gps-activity 0.7.3

A light-weight mobile module for analysis of GPS activity

GPS activity 🚛

A light-weight module for analysis of GPS activity. Package is designed to be trade-off solution for both researchers and developers in Waste Labs. Using gps_activity you can:

1. Cluster your time-series gps records to extract activity points
2. Join activity points with original plan or operation report
3. Estimate clustering performance

Installation ☁️ -> 🖥️

Using pip:

pip3 install gps_activity

Python package modules 📦

---

• extraction: clusters GPS records and extracts cluster activities (checkout module structure)
• linker: joins route plan and clustered gps records
• metrics: estimates clustering performance based on:
  1. internal source: ones that based on inter & intra cluster distances
  2. external source: joined route plan and clustered gps records (output of linker module)

Extraction modules 🔵 🟣 ⚫️

---

Organized by preprocessing, fragmentation & clustering steps that ultimately are packed into ActivityExtractionSession object and orchestrated.

⚠️ ActivityExtractionSession assumptions and constrains

1. 1 vehicle = 1 session run: to avoid clusters overlap
2. No duplicated gps records over vehicle-timstamp: avoids division by zero during computing velocity

🚀 VHFDBSCAN: Velocity hardlimit fragmentation Density-based spatial clustering of applications with noise

• Fragmentation is performing by applying hardlimiting on velocity computed from lat, lon and datetime columns
• Clustering is performed by classical DBSCAN that considers non-cluster candidates as noise

from gps_activity import ActivityExtractionSession
from gps_activity.extraction.factory.preprocessing import PreprocessingFactory
from gps_activity.extraction.factory.fragmentation import VelocityFragmentationFactory
from gps_activity.extraction.factory.clustering import FDBSCANFactory


preprocessing = PreprocessingFactory.factory_pipeline(
    source_lat_column="lat",
    source_lon_column="lon",
    source_datetime="datetime",
    source_vehicle_id="plate_no",
    source_crs="EPSG:4326",
    target_crs="EPSG:2326",
)

fragmentation = VelocityFragmentationFactory.factory_pipeline(max_velocity_hard_limit=4)
clustering = FDBSCANFactory.factory_pipeline(eps=30, min_samples=3)

activity_extraction = ActivityExtractionSession(
    preprocessing=preprocessing,
    fragmentation=fragmentation,
    clustering=clustering,
)

activity_extraction.predict(gps)

🚀 VHFSTCM: Velocity hardlimit fragmentation spatio-temporal clustering model

• Fragmentation is performing by applying hardlimiting on velocity computed from lat, lon and datetime columns
• Clustering is performed according steps:
  1. Generated adjacent proximity mask (if cluster pair distance <= eps)
  2. Clusters ID are generated according: proximity mask and fragmentation flag
  3. GPS records grouped by cluster_id and aggregated cluster time span
  4. Cluster is validated if time span >= min_duration_sec
  5. Validated cluster ids are set to original GPS records

from gps_activity import ActivityExtractionSession
from gps_activity.extraction.factory.preprocessing import PreprocessingFactory
from gps_activity.extraction.factory.fragmentation import VelocityFragmentationFactory
from gps_activity.extraction.factory.clustering import STCMFactory


preprocessing = PreprocessingFactory.factory_pipeline(
    source_lat_column="lat",
    source_lon_column="lon",
    source_datetime="datetime",
    source_vehicle_id="plate_no",
    source_crs="EPSG:4326",
    target_crs="EPSG:2326",
)

fragmentation = VelocityFragmentationFactory.factory_pipeline(max_velocity_hard_limit=4)
clustering = STCMFactory.factory_pipeline(
    source_vehicle_id_column="plate_no",
    eps=30,
    min_duration_sec=60
)

stcm = ActivityExtractionSession(
    preprocessing=preprocessing,
    fragmentation=fragmentation,
    clustering=clustering,
)

Linker module implementation 🔵 🟣 ⚫️

Overview linker module components

# Initilize linkage components
from gps_activity import ActivityLinkageSession
from gps_activity.linker.factory import PreprocessingFactory
from gps_activity.linker.factory import ClusterAggregationFactory
from gps_activity.linker.factory import JoinValidatorFactory
from gps_activity.linker.factory import SpatialJoinerFactory
from gps_activity.linker.factory import CoverageStatisticsFactory


MAX_DISTANCE = 100
MAX_DAYS_DISTANCE = 1


gps_link_preprocess_pipeline = PreprocessingFactory.factory_pipeline(
    source_lat_column="lat",
    source_lon_column="lon",
    source_datetime="datetime",
    source_vehicle_id="plate_no",
    source_crs=WSG_84,
    target_crs=HK_CRS,
    generate_primary_key_for="gps",
    source_composite_keys=["plate_no", "datetime", "lat", "lon"],
)

plans_link_preprocess_pipeline = PreprocessingFactory.factory_pipeline(
    source_lat_column="lat",
    source_lon_column="lng",
    source_datetime="datetime",
    source_vehicle_id="re-assigned by Ricky",
    source_crs=WSG_84,
    target_crs=HK_CRS,
    generate_primary_key_for="plan",
    source_composite_keys=["CRN#"],
)

cluster_agg_pipeline = ClusterAggregationFactory.factory_pipeline(
    source_lat_column="lat",
    source_lon_column="lon",
    source_datetime="datetime",
    source_vehicle_id="plate_no",
    source_crs=WSG_84,
    target_crs=HK_CRS,
)

spatial_joiner = SpatialJoinerFactory.factory_pipeline(how="inner", max_distance=MAX_DISTANCE)
spatial_validator = JoinValidatorFactory.factory_pipeline(max_days_distance=MAX_DAYS_DISTANCE,
                                                          ensure_vehicle_overlap=True)
coverage_stats_extractor = CoverageStatisticsFactory.factory_pipeline()


gps_linker_session = ActivityLinkageSession(
    gps_preprocessor=gps_link_preprocess_pipeline,
    plan_preprocessor=plans_link_preprocess_pipeline,
    cluster_aggregator=cluster_agg_pipeline,
    spatial_joiner=spatial_joiner,
    spatial_validator=spatial_validator,
    coverage_stats_extractor=coverage_stats_extractor,
)


linker_results = gps_linker_session.transform({
    "gps": clustered_gps,
    "plan": plans,
})

Metrics module implementation 📊

• NOTE: This module is highly experimental
• NOTE: This module depends on linker module

from gps_activity.metrics import ActivityMetricsSession
from gps_activity.metrics.models import Metrics


metrics = ActivityMetricsSession(beta=2)
metrics = metrics.transform(linker_results)