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License information was derived automatically
The "Forest Proximate People" (FPP) dataset is one of the data layers contributing to the development of indicator #13, “number of forest-dependent people in extreme poverty,” of the Collaborative Partnership on Forests (CPF) Global Core Set of forest-related indicators (GCS). The FPP dataset provides an estimate of the number of people living in or within 5 kilometers of forests (forest-proximate people) for the year 2019 with a spatial resolution of 100 meters at a global level.
For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L. Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: A new methodology and global estimates. Background Paper to The State of the World’s Forests 2022 report. Rome, FAO.
Contact points:
Maintainer: Leticia Pina
Maintainer: Sarah E., Castle
Data lineage:
The FPP data are generated using Google Earth Engine. Forests are defined by the Copernicus Global Land Cover (CGLC) (Buchhorn et al. 2020) classification system’s definition of forests: tree cover ranging from 15-100%, with or without understory of shrubs and grassland, and including both open and closed forests. Any area classified as forest sized ≥ 1 ha in 2019 was included in this definition. Population density was defined by the WorldPop global population data for 2019 (WorldPop 2018). High density urban populations were excluded from the analysis. High density urban areas were defined as any contiguous area with a total population (using 2019 WorldPop data for population) of at least 50,000 people and comprised of pixels all of which met at least one of two criteria: either the pixel a) had at least 1,500 people per square km, or b) was classified as “built-up” land use by the CGLC dataset (where “built-up” was defined as land covered by buildings and other manmade structures) (Dijkstra et al. 2020). Using these datasets, any rural people living in or within 5 kilometers of forests in 2019 were classified as forest proximate people. Euclidean distance was used as the measure to create a 5-kilometer buffer zone around each forest cover pixel. The scripts for generating the forest-proximate people and the rural-urban datasets using different parameters or for different years are published and available to users. For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L., Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: a new methodology and global estimates. Background Paper to The State of the World’s Forests 2022. Rome, FAO.
References:
Buchhorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N.E., Herold, M., Fritz, S., 2020. Copernicus Global Land Service: Land Cover 100m: collection 3 epoch 2019. Globe.
Dijkstra, L., Florczyk, A.J., Freire, S., Kemper, T., Melchiorri, M., Pesaresi, M. and Schiavina, M., 2020. Applying the degree of urbanisation to the globe: A new harmonised definition reveals a different picture of global urbanisation. Journal of Urban Economics, p.103312.
WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University, 2018. Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645
Online resources:
GEE asset for "Forest proximate people - 5km cutoff distance"
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
The world's population has undergone remarkable growth, exceeding 7.5 billion by mid-2019 and continuing to surge beyond previous estimates. Notably, China and India stand as the two most populous countries, with China's population potentially facing a decline while India's trajectory hints at surpassing it by 2030. This significant demographic shift is just one facet of a global landscape where countries like the United States, Indonesia, Brazil, Nigeria, and others, each with populations surpassing 100 million, play pivotal roles.
The steady decrease in growth rates, though, is reshaping projections. While the world's population is expected to exceed 8 billion by 2030, growth will notably decelerate compared to previous decades. Specific countries like India, Nigeria, and several African nations will notably contribute to this growth, potentially doubling their populations before rates plateau.
This dataset provides comprehensive historical population data for countries and territories globally, offering insights into various parameters such as area size, continent, population growth rates, rankings, and world population percentages. Spanning from 1970 to 2023, it includes population figures for different years, enabling a detailed examination of demographic trends and changes over time.
Structured with meticulous detail, this dataset offers a wide array of information in a format conducive to analysis and exploration. Featuring parameters like population by year, country rankings, geographical details, and growth rates, it serves as a valuable resource for researchers, policymakers, and analysts. Additionally, the inclusion of growth rates and world population percentages provides a nuanced understanding of how countries contribute to global demographic shifts.
This dataset is invaluable for those interested in understanding historical population trends, predicting future demographic patterns, and conducting in-depth analyses to inform policies across various sectors such as economics, urban planning, public health, and more.
This dataset (world_population_data.csv
) covering from 1970 up to 2023 includes the following columns:
Column Name | Description |
---|---|
Rank | Rank by Population |
CCA3 | 3 Digit Country/Territories Code |
Country | Name of the Country |
Continent | Name of the Continent |
2023 Population | Population of the Country in the year 2023 |
2022 Population | Population of the Country in the year 2022 |
2020 Population | Population of the Country in the year 2020 |
2015 Population | Population of the Country in the year 2015 |
2010 Population | Population of the Country in the year 2010 |
2000 Population | Population of the Country in the year 2000 |
1990 Population | Population of the Country in the year 1990 |
1980 Population | Population of the Country in the year 1980 |
1970 Population | Population of the Country in the year 1970 |
Area (km²) | Area size of the Country/Territories in square kilometer |
Density (km²) | Population Density per square kilometer |
Growth Rate | Population Growth Rate by Country |
World Population Percentage | The population percentage by each Country |
The primary dataset was retrieved from the World Population Review. I sincerely thank the team for providing the core data used in this dataset.
© Image credit: Freepik
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Non-Hispanic population of White Earth by race. It includes the distribution of the Non-Hispanic population of White Earth across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of White Earth across relevant racial categories.
Key observations
With a zero Hispanic population, White Earth is 100% Non-Hispanic. Among the Non-Hispanic population, the largest racial group is White alone with a population of 76 (100% of the total Non-Hispanic population).
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.
