Mirror of DOI 10.58132/FSJGSP, version 1.0

README.md

@@ -1,3 +1,177 @@
 ---
 license: cc-by-4.0
+language:
+  - en
+pretty_name: StatMapCorpus
+size_categories:
+  - 10K<n<100K
+annotations_creators:
+  - machine-generated
+task_categories:
+  - image-classification
+tags:
+  - cartography
+  - thematic-maps
+  - statistical-maps
+  - choropleth
+  - vlm-annotations
+  - silver-standard
+  - web-crawl
+configs:
+  - config_name: default
+    data_files:
+      - split: full
+        path: deposit/statmapcorpus_dataset_v1.parquet
 ---
+
+# StatMapCorpus v1
+
+23,549 English-language statistical maps identified in MapPool, annotated for cartographic
+method by vision-language models.
+
+> **This repository is a mirror.** The citable source of record is the deposit in Dane
+> Badawcze UW (University of Warsaw, ICM): **https://doi.org/10.58132/FSJGSP**, version 1.0.
+> The nine files in `deposit/` are byte-identical to that release; `deposit/schema.json`
+> carries their sha256 sums so you can verify this yourself. Cite the DOI, not this URL.
+
+This deposit contains annotations and identifiers only. No images are redistributed; each
+record points to an image on its original host, and an included script retrieves them.
+
+## Before you use it
+
+**The annotation layer is a silver standard.** Columns 16-27 of the table were generated by
+vision-language models and have not been validated against human coding. A human-coded
+validation is planned for a subsequent version. If you intend to use this corpus to
+evaluate a model, read that sentence again: the labels are a model's opinion, not ground
+truth.
+
+**The corpus is not a random sample of statistical maps.** It inherits the composition of a
+web crawl and the selection criteria that produced it. Four thresholds shape every
+distribution you will see: records scoring below -1.0 on the keyword filter were dropped,
+the corpus was drawn from classifier scores of 0.80 and above, images smaller than 400 px
+on their shorter side were excluded, and no more than five images were kept per source
+domain.
+
+Three further points from `deposit/CODEBOOK.md` matter before any analysis:
+
+- **Splits.** Group on `phash`, not on `duplicate_group_id`. 3,294 rows share a perceptual
+  hash with another row without being byte-identical, and partitioning on the byte-level
+  column alone will leak between train and test.
+- **Class balance.** The method labels are multi-label and severely imbalanced: choropleth
+  covers 88.7% of records, four classes sit below 1.5%. Report macro-averaged metrics.
+- **Null in `legend_is_classed`.** A third value meaning the classed-versus-continuous
+  distinction does not apply, not a missing one. It affects a quarter of the corpus, and it
+  is not missing at random: it correlates with cartographic method and with the annotating
+  model's self-reported confidence.
+
+## Files
+
+Everything under `deposit/` is the published release, unmodified.
+
+| File | Contents |
+|---|---|
+| `statmapcorpus_dataset_v1.parquet` | The annotation table. 23,549 rows, 30 columns. |
+| `embeddings_clip_vitl14.npz` | CLIP ViT-L/14 image embeddings, float32, 23,549 × 768, aligned row-for-row with the Parquet table by `uid`. |
+| `download_images.py` | Retrieves the images and verifies each download against the published checksums. |
+| `positive_keywords.json` `negative_keywords.json` | The weighted phrase dictionaries used in the first selection step. |
+| `schema.json` | Machine-readable field list and file checksums. |
+| `CODEBOOK.md` | Every column: type, permitted values, observed distribution, and caveats for analysis. |
+| `NOTICE.md` | Attribution, derivation chain, licensing and removal procedure. |
+| `README.md` | The deposit's own README, as published. |
+
+The dataset viewer shows the Parquet table only. The embeddings are a separate NPZ file and
+have to be loaded directly.
+
+```python
+from huggingface_hub import hf_hub_download
+import numpy as np, pandas as pd
+
+repo = "msolarz/StatMapCorpus"          # adjust if the namespace differs
+df = pd.read_parquet(hf_hub_download(repo, "deposit/statmapcorpus_dataset_v1.parquet",
+                                     repo_type="dataset"))
+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
+
+Candidates were drawn from MapPool (Schnürer 2024), a collection of 75.9 million map-like
+images derived from CommonPool/DataComp, and narrowed in three stages: a multilingual
+keyword filter over the alt-text inherited from MapPool, an SVM-RBF classifier over CLIP
+ViT-L/14 embeddings trained by active learning, and a cascade of vision-language model
+gates applied to the images that could still be retrieved. The gates ran on Claude Haiku
+4.5 (is this a map, and is it a statistical map with quantitative data on administrative
+units, lettered in English) and Claude Sonnet 4.6 (cartographic method and legend type).
+Annotations were generated on 1-2 August 2026. The cascade reduced 75.9 million candidates
+to 54.9 million after the keyword filter, roughly 1.68 million after the classifier and
+23,549 after the vision-language gates.
+
+The alt-text itself is not redistributed here; it belongs to MapPool and can be joined back
+on `uid`. The two phrase dictionaries are included so that the first step can be reproduced
+against that source.
+
+Every record is a statistical map: numeric values attached to administrative or statistical
+units, lettered in English where the map carries any text, and carrying at least one
+identified cartographic method from a seven-class taxonomy (choropleth, diagrams, dot
+density, isolines, cartogram, flow map, heat map). Classification is multi-label; the
+taxonomy is defined in `deposit/CODEBOOK.md`.
+
+## Getting the images
+
+```
+cd deposit
+python download_images.py --data statmapcorpus_dataset_v1.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 re-encoded the file and the
+annotations continue to apply. Where neither matches, the content has changed and the
+annotations do not.
+
+All 23,549 records were reachable on 1 August 2026. Because the population comes from a web
+crawl, the reachable fraction only decreases from that date.
+
+## Licence
+
+Annotations, metadata and documentation are 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 rather than in the repository metadata. Rights in the images themselves
+remain with their original owners; they are neither redistributed nor licensed here.
+
+`deposit/NOTICE.md` carries the full attribution, the derivation chain from MapPool and
+CommonPool/DataComp, and the procedure for requesting removal of a record. Removal requests
+from rights holders and erasure requests under Art. 17 GDPR are honoured; they 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 for cartographic method by vision-language models},
+  year      = {2026},
+  publisher = {Dane Badawcze UW},
+  version   = {V1},
+  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.*
+
+## Versions
+
+| Tag | Deposit version | Notes |
+|---|---|---|
+| `v1.0` | V1 (2026-08-24) | Initial release. Model-generated annotations, no human validation. |
+
+## Contact
+
+Marta Solarz, Faculty of Geography and Regional Studies, University of Warsaw —
+m.solarz2@uw.edu.pl
+
+Funded by the University of Warsaw Excellence Initiative — Research University (IDUB),
+Action IV.4.1, Microgrants for Doctoral Candidates 2026, grant BOB-IDUB-622-272-2026.

