Search

Zum Schulerfolg von Gefl��chteten liegen bisher kaum quantitative Ergebnisse vor. Vorgestellt wird ein sekund��ranalytischer Ansatz, den Schulerfolg von Gefl��chteten basierend auf Daten des Ausl��nderzentralregisters und der Schulstatistik n��herungsweise zu untersuchen. Eine exemplarische Analyse f��r das Bundesland Nordrhein-Westfalen (NRW) zeigt auf, dass Gefl��chtete im Vergleich zu Nichtgefl��chteten einen erheblich geringeren Schulerfolg aufweisen. (DIPF/Orig.), This article describes a secondary analytical approach to approximately examine the school success of refugees based on data from the German central register of foreigners and official school statistics. An exemplary analysis of the federal state of North Rhine-Westphalia (NRW) demonstrates that, in comparison with non-refugees, refugees are significantly less likely to succeed at school. (DIPF/Orig.)

Berlin : Rat für Migration e.V. 2022, 15 S. - (RfM-Debatte; 2022), Kommentiert wird die aktuelle Debatte hinsichtlich der Messung von Migration – wonach etwa vorgeschlagen wird, nicht mehr den ‚Migrationshintergrund‘, sondern ausschließlich ‚Eingewanderte‘ zu erfassen, insbesondere jedoch solle der ‚Migrationshintergrund‘ durch die Erhebung von subjektiver Selbstwahrnehmung und selbstwahrgenommener Fremdzuschreibung ersetzt werden. Der Beitrag diskutiert u.a. die zentrale Frage, ob die Messung des ‚Migrationshintergrundes‘ obsolet wird und welche Konsequenzen mit dessen Nicht-Erhebung u.a. für verschiedene Statistiken und Studien verbunden wären. In diesem Zusammenhang wird knapp auf die Funktion des Konstrukts ‚Migrationshintergrund‘ eingegangen und es werden Vorschläge hinsichtlich der Operationalisierung und zu möglichen Alternativbegriffen gemacht. (Autor)

Changing the coordinate system of grids storing population counts is typically a challenging task, due to the need to keep the population totals unchanged (i.e. volume preservation). The Population Warp tool (GHS-POPWARP), version 1, is an information system developed in the framework of the Global Human Settlement Layer (GHSL) project to convert geospatial raster grids of population counts between different map projections and/or resolutions, while preserving population volumes. The GHS-POPWARP v1 is a flexible tool to produce geospatial population counts grids in GeoTIFF format starting from another grid in a different coordinate system and/or resolution. The tool operationalises a stable and rigorous workflow long used in the Global Human Settlement Layer to handle gridded population data. The GHS-POPWARP v1 tool allows the creation of population counts grids via a sequence of oversampling, structure conversion and reprojection geospatial procedures. The principal purpose of the tool is to expand the interoperability of GHSL tools which require population grid data as input (e.g. GHS-DUG that strictly requires a kilometric population grid in equal area projection) or to simplify the warping of population grids for users less familiar with geospatial processing. The tool is offered as a contribution to capacity enhancement in the framework of the multi-stakeholder effort for the uptake of the Degree of Urbanisation (DEGURBA) classification method. Degree of Urbanisation refers to the people-based harmonised definition of cities and settlements recommended by the 51st Session of the United Nations Statistical Commission as the methodology to delineate cities and rural areas for international statistical comparison. The GHS-POPWARP, as all GHSL tools, is issued with an end-user licence agreement, included in the download package., JRC.E.1-Disaster Risk Management

Der Beitrag adressiert die Klassifikation von Migration durch statistische Erhebungen zu Migration, zum Migrationshintergrund und zu Sprache. Ausgegangen wird zunächst von bisherigen Befunden dazu, dass es nicht den einen ‚Migrationshintergrund‘ gibt, sondern höchst unterschiedliche statistische Ansätze, Migration zu messen und eine Unterscheidbarkeit zwischen Personen mit und ohne Migrationshintergrund herzustellen. Die unterschiedenen Schüler*innen können erst dadurch unterschiedlich beschult und beforscht werden. Der Beitrag verfolgt verschiedene Ziele. Zunächst werden unterschiedliche Operationalisierungen des Migrationshintergrundes dargestellt, die in verschiedenen Erhebungen und Statistiken verwendet werden. Dann wird veranschaulicht, dass sich vereinzelt auch innerhalb derselben Erhebung unterschiedliche Operationalisierungen finden. Die Ursachen hierfür sowie die hiermit einhergehenden Probleme werden thematisiert. Anschließend wird aufgezeigt, dass verschiedene Bezeichnungen für den ‚Migrationshintergrund‘ verwendet werden. Terminologische Unterschiede sind nicht nur zwischen unterschiedlichen Studien zu beobachten, sondern im Zeitverlauf z.T. sogar für dieselbe Studie mit derselben Operationalisierung. Weiter werden die verschiedenen Konzepte der Erfassung von Sprache in ausgewählten Erhebungen kontrastierend vorgestellt, die z.T. in die Bestimmung des ‚Migrationshintergrunds‘ einfließen. Veranschaulicht werden kann auch, dass selbst in wissenschaftlichen Publikationen falsche – z.B. nicht trennscharfe – migrationsbezogene Begrifflichkeiten verwendet werden oder inhaltlich unzutreffende Zuordnungen und damit sozial wirksame Unterscheidungen erfolgen. Die genannten Aspekte werden anhand von einschlägigen Datensätzen untersucht, die in der Bildungsforschung häufig Verwendung finden, wie die internationale Schulleistungsstudie PISA (Programme for International Student Assessment), das Nationale Bildungspanel NEPS, die amtliche repräsentative Bevölkerungsstatistik (Mikrozensus) sowie die amtlichen Schulstatistiken der Bundesländer. (DIPF/Orig.)

This document presents the contributions discussed at the second institutional workshop on Artificial Intelligence (AI), organized by the Joint Research Centre (JRC) of the European Commission. This workshop was held on 05th July 2019 at the premises of the JRC in Ispra (Italy), with video-conference to all JRC's sites. The workshop aimed to gather JRC specialists on AI and Big Data and share their experience, identify opportunities for meeting EC demands on AI, and explore synergies among the different JRC's working groups on AI. In comparison with the first event, according to the JRC Director General Vladimír Šuchav, the activities and results presented in this second workshop demonstrated a significant development of AI research and applications by JRC in different policy areas. He suggested to think about replicating the event at the premises of diverse policy DGs in order to present and discuss the clear opportunities created by JRC activities. After the opening speech by the JRC Director General Vladimír Šuchav, the research and innovation presentation were anticipated by two presentations by Alessandro Annoni and Stefano Nativi. The first presentation dealt with the results of one year of AI@JRC and six months of fully operational AI&BD community of practice1. The second presentation reported the results of the AI competences survey at JRC. The research and innovation contributions consisted in flash presentations (5 minutes) covering a wide range of areas. This report is structured according to the diverse domain areas addressed by the presenters. While the first part of the workshop was mainly informative, in the second part we collectively discussed about how to move on and evolve the AI&BD community of practice., JRC.B.6-Digital Economy