Racial categories include:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for White Earth Population by Race & Ethnicity. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Notes for the 13.10.2019 update- The months of January 2019 to July 2019 were added to the ERA5 reconstructions- The robustness of the ERA5 reconstruction was improved for a few Greenland and Antarctica mascons by better handling a special case occuring when air temperature is always lower than 0°C during the calibration period.- The updated ERA5 time series might differ from the previous version (especially individual ensemble members). With the exception of the special case mentioned above, these differences are not significant.List of all filesReadme file 00_readme.txtMonthly grids - ensemble means 01_monthly_grids_ensemble_means_allmodels.zipMonthly grids - ensembles, model 1 to 6 02_monthly_grids_ensemble_JPL_MSWEP_1979_2016.zip 02_monthly_grids_ensemble_JPL_GSWP3_1901_2014.zip 02_monthly_grids_ensemble_JPL_ERA5_1979_201907.zip 02_monthly_grids_ensemble_GSFC_MSWEP_1979_2016.zip 02_monthly_grids_ensemble_GSFC_GSWP3_1901_2014.zip 02_monthly_grids_ensemble_GSFC_ERA5_1979_201907.zipDaily grids - ensemble means, model 1 to 6 03_daily_grids_ensemble_means_JPL_MSWEP_1979_2016.zip 03_daily_grids_ensemble_means_JPL_GSWP3_1901_2014.zip 03_daily_grids_ensemble_means_JPL_ERA5_1979_201907.zip 03_daily_grids_ensemble_means_GSFC_MSWEP_1979_2016.zip 03_daily_grids_ensemble_means_GSFC_GSWP3_1901_2014.zip 03_daily_grids_ensemble_means_GSFC_ERA5_1979_201907.zipGlobal averages - daily and monthly time series 04_global_averages_allmodels.zipContent of readmeGRACE TWS Reconstruction (GRACE_REC_v03)The dataset contains reconstructed time series of daily and monthly anomalies of terrestrial water storage (TWS) based on two different GRACE solutions and three different meteorological forcing datasets. There is a total of 6 different models:JPL_MSWEP - trained with GRACE JPL mascons, forced with MSWEP forcing (1979-2016)JPL_GSWP3 - trained with GRACE JPL mascons, forced with GSWP3 forcing (1901-2014)JPL_ERA5 - trained with GRACE JPL mascons, forced with ERA5 forcing (1979-present)GSFC_MSWEP - trained with GRACE GSFC mascons, forced with MSWEP forcing (1979-2016)GSFC_GSWP3 - trained with GRACE GSFC mascons, forced with GSWP3 forcing (1901-2014)GSFC_ERA5 - trained with GRACE GSFC mascons, forced with ERA5 forcing (1979-present)The reconstruction aims at reproducing the sub-decadal climate-driven variability observed in the GRACE data. Seasonal cycle and human impacts on TWS are not reconstructed. A GRACE-based seasonal cycle is provided for convenience. Long-term signals (trends over a period >15 years) are removed during the model calibration procedure but are still present in the final dataset and mainly represent precipitation-driven trends. The interpretation of the reconstructed long-term trends should be done with the awareness that there can be some uncertainty in the reconstructed trends.For most applications, uncertainty ranges can be derived from the 100 ensemble members available for each model.The grids are stored in NetCDFv4 files in units of mm (kg m^-2). Although the data is provided on a 0.5 degrees grid, the effective spatial resolution should be considered to be 3 degrees, similar to the original resolution of the GRACE datasets. This might need to be taken into account when comparing this dataset against other sources.The global means are stored as csv files in units of Gt of water. To convert back to mm of water, use the land area values given in the reference paper below.When using this dataset, please cite:Humphrey, V., & Gudmundsson, L. (2019). GRACE-REC: a reconstruction of climate-driven water storage changes over the last century. Earth System Science Data, 11(3), 1153-1170.Vincent Humphrey, October 2019California Institute of TechnologyYour feedback is always welcome:vincent.humphrey[-a-t-]caltech.edu (vincent.humphrey[-a-t-]bluewin.ch) Abstract
The amount of water stored on continents is an important constraint for water mass and energy exchanges in the Earth system and exhibits large inter-annual variability at both local and continental scales. From 2002 to 2017, the satellites of the Gravity Recovery and Climate Experiment mission (GRACE) have observed changes in terrestrial water storage (TWS) with an unprecedented level of accuracy. In this paper, we use a statistical model trained with GRACE observations to reconstruct past climate-driven changes in TWS from historical and near real time meteorological datasets at daily and monthly scales. Unlike most hydrological models which represent water reservoirs individually (e.g. snow, soil moisture, etc.) and usually provide a single model run, the presented approach directly reconstructs total TWS changes and includes hundreds of ensemble members which can be used to quantify predictive uncertainty. We compare these data-driven TWS estimates with other independent evaluation datasets such as the sea level budget, large-scale water balance from atmospheric reanalysis and in-situ streamflow measurements. We find that the presented approach performs overall as well or better than a set of state-of-the-art global hydrological models (Water Resources Reanalysis version 2). We provide reconstructed TWS anomalies at a spatial resolution of 0.5°, at both daily and monthly scales over the period 1901 to present, based on two different GRACE products and three different meteorological forcing datasets, resulting in 6 reconstructed TWS datasets of 100 ensemble members each. Possible user groups and applications include hydrological modelling and model benchmarking, sea level budget studies, assessments of long-term changes in the frequency of droughts, the analysis of climate signals in geodetic time series and the interpretation of the data gap between the GRACE and the GRACE Follow-On mission.Check reference for additional details and caveats.ReferenceHumphrey, V., & Gudmundsson, L. (2019). GRACE-REC: a reconstruction of climate-driven water storage changes over the last century. Earth System Science Data, 11(3), 1153-1170.
TROPIS is the acronym for the Tree Growth and Permanent Plot Information System sponsored by CIFOR to promote more effective use of existing data and knowledge about tree growth.
TROPIS is concerned primarily with information about permanent plots and tree
growth in both planted and natural forests throughout the world. It has five
components:
- a network of people willing to share permanent plot data and tree
growth information;
- an index to people and institutions with permanent plots;
- a database management system to promote more efficient data management;
- a method to find comparable sites elsewhere, so that observations
can be supplemented or contrasted with other data; and
- an inference system to allow growth estimates to be made in the
absence of empirical data.
- TROPIS is about people and information. The core of TROPIS is an
index to people and their plots maintained in a relational
database. The database is designed to fulfil two primary needs:
- to provide for efficient cross-checking, error-checking and
updating; and to facilitate searches for plots matching a wide range
of specified criteria, including (but not limited to) location, forest
type, taxa, plot area, measurement history.
The database is essentially hierarchical: the key element of the
database is the informant. Each informant may contribute information
on many plot series, each of which has consistent objectives. In turn,
each series may comprise many plots, each of which may have a
different location or different size. Each plot may contain many
species. A series may be a thinning or spacing experiment, some
species or provenance trials, a continuous forest inventory system, or
any other aggregation of plots convenient to the informant. Plots need
not be current. Abandoned plots may be included provided that the
location is known and the plot data remain accessible. In addition to
details of the informant, we try to record details of additional
contact people associated with plots, to maintain continuity when
people transfer or retire. Thus the relational structure may appear
complex, but ensures data integrity.
At present, searches are possible only via mail, fax or email requests
to the TROPIS co-ordinator at CIFOR. Self-service on-line searching
will also be available in 1997. Clients may search for plots with
specified taxa, locations, silvicultural treatment, or other specified
criteria and combinations. TROPIS currently contains references to
over 10,000 plots with over 2,000 species contributed by 100
individuals world-wide.
This database will help CIFOR as well as other users to make more
efficient use of existing information, and to develop appropriate and
effective techniques and policies for sustainable forest management
world-wide.
