It includes past and ongoing undertakings on making use of scanner data from supermarket chains and other retailers, as well as online prices obtained from web-scraping to generate price indices in the People’s Republic of China, Japan, and the Republic of Korea.īig Data improves accuracy, granularity of stats on poverty, welfare The UN Statistics Division has developed an inventory of Big Data projects. Digital traces of mobile phone use can likewise help track population movements and examine people’s behavior during disaster events. Other examples include call detail records with information on mobile customer behavior being used to proxy the poverty status of mobile users.
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In the Philippines, the World Bank has teamed up with ride-hailing service provider Grab to launch the Open Traffic Initiative, which uses Grab driver data to yield near real-time traffic data and statistics, including speed, flow, and delays at intersections to study critical areas in traffic management. Big Data is being increasingly explored for development purposes.įor instance, in Jakarta, Twitter conversations on the price of rice have provided an innovative way to monitor actual prices. While rarely direct substitutes for data collected from censuses and surveys, administrative-based data can help reduce both the cost of statistics generation and the burden on respondents of surveys and censuses.īig Data from electronic devices, social media, search engines, and sensors tracking devices and satellite imagery now provides a novel data source for NSSs, with 3 Vs-(large) volume, velocity, and variety-to complement statistics from traditional sources. Most of the respondents view Big Data as a promising way to address data gaps for SDGs, but only a limited number have current Big Data projects.ĪDB, UNESCAP helping countries report disaggregated statsĭecades ago, NSSs generated statistics only from administrative data, censuses, and surveys. SAE approaches help to obtain more granular data on poverty or nutrition.Ī few NSOs also reported about their levels of access to aerial photos/satellite imagery, mobile data, web-scraped online price data, and social media data.
Over half (56%) of the countries are utilizing small area estimation (SAE) methods that strengthen direct survey estimates for small areas (or small sub-populations) with auxiliary information such as census records).
Many NSOs acknowledge that the only way that they will be able to meet SDGs’ disaggregated data requirements is to utilize innovative methods and data sources. The disaggregation is sparse for some SDG indicators by sex, and it is even scarcer-if not absent-for disabled persons and indigenous peoples (see Figure 1). In this survey, the national statistical organizations (NSOs) of 16 ADB developing member countries reported disaggregation of statistics by location for several SDG indicators. To better understand how ready national statistical systems are for the SDG era, ADB and the UN Economic and Social Commission for Asia and the Pacific (UNESCAP) have taken stock of 22 countries’ experience in disaggregating SDG indicators and using multiple types of data sources. However, with over 230 SDG indicators-many of which require disaggregation by location, sex, gender, age, income, and other relevant dimensions-collecting the necessary granular data to monitor all SDGs and targets is no easy feat for national statistical systems (NSS). The Sustainable Development Goals (SDGs) offer specific, time-bound, and quantifiable targets in sync with national development plans and priorities.
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