The Population To Grid tool (POP2G) –version 1.0- is an information system developed in the framework of the Global Human Settlement Layer (GHSL) to produce geospatial grids of population counts at different spatial resolutions. The POP2G v1.0 is a flexible tool to produce geospatial population grids in GeoTIFF format from census data. The tool operationalises the workflow developed for the production of the Global Human Settlement Layer Population Grid layers (GHS-POP). The POP2G v1.0 tool allows the creation of population grids at 50 m, 250 m, and 1 km spatial resolutions, handling census data stored as point or polygon vector data (in latter case requires additional covariate as input for dasymetric disaggregation). The principal purpose of the tool is the production of the population grid used as input for the Degree of Urbanisation Grid (DUG) also produced in the GHSL framework. However the potential uses of the tool and population grids go far beyond this main application. The tool is a capacity enhancement asset in the framework of the multi-stakeholder effort to develop a people-based harmonised definition of cities and settlements that helps the assessment of the feasibility of applying a global definition of cities/urban areas in support of global monitoring of SDGs and the New Urban Agenda urban targets. The POP2G, as all GHSL tools, is issued with an end-user licence agreement, included in the download package, JRC.E.1-Disaster Risk Management

In dem Beitrag werden die aktuellen inklusionsrelevanten Kennzahlen (Schuljahre 2014/15 und 2018/19) und die Schulabschlüsse von Schüler*innen mit einem Förderbedarf im Bereich Lernen erstmals für sieben Bundesländer in Zeitreihe (2015 bis 2018) berichtet. Hierbei erfolgt die Darstellung der Schulabschlüsse für Allgemeine Schulen im Vergleich zu Förderschulen und weiter differenziert nach Schulform. Die Ergebnisse basieren auf Daten der amtlichen Schulstatistik und zeigen – mit Ausnahme eines Bundeslands –, dass der Anteil der Schüler*innen mit einem Förderschwerpunkt im Bereich Lernen, die mindestens einen Hauptschulabschluss erreichen, an Allgemeinen Schulen höher ausfällt als an Förderschulen. Die Aussagekraft der vergleichenden Ergebnisse wird vor dem Hintergrund der Limitationen der vorhandenen Daten problematisiert. Es wird auf die Notwendigkeit schulischer Individualdatensätze verwiesen, um vertiefende Analysen durchführen zu können, anhand derer sich die Disparitäten in den Schulabschlussquoten zwischen den Ländern erklären lassen. (DIPF/Orig.), The article reports the inclusion relevant key figures (school years 2014/15 and 2018/19) and the graduation rates of students with special educational needs in the area of learning for seven German federal states in time series (2015 to 2018). The graduation rates are presented for general schools in comparison to special schools and further differentiated by school type. The results are based on data from the official school statistics and show, with the exception of one federal state, that students with special educational needs in learning are more likely to achieve at least a lower secondary certificate (Hauptschulabschluss) in regular schools than in special schools. The informational value of the comparative results is discussed with regard to the limitations of the available data. Reference is made to the importance of individual data in order to conduct in-depth analyses that can be used to explain the disparities in graduation rates between the federal states. (DIPF/Orig.)

Vorgestellt wird ein sekund��ranalytischer Ansatz, der es erm��glicht, die Bildungssituation von Gefl��chteten in NRW n��herungsweise zu untersuchen. Anhand von Daten des Ausl��nderzentralregisters (AZR), die u.a. Informationen zum aufenthaltsrechtlichen Status der nichtdeutschen Bev��lkerung enthalten, wird eine Operationalisierung von ���Gefl��chteten��� erarbeitet. Erg��nzend und zum Vergleich wird eine bestehende Operationalisierung von ���Schutzsuchenden��� des Statistischen Bundesamtes herangezogen. Die Informationen zum aufenthaltsrechtlichen Status lassen sich mit der in der Schulstatistik enthaltenen Staatsangeh��rigkeitsinformation verkn��pfen, wor��ber sich n��herungsweise die Bildungssituation von Gefl��chteten anhand von Daten der amtlichen Schulstatistik absch��tzen l��sst. Werden die erzielten Bildungsergebnisse f��r Gefl��chtete in Abh��ngigkeit von der jeweiligen Operationalisierungsvariante verglichen, dann ergeben sich nur relativ geringe Unterschiede zwischen den beiden Varianten. Eine tiefergehende Analyse der Bildungssituation von Gefl��chteten in NRW erfolgt anhand der auf AZR-Daten basierenden Operationalisierung von Gefl��chteten. Diese Operationalisierung wird f��r wissenschaftliche Analysen empfohlen und u.a. aus Pr��zisionsgr��nden favorisiert. Gegen��ber Nichtgefl��chteten zeigen sich Unterschiede in der Bildungsbeteiligung derart, dass Gefl��chtete in den Jahrgangsstufen 5 bis 9 seltener Gymnasien, hingegen h��ufiger Hauptschulen besuchen. Zudem weisen Gefl��chtete in NRW einen erheblich geringeren Schulerfolg auf: Sie verlassen die Schule im Vergleich zu Nichtgefl��chteten h��ufiger maximal mit Hauptschulabschluss (d.h. entweder ohne Hauptschulabschluss oder mit Hauptschulabschluss) und erlangen erheblich seltener die (Fach-)Hochschulreife. Dar��ber hinaus zeigt sich, dass in NRW sowohl hinsichtlich der Bildungsbeteiligung als auch des Schulerfolgs von Gefl��chteten ��� auch und insbesondere im Vergleich zu Nichtgefl��chteten ��� deutliche Disparit��ten auf Ebene der Kreise und kreisfreien St��dte bestehen. F��r ein potentielles administratives Monitoring der Bildungssituation von Gefl��chteten in NRW wird empfohlen, das bestehende ���Schutzsuchenden���-Konzept des Statistischen Bundesamtes zu verwenden. Das ���Schutzsuchenden���-Konzept wird ��� etwa aufgrund gr����erer Stichtagsunterschiede gegen��ber der Schulstatistik ��� im Vergleich zur AZR-Operationalisierung zwar als etwas weniger pr��zise eingesch��tzt, jedoch l��sst sich hier��ber ein kontinuierliches Monitoring der Bildungssituation von Gefl��chteten in NRW mit erheblich geringerem Aufwand realisieren. (DIPF/Orig.)