TROPIS is supported by the Government of Japan.
This information is from the CIFOR web site.
https://choosealicense.com/licenses/cc0-1.0/https://choosealicense.com/licenses/cc0-1.0/
Dataset Card for 100 Richest People In World
Dataset Summary
This dataset contains the list of Top 100 Richest People in the World Column Information:-
Name - Person Name NetWorth - His/Her Networth Age - Person Age Country - The country person belongs to Source - Information Source Industry - Expertise Domain
Join our Community
Supported Tasks and Leaderboards
[More Information Needed]
Languages
[More Information Needed]… See the full description on the dataset page: https://huggingface.co/datasets/nateraw/100-richest-people-in-world.
The World Religion Project (WRP) aims to provide detailed information about religious adherence worldwide since 1945. It contains data about the number of adherents by religion in each of the states in the international system. These numbers are given for every half-decade period (1945, 1950, etc., through 2010). Percentages of the states' populations that practice a given religion are also provided. (Note: These percentages are expressed as decimals, ranging from 0 to 1, where 0 indicates that 0 percent of the population practices a given religion and 1 indicates that 100 percent of the population practices that religion.) Some of the religions (as detailed below) are divided into religious families. To the extent data are available, the breakdown of adherents within a given religion into religious families is also provided.
The project was developed in three stages. The first stage consisted of the formation of a religion tree. A religion tree is a systematic classification of major religions and of religious families within those major religions. To develop the religion tree we prepared a comprehensive literature review, the aim of which was (i) to define a religion, (ii) to find tangible indicators of a given religion of religious families within a major religion, and (iii) to identify existing efforts at classifying world religions. (Please see the original survey instrument to view the structure of the religion tree.) The second stage consisted of the identification of major data sources of religious adherence and the collection of data from these sources according to the religion tree classification. This created a dataset that included multiple records for some states for a given point in time. It also contained multiple missing data for specific states, specific time periods and specific religions. The third stage consisted of cleaning the data, reconciling discrepancies of information from different sources and imputing data for the missing cases.
The Global Religion Dataset: This dataset uses a religion-by-five-year unit. It aggregates the number of adherents of a given religion and religious group globally by five-year periods.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This layer contains WorldPop's 100m resolution annual estimates of population density from the year 2000 to 2020. Usage notes: This layer is configured to be viewed only at a scale range for large-scale maps, i.e., zoomed into small areas of the world. Because the underlying data for this layer is relatively large and because raster pyramids cannot accurately represent aggregated population density, there are no pyramids. Thus, this layer may at times require 10 to 15 seconds to draw. We recommend using this layer in conjunction with WorldPop's 1-km resolution Population Density layer to create web maps that allow users to pan and zoom to wider areas; this web map contains an example of this combination. The population estimates in this layer are derived WorldPop's total population data, which use a Top-down unconstrained method which estimates the total population for each cell with a Random Forest-based dasymetric model (Stevens, F. R., Gaughan, A. E., Linard, C., & Tatem, A. J. (2015). Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PloS one, 10(2), e0107042) and converts these values to population density by dividing the number of people in each pixel by the pixel surface area. This diagram visually describes this model that uses known populated locations to analyze imagery to find similarly populated locations. The DOI for the original WorldPop.org total population population data is 10.5258/SOTON/WP00645.Recommended Citation: WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation. Accessed from https://worldpop.arcgis.com/arcgis/rest/services/WorldPop_Total_Population_100m/ImageServer, which was acquired from WorldPop in December 2021.
https://vocab.nerc.ac.uk/collection/L08/current/UN/https://vocab.nerc.ac.uk/collection/L08/current/UN/
This database, and the accompanying website called ‘SurgeWatch’ (http://surgewatch.stg.rlp.io), provides a systematic UK-wide record of high sea level and coastal flood events over the last 100 years (1915-2014). Derived using records from the National Tide Gauge Network, a dataset of exceedence probabilities from the Environment Agency and meteorological fields from the 20th Century Reanalysis, the database captures information of 96 storm events that generated the highest sea levels around the UK since 1915. For each event, the database contains information about: (1) the storm that generated that event; (2) the sea levels recorded around the UK during the event; and (3) the occurrence and severity of coastal flooding as consequence of the event. The data are presented to be easily assessable and understandable to a wide range of interested parties. The database contains 100 files; four CSV files and 96 PDF files. Two CSV files contain the meteorological and sea level data for each of the 96 events. A third file contains the list of the top 20 largest skew surges at each of the 40 study tide gauge site. In the file containing the sea level and skew surge data, the tide gauge sites are numbered 1 to 40. A fourth accompanying CSV file lists, for reference, the site name and location (longitude and latitude). A description of the parameters in each of the four CSV files is given in the table below. There are also 96 separate PDF files containing the event commentaries. For each event these contain a concise narrative of the meteorological and sea level conditions experienced during the event, and a succinct description of the evidence available in support of coastal flooding, with a brief account of the recorded consequences to people and property. In addition, these contain graphical representation of the storm track and mean sea level pressure and wind fields at the time of maximum high water, the return period and skew surge magnitudes at sites around the UK, and a table of the date and time, offset return period, water level, predicted tide and skew surge for each site where the 1 in 5 year threshold was reached or exceeded for each event. A detailed description of how the database was created is given in Haigh et al. (2015). Coastal flooding caused by extreme sea levels can be devastating, with long-lasting and diverse consequences. The UK has a long history of severe coastal flooding. The recent 2013-14 winter in particular, produced a sequence of some of the worst coastal flooding the UK has experienced in the last 100 years. At present 2.5 million properties and £150 billion of assets are potentially exposed to coastal flooding. Yet despite these concerns, there is no formal, national framework in the UK to record flood severity and consequences and thus benefit an understanding of coastal flooding mechanisms and consequences. Without a systematic record of flood events, assessment of coastal flooding around the UK coast is limited. The database was created at the School of Ocean and Earth Science, National Oceanography Centre, University of Southampton with help from the Faculty of Engineering and the Environment, University of Southampton, the National Oceanography Centre and the British Oceanographic Data Centre. Collation of the database and the development of the website was funded through a Natural Environment Research Council (NERC) impact acceleration grant. The database contributes to the objectives of UK Engineering and Physical Sciences Research Council (EPSRC) consortium project FLOOD Memory (EP/K013513/1).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The "Tree Proximate People" (TPP) dataset provides an estimate of the number of people living in or within 1 kilometer of trees outside forests (forest-proximate people) for the year 2019 with a spatial resolution of 100 meters at a global level. Trees outside forests are defined as areas classified as croplands with at least 10% tree cover.
