The Pacific Observatory is the World Bank analytical program to explore and develop new information sources to mitigate the impact of data gaps in official statistics for Papua New Guinea (PNG) and the Pacific Island Countries (PICs).
This repository hosts the team's efforts to investigate how alternative data sources can be used to generate economic and sector statistics through cost-effective methods. The goal is to assess whether new data sources can produce indicators that are timely, have higher frequency and granularity.
The content is structured by topic of investigation, each thematic folder contains code, notebooks, outputs, and feasibility notes.
🔖 Night Time Lights Applications
This note explores socio-economic applications with Night Time Lights data.
Are lights at night a good proxy for economic activity or extractives?
Can lights be used to aid poverty mapping, estimate access to electrification, or estimate damages/recovery from natural disasters?
🔖 Market Prices Imputation
A machine learning imputation method to fill gaps in food prices from markets in Papua New Guinea.
This follows the estimation proposed by
Andree, Bo Pieter Johannes. 2021. Estimating Food Price Inflation from Partial Surveys. Policy Research Working Paper;No. 9886. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/36778 License: CC BY 3.0 IGO.
URI: http://hdl.handle.net/10986/36778
The machine learning imputation code is avilable here https://github.com/worldbank/Food-Price-Estimation
The code relies on WFP price surveys that are not available for PNG. The code has been adapted to run on IFPRI surveys available here https://www.ifpri.org/project/fresh-food-price-analysis-papua-new-guinea
This requires a few additional pre-processing steps to add coordinates and turn the IFPRI data into the required format. See pacific-observatory/data/prices/
After preparing the raw data, the following section in the main.R file of the price imputation code should be changed to read the data:
if("Papua New Guinea" %in% selected_country_list){
cat("adding PNG from file")
PNG <- read.csv("PNG_dec_prices_wc.csv") ##### <---------- Original PNG price data file name.
PNG$time_id <- NA
rawMarketPrices = rbind(rawMarketPrices, PNG[PNG$year>=data_startyear,])
rawMarketPrices$time_id <- generate_T(rawMarketPrices$year, rawMarketPrices$month)
} if("Papua New Guinea" %in% selected_country_list){
cat("adding PNG from file")
PNG <- read.csv("PNG_july2022_prices_wc.csv") ##### <---------- Point the code to the new file name.
PNG$time_id <- NA
rawMarketPrices = rbind(rawMarketPrices, PNG[PNG$year>=data_startyear,])
rawMarketPrices$time_id <- generate_T(rawMarketPrices$year, rawMarketPrices$month)
}Also make sure that Papua New Guinea is included in the country list:
selected_country_list = c("Afghanistan", "Papua New Guinea") To produce results for different time periods, change
data_startyear = 2009🔖 Aviation Statistics
Estimating tourism flows through aviation data.
🔖 Crop Mapping / Climate Indices
Monitor crop productivity and seasonality through vegetation indices.
Create new crop masks with limited training data and satellite imagery.
Sub-national database of climate indicators.
🔖 Automatic Identification System (AIS)
This section assess the feasibility of using AIS data to derive high-frequency and geospatially disaggregated indicators on trade and fishing intensity.
🔖 Text Mining
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