The ENACT grids are a set of consistent, seamless, multi-temporal and validated population density grids for the European Union that take into account major daily and monthly population variations. These grids were produced by combining official statistical data at regional level with geo-spatial data from conventional and non-conventional data sources. The grids display maximum population density at day- and night-time for each month of the year (i.e. 24 population grids in total), at a spatial resolution of 1 x 1 km resolution for the whole EU-28. This is a major contribution to the state-of-the-art information on population density in Europe, as prior grids considered only static residential population (i.e. night-time). The newly released grids were produced in the context of the JRC ENACT exploratory research project, “ENhancing ACTivity and population mapping. This document accompanies the public release of the ENACT 2011 Population Grids and describes the contents., JRC.E.1-Disaster Risk Management

The Degree of Urbanisation Grid (GHS-DUG) Tool (– version 4) is an information system developed in the framework of the Global Human Settlement Layer (GHSL) to produce geospatial grids to map settlement classes and extract related statistics. The settlement classes are derived from the “Degree of Urbanisation” method and ported to the GHSL environment through the GHSL Settlement Mode (GHSL SMOD). The GHS-DUG 4 is designed as a scalable tool allowing the application of the GHSL Settlement Model to the input data available to the user or to data made available in the GHSL Data Package 2019. This document contains the description of the GHS-DUG Tool use, the rationale of the differentiation between settlement classes and the comprehensive description of the outputs. The tool is a capacity enhancement asset in the framework of the multi-stakeholder effort for the uptake of the Degree of Urbanisation, the people-based harmonised definition of cities and settlements recommended by the 51st Session of the United Nations Statistical Commission as the method to delineate cities and rural areas for international statistical comparison. The GHS-DUG, as all GHSL Tools, is issued with an end-user licence agreement, included in the download package., JRC.E.1-Disaster Risk Management

The Degree of Urbanisation Grid (DUG) Tool (– version 3.1) is an information system developed in the framework of the Global Human Settlement Layer (GHSL) to produce geospatial grids to map settlement classes and extract related statistics. The settlement classes are derived from the “Degree of Urbanisation” method and ported to the GHSL environment through the GHSL Settlement Mode (GHSL SMOD). The DUG 3.1 is designed as a scalable tool allowing the application of the GHSL Settlement Model to the input data available to the user or to data made available in the GHSL Data Package 2019. This document contains the description of the DUG Tool use, the rationale of the differentiation between settlement classes and the comprehensive description of the outputs. The tool is a capacity enhancement asset in the framework of the multi-stakeholder effort to develop a people-based harmonised definition of cities and settlements that helps the assessment of the feasibility of applying a global definition of cities/urban areas in support of global monitoring of SDGs and the New Urban Agenda urban targets. The DUG, as all GHSL Tools, is issued with an end-user licence agreement, included in the download package., JRC.E.1-Disaster Risk Management

Anhang zum Bericht: Bildungsbeteiligung und Schulerfolg von Gefl��chteten in NRW ��� Sekund��ranalytische Potentiale von Daten der amtlichen Schulstatistik.

Der Beitrag diskutiert die Frage, in welcher Weise die Beschulungsstrategien regionaler Schulsysteme die Bildungsteilhabe neu migrierter Schüler/-innen differentiell beeinflussen. Auf Basis verfügbarer schulstatistischer Daten werden hierfür die Bildungswege neu migrierter Schüler/-innen in Nordrhein-Westfalen rekonstruiert: Im Ergebnis zeigen sich zum einen erhebliche regionale Disparitäten bei der Verteilung der sogenannten 'Seiteneinsteiger' auf Sekundarschulformen; zum anderen lässt sich ein überregionales Allokationsmuster rekonstruieren, das zu einer zielgruppenspezifischen Negativselektion dieser Schüler/-innengruppe in niedrig qualifizierende Schulformen beiträgt. (DIPF/Orig.), This paper discusses the question how the schooling strategies of regional school systems differentially influence the educational participation of new migrant pupils. Based on data from official school statistics, the article reconstructs educational pathways of new migrant pupils in North Rhine-Westphalia: The findings show considerable regional disparities in the distribution of 'newcomers' to secondary school types on the one hand; on the other hand, a supra-regional allocation pattern can be reconstructed contributing to a target group-specific negative selection of these pupils into low qualifying school types. (DIPF/Orig.)

The Degree of Urbanisation Territorial Units Classifier (GHS-DU-TUC) Tool (– version 1) is an information system developed in the framework of the Global Human Settlement Layer (GHSL) to produce the classification of territorial units based on the Degree of Urbanisation and extract related statistics. The tool classifies territorial units by Degree of Urbanisation at Level 1 (3 classes) and Level 2 (7 classes) based on population majority by settlement classes derived from the “Degree of Urbanisation” method and ported to the GHSL environment through the GHSL Settlement Model (GHSL SMOD). The GHS-DU-TUC 1 is designed as an operational tool to perform the second step required to apply the Degree of Urbanisation released as standalone tool and as ArcGIS Toolbox. Once the first step produces the settlement classification grid (i.e. with the GHS-DUG Tool), the user runs the GHS-DU-TUC that requires this settlement classification grid, the population grid used to produce the settlement classification grid (i.e. produced with the GHS-POP2G Tool) and a geometry of territorial units to be classified by Degree of Urbanisation. This tool is conceptualised to be deployed after the application of the GHSL tools GHS-POP2G and GHS-DUG but it accepts in input population grids produced by means of any other procedure respecting the described constrains. This document contains the description of the GHS-DU-TUC Tool use, the rationale for the second step to apply the Degree of Urbanisation (the classification of territorial units) and the comprehensive description of the outputs. The tool is a capacity enhancement asset in the framework of the multi-stakeholder effort for the uptake of the Degree of Urbanisation, the people-based harmonised definition of cities and settlements recommended by the 51st Session of the United Nations Statistical Commission as the method to delineate cities and rural areas for international statistical comparison. The GHS-DU-TUC, as all GHSL Tools, is issued with an end-user licence agreement, included in the download package., JRC.E.1-Disaster Risk Management

The Population To Grid tool (GHS-POP2G) –version 2- is an information system developed in the framework of the Global Human Settlement Layer (GHSL) to produce geospatial grids of population counts at different spatial resolutions. The GHS-POP2G v2 is a flexible tool to produce geospatial population grids in GeoTIFF format from census data. The tool operationalises the workflow developed for the production of the Global Human Settlement Layer Population Grid layers (GHS-POP). The GHS-POP2G v2 tool allows the creation of population grids at 50 m, 100 m, 250 m, and 1 km spatial resolutions, handling census data stored as point or polygon vector data (in latter case requires additional covariate as input for dasymetric disaggregation). The principal purpose of the tool is the production of the population grid used as input for the Degree of Urbanisation Grid (GHS-DUG) also produced in the GHSL framework. However the potential uses of the tool and population grids go far beyond this main application. The tool is a capacity enhancement asset in the framework of the multi-stakeholder effort to develop a people-based harmonised definition of cities and settlements that helps the assessment of the feasibility of applying a global definition of cities/urban areas in support of global monitoring of SDGs and the New Urban Agenda urban targets. The GHS-POP2G, as all GHSL tools, is issued with an end-user licence agreement, included in the download package., JRC.E.1-Disaster Risk Management

The presence of green spaces within city centres has been recognized as a valuable component of the city landscape. Vegetation provides a variety of benefits including energy saving, improved air q...

