# UK Planning Applications - Per Application Entity (`nexgensignal/uk-planning-application-records`) Actor

UK planning applications (planning.data.gov.uk) as clean per-record data - reference, site point, entry/start/end/decision dates, typology, local planning authority. ~100,627 entities. Free-text description and applicant data dropped. OGL v3.0. $0.05 per record.

- **URL**: https://apify.com/nexgensignal/uk-planning-application-records.md
- **Developed by:** [NexGen Signal](https://apify.com/nexgensignal) (community)
- **Categories:** Business, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $33.50 / 1,000 planning application records

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## UK Planning Applications - Per Application Entity

The UK's planning applications from the national **Planning Data** platform (`planning.data.gov.uk`) as clean,
per-application records - reference, site point, the entry/start/end/decision dates, typology, quality and the
local planning authority. Application events, not people.

### What one record represents

The source is the government Planning Data platform's entity API, filtered to the **planning-application**
dataset. Each record is **one planning-application entity**: its local-authority reference, the dataset prefix,
the platform entity id, the entity name, its typology, the organisation-entity id of the local planning
authority that owns it, a representative geographic point for the site, a data-quality flag, and the entry,
start, end and decision dates. It is the application-event view: what was applied for, where, under which
authority, and when it moved through its dates.

### Coverage and volume

The platform reports an exact total for the dataset - about **100,627** planning-application entities at the time
of writing - and the Actor exposes that count on every run's receipt (`reported_total`). You raise **Maximum
records** to pull the whole set or lower it to sample; paging is by the entity API's own limit/offset, so a run
is a stable sweep of the dataset. Because the platform ingests from every local planning authority in England
that publishes to it, the set grows as authorities are onboarded and as applications are added.

### Person data: description dropped, no applicant field

A planning application can name an applicant and an agent. This cell is built to sell **application events**, not
the people behind them, and it removes personal data structurally. The free-text `description` field - which can
paraphrase who is doing what - is dropped and never delivered, as is the heavy WKT `geometry` blob. The entity
API at this grain carries no applicant or agent name field at all; the delivered record is limited to the
reference, the site point, the dates, the typology and the authority. A blocklist assertion rejects any
applicant/agent/contact field before delivery, so if the upstream schema ever changed to include one, the run
would fail rather than leak it. What you get is a person-free record of which application, at which site, under
which authority, reached which decision on which date.

### Distinct from planning-constraints

This dataset is the **applications** view. The fleet's `uk-planning-constraints` product draws from the *same
host* but a *different dataset* - the designations that constrain development (conservation areas, flood zones,
listed buildings, tree preservation orders and the like). Constraints describe the land; applications describe
what someone asked to build on it. They share the platform and its geography but not a single record: joining
them by site is a real analysis (which applications sit inside which constraints), which is exactly why they are
two cells and not one.

### Licence

Open Government Licence v3.0 (OGL v3.0). You are free to copy, publish, distribute, adapt and exploit the information commercially and non-commercially, provided you acknowledge the source with the required attribution statement and licence link. The required attribution statement rides on every record.

### Who buys this, and how they use it

This cell is bought by teams that need the source's full published set as a typed, keyed table they can hold and
refresh, rather than a page they scrape. Market-intelligence and lead-generation teams use it to size a market
and track who is active in it; analysts and journalists use it to build a longitudinal series that the source's
own portal does not expose; data engineers use it as a clean upstream feed into a warehouse, keyed so it upserts
without duplication. The common thread is that the record grain and the stable key are chosen so the output is a
building block, not a one-off export: you run it on a schedule, keep the delta, and join it to your other
sources on the identifiers it preserves verbatim.

