README.md

---
license: other
language:
  - en
pretty_name: Hackney Economic Atlas
tags:
  - geospatial
  - urban
  - london
  - hackney
  - economics
size_categories:
  - 1M<n<10M
configs:
  - config_name: placeholder
    data_files: README.md
---

# Hackney Economic Atlas (`kaomapi-2026`)

A high-dimensional, high-granularity open-data atlas of the **London Borough of Hackney**
(`E09000012`), built to power **agentic systems for economic decision-making** — helping small
businesses pick venues, renters analyse areas, and councils target intervention, by modelling how
money flows across businesses, workers, and markets. Designed for local processing on an NVIDIA
DGX Spark.

> **Status: built (2026-06-06).** Private dataset repo. **19 data domains + spine, ~151
> artifacts**, each with provenance + QC + a per-dataset `DATA_DICTIONARY.md`. The 33 GB of raw
> national source bulks are archived under `raw/`. A few layers await one-time user action (see
> "Pending" below).

## Layout

```
spine/        geography & identity backbone (boundary, wards, OA/LSOA/MSOA21, ONSPD, OS UPRN/roads, OSM)
curated/      domain = businesses | property | mobility | people | environment | publicmoney | visual
              | heritage | water | health | education | economy | transport | housing | buildings
              | costs | consumer | infrastructure | corpus | datastore
              keyed to (uprn?/lat/lon/geo_code, period, measure, value, source_id)
manifest.parquet   one row per artifact: path, domain, rows, bytes, sha256, source_url, licence, public
raw/          the source national bulks (ONSPD, OS UPRN/Roads, OSM, Companies House, EPC, Land
              Registry Price Paid, crime/STATS19, Colouring, Sentinel-2, LIDAR …) for re-derivation
```

Domains of note: **costs** = regulatory/tax/fee layer (business rates, CIL, council tax, ULEZ,
NLW/VAT/CT, utilities) for P&L and cost-of-living modelling; **consumer** = CDRC engineered indices
(IUC, AHAH); **infrastructure** = heat networks, mobile coverage, public toilets, imagery catalogue;
**corpus** = RAG text (Local Plan + London Plan policy, licensing & planning committee reports) plus
`webcast_catalogue.parquet` — the **NeMo-ASR worklist** of council-meeting recordings to transcribe;
**datastore** = Hackney's slices of the wider **London Datastore** (wellbeing/zen index, rents,
measured health, demographics, creative geography, and the cross-borough analysis leads) — each value
carries its **London rank/percentile** for context (e.g. emergency admissions 33/33 lowest while
deprivation 1/33 highest).

Each domain folder also ships `provenance.json`, `QC_REPORT.md`, and `DATA_DICTIONARY.md` (a
per-dataset column dictionary). Collector scripts live in the **git repo** (not here). Live-feed
frames are collected by cron (see git `docs/CRON_TODO.md`).

## Pending (one-time user action — not yet ingested)
- **CCOD/OCOD** property ownership — API key works, both free, but the dataset **licence must be
  signed once** at `use-land-property-data.service.gov.uk`. Then the collector path runs.
- **EGMS InSAR** ground motion/subsidence — needs a free **EU Login** (catalogued in
  `infrastructure/imagery_catalogue.parquet`).
- Skipped (documented): HMO register (pay-walled portal), Shopfront Design Guide SPD (scanned, no
  text layer — needs OCR).

## Usage — loading from Hugging Face

Every artifact is parquet, readable directly over `hf://` — no download step.

**DuckDB** (recommended for SQL/joins):
```python
import duckdb
con = duckdb.connect()
con.sql("INSTALL httpfs; LOAD httpfs;")
REPO = "hf://datasets/kaomapi/kaomapi-2026"
con.sql(f"SELECT * FROM '{REPO}/manifest.parquet'")                       # what's in the atlas
con.sql(f"SELECT * FROM '{REPO}/curated/businesses/voa_rating_list.parquet' LIMIT 5")
# join any layer to the geography spine (UPRN address grain):
con.sql(f"""
  SELECT u.uprn, v.rateable_value, f.business_name
  FROM '{REPO}/spine/uprn_hackney.parquet' u
  JOIN '{REPO}/curated/businesses/voa_rating_list.parquet' v USING (uprn)
  LEFT JOIN '{REPO}/curated/businesses/fsa_hygiene.parquet' f USING (uprn)
  LIMIT 20
""")
```

**pandas / polars**:
```python
import pandas as pd
df = pd.read_parquet("hf://datasets/kaomapi/kaomapi-2026/curated/people/claimant_count.parquet")
```

**cuDF (RAPIDS, on the DGX Spark)** — same paths, GPU dataframes:
```python
import cudf
gdf = cudf.read_parquet("hf://datasets/kaomapi/kaomapi-2026/curated/mobility/hsp_delays.parquet")
```

**huggingface_hub** (bulk pull):
```python
from huggingface_hub import snapshot_download
snapshot_download("kaomapi/kaomapi-2026", repo_type="dataset", local_dir="atlas")
```

Start from `manifest.parquet` to discover every layer, its grain, source and licence. The
geography join key is ONS 2021 codes (`oa21cd`/`lsoa21cd`/`msoa21cd`/`osward`) or `uprn` at
address grain (in `spine/uprn_hackney.parquet`).

## Provenance & licensing

Each domain ships a `provenance.json` and `QC_REPORT.md`. Sources are OGL v3.0 / CC-BY unless
noted. Some layers are aggregated rather than raw where the source licence restricts
redistribution (e.g. EPC certificates, Land Registry CCOD/OCOD ownership). Nothing person-level
is published beyond what the original source already publishes openly.

## Geography

The join backbone is ONS 2021 geography (OA → LSOA → MSOA → ward → borough) plus OS Open UPRN at
address grain. Built with the tooling in the companion analysis repo.

---
*Hackney spans ~280k residents, ~19 km², 21 wards, ~149 LSOAs, ~795 OAs, ~184k UPRNs.*