Livestock density Buffalo

Key: fao_livestock_buffalo | Secondary data | Category: Animals

The GeoTIFF of the data source provides only a few pixels for the area of Ghana, which also all have the value 0. The rest of the pixel inside Ghana are NODATA values.

Sources of Data Layer:

Gridded livestock density (Global - 2020 - 10 km) - GLW4

Data Layer information

Source file is available (Show file)

import os
import re

import numpy as np
from datalayers.datasources.base_layer import (
    BaseLayer,
    LayerTimeResolution,
    LayerValueType,
)
from datalayers.datasources.tiff_layer import TiffLayer


class FaoLivestockBuffalo(TiffLayer):
    def __init__(self) -> None:
        super().__init__()
        self.value_type: LayerValueType = LayerValueType.FLOAT

    def download(self):
        urls = [
            "https://storage.googleapis.com/fao-gismgr-glw4-2020-data/DATA/GLW4-2020/MAPSET/D-DA/GLW4-2020.D-DA.BFL.tif"
        ]

        for url in urls:
            self._save_url_to_file(url)

    def consume(self, file, band, shape):
        x = re.search(r"[0-9]{4}", os.path.basename(file))
        year = int(x[0])

        self.add_value(shape, year, np.nanmean(band))

Data is available locally

Data is loaded into the database

Operation	mean
Value type	`float`
Database unit	None
Smallest spatial resolution	`district`
Smallest temporal resolution	`year`

Data availability per spatial unit within the integrated time period
Shape type	First	Last	Availability
Country			100.00 %
Region			6.25 %
District			0.38 %
Total	2020	2020	1.08 %

Analytical overview

Source information

[1 of 1] Gridded livestock density (Global - 2020 - 10 km) - GLW4

Data

https://data.apps.fao.org/catalog//iso/9d1e149b-d63f-4213-978b-317a8eb42d02

Source: FAO Livestock Systems

No description provided.

Citation

This source is included in the Data Layer citation.

No citation information available.

Information	Description
Format	image/tiff
Format unit	head/km²
Projection	EPSG:4326
Spatial resolution	Approximately 10km (0.08333 degrees) 0.08 degrees × 0.08 degrees
Spatial coverage	global 90.00 -90.00 -180.00 180.00
Temporal resolution	annually
Temporal coverage	2020
Language	eng
License	CC-BY-NC-SA-3.0-IGO
Date published	2024-07-15
Date last accessed	2025-06-25

import requests from urllib.parse import urlencode import pandas as pd import matplotlib.pyplot as plt # set host, like http://localhost:8000, to where your Data Hub is running DH_HOST = "" # Your personal API authentication token # ATTENTION: tokens should not be included in production code! # I.e, use a environment variable to store the token and set it like this: # DH_TOKEN = os.environ["DH_TOKEN"] DH_TOKEN = "" def dh_data(query: dict) -> pd.DataFrame: response = requests.get( f"{DH_HOST}/api/datalayers/data?{urlencode(query)}", headers={"X-API-Key": DH_TOKEN} ) if response.status_code == 200: d = response.json() else: raise Exception(f"Request failed ({response.status_code}): {response.text}") df = pd.DataFrame(d["data"]) df[d["temporal_column"]] = pd.to_datetime(df[d["temporal_column"]], format="ISO8601") return df fao_livestock_buffalo_df = dh_data({ 'datalayer_key': 'fao_livestock_buffalo', # Optional filters to narrow down the returned data: #'shape_key': '', #'shape_type': '', #'start_date': '', #'end_date': '', })

# ===== LOAD LIBRARIES ===== library(httr) library(jsonlite) # ===== CONFIGURATION ===== # set host, like http://localhost:8000, to where your Data Hub is running DH_HOST <- "" # Your personal API authentication token # ATTENTION: tokens should not be added to production code! # I.e, use a .Renviron file to store the token and set it like this: # DH_TOKEN <- Sys.getenv("DH_TOKEN") DH_TOKEN <- "" # ===== MAIN FUNCTION ===== dh_data <- function(query) { url <- paste0(DH_HOST, "/api/datalayers/data") response <- GET( url, query = query, add_headers("X-API-Key" = DH_TOKEN) ) # Check if the request was successful (status code 200) if (status_code(response) == 200) { d <- fromJSON(content(response, "text", flatten = TRUE, encoding = "UTF-8")) } else { stop(sprintf("Request failed (%d): %s", status_code(response), content(response, as = "text"))) } df <- as.data.frame(d$data) # Convert the time column to proper date type temporal_column <- d$temporal_column temporal_format <- d$temporal_format # R would fill in missing date parts with the current date. # as.Date("2020",format="%Y") -> 2020-<current month>-<current day> and not 2020-01-01 if (temporal_column == "year") { df[[temporal_column]] <- as.Date(paste0(as.character(df[[temporal_column]]), "-01-01")) temporal_format <- "%Y-%m-%d" } df[[temporal_column]] <- as.Date(as.character(df[[temporal_column]]), format = temporal_format) return(df) } # ===== EXAMPLE USAGE ===== fao_livestock_buffalo_df <- dh_data(list( datalayer_key = "fao_livestock_buffalo" # Optional filters: # shape_key = "", # shape_type_key = "", # start_date = "", # end_date = "" ))