{
  "format": "dataset-manifest/1",
  "slug": "energy-mapping-insights",
  "version": 2,
  "seed": "energy-mapping-insights:1",
  "spec": {
    "format": "dataset/1",
    "slug": "energy-mapping-insights",
    "name": "Energy Mapping Insights",
    "tagline": "Geographical layers of energy infrastructure and service boundaries for planning in the energy sector.",
    "description": "Energy organizations analyze geographical data to optimize planning and resource management. They focus on energy infrastructure, service boundaries, and environmental features, all crucial for efficient operations. The dataset provides insights into organizations involved in energy distribution, detailing their mapping workspaces and the distinct layers they utilize for analysis. Each entry represents a specific mapping project, showcasing how different data layers contribute to decision-making processes.\n\nThe primary table, energy_mappings, contains rows that correspond to individual mapping projects within these organizations. Each row includes a unique identifier for the mapping project, along with critical attributes such as the type of energy infrastructure being analyzed, the geographical area covered, and the specific layers used in the mapping process. By connecting this data, users can explore how different organizations approach similar challenges, offering opportunities to identify trends and best practices across the sector.\n\nThis dataset allows developers to practice formulating queries that reveal insights into energy distribution patterns, resource allocation efficiency, and the adoption of various mapping techniques. Questions such as which areas are prioritized for renewable energy initiatives and how different layers impact resource management can be explored in depth. \n\nAll data within this dataset is synthetic, designed to simulate real-world scenarios while ensuring user privacy and data security. This provides a safe environment for developers to hone their skills and gain practical experience in working with geographical data specific to the energy sector.",
    "domain": "energy",
    "shape": "multi-tenant",
    "useCases": [
      "orm-seeding",
      "api-mocking"
    ],
    "keywords": [
      "energy",
      "geographical data",
      "GIS",
      "mapping",
      "renewable energy",
      "infrastructure",
      "service boundaries",
      "environmental features"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "energy_mappings": {
        "description": "This table represents organizations in the energy sector, detailing their GIS workspaces and the layers they employ. Each row corresponds to a unique organization and includes their mapping configurations and layer types.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each organization."
          },
          "organization_name": {
            "kind": "fullName",
            "description": "The name of the energy organization."
          },
          "workspace": {
            "kind": "words",
            "min": 1,
            "max": 3,
            "description": "The name or identifier of the mapping workspace.",
            "pool": [
              "solar",
              "wind",
              "grid",
              "efficiency",
              "sustainability",
              "renewable",
              "conservation",
              "analysis",
              "optimization",
              "monitoring",
              "demand",
              "usage",
              "forecast",
              "integration",
              "deployment",
              "strategy",
              "innovation",
              "assessment",
              "performance",
              "technology",
              "resource"
            ]
          },
          "layer_type": {
            "kind": "enum",
            "values": [
              "vector",
              "raster",
              "hybrid"
            ],
            "weights": [
              0.5,
              0.3,
              0.2
            ],
            "description": "The type of geographical layer used in the mapping."
          },
          "is_renewable": {
            "kind": "bool",
            "trueRate": 0.4,
            "description": "Indicates if the layer is related to renewable energy initiatives."
          },
          "location": {
            "kind": "city",
            "description": "The primary location or region the organization serves."
          },
          "data_source": {
            "kind": "url",
            "description": "Link to the data source for the mapping layer."
          }
        }
      }
    },
    "structure": "single-table",
    "traits": [
      "high-cardinality",
      "categorical",
      "nullable"
    ],
    "analysisQuestions": [
      "What types of mapping layers are most commonly used by energy organizations?",
      "How does the prevalence of renewable energy layers vary by geographic region?",
      "Which organizations are focusing on hybrid mapping solutions in their GIS?",
      "What are the primary locations served by organizations in the energy sector?"
    ],
    "suggestedQueries": [
      {
        "title": "List all organizations with vector layers",
        "sql": "SELECT organization_name FROM energy_mappings WHERE layer_type = 'vector';",
        "dialect": "postgres"
      },
      {
        "title": "Count organizations by layer type",
        "sql": "SELECT layer_type, COUNT(*) FROM energy_mappings GROUP BY layer_type;",
        "dialect": "postgres"
      },
      {
        "title": "Find renewable layer organizations by location",
        "sql": "SELECT organization_name, location FROM energy_mappings WHERE is_renewable = TRUE;",
        "dialect": "postgres"
      },
      {
        "title": "Get organizations using hybrid layers in specific locations",
        "sql": "SELECT organization_name FROM energy_mappings WHERE layer_type = 'hybrid' AND location IN ('RegionA', 'RegionB');",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Energy Sector Analysis",
        "body": "Analysts in the energy sector can use this dataset to explore the geographic mapping practices of different organizations. By understanding the types of layers utilized, they can identify trends in energy distribution and pinpoint areas that may require further development or investment."
      },
      {
        "title": "Software Development for GIS Tools",
        "body": "Developers working on GIS applications can leverage this synthetic dataset to simulate user interactions and test their software's functionality. By integrating realistic energy mapping data, they can ensure their tools meet the needs of users in the energy sector."
      },
      {
        "title": "Educational Purposes",
        "body": "Students and educators can utilize this dataset to teach concepts related to GIS, energy distribution, and environmental management. It provides a practical resource for assignments and projects that require real-world data analytics without the complexities of accessing actual datasets."
      }
    ]
  },
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    "energy_mappings": 200
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  "stats": {
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          "min": "Alice Almeida",
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        "workspace": {
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          "distinct": 3,
          "min": "hybrid",
          "max": "vector"
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        "is_renewable": {
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          "min": "false",
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        "location": {
          "nulls": 0,
          "distinct": 62,
          "min": "Amsterdam",
          "max": "Zurich"
        },
        "data_source": {
          "nulls": 0,
          "distinct": 196
        }
      }
    }
  },
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