For more detail, such as the theory behind, the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L., Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: a new methodology and global estimates. Background Paper to The State of the World’s Forests 2022 report. Rome, FAO.
Contact points:
Maintainer: Leticia Pina
Maintainer: Sarah E., Castle
Data lineage:
The TPP data are generated using Google Earth Engine. Trees outside forests (TOFs) are defined by the Copernicus Global Land Cover (CGLC) (Buchhorn et al. 2020) fractional cover data layer using a minimum of 10% tree cover on croplands lands. Any area classified as land with TOFs sized ≥ 1 ha in 2019 was included in this definition. Lands classified as forests in CGLC were excluded from the analysis. Croplands were defined using the FAO-LCCS2 land use classification layer from MODIS Land Cover (MCD12Q1.006). Croplands were defined as the total of three classifications: 1) “Herbaceous Croplands”: dominated by herbaceous annuals (<2m) with at least 60% cover and a cultivated fraction >60%, 2) “Natural Herbaceous/Croplands Mosaics”: mosaics of small-scale cultivation 40-60% with natural shrub or herbaceous vegetation, and 3) “Forest/Cropland Mosaics”: mosaics of small-scale cultivation 40-60% with >10% natural tree cover. Population density was defined by the WorldPop global population data for 2019 (WorldPop 2018). High density urban populations were excluded from the analysis. High density urban areas were defined as any contiguous area with a total population (using 2019 WorldPop data for population) of at least 50,000 people and comprised of pixels all of which met at least one of two criteria: either the pixel a) had at least 1,500 people per square km, or b) was classified as “built-up” land use by the CGLC dataset (where “built-up” was defined as land covered by buildings and other manmade structures) (Dijkstra et al. 2020). Using these datasets, any rural people living in or within 1 kilometer of TOFs on croplands in 2019 were classified as tree proximate people. Euclidean distance was used as the measure to create a 1-kilometer buffer zone around each TOF pixel. The scripts for generating the tree-proximate people and the rural-urban datasets using different parameters or for different years are published and available to users. For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L., Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: a new methodology and global estimates. Background Paper to The State of the World’s Forests 2022. Rome, FAO.
References:
Buchhorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N.E., Herold, M., Fritz, S., 2020. Copernicus Global Land Service: Land Cover 100m: collection 3 epoch 2019. Globe.
Dijkstra, L., Florczyk, A.J., Freire, S., Kemper, T., Melchiorri, M., Pesaresi, M. and Schiavina, M., 2020. Applying the degree of urbanisation to the globe: A new harmonised definition reveals a different picture of global urbanisation. Journal of Urban Economics, p.103312.
WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University, 2018. Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645
Online resources:
GEE asset for "Tree proximate people – Croplands, 1km cutoff distance"
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Historical chart and dataset showing World death rate by year from 1950 to 2025.
The "Tree Proximate People" (TPP) dataset is one of the data layers contributing to the development of indicator #13, “number of forest-dependent people in extreme poverty,” of the Collaborative Partnership on Forests (CPF) Global Core Set of forest-related indicators (GCS). The TPP dataset provides an estimate of the number of people living in or within 1 kilometers of trees outside forests (forest-proximate people) for the year 2019 with a spatial resolution of 100 meters at a global level. Trees outside forests are defined as areas classified as agricultural lands with at least 10% tree cover. Code available to update annually using Google Earth Engine. For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Madrid, M., & Pina, L. 2022. The number of forest- and tree-proximate people: a new methodology and global estimates. Rome, FAO.
Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.
April 9, 2020
April 20, 2020
April 29, 2020
September 1st, 2020
February 12, 2021
new_deaths
column.February 16, 2021
The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.
The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.
The AP is updating this dataset hourly at 45 minutes past the hour.
To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.
Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic
Filter cases by state here
Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac
Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true
Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.
Pull the 100 counties with the highest per-capita confirmed cases here
Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.
The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.
@(https://datawrapper.dwcdn.net/nRyaf/15/)
<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>
Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here
This data should be credited to Johns Hopkins University COVID-19 tracking project
Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
License information was derived automatically
The Rural Access Index (RAI) is a measure of access, developed by the World Bank in 2006. It was adopted as Sustainable Development Goal (SDG) indicator 9.1.1 in 2015, to measure the accessibility of rural populations. It is currently the only indicator for the SDGs that directly measures rural access.The RAI measures the proportion of the rural population that lives within 2 km of an all-season road. An all-season road is one that is motorable all year, but may be temporarily unavailable during inclement weather (Roberts, Shyam, & Rastogi, 2006). This dataset implements and expands on the most recent official methodology put forward by the World Bank, ReCAP's 2019 RAI Supplemental Guidelines. This is, to date, the only publicly available application of this method at a global scale.MethodologyReCAP's methodology provided new insight on what makes a road all-season and how this data should be handled: instead of removing unpaved roads from the network, the ones that are classified as unpaved are to be intersected with topographic and climatic conditions and, whenever there’s an overlap with excess precipitation and slope, a multiplying factor ranging from 0% to 100% is applied to the population that would access to that road. This present dataset developed by SDSN's SDG Transformation Centre proposes that authorities ability to maintain and remediate road conditions also be taken into account.Data sourcesThe indicator relies on four major items of geospatial data: land cover (rural or urban), population distribution, road network extent and the “all-season” status of those roads.Land cover data (urban/rural distinction)Since the indicator measures the acess rural populations, it's necessary to define what is and what isn't rural. This dataset uses the DegUrba Methodology, proposed by the United Nations Expert Group on Statistical Methodology for Delineating Cities and Rural Areas (United Nations Expert Group, 2019). This approach has been developed by the European Commission Global Human Settlement Layer (GHSL-SMOD) project, and is designed to instil some consistency into the definitions based on population density on a 1-km grid, but adjusted for local situations.Population distributionThe source for population distribution data is WorldPop. This uses national census data, projections and other ancillary data from countries to produce aggregated, 100 m2 population data. Road extentTwo widely recognized road datasets are used: the real-time updated crowd-sourced OpenStreetMap (OSM) or the GLOBIO’s 2018 GRIP database, which draws data from official national sources. The reasons for picking the latter are mostly related to