vary dramatically, from the sub-city level to being organizationally correct but physically misleading.
The following table breaks down the geolocation results for specific IP addresses along the route:
| IP Address / Network | Geolocation Result (as per MaxMind) | Analysis of Accuracy |
Case 1: High Precision<br>138.37.x.x | Tower Hamlets, London, UK (within 5km radius) | The user’s own IP address, registered to Queen Mary University, is geolocated with high precision. This is a direct result of the university providing a specific and accurate address during its RIR registration. |
Case 2: Organizational vs. Technical Location<br>146.97.143.217 | Birmingham, UK (within 20km radius) | This IP address belongs to JANET (Joint Academic Network). The result points to Birmingham, JANET’s registered headquarters. However, the network traffic is not physically routing through this location; the data reflects an administrative reality, not the technical path. |
Case 3: Suspected Inaccuracy<br>62.40.98.102 | The Netherlands | This IP address, part of the GÉANT (European academic network), is geolocated to the Netherlands. At this hop, however, the round trip time jumps from 16ms to over 200ms, a significant latency increase suggesting the physical path is far longer than one contained within Europe. |
The “Kansas Farmhouse” problem
A critical anomaly in IP geolocation arises when registration data lacks specificity. If an organization registers an IP block but only provides its country (e.g., “America”) without city or state details, a geolocation database may default to a calculated geographic center for that country.
This default behavior leads to a significant anomaly known as the “Kansas Farmhouse” problem. The geographic center of the United States is a point in rural Kansas. Consequently, any IP address with only country-level registration data points to this specific latitude and longitude. This has led to erroneous conclusions where individuals believe they have found the precise location of a device, when in fact the data’s precision was limited to the country level. This phenomenon underscores the danger of interpreting geolocation data without a clear understanding of its potential lack of granularity.
The variable accuracy of IP geolocation, and its widespread use for enforcing geo-restrictions, creates a strong incentive for users to actively obfuscate their location, leading directly to the use of circumvention techniques.
5.0 circumvention and obfuscation techniques
Because IP geolocation relies on the user’s public-facing IP address, users can employ techniques to mask their true location. This is frequently done to bypass geo-restrictions, such as accessing a streaming service like Netflix that offers different content libraries in different countries.
The two primary methods for circumventing IP geolocation are:
- Virtual Private Networks (VPNs) A VPN creates an encrypted tunnel for a user’s internet traffic, routing it from the user’s device to a server operated by the VPN provider. The traffic then exits onto the public internet from that server, which can be located anywhere in the world. This makes the user’s connection appear to originate from the VPN server’s location, not their own. Various tunneling methods are used to achieve this, from encapsulating one IP packet inside another to creating an SSH tunnel, but the principle remains the same: manipulating the network egress point to adopt a different geographical identity.
- Changing Network Connection A user’s public IP address is assigned by their current network provider. Simply switching networks can result in a new IP address with a different associated location. For instance, a user on a fixed-line university network in Tower Hamlets, London, can switch to a mobile hotspot. The connection is now managed by a different provider (e.g., BT Cellular) and may be assigned a new IPv4 and IPv6 address registered to a different location, such as Crawley. This simple action changes the geolocated position without requiring specialized software.
These techniques demonstrate that the location inferred from an IP address is tied to the network, not immutably to the user’s physical device.
6.0 conclusion: an inferential tool with inherent limitations
IP-based geolocation is an inferential, not a precise, technology. It does not function like the Global Positioning System (GPS), which can pinpoint a device’s exact coordinates. Instead, it infers location from a chain of administrative data originating from IP address registrations.
This method is generally reliable at the country level, with studies suggesting over 90% accuracy for IPv4 addresses. This makes it an essential tool for broad regulatory compliance and the enforcement of region-locked content.
However, its precision at the city or sub-city level is highly variable and entirely dependent on the quality of the registration data provided by the IP block owner. This can result in outputs that are organizationally correct but technically misleading, or in cases of non-specific data, wildly inaccurate defaults.
While IP geolocation is an indispensable tool for the modern internet, its outputs must be interpreted with a clear understanding of its underlying mechanisms and significant limitations. To treat its data as a precise indicator of physical location is to risk drawing false and potentially harmful conclusions.