{
  "format": "dataset-manifest/1",
  "slug": "real-time-neighborhood-safety-and-security-monitoring",
  "version": 1,
  "seed": "real-time-neighborhood-safety-and-security-monitoring:1",
  "spec": {
    "format": "dataset/1",
    "slug": "real-time-neighborhood-safety-and-security-monitoring",
    "name": "Community Safety Monitoring Dataset",
    "tagline": "Surveillance data including cameras, sensors, incident reports, guard shifts, and patrol routes for neighborhood safety.",
    "description": "Neighborhood safety thrives on the proactive monitoring of various security elements, ensuring communities remain secure and responsive to disturbances. Surveillance cameras capture real-time video feeds, while sensors detect movement and environmental changes, which are crucial in identifying incidents that may require immediate attention. Guards conduct regular patrols, following specified routes to maintain a visible security presence. The seamless integration of these components helps create a comprehensive safety net that can respond quickly to any issues that arise, effectively enhancing community safety.\n\nIn this dataset, each table plays a pivotal role in supporting daily operations. The 'cameras' table tracks individual camera locations and their operational statuses, providing essential information for monitoring. The 'sensors' table details sensor placements and types, focusing on their readings and alerts related to unusual activity. The 'incidents' table records specific security events, noting the time, location, and type of incident, helping to identify patterns. The 'guards' table contains information on security staff, including their shifts and assigned patrol areas, while the 'patrols' table outlines the routes taken by guards. By linking these tables, developers can join data from cameras, sensors, and incidents to analyze security trends, cross-referencing guard activities to see their impact on incident occurrences.\n\nQuestions this data can answer include which areas experience the most incidents, at what times these incidents typically occur, and how effectively patrols are responding to reported disturbances. It is particularly useful for practicing data analysis techniques, visualizations, and developing insights into community safety measures. Since this data is synthetic, it provides a safe environment for testing hypotheses and refining analytical skills without real-world implications.",
    "domain": "iot",
    "shape": "time-series",
    "useCases": [
      "analytics-demo",
      "bi-dashboards"
    ],
    "keywords": [
      "community safety",
      "incident tracking",
      "sensor data",
      "surveillance",
      "neighborhood monitoring",
      "security",
      "guard patrols"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "cameras": {
        "description": "This table tracks the surveillance cameras installed in various neighborhoods, linking to incidents and sensor data. Each row represents a specific camera and its details.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each camera."
          },
          "location": {
            "kind": "words",
            "min": 5,
            "max": 50,
            "description": "Description of the camera's physical location."
          },
          "status": {
            "kind": "enum",
            "values": [
              "active",
              "inactive"
            ],
            "description": "Current operational status of the camera."
          },
          "last_maintenance": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date of the last maintenance performed on the camera.",
            "trend": "seasonal"
          }
        }
      },
      "sensors": {
        "description": "This table captures data from various sensors positioned throughout neighborhoods, linking to incidents and monitoring activities. Each row corresponds to a specific sensor.",
        "rows": 150,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each sensor."
          },
          "sensor_type": {
            "kind": "enum",
            "values": [
              "motion",
              "temperature",
              "light"
            ],
            "description": "Type of sensor deployed."
          },
          "location": {
            "kind": "words",
            "min": 5,
            "max": 50,
            "description": "Description of the sensor's location."
          },
          "last_reading": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Timestamp of the last recorded reading from the sensor."
          }
        }
      },
      "incidents": {
        "description": "This table logs security incidents reported in the neighborhood, linking back to cameras and sensors that may have recorded the event. Each row corresponds to an individual incident.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each incident."
          },
          "incident_type": {
            "kind": "enum",
            "values": [
              "theft",
              "vandalism",
              "assault"
            ],
            "description": "Type of incident reported."
          },
          "camera_id": {
            "kind": "fk",
            "ref": "cameras.id",
            "description": "Camera that recorded the incident."
          },
          "sensor_id": {
            "kind": "fk",
            "ref": "sensors.id",
            "nullable": true,
            "description": "Sensor that detected activity during the incident, if applicable."
          },
          "timestamp": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Date and time when the incident occurred.",
            "dayProfile": "waking"
          },
          "location": {
            "kind": "words",
            "min": 5,
            "max": 50,
            "description": "Location where the incident took place."
          }
        }
      },
      "guards": {
        "description": "This table tracks the security guards assigned to patrol routes in the neighborhood, linking to patrols and incidents they may respond to. Each row represents a guard's shift.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each guard."
          },
          "full_name": {
            "kind": "fullName",
            "description": "Full name of the security guard."
