Known gaps and limitations

We're confident in what's in this dataset, but here's an honest accounting of what we know we're missing.

What we know we don't have

Small-township records that aren't online

Many small townships and rural counties don't post agendas, minutes, or signed ordinances on the web. When we know a moratorium exists from news coverage but can't pull the underlying instrument, we record it with a [VERIFY] note in verify_notes rather than guessing at the ordinance number or exact date. 180 of the 323 inventory rows have at least one such evidence-ceiling note (has_verify_tags = True), down from 123 of 222 in v2026.04.4 after a targeted verification pass.

Records behind authentication or CAPTCHA gates

Some primary sources (notably the NC eCourts portal at portal-nc.tylertech.cloud, several Legistar instances, and certain Granicus-archived meetings) are protected by Akamai-style human-verification challenges that defeat automated retrieval. For affected entries, we use the best secondary source (county press releases, local news) and document the evidence ceiling.

Coverage of the May-July 2026 window (v2026.07)

All 50 states were swept month by month for May, June, and July 2026, so counts inside that window are not coverage-limited. This is the first window in the dataset's history where that is true.

10 states recorded no local adoption during the window: Alaska, Arizona, Delaware, Hawaii, Idaho, Louisiana, Rhode Island, Vermont, West Virginia, and Wyoming. Those absences are findings, not gaps. Wyoming is the sharpest case -- Cheyenne's proposed twelve-month moratorium was rejected 9-1 on second reading, so the state's absence is a decision its council made, not a place nobody looked.

What the sweep was worth. Conversion changed several states' counts dramatically, and none of it was because anything changed on the ground:

Take that as the measure of what single-source coverage of this topic misses.

Earlier windows were not swept this way. Anything dated before 2026-05-01 entered the dataset through document search and opportunistic discovery, so those counts remain lower bounds. A time series across the 2026-05 boundary will show a step that is partly method, not only events.

The machine-readable record is data/sweep_coverage.json, derived by scripts/update_sweep_coverage.py and mirrored into summary_stats.json under sweep_coverage.

Extension and rescission events after the cutoff

Any moratorium extended, replaced, or rescinded after 2026-07-31 won't be reflected until the next release. In v2026.07 we re-researched every row whose recorded term had provably expired and every pending row older than 60 days, so the backlog of stale statuses is cleared as of the snapshot date — but 244 instruments are currently in force and many carry sunsets in the next few months.

Disagreement with a sibling dataset (added v2026.07)

A sibling research project maintains its own state-level rollup of data-center moratoria under different inclusion criteria. Reconciling it against this inventory (scripts/reconcile_sibling_tracker.py) found the two disagree in 31 of 34 shared states, in both directions — and it names 44 jurisdictions across 23 states that this inventory has no row for, including seven states where we currently record none at all (Florida, Nevada, New Mexico, Rhode Island, South Carolina, Texas, Utah).

We did not merge those counts. Two datasets built to different definitions should not be reconciled by overwriting one with the other, and the disagreement is itself informative: it puts a floor under how incomplete single-source coverage of this topic is. All 44 leads were verified individually; roughly three quarters proved real, and five states (Florida, Nevada, New Mexico, South Carolina, Texas) entered the dataset as a result. The false positives clustered into four failure modes: advocacy mistaken for adoption, jurisdiction confusion, proposals counted as votes, and one instrument that was adopted and then ruled legally invalid by the jurisdiction's own counsel.

What this means if you are using the data: for the 2026-05 to 2026-07 window the state list is now an enumeration rather than a lower bound, because every state was swept. Outside that window it remains a lower bound.

The clause taxonomy lags the inventory (v2026.07)

The 44-clause taxonomy analysis (data/structured_extractions.jsonl, data/clause_extraction_analysis.json, and the definitional-approaches, findings-impact, and sector-specific-clauses tables) rests on a cohort of 348 documents across 211 jurisdictions, all collected before 2026-04-28.

v2026.07 nearly doubled the inventory without re-running that extraction, so the clause percentages now describe roughly half the rows. This was deliberate: the 50-state document sweep was still running, and extracting from a partial corpus would have produced a cohort mixing two collection snapshots — worse than a clean one taken later. Of the 200 instruments added this cycle, about 91 have a primary source that could yield an extractable document; the remaining 109 are news-only and would contribute nothing to a clause-level analysis regardless.

What this means: the inventory-derived tables (top-states, temporal-distribution, state-sector-counts, moratorium-inventory) are current. The three clause tables are not, and should be cited as describing a pre-April-2026 sample rather than the present inventory.

Separately, tables/clause-prevalence.tex has no generator in this repository and cannot be rebuilt from the shipped data. It is carried forward from an earlier release.

