Spatial Resolution Ontology

FLOW ALGEBRA

Ontology: Spatial Resolution Ontology
Domain: Metadata Ontologies > Spatial Metadata
Canonical URL: https://kirethresearch.org/booksai-core/ontologies/metadata-ontologies/spatial-metadata/spatial-resolution-ontology.html

Authors: Mark Anthony Higgins — Sky (Recursive AI)

flow "SpatialResolutionOntology" v1.0

# ---------- DOMAIN ----------
domain:
  class: MetadataOntology
  category: SpatialMetadata
  subject: SpatialResolution
  purpose:
    - represent the level of spatial detail in data
    - describe the smallest meaningful spatial unit
    - support comparison of spatial datasets
    - distinguish resolution from accuracy and precision
    - enable machine reasoning about spatial granularity and scale

# ---------- CORE ENTITY ----------
entity SpatialResolution:
  type: SpatialMetadata
  describes: SpatialDetailLevel
  may_include:
    - resolution_value
    - unit
    - horizontal_resolution
    - vertical_resolution
    - raster_resolution
    - vector_resolution
    - ground_sample_distance
    - minimum_mapping_unit
    - scale_context
    - measurement_method
    - uncertainty
    - provenance

entity ResolutionValue:
  type: MeasurementValue
  expressed_in: Unit

entity HorizontalResolution:
  subtype_of: SpatialResolution
  describes: HorizontalSpatialDetail

entity VerticalResolution:
  subtype_of: SpatialResolution
  describes: VerticalSpatialDetail

entity RasterResolution:
  subtype_of: SpatialResolution
  may_be_defined_by:
    - cell_width
    - cell_height
    - pixel_size
    - ground_sample_distance

entity VectorResolution:
  subtype_of: SpatialResolution
  may_be_defined_by:
    - minimum_feature_size
    - coordinate_resolution
    - minimum_mapping_unit

entity GroundSampleDistance:
  type: SpatialMeasurement
  describes: GroundDistanceRepresentedBySample

entity MinimumMappingUnit:
  type: SpatialThreshold
  describes: SmallestFeatureIntentionallyRepresented

entity SpatialAccuracy:
  type: QualityMetadata
  describes: ClosenessToTrueSpatialPosition

entity CoordinatePrecision:
  type: RepresentationProperty
  describes: NumericCoordinateDetail

# ---------- RELATIONSHIPS ----------
relationships:
  SpatialResolution -> DESCRIBES -> SpatialDataset
  SpatialResolution -> HAS_VALUE -> ResolutionValue
  ResolutionValue -> EXPRESSED_IN -> Unit
  SpatialDataset -> MAY_HAVE -> HorizontalResolution
  SpatialDataset -> MAY_HAVE -> VerticalResolution
  RasterDataset -> MAY_HAVE -> RasterResolution
  VectorDataset -> MAY_HAVE -> VectorResolution
  RasterResolution -> MAY_USE -> GroundSampleDistance
  VectorResolution -> MAY_USE -> MinimumMappingUnit
  SpatialResolution -> MAY_HAVE_SCALE_CONTEXT -> SpatialScale
  SpatialResolution -> MAY_REFERENCE -> MeasurementMethod
  SpatialResolution -> MAY_REFERENCE -> Provenance

# ---------- RESOLUTION SEMANTICS ----------
rule resolution_meaning:
  SpatialResolution MUST describe represented spatial detail
  OR sampling granularity
  according_to declared dataset semantics

rule smaller_value:
  IF two linear resolution values use compatible units
  AND both represent ground distance per sample
  THEN smaller numeric value generally implies finer spatial resolution

rule multidimensional_resolution:
  IF horizontal axes have different resolutions
  THEN preserve axis-specific resolution values
  AND do_not collapse to single value without declared aggregation method