its ability to provide information on the surface (pavement) of these roads, to the detriment of the timeliness of the data, which is restrained to the year 2018. Additionally, data from Microsoft Bing's recent Road Detection project is used to ensure completeness. This dataset is completely derived from machine learning methods applied over satellite imagery, and detected 1,165 km of roads missing from OSM.Roads’ all-season statusThe World Bank's original 2006 methodology defines the term all-season as “… a road that is motorable all year round by the prevailing means of rural transport, allowing for occasional interruptions of short duration”. ReCAP's 2019 methodology makes a case for passability equating to the all-season status of a road, along with the assumption that typically the wet season is when roads become impassable, especially so in steep roads that are more exposed to landslides.This dataset follows the ReCAP methodology by creating an passability index. The proposed use of passability factors relies on the following three aspects:• Surface type. Many rural roads in LICs (and even in large high-income countries including the USA and Australia) are unpaved. As mentioned before, unpaved roads deteriorate rapidly and in a different way to paved roads. They are very susceptible to water ingress to the surface, which softens the materials and makes them very vulnerable to the action of traffic. So, when a road surface becomes saturated and is subject to traffic, the deterioration is accelerated. • Climate. Precipitation has a significant effect on the condition of a road, especially on unpaved roads, which predominate in LICs and provide much of the extended connectivity to rural and poor areas. As mentioned above, the rainfall on a road is a significant factor in its deterioration, but the extent depends on the type of rainfall in terms of duration and intensity, and how well the roadside drainage copes with this. While ReCAP suggested the use of general climate zones, we argue that better spatial and temporal resolutions can be acquired through the Copernicus Programme precipitation data, which is made available freely at ~30km pixel size for each month of the year.• Terrain. The gradient and altitude of roads also has an effect on their accessibility. Steep roads become impassable more easily due to the potential for scour during heavy rainfall, and also due to slipperiness as a result of the road surface materials used. Here this is drawn from slope calculated from SRTM Digital Terrain data.• Road maintenance. The ability of local authorities to remediate damaged caused by precipitation and landslides is proposed as a correcting factor to the previous ones. Ideally this would be measured by the % of GDP invested in road construction and maintenance, but this isn't available for all countries. For this reason, GDP per capita is adopted as a proxy instead. The data range is normalized in such a way that a road maxed out in terms of precipitation and slope (accessibility score of 0.25) in a country at the top of the GDP per capita range is brought back at to the higher end of the accessibility score (0.95), while the accessibility score of a road meeting the same passability conditions in a country which GDP per capita is towards the lower end is kept unchanged.Data processingThe roads from the three aforementioned datasets (Bing, GRIP and OSM) are merged together to them is applied a 2km buffer. The populations falling exclusively on unpaved road buffers are multiplied by the resulting passability index, which is defined as the normalized sum of the aforementioned components, ranging from 0.25 to. 0.9, with 0.95 meaning 95% probability that the road is all-season. The index applied to the population data, so, when calculated, the RAI includes the probability that the roads which people are using in each area will be all-season or not. For example, an unpaved road in a flat area with low rainfall would have an accessibility factor of 0.95, as this road is designed to be accessible all year round and the environmental effects on its impassability are minimal.The code for generating this dataset is available on Github at: https://github.com/sdsna/rai
Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
License information was derived automatically
World Population Data from the United Nations (UN), United Nations Department of Economic and Social Affairs Population Division World Population Prospects 2022
Notes
File (CSV, 6 KB)
Location notes.
**Demographic Indicators ** Indicator reference (CSV, 4 KB) 1950-2100, medium (ZIP, 7.77 MB) 2022-2100, other scenarios (ZIP, 34.76 MB) Demographic Indicators:
Total Population, as of 1 January (thousands)
Total Population, as of 1 July (thousands)
Male Population, as of 1 July (thousands)
Female Population, as of 1 July (thousands)
Population Density, as of 1 July (persons per square km)
Population Sex Ratio, as of 1 July (males per 100 females)
Median Age, as of 1 July (years)
Natural Change, Births minus Deaths (thousands)
Rate of Natural Change (per 1,000 population)
Population Change (thousands)
Population Growth Rate (percentage)
Population Annual Doubling Time (years)
Births (thousands)
Births by women aged 15 to 19 (thousands)
Crude Birth Rate (births per 1,000 population)
Total Fertility Rate (live births per woman)
Net Reproduction Rate (surviving daughters per woman)
Mean Age Childbearing (years)
Sex Ratio at Birth (males per 100 female births)
Total Deaths (thousands)
Male Deaths (thousands)
Female Deaths (thousands)
Crude Death Rate (deaths per 1,000 population)
Life Expectancy at Birth, both sexes (years)
Male Life Expectancy at Birth (years)
Female Life Expectancy at Birth (years)
Life Expectancy at Age 15, both sexes (years)
Male Life Expectancy at Age 15 (years)
Female Life Expectancy at Age 15 (years)
Life Expectancy at Age 65, both sexes (years)
Male Life Expectancy at Age 65 (years)
Female Life Expectancy at Age 65 (years)
Life Expectancy at Age 80, both sexes (years)
Male Life Expectancy at Age 80 (years)
Female Life Expectancy at Age 80 (years)
Infant Deaths, under age 1 (thousands)
Infant Mortality Rate (infant deaths per 1,000 live births)
Live births Surviving to Age 1 (thousands)
Deaths under age 5 (thousands)
Under-five Mortality Rate (deaths under age 5 per 1,000 live births)
Mortality before Age 40, both sexes (deaths under age 40 per 1,000 live births)
Male mortality before Age 40 (deaths under age 40 per 1,000 male live births)
Female mortality before Age 40 (deaths under age 40 per 1,000 female live births)
Mortality before Age 60, both sexes (deaths under age 60 per 1,000 live births)
Male mortality before Age 60 (deaths under age 60 per 1,000 male live births)
Female mortality before Age 60 (deaths under age 60 per 1,000 female live births)
Mortality between Age 15 and 50, both sexes (deaths under age 50 per 1,000 alive at age 15)
Male mortality between Age 15 and 50 (deaths under age 50 per 1,000 males alive at age 15)
Female mortality between Age 15 and 50 (deaths under age 50 per 1,000 females alive at age 15)
Mortality between Age 15 and 60, both sexes (deaths under age 60 per 1,000 alive at age 15)
Male mortality between Age 15 and 60 (deaths under age 60 per 1,000 males alive at age 15)
Female mortality between Age 15 and 60 (deaths under age 60 per 1,000 females alive at age 15)
Net Number of Migrants (thousands)
Net Migration Rate (per 1,000 population)
Fertility
1950-2100, single age (ZIP, 78.01 MB)
1950-2100, 5-year age groups (ZIP, 22.38 MB)
Age-specific Fertility Rate (ASFR)
Percent Age-specific Fertility Rate (PASFR)
Births (thousands)
**Life Tables ** 1950-2021, medium (ZIP, 68.72 MB) 2022-2100, medium (ZIP, 74.62 MB) Abridged life tables up to age 100 by sex and both sexes combined providing a set of values showing the mortality experience of a hypothetical group of infants born at the same time and subject throughout their lifetime to the specific mortality rates of a given year, from 1950 to 2100. Only medium is available.
