This dataset contains Who, What, and Where(3W) data for the Eastern Samar Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Guimaras Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Iloilo Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Leyte Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Maguindanao Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Negros Occidental Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Negros Oriental Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Northern Samar Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Nueva Ecija Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Occidental Mindoro Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Oriental Mindoro Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Palawan Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Pangasinan Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Samar Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Siquijor Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the Southern Leyte Province in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset contains Who, What, and Where(3W) data for the National Capital Region in the Philippines. The operational presence of the various organisations (who) by sector (what), and location (where) at the province level.
This dataset has been consolidated from NDRMC / DROMIC reports from 2014 - 2020 to summarize the number of people affected and houses damaged in the Philippines as a result of typhoons.
The UNHCR Livelihoods Monitoring Framework takes a program-based approach to monitoring, with the aim of tracking both outputs and the impact of UNHCR dollars spent on programming (either via partners or through direct implementation).
The process for developing the indicators began in 2015 with a review of existing tools and approaches. Consultations were held with governments, the private sector, field-based staff and civil society partners to devise a set of common, standardized measures rooted in global good practices.
Since 2017, a data collection (survey) has been rolled out globally, and the participating operations conducted a household surveys to a sample of beneficiaries of each livelihoods project implemented by UNHCR and its partner. The dataset consists of baseline and endline data from the same sample beneficiaries, in order to compare before and after the project implementation and thus to measure the impact.
More info is available on the official website: https://lis.unhcr.org
The Common Operational Datasets is a tool that can help coordinators and managers from government agencies and humanitarian organizations determine the population demographics for advocacy, fundraising and programming at the very onset of an emergency. These CODs work by automatically providing the user with a simple way to access the ‘best available baseline data’ for the Philippines and subnational levels in consideration of the availability, scope, limitation, and quality of data coming from various government sources.
Philippines administrative level 0-2 sex and age disaggregated 2022, 2023, 2024, and 2025 population statistics
REFERENCE YEAR: 2022 (and 2023, 2024, 2025)
These CSV population statistics files are suitable for database or ArcGIS joins to the Philippines - Subnational Administrative Boundaries.
See caveats.
In partnership with Yale, Meta launched a climate change opinion survey that explores public climate change knowledge, attitudes, policy preferences, and behaviors. The 2022 survey includes respondents from nearly 200 countries and territories. We are sharing country level data from this survey, providing policymakers, research institutions, and nonprofits with an international view of public climate change opinion.
For more information please see https://dataforgood.facebook.com/dfg/tools/climate-change-opinion-survey
If you're interested in becoming a research partner and accessing record level data, please email dataforgood@fb.com.
NOTE: We plan to no longer update this dataset after May 22 2022.
These data sets are intended to inform researchers and public health experts about how populations are responding to physical distancing measures. In particular, there are two metrics, Change in Movement and Stay Put, that provide a slightly different perspective on movement trends. Change in Movement looks at how much people are moving around and compares it with a baseline period that predates most social distancing measures, while Stay Put looks at the fraction of the population that appear to stay within a small area during an entire day.
Full details, including the privacy protections in this data, are available here: https://research.fb.com/blog/2020/06/protecting-privacy-in-facebook-mobility-data-during-the-covid-19-response/
A dataset containing confirmed dengue cases and deaths from the Department of Health-Epidemiology Bureau in the Philippines.
Notes: Reported dengue cases includes suspect, probable and confirmed. Deaths were included in the total number of cases.