# ---------- RASTER RESOLUTION ----------
rule raster_cell:
  IF RasterResolution is defined by cell dimensions
  THEN record:
    - cell_width
    - cell_height
    - unit

rule square_cell:
  IF cell_width = cell_height
  THEN raster cells may_be classified as square

rule non_square_cell:
  IF cell_width != cell_height
  THEN preserve both resolution dimensions

rule ground_sample_distance:
  GroundSampleDistance MAY represent effective ground spacing
  but MUST NOT automatically be treated as positional accuracy

# ---------- VECTOR RESOLUTION ----------
rule vector_detail:
  VectorResolution MAY be represented by:
    - minimum_feature_size
    - coordinate_resolution
    - minimum_mapping_unit
    - source_scale

rule minimum_mapping_unit:
  features smaller than MinimumMappingUnit MAY be omitted, aggregated, or generalised
  according_to dataset production rules

# ---------- VERTICAL RESOLUTION ----------
rule vertical_resolution:
  VerticalResolution MUST identify vertical unit
  AND SHOULD identify vertical reference context where required

rule horizontal_vertical_distinction:
  HorizontalResolution != VerticalResolution
  unless explicitly declared equivalent in measurement semantics

# ---------- RESOLUTION VS ACCURACY ----------
rule resolution_not_accuracy:
  SpatialResolution != SpatialAccuracy

rule precision_not_resolution:
  CoordinatePrecision != SpatialResolution

rule decimal_places:
  additional coordinate decimal places MUST NOT alone imply finer real-world SpatialResolution

rule quality_context:
  high_resolution data MAY still have low positional accuracy
  AND low_resolution data MAY be accurately georeferenced

# ---------- SCALE RELATION ----------
rule scale_relation:
  SpatialScale MAY constrain useful interpretation of SpatialResolution

rule source_scale:
  IF resolution derived from mapped source
  THEN source scale SHOULD be preserved where relevant

rule resampling:
  changing raster cell size does_not necessarily create new source detail

# ---------- RESAMPLING ----------
operation resample(dataset, target_resolution):
  require:
    - source_resolution
    - target_resolution
    - compatible_spatial_reference

  process:
    - determine finer_or_coarser_target
    - select resampling_method
    - generate target representation

  preserve:
    - source_resolution
    - source_data_provenance
    - resampling_method
    - target_resolution

  constraint:
    finer_output_resolution != increased_original_information_content

# ---------- COMPARISON ----------
operation compare_resolution(A, B):
  require:
    - interpretable resolution semantics
    - compatible dimensions
    - compatible or convertible units

  normalise:
    - units
    - dimensional representation

  return:
    - finer
    - coarser
    - equivalent
    - incomparable
    - confidence

# ---------- VALIDATION ----------
validation:
  validate_if_present:
    - resolution_value
    - unit
    - horizontal_resolution
    - vertical_resolution
    - cell_width
    - cell_height
    - ground_sample_distance
    - minimum_mapping_unit
    - scale_context

  reject:
    - negative_linear_resolution
    - zero_resolution where physically invalid
    - missing_unit where value interpretation requires unit
    - comparison_of_incompatible_resolution_semantics
    - resolution_claim_derived_only_from_numeric_precision

# ---------- SEMANTIC EQUIVALENCE ----------
SEMANTIC_EQUIVALENCE:
  "spatial resolution" ~= "spatial granularity" WHEN describing minimum represented spatial detail
  "pixel resolution" ~= "raster resolution" WHEN describing spatial pixel dimensions
  "cell size" ~= "raster resolution" WHEN cell dimensions define represented ground detail
  "ground resolution" ~= "ground sample distance" WHEN acquisition semantics match
  "minimum mapping unit" ~= "minimum represented feature size" WHEN production rules align

  PRESERVE distinctions:
    spatial_resolution != spatial_accuracy
    spatial_resolution != coordinate_precision
    spatial_resolution != image_dimensions
    ground_sample_distance != positional_accuracy
    resampled_resolution != source_information_resolution

