deposit/CODEBOOK.md

CODEBOOK

Quick summary

• One row per map in the main table: 23,549 rows, 30 columns, one Parquet file.
• Embeddings in a separate NPZ file, one-to-one with the rows by uid.
• candidates.parquet: all 203,371 candidates of Stage 3 with the outcome of each step, so that the selection can be reproduced and rejected images studied.
• validation.csv: the human and repeat-run labels of the Technical Validation; summarised in VALIDATION.md.
• 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 files.
• For how the corpus was selected and annotated, see README.md.

Gates

The data descriptor numbers the steps of the cascade as Gates 1-4. In the pipeline code Gate 1 is the size gate and Gates 2-4 are step1-step3.

Gate	Pipeline step	Model	Decides
Gate 1	size gate	rule, no model	shorter side of the image at least 400 px
Gate 2	step1	Claude Haiku 4.5	the image is a map, and the map covers at least about half of it; writes short_description
Gate 3	step2	Claude Haiku 4.5	statistical map, data referenced to administrative or statistical units, quantitative data (phenomena measured by sensors or physical models are excluded); language of the lettering (must be English); records has_text
Gate 4	step3	Claude Sonnet 4.6	the seven thematic map types, the legend type and a confidence rating; at least one type required

File: statmapcorpus_dataset.parquet

Identical in columns and values to statmapcorpus_dataset_v1.parquet of version 1; only the file name and the dataset entry of the file metadata changed.

Columns

#	Column	Type	Description and observed values
1	uid	str	32-char lowercase hex, unique. Inherited from MapPool. Join key to the NPZ, to candidates.parquet and to validation.csv.
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 (Stage 1).
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	Stage 2 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. Gate 1 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 written at Gate 2 by Claude Haiku 4.5. 122-557 chars; median 222; typically one or two sentences. 23 duplicated values.
17	has_text	bool	Gate 3: whether the map carries any lettering at all - title, legend, labels or source. True 23,532; False 17. Gate 3 required English lettering, so the 17 maps without lettering are in the corpus only because the model recorded English as their language; maps without lettering are represented only incidentally (see VALIDATION.md).
18	method_choropleth	bool	M1 choropleth. True 20,888 (88.7%).
19	method_diagrams	bool	M2 diagram. True 3,868 (16.4%).
20	method_dot_density	bool	M4 dot density. True 215 (0.9%).
21	method_isolines	bool	M3 isoline. True 275 (1.2%).
22	method_cartogram	bool	M6 cartogram. True 302 (1.3%).
23	method_flow_map	bool	M7 flow map. True 198 (0.8%).
24	method_heat_map	bool	M5 heat map. True 311 (1.3%).
25	methods_count	int64	Number of thematic map types: 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 type was required for inclusion (Gate 4).
26	legend_is_classed	object, null in 6,053 (25.7%)	Gate 4. True 14,857: legend divided into discrete classes. False 2,639: continuous or unclassed legend. Null means not applicable: the classed/continuous distinction does not apply (no legend, a qualitative legend or an illegible one); it is not a missing value - see "Using the data".
27	classification_confidence	str	Gate 4: the annotating model's own confidence in the thematic map types it assigned. high 18,644; medium 4,899; low 6. Self-reported and not calibrated; it did not predict agreement with the human coders (VALIDATION.md).
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	Gate 4 annotation time. 1 Aug 2026 15:43 - 2 Aug 2026 09:55 UTC.

Columns 16-27 are model-generated. Their precision against two independent human coders, their repeatability over repeated model runs and an audit of the images the cascade rejected are reported in VALIDATION.md and in the Technical Validation section of the data descriptor; the underlying labels are in validation.csv.

Properties shared by every record

The following hold for all 23,549 records, by construction. They were checked per record during annotation but are not stored as columns: a value identical in every row states the dataset's definition rather than describing an individual map.

Property	Value	Gate	Field in validation.csv
is_map	true	Gate 2	-
is_map_dominant	true	Gate 2	-
is_statistical_map	true	Gate 3	i2 (coders), is_statistical_map (retest)
has_admin_units	true: data referenced to administrative or statistical units	Gate 3	i3, has_admin_units
has_quantitative_data	true	Gate 3	i4, has_quantitative_data
map_language	en	Gate 3	map_language (retest)
methods_count	≥ 1: at least one thematic map type	Gate 4	-

Anything failing one of these was excluded before the corpus was formed, so this file cannot be used to study how often maps fail them; candidates.parquet can. The values are also written into the parquet file's key-value metadata, so they travel with the data:

import pyarrow.parquet as pq
meta = pq.read_schema("statmapcorpus_dataset.parquet").metadata
meta[b"inclusion_criteria"], meta[b"source_corpus"], meta[b"pipeline_version"]

Thematic map types (M1-M7)

Multi-label: the seven flags are independent and multi-type maps are common. The typology follows cartographic textbooks; the definitions below are those of Table 3 of the data descriptor and summarise the instructions given to the annotating model at Gate 4 (full wording in src/03_04_retrieval_and_cascade/multiturn/prompts/step3.md of the code repository). The column names keep the prefix method_ and the name methods_count of version 1; each method_* column holds one thematic map type label.

