LACeS Anycast Census

LACeS: an Open, Fast, Responsible and Efficient Longitudinal Anycast Census System

Contact: remi.hendriks@utwente.nl

• Downloading census data
• LACeS Explorer — interactive web interface
• Using the Python helper
• Understanding the dataset
  • Data recommendations
  • Detection methods
• Data structure
  • Columns
  • Geolocation accuracy metrics
  • Unicast geolocation results
• Measurement methodology
• Running your own census
• Citation

The parquet files in this repository can be downloaded in alternative formats via the manycast.net REST API:

Replace latest with a specific date (e.g., 2026-03-22) to download a historical snapshot. Both IPv4 and IPv6 are available with prefixes IPv4- and IPv6-.

Example:

curl -O https://manycast.net/api/v1/export/IPv4-2026-03-22.csv.gz

See the full API documentation for querying individual prefixes, ASNs, and daily statistics.

We provide an interactive dashboard for querying, visualizing, and exploring the census data:

Search & lookup. Enter any IP address, prefix, ASN, domain, or TLD to look up anycast status, geolocation of detected PoPs, and related network information. Historical snapshots are available for all census dates since March 21, 2024.

Compare mode. Select two different census dates to track how anycast deployment changed over time.

RIPE Atlas integration. Run the geolocation algorithm on public RIPE Atlas results.

Analytics. Statistics over time, anycast by region, Hilbert curve of anycast space, ...

The census_helper.py file provides convenience functions for downloading and filtering the dataset. Requires pandas, pyarrow, and requests.

import census_helper

# Download latest snapshot and filter to high-confidence anycast prefixes
census = census_helper.download_latest("v4")
anycast = census_helper.filter_anycast(census, "v4")

# Or use comprehensive coverage (includes borderline cases)
anycast = census_helper.filter_anycast(census, "v4", confidence="comprehensive")

python census_helper.py --ip-version v4 --date latest --prefixes-only
python census_helper.py --ip-version v4 --date 2026-03-22 --confidence comprehensive

Depending on requirements we recommend the following filtering:

High confidence:

(AB > 3) || (GCD > 1)

Comprehensive coverage:

(AB > 1) || (GCD > 1)

The census uses two detection methods:

Anycast-based (AB): Detects anycast using anycast. A prefix is considered anycast if multiple PoPs receive replies. See MAnycast2 for details. Three probe protocols are used: ICMP/ping, TCP SYN/ACK, and DNS/UDP.

• False positives (FPs): This method is known to produce FPs, especially when the number of receiving PoPs is < 4.

Latency-based (GCD): Detects anycast using latency measurements and Great-Circle-Distance calculations.

• False negatives (FNs): Highly accurate, but struggles with detecting regional anycast (i.e., anycast deployed within a tight geographic area).

NOTE We provide unicast geolocation results when AB > 1 but GCD == 1. This may help with determining whether AB results are FPs or GCD results are FNs.

Latest files (updated daily):

IPv4-latest.parquet
IPv6-latest.parquet
IPv4-latest.csv
IPv6-latest.csv
stats-latest

Historical files (since March 21, 2024):

YYYY/MM/DD/IPv4.parquet
YYYY/MM/DD/IPv6.parquet
YYYY/MM/DD/IPv4.csv
YYYY/MM/DD/IPv6.csv
YYYY/MM/DD/stats

The last three fields help in determining the confidence of geolocation results.

Limited data is provided in CSV format for ease of access via GitHub's Web UI:

prefix,number_of_sites,backing_prefix
1.1.1.0/24,67,1.1.1.0/24

We use the USC/ISI ANT IPv4 hitlist (ranked ICMP/ping responsive IP addresses per /24), supplemented by:

• Public DNS nameservers
• OpenINTEL infra:ns records

We use:

• AAAA records from OpenINTEL
• IPv6Hitlist
• IPv6-SRA from TU Dresden and HAW Hamburg

To minimize daily probing impact, we scan at /24 granularity. However, some prefixes contain mixed unicast and anycast IP addresses (see paper for details) which we detect using multi-target probing.

We make all measurement and analysis tooling publicly available under the MPL 2.0 license:

MAnycastR provides implementations for AB and GCD measurements

MiGreedy is our optimized implementation of iGreedy's algorithm for IP geolocation, designed for large-scale production censuses. It supports unicast geolocation, improved detection using intersection, and confidence/accuracy metrics.

When using this dataset for academic research, please cite the following paper:

@inproceedings{10.1145/3730567.3764484,
  author = {Hendriks, Remi and Luckie, Matthew and Jonker, Mattijs and van Rijswijk-Deij, Roland},
  title = {LACeS: an Open, Fast, Responsible and Efficient Longitudinal Anycast Census System},
  year = {2025},
  booktitle = {Proceedings of the 2025 Internet Measurement Conference}
}