deposit/CODEBOOK.md

@@ -0,0 +1,117 @@
+# CODEBOOK
+
+## Quick summary
+
+- Row per image.
+- 23,549 rows, 30 columns, one Parquet file. 
+- Embeddings stored in a separate NPZ file, one-to-one with rows based on `uid`. 
+- Timestamps are ISO-8601 local-naive strings; the timezone is UTC and is not written into the value.
+- Every count in this document is computed from the published file. 
+- For details on selection & annotation of the corpus, see README.
+
+## File: statmapcorpus_dataset_v1.parquet
+
+### Columns:
+
+| # | Column | Type | Description and observed values |
+|---|---|---|---|
+| 1 | `uid` | str | 32-char lowercase hex, unique. Inherited from MapPool. Join key to the NPZ. |
+| 2 | `url` | str | Image location on its original host. 18,284 https; 5,265 http. Inherited from MapPool, not normalised. |
+| 3 | `domain` | str | Host from `url`. 16,374 distinct; capped at five images per domain during selection. |
+| 4 | `keyword_score` | float64 | Sum of the weights of the distinct dictionary phrases matched in the MapPool alt-text; each phrase counts at most once per record. Positive phrases contribute +0.5 to +5.0, negative ones -0.5 to -5.0. Range -1.0 to 23.5; median 0.0; 39 distinct values. Records scoring below -1.0 were dropped. |
+| 5 | `pos_keys` | str, empty where none | Pipe-separated positive phrases matched. 557 distinct here; commonest `state`, `covid`, `population`, `election`. |
+| 6 | `neg_keys` | str, empty where none | Pipe-separated negative phrases matched. 140 distinct here; commonest `screen`, `city`, `image`, `urban`. |
+| 7 | `pred_proba` | float64 | Classifier probability that the image is a statistical map. 0.800015-1.0; median 0.95. The corpus was drawn from records at 0.80 and above, so this column is truncated and is not the classifier's output distribution. |
+| 8 | `image_width` | int64 | Pixels. 400-16,656. |
+| 9 | `image_height` | int64 | Pixels. 400-12,960. Selection required `min(width, height)` of at least 400. |
+| 10 | `image_max_dim` | int64 | Equals `max(width, height)` in every row. Redundant, kept for convenience. |
+| 11 | `image_format` | str | JPEG 13,249; PNG 10,092; WEBP 194; BMP 14. |
+| 12 | `image_size_bytes` | int64 | 6,571-20,738,937; median 104,548. |
+| 13 | `sha256` | str | 64-char hex of the annotated bytes. 22,499 distinct. Exact-match check after download. |
+| 14 | `phash` | str | 16-char hex, 64-bit perceptual hash. 20,399 distinct. Matches after host re-encoding, where the annotations still apply. |
+| 15 | `retrieved_at` | str | Fetch time. All 1 Aug 2026, 11:19-15:26 UTC. |
+| 16 | `short_description` | str | Content description generated by Claude Haiku 4.5. 122-557 chars; median 222; typically one or two sentences. 23 duplicated values. |
+| 17 | `has_text` | bool | Whether the map carries any lettering at all - title, legend, labels or source. `False` (17 rows) means a graphically clean map; `True` 23,532. |
+| 18 | `method_choropleth` | bool | M1. `True` 20,888 (88.7%). |
+| 19 | `method_diagrams` | bool | M2. `True` 3,868 (16.4%). |
+| 20 | `method_dot_density` | bool | M4. `True` 215 (0.9%). |
+| 21 | `method_isolines` | bool | M3. `True` 275 (1.2%). |
+| 22 | `method_cartogram` | bool | M6. `True` 302 (1.3%). |
+| 23 | `method_flow_map` | bool | M7. `True` 198 (0.8%). |
+| 24 | `method_heat_map` | bool | M5. `True` 311 (1.3%). |
+| 25 | `methods_count` | int64 | Sum of the seven `method_*` columns; verified in every row. 1 → 21,158; 2 → 2,278; 3 → 110; 4 → 2; 5 → 1. Never 0: at least one method was required for inclusion. |
+| 26 | `legend_is_classed` | object, **null in 6,053 (25.7%)** | `True` 14,857; legend divided into discrete classes. `False` 2,639; continuous or unclassed legend. Null is a third value meaning the distinction does not apply, not a missing one - see "Using the data". |
+| 27 | `classification_confidence` | str | The annotating model's own confidence in the representation methods it identified. `high` 18,644; `medium` 4,899; `low` 6. Self-reported, not derived. |
+| 28 | `is_duplicate_byte` | bool | `True` 1,050. Marks redundant copies; one representative per group stays `False`. |
+| 29 | `duplicate_group_id` | str, null in 21,663 | Equals `sha256[:16]`. 1,886 rows in 836 groups. Null means no byte-identical twin. |
+| 30 | `annotated_at` | str | Annotation time. 1 Aug 2026 15:43 - 2 Aug 2026 09:55 UTC. |
+
+Columns 16-27 are model-generated. They have not been checked against human coding; a
+human-coded validation is planned for a subsequent version.
+
+### Cartographic method taxonomy (M1-M7)
+
+Multi-label: the seven flags are independent and hybrid maps are common. Definitions
+follow the standard thematic-cartography textbooks.
+
+| ID | Column | Definition |
