7 Fundamentals of Spatial Analysis

Spatial analysis and data preparation

Spatial analysis uses the locations and shapes of geographic features to answer questions about where things are, how they relate, and how patterns vary across an area. A spatial query tests a condition involving location or geometry, while a spatial analysis operation creates or summarizes information based on those relationships.

Prepare suitable data

Before analysis, check that layers use compatible coordinate systems and that their geometries and attributes suit the question. Coordinate systems determine how locations are represented: projected systems commonly express coordinates in linear units, while geographic systems use angular units. Distance and area calculations depend on both the coordinate system and the method used.

For local distance analysis, use an appropriate projected coordinate system or a geodesic method, and confirm the units. Also check for missing or invalid geometries, mismatched dates or definitions, and differences in spatial detail. A precise-looking result cannot correct unsuitable input data.

Select features by conditions

Attribute selection chooses features using table values, such as parcels whose land-use type is “commercial.” Spatial selection chooses features using their geometric relationship to another feature or layer. The selected features can remain in the original layer or be exported as a new layer.

Common geometric predicates describe different relationships:

• Intersects: the geometries share at least one point; they may overlap or simply touch.

• Within / contains: one geometry lies inside another. Exact boundary behavior depends on the predicate used.

• Touches, overlaps, crosses, and disjoint: other ways to describe geometric relationships.

For example, a spatial selection could find schools that intersect a city boundary or parcels within a flood zone. Spatial-selection tools evaluate a predicate between features in the input and comparison layers.

Buffers and proximity

A Buffer defines an area around an input feature at a specified distance. A point buffer represents a radius around a location, a line buffer represents a corridor along a route, and a polygon buffer expands or contracts an area. Buffers can answer questions such as which homes are within 500 meters of a road or what land lies within 1 kilometer of a river.

Buffers may be created separately for each feature or dissolved into a combined area. Planar buffers use distances in the coordinate system’s units; geodesic methods account for Earth’s curvature. Choose the distance method and units carefully. If the question is simply which features fall within a distance, a direct within-distance query may be preferable to constructing buffer polygons.

Proximity analysis examines distance or nearness between features. It can be used to find the nearest hospital to each school, identify homes within walking-distance approximations of transit stops, or locate roads with no nearby services. A within-distance query returns features at or below a chosen distance. A nearest-feature query identifies the closest feature, which may or may not be within an acceptable distance.

Straight-line distance is not the same as travel distance or travel time. A road, river, terrain, or transit network can make an actual route much longer than the map distance. Use network analysis when the question concerns movement along connected routes.

Combine layers with overlay

Overlay combines geometries from two or more layers to reveal where their features coincide and often transfers their attributes into the result. For example, overlaying land-use polygons with watershed boundaries can show which land-use types occur in each watershed. Overlay is more than visually placing maps on top of one another: it computes new spatial relationships or geometries.

Common operations have different effects:

• Intersect keeps the shared portions of the input features. If the inputs do not overlap, the intersection is empty.

• Union combines polygon coverage from the inputs while retaining the areas and attributes represented by each layer.

• Erase / difference keeps portions of one input that are outside another.

• Clip keeps the portion of an input inside a boundary.

For example, clipping a road layer to a study-area boundary produces road segments inside that boundary.

Summarize spatial patterns

Spatial summaries aggregate information by location or area. A point-in-polygon summary can count clinics in each district; a line summary can calculate road length by district; and a polygon summary can calculate how much of each land-cover type lies within each watershed. Summary statistics may include counts, sums, averages, or other measures.

Counts alone can mislead when areas differ in population or size. To compare districts more fairly, consider a rate or density, such as clinics per 10,000 residents or road length per square kilometer. Make the denominator, time period, and spatial unit clear. Aggregating data into different boundaries can change the apparent pattern, so interpret results in light of the chosen areas and scale.

Workflow and interpretation

A practical workflow connects the question to the operation and then checks how the result was produced:

1. Frame the question: specify the features, relationship, distance, or summary needed.

2. Inspect the data: check coordinate systems, units, geometry validity, attributes, and dates.

3. Choose the operation: selection, buffer, proximity, overlay, or spatial summary.

4. Run and inspect the result: check selected counts, map boundaries, and attribute values for unexpected outcomes.

5. Communicate the method: record key assumptions, distance units, predicates, and summary measures.

Spatial queries select features based on attribute or geometric conditions. Buffers define areas around features, proximity analysis measures nearness, overlay combines layers to identify shared areas and relationships, and spatial summaries aggregate counts, lengths, areas, or attributes by place. Reliable conclusions depend on suitable coordinate systems, clear definitions, appropriate units, and careful interpretation of the data’s scale and limits.