EGU2020-18956
https://doi.org/10.5194/egusphere-egu2020-18956
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

From Silos to FAIR Services: Interoperable application of geospatial data for longitudinal surveys in the Social Sciences.

Peter Löwe, Tobias Gebel, and Hans Walter Steinhauer
Peter Löwe et al.
  • German Institute for Economic Research (DIW Berlin), Berlin, Germany (ploewe@diw.de)

Due to the european INSPIRE directive to establish an infrastructure for spatial information in Europe, the number of national data sources in Europe which are open to the public or at least science continues to grow. 

However, challenges remain to enable easy access for society and science  to these previously unavailable data silos based on standardized web-services, as defined by the Open Geospatial Consortium (OGC). This is crucial to ensure sustainable data generation and reuse according to the FAIR principles (Findable, Accessible, Interoperable, Reusable). 

We report on an interdisciplinary application, using spatial data to improve longitudinal surveys in the social sciences, involving building plans encoded in CityGML, PostGIS, MapServer and R.

The Socio-economic Panel (SOEP) as part of the German Institute for Economic Research (DIW Berlin) provides longitudinal data on persons living in private households across Germany. Lately the SOEP sampled households in certain neighborhoods within cities, areas of the so called „Soziale Stadt“ (social town). Because of restricted area, spatially referenced data has been used. Information on the level of census tiles provided by the Federal Statistical Office was used to form regional clusters. 

Within these clusters addresses, spatially referenced by the German Federal Agency for Cartography and Geodesy (BKG), have been sampled. This way, we made sure addresses are within the neighborhoods to be surveyed. As this procedure turned out to reduce organizational burden for the survey research institute as well as for the interviewers and at the same time allows for generating random household samples, it is considered for future use. Yet, addresses can belong to residential buildings as well as cinemas or hotels. 

To meet with this obstacle we evaluate the use of 3D Building Models provided by the German Federal Agency for Cartography and Geodesy (BKG).
This data is distributed as compressed data archives for the 16 states of Germany, each containing very large numbers of CityGML files containing  LoD1 data sets for buildings. The large storage footprint of these data sets makes their reuse by social scientists using standard  statistical software (such as R or Stata) on desktop computers difficult at best. This is overcome by providing flexible access to Areas of Interest (AOI) through OGC Webservices (WMS/WFS) based on a PostGIS database. The ingestion process is based on the new GMLAS driver of the ogr software project for Complex Features encoded in Geographic Markup Language (GML) based on application schemas.

How to cite: Löwe, P., Gebel, T., and Steinhauer, H. W.: From Silos to FAIR Services: Interoperable application of geospatial data for longitudinal surveys in the Social Sciences., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18956, https://doi.org/10.5194/egusphere-egu2020-18956, 2020

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Presentation version 2 – uploaded on 04 May 2020
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  • CC1: CityGML import routines, Daniel Heydebreck, 05 May 2020

    Recently, I came into contact with the CityGML format. On the one hand, the format is great because it is open, machine- and human-readable (theoretically, at least) and documented. On the other hand, one needs software/drivers to read the data properly. The issues mentioned in the presentation were those, which I also expected when I got briefly familiar with this format.

    I am not very familiar with GDAL (just used it for a few conversions in the past): Can the gdal ingdest function/driver be used to convert CityGML data in an arbitrary output file format -- e.g. netCDF?

    • AC1: Reply to CC1, Peter Löwe, 05 May 2020

      Hello CC1,

      conversion to NetCDF should work, as drivers for both formats are available for gdal/ogr:

      This article helped us to understand how the GMLAS-driver can be used:

       

      • CC2: Reply to AC1, Daniel Heydebreck, 05 May 2020

        Thanks, Peter, for the quick reply. Unfortunately, the URL to the article got lost in your reply.

Presentation version 1 – uploaded on 04 May 2020 , no comments