ID	Thematic map type	Column	Definition used for labelling
M1	Choropleth	method_choropleth	Areal units filled with colour, shading or hatching that varies with the mapped value; excludes uniformly filled reference maps.
M2	Diagram	method_diagrams	Symbols or small charts placed on the map to encode magnitude: proportional, graduated and structural symbols, pie and bar charts; excludes constant-size markers.
M3	Isoline	method_isolines	Contours of equal value, as lines or as filled bands with a classed legend, whose boundaries do not follow administrative or statistical units.
M4	Dot density	method_dot_density	Many small uniform dots scattered within areas, each representing a fixed count.
M5	Heat map	method_heat_map	Continuous, smoothly blending colour surface (raster, kernel density or gridded) without class boundaries.
M6	Cartogram	method_cartogram	Geometry of units visibly deformed so that their size encodes a data value.
M7	Flow map	method_flow_map	Directed lines, arrows or curves connecting locations to represent movement, often with width encoding magnitude.

Maps matching none of the seven were rejected at Gate 4 and are not in the corpus (97 images; see candidates.parquet).

File: embeddings_clip_vitl14.npz

Unchanged from version 1.

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.

File: candidates.parquet

203,371 rows × 24 columns. One row for every record selected in Stage 3 (classifier probability at least 0.80, at most five images per domain), with the outcome of each step from the URL check to Gate 4. A gate column is null when the record never reached that gate, so the gates can be read as a funnel. Raw model responses, descriptions of rejected images and local file paths are not included; the labels of the corpus maps are in the main table.

Column	Type	Non-null	Values	Description
uid	object	203,371	203,371 distinct, e.g. 00003812eafb6f4ff543ea7ad1b00460	MapPool record identifier; joins to the main table for the 23,549 corpus maps
url	object	203,371	203,371 distinct, e.g. http://fjb.blogs.com/.a/6a00e54f841b7e88330168e…	image location on its original host, as inherited from MapPool
domain	object	203,371	104,557 distinct, e.g. fjb.blogs.com	host from url; at most five candidates per domain
keyword_score	float64	203,371	-1 .. 23.5 (median 0)	Stage 1 dictionary score (see the main table)
pred_proba	float64	203,371	0.8 .. 1 (median 0.9449)	Stage 2 classifier probability; 0.80 and above by selection
url_status	object	203,371	alive 122,963; dead 80,277; error 131	HTTP HEAD check on 30 July or 1 August 2026: alive, dead (error status or no response) or error (request could not be made)
url_http_code	Int64	176,272	118 .. 999 (median 200)	HTTP status code of the check; null when no response was received
download_status	object	122,963	success 116,573; not_image 5,770; failed 620	download on 1 August 2026: success, not_image (the file could not be decoded as an image) or failed; null when the URL check failed and no download was attempted
image_width	Int64	116,573	1 .. 16,656 (median 640)	pixels; null unless download_status = success
image_height	Int64	116,573	1 .. 15,417 (median 450)	pixels; null unless download_status = success
image_format	object	116,573	JPEG 71,945; PNG 43,593; WEBP 930; BMP 63; GIF 35; TIFF 7	decoded file format; null unless download_status = success
gate1_pass	boolean	116,573	true 68,035; false 48,538	Gate 1: shorter side at least 400 px
gate2_is_map	boolean	68,035	true 67,698; false 337	Gate 2: the image shows a map
gate2_map_dominant	boolean	68,035	true 67,273; false 762	Gate 2: the map, with its legend and marginal elements, covers at least about half of the image
gate2_pass	boolean	68,035	true 67,273; false 762	Gate 2 passed: gate2_is_map and gate2_map_dominant
gate3_is_statistical	boolean	67,273	true 54,067; false 13,206	Gate 3: statistical map
gate3_admin_units	boolean	67,273	true 64,148; false 3,125	Gate 3: data referenced to administrative or statistical units
gate3_quantitative	boolean	67,273	true 43,021; false 24,252	Gate 3: quantitative data
gate3_has_text	boolean	67,273	true 63,806; false 3,467	Gate 3: the map carries any lettering (recorded, not a criterion)
gate3_language	object	67,273	46 distinct, e.g. unknown	Gate 3: dominant language of the lettering, ISO 639-1 code; unknown when the model could not assign one (including maps without lettering), other for a language outside its list
gate3_pass	boolean	67,273	true 23,646; false 43,627	Gate 3 passed: the three content conditions and gate3_language = en
gate4_n_types	Int64	23,646	0 → 97; 1 → 21,158; 2 → 2,278; 3 → 110; 4 → 2; 5 → 1	Gate 4: number of thematic map types assigned (0-7)
gate4_pass	boolean	23,646	true 23,549; false 97	Gate 4 passed: at least one type
in_corpus	bool	203,371	true 23,549; false 179,822	the record is in statmapcorpus_dataset.parquet

