E2 · Publication Volume 24

File and Exchange Formats

CSV, JSON, GeoJSON, Parquet, GeoPackage, LAS and glTF

Learning objectives

  • Explain why CSV, JSON, GeoJSON, Parquet, GeoPackage, LAS and glTF require explicit semantic modelling.
  • Design identities, relations and constraints that preserve file and exchange formats across exchange.
  • Separate hard release gates from diagnostic metrics and interpretation choices.
  • Produce three validated distributions and a use-case-specific format decision record from synthetic evidence.

The lesson is complete only when the learner can defend both the model and the release decision. A neat schema without evidence, tests or declared limitations is an unverified design. The assessed artefact must make assumptions visible and distinguish source assertions from derived conclusions.

Decision context

Choose a format from required semantics, geometry, scale, access pattern, portability, validation and preservation needs. The same dataset may have several distributions, each derived from one canonical contract. No distribution should silently invent or discard meaning.

Start with a decision record: name the intended use, the evidence required, the consequence of error, the accepted uncertainty and the role authorised to accept residual risk. Then ask whether the proposed model can answer the decision question without relying on filename conventions, row order, undocumented defaults or someone’s memory. This prevents technology selection from concealing a missing semantic requirement.

The same record may be fit for one use and unfit for another. A rapid exploratory view can tolerate conditions that a released exchange package cannot. Fitness is therefore stated against a use, contract version and quality gate rather than attached permanently to the data.

Core concept

A format is an encoding choice, not a complete data model. Delimited text is easy to inspect but weakly typed; object notation carries nested structure; columnar files support analytical scans; geospatial containers bind geometry and metadata; point-cloud and three-dimensional asset formats specialise for particular representations.

The working scope is CSV, JSON, GeoJSON, Parquet, GeoPackage, LAS and glTF. For each item in that scope, distinguish the thing itself, the label used by a source, the claim made about it and the record that carries the claim. Identity is not a display name; a value is not its unit; an observation is not a model; current is not the same as valid. These distinctions create explicit places for correction, uncertainty and competing interpretations.

A good semantic design can be explained as a set of sentences before it is encoded. Each sentence identifies a subject, a property or relationship, an object or result, and the context under which the claim holds. Physical tables and files are then projections of those sentences, not their source of meaning.

Semantic model

Separate the logical schema from physical encoding. The logical layer defines entities, properties, types, units, cardinalities and constraints. A format profile maps those elements to columns, objects, layers, records or buffers and declares character encoding, coordinate order, null representation, precision and extension use.

Test every proposed record against seven questions: What has identity? What type is it? Which property or relationship is asserted? Which spatial and temporal context applies? Which state or qualifier modifies the assertion? Which evidence supports it? Which version and activity produced the stored representation? Missing answers become explicit contract gaps.

Normalisation is used to separate independent facts, not to maximise the number of tables. A compact nested object can be semantically sound if the same identities, constraints and provenance remain explicit. Conversely, a highly normalised database can still be ambiguous when relationships and units exist only in documentation or application code.

Constraints and invariants

| Invariant | Executable or review test | | --- | --- | | Format profile is versioned | Declare normative options and prohibit ambiguous defaults. | | Encoding is explicit | Specify character encoding, newline and escaping where applicable. | | Geometry semantics are retained | Carry coordinate reference, axis order, dimensionality and validity. | | Round trips are tested | Compare logical records before and after export–import cycles. |

An invariant is a condition that must remain true across storage, export, correction and reprocessing. Implement it as close to the authoritative boundary as practical and repeat the check at exchange boundaries. Record rule identifier, version, severity, evaluated scope, observed value and outcome so a failure can be reproduced.

Hard gates protect identity, semantic validity, required provenance and authorised use. Diagnostic checks reveal unusual values or patterns but require interpretation. Never convert a diagnostic threshold into deletion or correction without a reviewed rule and preserved source evidence.

Quantitative reasoning

Compare formats using a weighted decision score S_f = sum_i w_i r_{fi}, where weights w_i reflect the use case and ratings r_{fi} are supported by tests. Do not treat the score as universal. Also record round-trip loss count, validation coverage, read amplification and compressed size.

Every reported ratio states its numerator, denominator, exclusions and evaluation time. Stratify results by source, entity type, contract version or processing run where aggregation could hide a local failure. Counts accompany percentages so a seemingly large change based on a tiny denominator remains visible.

Precision is part of meaning. Do not add decimal places merely because a storage type permits them, and do not round identity, interval or coordinate fields without a declared tolerance and test. Quantitative summaries support a release decision; they do not replace semantic review.

Evidence and uncertainty

A successful parser run is not proof of semantic fidelity. Evidence includes schema validation, geometry validation, record counts, key-set equality, value-state comparisons, precision checks and content fingerprints. For lossy representations, document exactly which information is intentionally omitted.

