We announce a new version of the online data mining tool “Medline Trend” (http://dan.corlan.net/medline-trend.html) that extends the yearly trends with spatial (geographic) statistics. In a nutshell, the user introduces anormal query, such as the name of a disease, and obtains counts of PubMed entries (papers) for each countryand time interval. The country of origin is detected from the address (AD) field.In contrast with the date of publication, the presence and significance of at least one recognisable country namein the address field is more variable and the address field itself seems to only have been introduced in Medlineabout 20 years ago. For example, at the date of writing, in the 2008–2012 interval, only about 3.34 million entriesout of 4.052 million had a recognisable country name. For earlier years, the rate is even lower.In this paper, we propose a number of indices that are not directly influenced by the address field variability. Theyare based on the relative value of one spatial index to others, computed for the same time interval or region.The annualised rate of change of the number of papers for a keyowrd and region over a time interval is thecompund anual rate of change of the number of entries fulfilling the search criteria fitted to the actually observedcounts.The relative interest is the proportion of entries on a topic (such as ‘tuberculosis’) in a geographical region andover a specified time interval compared to all entries originating from that region during the same time interval.We found that, at least for tuberculosis, there is a strong and consistent log-log relationship between the relativeinterest and the prevalence of the disease in that area and period.The interest–size corelation index is the Spearman (ρ) correlation between the absolute output of a countryand the relative interest for a given keyword. It might grossly indicate whether a topic attracts more interest incountries with more rather than less developed scientific systems. It is for example 0.26 for ‘echocardiography’,-0.48 for ‘tuberculosis’, 0.72 for ‘stem cell’, 0.15 for ‘diabetes’, -0.06 for ‘malaria’.Spatiotemporal trends might sometimes provide insightful clues into the quantitative mechanisms that lead toadoption of a particular research thematic, but their application requires attension to numerous limitations andcaveats, in addition to the usual limitations of paper count statistics.