IP geolocation accuracy levels
As mentioned above, accuracy is variable across country, region and city. Also, if one database isn’t updated as frequently as others, you can see disparities between different IP geolocation APIs.
The quality of the accuracy of IP geolocation depends on which database is being used; these vary from region to region. There are five major IP geolocation databases:
- African Network Information Centre (AfriNIC)
- American Registry for Internet Numbers (ARIN)
- Asia-Pacific Network Information Centre (APNIC)
- Latin American and Caribbean Internet Address Registry (LACNIC)
- RIPE Network Coordination Centre (RIPE NCC)
The accuracy of an IP address will vary largely from country to country. In parts of the world where there are many smaller countries, there is a higher chance that you get a false match on a specific country. In places like the U.S., it is almost always going to be accurate. Overall, the likelihood that your country match is accurate is about 95-99%.
However, when you start breaking it down to city level, the accuracy of the exact location of the user becomes far more variable. This is because the IP address that is identified is coming directly from a user’s Internet Service Provider (ISP). Most IPSs make use of dynamic IP addresses, particularly if they are relying on IPv4, of which there are a limited number of distinct addresses. This means that they may be using a random IP from a nearby town or city which may or may not be accurate as to where an individual who is accessing a site is physically located.
The information provided to the querying user is likely to be accurate to where the specific node exists, but not necessarily the exact location of the user. As a result, in order of accuracy, you will most likely get the correct country, but not necessarily the correct city or town (though it may within a reasonable range of distance), and almost certainly not the exact physical address of the user. If a user is attempting to access your site from a business, the results are more likely to be accurate, as many companies make use of static IP addresses.
Also, many IP geolocation services segment their products into different tiers. Some vendors will segment their database by the number of API calls they permit per month and frequency ie number per second. However, some will also ‘hide’ greater levels of accuracy behind higher product tiers. For example, a product might not be capable of sourcing IP geolocation data more accurate than country at the level 1 tier with more detailed data only becoming available at level 2 (at a higher cost).
How to Improve Geolocation Accuracy
Based on my experience and testing with IP geolocation, here are some tips on how to improve its accuracy.
- Use a High-Quality IP Geolocation Database:
- The accuracy of geolocation largely depends on the quality of the database you use. Opt for reputable providers who regularly update their databases with accurate and comprehensive data. Providers like MaxMind, IP2Location, and AbstractAPI are known for their reliability.
- Combine Multiple Data Sources:
- Relying on a single data source can limit accuracy. By combining data from multiple providers, you can cross-verify and improve the reliability of your geolocation results. Some services offer multi-source integration to enhance accuracy.
- Regular Database Updates:
- IP address allocations and geographic mappings change frequently. Ensure your IP geolocation database is updated regularly. Most premium providers offer weekly or monthly updates. Automate the update process to avoid relying on outdated data.
- Leverage User-Provided Data:
- Enhance geolocation data with user-provided information. For instance, if a user shares their location via a form or allows browser-based geolocation, use this data to refine IP-based guesses. This hybrid approach can significantly improve accuracy.
- Implement Fallback Mechanisms:
- Use fallback mechanisms when high accuracy is crucial. For example, if IP geolocation provides a broad location, ask users to confirm or refine their location via a prompt. This can be particularly useful for applications where precise location matters, such as local services or delivery apps.
- Consider Network Anomalies:
- Recognize that certain networks, such as VPNs, proxies, and mobile carriers, can skew geolocation data. Use services that detect and account for these anomalies. For instance, some databases can flag known VPN IP addresses, allowing you to handle them differently.
- Contextual Awareness:
- Use contextual data to refine geolocation. For example, if you know a user is accessing your service from a specific type of device or network, adjust your geolocation expectations accordingly. Mobile devices might have different geolocation patterns compared to desktop users.
- Machine Learning and Analytics:
- Implement machine learning algorithms to analyze and predict more accurate geolocation data. By studying patterns and trends in your user data, you can fine-tune your geolocation models to improve accuracy over time.
- User Feedback Loop:
- Establish a feedback loop where users can report incorrect geolocations. Use this feedback to continuously improve your geolocation accuracy. This can be as simple as a prompt asking users if their detected location is correct.
- Geolocation API Tuning:
- Some geolocation APIs allow tuning based on specific use cases. For example, you might adjust settings for better city-level accuracy versus country-level accuracy depending on your needs. Explore the configuration options provided by your geolocation service.
Practical Testing and Implementation
In my testing, I combined the following strategies for optimal results:
- Testing Multiple Providers: I evaluated several IP geolocation providers to compare their accuracy. MaxMind and AbstractAPI provided the best results in my case.
- Automated Updates: I set up an automated system to update the IP geolocation database weekly, ensuring I always used the latest data.
- User Data Integration: I integrated optional user location input to refine geolocation predictions. For example, asking users for their city when signing up significantly improved accuracy for subsequent visits.
- Contextual Adjustments: I adjusted my approach based on network types. For example, mobile users often had broader location data, so I used additional checks for better precision.
By combining these strategies, I was able to enhance the accuracy of IP geolocation significantly, providing more reliable location data for my applications.
