Skip to content

Instantly share code, notes, and snippets.

@sgibbes

sgibbes/Querying_Country_Pages_Statistics_from_GFW_API.ipynb

Last active April 17, 2018 16:17
Show Gist options
  • Save sgibbes/9f834a4cb51b400713877cfd08229fa6 to your computer and use it in GitHub Desktop.
Save sgibbes/9f834a4cb51b400713877cfd08229fa6 to your computer and use it in GitHub Desktop.
Download ZIP
This is an example of how to return annual forest loss statistics within Indonesia by querying the GFW API. I'll show all possible boundaries to query by, and an example of looking at forest loss within Indonesia Primary Forest
Display the source blob
Display the rendered blob
Raw
Querying_Country_Pages_Statistics_from_GFW_API.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 153,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"from pprint import pprint\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'dataset': '499682b1-3174-493f-ba1a-368b4636708e'}"
]
},
"execution_count": 154,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The data on country pages is stored in this table: \n",
"{'dataset': '499682b1-3174-493f-ba1a-368b4636708e'}"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {},
"outputs": [],
"source": [
"# create the url to query the data\n",
"url = 'https://production-api.globalforestwatch.org/v1/query/499682b1-3174-493f-ba1a-368b4636708e?'\n",
"sql = 'SELECT polyname from data GROUP BY polyname'"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{u'data': [{u'polyname': u'gadm28'},\n",
" {u'polyname': u'wdpa'},\n",
" {u'polyname': u'kba'},\n",
" {u'polyname': u'mining'},\n",
" {u'polyname': u'plantations'},\n",
" {u'polyname': u'landmark'},\n",
" {u'polyname': u'mangroves'},\n",
" {u'polyname': u'plantations__mining'},\n",
" {u'polyname': u'ifl_2013'},\n",
" {u'polyname': u'aze'},\n",
" {u'polyname': u'ifl_2013__wdpa'},\n",
" {u'polyname': u'mangroves__wdpa'},\n",
" {u'polyname': u'mangroves__kba'},\n",
" {u'polyname': u'plantations__wdpa'},\n",
" {u'polyname': u'ifl_2013__kba'},\n",
" {u'polyname': u'tiger_cl'},\n",
" {u'polyname': u'plantations__kba'},\n",
" {u'polyname': u'plantations__idn_forest_moratorium'},\n",
" {u'polyname': u'plantations__idn_mys_peatlands'},\n",
" {u'polyname': u'plantations__oil_palm'},\n",
" {u'polyname': u'ifl_2013__landmark'},\n",
" {u'polyname': u'managed_forests'},\n",
" {u'polyname': u'plantations__wood_fiber'},\n",
" {u'polyname': u'plantations__tiger_cl'},\n",
" {u'polyname': u'primary_forest'},\n",
" {u'polyname': u'idn_forest_moratorium'},\n",
" {u'polyname': u'mangroves__landmark'},\n",
" {u'polyname': u'ifl_2013__mining'},\n",
" {u'polyname': u'primary_forest__idn_forest_moratorium'},\n",
" {u'polyname': u'primary_forest__wdpa'},\n",
" {u'polyname': u'primary_forest__kba'},\n",
" {u'polyname': u'mangroves__mining'},\n",
" {u'polyname': u'ifl_2013__tiger_cl'},\n",
" {u'polyname': u'ifl_2013__managed_forests'},\n",
" {u'polyname': u'plantations__managed_forests'},\n",
" {u'polyname': u'oil_palm'},\n",
" {u'polyname': u'idn_mys_peatlands'},\n",
" {u'polyname': u'mangroves__idn_forest_moratorium'},\n",
" {u'polyname': u'ifl_2013__aze'},\n",
" {u'polyname': u'wood_fiber'},\n",
" {u'polyname': u'plantations__landmark'},\n",
