Geospatial Operations

7 specialized tools for spatial queries, distance calculations, and GIS operations using PostGIS.

Requirements: PostGIS extension (v3.0+)

📊 Overview

Tool	Purpose	Geometry Type
`spatial_search`	Find geometries within area	Point, Polygon, LineString
`distance_calculate`	Calculate distances	Point-to-point, point-to-geometry
`within_radius`	Find points within radius	Point, Circle
`geometry_validate`	Validate geometry data	All types
`coordinate_transform`	Transform coordinate systems	SRID conversion
`spatial_index_create`	Create spatial indexes	GIST, BRIN
`geo_stats`	Spatial statistics	Area, length, bounds

🔧 Tool Details

spatial_search

Search for geometries within a specified area or bounding box.

Parameters:

table_name (string, required): Table with geometry column
geometry_column (string, required): Column containing geometries
search_geometry (string, required): WKT geometry to search within
operation (string, optional): within, intersects, contains (default: within)
limit (integer, optional): Maximum results

Returns:

Matching geometries
Distance/area calculations
Geometry properties

Example:

# Find points within polygon
result = spatial_search(
    table_name="locations",
    geometry_column="geom",
    search_geometry="POLYGON((0 0, 10 0, 10 10, 0 10, 0 0))",
    operation="within"
)
# Returns: [
#   {"id": 1, "name": "Store A", "geom": "POINT(5 5)"},
#   {"id": 2, "name": "Store B", "geom": "POINT(7 3)"},
#   ...
# ]

# Find all geometries intersecting a line
result = spatial_search(
    table_name="roads",
    geometry_column="path",
    search_geometry="LINESTRING(0 0, 10 10)",
    operation="intersects"
)

Operations:

within - Geometries completely inside search area
intersects - Geometries that touch/cross search area
contains - Search area is completely inside geometry

Use Cases:

Store locator
Service area analysis
Delivery zone checking
Proximity queries

distance_calculate

Calculate distances between geometries or points.

Parameters:

table_name (string, required): Source table
geometry_column (string, required): Geometry column
reference_geometry (string, required): WKT reference point/geometry
unit (string, optional): meters, kilometers, miles, feet (default: meters)
limit (integer, optional): Nearest N results

Returns:

Distance values in specified unit
Sorted by distance (nearest first)

Example:

# Find nearest stores
result = distance_calculate(
    table_name="stores",
    geometry_column="location",
    reference_geometry="POINT(-122.4194 37.7749)",  # San Francisco
    unit="kilometers",
    limit=5
)
# Returns: [
#   {"id": 42, "name": "Downtown Store", "distance_km": 0.5},
#   {"id": 18, "name": "Market St Store", "distance_km": 1.2},
#   ...
# ]

# Calculate distance to all locations
result = distance_calculate(
    table_name="warehouses",
    geometry_column="location",
    reference_geometry="POINT(-74.0060 40.7128)",  # New York
    unit="miles"
)

Supported Units:

meters (default)
kilometers
miles
feet

Use Cases:

Nearest neighbor search
Distance-based sorting
Travel time estimation
Service radius verification

within_radius

Find all points within a specified radius.

Parameters:

table_name (string, required): Source table
geometry_column (string, required): Point geometry column
center_point (string, required): WKT center point
radius (number, required): Search radius
unit (string, optional): Distance unit (default: meters)

Returns:

Points within radius
Distance from center
Sorted by distance

Example:

# Find stores within 5km
result = within_radius(
    table_name="stores",
    geometry_column="location",
    center_point="POINT(-122.4194 37.7749)",
    radius=5,
    unit="kilometers"
)
# Returns: [
#   {
#     "id": 1,
#     "name": "Downtown Store",
#     "location": "POINT(-122.4183 37.7750)",
#     "distance_km": 0.12
#   },
#   ...
# ]

# Find all points within 1 mile
result = within_radius(
    table_name="customers",
    geometry_column="address_point",
    center_point="POINT(-74.0060 40.7128)",
    radius=1,
    unit="miles"
)

Use Cases:

Radius search
Geofencing
Coverage analysis
Service area definition

geometry_validate

Validate and check geometry data quality.

