A Beginner's Guide to Geographic Virtual Communities Research Figure 4. A choropleth map showing self-focus in the Russian Wikipedia through "indegree sums", which indicate how many articles in a Wikipedia link to articles about places in a geographic region. We were careful to use a proper data classification strategy in this cartographic product Shapefile or Google's KML file format (from stakeholder websites 22 or via a web search). Once we executed the aggregation, we were able to perform both statistical and visual analyses of the results. We will leave the rather detailed statistical analyses to readers who download the paper, but the visual reporting both elucidates the power of geographical analyses and presents an opportunity to briefly touch upon appropriate cartographic techniques for reporting these types of results. Figure 4 shows the rather extreme nature of our results: Russia is the destination of the most links in the Russian Wikipedia (by far). This was repeated across nearly all 15 languages. In order to truthfully convey the results of our study in map form (Figure 4 appeared in our paper), we made absolutely sure that our data classification strategy accurately represented our findings. A cartographic novice or an expert manipulator could easily exploit the map's legend to naively or unscrupulously alter the reader's impression of the data, especially given the lesser-known units of "inlinks". It is also possible through naïveté to produce maps that are simply very difficult for the reader to interpret. Before producing a choropleth (i.e. colored-polygon) map, it is important that the researcher be familiar with the standard methods of data classification (e.g. quantile, natural breaks, etc.). Many websites 23 provide good tutorials on this topic. However, consulting a GIS or cartogra- phy textbook, (e.g. (Slocum, McMaster, Kessler, & Howard, 2009) or reading the entertaining "How to Lie With Maps" (Monmonier, 1996) is of course a more complete solution. Hopefully, through this case study the reader has gained a greater understanding of how geog- raphy can enable exciting virtual communities research. Readers should also be able to repeat many of the steps above in their own work. NEXT STEPS: WHERE TO GO FROM HERE In this chapter, we have covered what we believe to be the minimal information required to begin examining virtual communities with a geographic lens. However, this chapter is by no means a replacement for a solid GIS course series. The 344