185 lines
7.4 KiB
Plaintext
185 lines
7.4 KiB
Plaintext
GEOSPATIAL DATA RELATIONSHIPS IN PERSONAL TRACKER
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==================================================
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This document explains how the geospatial datasets in the timeline_csv folder are interconnected
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and structured to provide a complete picture of location-based personal tracking data.
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OVERVIEW
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--------
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The location tracking system uses a hierarchical approach with semantic segments as the master
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index that coordinates different types of location data. The data is organized into movement
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periods (travel) and stationary periods (visits), creating a complete chronological timeline.
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CORE DATASETS AND THEIR RELATIONSHIPS
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=====================================
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1. SEMANTIC_SEGMENTS.CSV - The Master Index
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--------------------------------------------
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Purpose: Acts as the central orchestrator that defines time-based segments
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Key Fields:
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- segment_index: Unique identifier linking all other datasets
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- startTime/endTime: Time boundaries for each segment
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- has_visit: Boolean indicating if segment contains visit data
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- has_timeline_path: Boolean indicating if segment contains movement data
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This dataset defines the temporal structure and determines which other datasets contain
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data for each time period.
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2. TIMELINE_PATH_POINTS.CSV - Movement Data
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--------------------------------------------
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Purpose: GPS tracking data during travel/movement periods
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Key Fields:
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- segment_index: Links to semantic_segments
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- point_index: Order of GPS points within a segment
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- time: Precise timestamp for each GPS reading
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- lat/lon: Geographic coordinates
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- raw_point: Original coordinate string
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Relationship: Contains data ONLY for segments where has_timeline_path=1 in semantic_segments.
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These represent periods when the person was moving between locations.
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3. VISITS.CSV - Stationary Location Data
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-----------------------------------------
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Purpose: Information about places where the person stayed for extended periods
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Key Fields:
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- segment_index: Links to semantic_segments
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- top_place_id: Google Places API identifier
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- top_semantic_type: Category (HOME, WORK, UNKNOWN, etc.)
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- top_lat/top_lon: Geographic coordinates of the visit location
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- startTime/endTime: Duration of the visit
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- visit_probability: Confidence that this was actually a visit
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Relationship: Contains data ONLY for segments where has_visit=1 in semantic_segments.
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These represent periods when the person was stationary at a specific location.
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4. FREQUENT_PLACES.CSV - Location Reference Data
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------------------------------------------------
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Purpose: Registry of commonly visited locations with semantic labels
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Key Fields:
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- placeId: Google Places API identifier (links to visits.top_place_id)
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- label: Semantic meaning (HOME, WORK, or empty for unlabeled)
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- lat/lon: Geographic coordinates
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Relationship: Acts as a lookup table for visits.csv. The placeId field provides
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cross-references to identify and categorize frequently visited locations.
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5. RAW_SIGNALS.CSV - Raw GPS Data
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---------------------------------
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Purpose: Unprocessed GPS signals from the device
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Key Fields:
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- raw_index: Sequential identifier
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- timestamp: When the GPS signal was recorded
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- lat/lon: Geographic coordinates
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- accuracyMeters: GPS accuracy measurement
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- altitudeMeters: Elevation data
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- speedMetersPerSecond: Movement speed
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- source: Data source type
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Relationship: This is the foundation data that gets processed into timeline_path_points
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and visits. It represents the raw GPS signals before semantic interpretation.
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SUPPORTING DATASETS
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===================
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6. FREQUENT_TRIPS.CSV - Trip Pattern Analysis
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----------------------------------------------
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Purpose: Analysis of regular travel patterns (like commutes)
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Key Fields:
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- trip_index: Unique identifier for trip patterns
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- startTimeMinutes/endTimeMinutes: Time of day patterns
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- durationMinutes: Typical trip duration
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- commuteDirection: HOME_TO_WORK or WORK_TO_HOME
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- waypoint_count: Number of stops in the trip
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7. FREQUENT_TRIP_WAYPOINTS.CSV - Trip Waypoint Details
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------------------------------------------------------
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Purpose: Specific locations that are part of frequent trips
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Key Fields:
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- trip_index: Links to frequent_trips.csv
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- waypoint_order: Sequence of stops in the trip
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- waypoint_id: Links to frequent_places.placeId
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8. FREQUENT_TRIP_MODE_DISTRIBUTION.CSV - Transportation Analysis
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---------------------------------------------------------------
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Purpose: Analysis of transportation methods used
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Key Fields:
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- trip_index: Links to frequent_trips.csv
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- mode: Transportation type (WALKING, DRIVING, etc.)
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- percentage: How often this mode was used for this trip
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9. TRAVEL_MODE_AFFINITIES.CSV - User Preferences
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------------------------------------------------
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Purpose: User's preferred transportation methods
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Key Fields:
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- mode: Transportation type
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- affinity: Preference score
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DATA FLOW AND RELATIONSHIPS
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============================
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1. RAW COLLECTION:
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raw_signals.csv contains all GPS pings from the device
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2. TEMPORAL SEGMENTATION:
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semantic_segments.csv divides time into logical periods based on movement patterns
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3. MOVEMENT vs. STATIONARY CLASSIFICATION:
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- Movement periods → timeline_path_points.csv (detailed GPS tracking)
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- Stationary periods → visits.csv (location identification and categorization)
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4. LOCATION IDENTIFICATION:
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frequent_places.csv provides semantic meaning to visited locations
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5. PATTERN ANALYSIS:
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frequent_trips.csv, frequent_trip_waypoints.csv, and frequent_trip_mode_distribution.csv
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analyze regular patterns and transportation preferences
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EXAMPLE DATA FLOW
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==================
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Segment 0 (Movement): 2013-12-31 22:00 - 2014-01-01 00:00
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- semantic_segments: has_timeline_path=1, has_visit=0
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- timeline_path_points: Contains GPS coordinates during this travel period
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- visits: No data for this segment
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Segment 1 (Visit): 2013-12-31 22:29 - 2014-01-01 17:10
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- semantic_segments: has_timeline_path=0, has_visit=1
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- timeline_path_points: No data for this segment
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- visits: Shows visit to place ChIJyaJWtZVqdkgRZHVIi0HKLto (HOME)
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- frequent_places: Confirms this placeId is labeled as "HOME"
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QUERYING STRATEGIES
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===================
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To get complete journey information:
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1. Query semantic_segments for time range
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2. For movement segments: Join with timeline_path_points on segment_index
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3. For visit segments: Join with visits on segment_index
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4. Enhance visit data by joining visits.top_place_id with frequent_places.placeId
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To analyze location patterns:
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1. Use frequent_places for location categories
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2. Use frequent_trips for commute patterns
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3. Use travel_mode_affinities for transportation preferences
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COORDINATE SYSTEMS
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==================
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All latitude/longitude data uses WGS84 decimal degrees:
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- Latitude: Positive = North, Negative = South
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- Longitude: Positive = East, Negative = West
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- Precision: Typically 6-7 decimal places (meter-level accuracy)
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TIME ZONES
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==========
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All timestamps include timezone information (typically +00:00 or +01:00 for UK data).
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Time ranges in semantic_segments define the boundaries for linking other datasets.
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DATA COMPLETENESS
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=================
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- Not all segments have both movement and visit data
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- Some segments may have neither (gaps in tracking)
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- Visit probability scores indicate confidence levels
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- Missing coordinates in raw_signals are represented as empty fields
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This hierarchical structure allows for both detailed movement tracking and high-level
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pattern analysis while maintaining semantic meaning about the places visited. |