Worst-case optimal join algorithms are attractive from a theoretical point of view, as they offer asymptotically better runtime than binary joins on certain types of queries. In particular, they avoid enumerating large intermediate results by processing multiple input relations in a single multi-way join. However, existing implementations incur a sizable overhead in practice, primarily since they rely on suitable ordered index structures on their input. Systems that support worst-case optimal joins often focus on a specific problem domain, such as read-only graph analytic queries, where extensive precomputation allows them to mask these costs. In this paper, we present a comprehensive implementation approach for worst-case optimal joins that is practical within general-purpose relational database management systems supporting both hybrid transactional and analytical workloads. The key component of our approach is a novel hash-based worst-case optimal join algorithm that relies only on data structures that can be built efficiently during query execution. Furthermore, we implement a hybrid query optimizer that intelligently and transparently combines both binary and multi-way joins within the same query plan. We demonstrate that our approach far outperforms existing systems when worst-case optimal joins are beneficial while sacrificing no performance when they are not.

Der Beitrag widmet sich der Prävalenz von Schülerinnen und Schülern mit einem diagnostizierten sonderpädagogischen Förderbedarf im Bereich Lernen – und diskutiert diese im Zusammenhang mit sozialer Benachteiligung. Auf Basis von Daten der amtlichen Schulstatistik werden für das Land Nordrhein-Westfalen (Schuljahr 2016/17) inklusionsrelevante Kennzahlen differenziert nach Jahrgangsstufe, Kreisen und kreisfreien Städten dargestellt. Die Ergebnisse zeigen erhebliche Disparitäten hinsichtlich der Region, des Förderortes (Allgemeine Schule versus Förderschule) und der Jahrgangsstufe auf. Insbesondere ab der Sekundarstufe I werden Schülerinnen und Schüler im Bereich Lernen verstärkt an Förderschulen separiert unterrichtet. Als mögliche Erklärungen der festgestellten Unterschiede in der Prävalenz werden die Abwesenheit klarer Kriterien für die Diagnostik des Förderbedarfs Lernen sowie sozio-demografische Unterschiede der Regionen und der Schülerschaft thematisiert. Die Befunde unterstreichen letztlich die Benachteiligung im Bildungssystem bei ohnehin erschwerten Ausgangslagen dieser Schülerinnen und Schüler. (DIPF/Orig.) In the context of social disadvantage, the article addresses the prevalence of students with a diagnosed special educational need (SEN) in the area of learning disabilities. On the basis of data from official school statistics for the state of North Rhine-Westphalia (school year 2016/17), inclusion-relevant indicators are presented – differentiated by grade levels and school districts. The results show considerable disparities with regard to the region, the type of school (regular schools versus special schools) and the grade levels. Particularly from the secondary level onwards, students with learning disabilities are increasingly enrolled in special schools. As explanations for the differences in prevalence, two possible causes are discussed: the absence of clear criteria for diagnosing SEN in the field of learning and socio-demographic disparities between regions and students. The findings ultimately underline the disadvantaged status of these students in the education system, given their already difficult starting conditions. (DIPF/Orig.)

The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics and knowledge describing the human presence on the planet Earth. The GHSL operates in a fully open and free data and methods access policy, building the knowledge supporting the definition, the public discussion and the implementation of European policies and the international frameworks as the 2030 Development Agenda and the related thematic agreements. The GHSL supports the GEO Human Planet Initiative (HPI) that is committed to developing a new generation of measurements and information products providing new scientific evidence and a comprehensive understanding of the human presence on the planet and that can support global policy processes with agreed, actionable and goal-driven metrics. The Human Planet Initiative relies on a core set of partners committed in coordinating the production of the global settlement spatial baseline data. One of the core partners is the European Commission, Directorate General Joint Research Centre, Global Human Settlement Layer project. The Global Human Settlement Layer project produces global spatial information, evidence-based analytics, and knowledge describing the human presence in the planet. This document describes the public release of the GHSL Data Package 2019 (GHS P2019)., JRC.E.1-Disaster Risk Management

The Global Human Settlement Layer Urban Centres Database (GHS-UCDB) is the most complete database on cities to date, publicly released as an open and free dataset - GHS STAT UCDB2015MT GLOBE R2019A V1.0. The database represents the global status on Urban Centres in 2015 by offering cities location, their extent (surface, shape), and describing each city with a set of geographical, socio-economic and environmental attributes, many of them going back 25 or even 40 years in time. Urban Centres are defined in a consistent way across geographical locations and over time, applying the “Global Definition of Cities and Settlements” developed by the European Union to the Global Human Settlement Layer Built-up (GHS-BUILT) areas and Population (GHS-POP) grids. This report contains the description of the dimensions and the derived attributes that characterise the Urban Centres in the database. The document includes notes about methodology and sources. The GHS-UCDB contains information for more than 10,000 Urban Centres and it is the baseline data of the analytical results presented in the Atlas of the Human Planet 2018., JRC.E.1-Disaster Risk Management

Basierend auf Daten der amtlichen Schulstatistik wird exemplarisch für ein Bundesland anhand von inklusionsrelevanten Kennzahlen die Entwicklung der schulischen Inklusion überprüft. Für die Schuljahre 2012/13 bis 2016/17 wird untersucht, wie sich die Anzahl der Schuler*innen mit sonderpädagogischem Förderbedarf entwickelt hat und wie viele hiervon an Schulformen der Allgemeinen Schule und an Förderschulen lernen. Darüber hinaus wird mit der GU-Quote eine neue Kennzahl eingeführt, die abbildet, wie sich der Anteil der Schulen, die Inklusion im Sinne der Operationalisierung durchfuhren, entwickelt. Weiter werden die zwischen den Kommunen bestehenden erheblichen regionalen Disparitäten hinsichtlich inklusionsrelevanter Kennzahlen aufgezeigt. (DIPF/Orig.), On the basis of data from official school statistics, the development of inclusive education is examined for one federal state with regard to inclusion-relevant key figures. For the school years 2012/13 to 2016/17, the number of students with special educational needs is analyzed and whether they are taught in different general or special schools. In addition, a new indicator, the GU-quota [quota of common learning], is introduced, which shows the quota of schools providing inclusive education. Furthermore, it is referred to the considerable regional disparities which exist between the municipalities in terms of the different quotas. (DIPF/Orig.)