Reading the funnel

Step	Remaining	Rejected
Stage 3 candidates	203,371
URL alive	122,963	dead 80,277; error 131
Downloaded and decoded	116,573	not an image 5,770; failed 620
Gate 1 passed	68,035	too small 48,538
Gate 2 passed	67,273	not a map 337; map not dominant 425
Gate 3 passed	23,646	content 25,984; language alone 17,643
Gate 4 passed = corpus	23,549	no type 97

Of the 17,643 images rejected at Gate 3 for language alone, 15,728 were assigned another language and 1,915 unknown; 937 of the latter carry no lettering at all.

Example - every image that met all content conditions of Gate 3 but was rejected for its language:

c = pd.read_parquet("candidates.parquet")
ok = c.gate3_is_statistical & c.gate3_admin_units & c.gate3_quantitative
rejected_for_language = c[ok.fillna(False) & c.gate3_language.ne("en")]

The images themselves are not distributed. download_images.py reads the uid and url columns, so it can fetch any subset of this file, but no checksum is recorded for the rejected images: there is nothing to verify a later download against.

File: validation.csv

11,718 rows. The published record of the three validation exercises of the Technical Validation, one row per observation (long format). VALIDATION.md summarises the results; the scripts that reproduce every number from this file and the main table are in the validation/ directory of the code repository.

Block	Rows	Maps	source	field
coders	3,768	157	coder1, coder2	has_text, i2, i3, i4, legend_is_classed, m1, m2, m3, m4, m5, m6, m7
retest	7,800	300	run1, run2	has_admin_units, has_quantitative_data, has_text, is_statistical_map, legend_is_classed, m1, m2, m3, m4, m5, m6, m7, map_language
audit	150	150	coder1	meets_all_criteria

Maps per stratum: audit DECOY 20; audit G2_not_a_map 15; audit G2_not_dominant 15; audit G3_content 60; audit G3_language 20; audit G4_no_type 20; retest random 143; retest validation 157.

Column	Description
block	coders: two independent human coders on the 157-map validation sample. retest: two repeated runs of Gates 3 and 4 on 300 corpus maps. audit: blind coding of 130 images rejected by the cascade and 20 corpus maps as controls.
uid	MapPool record identifier. Joins to the main table (coders, retest, audit controls) or to candidates.parquet (audit).
stratum	coders: empty, because a map enters the stratified sample through any of its labels; see inclusion_probability. retest: validation (the 157 validation maps) or random (143 maps drawn at random from the rest of the corpus). audit: G2_not_a_map, G2_not_dominant, G3_content, G3_language, G4_no_type (the gate and reason of rejection) or DECOY (corpus maps added as controls).
source	coders: coder1 or coder2. retest: run1 or run2; the production labels are the main table. audit: coder1.
field	coders: i2 statistical map, i3 administrative or statistical units, i4 quantitative data, has_text, m1-m7 (the thematic map types, numbered as in this codebook), legend_is_classed. retest: the same type and legend fields and has_text, plus is_statistical_map, has_admin_units, has_quantitative_data, map_language. audit: meets_all_criteria.
value	coders and audit: yes, no or cannot_assess; for legend_is_classed: classed, continuous or other (not applicable: no legend, a qualitative one or an illegible one; null in the main table). retest: true or false; classed, continuous or other; a language code for map_language.
inclusion_probability	coders only: probability that the map entered the stratified sample; its reciprocal is the Horvitz-Thompson weight.
population_n	audit only: size of the stratum among the rejected images (for DECOY, the corpus maps eligible as controls), needed to extrapolate.
response_time_s	audit only: seconds the coder spent on the map.

coder1 is the author of the dataset, who designed the pipeline; coder2 an independent coder. Neither saw model output while coding. Disputed judgements are kept as recorded; the three reference standards built from them (A, B, C) are explained in VALIDATION.md.

Using the data

Group on phash, not on duplicate_group_id, when building splits. Columns 28-29 of the main table 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 thematic map type labels are multi-label and severely imbalanced: choropleth covers 88.7% of records while the five rarest types each sit below 1.5%. Precision also differs by type: it was high for choropleths, diagrams and cartograms, lower for heat maps, dot density and flow maps and lowest for isolines (VALIDATION.md), so per-type results on the rare types carry label noise.

Do not impute null (not applicable) 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 type: 2.2% for isolines, 20.6% for choropleth, 45-60% for diagram maps, 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 type 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. In the Parquet file the column is a boolean with nulls; pandas reads it as object (Python None mixed with bool) unless it is read with pd.read_parquet(..., dtype_backend="numpy_nullable").