Which Settings to Use

The ideal settings to use depends on the project. Here are some suggestions for some common scenarios:

  1. Global events datasets
  2. Cross-cultural surveys
  3. The case study
  4. Country statistics at different points in time

1: Global Events Datasets
For visualizing and analyzing the spatial distribution of global events data it might be most natural to use city-mode to get the most exact positions as possible. If you want as many matches as possible you may want to use the default “GNS” for the “coordinates provider” option because GNS appears to have the most extensive coverage and get higher match-rates. However, GNS is so extensive that it also has many local and peripheral placenames with identical placenames, in which case an event that occurred in a very large and famous city like New York, USA may be placed in a small-town with the same name halfway across the country, simply because the software had to choose arbitrarily between the identically named locations. If your data has fewer cases or tends to occur in larger population centers, it might be more important that each case is placed correctly and so you may therefore want to instead go with the “GeoNames” coordinates provider to only include the larger and more relevant locations.

2: Cross-Cultural Surveys
Although most multi-national surveys by organizations like Gallup, World Values Survey, or Pew are most often used to compare national-level opinions, it is quite common in such datasets to include a variable for the geographic area within each country that the respondent was given the survey. Unfortunately, the way this “region” variable is recorded varies from country to country, some only using vague descriptions such as “northern Italy” or “north-eastern Russia”. Others contain specific province-names that can be georeferenced but are often widely different in size and at different levels of the administrative chain. If you are only interested in displaying a continual surface of response values and the differences in province levels between countries does not matter to your project, then the “Any” option for “Administrative Level” will result in the highest match-rates and require no processing after georeferencing, though you should still watch out for multiple levels being present in the same country due to nested duplicate names (e.g. the higher level “New York” state would hide the lower level “New York” district and its neighbors and result in overlapping province shapes). If you think the level of the administrative chain may matter and you want to standardize to the same level across the different countries, then you can force lower-level name matches to be scaled upwards to a level of your choice. The backdraw here is that the resulting shapefile will very likely contain multiple shapes of the same upper-level province (one for each of its constituent sub-districts) and it therefore becomes important that after georeferencing you use a GIS to dissolve the shapefile so that only one shape and aggregated value is given for each province and its attribute fields. It also may lead to a lower match-rate because provinces higher than the level you specified will not be included in the output.

3: The Case Study
If you are engaged in a case study that looks in-depth at only one or more countries there are several settings you can use to highlight different aspects of your country case study. Use city-mode to highlight important events or locations of socio-economic-political importance, to explore the spatial distribution of the opinions of interview respondent opinions, or to quantitatively model the spatial distribution of events. Use province-mode to visualize contextual factors and statistics that are often available from national statistical reports, or to use such contextual factors to explain or predict city-events in a statistical model. Using province mode, just remember to set the “administrative unit” option to the correct level at which your contextual data was recorded.

4: Country Statistics at Different Points in Time
There are many datasets that record changes in how countries score on different variables over a range of years. It is not always straight forward how to visualize such changes because most country shapefiles usually represent only how country-borders look today. By clicking the “clock” icon next to the “Country Field” input in Easy Georeferencer’s country-mode, it becomes possible to visualize country-statistics for any point in time while accounting for the relevant name- and boundary-changes. By choosing the “Varying” option and selecting the field that contains the year value of your historical dataset you can create an equivalent historical shapefile. Different maps at different points in history can then be correctly represented by using simple select queries in a GIS.


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