--- license: cc-by-4.0 language: - en pretty_name: StatMapCorpus size_categories: - 10K **This repository is a mirror.** The citable source of record is the deposit in Dane > Badawcze UW (University of Warsaw): **https://doi.org/10.58132/FSJGSP**, version 2.0. > The thirteen files in `deposit/` are byte-identical to that release; `deposit/schema.json` > carries their SHA-256 sums so you can verify this yourself. Cite the DOI, not this URL. The deposit contains annotations, identifiers, image embeddings and documentation. No images are redistributed: each record points to an image on its original host, and an included script retrieves the images and verifies them against the checksums recorded at annotation time. - **Code:** https://github.com/MartaSolarz/StatMapCorpus (MIT), archived at https://doi.org/10.5281/zenodo.23215421. ## Before you use it - **The labels are a silver standard.** Against two human coders, precision was 0.90–0.97 for choropleths, diagrams and cartograms, 0.63–0.71 for heat maps, dot density and flow maps, and 0.52 for isolines (`deposit/VALIDATION.md`). The errors are systematic, not random noise. Labels for these four types should be verified before they are used as ground truth; `deposit/validation.csv` lets you estimate the error rate for a chosen subset. - **Splits.** Group records by `phash`, not by `duplicate_group_id`: 3,294 rows share a perceptual hash with another row without being byte-identical. Grouping also by `domain` further reduces leakage between maps from the same publisher. - **Class balance.** The type labels are multi-label and highly imbalanced. Choropleth covers 88.7% of records, and each of the five rarest types covers less than 1.5%. Report macro-averaged metrics. - **Legend type.** A null value in `legend_is_classed` means *not applicable* (no legend, a qualitative legend or an illegible one), not a missing value. - **Composition.** The corpus is shaped by the selection design: a web crawl, a classifier threshold of 0.80 and at most five images per domain. It is not a representative sample of statistical maps on the web, and it is not exhaustive even among the candidates examined. ## Files Everything under `deposit/` is the published release, unmodified. | File | Contents | |---|---| | `statmapcorpus_dataset.parquet` | The main table: one row per map, 23,549 rows, 30 columns. | | `embeddings_clip_vitl14.npz` | CLIP ViT-L/14 image embeddings, float32, 23,549 × 768, keyed by `uid`. | | `candidates.parquet` | All 203,371 candidates sampled in Stage 3, with the outcome of each step from the URL check to Gate 4. | | `validation.csv` | Labels of two human coders for the 157-map validation sample, model labels from two repeated runs of Gates 3 and 4 for 300 maps, and a blind audit of 150 images. | | `positive_keywords.json`, `negative_keywords.json` | The Stage 1 dictionaries: phrases and their weights. | | `download_images.py` | Retrieves the images and verifies them by SHA-256 and perceptual hash. | | `schema.json` | Field names, data types and SHA-256 checksums of all files. | | `CODEBOOK.md` | Every column of every data file: type, permitted and observed values, caveats. | | `VALIDATION.md` | Results of the technical validation. | | `DATASHEET.md` | The dataset documented following *Datasheets for Datasets*. | | `NOTICE.md` | Provenance, licences and the procedure for removal requests. | | `README.md` | The deposit's own README, as published. | The dataset viewer shows the main table only. The other files have to be loaded directly. ```python from huggingface_hub import hf_hub_download import numpy as np, pandas as pd repo = "msolarz/StatMapCorpus" df = pd.read_parquet(hf_hub_download(repo, "deposit/statmapcorpus_dataset.parquet", repo_type="dataset"), dtype_backend="numpy_nullable") npz = np.load(hf_hub_download(repo, "deposit/embeddings_clip_vitl14.npz", repo_type="dataset")) uids, emb = npz["uids"], npz["emb"] # emb.shape == (23549, 768) ``` ## How the corpus was built MapPool (Schnürer 2024) holds 75.9 million map-like images from the CommonPool collection of DataComp, each with its URL, alt-text and CLIP embedding. StatMapCorpus was selected from it in four stages, ordered by increasing cost per record: 1. **Keyword filter.