بنقرة واحدة
map-visualization
Tips and useful information when an user wants to visualize in a map as result
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
القائمة
Tips and useful information when an user wants to visualize in a map as result
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
How to use the CARTO CLI to interact with the platform
How to write, validate, and upload CARTO Workflows
How to enrich component notes and input type documentation served by the local workflows-api
Builds Geographically Weighted Regression (GWR) workflows in CARTO. Triggers when the user mentions GWR, geographically weighted regression, spatially varying relationships, local regression, local coefficients, spatial regression, "what drives X in different areas", "why do prices vary spatially", "local factors affecting Y", varying coefficients, coefficient maps, spatial non-stationarity, or wants to model how the relationship between a dependent variable and predictors changes across geography. Produces per-cell regression coefficients that reveal how predictor importance shifts from place to place.
Builds Getis-Ord Gi* hotspot analysis workflows in CARTO. Triggers when the user mentions hotspots, coldspots, spatial clusters, Getis-Ord, Gi*, cluster detection, concentration areas, "where do X cluster", spacetime hotspot, temporal clusters, time-varying patterns, hotspot trends, emerging hotspots, Mann-Kendall, or wants to find statistically significant spatial or spatiotemporal patterns in point or grid data.
Builds Moran's I spatial autocorrelation workflows in CARTO. Triggers when the user mentions spatial autocorrelation, Moran's I, spatial dependency, spatial correlation, spatial outliers, HH HL LH LL quadrants, high-high clusters, low-low clusters, spatial weight matrix, "is there clustering", "are values spatially correlated", local indicators of spatial association, LISA, spatial randomness test, or wants to determine whether a variable exhibits spatial clustering, dispersion, or randomness across a gridded dataset. Also relevant when the user needs to classify locations into cluster types (HH, HL, LH, LL) rather than just identifying hotspots and coldspots.
| name | map-visualization |
| description | Tips and useful information when an user wants to visualize in a map as result |
This skill covers how to create map visualizations from workflow outputs using the native.createmap component and best practices for spatial data handling.
native.createmap ComponentThe native.createmap component creates a CARTO Builder map from workflow output. This is the recommended way to visualize workflow results.
Fetch the full schema with:
carto workflows components get native.createmap --connection <connection> --json
Key inputs:
| Input | Type | Description |
|---|---|---|
source | Table | The input table to visualize |
name | String | Name for the created map |
geo | Column | The geometry/spatial column to use for rendering |
{
"id": "create-map-1",
"type": "generic",
"data": {
"name": "native.createmap",
"label": "Create Map",
"inputs": [
{
"name": "source",
"type": "Table",
"value": ""
},
{
"name": "name",
"type": "String",
"value": "My Analysis Results"
},
{
"name": "geo",
"type": "Column",
"value": "geom"
}
],
"outputs": []
},
"position": { "x": 800, "y": 100 }
}
<critical_rule name="preserve-spatial-indices"> Do NOT convert H3 or Quadbin indices to geometry for map visualization.
CARTO Builder natively understands H3 and Quadbin spatial indices. When your workflow output contains these columns, leave them as-is:
h3, h3_index)quadbin, qb_index)Builder will automatically:
Only use native.h3boundary or native.quadbinboundary when:
Correct - Let Builder handle H3 visualization:
source -> h3frompoint -> groupby (count per cell) -> createmap
geo: "h3"
Unnecessary - Adding h3boundary just for visualization:
source -> h3frompoint -> groupby -> h3boundary -> createmap
geo: "h3_geo"
The second approach works but adds unnecessary computation. Builder can render H3 cells directly.
When using native.createmap, the geo input must reference a valid spatial column:
| Data Type | Column to Use |
|---|---|
| H3 | The H3 column directly (e.g., h3, index) - Builder renders cells natively |
| Quadbin | The Quadbin column directly (e.g., quadbin) - Builder renders cells natively |
| Point / Line / Polygon / MultiPolygon | The geometry column name (e.g., geom, geometry, geom_buffer) |
Check output schema before createmap: Validate the workflow and inspect available columns to ensure the geometry column exists
Name maps descriptively: The name input becomes the map title in Builder - use meaningful names
One map per workflow output: Each createmap component creates a separate map. For multiple layers, create multiple maps or use Builder to combine them
Spatial indices are preferred: For aggregated data (e.g., counts per H3 cell), spatial indices render faster than polygon geometries
| Issue | Cause | Solution |
|---|---|---|
| Map shows no data | Wrong geo column specified | Check output schema for correct geometry column name |
| Map renders slowly | Using extracted polygons instead of spatial indices | Use H3/Quadbin columns directly instead of boundary components |
| "Column not found" error | Geometry column was renamed or dropped | Trace the workflow to ensure geometry is preserved through all transformations |