Parameters:

table_name (string, required): Table to validate
geometry_column (string, required): Geometry column
fix_invalid (boolean, optional): Attempt to fix invalid geometries

Returns:

Validation results
Error descriptions
Fixed geometries (if requested)

Example:

result = geometry_validate(
    table_name="parcels",
    geometry_column="boundary",
    fix_invalid=True
)
# Returns: {
#   "total_geometries": 1000,
#   "valid": 985,
#   "invalid": 15,
#   "fixed": 12,
#   "errors": [
#     {
#       "id": 42,
#       "error": "Self-intersection",
#       "fixed": True
#     },
#     {
#       "id": 108,
#       "error": "Ring Self-intersection",
#       "fixed": False
#     }
#   ]
# }

Common Validation Issues:

Self-intersecting polygons
Duplicate vertices
Invalid ring orientation
Unclosed polygons
Null geometries

Use Cases:

Data quality assurance
Import validation
GIS data cleaning
Topology correction

coordinate_transform

Transform geometries between coordinate reference systems (SRID).

Parameters:

table_name (string, required): Source table
geometry_column (string, required): Geometry column
source_srid (integer, required): Source SRID
target_srid (integer, required): Target SRID
update_table (boolean, optional): Update geometries in place

Returns:

Transformed geometries
Transformation statistics
SRID information

Example:

# Transform from WGS84 (4326) to Web Mercator (3857)
result = coordinate_transform(
    table_name="locations",
    geometry_column="geom",
    source_srid=4326,
    target_srid=3857,
    update_table=False
)

# Common transformations
# WGS84 (GPS): 4326
# Web Mercator (Google Maps): 3857
# UTM Zone 10N: 32610
# State Plane CA Zone 3: 2227

Common SRIDs:

4326 - WGS84 (GPS coordinates, lat/lon)
3857 - Web Mercator (Web maps)
2163 - US National Atlas Equal Area
32601-32660 - UTM Northern Hemisphere
32701-32760 - UTM Southern Hemisphere

Use Cases:

Map projection conversion
GPS coordinate transformation
GIS data integration
Spatial analysis preparation

spatial_index_create

Create optimized spatial indexes for faster queries.

Parameters:

table_name (string, required): Target table
geometry_column (string, required): Geometry column
index_type (string, optional): gist (default) or brin
index_name (string, optional): Custom index name

Returns:

Index creation status
Estimated performance improvement
Index size

Example:

# GIST index (recommended for most cases)
result = spatial_index_create(
    table_name="locations",
    geometry_column="geom",
    index_type="gist"
)

# BRIN index (for very large, spatially sorted data)
result = spatial_index_create(
    table_name="sensor_readings",
    geometry_column="location",
    index_type="brin"
)

Index Types:

GIST (Generalized Search Tree):

Best for: Most spatial queries
Pros: Fast searches, good for mixed workloads
Cons: Larger index size, slower to build

BRIN (Block Range Index):

Best for: Very large, spatially clustered data
Pros: Small index size, fast to build
Cons: Slower searches, requires spatial clustering

Use Cases:

Query performance optimization
Large dataset handling
Production readiness

geo_stats

Calculate spatial statistics on geometry data.