The Atlas of the Human Planet 2019 presents key human settlements and urbanisation statistics for 239 countries based on the progress made towards the development of a people-based global harmonised definition of cities and rural areas. Figures and statistics presented in the Atlas 2019 are the result of massive automatic big data processing carried out at the European Commission Directorate General Joint Research Centre in the framework of the Global Human Settlement Layer (GHSL) combining satellite imagery and census information to map settlements, understand their characteristics, and report about their changes over 40 years’ time (1975 – 2015). The Atlas explains the fundamentals of the GHSL, and the service it provides to upscale to the globe the Degree of Urbanisation method (currently adopted as European Union Regulation). Based on the global application of the method the Atlas presents a global urbanisation brief, a commented series of highlights on global human settlement development trajectories, supported by 239 urbanisation briefs, which form the knowledge base for the next generation of urban and territorial policy, development and cooperation action, and global reporting on progress made towards meeting the Sustainable Development Goals and the 2030 Development Agenda as a whole., JRC.E.1-Disaster Risk Management

The combination of Earth Observation and population data produces new information that describes inequalities across the globe in an original, objective and spatially distinct way.The new information contributes to a better understanding of the spatial distribution of wealth and poverty around the globe.The approach has potential for the monitoring and detection of changes in spatial patterns of inequality., JRC.E.1-Disaster Risk Management

The ESM_2015 is the latest release of the European Settlement Map produced in the frame of the GHSL project. It is produced with the Global Human Settlement Layer (GHSL) technology of the Joint Research Centre (JRC) in collaboration with the Directorate General of Regional and Urban Policy. The workflow was executed on the JRC Big Data Analytics platform. It follows-up on the previous ESM_2012 derived from 2.5 m resolution SPOT-5/6 images acquired in the context of the pan-European GMES/Copernicus (Core_003) dataset for the reference year 2012. The ESM_2015 product exploits the Copernicus VHR_IMAGE_2015 dataset made of satellite images Pleiades, Deimos-02, WorldView-2, WorldView-3, GeoEye-01 and Spot 6/7 ranging from 2014 to 2016. Unlike the previous ESM versions, the built-up extraction is realized through supervised learning (and not only by means of image filtering and processing techniques) based on textural and morphological features. The workflow is fully automated and it does not include any post-processing. For the first time a new layer containing non-residential buildings was derived by using only remote sensing imagery and training data. The produced built-up map is delivered at 2 m pixel resolution (level 1 layer) while the residential/non-residential layer (level 2) is delivered at 10 m spatial resolution. ESM_2015 offers new opportunities in Earth observation related research by allowing to study urbanisation and related features across Europe in urban and rural areas, from continental to country perspective, from regional to local, until single blocks. ESM_2015 was validated against the LUCAS 2015 survey database both at 2 and 10 meters resolution (including also the non-residential class). The validation has resulted in a Balanced Accuracy of 0.81 for the 2 m resolution built-up layer and of 0.71 for the 10 m non-residential built-up layer., JRC.E.1-Disaster Risk Management

This document presents the contributions presented at the first internal workshop on Artificial Intelligence (AI), organized by the Joint Research Centre (JRC) of the European Commission. This workshop was held on 23rd May at the premises of the JRC in Ispra (Italy), with video-conference to all JRC's sites. The workshop aimed to gather JRC specialists on AI to share their experience, to identify opportunities for meeting the EC demands on AI, and explore synergies among different JRC's working groups on AI. The full-day session workshop was organized around three main topical strands entitled Policy support, New Initiatives and Technology Development. Contributions covered a wide range of areas, including applications of AI to Cybersecurity, Transport, Environment, Health and other specific issues. This report is structured according to those main topics of study. According to the JRC Director General Vladimír Šucha: "The workshop was very stimulating and interesting presenting a broad spectrum of activities and competencies across JRC. It gave a great opportunity to build a strong and hopefully useful position in the field of AI/ML". While the first part of the workshop was mainly informative, in the second part we collectively discussed about JRC priorities and the set-up of a Community of Practice (now available at https://webgate.ec.europa.eu/connected/groups/community-of-practice-ai-and-big-data) dealing with AI and Big Data. Finally, the preliminary results of the online survey were presented. All colleagues were excellent in communicating their scientific activity in a flash and efficient way., JRC.B.6-Digital Economy

Urban transformation is an emerging topic in recent years and its socio-economical and environmental impacts have become increasingly evident to scientists and policymakers. The ability to analyse and understand this process was limited by the lack of comprehensive datasets and formalised methodologies. The Global Human Settlement Layer (GHSL) suite accommodates this need by providing worldwide, multi-epoch information on human settlements. In particular, the GHSL suite includes three main layers that map built-up density (GHS-BUILT), population density (GHS-POP) and human settlements (GHS-SMOD). These grid-based layers are produced in a consistent and harmonised way for four epochs (1975-1990-2000-2015) allowing the comparison between different periods. In this technical report, we present a formalised methodology (workflow) to characterise and analyse the dynamics of settlements. This workflow proposes a taxonomy of all possible GHS-SMOD classification combinations between two epochs; it quantifies and analyses changes of built-up surface and population density in each taxonomical class. We show the workflow capability by using the region of New York (United States of America) as a case study. The presented workflow has a direct application to characterise urban dynamics using the GHSL. Such approach supports the assessment of urbanization processes, by monitoring urban expansion, contraction and rural-urban transitions, and by measuring sustainable urban development metrics. The application of this workflow allows taking into account, both and separately, the net and the gross change in built-up surface and population density within a urban settlement evolution. Moreover, the application of this workflow to the GHSL suite benefits of the full ranges of GHSL features (global coverage, data consistency and open and free data format)., JRC.E.1-Disaster Risk Management

The GEO Human Planet Initiative is committed to developing a new generation of measurements and information products that provide new scientific evidence and a comprehensive understanding of the human presence on the planet and that can support global policy processes with agreed, actionable and goal-driven metrics. The Human Planet Initiative relies on a core set of partners committed in coordinating the production of the global settlement spatial baseline data, and an enlarged community of partners developing experimental activities on using the new baseline data for derived post-2015 indicators. One of the core partners is the European Commission, Directorate General Joint Research Centre, Global Human Settlement Layer project. The Global Human Settlement Layer project produces global spatial information, evidence-based analytics, and knowledge describing the human presence in the planet. This document describes the Community pre-Release of the GHSL Data Package 2018 - GHS CR2018 - created for the members of the GEO Human Planet Initiative. The data in this data package have the purpose to gather feedbacks from the GEO Human Planet Initiative community, in preparation for the public release of these data. Disclaimer: the data included in the Community Pre-Release of the GHSL Data Package 2018 should be considered preliminary and not yet validated. It may differ substantially with the data that will be included in the public final Release 2018. The JRC data are provided "as is" and "as available" in conformity with the JRC Data Policy1 and the Commission Decision on reuse of Commission documents (2011/833/EU). Although the JRC guarantees its best effort in assuring quality when publishing these data, it provides them without any warranty of warranty of any kind, either express or implied, including, but not limited to, any implied warranty against infringement of third parties' property rights, or merchantability, integration, satisfactory quality and fitness for a particular purpose. The JRC has no obligation to provide technical support or remedies for the data. The JRC does not represent or warrant that the data will be error free or uninterrupted, or that all non-conformities can or will be corrected, or that any data are accurate or complete, or that they are of a satisfactory technical or scientific quality. The JRC or as the case may be the European Commission shall not be held liable for any direct or indirect, incidental, consequential or other damages, including but not limited to the loss of data, loss of profits, or any other financial loss arising from the use of the JRC data, or inability to use them, even if the JRC is notified of the possibility of such damages., JRC.E.1-Disaster Risk Management

