The report reflects data for the 24-hour period leading up to midnight at the start of March 1, 2024 UTC
The significance of precise IP address geolocation is hard to overstate. Often, it serves as the only non-intrusive tool at our disposal to identify the geographical origins of our online customers and visitors. This invaluable data allows us to better cater to their needs by understanding and accommodating their geographical properties.
However, it's important to acknowledge that IP geolocation cannot guarantee 100% accuracy. There are inherent limitations to this technology, some of which we've explored in our blog post, How accurate can IP Geolocation get?.
Estimating the overall accuracy of IP geolocation is a complex task. It requires comprehensive testing against a vast array of IP addresses, each with different locations, sources, and providers. Ideally, this testing should be conducted against our potential customer base, associating IP addresses with real individuals, much like our visitors. However, this kind of ground-truth reference data is rare and must be collected with careful consideration.
One might ask, 'Where does BigDataCloud source its reference data for estimating and comparing IP geolocation accuracy across different providers?' This is a critical question as the quality and reliability of our reference data directly impact the validity of our accuracy assessments.
Our reference data is meticulously gathered from a variety of dependable sources.
Firstly, we request our website visitors to share their location with us. You may have noticed it when you visited our What is my IP Address? page. This real-time, user-verified location data serves as a valuable reference point for assessing the accuracy of IP geolocation services.
Then, we utilise a range of iOS and Android apps designed for network professionals and enthusiasts. These apps contribute to our data collection efforts by providing us with location information associated with the public IP addresses used by the app users.
We also offer a FREE Client-side reverse geocoding API, an industry-first service without access rate or volume limitations. This service ensures that the requested location likely reflects the caller's IP address and actual service location, providing another layer of reference data for our accuracy assessments.
At BigDataCloud, we are committed to maintaining the highest standards of data privacy and security. We strictly refrain from gathering any data that could establish a connection between a user's IP address or location and their actual identity. We do not seek to obtain information about end-users or their activities, nor do we engage in surveillance or store personal data associated with IP addresses, even for internal purposes.
In conclusion, our reference data is thoughtfully and responsibly collected, with a focus on providing the most accurate and comprehensive assessment of IP geolocation accuracy across different providers, while upholding the utmost respect for user privacy.
As soon as we receive an IP address/location pair through any of our collection channels, we immediately conduct an IP Geolocation check against our latest dataset and store the accuracy results. We also perform the same check against competitive datasets, automatically generating a comprehensive report, as presented below.
Importantly, the data we collect is ideally suited to represent real-life internet users, website visitors, mobile app users, and IoT devices worldwide. However, it is less likely to include infrastructural segments such as network routers or public websites.
We also go to great lengths to exclude abusers from our data. However, we do include VPN and other anonymisers, likely in the same way and proportion one would expect from the traffic hitting a random website or another public service.
We often receive several location samples for the same IP address, for example, when the user is on the move. We report based on the best hit point accuracy record, so it is based on distinct IP addresses regardless of the number of occurrences.
We apply the exact same algorithm and sample categorisation for all datasets, ensuring we are constantly comparing like with like.
This report can be customised to represent IP addresses reported from a selected country or territory only.
We are aiming to include as many IP Geolocation original data vendors as possible. We guarantee that we're making absolutely no use of another provider's data beyond the scope of this report.
If you are an IP Geolocation provider, please get in touch with us and let us access your data, API access is prefered, and we will happily include it in our automated report. We promise to handle your data with great respect and confidentiality expected.
* The blocks count field represents the total number of unique records presented in the dataset. This value is generally applicable to flat data sources. However, the BigDataCloud database does not have such a metric available as we get down to a single IP address resolution. Therefore, we offer a total number of network segments detected by the likelihood of serving the same territory and purpose.
BigDataCloud's innovative patented technology enables accurate estimation of the geographical area served by an IP address, rather than a specific device or location. This area is known as the confidence area, and it is represented by a polygon. Some other providers refer to it as an accuracy radius represented by a circle.
The chart provided below showcases the accuracy of this area. If the ground-truth location data is within the confidence area or the accuracy radius of the IP address, it is considered a hit. Therefore, the percentage of location data that hits within this area is known as the Hit Ratio.
Point accuracy is a critical aspect of IP geolocation that often gets overlooked. It represents the actual error in the straight distance detected between the actual location reported to the one estimated by an IP Geolocation. In simpler terms, it's a measure of how close the estimated location of an IP address is to its actual physical location.
Wired consumers form a significant portion of the data we analyze in our IP geolocation accuracy reports. This segment primarily represents fixed network installations such as wired WiFi, and includes both home and office consumers.
Understanding the geolocation of wired consumers is crucial for many businesses. Whether it's for delivering tailored content, improving service delivery, or enhancing security measures, accurate IP geolocation data can provide valuable insights.
However, the detection of fixed networks presents a complex issue. The dynamic nature of IP addresses, the use of VPNs and proxies, and other factors can lead to inaccuracies or false-positive cases.
Cellular consumers represent a significant and growing segment in our IP geolocation accuracy reports. This category includes users accessing the internet through mobile networks, a group that has seen exponential growth in recent years.
The geolocation of cellular consumers is crucial for businesses aiming to deliver location-specific services or content. Whether it's for mobile advertising, content customization, or fraud prevention, accurate IP geolocation data can provide invaluable insights.
This category represents the IP addresses that were detected as servicing hosting networks, VPNs, proxies and mixed networks. The mixed networks are the network subnets that serve legit customers like cellular users and are also subleased to host some VPN or proxy services.
In addition, this category also usually includes residential proxies.
The chart below represents the country estimation accuracy of the IP geolocation data providers.