Spatial Predicates In Qgis – A Deep Dive

What are Spatial Predicates and Why They Matter

In GIS, spatial predicates are functions that test the spatial relationship between two geographic features. They allow users to analyze how points, lines, and polygons interact with each other in two-dimensional space. Mastering spatial predicates unlocks advanced spatial analysis and mapping capabilities.

Some key applications of spatial predicates include:

  • Identifying features that fall within a defined area of interest
  • Finding overlaps between zones and jurisdictions
  • Modeling diffusion and accessibility based on proximity
  • Detecting topological errors in vector data
  • Optimizing spatial queries for faster analysis and visualization

QGIS includes a robust set of spatial predicate functions powered by PostGIS. Learning these functions deeply allows GIS analysts to solve spatial problems quickly with precise, customized tests.

Key Functions for Spatial Analysis in QGIS

ST_Intersects – Finding Overlapping Geometries

ST_Intersects() tests if two vector features share any portion of space, detecting any spatial overlap between their geometries. It returns TRUE if the boundaries or interiors intersect at all and FALSE if the features do not overlap.

Use cases include:

  • Identifying which parcels fall within a flood zone polygon
  • Selecting roads that cross ecological corridors defined as linestrings
  • Flagging buildings whose footprints overlap wetland boundaries

ST_Intersects only requires two geometries as input. This versatility across geometry types makes it an essential predicate for feature selection and overlay analysis.

ST_Contains – Testing if Features are Within Others

ST_Contains() evaluates if the first geometry completely surrounds the second, containing its entire boundary and interior points. This predicate uniquely tests for an exact containment relationship.

Typical use cases are:

  • Selecting suburban blocks fully within a city’s limits
  • Identifying wells inside a polygon watershed unit
  • Flagging line highways running through a national park perimeter

Testing for containment relationships allows users to precisely isolate features falling fully inside target containers. ST_Contains enables robust, specialized area of interest selection.

ST_Overlaps – Detecting Partial Overlaps Between Shapes

ST_Overlaps() reveals intersections where features share space without passing through each other completely. It flags partial overlaps, unlike ST_Within and ST_Contains tests.

Common applications include:

  • Finding zoning polygons that intrude into neighboring districts
  • Marking utility lines from different providers crossing over each other
  • Identifying countries disputing border regions with fuzzy boundaries

ST_Overlaps empowers analytic flagging of complex spatial boundaries and relationships missing clean containment semantics.

ST_Touches – Checking for Shared Boundaries

ST_Touches() specifically detects adjacency between geometries, where features connect along immediate borders without overlapping. This predicate identifies precise aligned edges.

Use cases are:

  • Selecting US states sharing state lines
  • Marking coincidentparcel plat boundary segments
  • Connecting shoreline segments to trace water bodies

ST_Touches supports advanced topology-aware analyses leveraging shared precise spatial boundaries between features.

Writing Spatial Queries with Predicates

Basic Syntax and Examples

The basic syntax for using spatial predicates in SQL queries in QGIS takes the form:

SELECT target_fields
FROM feature_layer
WHERE ST_Predicate(geometry1, geometry2);

For example, finding all land parcels touching a lake perimeter:

SELECT, parcels.land_use 
FROM parcels
WHERE ST_Touches(parcels.geometry, lakes.perimeter);

Multiple predicates can also be nested to refine selections:

FROM buildings
WHERE ST_Within(buildings.footprint, city_limit)
AND ST_Overlaps(buildings.footprint, floodplain);

Optimizing Performance of Spatial Queries

Applying spatial indexes on geometry fields and simplifying complex polygon geometries can significantly improve spatial query processing time. Always test queries on a subset before running on full dataset.

Use EXPLAIN to check if indexes are utilized by the spatial predicate queries. If not, create a spatial GiST index on geometries:

CREATE INDEX buildings_geo_idx
  ON buildings
  USING GIST (footprint);

Simplifying geometries also reduces processing time for overlays and topology-based predicates:

UPDATE lakes
SET simplified_perimeter = ST_Simplify(perimeter, 0.0005);

Test queries with EXPLAIN ANALYZE before execution at scale. Check for full table scans and assess I/O time versus CPU time.

Going Beyond the Basics – Advanced Usage Tips

Combining Multiple Predicates

SQL predicates like AND/OR allow chaining spatial predicates for advanced multi-predicate queries:

SELECT buildings.*
FROM buildings
WHERE ST_Within(buildings.footprint, old_town)
OR ST_Overlaps(buildings.footprint, new_development);

This approach enables complex logic like flagging features satisfying any conditions in a set. Syntax forms like IN query subsets based on spatial batches.

Applying Predicates to Complex Geometries

QGIS handles applying predicates to multi-part geometries with sub-element tests:

SELECT, roads.type 
FROM roads
WHERE ST_Intersects(caves.boundary, roads.lines)
OR ST_Contains(lakes.waterbody, roads.lines); 

Geometry component tags like roads.lines allow predicates to target spatial subsets specifically. This grants flexibility querying complex real-world data.

Common Issues and Solutions

Debugging Errors and Unexpected Outputs

Validate predicates match geometry types. Check SRIDs match before testing relationships. Print geometries before intersecting to inspect structures.

Review spatial metadata tables for geometry dimensionality, validity, constraints. Use ST_IsValid() to catch invalid ring orientations, self-intersections.

SELECT buildings.* 
FROM buildings
WHERE NOT ST_IsValid(buildings.footprint);

Handle geometry exceptions before applying computational geometry predicates.

Troubleshooting Slow Query Performance

EXPLAIN ANALYZE diagnostics identify the longest query phases. Target index creation, geometry simplification, spatial partitioning for these hotspots.

Test parallelization, hardware optimization for intensive predicates like overlap detection. Benchmark alternatives like PostGIS topology for certain use cases.

Generate spatial statistics summaries to understand data complexity by:

SELECT ST_MemSize(geom), ST_NPoints(geom) 
FROM buildings;

Tailor data structures, query plans based on distribution analyses.

Leave a Reply

Your email address will not be published. Required fields are marked *