          },
          "shift_start": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Start time of the guard's shift.",
            "dayProfile": "business"
          },
          "shift_end": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "End time of the guard's shift.",
            "dayProfile": "business"
          }
        }
      },
      "patrols": {
        "description": "This table details the patrol routes taken by security guards, linking back to incidents they may encounter. Each row corresponds to a specific patrol.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each patrol."
          },
          "guard_id": {
            "kind": "fk",
            "ref": "guards.id",
            "description": "Guard assigned to the patrol route."
          },
          "start_location": {
            "kind": "words",
            "min": 5,
            "max": 50,
            "description": "Starting point of the patrol route."
          },
          "end_location": {
            "kind": "words",
            "min": 5,
            "max": 50,
            "description": "End point of the patrol route."
          },
          "timestamp": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Timestamp of when the patrol started.",
            "dayProfile": "waking"
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "timestamped",
      "categorical",
      "nullable"
    ],
    "analysisQuestions": [
      "What types of incidents are most frequently reported?",
      "How do sensor readings correlate with incident occurrences?",
      "At what times are incidents most likely to happen?",
      "Which neighborhoods have the highest frequency of disturbances?",
      "How effective are the patrols in reducing incident reports?"
    ],
    "suggestedQueries": [
      {
        "title": "List all active cameras.",
        "sql": "SELECT * FROM cameras WHERE status = 'active';",
        "dialect": "postgres"
      },
      {
        "title": "Count incidents by type.",
        "sql": "SELECT incident_type, COUNT(*) FROM incidents GROUP BY incident_type;",
        "dialect": "postgres"
      },
      {
        "title": "Find all incidents linked to a specific camera.",
        "sql": "SELECT * FROM incidents WHERE camera_id = $1;",
        "dialect": "postgres"
      },
      {
        "title": "Get average incidents per hour.",
        "sql": "SELECT EXTRACT(HOUR FROM timestamp) AS hour, COUNT(*) FROM incidents GROUP BY hour;",
        "dialect": "postgres"
      },
      {
        "title": "List patrols conducted by a specific guard.",
        "sql": "SELECT * FROM patrols WHERE guard_id = $1;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Security Analysis for Local Authorities",
        "body": "Local authorities can utilize this dataset to analyze security incidents within neighborhoods, identifying patterns and areas needing more surveillance. By understanding the types and frequencies of incidents, they can allocate resources more effectively and implement community safety initiatives."
      },
      {
        "title": "Real-Time Monitoring for Security Firms",
        "body": "Security firms can leverage this dataset to monitor the effectiveness of their surveillance systems in real-time. By analyzing data from cameras and sensors, they can optimize their patrol routes and response times, ensuring a quicker reaction to incidents."
      },
      {
        "title": "Research on Urban Safety Trends",
        "body": "Urban planners and researchers can use this dataset to study the impact of urban design on community safety. By correlating incident reports with sensor data and patrol activities, they can develop insights that inform safer city planning and community designs."
      },
      {
        "title": "Development of Predictive Analytics Tools",
        "body": "Data scientists can apply machine learning algorithms to this dataset to create predictive analytics tools that anticipate potential incidents based on historical data. These tools can help in proactively addressing security challenges before they escalate."
      }
    ]
  },
  "counts": {
    "cameras": 200,
    "guards": 100,
    "patrols": 300,
    "sensors": 150,
    "incidents": 500
  },
  "stats": {
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      "rows": 200,
      "columns": {
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          "distinct": 200,
          "min": 1,
          "max": 200
        },
        "location": {
          "nulls": 0,
          "distinct": 200
        },
        "status": {
          "nulls": 0,
          "distinct": 2,
          "min": "active",
          "max": "inactive"
        },
        "last_maintenance": {
          "nulls": 0,
          "distinct": 181,
          "min": "2020-01-06",
          "max": "2023-12-25"
        }
      }
    },
    "sensors": {
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      "columns": {
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          "nulls": 0,
          "distinct": 150,
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        "sensor_type": {
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          "distinct": 3,
          "min": "light",
          "max": "temperature"
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        "location": {
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        "last_reading": {
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          "min": "2023-01-01 04:07:31",
          "max": "2023-12-31 10:21:01"
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    "incidents": {
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        },
        "incident_type": {
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          "distinct": 3,
          "min": "assault",
          "max": "vandalism"
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        "camera_id": {
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          "distinct": 189,
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        "sensor_id": {
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        "timestamp": {
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          "min": "2023-01-01 22:21:39",
          "max": "2023-12-31 18:24:48"
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    "guards": {
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