iiRecord
Agentic AI Atlas · Geospatial Analytics (PostGIS + Python + Leaflet/Mapbox + GeoPandas)
stack-profile:geospatial-analyticsa5c.ai
II.
StackProfile JSON

stack-profile:geospatial-analytics

Structured · live

Geospatial Analytics (PostGIS + Python + Leaflet/Mapbox + GeoPandas) json

Inspect the normalized record payload exactly as the atlas UI reads it.

File · domain/stack-profiles/deep-stacks-2.yamlCluster · domain
Record JSON
{
  "id": "stack-profile:geospatial-analytics",
  "_kind": "StackProfile",
  "_file": "domain/stack-profiles/deep-stacks-2.yaml",
  "_cluster": "domain",
  "attributes": {
    "displayName": "Geospatial Analytics (PostGIS + Python + Leaflet/Mapbox + GeoPandas)",
    "description": "A geospatial data analysis and visualization stack: PostGIS extends\nPostgreSQL with spatial data types, indexing, and query functions;\nPython with GeoPandas provides the data manipulation layer for spatial\njoins, buffers, and coordinate transformations; and Leaflet or Mapbox\nGL JS renders interactive maps in the browser with layer controls,\npopups, and custom tile sources.\n\nThe pipeline ingests geospatial data from shapefiles, GeoJSON, GPS\nfeeds, or satellite imagery, stores it in PostGIS with spatial indexes\nfor efficient bounding-box and nearest-neighbor queries, processes it\nwith GeoPandas for analysis, and serves results through a FastAPI\nendpoint for the frontend map. This stack powers logistics route\noptimization, urban planning tools, environmental monitoring dashboards,\nand location intelligence platforms. The key tradeoff is the learning\ncurve of spatial data models and coordinate reference systems, plus the\nstorage and query cost of high-resolution geometries.\n",
    "composes": [
      "language:python",
      "language:sql",
      "language:typescript",
      "framework:fastapi",
      "library:pandas",
      "library:numpy",
      "library:matplotlib",
      "framework:react"
    ]
  },
  "outgoingEdges": [
    {
      "from": "stack-profile:geospatial-analytics",
      "to": "language:python",
      "kind": "composed_of"
    },
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      "to": "language:sql",
      "kind": "composed_of"
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      "to": "language:typescript",
      "kind": "composed_of"
    },
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      "to": "framework:fastapi",
      "kind": "composed_of"
    },
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      "to": "library:pandas",
      "kind": "composed_of"
    },
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      "kind": "composed_of"
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      "kind": "composed_of"
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      "to": "framework:react",
      "kind": "composed_of"
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      "to": "role:data-scientist",
      "kind": "used_by_role"
    },
    {
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      "to": "role:data-engineer",
      "kind": "used_by_role"
    },
    {
      "from": "stack-profile:geospatial-analytics",
      "to": "role:fullstack-engineer",
      "kind": "used_by_role"
    },
    {
      "from": "stack-profile:geospatial-analytics",
      "to": "workflow:data-pipeline-deployment",
      "kind": "follows_workflow"
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      "to": "workflow:dashboard-development-cycle",
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      "to": "domain:logistics",
      "kind": "applies_to"
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      "to": "domain:government",
      "kind": "applies_to"
    },
    {
      "from": "stack-profile:geospatial-analytics",
      "to": "skill-area:geospatial-data-analysis",
      "kind": "requires_skill_area"
    },
    {
      "from": "stack-profile:geospatial-analytics",
      "to": "skill-area:data-visualization",
      "kind": "requires_skill_area"
    },
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      "kind": "requires_skill_area"
    },
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      "to": "skill-area:backend-data-persistence",
      "kind": "requires_skill_area"
    },
    {
      "from": "stack-profile:geospatial-analytics",
      "to": "skill-area:postgres-tuning",
      "kind": "requires_skill_area"
    }
  ],
  "incomingEdges": []
}