mx: Central death rate, nmx, for the age interval (x, x+n)
qx: Probability of dying (nqx), for an individual between age x and x+n
px: Probability of surviving, (npx), for an individual of age x to age x+n
lx: Number of survivors, (lx), at age (x) for 100000 births
dx: Number of deaths, (ndx), between ages x and x+n
Lx: Number of person-years lived, (nLx), between ages x and x+n
Sx: Survival ratio (nSx) corresponding to proportion of the life table population in age group (x, x+n) who are alive n year later
Tx: Person-years lived, (Tx), above age x
ex: Expectation of life (ex) at age x, i.e., average number of years lived subsequent to age x by those reaching age x
ax: Average number of years lived (nax) between ages x and x+n by those dying in the interval
Life Tables 1950-2021 (ZIP, 94.76 MB) 2022-2100 (ZIP, 101.66 MB) Single age life tables up to age 10...
The dataset is a relational dataset of 8,000 households households, representing a sample of the population of an imaginary middle-income country. The dataset contains two data files: one with variables at the household level, the other one with variables at the individual level. It includes variables that are typically collected in population censuses (demography, education, occupation, dwelling characteristics, fertility, mortality, and migration) and in household surveys (household expenditure, anthropometric data for children, assets ownership). The data only includes ordinary households (no community households). The dataset was created using REaLTabFormer, a model that leverages deep learning methods. The dataset was created for the purpose of training and simulation and is not intended to be representative of any specific country.
The full-population dataset (with about 10 million individuals) is also distributed as open data.
The dataset is a synthetic dataset for an imaginary country. It was created to represent the population of this country by province (equivalent to admin1) and by urban/rural areas of residence.
Household, Individual
The dataset is a fully-synthetic dataset representative of the resident population of ordinary households for an imaginary middle-income country.
ssd
The sample size was set to 8,000 households. The fixed number of households to be selected from each enumeration area was set to 25. In a first stage, the number of enumeration areas to be selected in each stratum was calculated, proportional to the size of each stratum (stratification by geo_1 and urban/rural). Then 25 households were randomly selected within each enumeration area. The R script used to draw the sample is provided as an external resource.
other
The dataset is a synthetic dataset. Although the variables it contains are variables typically collected from sample surveys or population censuses, no questionnaire is available for this dataset. A "fake" questionnaire was however created for the sample dataset extracted from this dataset, to be used as training material.
The synthetic data generation process included a set of "validators" (consistency checks, based on which synthetic observation were assessed and rejected/replaced when needed). Also, some post-processing was applied to the data to result in the distributed data files.
This is a synthetic dataset; the "response rate" is 100%.
Data for Figure 10.10 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 10.10 shows observed and projected changes in austral summer (December to February) mean precipitation in Global Precipitation Climatology Centre (GPCC), Climatic Research Unit Time-Series (CRU TS) and 100 members of the Max-Planck-Institut für Meteorologie Earth-System Model (MPI-ESM). --------------------------------------------------- How to cite this dataset --------------------------------------------------- When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Doblas-Reyes, F.J., A.A. Sörensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, W.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking Global to Regional Climate Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1363–1512, doi:10.1017/9781009157896.012. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has two panels, with data provided for both panels. Panel (a) consists of two maps, panel (b) shows multiple timeseries and boxplots. --------------------------------------------------- List of data provided --------------------------------------------------- The dataset contains data of relative precipitation anomalies over 1950-2100 with respect to 1995-2014 average for global, S.E.South-America, Sao Paulo and Buenos Aires for: - Observational data (GPCC and CRU TS) - Model data (100 runs of MPI-ESM) --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- Panel (a): - Data files: Modelled precipitation rate OLS linear trends between 2015-2070 with respect to 1995-2014 average over S.E. South America region, from left to right (MPI-ESM member with min (driest) and max (wettest) trends): Fig_10_10_panel-a_mapplot_trend_SES_DJF_MPI-GE_min_single-MultiModelMean_trend-min-median-max.nc, Fig_10_10_panel-a_mapplot_trend_SES_DJF_MPI-GE_max_single-MultiModelMean_trend-min-median-max.nc Panel (b): - Data files: Precipitation rate anomalies 1950-2100 with respect to 1995-2014 average for the global mean, S.E.South-America mean, Sao Paulo mean and Buenos Aires mean of GPCC (dark blue), CRU (dark brown), members of the MPI-ESM (grey), the MPI-ESM member with the driest (brown) and wettest (green) trend: Fig_10_10_panel-b_timeseries_global.csv, Fig_10_10_panel-b_timeseries_SES.csv, Fig_10_10_panel-b_timeseries_SaoPaulo.csv, Fig_10_10_panel-b_timeseries_BuenosAires.csv - Data files: Underlying data points of the boxplot showing MPI-ESM modelled precipitation rate OLS linear trends over all members between 2015-2070 with respect to 1995-2014 average for the global mean, S.E.South-America mean, Sao Paulo mean and Buenos Aires mean: Fig_10_10_panel-b_boxplot_BuenosAires.csv, Fig_10_10_panel-b_boxplot_global.csv, Fig_10_10_panel-b_boxplot_SaoPaulo.csv, Fig_10_10_panel-b_boxplot_SES.csv; OLS - ordinary least squares regression. --------------------------------------------------- Notes on reproducing the figure from the provided data --------------------------------------------------- The code for ESMValTool is provided. --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the figure on the IPCC AR6 website - Link to the report component containing the figure (Chapter 10) - Link to the Supplementary Material for Chapter 10, which contains details on the input data used in Table 10.SM.11 - Link to the code for the figure, archived on Zenodo.