" {u'polyname': u'ifl_2013__idn_forest_moratorium'},\n",
" {u'polyname': u'primary_forest__managed_forests'},\n",
" {u'polyname': u'primary_forest__oil_palm'},\n",
" {u'polyname': u'primary_forest__wood_fiber'},\n",
" {u'polyname': u'mangroves__aze'},\n",
" {u'polyname': u'primary_forest__idn_mys_peatlands'},\n",
" {u'polyname': u'plantations__aze'},\n",
" {u'polyname': u'mangroves__idn_mys_peatlands'},\n",
" {u'polyname': u'ifl_2013__oil_palm'},\n",
" {u'polyname': u'primary_forest__tiger_cl'},\n",
" {u'polyname': u'mangroves__oil_palm'},\n",
" {u'polyname': u'mangroves__managed_forests'},\n",
" {u'polyname': u'primary_forest__aze'},\n",
" {u'polyname': u'ifl_2013__wood_fiber'},\n",
" {u'polyname': u'mangroves__tiger_cl'},\n",
" {u'polyname': u'ifl_2013__idn_mys_peatlands'},\n",
" {u'polyname': u'mangroves__wood_fiber'},\n",
" {u'polyname': u'primary_forest__landmark'},\n",
" {u'polyname': u'primary_forest__mining'}],\n",
" u'meta': {u'cloneUrl': {u'body': {u'dataset': {u'application': [u'your',\n",
" u'apps'],\n",
" u'datasetUrl': u'/v1/query/499682b1-3174-493f-ba1a-368b4636708e?sql=SELECT%20polyname%20from%20data%20GROUP%20BY%20polyname'}},\n",
" u'http_method': u'POST',\n",
" u'url': u'/v1/dataset/499682b1-3174-493f-ba1a-368b4636708e/clone'}}}\n"
]
}
],
"source": [
"# send request to the api using specific sql string\n",
"properties = {'sql': sql}\n",
"r = requests.get(url, params = properties)\n",
"\n",
"# this returns all possible boundaries that data can be summarized by\n",
"pprint(r.json())"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{u'data': [{u'area': 745229.7349489555,\n",
" u'iso': u'IDN',\n",
" u'polyname': u'gadm28',\n",
" u'year': 2001},\n",
" {u'area': 856933.7275843099,\n",
" u'iso': u'IDN',\n",
" u'polyname': u'gadm28',\n",
" u'year': 2002},\n",
" {u'area': 545401.5946592465,\n",
" u'iso': u'IDN',\n",
" u'polyname': u'gadm28',\n",
" u'year': 2003},\n",
" {u'area': 1290529.9424487203,\n",
" u'iso': u'IDN',\n",
" u'polyname': u'gadm28',\n",
" u'year': 2004},\n",
" {u'area': 1184006.661106944,\n",
" u'iso': u'IDN',\n",
" u'polyname': u'gadm28',\n",
" u'year': 2005},\n",
" {u'area': 1434923.6626982987,\n",
" u'iso': u'IDN',\n",
" u'polyname': u'gadm28',\n",
" u'year': 2006},\n",
" {u'area': 1388837.0555543378,\n",
" u'iso': u'IDN',\n",
" u'polyname': u'gadm28',\n",
" u'year': 2007},\n",
" {u'area': 1397189.6777070984,\n",
" u'iso': u'IDN',\n",
" u'polyname': u'gadm28',\n",
" u'year': 2008},\n",
" {u'area': 1946559.7092499286,\n",
" u'iso': u'IDN',\n",
" u'polyname': u'gadm28',\n",
" u'year': 2009},\n",
" {u'area': 1280661.4490504116,\n",
" u'iso': u'IDN',\n",
" u'polyname': u'gadm28',\n",
" u'year': 2010},\n",
" {u'area': 1544765.5635640025,\n",
" u'iso': u'IDN',\n",
" u'polyname': u'gadm28',\n",
" u'year': 2011},\n",
" {u'area': 2262183.5242625773,\n",
" u'iso': u'IDN',\n",
" u'polyname': u'gadm28',\n",
" u'year': 2012},\n",
" {u'area': 1140017.9056999013,\n",
" u'iso': u'IDN',\n",
" u'polyname': u'gadm28',\n",
" u'year': 2013},\n",
" {u'area': 1896862.4773496017,\n",
" u'iso': u'IDN',\n",
" u'polyname': u'gadm28',\n",
" u'year': 2014},\n",
" {u'area': 1748403.016772665,\n",
" u'iso': u'IDN',\n",
" u'polyname': u'gadm28',\n",
" u'year': 2015},\n",
" {u'area': 2424111.9581938162,\n",