+|---|---|---|
+| M1 | `method_choropleth` | Reference units filled with colour representing a data value. Classed (discrete) or unclassed (continuous). Values typically normalised: densities, rates, percentages. |
+| M2 | `method_diagrams` | Point or areal symbols - circles, squares, triangles, pie charts, icons - sized or structured in proportion to value. Covers proportional (size ∝ value), graduated (discrete size classes) and structural variants (pie charts on a map, multivariate glyphs). Subtypes are not distinguished. |
+| M3 | `method_isolines` | Isarithmic map: lines joining points of equal value, or fills between them. Colour-zone boundaries are smooth curves, not administrative edges. |
+| M4 | `method_dot_density` | Uniform dots, each standing for a fixed count (e.g. 1 dot = 1,000 people). Dot size carries no value; local dot density does. |
+| M5 | `method_heat_map` | Continuous interpolated surface (kernel density, IDW, kriging) showing local intensity without discrete zone boundaries. Distinguished from M3 by the absence of discrete isolines; the colour transition is smooth. |
+| M6 | `method_cartogram` | Unit geometry distorted in proportion to value. Contiguous (warped borders), non-contiguous (units float), Dorling (one circle per unit), hex-tile / tile grid. Data are encoded by geometry, not colour. |
+| M7 | `method_flow_map` | Arrows or directional lines representing movement between units (migration, trade, transport). Value typically encoded by line thickness or length. |
+
+Maps matching none of the seven were rejected during selection and are not in the corpus.
+
+## File: `embeddings_clip_vitl14.npz`
+
+`uids` - 23,549 strings, same values and same order as the `uid` column.
+`emb` - float32, shape (23,549, 768), L2-normalised at source (observed norms
+1.0000 ± 0.0006), no NaN or infinite values.
+
+These are OpenAI CLIP ViT-L/14 image features (`open_clip`: model `ViT-L-14`, pretrained
+tag `openai`; Hugging Face: `openai/clip-vit-large-patch14`), computed by DataComp for
+CommonPool and passed through MapPool unchanged. Standard CLIP preprocessing applies:
+bicubic resize, 224×224 centre crop, OpenAI channel means and standard deviations.
+
+Note that CommonPool resized images so that the largest dimension did not exceed 512 px
+*before* extracting features. Images in this corpus run up to 16,656 px wide, so the
+embedding encodes a heavily downscaled view. Fine detail - legend text, thin borders,
+small labels - is unlikely to survive into the vector, which matters if you intend to use
+these embeddings to detect textual or fine-grained cartographic properties.
+
+## Using the data
+
+**Group on `phash`, not on `duplicate_group_id`, when building splits.** Columns 28-29
+cover byte-identical files only. 5,180 rows share a `phash` with at least one other row,
+but only 1,886 of them carry a `duplicate_group_id`: the remaining 3,294 are re-encodings
+and rescalings that `sha256` grouping cannot see. Relaxing the match to a Hamming distance
+of 2, which catches typical re-encodings, raises the affected total to 6,310 rows.
+Partitioning on `duplicate_group_id` alone will leak between train and test.
+
+**Report macro-averaged metrics, not accuracy.** The method labels are multi-label and
+severely imbalanced: choropleth covers 88.7% of records while four classes sit below 1.5%.
+
+**Do not impute null in `legend_is_classed` as `False`.** The annotating model was
+instructed to return null when the classed-vs-continuous distinction does not apply: no
+legend is present, the legend is qualitative (unordered hues for nominal categories)
+rather than a quantitative scale, or it is illegible. Ties were to be broken toward `True`,
+so null is not a catch-all for uncertainty.
+
+The three causes are not separable in this release. Their relative weight can be read
+indirectly: the null rate is flat across resolution quartiles (24.6-27.9%), so
+illegibility is not the main driver, but it varies sharply by method: 2.2% for isolines,
+20.6% for choropleth, 45-60% for diagrams, flow maps, dot density, heat maps and
+cartograms, where value is carried by symbols rather than by a classed fill. Among
+choropleth-only maps, where a classed legend would be expected, 3,414 of 18,683 (18.3%)
+are still null.
+
+Null is not missing at random; it correlates with method and with
+`classification_confidence` (16.3% of `high`, 61.5% of `medium`), so a complete-case
+analysis over the 17,496 resolved rows is biased toward maps with a legible quantitative
+legend. Report that denominator explicitly. The column is stored as `object` rather than a
+nullable boolean dtype; worth fixing in a future version so that readers do not get Python
+`None` mixed with `numpy.bool_`.

