---

Relocations and Trajectories

Setup

Ecoscope

ECOSCOPE_RAW = "https://raw.githubusercontent.com/wildlife-dynamics/ecoscope/master"

# !pip install ecoscope

import os
import sys

import geopandas as gpd
import pandas as pd
import shapely

import ecoscope

ecoscope.init()

Google Drive Setup

output_dir = "Ecoscope-Outputs"

if "google.colab" in sys.modules:
    from google.colab import drive

    drive.mount("/content/drive/", force_remount=True)
    output_dir = os.path.join("/content/drive/MyDrive/", output_dir)

os.makedirs(output_dir, exist_ok=True)

Relocations

Read in sample data in MovBank format

ecoscope.io.download_file(
    f"{ECOSCOPE_RAW}/tests/sample_data/vector/movbank_data.csv",
    os.path.join(output_dir, "movbank_data.csv"),
)

data = pd.read_csv(os.path.join(output_dir, "movbank_data.csv"))

Make Data Timezone Aware and Sort by Time

data["timestamp"] = pd.to_datetime(data["timestamp"], utc=True)
data.sort_values(["timestamp"], inplace=True)

Create GeoDataFrame Using Lat/Lon Point Coordinates

gdf = gpd.GeoDataFrame(data, geometry=gpd.points_from_xy(x=data["location-long"], y=data["location-lat"]), crs=4326)

Display columns:

gdf.columns

Create Relocations from Specified Time, Subject Id, and Observation Id

relocs = ecoscope.base.Relocations.from_gdf(
    gdf,
    groupby_col="individual-local-identifier",
    time_col="timestamp",
    uuid_col="event-id",
)

When the dataframe becomes a Relocations it will gain fixtime, groupby_col, junk_status columns that are expected by Ecoscope analyses

relocs.columns

Optionally drop Non-Essential Columns

relocs = relocs[["groupby_col", "fixtime", "junk_status", "geometry"]]

relocs

Which unique subjects are contained in the groupby_col column?

relocs["groupby_col"].unique()

Visualize Data

relocs[["groupby_col", "fixtime", "geometry"]].explore()

Mark Points as Junk Based on Coordinates

coord_filter = ecoscope.base.RelocsCoordinateFilter(
    min_x=-5,
    max_x=1,
    min_y=12,
    max_y=18,
    filter_point_coords=[[180, 90], [0, 0]],
)
relocs.apply_reloc_filter(coord_filter, inplace=True)

Count values marked as junk:

relocs["junk_status"].value_counts()

Mark Points as Junk Based on Speed

speed_filter = ecoscope.base.RelocsSpeedFilter(max_speed_kmhr=4.0)
relocs.apply_reloc_filter(speed_filter, inplace=True)

relocs["junk_status"].value_counts()

Remove Flagged Fixes

relocs.remove_filtered(inplace=True)

Remove Duplicate Fixes

print(relocs.duplicated(subset=["fixtime", "geometry"]).any())

relocs = relocs.drop_duplicates(subset=["fixtime", "geometry"])

Visualize:

relocs["geometry"].explore()

Determine whether each relocation is a day or night

relocs["is_night"] = ecoscope.analysis.astronomy.is_night(relocs.geometry, relocs.fixtime)

Break Relocations Geometry Into X and Y

relocs["lat"] = relocs["geometry"].y
relocs["lon"] = relocs["geometry"].x

Convert to UTM and Back to WGS 84

relocs = relocs.to_crs(relocs.estimate_utm_crs())
relocs["extra__northing"] = relocs["geometry"].y
relocs["extra__easting"] = relocs["geometry"].x
relocs = relocs.to_crs(4326)

relocs.columns

Export to Compressed CSV

relocs.to_csv(os.path.join(output_dir, "relocs_clean.csv.zip"), header=True, index=True, compression="zip")

Trajectory

Read in Relocations

relocs from the previous steps can be used instead of redefining it here.

ecoscope.io.download_file(
    f"{ECOSCOPE_RAW}/tests/sample_data/vector/er_relocs.csv.zip",
    os.path.join(output_dir, "er_relocs.csv.zip"),
)

data = pd.read_csv(os.path.join(output_dir, "er_relocs.csv.zip"), header=0, index_col=0)

Parse Geometry from WKT

gdf = gpd.GeoDataFrame(data, geometry=data["geometry"].map(lambda x: shapely.wkt.loads(x)), crs=4326)

relocs = ecoscope.base.Relocations.from_gdf(gdf)

Create Trajectory from Relocations

traj = ecoscope.base.Trajectory.from_relocations(relocs)

traj

traj.columns

Visualize:

traj["geometry"].explore()

Filter Segments

traj_seg_filter = ecoscope.base.TrajSegFilter(
    min_length_meters=0.0,
    max_length_meters=float("inf"),
    min_time_secs=0.0,
    max_time_secs=4 * 60 * 60,
    min_speed_kmhr=0.0,
    max_speed_kmhr=8.0,
)
traj.apply_traj_filter(traj_seg_filter, inplace=True)

traj["junk_status"].value_counts()

traj.remove_filtered(inplace=True)

Visualize:

traj["geometry"].explore()

Optionally Drop Non-Essential Columns

traj = traj[
    [
        "groupby_col",
        "segment_start",
        "segment_end",
        "timespan_seconds",
        "dist_meters",
        "speed_kmhr",
        "heading",
        "geometry",
    ]
]

Calculate Summary Statistics

traj.groupby("groupby_col").apply(pd.DataFrame.describe)

Export to GeoPackage for ArcGIS or QGIS

traj.to_file(os.path.join(output_dir, "traj.gpkg"))

Convert a Trajectory object to a Relocation object

new_relocs = traj.to_relocations()

Upsampling

upsampled_relocs = traj.upsample("180S")

Downsampling, with interpolation

downsampled_relocs_int = traj.downsample("10800S", interpolation=True)

Downsampling, without interpolation

downsampled_relocs_noint = traj.downsample("10800S", tolerance="900S")