### Field-by-field, and why the grain is what it is

Every field in the record is either a source-native identifier, a source-native attribute, or one of the six
provenance fields (`source`, `source_dataset`, `licence`, `attribution`, `caveat`, `observed_at`) the fleet
attaches to every record. Nothing is derived or inferred beyond the small, documented transforms noted above,
and nothing is dropped silently: the person-handling section spells out exactly which fields are excluded and
why. The grain - one record per the natural unit of the source - is deliberate: it keeps each row independently
meaningful, keeps the key stable across runs so re-running is a cheap upsert rather than a re-import, and lets
you aggregate up to whatever unit you need without having to unpick a pre-joined table. If you need a different
grain, you compose it downstream from these rows; the cell's job is to deliver the atomic, person-safe,
licence-clean records that everything else is built from.

### Reconciling counts honestly

Where the live count differs from any previously published figure, the live measure is the honest one and is what
this listing quotes; sources re-issue and consolidate their data over time, so a figure drifts. The run receipt
always states what was actually delivered and charged and confirms the two agree, so every run is auditable
against itself regardless of what any external index expected.

### Sibling Actors

### Provenance and compliance

Every run reads the door host's `robots.txt` at runtime; the gate result (URL, status, byte length and, where a
policy is served, its SHA-256) is written to the run's `RUN_RECEIPT`. Where the host serves no applicable
robots rule, or redirects its policy to another host, the gate records that (flagged) and proceeds on the
licence, which grants re-use. The endpoint is keyless. The Actor never bypasses a block or fetches through a
mirror, and it reads only the public listing endpoint - never a per-record detail page.

### Data quality and freshness

Numeric columns are delivered as real numbers and every other column as a string or null, so the dataset loads
without a cleaning pass. Delivery is keyed on a stable source identifier, so the data is safe to diff,
deduplicate or upsert. Every run re-reads the live door, so the data is as fresh as the source publishes, and
each record's `observed_at` stamp dates the snapshot. The run's `RUN_RECEIPT` records the source URL and how
many records were delivered and charged, and confirms `charge_equals_delivered`.

### Billing, delivery and joins

Pricing is per record: you are billed only for records the Actor actually delivers, with the charge raised after
each record is pushed (push-then-charge), so a failed or empty run costs nothing. The **Maximum records** cap
bounds every run, so you control spend precisely - sample cheaply, then raise it. Every record is a flat, typed
object keyed on a stable id, so the data loads without a cleaning pass, diffs cleanly between runs, and upserts
into a table you maintain over time; re-running keeps that table current without re-paying for rows you already
hold, and each receipt reconciles delivered against charged. Because the source's own identifiers are preserved
verbatim, the dataset joins cleanly onto other sources keyed on the same identifier.

### Scaling and scheduling

Set **Maximum records** low to sample the shape of the data cheaply, then raise it once the cell fits your use.
The Actor delivers incrementally and streams its source, so memory stays flat regardless of how many records you
request, and you are billed only for what is delivered. Because the source republishes on its own cadence, a
scheduled run keeps a downstream table current: new and changed records upsert over the old ones on the stable
key, and the `observed_at` stamp on every record tells you when each was last seen live. There is no
subscription and no minimum - the per-record price and the record cap together mean the spend on any run is
known in advance and matched exactly to the data you receive.

# Actor input Schema

## `maxRecords` (type: `integer`):

Maximum records delivered and billed. You are billed only for records actually delivered. Raise it to pull the full set.

## Actor input object example

```json
{
  "maxRecords": 500
}
```

# Actor output Schema

## `results` (type: `string`):

The delivered UK planning application entity record.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "maxRecords": 500
};

// Run the Actor and wait for it to finish
const run = await client.actor("nexgensignal/uk-planning-application-records").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "maxRecords": 500 }

# Run the Actor and wait for it to finish
run = client.actor("nexgensignal/uk-planning-application-records").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "maxRecords": 500
}' |
apify call nexgensignal/uk-planning-application-records --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,nexgensignal/uk-planning-application-records"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/uRX7sB6mTARQwfnUU/builds/VtOTRkcqYTLRXPNJo/openapi.json