The experiments with the GFDL model used here were performed using the coupled ocean-atmosphere model described in Manabe et al. (1991) and Stouffer et al., (1994) and references therein. The model has interactive clouds and seasonally varying solar insolation. The atmospheric component has nine finite difference (sigma) levels in the vertical. This version of the model was run at a rhomboidal resolution of 15 waves (R15) yielding an equivalent resolution of about 4.5 degrees latitude by 7.5 degrees longitude. The model has global geography consistent with its computational resolution and seasonal (but not diurnal) variation of insolation. The ocean model is based on that of Byan and Lewis (1979) with a spacing between gridpoints of 4.5 degrees latitude and 3.7 degrees longitude. It has 12 unevenly spaced levels in the vertical dimension. To reduce model drift, the fluxes of heat and water are adjusted by amounts which vary seasonally and geographically, but do not change from one year to another. The model also includes a dynamic sea-ice model (Bryan, 1969) which allows the system additional degrees of freedom. The 1000-year unforced simulation used here is described in Manabe and Stouffer (1996). The drift in global-mean temperature during this unforced simulation is very small at about -0.023 degrees C per century. The two GFDL-R15 climate change experiments used here use the IS92a scenario of estimated past and future greenhouse gas (GGa1) and combined greenhouse gas and sulphate aerosol (GSa1) forcing for the period 1765-2065 (Haywood et al., 1997). For the GGa1 experiment only the 100-year segment from 1958-2057 are available through the IPCC DDC. The radiative effects of all greenhouse gases is represented in terms of an equivalent CO2 concentration, and the direct radiative sulphate aerosol forcing is parameterised in terms of specified spatially dependent surface albedo changes (following Mitchell et al., 1995). Results from these climate change experiments are discussed in Haywood et al. (1997). The model's climate sensitivity is about 3.7 degrees C. Like B1, the B2 world is one of increased concern for environmental and social sustainability, but the character of this world differs substantially. Education and welfare programs are widely pursued leading to reductions in mortality and, to a lesser extent, fertility. The population reaches about 10 billion people by 2100, consistent with both the United Nations and IIASA median projections. Income per capita grows at an intermediary rate to reach about US$12,000 by 2050. By 2100 the global economy might expand to reach some US$250 trillion. International income differences decrease, although not as rapidly as in scenarios of higher global convergence (A1, B1). Local inequity is reduced considerably through the development of stronger community support networks. Generally high educational levels promote both development and environmental protection. Indeed, environmental protection is one of the few remaining truly international priorities. However, strategies to address global environmental challenges are less successful than in B1, as governments have difficulty designing and implementing agreements that combine environmental protection with mutual economic benefits. The B2 storyline presents a particularly favorable climate for community initiative and social innovation, especially in view of high educational levels. Technological frontiers are pushed less than in A1 and B1 and innovations are also regionally more heterogeneous. Globally, investment in R and D continues its current declining trend, and mechanisms for international diffusion of technology and know-how remain weaker than in scenarios A1 and B1 (but higher than in scenario A2). Some regions with rapid economic development and limited natural resources place particular emphasis on technology development and bilateral co-operation. Technical change is therefore uneven. The energy intensity of GDP declines at about one percent per year, in line with the average historical experience of the last two centuries. Land-use management becomes better integrated at the local level in the B2 world. Urban and transport infrastructure is a particular focus of community innovation, contributing to a low level of car dependence and less urban sprawl. An emphasis on food self-reliance contributes to a shift in dietary patterns towards local products, with reduced meat consumption in countries with high population densities. Energy systems differ from region to region, depending on the availability of natural resources. The need to use energy and other resources more efficiently spurs the development of less carbon-intensive technology in some regions. Environment policy cooperation at the regional level leads to success in the management of some transboundary environme... Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Fdpennington.178.1 for complete metadata about this dataset.
Background Human-induced land use change (LUC), driven by activities such as forestry, logging, and the production of agricultural commodities (e.g. fruits, nuts, and meat) significantly impacts the Global Commons, encompassing the climate system, ice sheets, land biosphere, oceans, and the ozone layer. The convertion of natural forests into areas dedicated to these activities lead to disrupted ecosystems (Foley et al. 2005), severely degraded biodiversity (Newbold et al. 2015), and the release of substantial amounts of greenhouse gases (GHGs) into the atmosphere (Hong et al. 2021), further exacerbating climate change and ocean acidification (Doney et al. 2009). The expansion of the agricultural frontier is identified as the predominant direct cause of deforestation globally, with other industries like timber and mining also playing significant roles (Curtis et al. 2018). To achieve global climate targets, forestry, and other land use GHG emissions must decrease along a nonlinear trajectory and reach carbon neutrality by 2050 (Rockström et al. 2017). However, to successfully address this road map, improving our understanding of deforestation drivers is urgently needed. Summary This dataset is the result of data processing performed to estimate the extent to which commodities and other agricultural products have replaced forests, while mapping the CO2 emission impact making use of the best available spatially explicit data. Results are reported globally for 52 products at national level, as well as agroecological and thermal zones (FAO & IIASA) and a 50km cell vector grid. In order to detect spatially-explicit deforestation drivers, the current extent of commodities and agricultural products was overlapped with global annual tree cover loss in the 10-year period from 2014 to 2023. Carbon stocks in the deforested areas were then assumed to have been emmited into the atmosphere. Recent, detailed crop and pasture maps for relevant commodities were used whenever available, and coarser resolution datasets were used as supplements when needed. Operations were performed in Google Earth Engine. Datasets used Forest and biomass carbon distribution The Global Forest Change dataset (Hansen et al., 2013) is used to estimate deforestation between 2014 and 2023. This tree cover loss dataset measures the first instance of complete removal of tree cover canopy at a 30-meter resolution for all woody vegetation over 5 meters in height. The WCMC Above and Below Ground Biomass Carbon Density (Soto-Navarro et al., 2020), for reference year 2010 at 300m pixel, is overlapped with resulting deforested areas pixels to dermine the biomass carbon present in the areas before deforestation. Generalized deforestation drivers Tree cover loss by dominant driver (Curtis et al., 2022) in 2023 is used to determine wide categories of deforestation drivers (commodities, shifting agriculture, forestry, wildfire and urbanization). Pixels indicating deforestation in the Global Forest Change dataset (Hansen et al., 2013) that overlap the commodities and shifting agriculture pixels from this dataset (Curtis et al., 2022) have their drivers further detailed with the data sources listed in the below. EarthStat pasture areas layer (Ramankutty et al., 2008) is used to identify areas for which specific livestock categories are to be defined. The project provides pasture areas for reference year 2000 at ~10km resolution. Detailed deforestation drivers The Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) commodity distibution layer (Becker-Reshef et al., 2023) is used to identify specific commodities (winter wheat, spring