" u'iso': u'IDN',\n",
" u'polyname': u'gadm28',\n",
" u'year': 2016}],\n",
" u'meta': {u'cloneUrl': {u'body': {u'dataset': {u'application': [u'your',\n",
" u'apps'],\n",
" u'datasetUrl': u'/v1/query/499682b1-3174-493f-ba1a-368b4636708e?sql=SELECT%20polyname%2C%20year_data.year%20as%20year%2C%20SUM%28year_data.area_loss%29%20as%20area%20FROM%20data%20WHERE%20polyname%20%3D%20%27gadm28%27%20AND%20iso%20%3D%20%27IDN%27%20AND%20thresh%3D%2030%20GROUP%20BY%20polyname%2C%20iso%2C%20nested%28year_data.year%29'}},\n",
" u'http_method': u'POST',\n",
" u'url': u'/v1/dataset/499682b1-3174-493f-ba1a-368b4636708e/clone'}}}\n"
]
}
],
"source": [
"# Now, to query loss within Indonesia: Specify gadm28 as the polyname and IDN as the iso:\n",
"sql = \"SELECT polyname, year_data.year as year, SUM(year_data.area_loss) as area FROM data WHERE polyname = 'gadm28' AND iso = 'IDN' AND thresh= 30 GROUP BY polyname, iso, nested(year_data.year)\"\n",
"\n",
"properties = {'sql': sql}\n",
"r = requests.get(url, params = properties)\n",
"\n",
"pprint(r.json())\n"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" area iso polyname year\n",
"0 7.452297e+05 IDN gadm28 2001\n",
"1 8.569337e+05 IDN gadm28 2002\n",
"2 5.454016e+05 IDN gadm28 2003\n",
"3 1.290530e+06 IDN gadm28 2004\n",
"4 1.184007e+06 IDN gadm28 2005\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10eb2f250>"
]
},
"execution_count": 158,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10dcc3e50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create a plot of loss data within Indonesia\n",
"\n",
"# load into pandas dataframe\n",
"idn_loss = pd.DataFrame(r.json().get('data'))\n",
"print idn_loss.head()\n",
"\n",
"# plot the dataframe\n",
"idn_loss.plot(x='year', y='area', kind='bar', color='#FE5A8D', title='Forest Loss within Indonesia')\n"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {},
"outputs": [],
"source": [
"# Now, to query loss within Indonesia's Primary Forest: Specify primary_forest as the polyname and IDN as the iso:\n",
"\n",
"sql = \"SELECT polyname, year_data.year as year, SUM(year_data.area_loss) as area FROM data WHERE polyname = 'primary_forest' AND iso = 'IDN' AND thresh= 30 GROUP BY polyname, iso, nested(year_data.year)\"\n",
"\n",
"properties = {'sql': sql}\n",
"prf_request = requests.get(url, params = properties)\n"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" area iso polyname year\n",
"0 208336.702883 IDN primary_forest 2001\n",
"1 278416.723108 IDN primary_forest 2002\n",
"2 240911.253722 IDN primary_forest 2003\n",
"3 478764.061310 IDN primary_forest 2004\n",
"4 488863.596515 IDN primary_forest 2005\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10e04ead0>"
]
},
"execution_count": 160,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e518e90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create a plot of loss data within Indonesia's Primary Forest\n",
"\n",
"# load into pandas dataframe\n",
"prf_loss = pd.DataFrame(prf_request.json().get('data'))\n",
"print prf_loss.head()\n",
"\n",
"# plot the dataframe\n",
"prf_loss.plot(x='year', y='area', kind='bar', color='#FE5A8D', title=\"Forest Loss within Indonesia's Primary Forest\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment