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The National Spatial Data Infrastructure (NSDI) is defined as the technologies, policies and people necessary to promote sharing of geospatial data throughout all levels of government, the private and non-profit sectors and the academic community. The US Census Bureau is the federal agency lead for administrative units data, one of the seven data themes identified by the NSDI framework. The administrative unit is a unit with administrative responsibilities. These units are organized as nodes/lines/areas feature data. The OpenGIS Geography Markup Language (GML) is the XML grammar to express the geographic features. This study at the US Census Bureau investigates how the general-purpose GML standard could be leveraged and extended to describe the most comprehensive geographic dataset with national coverage in the US. Challenges and problems in dealing with data volume, GML document structure, GML schema design and GML document naming are analyzed, followed by proposed solutions proven for feasibility. Our results show that one key point in making a successful GML deployment for NSDI is to reflect the characteristics of the geographic data through a carefully designed GML schema, structure and organization. The lessons learned may be useful to others transforming NSDI framework data and other large geospatial datasets into GML structures.

International audience ; Up to today, dealing with application schema features, such as data harmonised to the INSPIRE data models, also known as complex GML, was challenging for users due to lack of appropriate tools in the commonly used client applications. Recent advances in GDAL/OGR and QGIS will allow easier handling of appschema data. A consortium of titellus, Spatialys, Camptocamp, Oslandia and BRGM has taken this task in the scope of the European Union's Earth observation programme Copernicus, as part of the tasks delegated to the European Environment Agency. The functionality will become available from the upcoming 3.0 version of QGIS. In this workshop we will guide you through the world of application schema in GDAL/OGR and QGIS. Since its introduction the GML Application Schema (GMLAS) driver has received quite some positive feedback, being for example able to address national schemas used in Finland or Japan, beyond the initial use cases it was developed for (Inspire, GeoSciML, GroundWaterML) We will start the workshop with a section on dataset discovery from within QGIS, by querying a spatial catalogue. We will from there connect to a WFS endpoint to retrieve appschema features. We'll explain how GMLAS is able to automatically set up a relational datamodel in PostGIS by analysing the application schema and load the GML into the database. QGIS will then be able to visualise the data in a map view. You are invited to modify the data and finally export the data from QGIS as application schema GML. We also invite you to bring some of your personal data to load as and/or transform to an application schema of your choice. At the workshop we'll have core developers of OGR, QGIS.

International audience ; Up to today, dealing with application schema features, such as data harmonised to the INSPIRE data models, also known as complex GML, was challenging for users due to lack of appropriate tools in the commonly used client applications. Recent advances in GDAL/OGR and QGIS will allow easier handling of appschema data. A consortium of titellus, Spatialys, Camptocamp, Oslandia and BRGM has taken this task in the scope of the European Union's Earth observation programme Copernicus, as part of the tasks delegated to the European Environment Agency. The functionality will become available from the upcoming 3.0 version of QGIS. In this workshop we will guide you through the world of application schema in GDAL/OGR and QGIS. Since its introduction the GML Application Schema (GMLAS) driver has received quite some positive feedback, being for example able to address national schemas used in Finland or Japan, beyond the initial use cases it was developed for (Inspire, GeoSciML, GroundWaterML) We will start the workshop with a section on dataset discovery from within QGIS, by querying a spatial catalogue. We will from there connect to a WFS endpoint to retrieve appschema features. We'll explain how GMLAS is able to automatically set up a relational datamodel in PostGIS by analysing the application schema and load the GML into the database. QGIS will then be able to visualise the data in a map view. You are invited to modify the data and finally export the data from QGIS as application schema GML. We also invite you to bring some of your personal data to load as and/or transform to an application schema of your choice. At the workshop we'll have core developers of OGR, QGIS.

International audience ; Up to today, dealing with application schema features, such as data harmonised to the INSPIRE data models, also known as complex GML, was challenging for users due to lack of appropriate tools in the commonly used client applications. Recent advances in GDAL/OGR and QGIS will allow easier handling of appschema data. A consortium of titellus, Spatialys, Camptocamp, Oslandia and BRGM has taken this task in the scope of the European Union's Earth observation programme Copernicus, as part of the tasks delegated to the European Environment Agency. The functionality will become available from the upcoming 3.0 version of QGIS. In this workshop we will guide you through the world of application schema in GDAL/OGR and QGIS. Since its introduction the GML Application Schema (GMLAS) driver has received quite some positive feedback, being for example able to address national schemas used in Finland or Japan, beyond the initial use cases it was developed for (Inspire, GeoSciML, GroundWaterML) We will start the workshop with a section on dataset discovery from within QGIS, by querying a spatial catalogue. We will from there connect to a WFS endpoint to retrieve appschema features. We'll explain how GMLAS is able to automatically set up a relational datamodel in PostGIS by analysing the application schema and load the GML into the database. QGIS will then be able to visualise the data in a map view. You are invited to modify the data and finally export the data from QGIS as application schema GML. We also invite you to bring some of your personal data to load as and/or transform to an application schema of your choice. At the workshop we'll have core developers of OGR, QGIS.

