Benchmarking
Starting to work with georeferenced data can be a challenge. This site brings several useful tools and case studies together, so that a new project can be measured against approaches that are already established.
Tools
Look at it
Geodata Visualizer
A good starting point when working with georeferenced data is to look at a map. The following tool provides an interactive map of regional indicators for Germany: pick a measure, see it across districts, and read a single region against the distribution. It was built at the SOEP RegioHub and still carries its working title, SOEP RegioHub Data Explorer.
Nine indicators at the moment, drawn from the SOEP, from INKAR and from official statistics: hospital beds, population density, body mass index, satisfaction with one’s own health, worries about one’s economic situation, life satisfaction, electoral turnout, trust, and a Twitter sociology index. Each one names its source in the map.
Compute with it
Software toolbox
Four R packages from the RegioHub, each solving one recurring step in regional analysis, all open source.
inkr: all of INKAR in a local DuckDB database, so the indicators are queryable offline instead of clicked out of a portal.nutscoder: geocoding to NUTS region codes, which is the join key most spatial linkages need.badata: regional labour-market data from the Bundesagentur für Arbeit.deairdata: a wrapper around the air-quality API of the Umweltbundesamt.
Further guidance
For more guidelines, descriptions of geocoding processes, and a discussion about advantages and disadvantages of geodata, also see Why Geodata?. Among others, the webpage discusses
- changing administrative boundaries and the territorial reforms that break a time series,
- the assumption that everyone inside a boundary is exposed to the same context,
- the choice between distance-based, radius-based and grid-based measures,
- and what a residential coordinate cannot tell you.
Case studies
Case studies are in preparation and will be collected here, next to the tools above.