A Practical Process for Solving PostGIS Problems
When solving a PostGIS problem, the hardest part is often not writing SQL. It is figuring out what the spatial problem actually is.
A Practical Process for Solving PostGIS Problems
When solving a PostGIS problem, the hardest part is often not writing SQL. It is figuring out what the spatial problem actually is.
A useful rule is:
Reason about the spatial problem first. Write the SQL second.
The process below is designed to work across different datasets and types of spatial analysis.
1. Understand the Question
Start by translating the question into plain language.
Ask:
- What is the question actually asking?
- What should the final answer contain?
- Is this a question about location, distance, proximity, containment, intersection, overlap, or another spatial relationship?
For example:
“Which areas are within 500 meters of a river?”
Translate that into:
Find the areas and determine which ones are within 500 meters of the river.
Don’t think about SQL yet.
2. Identify the Geographic Objects
Determine the real-world objects involved.
Typical PostGIS data might represent:
| Object | Typical geometry |
|---|---|
| Address | Point |
| Bus stop | Point |
| Street | LineString |
| River | LineString |
| Parcel | Polygon |
| Neighborhood | Polygon |
| Administrative area | Polygon |
Ask:
What geographic objects am I working with?
This establishes the objects that will participate in the spatial analysis.
3. Understand the Available Data
Now determine what data is available to answer the question.
Look for:
- datasets or tables;
- identifying attributes;
- geometry columns;
- geometry types;
- SRID/CRS;
- unique identifiers;
- relationships between datasets.
At this stage, the question is:
Do I have the right data to answer the question?
You are building a mental model of the data before designing the query.
4. Determine the Spatial Relationship
This is the central spatial reasoning step.
Ask:
What spatial relationship must be established to answer the question?
Common concepts include:
| Question | Spatial relationship |
|---|---|
| What is inside this area? | Containment |
| What intersects this feature? | Intersection |
| What touches this boundary? | Touching |
| What overlaps this polygon? | Overlap |
| What is closest to this feature? | Proximity |
| What is within a certain distance? | Distance |
| What crosses this feature? | Crossing |
The important thing is to identify the relationship first.
Don’t start by searching for a function name.
5. Check Spatial Compatibility
Before implementing the relationship, check whether the geometries are appropriate for the operation.
Consider:
- geometry type;
- SRID;
- CRS;
- coordinate units;
geometryversusgeography;- dimensionality where relevant.
This matters particularly for distance calculations.
For example:
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ST_DWithin(geom1, geom2, 1000)
does not automatically mean 1,000 meters.
The meaning of 1000 depends on the data type and coordinate system.
So ask:
Are these geometries compatible with the operation I want to perform, and do I understand the units?
6. Determine How Many Results You Expect
Now think about cardinality.
Ask:
How many results should this relationship logically produce?
Possibilities include:
- one result;
- several results;
- one result for each feature;
- many-to-many relationships.
For example, a road may intersect several neighborhood polygons.
Therefore:
“Which neighborhoods intersect this road?”
may legitimately return several rows.
At this point, determine what the question requires:
- all matches;
- one match;
- the nearest match;
- the largest overlap;
- an aggregate;
- a ranked result.
The spatial relationship alone does not always determine the final answer.
7. Separate Attribute and Spatial Logic
Now divide the problem into two kinds of conditions.
Attribute logic
For example:
Which feature am I looking for?
Which category?
Which date?
Which status?
Spatial logic
For example:
Which features intersect it?
Which features contain it?
Which features are within 500 meters?
Think of the query as:
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Attribute conditions
+
Spatial conditions
↓
Required result
This separation becomes especially useful when spatial queries become more complicated.
8. Translate the Spatial Concept into a PostGIS Function
Only now should you move from spatial reasoning to PostGIS syntax.
For example:
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Containment
↓
ST_Contains / ST_Within / ST_Covers
Intersection
↓
ST_Intersects
Distance
↓
ST_DWithin / ST_Distance
Touching
↓
ST_Touches
The function should be chosen after the relationship is understood.
Two common traps
Argument order matters.
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ST_Contains(A, B)
means:
A contains B.
Switching the arguments changes the question.
Also, containment functions can differ in how they treat boundaries. For example, ST_Contains does not consider a point exactly on a polygon boundary to be contained in the same way that ST_Covers does.
The general lesson is:
Know exactly what the function means, including argument order and boundary behavior.
9. Design and Write the SQL
Now you have enough information to construct the query.
At this point you should know:
- what you are looking for;
- which data represents it;
- what spatial relationship is required;
- whether the geometries are compatible;
- how many results you expect;
- what attribute filters are needed;
- what the final output should contain;
- which PostGIS function expresses the spatial relationship.
The SQL is now an implementation of reasoning you have already completed.
10. Validate the Result
A query that runs successfully is not necessarily a correct spatial query.
Check:
- Does the number of rows make sense?
- Are there unexpected duplicates?
- Did the spatial relationship produce the expected matches?
- Are the distances or measurements reasonable?
- Are the units correct?
- Does the result make geographic sense?
- Does the result look correct when visualized?
For spatial analysis, validation is essential because SQL can be syntactically correct while expressing the wrong spatial logic.
Worked Example: Atlantic Commons
Consider this question:
What neighborhood and borough is Atlantic Commons in?
Step 1 — Understand the question
We need to identify a street and determine its relationship to a neighborhood.
Step 2 — Identify the objects
The problem involves:
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Street
Neighborhood
Borough
Step 3 — Understand the data
Suppose the data contains:
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nyc_streets
name
geom
nyc_neighborhoods
name
boroname
geom
The street has a line geometry, while the neighborhood has a polygon geometry.
Step 4 — Determine the spatial relationship
We need to determine:
Which neighborhood geometry intersects the street geometry?
The relationship is therefore intersection.
Step 5 — Check compatibility
We need to verify that the two geometries use compatible SRIDs and that the geometry types are appropriate.
Here:
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street → LINESTRING
neighborhood → POLYGON
That is an appropriate combination for an intersection test.
Step 6 — Consider cardinality
A street can cross more than one neighborhood.
So ST_Intersects() may return multiple neighborhoods.
For a simple workshop exercise, returning all intersecting neighborhoods may be the intended interpretation. In another problem, we might need a rule such as largest overlap or nearest polygon.
Step 7 — Separate the logic
Attribute condition:
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street name = 'Atlantic Commons'
Spatial condition:
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street intersects neighborhood
Step 8 — Choose the function
The spatial concept is intersection, so we use:
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ST_Intersects(...)
Step 9 — Write the SQL
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SELECT n.name, n.boroname
FROM nyc_neighborhoods AS n
JOIN nyc_streets AS s
ON ST_Intersects(n.geom, s.geom)
WHERE s.name = 'Atlantic Commons';
The important point is that the SQL was the last step of the reasoning process, not the first.
One More Practical Consideration: Performance
Once the logic is correct, consider how the query will perform on larger datasets.
Spatial indexes, typically GiST indexes, are important for many spatial predicates such as:
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ST_Intersects(...)
ST_DWithin(...)
For example:
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CREATE INDEX nyc_streets_geom_idx
ON nyc_streets
USING GIST (geom);
This is a performance consideration after the spatial problem has been defined correctly.
Do not try to optimize a query before you know that it asks the right spatial question.
The Core Workflow
Keep this sequence in mind:
Understand → Identify → Inspect → Define relationship → Check compatibility → Consider cardinality → Separate logic → Choose function → Write SQL → Validate
That is the repeatable process.
The specific PostGIS functions, tables, and datasets will change from problem to problem. The reasoning process does not.