Für sechs Bundesländer wird der Schulerfolg – im Sinne von formal erlangten Abschlüssen – von Schülerinnen und Schülern mit sonderpädagogischem Förderbedarf an Allgemeinen Schulen und an Förderschulen untersucht. Die Analysen basieren auf Daten der amtlichen Schulstatistik. In diesem Zusammenhang wird die Kennzahl ‚GU-Abschlüsse‘ eingeführt. Die Ergebnisse zeigen für das Abgangsjahr 2016, dass die Schülerinnen und Schüler mit sonderpädagogischem Förderbedarf an Allgemeinen Schulen – insbesondere im Förderschwerpunkt Lernen – häufiger einen qualifizierten Schulabschluss erreichen als ihre Peers an Förderschulen. Abschließend werden die Ergebnisse unter Berücksichtigung der Limitationen der Datenbasis sowie unter Einbezug weiterer inklusionsrelevanter Kennzahlen diskutiert., In the present paper the graduation rates of students with special educational needs (SEN) in regular schools and in special schools are examined. The analyses are based on data from official school statistics. In this context, the indicator “GU-Abschlüsse” (graduation rates of students with SEN in regular schools) is introduced. The results for 2016 show higher graduation rates for students with SEN – especially students with learning disabilities – in inclusive settings than in special schools. Finally, the results are discussed taking into account the limitations of the data base as well as other inclusion-relevant key figures.

Spatial inequalities across the globe are not easy to detect and satellite data have shown to be of use in this task. Earth Observation (EO) data combined with other information sources can provide complementary information to those derived from traditional methods. This research shows patterns of inequalities emerging by combining global night lights measured from Earth Observation, population density and built-up in 2015. The focus of the paper is to describe the spatial patterns that emerge by combing the three variables. This work focuses on processing EO data to derive information products, and in combining built-up- and population density with nighttime emission. The built-up surface was derived entirely from remote sensing archives using artificial intelligence and pattern recognition techniques. The built-up was combined with population census data to derive population density. Also the nighttime emission data were available from EO satellite sensors. The three layers are subsequently combined as three colour compositions based on the three primary colours (i.e. red green and blue) to display the “human settlement spatial pattern” maps. These GHSL nightlights provide insights in inequalities across the globe. Many patterns seem to be associated with countries income. Typically, high income countries are very well lit at night, low income countries are poorly lit at night. All larger cities of the world are lit at night, those in low-income countries are often less well lit than cites in high-income countries. There are also important differences in nightlights emission in conflict areas, or along borders of countries. This report provides a selected number of patterns that are described at the regional, national and local scale. However, in depth analysis would be required to assess more precisely that relation between wealth access to energy and countries GDP, for example. This work also addresses regional inequality in GHSL nightlights in Slovakia. The country was selected to address the deprivation of the Roma minority community. The work aims to relate the information from the GHSL nightlights with that collected from field survey and census information conducted at the national level. Socio-economic data available at subnational level was correlated with nightlight. The analysis shows that despite the potential of GHSL nightlights in identifying deprived areas, the measurement scale of satellite derived nightlights at 375 x 375 m and 750 x 750 m pixel size is too coarse to capture the inequalities of deprived communities that occur at finer scale. In addition, in the European context the gradient of inequality is not strong enough to produce strong evidence. Although there is a specific pattern of GHSL nightlights in settlements with high Roma presence, this cannot be used to identify such areas among the others. This work is part of the exploratory data analysis conducted within the GHSL team. The exploratory analysis will be followed by more quantitative assessments that will be available in future work., JRC.E.1-Disaster Risk Management

The Atlas of the Human Planet 2018 describes the Urban Centre Database, which was produced in the framework of the Global Human Settlement Layer (GHSL) project by applying a global definition of cities and settlements to the GHSL data. The Atlas presents the key findings of the analysis of geographic, environmental and socio-economic variables that were gathered from free and open sources for each urban centre in the world., JRC.E.1-Disaster Risk Management

People, planet, prosperity and peace are four priorities shared by Africa and Europe, and areas where opportunities for beneficial cooperation abound. Over the past three decades, the European Commission’s Joint Research Centre (JRC) has worked with many organisations and institutions across Africa. This report and its accompanying interactive online service ‘Africa StoryMaps’ present the key findings from this collaboration, and set out options the decision-making, research and education communities may consider. The report focuses on the African dimension of the partnership. It explores the opportunities and challenges arising from the fact that Africa has over twice the population of the European Union (EU), is the world’s most youthful continent, has an economy that is growing faster than that of the EU, is almost seven times larger geographically, yet is vulnerable to diverse internal and external stresses. Tell us what you think about this report. Please fill out and return our feedback form (PDF) at https://ec.europa.eu/jrc/en/file/document/africa-report-feedback-form., JRC.D.6-Knowledge for Sustainable Development and Food Security

This user guide accompanies the MASADA tool which is a public tool for the detection of built-up areas from remote sensing data. MASADA stands for Massive Spatial Automatic Data Analytics. It has been developed in the frame of the “Global Human Settlement Layer” (GHSL) project of the European Commission’s Joint Research Centre, with the overall objective to support the production of settlement layers at regional scale, by processing high and very high resolution satellite imagery. The tool builds on the Symbolic Machine Learning (SML) classifier; a supervised classification method of remotely sensed data which allows extracting built-up information using a coarse resolution settlement map or a land cover information for learning the classifier. The image classification workflow incorporates radiometric, textural and morphological features as inputs for information extraction. Though being originally developed for built-up areas extraction, the SML classifier is a multi-purpose classifier that can be used for general land cover mapping provided there is an appropriate training data set. The tool supports several types of multispectral optical imagery. It includes ready-to-use workflows for specific sensors, but at the same time, it allows the parametrization and customization of the workflow by the user. Currently it includes predefined workflows for SPOT-5, SPOT-6/7, RapidEye and CBERS-4, but it was also tested with various high and very high resolution1 sensors like GeoEye-1, WorldView-2/3, Pléiades and Quickbird., JRC.E.1-Disaster Risk Management

Basierend auf Daten der amtlichen Schulstatistik wird die Bildungsbeteiligung von geflüchteten Kindern und Jugendlichen an allgemeinbildenden Schulen in Deutschland quantitativ-explorativ untersucht. Es werden föderale und insbesondere regionale Unterschiede in der Bildungsbeteiligung von Schülerinnen und Schülern ausgewählter Staatsangehörigkeiten zwischen 2005 und 2015 systematisch analysiert. Hierbei geht es um die Frage nach strukturellen Rahmenbedingungen für ungleiche Bildungsteilhabe. Die Befunde zeigen weiterhin bestehende Unterschiede im Schulformbesuch von Geflüchteten im Vergleich zu deutschen Schülerinnen und Schülern, die im Zeitverlauf zurückgehen. Zudem werden (für den Fall NRW) systematische regionale Unterschiede innerhalb eines Bundeslandes belegt, was auf die Bedeutung regionaler Disparitäten als eigenständiges Erklärungsangebot für Bildungsungleichheit verweist. (DIPF/Orig.), This article presents a quantitative and exploratory study on the educational participation of refugee children based on data from official school statistics. Comparative analyses were made between the federal states in Germany (Bundesländer) and regions within North Rhine-Westphalia (NRW) regarding the educational participation of students of selected nationalities between 2005 and 2015. Our findings show that there are disparities between refugee and German citizen students regarding the attendance of different school types. However, the disparities are decreasing with time. Furthermore, systematic regional differences within NRW are documented, which could lead to a separate explanation for educational inequality. (DIPF/Orig.)

