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"

Occurrence dataset: A relatively large (~1500) dataset of fossil mammal occurrence data for the Paleocene, Eocene and Oligocene (66 Ma - 23 Ma) of Mongolia and Northern China above 30 degrees North. Occurrence data comprises species or genus name, specimen information where possible, geological unit specimen was found in, age (range) of specimen and/or geological unit and any other relevant information. Data taken from multiple sources. The majority comes from the Palaeobiology Database (PBDB), an open-access community dataset of global fossil occurrences (and some trait data) for all time periods and taxonomic groups. Our dataset used only the mammal records from our study region and time period. A very small amount of data (10's of occurrences) was taken from the NOW (New and Old Worlds) Database of fossil mammals (NOW database), another open-access community dataset. This database contains only mammal occurrence and trait data for fossil mammals throughout geological history and across the world. Additional occurrence data (~100) was collected first hand from the literature by Dr Gemma Benevento.

Body Size dataset: Lower first molar (m1) length and width (which can be used to estimate mammal body size) was collected for approximately 60% of the individual species in the occurrence dataset (~430 species).

Abstract

Since 2018, the Listening to the Citizens of Uzbekistan Survey (L2CU) has been implemented by the World Bank with the support of the UK Government. Around 1,500 households across the country regularly participate in the phone survey that is carried out every month. The L2CU collects information on public’s perception of overall socio-economic conditions and policy reforms, migration, employment, access to public service, household income, savings, food security, and coping mechanisms. That helps to inform the Government’s crucial decision-making for delivering essential social and economic reforms, implementing poverty reduction initiatives, and improving the well-being of citizens.

Geographic coverage

National, urban and rural

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of the L2CU is a subsample of the 2018 baseline survey. The 2018 baseline survey was conducted face-to-face interviews with a nationally and urban/rural representative sample of 4,000 households from 200 PSU (mahallas – lowest administrative unit) in Uzbekistan. The baseline survey took the two-stage sampling approach, and the sample was stratified for all 14 regions, taking into consideration settlement type (urban and rural) within each region, except for Tashkent city which fully refers to urban area. As a result, 27 strata were formed where 1 stratum was dedicated to Tashkent city and 26 strata to 13 regions. In the first stage, 200 PSU were selected with probability proportion to the population size of 27 strata and in the second stage, 20 households are randomly selected in each PSU. In the baseline, households were asked to provide phone number of their most knowledgeable member for future contact during short phone interviews (CATI - Computer Assisted Telephone Interview).

After completion of the 2018 baseline survey, L2CU monthly interviewers began regularly calling a randomly selected panel of 1,500 households over the phone to conduct short interviews, following a set monthly schedule agreed to by the participating household. For the phone L2CU survey, 7-8 households are randomly selected in each of 200 clusters in the Baseline.

To ensure that non-response in the first round (and attrition in subsequent rounds) did not affect the required sample size for survey representativeness, households that refused to participate were replaced with other households drawn from the same PSU in the baseline data.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire was administered in local languages (i.e. Uzbek and Russian) with about varying length (about 20 minutes).

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the NBT with the support of the WB team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes.