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Using the statistical language R as a Geographic Information System [workshop]

Authors: Jünger, Stefan;

Using the statistical language R as a Geographic Information System [workshop]

Abstract

Geospatial data have become an emerging topic in social science research. When we aim to use these data in our study, we have to rely on a set of analytical skills facilitated by the exploitation of Geographic Information Systems (GIS). We also have to be aware of changes in our data management workflows, originating in issues concerning data privacy and new data types. Meanwhile, GIS is a collection of tools to visualize, process, and analyze geospatial data. Yet, using GIS can get intimidating since we often have to rely on foreign software solutions to achieve our geospatial data processing goals. The good news is that we can also use the statistical software R as a proper GIS nowadays. Over the last years, the range of supported file formats in R was expanded. Most importantly, it became so much easier to wrangle complex geospatial data. In this online workshop, participants learned about using R as a GIS and applying some first geospatial methods. The event focused on showing participants the most common data formats, import data, and process them for further analysis, and also creating maps and other features. Video can be viewed on the CESSDA Training YouTube Channel. Exercises are available on Stefan Juergen’s GitHub channel: https://github.com/StefanJuenger/CESSDA-R-GIS

Keywords

geo-spatial, data, statistics, data usage, geographic system, data discovery, online workshoop

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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