Der Bildungs(miss)erfolg von Kindern und Jugendlichen mit Migrationshintergrund wird in Deutschland intensiv diskutiert. Dabei wird in der Regel von einer grundsätzlichen Benachteiligung von Schüler_innen mit ausländischer Staatsangehörigkeit sowie der etwas weiter gefassten Gruppe von Schüler_innen mit Migrationshintergrund ausgegangen. Hingegen wird die Gruppe der vietnamesischen Schüler_innen in Deutschland eher als Musterbeispiel dargestellt. In der öffentlichen Wahrnehmung gelten sie als »gut integriert« und daher als nicht benachteiligt. Dies kann neben der Tatsache, dass es sich bei ihnen um eine quantitativ kleine Zuwanderungsgruppe handelt, ein Grund dafür sein, dass bisher wenige Studien zu vietnamesischen Schüler_innen vorliegen. Im Folgenden soll ein Überblick über die Bildungsbeteiligung – im Sinne des Besuchs verschiedener Schulformen – von vietnamesischen Schüler_innen in Deutschland sowie zu den sozioökonomischen Rahmenbedingungen in ihren Familien gegeben werden. Zunächst wird ihre Bildungsbeteiligung in Deutschland insgesamt sowie in den Bundesländern in einer Zeitreihe für die Jahre 2005 bis 2015 dargestellt. Dabei werden Daten der amtlichen Statistik verwendet, die Informationen zu vietnamesischen Staatsangehörigen bereitstellt. Daraufhin werden ihre bildungsrelevanten Sozialisationsbedingungen skizziert und diskutiert. (DIPF/Orig.)

LUE tool stands for Land Use Efficiency tool and it is a tool developed by the Global Human Settlement Layer (GHSL) Team. This tool, developed in Python language, allows user calculating easily and quickly some indicators on the change of land in an area of interest. The tool is designed to be use with GHS Layers on Built-up area and population, but it can be easily adapted also to other input data. This guide provides instructions about installing and using the LUE tool in the open source software QGIS and provides suggestions for the output interpretation., JRC.E.1-Disaster Risk Management

This user guide accompanies the DUG tool which is a public tool for applying the “Degree of urbanisation” (DEGURBA) model at one kilometer grid. DUG stands for Degree of Urbanisation Grid. It has been developed in the frame of the “Global Human Settlement Layer” (GHSL) project of the European Commission’s Joint Research Centre, with the overall objective to support the DEGURBA activities. The tool builds on the GHS SMOD model that implements settlement model classifier at 1 km grid. The tool uses population and built-up grids as input data, and optionally a water mask. It has been developed and tested using GHS P2016 datasets ; however other grids can be used on user responsibility. This user guide is a comprehensive guide to all aspects of using the DUG tool. It includes instructions for the set-up of the software, the use of the tool and the manipulation of the data. It presents briefly the basic principles and background information on the methodology and its implementation. Some guidelines on the parametrization are also provided., JRC.E.1-Disaster Risk Management

Nordrhein-Westfalen (NRW) erfasst im Rahmen einer auf Aggregatdaten basierenden Schulstatistik einen Migrationshintergrund, der von den KMK-Vorgaben abweicht. Das Erhebungskonzept der NRW-Statistik bietet seit wenigen Jahren aufgrund von zusätzlichen Schülermerkmalen und ergänzenden Elterninformationen neue analytische Möglichkeiten. Für die Grundgesamtheit der Schülerinnen und Schüler in NRW kann exemplarisch überprüft werden, inwiefern Ergebnisse zur Bildungsbeteiligung von der gewählten Operationalisierung des Migrationshintergrundes abhängen. Es zeigt sich, dass ein unter Einbezug von Elternmerkmalen weiter gefasster Migrationshintergrund mit geringeren Förderschulbesuchs- und höheren Gymnasialbesuchsanteilen einhergeht und dass die schulformbezogenen Bildungsdisparitäten im Vergleich zu Schülerinnen und Schülern ohne Migrationshintergrund geringer ausfallen. Zudem werden die schulstatistischen Potenziale des Datensatzes gegenüber anderen Erhebungsansätzen – etwa gemäß KMK-Empfehlung – diskutiert. (DIPF/Orig.), This article exemplarily highlights a new statistical approach to determining information on the migration background of students at aggregate level in the Federal State North Rhine-Westphalia (NRW). This approach provides new analytical opportunities due to additionally collected student characteristics and supplementary information on their parents. In particular, the data enable analyses of the educational participation of students depending on the specific operationalization of migration background. We find that a migration background following a broader definition (i.e., which additionally relies on parental migration characteristics) is linked to lower attendance rates in schools for children with special needs and higher attendance rates in upper secondary schools (Gymnasien). Following this approach we also find that disparities in attendance of different school types are lower in comparison to students without a migration background. Keywords: foreign students, migration, migration background, citizenship, educational participation, generation, school statistics. (DIPF/Orig.)

Journal for educational research online 9 (2017) 1, S. 144-168, Der Beitrag gibt Auskunft über den Stand der Erfassung von Migration in den amtlichen Schulstatistiken der Bundesländer. Hierauf aufbauend werden die Anteile von Schülerinnen und Schülern mit nichtdeutscher Staatsangehörigkeit sowie von Schülerinnen und Schülern mit Migrationshintergrund nach der jeweiligen landesspezifischen Migrationsdefinition dargestellt und hinsichtlich Validität, Vergleichbarkeit und Zusammenführbarkeit in eine Bundesstatistik diskutiert. Anhand der verfügbaren Migrationsmerkmale wird weiter die Bildungsbeteiligung von Schülerinnen und Schülern mit Migrationshintergrund insbesondere hinsichtlich des Gymnasialbesuchs analysiert. (DIPF/Orig.), The paper informs about the status of the collection of migrational data in official school statistics in the German federal states. The proportion of students without a German citizenship as well as students with migration background is presented and differentiated by the specific definition of migration background in the federal states. I discuss the validity and comparability of the definitions as well as limitations of merging the different federal to a national school statistic. Based on the available migration attributes the educational participation of students with migration background is analyzed – with a special focus on the attendance of upper secondary school (Gymnasium). (DIPF/Orig.)

