Natural Resource Management Index (NRMI), 2010 Release
简介
2010 年发布的自然资源管理指数 (NRMI) 是 157 个国家的综合指数,该指数根据四项接近目标的指标的平均值得出,这四项指标分别是生态区域保护(受保护生物群落的加权平均百分比)、改善卫生设施的获取、改善水源的获取和儿童死亡率。2010 年发布的 NRMI 包括 2006、2007、2008、2009 和 2010 年的时间序列 NRMI。需要注意的是,由于数据和方法的变化,此版本中提供的 2006-2008 年 NRMI 与 2006-2008 年发布的 NRMI 不能直接比较。该数据集由哥伦比亚大学国际地球科学信息网络中心 (CIESIN) 与耶鲁大学耶鲁环境法律与政策中心 (YCELP) 合作制作和发布。
摘要
Resource Type Dataset
Metadata Created Date November 12, 2020
Metadata Updated Date April 24, 2025
Publisher SEDAC
Identifier C1000000007-SEDAC
Data First Published 2010-12-31
Language en-US
Data Last Modified 2025-04-23
Category NRMI, geospatial
Public Access Level public
Bureau Code 026:00
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id e0de2b92-6530-49d3-984e-b3918a18cc0f
Harvest Source Id 58f92550-7a01-4f00-b1b2-8dc953bd598f
Harvest Source Title NASA Data.json
Homepage URL https://doi.org/10.7927/H49G5JRZ
Metadata Type geospatial
Old Spatial -180.0 -55.0 180.0 90.0
Program Code 026:001
Related Documents https://doi.org/10.7927/H45Q4T1N, https://doi.org/10.7927/H41Z4299, https://doi.org/10.7927/H4NZ85MP, https://doi.org/10.7927/H46M34RP, https://doi.org/10.7927/H4G73BM2, https://doi.org/10.7927/H48913TX, https://doi.org/10.7927/H4SQ8XGT, https://doi.org/10.7927/6t8a-es66, https://doi.org/10.7927/r6mv-sv82
Source Datajson Identifier True
Source Hash e88f282bfda7fc8104b5a1cdb67d316525816e1845f7c1d045a266a50cb5957a
Source Schema Version 1.1
Spatial
Temporal 2004-01-01T00:00:00Z/2009-12-31T00:00:00Z
代码
!pip install leafmap
!pip install pandas
!pip install folium
!pip install matplotlib
!pip install mapclassify
import pandas as pd
import leafmap
url = "https://github.com/opengeos/NASA-Earth-Data"
df = pd.read_csv(url, sep="\t")
df
leafmap.nasa_data_login()
results, gdf = leafmap.nasa_data_search(
short_name="CIESIN_SEDAC_NRMI_2010",
cloud_hosted=True,
bounding_box=(-180.0, -55.0, 180.0, 90.0),
temporal=("2004-01-01", "2009-12-31"),
count=-1, # use -1 to return all datasets
return_gdf=True,
)
gdf.explore()
leafmap.nasa_data_download(results[:5], out_dir="data")