Session 2 : Cooperation between NMAs and NSIs

Population and Housing Statistics - The use of 3D models
Vilni Verner Holst Bloch (Statistics Norway, Norway)

Location, location, location. These are the three arguments used in housing prices, access to green areas, exposure to noise, solar influx, local climate, air pollution, flooding, and a wide range of other areas. To make an analysis of these topics one need a georeferenced point based register with the statistical units of interest, a 3D model of the geographic area of interest, and models to make indicators. This study explores the possibilities in applied georeferenced point based statistics and future cooperation between national statistical institutes (NSIs) and national mapping agencies (NMAs). Geographic 3D data is getting more and more accessible, cheaper and with higher resolutions and more possibilities of making statistics relevant down to smaller and smaller geographic areas. The use of detailed terrain data may contribute to and fill out gaps in the search for search for sustainable development goals for the next decades on a local to global level.

A questionnaire for collecting opinions for Statistical Units Data Specifications (UNGGIM-Europe, WGA)

Ignacio Duque (National Statistical Institute, Spain)

During the meeting in Paris in January 2016 the “Work Group A” (UN-GGIM-Europe) took a decision about the Core Data Themes, including Statistical Units.
Core data can be seen as the authoritative, harmonized and homogeneous framework data which both national and international users need to either fulfil their requirements or to geo-reference and locate their own thematic data. In the latter case, core data may be used as a framework on which other richer, more detailed, thematic geospatial and statistical data would rely. UN-GGIM: Europe believes that core data should be produced once for national and regional uses with maximum resolution, and would then be provided to international users if necessary through generalizing and aggregating processes. Core data is the data that is the most widely used, either directly or as a framework. Due to the United Nations context, WG A decided to focus the user requirements survey on the SDG-related requirements. Core data will be the data of main value for the UN Sustainable Development Goals (SDGs).
‘Statistical Units’ are the geographic part of a wide range of statistical data. The geography of statistical units is the mandatory bridge that connects the territory and statistical data. Simply combined with basic population information, theme SU provides a location associated with a number of persons and possibly with their characteristics (gender, age, etc.). Statistical Units may also be combined with more specialized statistics, such as socio-economic data or human health data, allowing various analyses about poverty, employment, education, health etc.
This theme is starting point for almost all studies at medium or large scales (small denominator scales), enabling deciders to identify the areas with major issues on a given topic. As Administrative units, NUTS/LAU may be used for money allocation. The INSPIRE theme ‘Population Distribution’ (PD), though of wide use for SDGs, has not been considered as core data because it is not geographic information. Theme PD is statistical information that may be combined with geographic data, generally themes ‘Administrative Units’ or ‘Statistical Units’ in order to perform powerful analysis.
The idea is to collect comments and suggestions for this document of specifications with a very brief questionnaire (with background documents). The presentations explain the questionnaire: aim, detailed information, target respondents and time span for collecting the forms. The questionnaires was designed after a round of consultation with Coordinators of Group A, Eurostat, EFGS steering committee and others.

Collaboration between French NMA and NSI (from NMA point of view)
François Chirié, Dominique Laurent (IGN, France)

IGN France (French NMA) has many relationships with INSEE (French NSI). First, IGN and INSEE have common interest in statistics: in 2012, IGN merged with the IFN (French Forest Inventory) and so, is now in charge of the public task to collect, manage and publish the statistical data of the forest inventory.
IGN carries out tasks requested by INSEE ; for instance, IGN redefined urban units, according to United Nations recommendations, using both topographic data from IGN (mainly buildings) and population data from INSEE.
IGN and INSEE are also partners in the upgrade of statistical units data, called IRIS. IRIS are enumeration districts of 2 000 inhabitants. Purpose of this upgrade is to provide more accurate geometry to these IRIS by realigning their boundaries on the IGN background large scale topographic data.
IGN’s mission is not only to produce data but it is also to integrate data from various origins. IGN has therefore an ambitious collaborative strategy and is developing partnerships in order to update and enrich its datasets. Some partnership with INSEE already took place e.g. IGN has used INSEE data about public facilities in order to populate a collaborative platform on “establishments open to the public”. And there is room for further partnership as INSEE has a database named RIL containing information about addresses and buildings that could be a very relevant data source to enrich the IGN topographic data base and the national address database (BAN).

Resource Centre for GIS and Geospatial Information in Statistics Norway
Trine Haagensen (Statistics Norway, Norway)

In order to produce cost-effective land use statistics covering Norway, existing cartographic databases and registers has been used. Statistics Norway has created a hierarchical classification system, “Standard for classification of land use and land cover”, which is based on both national and international standards and nomenclatures. The method applied is based on utilising the highest quality data sources available, but where no optimal data source exists, the next best quality data sources are used. In practical terms, the method is an automatic geographic information system (GIS) that defines, classifies and assembles the data into a hierarchy. In the statistics on land use and land resources all areas with buildings are classified as built-up. In addition, the building types within a built-up area determine the classification of that area.