Nordrhein-Westfalen (NRW) erfasst im Rahmen einer auf Aggregatdaten basierenden Schulstatistik einen Migrationshintergrund, der von den KMK-Vorgaben abweicht. Das Erhebungskonzept der NRW-Statistik bietet seit wenigen Jahren aufgrund von zusätzlichen Schülermerkmalen und ergänzenden Elterninformationen neue analytische Möglichkeiten. Für die Grundgesamtheit der Schülerinnen und Schüler in NRW kann exemplarisch überprüft werden, inwiefern Ergebnisse zur Bildungsbeteiligung von der gewählten Operationalisierung des Migrationshintergrundes abhängen. Es zeigt sich, dass ein unter Einbezug von Elternmerkmalen weiter gefasster Migrationshintergrund mit geringeren Förderschulbesuchs- und höheren Gymnasialbesuchsanteilen einhergeht und dass die schulformbezogenen Bildungsdisparitäten im Vergleich zu Schülerinnen und Schülern ohne Migrationshintergrund geringer ausfallen. Zudem werden die schulstatistischen Potenziale des Datensatzes gegenüber anderen Erhebungsansätzen – etwa gemäß KMK-Empfehlung – diskutiert. (DIPF/Orig.) This article exemplarily highlights a new statistical approach to determining information on the migration background of students at aggregate level in the Federal State North Rhine-Westphalia (NRW). This approach provides new analytical opportunities due to additionally collected student characteristics and supplementary information on their parents. In particular, the data enable analyses of the educational participation of students depending on the specific operationalization of migration background. We find that a migration background following a broader definition (i.e., which additionally relies on parental migration characteristics) is linked to lower attendance rates in schools for children with special needs and higher attendance rates in upper secondary schools (Gymnasien). Following this approach we also find that disparities in attendance of different school types are lower in comparison to students without a migration background. Keywords: foreign students, migration, migration background, citizenship, educational participation, generation, school statistics. (DIPF/Orig.)

Wuppertal : Universitätsbibliothek Wuppertal 2016, 24 S. - (Schumpeter Discussion Papers; SDP 16001), Der Beitrag gibt Auskunft über den Stand der Erfassung von Migration in den amtlichen Schulstatistiken der Bundesländer. Hierauf aufbauend werden die Anteile von Schülerinnen und Schülern mit nichtdeutscher Staatsangehörigkeit sowie von Schülerinnen und Schülern mit Migrationshintergrund nach der jeweiligen landesspezifischen Migrationsdefinition dargestellt und hinsichtlich Validität, Vergleichbarkeit und Zusammenführbarkeit in eine Bundesstatistik diskutiert. Anhand der verfügbaren Migrationsmerkmale wird weiter die Bildungsbeteiligung von Schülerinnen und Schülern mit Migrationshintergrund insbesondere hinsichtlich des Gymnasialbesuchs analysiert. (DIPF/Orig.), The paper provides information on the status of the collection of migrational data in official school statistics of the federal states in Germany. Based on this, the proportion of students without a German citizenship as well as students with migration background will be presented and differentiated by the specific definition of migration background in the federal states. Furthermore, the validity and comparability of the definitions will be discussed as well as the merging of the federal school statistics into a national school statistic. Based on the available migration attributes the educational participation of students with migration background will be analyzed – with a special focus on the attendance of secondary schools. (DIPF/Orig.)

Sonderpädagogische Förderung heute 61 (2016) 2, S. 194-204, Zum Schulformbesuch von Flüchtlingen und Asylbewerbern in Deutschland liegen bislang nur sehr wenige empirische Befunde vor. Für ein Bundesland kann exemplarisch die Bildungsbeteiligung von Flüchtlingen und Asylbewerbern analysiert werden, da die auf Individualdaten basierende Schulstatistik des Landes Brandenburg für die Grundgesamtheit der Schülerinnen und Schüler auch den Flüchtlings- und Asylbewerberstatus erfasst. Für diese Schüler zeigen sich Nachteile hinsichtlich des Schulformbesuchs, z.B. da sie im Vergleich zu anderen Schülern ohne deutsche Staatsangehörigkeit seltener Gymnasien besuchen., There is few knowledge in terms of school attendance of refugees and asylum seekers in Germany. Their educational participation will exemplarily be analyzed for one federal state (Brandenburg) based on school statistics. Official school statistics in Brandenburg base on individual data and provide information for the population of students about their possible status as a refugee or asylum seeker. With regard to their educational participation disadvantages can be found: E.g. they attend less frequently upper secondary schools (Gymnasium) compared to other foreign students.

The JRC’s Global Human Settlement Layer (GHSL) and the derived population data were integrated with night-time satellite imagery to assess the humanitarian impact of the Syrian conflict. The results demonstrate that the methodology allows estimating in a timely and consistent manner the number of affected people during crisis. Estimates of affected people that match with the official figures including registered refugees and IDPs were obtained with this method. The approach has a potential in estimating in an objective and timely way the impacts of humanitarian crisis. Prospective studies can make use of the temporal and spatial advantages of open-access geospatial data (night-time satellite imagery and GHSL derived products) in the field of disaster risk management to investigate the role of social dynamics over space and time in the occurrences of disasters and provide evidence-based knowledge to support disaster risk reduction plans and actions., JRC.E.1-Disaster Risk Management

Der Beitrag gibt Auskunft über den Stand der Erfassung von Migration in den amtlichen Schulstatistiken der Bundesländer. Hierauf aufbauend werden die Anteile von Schülerinnen und Schülern mit nichtdeutscher Staatsangehörigkeit sowie von Schülerinnen und Schülern mit Migrationshintergrund nach der jeweiligen landesspezifischen Migrationsdefinition dargestellt und hinsichtlich Validität, Vergleichbarkeit und Zusammenführbarkeit in eine Bundesstatistik diskutiert. Anhand der verfügbaren Migrationsmerkmale wird weiter die Bildungsbeteiligung von Schülerinnen und Schülern mit Migrationshintergrund insbesondere hinsichtlich des Gymnasialbesuchs analysiert. The paper provides information on the status of the collection of migrational data in official school statistics of the federal states in Germany. Based on this, the proportion of students without a German citizenship as well as students with migration background will be presented and differentiated by the specific definition of migration background in the federal states. Furthermore, the validity and comparability of the definitions will be discussed as well as the merging of the federal school statistics into a national school statistic. Based on the available migration attributes the educational participation of students with migration background will be analyzed - with a special focus on the attendance of secondary schools.

Die Daten der Volkszählung erlauben es, den Schulbesuch nach Kreisen und dem Migrationshintergrund der Schülerinnen und Schüler an ihrem Wohnort darzustellen. Damit liefern sie wichtige Informationen über die regionalen Bedingungen des Schulwesens, die die Schulstatistik nicht liefert. (DIPF/Orig.)