wheat, maize, rice and soybean) to deforestation pixels pertaining to the 'commodities' class. The ressource provides commodity distribution mapping at 5km pixel resolution. Values are provided as percentage of pixel area occupied by given crop. The Spatial Production Allocation Model (SPAM) physical area layer (You et al., 2014) for reference year 2020 is used to detail drivers pertaining to the 'shifting agriculture' class. The dataset covers 46 crops and crop groups at ~9km pixel resolution. Values are provided as percentage of pixel area occupied by given crop or crop group. The Gridded Livestock of the World (GLW3) (Gilbert et al., 2022) is used to determine which species (cattle, goat, sheep or horse) of livestock is raised in areas identified as pasture in the EarthStat layer and pertaining to the 'commodities' class. The project provides livestock distribution for reference year 2015 at ~9km resolution. Values are provided as number of individuals located within the pixel. Values were converted into percentage of pixel area covered by grazing field for given species based on species density thresholds. Data processing Most of data processing takes place in Google Earth Engine, with scripts redacted in javascript. In summary, two strategies were implemented: Proportional driver distribution strategy: When deforestation pixels (Hansen et al., 2013) overlapped with pixels from at least one of the detailed deforestation drivers data sources, the driver describe in the latter were associated with that deforested area. Whenever more than one of these data sources had non-null pixels overlapping the area, a proportional distribution was assumed (i.e. if SPAM indicated 100% of the area to be covered by cowpea crops, GEOGLAM 100% by maize, and GLW3 100% by cattle grazing fields, the pixel is assumed to have 33.3% of its deforested area associated with each of these drivers). Main driver strategy: When deforestation pixels did not overlap with any non-null pixels from any of the detailed drivers sources, the pixel is assumed to have the entirety of its deforested area associated with one single main driver resulting from a crop-livestock mosaic. The mosaic is created by taking the highest value from each of the crop or livestock distribution rasters, and then assigning the raster category to be the new pixel value, ultimately creating a category raster layer containing the main crop, crop group or livestock species occupying that pixel area. Null or zero values in this mosaic are filled-in by nearest neighbour analysis, to a limit of 20 pixels expansion. This was enough to ensure that all deforestation pixels had at least one detailed driver with which it could be associated. The logic behind this operation resides in the fact that the deforestation layer (Hansen et al., 2013) has a larger temporal coverage (with the more recent data point being the reference year 2023), while the detailed driver layers can be as old as reference year 2015. This means we're assuming the main deforestation drivers continued to expand their limits to neighbouring areas during the years for which no data is available. Resulting rasters from both strategies are put together and a zonal statistics operation is performed in order to populate the vector grid cells. Files This repository contains the following files: deforested_area_by_LUC_driver_2014_2023.CSV contains the deforested area (hectares) and the corresponding driver in each grid cell (idenfied by the id field) in each year, in CSV text format. carbon_emissions_by_LUC_driver_2014_2023.CSV contains the carbon emitted (Mg CO2 eq.) and the corresponding driver in each grid cell (idenfied by the id field) in each year, in CSV text format. spatial_grid.gpkg contains the raw 50km cell grid, with identification of country (iso3 and name fields), region, and FAO agroecological zone (zone field) and thermal zone (thermal field), in Geopackage format. In order to visualize the data in a map, the user will need to join one of the csv files to this geopackage file by basing the join on the 'id' field. summary_showcase.png is an image showcasing maps created using the database, as well as a diagram showing the datasets used to create the final dataset. How to cite Iablonovski, G.; Berthet, E. C.; Roberts, S. (2024). Yearly CO2 emissions from anthropogenic land use change by main driver (2014-2023) [Data set]. Zenodo. https://zenodo.org/doi/10.5281/zenodo.13308514 Authors and contact Authors: Guilherme Iablonovski*, Etienne Charles Berthet, Sophie Roberts *Corresponding author: Guilherme Iablonovski (guilherme.iablonovski@unsdsn.org)
WorldPop Asia dataset details_DATASET: Alpha version 2010, 2015 and 2010 estimates of numbers of people per pixel (ppp) and people per hectare (pph), with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted.REGION: AsiaSPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator)PROJECTION: Geographic, WGS 84UNITS: Estimated persons per grid squareMAPPING APPROACH: Random ForestFORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org)FILENAMES: Example - ARM_ppp_v2c_2010_UNadj.tif = Armenia (ARM) population per pixel (ppp) modelling version 2c (v2c) map for 2010 (2010) adjusted to match UN national estimates (UNadj).DATE OF PRODUCTION: May 2016Also included: (i) Metadata html file, (ii) Population datasets produced using original census year data, (iii).kmz Google Earth file.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The "Forest Proximate People" (FPP) dataset is one of the data layers contributing to the development of indicator #13, “number of forest-dependent people in extreme poverty,” of the Collaborative Partnership on Forests (CPF) Global Core Set of forest-related indicators (GCS). The FPP dataset provides an estimate of the number of people living in or within 5 kilometers of forests (forest-proximate people) for the year 2019 with a spatial resolution of 100 meters at a global level.
For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L. Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: A new methodology and global estimates. Background Paper to The State of the World’s Forests 2022 report. Rome, FAO.
Contact points:
Maintainer: Leticia Pina
Maintainer: Sarah E., Castle
Data lineage:
The FPP data are generated using Google Earth Engine. Forests are defined by the Copernicus Global Land Cover (CGLC) (Buchhorn et al. 2020) classification system’s definition of forests: tree cover ranging from 15-100%, with or without understory of shrubs and grassland, and including both open and closed forests. Any area classified as forest sized ≥ 1 ha in 2019 was included in this definition. Population density was defined by the WorldPop global population data for 2019 (WorldPop 2018). High density urban populations were excluded from the analysis. High density urban areas were defined as any contiguous area with a total population (using 2019 WorldPop data for population) of at least 50,000 people and comprised of pixels all of which met at least one of two criteria: either the pixel a) had at least 1,500 people per square km, or b) was classified as “built-up” land use by the CGLC dataset (where “built-up” was defined as land covered by buildings and other manmade structures) (Dijkstra et al. 2020). Using these datasets, any rural people living in or within 5 kilometers of forests in 2019 were classified as forest proximate people. Euclidean distance was used as the measure to create a 5-kilometer buffer zone around each forest cover pixel. The scripts for generating the forest-proximate people and the rural-urban datasets using different parameters or for different years are published and available to users. For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L., Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: a new methodology and global estimates. Background Paper to The State of the World’s Forests 2022. Rome, FAO.
References:
Buchhorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N.E., Herold, M., Fritz, S., 2020. Copernicus Global Land Service: Land Cover 100m: collection 3 epoch 2019. Globe.
Dijkstra, L., Florczyk, A.J., Freire, S., Kemper, T., Melchiorri, M., Pesaresi, M. and Schiavina, M., 2020. Applying the degree of urbanisation to the globe: A new harmonised definition reveals a different picture of global urbanisation. Journal of Urban Economics, p.103312.
WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University, 2018. Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645
Online resources:
GEE asset for "Forest proximate people - 5km cutoff distance"