deposit/NOTICE.md

@@ -0,0 +1,67 @@
+# NOTICE
+
+StatMapCorpus distributes **annotations and image identifiers only**. It does not contain,
+mirror or redistribute any images. Each record points to an image hosted by a third party
+(`url`) and carries verification surrogates (`sha256`, `phash`, `image_width`,
+`image_height`) that let a user confirm a file retrieved today is the file that was
+annotated.
+
+## Provenance
+
+```
+Common Crawl  →  CommonPool / DataComp  →  MapPool (Schnürer 2024)  →  StatMapCorpus
+```
+
+- **MapPool** — Schnürer, R. (2024). *MapPool — Bubbling up an extremely large corpus of
+  maps for AI.* https://infoscience.epfl.ch/handle/20.500.14299/240495 —
+  https://huggingface.co/datasets/sraimund/MapPool — 75,890,407 map-like images
+  identified in CommonPool by a support vector machine, released under CC BY 4.0.
+  StatMapCorpus inherits `uid`, `url` and the CLIP ViT-L/14 image embeddings (`l14_img`)
+  from MapPool, which in turn carries them from CommonPool. **Modified:** a subset was
+  selected and independently re-annotated.
+- **CommonPool / DataComp** — https://www.datacomp.ai — an index of image–text pairs.
+  Its authors distribute the url–text samples and metadata under CC BY 4.0 and state that
+  the individual images remain under their own copyrights, that the index is provided as
+  is, and that any content downloaded from it is used at the user's own risk and subject
+  to the terms or copyright limitations accompanying that content.
+
+Like its parents, StatMapCorpus stores links rather than images, for copyright reasons.
+
+Neither Raimund Schnürer nor the DataComp authors are affiliated with StatMapCorpus or
+endorse it.
+
+## Licensing
+
+| Component | License |
+|---|---|
+| Annotations, metadata, documentation | **CC BY 4.0** — https://creativecommons.org/licenses/by/4.0/legalcode |
+| `download_images.py` | **MIT** — see the licence header in the file |
+| CLIP embeddings | derived from MapPool, **CC BY 4.0** (attribution to MapPool required) |
+| **Source images** | **rights remain with their original owners. Not redistributed. Not licensed by us.** |
+
+Downloading images retrieves files directly from their original hosts. Whether a given
+image may be used for a given purpose is determined by that image's own copyright status,
+not by this dataset's license. Users are responsible for their own compliance.
+
+## Removal requests
+
+**Rights holders.** Removal requests are honoured. Write to **Marta Solarz —
+m.solarz2@uw.edu.pl**, Faculty of Geography and Regional Studies, University of Warsaw,
+giving the `uid` or `url` of the affected record. Requests are acknowledged within 30
+days. Affected records are removed and a corrected version of the dataset is published;
+this does not wait for a scheduled release.
+
+**Personal data.** The corpus is derived from a web crawl and some linked images may
+depict identifiable individuals. Data subjects may request erasure of a record under
+Art. 17 GDPR at the address above, or through the University of Warsaw Data Protection
+Officer at iod@adm.uw.edu.pl.
+
+Because this dataset contains no images, removal means deleting the identifier and its
+annotations from the index — the image itself remains under its owner's sole control at
+all times.
+
+## Citation
+
+Solarz, Marta, 2026, "StatMapCorpus: 23,549 English-language statistical maps from
+MapPool, annotated for cartographic method by vision-language models",
+https://doi.org/10.58132/FSJGSP, Dane Badawcze UW, V1