** Weighted phrase dictionaries in 21 languages are matched against the alt-text; records scoring at least −1 are retained (54.9 million). 2. **Image classifier.** An SVM with a radial basis function kernel on the CLIP ViT-L/14 embeddings, trained with active learning; records with a predicted probability of at least 0.5 are passed on (about 1.68 million). 3. **Deduplication, sampling and retrieval.** URLs are normalised and deduplicated; records with a probability of at least 0.80 are sampled, at most five per domain (203,371 candidates); the URLs are checked and the images downloaded (116,573 images). 4. **Vision-language cascade.** Gate 1 (rule): the shorter side is at least 400 px. Gate 2 (Claude Haiku 4.5): a map covering at least about half of the image. Gate 3 (Claude Haiku 4.5): a statistical map with quantitative data referenced to administrative or statistical units, with English lettering. Gate 4 (Claude Sonnet 4.6): at least one of the seven thematic map types, the legend type and the model's confidence. The images were downloaded on 1 August 2026 and annotated on 1–2 August 2026. Each map carries seven non-exclusive thematic map type labels (choropleth, diagram, isoline, dot density, heat map, cartogram, flow map), a legend type, a one-sentence description, the model's self-reported confidence, image properties, SHA-256 and perceptual hashes, and a CLIP image embedding. The alt-text is not redistributed; it belongs to MapPool and can be joined on `uid`. ## Getting the images ``` cd deposit python download_images.py --data statmapcorpus_dataset.parquet --out images/ --verify-phash ``` Every downloaded file is checked against the `sha256` recorded at annotation time. Where the hash differs but the 64-bit `phash` still matches, the host has only re-encoded the file and the labels still apply. Where neither matches, the image has changed and its labels should not be used. All 23,549 images were retrievable on 1 August 2026, and the share of reachable URLs will decline over time. ## Licence Annotations, metadata and documentation are CC BY 4.0. The CLIP embeddings are derived from MapPool and are also CC BY 4.0. `download_images.py` is MIT; the Hub records a single licence per repository, so the MIT grant is stated here and in the file's own header. Rights in the images remain with their owners; they are neither redistributed nor licensed here. `deposit/NOTICE.md` gives the full attribution, the derivation chain from MapPool and CommonPool/DataComp, and the procedure for removal requests. Removal requests are handled against the deposit, and this mirror is updated to match. ## Citation ```bibtex @dataset{solarz2026statmapcorpus, author = {Solarz, Marta}, title = {{StatMapCorpus}: 23,549 English-language statistical maps from {MapPool}, annotated by thematic map type using vision-language models}, year = {2026}, publisher = {Dane Badawcze UW}, version = {V2}, doi = {10.58132/FSJGSP}, url = {https://doi.org/10.58132/FSJGSP} } ``` Please also cite MapPool, from which the identifiers and embeddings are inherited: Schnürer, R. (2024). *MapPool – Bubbling up an extremely large corpus of maps for AI.* Abstracts of the ICA 8, 22. https://doi.org/10.5194/ica-abs-8-22-2024 ## Versions | Tag | Deposit version | Notes | |---|---|---| | `v1.0` | V1 (2026-08-24) | Initial release. Model-generated annotations, no human validation. | | `v2.0` | V2 | Validation files, candidate list, datasheet; "cartographic method" renamed to "thematic map type"; file names without a version number. The main table has the same columns and values as in V1. | ## Contact Marta Solarz, Faculty of Geography and Regional Studies, University of Warsaw – m.solarz2@uw.edu.pl Funded by the University of Warsaw under the Excellence Initiative – Research University (IDUB) programme, through a microgrant for doctoral students (Action IV.4.1), BOB-IDUB-622-272-2026.