International audience ; Up to today, dealing with application schema features, such as data harmonised to the INSPIRE data models, also known as complex GML, was challenging for users due to lack of appropriate tools in the commonly used client applications. Recent advances in GDAL/OGR and QGIS will allow easier handling of appschema data. A consortium of titellus, Spatialys, Camptocamp, Oslandia and BRGM has taken this task in the scope of the European Union's Earth observation programme Copernicus, as part of the tasks delegated to the European Environment Agency. The functionality will become available from the upcoming 3.0 version of QGIS. In this workshop we will guide you through the world of application schema in GDAL/OGR and QGIS. Since its introduction the GML Application Schema (GMLAS) driver has received quite some positive feedback, being for example able to address national schemas used in Finland or Japan, beyond the initial use cases it was developed for (Inspire, GeoSciML, GroundWaterML) We will start the workshop with a section on dataset discovery from within QGIS, by querying a spatial catalogue. We will from there connect to a WFS endpoint to retrieve appschema features. We'll explain how GMLAS is able to automatically set up a relational datamodel in PostGIS by analysing the application schema and load the GML into the database. QGIS will then be able to visualise the data in a map view. You are invited to modify the data and finally export the data from QGIS as application schema GML. We also invite you to bring some of your personal data to load as and/or transform to an application schema of your choice. At the workshop we'll have core developers of OGR, QGIS.

International audience ; Up to today, dealing with application schema features, such as data harmonised to the INSPIRE data models, also known as complex GML, was challenging for users due to lack of appropriate tools in the commonly used client applications. Recent advances in GDAL/OGR and QGIS will allow easier handling of appschema data. A consortium of titellus, Spatialys, Camptocamp, Oslandia and BRGM has taken this task in the scope of the European Union's Earth observation programme Copernicus, as part of the tasks delegated to the European Environment Agency. The functionality will become available from the upcoming 3.0 version of QGIS. In this workshop we will guide you through the world of application schema in GDAL/OGR and QGIS. Since its introduction the GML Application Schema (GMLAS) driver has received quite some positive feedback, being for example able to address national schemas used in Finland or Japan, beyond the initial use cases it was developed for (Inspire, GeoSciML, GroundWaterML) We will start the workshop with a section on dataset discovery from within QGIS, by querying a spatial catalogue. We will from there connect to a WFS endpoint to retrieve appschema features. We'll explain how GMLAS is able to automatically set up a relational datamodel in PostGIS by analysing the application schema and load the GML into the database. QGIS will then be able to visualise the data in a map view. You are invited to modify the data and finally export the data from QGIS as application schema GML. We also invite you to bring some of your personal data to load as and/or transform to an application schema of your choice. At the workshop we'll have core developers of OGR, QGIS.

Das 'German Microdata Lab' (GML) wurde im Jahr 2003 durch die Zusammenlegung der ZUMA-Abteilungen Mikrodaten und Einkommen und Verbrauch gebildet. Zusammen mit der Abteilung Soziale Indikatoren, die die gesellschaftliche Entwicklung mit dem Instrumentarium der Sozialindikatorenforschung und der Sozialberichterstattung beobachtet und analysiert, bildet das GML den Arbeitsbereich 'gesellschaftliche Dauerbeobachtung'. Im vorliegenden Bericht werden die Arbeiten des GML im Jahr 2006 näher vorgestellt. Es werden die Aktivitäten zur Erschließung amtlicher Mikrodaten für die empirische Sozial- und Wirtschaftsforschung, der wissenschaftliche Service des GML sowie die Wissensvermittlung in Form von Nutzerberatungen und Betreuung von Gastwissenschaftlern beschrieben. Des weiteren wird die methodische Forschung des GML vorgestellt und ein Überblick über die im Jahr 2006 abgeschlossenen und laufenden Drittmittelprojekte gegeben. Der Anhang des Berichts enthält eine Liste aller Mitarbeiter des GML, die im Berichtsjahr tätig waren, die Programme der Workshops und der 1. EVS-Nutzerkonferenz sowie eine Bilanz der wissenschaftlichen Tätigkeit in Form von Publikationen und Vorträgen. (ICI2)

Der Bericht dokumentiert die Arbeiten des German Microdata Lab (GML) für das Jahr 2018. Das GML ist eine Serviceeinrichtung für die Sozialforschung und vertritt deren Interessen gegenüber der amtlichen Statistik. Das GML ist unabhängig von Statistischen Ämtern und anderen Datenproduzenten und wirkt als Schnittstelle zwischen empirischer Sozialforschung und amtlicher Statistik.

Der Bericht dokumentiert die Arbeiten des German Microdata Lab (GML) für das Jahr 2017. Das GML ist eine Serviceeinrichtung für die Sozialforschung und vertritt deren Interessen gegenüber der amtlichen Statistik. Das GML ist unabhängig von Statistischen Ämtern und anderen Datenproduzenten und wirkt als Schnittstelle zwischen empirischer Sozialforschung und amtlicher Statistik.

Der Bericht dokumentiert die Arbeiten des German Microdata Lab (GML) bei GESIS für das Jahr 2023. Das GML ist eine Serviceeinrichtung für die Sozialforschung und vertritt deren Interessen gegenüber der amtlichen Statistik. Das GML ist unabhängig von Statistischen Ämtern und anderen Datenproduzenten und wirkt als Schnittstelle zwischen empirischer Sozialforschung und amtlicher Statistik.