deposit/README.md

@@ -0,0 +1,94 @@
+# StatMapCorpus v1
+
+23,549 English-language statistical maps identified in MapPool, annotated for cartographic
+method by vision-language models.
+
+DOI: https://doi.org/10.58132/FSJGSP
+
+This deposit contains annotations and identifiers only. No images are redistributed; each
+record points to an image on its original host, and an included script retrieves them.
+
+## Files
+
+| File | Contents |
+|---|---|
+| `statmapcorpus_dataset_v1.parquet` | The annotation table. 23,549 rows, 30 columns. |
+| `embeddings_clip_vitl14.npz` | CLIP ViT-L/14 image embeddings, float32, 23,549 × 768, aligned row-for-row with the Parquet table by `uid`. |
+| `download_images.py` | Retrieves the images and verifies each download against the published checksums. |
+| `positive_keywords.json` `negative_keywords.json` | The weighted phrase dictionaries used in the first selection step. |
+| `schema.json` | Machine-readable field list and file checksums. |
+| `CODEBOOK.md` | Every column: type, permitted values, observed distribution, and caveats for analysis. |
+| `NOTICE.md` | Attribution, derivation chain, licensing and removal procedure. |
+
+## How the corpus was built
+
+Candidates were drawn from MapPool (Schnürer 2024), a collection of 75.9 million map-like
+images derived from CommonPool/DataComp, and narrowed in three stages: a multilingual
+keyword filter over the alt-text inherited from MapPool, an SVM-RBF classifier over CLIP
+ViT-L/14 embeddings trained by active learning, and a cascade of vision-language model
+gates applied to the images that could still be retrieved. The gates ran on Claude Haiku
+4.5 (is this a map, and is it a statistical map with quantitative data on administrative
+units, lettered in English) and Claude Sonnet 4.6 (cartographic method and legend type).
+Annotations were generated on 1-2 August 2026. The cascade reduced 75.9 million candidates
+to 54.9 million after the keyword filter, roughly 1.68 million after the classifier and
+23,549 after the vision-language gates.
+
+The alt-text itself is not redistributed here; it belongs to MapPool and can be joined
+back on `uid`. The two phrase dictionaries are included so that the first step can be
+reproduced against that source.
+
+Every record is a statistical map: numeric values attached to administrative or statistical
+units, lettered in English where the map carries any text, and carrying at least one
+identified cartographic method from a seven-class taxonomy (choropleth, diagrams, dot density, isolines, cartogram, 
+flow map, heat map). Classification is multi-label; the 
+taxonomy is defined in `CODEBOOK.md`.
+
+Four thresholds shape the distributions you will see in the data: records scoring below
+-1.0 on the keyword filter were dropped, the corpus was drawn from classifier scores of
+0.80 and above, images smaller than 400 px on their shorter side were excluded, and no
+more than five images were kept per source domain.
+
+## Getting the images
+
+```
+python download_images.py --data statmapcorpus_dataset_v1.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 re-encoded the file
+and the annotations continue to apply. Where neither matches, the content has changed and
+the annotations do not.
+
+All 23,549 records were reachable on 1 August 2026. Because the population comes from a
+web crawl, the reachable fraction only decreases from that date.
+
+## Before you use it
+
+Annotations are model-generated and have not been validated against human coding; treat
+the annotation layer as a silver standard. The corpus is not a random sample of
+statistical maps - it inherits the composition of a web crawl and the selection criteria
+that produced it.
+
+Three points from `CODEBOOK.md` are worth reading before any analysis:
+
+- **Splits.** Group on `phash`, not on `duplicate_group_id`. 3,294 rows share a perceptual
+  hash with another row without being byte-identical, and partitioning on the byte-level
+  column alone will leak between train and test.
+- **Class balance.** The method labels are multi-label and severely imbalanced: choropleth
+  covers 88.7% of records, four classes sit below 1.5%. Report macro-averaged metrics.
+- **Null in `legend_is_classed`.** A third value meaning the classed-vs-continuous
+  distinction does not apply, not a missing one. It affects a quarter of the corpus and is
+  not missing at random.
+
+## Licence, citation and contact
+
+Annotations, metadata and documentation are CC BY 4.0; `download_images.py` is MIT. Rights
+in the images themselves remain with their original owners. `NOTICE.md` carries the full
+attribution, the derivation chain from MapPool and CommonPool/DataComp, the citation, and
+the procedure for requesting removal of a record.
+
+Marta Solarz, Faculty of Geography and Regional Studies, University of Warsaw -
+m.solarz2@uw.edu.pl
+
+Funded by the University of Warsaw Excellence Initiative - Research University (IDUB),
+Action IV.4.1, Microgrants for Doctoral Candidates 2026, grant BOB-IDUB-622-272-2026.

