1. Nearest Neighbor
Find the closest feature.
For each feature in [source_layer], find the nearest feature in [target_layer]. Add the distance and ID of the nearest feature as attributes.
2. Distance Matrix
Calculate all pairwise distances.
Calculate the distance between every feature in [layer_1] and every feature in [layer_2]. Output as a distance matrix table.
3. Find Within Distance
Find all features within a distance.
Find all [target_layer] features within [distance] meters of each [source_layer] feature. List the matching features and their distances.
4. K Nearest Neighbors
Find K closest features.
For each feature in [source_layer], find the [k] nearest features in [target_layer]. Include distance to each neighbor in the output.
5. Distance to Nearest
Add distance to nearest as attribute.
Calculate the distance from each [layer_1] feature to the nearest [layer_2] feature. Add this distance as a new column called [column_name].
6. Spatial Join by Proximity
Join attributes based on proximity.
Join attributes from [source_layer] to [target_layer] based on proximity. Each target feature gets attributes from its nearest source feature.
7. Create Connection Lines
Draw lines to nearest features.
Create straight lines connecting each [source_layer] feature to its nearest [target_layer] feature. Style lines by distance.
8. Accessibility Score
Calculate accessibility based on proximity.
Calculate an accessibility score for each [area_layer] feature based on distance to [amenity_layer]. Closer amenities should give higher scores.
9. Hotspot Detection
Find clusters of nearby features.
Identify clusters where [layer_name] features are unusually close together. Highlight areas with high feature density.
10. Gap Analysis
Find underserved areas.
Identify areas more than [distance] meters from any [service_layer] feature. Highlight these underserved/gap areas on the map.