Planning Governance Assessment Tool
PGAT is a structured method for evaluating whether a planning decision process is properly supported and recorded. It focuses on how evidence is handled, how decision-critical issues are addressed at the point of determination, and whether the reasoning is robust enough to withstand scrutiny.
PGAT is outcome-neutral. It does not “take sides” on development. It assesses process quality: evidence, governance, and decision-law compliance.
What PGAT is not
- Not a planning merits opinion engine. PGAT does not decide whether a scheme is “good” or “bad”.
- Not an objection generator. PGAT does not exist to produce volume commentary or campaign content.
- Not a representation service. PGAT does not act on behalf of users or handle individual cases as a default service.
- Not a pressure or coordination tool. PGAT does not direct engagement at named individuals or public officers.
- Not a substitute for professional judgment. Users remain responsible for proportionality and lawful conduct
What PGAT does
PGAT produces structured, governance-led analysis designed to be calm, evidence-based, and suitable for professional review. Its core value is identifying where a decision becomes exposed due to procedural weakness or decision-critical gaps.
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Separates evidence from decision evidence
PGAT distinguishes between evidence that exists somewhere in the file and evidence that is actually carried into the decision (summarised, presented, and relied upon). This prevents “paper compliance” from being mistaken for decision integrity.
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Identifies decision-critical issues
PGAT flags issues that must be resolved (or lawfully addressed) at the stage being determined. It helps detect when “we’ll deal with it later” becomes an unlawful or unsafe deferral.
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Tests policy engagement quality
PGAT examines whether policy is meaningfully engaged with (operative requirements, thresholds, tests), rather than merely referenced. Token or superficial policy mention is treated as a governance weakness where policy is decision-critical.
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Flags evidential integrity problems
PGAT detects common evidence failures such as unreadable/stubbed documents, missing assessments, or reliance on assumptions where the decision requires demonstrated support.
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Assesses reasoning, recording, and defensibility
PGAT reviews whether the recorded reasoning is coherent, anchored in material considerations, and consistent with lawful decision-making expectations. It highlights indicators of decision exposure.
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Encourages restraint and disciplined use
PGAT is designed to reduce noise, not amplify it. In many cases, the correct outcome is improved understanding or inaction. The platform prioritises precision, proportionality, and governance clarity.
Summary: PGAT helps users understand whether a planning decision process is properly supported and
lawfully reasoned. It is professional-grade by design, while remaining readable to serious non-professionals who
are willing to learn the concepts and use them responsibly.
Platform Architecture
PGAT is implemented as a Python-based analytical system designed for document-heavy, evidence-led governance review.
Why Python?
PGAT is built in Python because of its strengths in structured text analysis, document processing, and deterministic rule evaluation. Planning decisions rely heavily on large, complex document sets, and Python provides mature, well-supported tooling for handling those inputs reliably.
The choice of Python reflects a design priority for clarity, repeatability, and analytical control rather than automation of outcomes.
Core technical characteristics
- Document-focused processing: PGAT is designed to work with planning documents such as committee reports, consultee responses, assessments, and notices.
- Deterministic analysis: Findings are produced by explicit analytical rules rather than probabilistic decision-making.
- Separation of analysis and presentation: The analytical engine operates independently of any user interface.
- Evidence integrity awareness: The system is able to detect limitations in source material, such as unreadable or incomplete documents, and treat these as governance risks rather than assumptions.
Technology components (high level)
PGAT uses a small number of established Python libraries for document handling, structured text processing, and data management. These are selected for stability and transparency, not novelty.
- Structured data processing and validation modules
- Rule-based analysis components implementing PGAT’s governance logic
- Optional language-rendering components used only to present findings clearly
Use of automation and AI
PGAT does not automate decisions and does not generate conclusions independently of its analytical rules. Where automated language generation is used, it operates strictly downstream of deterministic findings and serves only to improve readability.
The analytical outcome is always defined by the governance rules applied to the source material, not by statistical inference.
PGAT produces a machine readable .json file, AI reads this file and converts the findings into a readble document.
Deployment and integration
PGAT can be deployed as a standalone analytical program (.exe), its architecture allows it to operate independently of any particular interface, making it suitable for professional and organisational environments.
This separation ensures that analytical behaviour remains consistent regardless of how or where the results are viewed.
Design principle: PGAT is intentionally constrained. It analyses governance and decision-law integrity, but does not perform actions, coordinate engagement, or automate outcomes.
Also See
Other modules from the Governance Assessment Toolkit.
- DGAT - Disciplinary Accessment Tool
- RGAT - Risk Accessment Tool
- IGAT - Information (FOI) Accessment Tool
- PGAT - Planning Accessment Tool
- SGAT - Safeguarding Accessment Tool