deposit/download_images.py

@@ -0,0 +1,291 @@
+#!/usr/bin/env python3
+"""
+Download StatMapCorpus source images and verify them against the published checksums.
+
+StatMapCorpus ships URLs, not images (see NOTICE.md). This script fetches them and tells you,
+per file, whether what you got is what was annotated.
+
+    python download_images.py --data statmapcorpus_dataset_v1.parquet --out ./images
+
+Verification outcomes written to the report:
+    ok          sha256 matches -- byte-identical to the annotated file
+    recoded     sha256 differs, pHash within --phash-tolerance -- same map, re-encoded
+                by the host (annotations still apply; pixel-level work may not)
+    changed     sha256 and pHash both differ -- the URL now serves different content;
+                DO NOT treat the annotations as valid for this file
+    failed      could not be downloaded (dead link, timeout, HTTP error, or the host
+                returned something that is not an image -- e.g. a soft-404 page)
+
+Resumable: files already present and verified are skipped, including ones previously
+classified as 'recoded' (when --verify-phash is on). Downloads are atomic -- an
+interrupted or failed fetch never destroys a copy you already have. Interrupt freely.
+
+Requires: pandas, pyarrow, requests. Pillow + scipy only if you use --verify-phash
+(they are imported lazily, so the script runs without them otherwise).
+"""
+
+# Copyright (c) 2026 Marta Solarz
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import argparse, hashlib, os, threading, time, pandas as pd, requests
+
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+UA = ("StatMapCorpus-downloader/1.0 (academic dataset reconstruction; "
+      "contact: m.solarz2@uw.edu.pl)")
+
+# Content sniffing: leading bytes for the formats the dataset actually contains.
+MAGIC = (b"\xff\xd8\xff", b"\x89PNG\r\n\x1a\n", b"RIFF", b"BM", b"GIF8", b"II*\x00", b"MM\x00*")
+
+RETRY_STATUS = {408, 425, 429, 500, 502, 503, 504}
+
+_local = threading.local()
+
+
+def session_for_thread():
+    """One Session per worker thread: requests.Session is not documented as thread-safe."""
+    s = getattr(_local, "session", None)
+
+    if s is None:
+        s = requests.Session()
+        s.headers.update({"User-Agent": UA, "Accept": "image/*,*/*;q=0.8"})
+        adapter = requests.adapters.HTTPAdapter(pool_connections=4, pool_maxsize=4)
+        s.mount("http://", adapter)
+        s.mount("https://", adapter)
+        _local.session = s
+
+    return s
+
+
+def sha256_of(path):
+    h = hashlib.sha256()
+
+    with open(path, "rb") as f:
+        for chunk in iter(lambda: f.read(1 << 20), b""):
+            h.update(chunk)
+
+    return h.hexdigest()
+
+
+def phash_of(path):
+    """
+    Same parameters as the published `phash` column: 32x32 grayscale, 2D DCT,
+    top-left 8x8 block thresholded at its median (DC term included, which is why
+    bit 0 is always set), read row-major into 64 bits.
+    """
+    import numpy as np
+    from PIL import Image
+    from scipy.fftpack import dct
+
+    im = Image.open(path).convert("L").resize((32, 32), Image.LANCZOS)
+    d = dct(dct(np.asarray(im, dtype=np.float64), axis=0, norm="ortho"), axis=1, norm="ortho")
+    low = d[:8, :8]
+    val = 0
+
+    for b in (low > np.median(low)).flatten():
+        val = (val << 1) | int(b)
+
+    return f"{val:016x}"
+
+
+def hamming(a, b):
+    return bin(int(a, 16) ^ int(b, 16)).count("1")
+
+
+def classify_local(path, row, tol):
+    """Compare a file already on disk against the published checksums."""
+    if sha256_of(path) == row["sha256"]:
+        return "ok", None
+
+    if row.get("phash"):
+        try:
+            d = hamming(phash_of(path), row["phash"])
+            return ("recoded" if d <= tol else "changed"), f"hamming={d}"
+        except Exception as e:
+            return "changed", f"phash failed: {type(e).__name__}"
+
+    return "changed", "sha256 mismatch"
+
+
+def looks_like_image(path, content_type):
+    """Guard against soft-404s: hosts that serve an HTML error page with HTTP 200."""
+    if content_type and content_type.split(";")[0].strip().lower().startswith("text/"):
+        return False
+
+    with open(path, "rb") as f:
+        head = f.read(12)
+
+    return head.startswith(MAGIC)
+
+
+def download(url, dest, timeout, retries, backoff):
+    """
+    Fetch to a temporary file and return its path. Never touches `dest` itself,
+    so a failure here cannot destroy an existing copy.
+    """
+    tmp = dest + ".part"
+    last = None
+
+    for attempt in range(retries + 1):
+        try:
+            r = session_for_thread().get(url, timeout=timeout, stream=True)
+
+            if r.status_code in RETRY_STATUS and attempt < retries:
+                r.close()
+                last = requests.HTTPError(f"HTTP {r.status_code}")
+                time.sleep(backoff * (2 ** attempt))
+                continue
+
+            with r:
+                r.raise_for_status()
+                with open(tmp, "wb") as f:
+                    for chunk in r.iter_content(1 << 16):
+                        f.write(chunk)
+
+            return tmp, r.headers.get("Content-Type"), None
+        except (requests.Timeout, requests.ConnectionError, requests.HTTPError) as e:
+            last = e