Non-English-language jurisdictions

We didn't find any moratoria adopted in languages other than English, but a comprehensive sweep of bilingual border-region jurisdictions or Spanish-language Puerto Rico municipal records was not part of the methodology.

Federal moratoria and tribal-government moratoria

We document one tribal-government moratorium (Sault Tribe of Chippewa Indians, April 2026, on AI data centers on tribal/trust lands). There may be others we missed, particularly on Bureau of Indian Affairs–trust lands. Federal-level moratoria on federal land (BLM, USFS) are out of scope for this dataset, which focuses on local-government land-use authority.

What we tried and couldn't get

Geocoding caveats (added v2026.04.2)

531 of 323 jurisdictions are geocoded to WGS84 lat/lon via OSM Nominatim. The 2 blanks are aggregate meta-rows (Other Reported Local Moratoria, Michigan and Proposed or Rejected Local Pauses, Maryland) that aren't real geographic points.

Within-state name ambiguity. Several Ohio townships share names across multiple counties (e.g., 7 different "Washington Township"s, 3 "Plain Township"s, 4 "Lake Township"s). The geocoder picks the highest-rank match, which isn't always the moratorium-adopting jurisdiction. We caught and manually corrected 4 such cases in v2026.04.2:

• Lake Township, OH (Wood County, not Logan County)
• Plain Township, OH (Stark County, not Franklin County)
• Spencer Township, OH (Lucas County, not Lorain County)
• Waterville Township, OH (Lucas County, not Stark County)

If you're using the lat/lon for a point map and a township seems oddly placed, check the row's legal_basis and trigger text for county hints. We've also flagged Washington Township, OH (40.11, -83.13) as residually ambiguous — the article context doesn't uniquely identify the county.

The same failure recurred in v2026.07, and is now handled systematically. Automated geocoding placed Lyon Township, Michigan in Roscommon County when the moratorium belongs to Lyon Charter Township in Oakland County — 130 miles off, and identifiable only because the row's affected project (the "Project Flex" hyperscale campus near New Hudson) pins the location. That row is now labelled Lyon Charter Township (Oakland County).

Rather than silently patching coordinates, ambiguous and unresolvable cases are now recorded as declared overrides in scripts/apply_geo_overrides.py, each carrying a why field stating the evidence that settles which place is meant. The current overrides are:

Three "City of X" rows originally needed overrides. Investigating why turned up a real bug rather than a data problem: geocode_inventory.py defined a prefix-stripping regex and never called it, so every City of ... row fell straight through to manual review. The geocoder now retries with the governing-body prefix removed, and those overrides were retired — except Effingham, where the stripped form resolves to the surrounding county instead of the city.

The lessons that generalize: a jurisdiction name alone is not a geographic key in states with repeated township names; governing-body prefixes and suffixes ("City of", "Fiscal Court", "Charter Township") defeat geocoders; and a same-named county sitting around a city will outrank it. Expect to curate a few of these by hand each refresh — but check first whether the failure is a class the geocoder should handle.

Audit confidence. Geocoded coordinates were triple-checked across 89 verifications via three independent methods: spot-check against geographic knowledge, Wikipedia GeoSearch reverse-lookup (does the jurisdiction's name appear in nearby Wikipedia pages?), and nearby-page context analysis (when a township has no Wikipedia article, do nearby places confirm the right county?). Across all 89 verifications, zero confirmed wrong geocodes (after the 4 manual corrections above). Treat the lat/lon column as ≥99% accurate.

When new releases add new same-name townships, expect a small number of similar issues until the geocoder catches up.

What gets fixed in each release

• New moratoria adopted between releases get added.
• [VERIFY] flags get resolved as towns post their post-meeting minutes online.
• Outcomes of pending moratoria (extended/replaced/expired/rescinded) get updated.
• Errors flagged by the community via the issue tracker get corrected.

If you have access to one of the records above and want to share it, please open an issue. We'll add it and credit you in the next release.

Selection bias

This dataset is biased toward jurisdictions that:

1. Post agendas and minutes online
2. Have local newspapers or trade-press coverage
3. Have moratoria of large enough scope to attract attention

A small township in a rural county that adopts a 90-day data-center moratorium and never tells anyone is statistically very likely to be missing from this dataset. The bias is structural, not avoidable, and we don't try to correct for it. Treat the corpus as a high-confidence lower bound on the true count of moratoria, not as a probability sample.

Confidence on individual entries

The has_verify_tags, verify_count, and verify_notes fields tell you which rows are most certain and which have remaining open questions. When we use a row in the structured-extraction analysis, a confidence score (0.0 to 1.0) is also attached.

For any specific row you want to use in a publication, always read the verify_notes first and cite a primary source rather than just our row.