Build an evidence packet containing preserved source reference, acquisition or assertion context, applicable method, validation results, reviewer decision and links to derivatives. Classify uncertainty as observational, semantic, structural, parametric or policy-related where that distinction changes treatment. “Unknown” is a valid state when the evidence cannot justify a stronger claim.

Contradictory evidence remains available. The model may select one current assertion, but the reason, competing assertion and effective time are retained. This makes later reinterpretation possible without pretending the earlier evidence never existed.

Interfaces and storage

Package each distribution with media type, format version, profile identifier, schema, coordinate reference, units, checksum and provenance. If a format supports extensions, list required extensions and fallback behaviour. A consumer that lacks a required extension must fail explicitly, not approximate silently.

Design an interface from the logical contract outward. Specify identifiers, types, cardinalities, units, value states, coordinate and time references, version negotiation, validation behaviour and structured errors before choosing a serialisation. The physical representation then declares its mapping to those logical elements.

Storage optimisation may partition, compress, index or cache data, but it must not change identity or silently remove context. A derived representation points to immutable inputs and a processing manifest. A cache carries freshness and contract-version information and is never treated as the only evidence copy.

Governance and access

Maintain approved profiles rather than merely approved file extensions. Profile changes undergo compatibility tests with representative producers and consumers. Archival policy retains the schema, vocabulary and rendering-independent validation evidence needed to understand the file later.

Governance is expressed through named roles, review states and versioned decisions, not through references to a particular organisation. Define who may propose, validate, approve, supersede and withdraw each governed resource. The audit trail records the role and event while avoiding unnecessary personal data.

Apply least-necessary access to source evidence and derivatives. Access controls must not erase identifiers, lineage or quality metadata needed to understand an authorised release. When policy is unresolved, quarantine the output with a precise reason and escalation route.

Integration checkpoint

One logical contract maps to several fit-for-purpose formats
One logical contract maps to several fit-for-purpose formats

The diagram summarises the control flow for this lesson. Read it from source evidence through semantic structure and validation to a decision-ready artefact. Each arrow should correspond to a declared relationship or transformation; each boundary should have a contract; each released node should have an identity, version and provenance pointer.

Integrate the lesson by adding three validated distributions and a use-case-specific format decision record to the evolving synthetic data package. Verify that earlier artefacts still resolve and that the new model does not overwrite observations, identifiers, values or versions introduced in previous lessons. Record every changed assumption.

Synthetic worked example

A synthetic collar dataset is distributed as delimited text, object notation and a geospatial container. The learner applies one logical contract, declares each mapping, validates coordinate reference and null states, and performs round trips. The text distribution omits no required fields but requires a companion schema; the container carries stronger typing and geometry metadata.

Work the example in four passes:

  1. Preserve the received records and write the intended decision without correcting anything.
  2. Identify entities, claims, context, uncertainties and policy constraints; mark every unresolved item.
  3. Apply the versioned rules, create derivatives and record the exact transformation plus validation evidence.
  4. Issue an accept, reject or quarantine decision and show how an independent reviewer can reproduce it.

Because the example is entirely synthetic, its values demonstrate method only. The important result is the chain from received evidence to justified decision. If a required fact is absent, the worked solution records the gap rather than manufacturing a plausible value.

Practice task

Build three distributions of one synthetic spatial dataset. Publish a format profile for each, run schema and geometry validation, compare round-trip logical records, measure size and selected-read cost, then justify which distribution serves inspection, analytics and exchange.

Use the following acceptance criteria:

  • All required identifiers and references resolve to declared types.
  • Every transformation preserves the received evidence and records its derivation.
  • Invalid, unknown and inapplicable states remain distinct and machine-testable.
  • The output identifies the contract, vocabulary and processing versions used.
  • A second reader can reproduce the validation result without private knowledge.

Submit the source snapshot, authored contract or model, validation output, derivative, manifest and a short decision record. A screenshot alone is insufficient because it cannot demonstrate the exact input, version or rule execution.

Common failure modes

  • A file extension is treated as sufficient schema documentation.
  • Coordinate reference or axis order disappears during export.
  • A parser default changes empty text into numeric zero.
  • A required extension is ignored and geometry is approximated.

These failures share a pattern: convenient representation is mistaken for verified meaning. Diagnose the earliest boundary at which an assumption became implicit. Correct by restoring source evidence, making the assumption a versioned field or rule, rerunning dependent transformations and superseding—not overwriting—the affected release.

Do not repair a failure by adding an undocumented default. A blocked result with a specific missing dependency is safer and more reusable than a complete-looking result whose meaning cannot be reconstructed.

Review questions

  1. Why is a format not a data model?
  2. Which profile details remove ambiguous defaults?
  3. What constitutes round-trip evidence?
  4. When is a specialised format preferable to a general one?

For each answer, identify the governing invariant, the evidence needed to evaluate it and the appropriate release behaviour when the invariant fails. A strong answer distinguishes scientific uncertainty from missing semantics and distinguishes a recoverable warning from a hard contract violation.

Sources and further reading