Location and distribution of MPAs on the east side of Babeldaob. Data obtained from WDPA dataset
The 2021 Tonga HIES is the new update of this kind, after the 2015/2016, 2009 & 2001 versions. This survey aims to provide indicators on Household Living Standard using monetary aspect (amount of income and expenditure), non-monetary aspect (calory consumed, assets own, imputed rents…) and more social approach (education, health, food security status…).
PURSE SEINE fishery data compiled by the Western and Central Pacific Fisheries Commission (WCPFC). The WCPFC have compiled a public domain version of aggregated catch and effort data using operational, aggregate and annual catch estimates data provided by Commission Members (CCMs) and Cooperating Non-members (CNMs).
Data cover 1950 to 2021 and are grouped by 1°x1° latitude/longitude grids, year and month.
The data are described here:
https://www.wcpfc.int/public-domain
Tuna biomass (skipjack, albacore, yellowfin and bigeye tuna) variability over the period 1979-2010 simulated by the Spatial Ecosystem and Dynamics Model (SEAPODYM, http://www.seapodym.eu/ and https://github.com/PacificCommunity/seapodym-codebase).
Here, we provide the unfished biomass dynamics (i.e. without considering any fishing). For each of the four tuna species we provide both the total biomass (adults + juveniles) and the larvae abundance.
Projection of tuna biomass (skipjack, albacore, yellowfin and bigeye tuna) in response to climate change simulated by the Spatial Ecosystem and Population Dynamics Model (SEAPODYM, http://www.seapodym.eu/ and https://github.com/PacificCommunity/seapodym-codebase).
1/36° model hindcast of the Solomon sea developed as part of the SOSMOD project (SOlomon Sea high résolution MODeling).
Simulations performed by the MEOM group using NEMO (Nucleus for European Modelling of the Ocean) for the period 1989-2007.
Here we provide temperature and current velocity every 5 days.
The model configuration and results are described in:
Tuna biomass (skipjack and bigeye tuna) variability over the period 1998-2019 simulated by the Spatial Ecosystem and Population Dynamics Model (SEAPODYM).
Here, we provide the unfished biomass dynamics (i.e. without considering any fishing). For each of the four tuna species we provide both the total biomass (adults + juveniles) and the larvae abundance.
This model is described in :
The Maritime Zones Act No. 06 of 2010 states
2 Sovereignty of Vanuatu
The Sovereignty of Vanuatu comprises of:
(a) All islands within the archipelago including Mathew (Umaenupne) and Hunter (Leka) islands; and
(b) Any islands or reefs forming or formed within the Exclusive Economic Zone of Vanuatu.
The dataset is a 44-year hindcast (1979-2022) of the wave conditions in Tuvalu using the unstructured version of the third-generation wave model Simulating Waves Nearshore (UnSWAN).
Location and distribution of MPAs on the east side of Babeldaob. Data obtained from WDPA dataset
The country data contained herein is a subset of the Pacific Food Trade Database (PFTD) version 2.1. The PFTD was developed to facilitate more reliable analysis of Pacific food trade in terms of food security and nutrition. The PFTD version 2.1 includes data for 18 Pacific Islands Countries and Territories for the years 1995-2018. The classification system used for the PFTD (version 2.1) is HS92.
_Find more Pacific data on [PDH.stat](https://stats.pacificdata.org)._
A strong evidence base is needed to understand the socioeconomic implications of the coronavirus pandemic for the Solomon Islands. High Frequency Phone Surveys (HFPS) are set up to understand these implications over the years. This data is the fifth of the five planned rounds of mobile surveys.
The phone survey was conducted to gather data on the socio-economic impact of COVID-19 crisis in Vanuatu. Community transmission of COVID-19 in Vanuatu started only in March 2022 followed by the nation-wide lockdown and other restrictions. Round 1 HFPS survey was a timely process to observe the effect of the crisis on the country. Round 1 interviewed 2,515 households both in urban and rural regions of the country from July 2022 to September 2022.
## Overview
A geospatial dataset of point geometries with a land use / land cover label and several remote-sensing derived predictor variables that can be used to train and test a land use / land cover classifier.
This dataset was generated with support from a Climate Change AI Innovation Grant and the Australian Centre for International Agricultural Research.
Each of the point geometries was assigned one of the following class labels:
This dataset is an aggregation of the database from the Lowy Institute Pacific Aid Map which provides an overview of aid and development finance in the Pacific region.
_Find more Pacific data on [PDH.stat](https://stats.pacificdata.org)._
This selection includes data related to SPC member countries and territories for some of the indicators available in the original database published by the Asian Development Bank.
_Find more Pacific data on [PDH.stat](https://stats.pacificdata.org)._
Regional wave data were obtained by downscaling the 7-years hindcast of the Centre for Australian Weather and Climate Research (CAWCR) using the unstructured version of SWAN (Simulating WAves Nearshore; Booij et al., 1999).