+            retriable = not isinstance(e, requests.HTTPError) or "HTTP 5" in str(e)
+
+            if attempt < retries and retriable:
+                time.sleep(backoff * (2 ** attempt))
+                continue
+
+            break
+        except Exception as e:
+            last = e
+            break
+
+    if os.path.exists(tmp):
+        os.remove(tmp)
+
+    return None, None, f"{type(last).__name__}: {last}"
+
+
+def fetch(row, out_dir, timeout, verify_phash, tol, retries, backoff):
+    uid, url = row["uid"], row["url"]
+    ext = {"JPEG": ".jpg", "PNG": ".png", "WEBP": ".webp", "BMP": ".bmp"}.get(row.get("image_format"), ".img")
+    path = os.path.join(out_dir, uid + ext)
+
+    # Resume: trust what is already on disk if it verifies. With --verify-phash this
+    # also covers files previously classified 'recoded', so they are not re-fetched
+    # on every run.
+    if os.path.exists(path):
+        status, note = classify_local(path, row, tol if verify_phash else -1)
+
+        if status == "ok" or (status == "recoded" and verify_phash):
+            return uid, status, path, note
+
+    tmp, ctype, err = download(url, path, timeout, retries, backoff)
+
+    if tmp is None:
+        note = err
+
+        if os.path.exists(path):
+            note += " (existing local copy kept)"
+
+        return uid, "failed", path if os.path.exists(path) else None, note
+
+    if not looks_like_image(tmp, ctype):
+        os.remove(tmp)
+        return uid, "failed", None, f"not an image (Content-Type: {ctype or 'unset'}) -- likely a soft-404"
+
+    os.replace(tmp, path)                                  # atomic
+
+    if not verify_phash:
+        return (uid, "ok", path, None) if sha256_of(path) == row["sha256"] else (uid, "changed", path, "sha256 mismatch")
+
+    status, note = classify_local(path, row, tol)
+
+    return uid, status, path, note
+
+
+def main():
+    ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    ap.add_argument("--data", required=True, help="statmapcorpus_dataset_v1.parquet")
+    ap.add_argument("--out", required=True, help="output directory for images")
+    ap.add_argument("--workers", type=int, default=8, help="parallel downloads (default 8; be considerate of hosts)")
+    ap.add_argument("--timeout", type=int, default=30)
+    ap.add_argument("--retries", type=int, default=2, help="retries on timeout/connection error/5xx (default 2)")
+    ap.add_argument("--backoff", type=float, default=1.0, help="base seconds for exponential backoff (default 1.0)")
+    ap.add_argument("--limit", type=int, default=None, help="stop after N records (for testing)")
+    ap.add_argument("--skip-duplicates", action="store_true", help="skip byte-duplicate copies (is_duplicate_byte == True)")
+    ap.add_argument("--verify-phash", action="store_true", help="on sha256 mismatch, compare pHash to tell re-encoding from a content change (needs Pillow + scipy)")
+    ap.add_argument("--phash-tolerance", type=int, default=2)
+    ap.add_argument("--report", default="download_report.csv")
+    a = ap.parse_args()
+
+    if a.verify_phash:                                     # fail fast, not 20k files in
+        try:
+            import PIL, scipy, numpy                       # noqa: F401
+        except ImportError as e:
+            ap.error(f"--verify-phash needs Pillow, scipy and numpy ({e})")
+
+    df = pd.read_parquet(a.data)
+
+    if a.skip_duplicates:
+        df = df[df.is_duplicate_byte != True]
+
+    if a.limit is not None:
+        df = df.head(a.limit)
+
+    if df.empty:
+        print("nothing to download")
+        return
+
+    os.makedirs(a.out, exist_ok=True)
+    print(f"to download: {len(df):,}   workers: {a.workers}")
+
+    counts, results, t0 = {}, [], time.time()
+    rows = df.to_dict("records")
+
+    with ThreadPoolExecutor(max_workers=a.workers) as ex:
+        futs = {ex.submit(fetch, r, a.out, a.timeout, a.verify_phash,
+                          a.phash_tolerance, a.retries, a.backoff): r["uid"] for r in rows}
+
+        for i, fut in enumerate(as_completed(futs), 1):
+            uid, status, path, note = fut.result()
+            counts[status] = counts.get(status, 0) + 1
+            results.append({"uid": uid, "status": status, "path": path, "note": note})
+
+            if i % 200 == 0 or i == len(rows):
+                el = time.time() - t0
+                print(f"  {i:,}/{len(rows):,}  " +
+                      "  ".join(f"{k}={v:,}" for k, v in sorted(counts.items())) +
+                      f"  ({el/i:.2f}s/file, ~{el/i*(len(rows)-i)/60:.0f} min left)", flush=True)
+
+    pd.DataFrame(results).to_csv(a.report, index=False)
+    n = max(len(results), 1)
+
+    print(f"""
+--- SUMMARY ---
+{'ok':<10} {counts.get('ok', 0):>7,}  ({counts.get('ok', 0)/n*100:.1f}%)  byte-identical to the annotated file
+{'recoded':<10} {counts.get('recoded', 0):>7,}  same map, re-encoded by the host
+{'changed':<10} {counts.get('changed', 0):>7,}  DIFFERENT CONTENT -- annotations do not apply
+{'failed':<10} {counts.get('failed', 0):>7,}  could not be downloaded (link rot, soft-404)
+report: {a.report}
+""")
+
+    if counts.get("changed"):
+        print("WARNING: filter out 'changed' records before using the annotations.")
+
+
+if __name__ == "__main__":
+    main()

