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Following the 2013 Meeting on the Management of Statistical Information = Systems, a Task Team was set up to work out the key issues with using Big D= ata for official statistics, identify priority actions and formulate a proj= ect proposal. As part of its work, the Task Team gathered a significant amo= unt of information about the use of Big Data in various national and intern= ational statistical organizations. The Task Team recognised that this infor= mation could form the basis of a useful resource for the official statistic= s community, and therefore decided to further develop and publish this reso= urce in the form of an inventory.
It is unrealistic to assume that the inventory could record all possible= information relating to the use of Big Data for official statistics; such = an inventory would also be very difficult for a single person or organizati= on to maintain. The inventory is therefore not meant to be exh= austive, but aims to include at least the key resources that will = be most valuable for the official statistics community. Where a resource al= ready exists somewhere on the Internet, the inventory functions as a portal= , holding links to resources and documentation, to avoid duplication and re= duce problems of version control.
The inventory contains structured and searchable information about actua= l and planned use of Big Data in statistical organizations. This informatio= n is held in a standard template, searchable by statistical domain (using t= he Classification of Stati= stical Activities, an existing international standard), and type of Big= Data (using a classification developed by the Task Team).
The inventory is hosted on the UNECE wiki platform. The wiki approach ma= kes it easier for the inventory to be maintained by the whole statistical c= ommunity, thus avoiding the whole burden of maintenance falling on one indi= vidual or organization. This approach also ensures that the inventory is a = relevant and useful resource for all. Read access is now open to the public= (for inventory entries for which author organizations have given permissio= n). Edit access is restricted to staff of national or international statist= ical organizations with an interest in this topic, and must be requested by= contacting firstname.lastname@example.org. The infrastructure is maintained by UNE= CE, which will also act as a moderator if necessary. The 'owner' of the inv= entory is the High-Level Group on the Modernisation of Statistical Producti= on and Services, on behalf of the international statistical community.
The value of the inventory will be increased if members of the official = statistics community add their materials to it. This can be done by r= equesting a user account with edit access, and then clicking the button bel= ow to add an entry. Once you click the button, you will see a new pag= e which asks you to change the title. Fill in as many of the blank fi= elds as possible and press save to add your record to the list. Choos= e 'edit contents' to make any subsequent changes. Please contact = ;support.= email@example.com for assistance with your entry.
(To filter by type of big data source, choose from the drop down lis= t.)
(To sort by any column, click on the column heading)
|Inventory entries||Type of Big Data use= d*||Domain**|
|Australia (ABS) - Remote Sensing= for Agricultural and Land Use Estimation||Satellite images||Agriculture, forestr=
|Australia (ABS= ) - Social Linked (semantic) Data Processing for Various Statistical Uses= a>||Data from public adm= inistration||Education
Income and consumption
Population and migration=
|Eurostat - Consumer Price Index from internet price d= ata||Commercial transacti= ons||Prices|
|Eurostat - Mobile positioning data for tourism stati= stics||Mobile phone: call/t= ext times and positions||Tourism|
|Italy (I= stat) - Experimenting Web Scraping and Text Mining in the "Survey on i= nformation and communication technology in enterprises"||E-commerce||Information society<= /td>|
|Italy (Istat) - Specific purpose geographic basins and population sta= tistics using mobile phone tracking data||Mobile phone: call/t= ext times and positions||Population and migra= tion|
|New BD Proje= ct Example||Car location||Banking, insurance, = financial statistics|
|New Zealand - short-term pop= ulation movements during and after a natural disaster||Mobile phone: call/t= ext times and positions||Population and migra=
: All statistical results from our initial investigations are publis= hed in the report. The report does not cover access and supply of data.=
|OECD - Quality of Internet connections from Google dat= a||Internet searches||Information society<= /td>|
|Slovenia - Population statistics using mobile p= ositioning data||Mobile phone: call/t= ext times and positions||Population and migra= tion|
* Categories from the = Classification of Types of Big Data
** Categories from the Classification of = Statistical Activities