Parameters:

table_name (string, required): Source table
geometry_column (string, required): Geometry column
include_area (boolean, optional): Calculate areas
include_length (boolean, optional): Calculate lengths
include_bounds (boolean, optional): Calculate bounding boxes

Returns:

Spatial statistics
Aggregate measurements
Distribution analysis

Example:

result = geo_stats(
    table_name="parcels",
    geometry_column="boundary",
    include_area=True,
    include_length=True,
    include_bounds=True
)
# Returns: {
#   "total_geometries": 5000,
#   "geometry_types": {
#     "POLYGON": 4850,
#     "MULTIPOLYGON": 150
#   },
#   "area_stats": {
#     "total_sqm": 5000000,
#     "avg_sqm": 1000,
#     "min_sqm": 50,
#     "max_sqm": 10000
#   },
#   "perimeter_stats": {
#     "total_m": 250000,
#     "avg_m": 50,
#     "min_m": 10,
#     "max_m": 500
#   },
#   "bounds": {
#     "min_x": -122.5,
#     "min_y": 37.7,
#     "max_x": -122.3,
#     "max_y": 37.8
#   },
#   "srid": 4326
# }

Use Cases:

Data inventory
Quality assessment
Area calculations
Spatial extent analysis

🎯 Common Workflows

Store Locator

# 1. Find stores within 10km
nearby = within_radius(
    table_name="stores",
    geometry_column="location",
    center_point="POINT(-122.4194 37.7749)",
    radius=10,
    unit="kilometers"
)

# 2. Get exact distances
distances = distance_calculate(
    table_name="stores",
    geometry_column="location",
    reference_geometry="POINT(-122.4194 37.7749)",
    unit="kilometers",
    limit=5
)

# 3. Find stores in delivery area
in_zone = spatial_search(
    table_name="stores",
    geometry_column="location",
    search_geometry="POLYGON(...)",  # Delivery zone
    operation="within"
)

Service Coverage Analysis

# 1. Validate service area geometries
validation = geometry_validate(
    table_name="service_areas",
    geometry_column="boundary",
    fix_invalid=True
)

# 2. Calculate coverage statistics
stats = geo_stats(
    table_name="service_areas",
    geometry_column="boundary",
    include_area=True
)

# 3. Find customers in service area
covered = spatial_search(
    table_name="customers",
    geometry_column="address_point",
    search_geometry="POLYGON(...)",  # Service area
    operation="within"
)

GIS Data Integration

# 1. Transform coordinates
transform = coordinate_transform(
    table_name="imported_data",
    geometry_column="geom",
    source_srid=4269,  # NAD83
    target_srid=4326,  # WGS84
    update_table=True
)

# 2. Create spatial index
index = spatial_index_create(
    table_name="imported_data",
    geometry_column="geom",
    index_type="gist"
)

# 3. Validate geometries
validate = geometry_validate(
    table_name="imported_data",
    geometry_column="geom",
    fix_invalid=True
)

🚀 Best Practices

1. Always Use Spatial Indexes

# Create index before running queries
spatial_index_create(
    table_name="locations",
    geometry_column="geom",
    index_type="gist"
)

2. Use Appropriate SRID

# WGS84 (4326) for GPS data
# Web Mercator (3857) for web maps
# UTM for accurate distance measurements

# Transform to appropriate SRID for your use case
coordinate_transform(
    table_name="data",
    geometry_column="geom",
    source_srid=4326,
    target_srid=32610  # UTM Zone 10N for California
)

3. Validate Before Processing

# Always validate imported data
geometry_validate(
    table_name="new_data",
    geometry_column="geom",
    fix_invalid=True
)

📚 Related Documentation

Extension Setup - Install PostGIS
Core Database Tools - Basic operations
Statistical Analysis - Spatial statistics

🔗 External Resources

See Home for more tool categories.

Geospatial Operations

Geospatial Operations

📊 Overview

🔧 Tool Details

spatial_search

distance_calculate

within_radius

geometry_validate

coordinate_transform

spatial_index_create

geo_stats

🎯 Common Workflows

Store Locator

Service Coverage Analysis

GIS Data Integration

🚀 Best Practices

1. Always Use Spatial Indexes

2. Use Appropriate SRID

3. Validate Before Processing

📚 Related Documentation

🔗 External Resources

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