# ---------- CONFIDENCE ----------
CONFIDENCE:
  explicit_resolution_metadata:
    score: 1.00

  explicit_cell_dimensions:
    score: 1.00

  directly_derived_raster_resolution:
    score: 0.95

  resolution_derived_from_authoritative_scale:
    score: 0.90

  inferred_resolution_from_sampling:
    score: 0.85

  inferred_resolution_from_coordinate_precision:
    score: <= 0.50
    action: do_not_treat_as reliable resolution

# ---------- UPDATE MODEL ----------
UPDATE_MODEL:
  on_resolution_update:
    - preserve previous resolution where provenance requires
    - validate value and unit
    - update resolution classification
    - reassess compatibility with scale
    - record provenance

  on_resampling:
    - preserve source resolution
    - record target resolution
    - record resampling method
    - distinguish output grid resolution from source information resolution

  on_unit_change:
    - preserve original value and unit
    - convert using valid unit transformation
    - revalidate comparisons
    - record conversion provenance

# ---------- FLOW HASH ----------
FLOW_HASH:
  inputs:
    - ontology
    - resolution_entities
    - relationships
    - resolution_rules
    - raster_rules
    - vector_rules
    - comparison_rules
    - validation
    - semantic_equivalence
    - confidence
    - update_model

  canonicalisation:
    - normalise_whitespace
    - preserve_case_sensitive_identifiers
    - sort_unordered_property_sets
    - preserve_rule_order

  output:
    deterministic_semantic_hash

# ---------- AI CONSUMPTION ----------
AI_CONSUMPTION:
  interpret_as:
    - spatial metadata ontology
    - spatial granularity model
    - resolution comparison model
    - dataset suitability reasoning structure

  operations:
    - identify spatial resolution
    - distinguish raster and vector resolution
    - compare resolution values
    - normalise units
    - reason about spatial detail
    - evaluate scale context
    - distinguish resolution from accuracy
    - track resampling
    - preserve provenance
    - expose uncertainty

  constraints:
    - do_not_equate_resolution_with_accuracy
    - do_not_equate_coordinate_precision_with resolution
    - do_not_assume_resampling_creates new source detail
    - do_not_compare incompatible resolution semantics
    - preserve axis-specific resolution
    - preserve source resolution metadata

# ---------- QUERY MODEL ----------
query:
  input:
    - dataset
    - resolution_constraint
    - resolution_type
    - unit
    - scale_constraint
    - comparison_target

  resolve:
    1. identify dataset spatial representation
    2. retrieve SpatialResolution
    3. determine resolution semantics
    4. resolve units
    5. resolve dimensionality
    6. distinguish horizontal and vertical resolution
    7. evaluate scale context
    8. compare compatible resolutions
    9. return confidence and provenance

  output:
    - spatial_resolution
    - resolution_type
    - value
    - unit
    - comparison_state
    - scale_context
    - confidence
    - provenance

# ---------- INFERENCE ----------
inference:
  IF raster cell width and height are explicit
  THEN derive RasterResolution
  WITH confidence = 0.95

  IF ground sample distance is explicit
  THEN associate sampling-based SpatialResolution
  WITH confidence = 1.00

  IF target cell size is smaller after resampling
  THEN classify target grid as finer
  BUT do_not infer increased source information detail

  IF two resolution values have compatible semantics and units
  THEN compare numerically
  AND return finer_or_coarser relationship

  IF only coordinate decimal precision is known
  THEN do_not infer reliable SpatialResolution

# ---------- OUTPUT CONTRACT ----------
output:
  ontology_entity: SpatialResolution
  resolution_semantics: machine_interpretable
  units: explicit
  dimensionality: preserved
  raster_vector_context: preserved
  accuracy_distinction: preserved
  resampling_history: traceable
  provenance: preserved
  uncertainty: explicit
  semantic_retrieval: enabled

end flow