simd2016_pw_ca_quintile¶
PHS population-weighted quintile within ca; 1 = most deprived
| Table | Type | Nullable | Category | In the CSV export | SQL Server type |
|---|---|---|---|---|---|
history table (postcode_simd_history, SPD) |
int8 | no | PHS SIMD, population-weighted | yes | tinyint |
main table (postcode_simd, SSPL) |
int8 | no | PHS SIMD, population-weighted | yes | tinyint |
Provenance (the same in both tables)¶
Each table runs its own copy of the table-level checks, named .history. and .main..
Where it comes from
| Input | Field | Condition | Reaches the row by |
|---|---|---|---|
PHS/simd2016_18052020.csv (pinned, SHA256 3a98af3b181d…) |
SIMD2016CAQuintile |
DataZone2011Code = DataZone |
Scope: the row itself.
What was done to it: copied. Copied as published, 1 most deprived. (code: simd_ingest/core/phs.py: canonicalise_phs)
How it is checked (what each check tests; whether a build passed it is in that build's manifest.json["checks"])
source.hash.PHS/simd2016_18052020.csv: the pinned file's SHA256 equals the registry's: the bytes are exactly the ones reviewedphs.2016.source.schema: the PHS file's header is the declared onephs.2016.source.key_unique: each data zone appears once in the PHS filecross.2016.same_zones: PHS and the Government publish the same data zones for the editionjoin.<table>.phs.2016.every_record_matched: every record found its data zone in the edition: no empty cellreadback.<table>.attached_values: every attached value in the saved file is looked up again from the sources and equalphs.2016.source.CAQuintile.range: every published band is within its rangephs.2016.pw_ca_quintile.monotone: after any inversion, the band never falls as the rank rises, within its geographyphs.2016.rank1_in_band1: the most deprived zone is in band 1 of every band: the direction is rightjoin.<table>.simd2016_pw_ca_quintile.range: every joined band is within its range