deposit/embeddings_clip_vitl14.npz

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+oid sha256:cbc3a5f240d91751f6e89ca55558b64fc685f11274d3f2448ac7138bb2c2e35a
+size 43248325

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+  "motor": -1.0,
+  "screen": -1.0,
+  "town names": -1.0,
+  "seating chart": -2.0
+}

deposit/positive_keywords.json

@@ -0,0 +1,2727 @@
+{
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+  "simbol proporțional": 5.0,
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deposit/schema.json

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+{
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+  "generated_at": "2026-08-23T09:24:20+00:00",
+  "note": "schema.json is not listed below: a checksum file cannot carry its own checksum. Verify the other files with: shasum -a 256 <file>",
+  "files": [
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+      ]
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+    {
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+      "sha256": "9d553af6cd74dca3d4f6cfe2e9a66764240b804a243a2d70468d69edba67da3a",
+      "note": "script: retrieves the source images and verifies each download against sha256 / phash (MIT)"
+    },
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+      "sha256": "1a7036b2cd66ae3678e6d2e049e9279a3f3277245cdb330cc996dda218869b39",
+      "note": "counter-indicative phrases and their weights, same step"
+    },
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+      "note": "map-indicative phrases and their weights, used by the first selection step over the MapPool alt-text"
+    },
+    {
+      "file": "CODEBOOK.md",
+      "bytes": 9373,
+      "sha256": "886403e506daa96a80e3fd733271b1ba0f65ba567e6b8a5dee8f269399e4c279",
+      "note": "every column: type, observed values, caveats for analysis"
+    },
+    {
+      "file": "NOTICE.md",
+      "bytes": 3454,
+      "sha256": "68cb07865f4aa7041d080788a0d9a31cb6a27a1060ce6ee94d3cf170b25eba2b",
+      "note": "attribution, derivation chain, licensing, removal procedure"
+    },
+    {
+      "file": "README.md",
+      "bytes": 5081,
+      "sha256": "44446b07f7f41002a09fcc50300f0d593b94ac2c44785244e4ea6715ef37f699",
+      "note": "what the corpus is, how it was built, how to fetch the images"
+    }
+  ]
+}

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