Hot and cold spots in the US research: A spatial analysis of bibliometric data on the institutional level is a research paper published in Journal of Information Science (2018). On theSindex it has a DataRank of 0.449. It has been cited 19 times.
Spatial bibliometrics addresses the spatial aspects of scientific research activities. In this case study, we use the Getis–Ord G∗ i ( d) statistic for bibliometric data on US institutions to identify hot spots of institutions on a map publishing many high-impact papers. The study is based on a dataset with performance data (proportion and number of papers belonging to the 10% most frequently cited papers) and geo-coordinates for all institutions in the United States from the SCImago group (and Scopus). The Getis-Ord Gi* statistic returns, for each institution on a map, a z score. Higher z scores point to intense clustering of institutions, which have published a large proportion or number of highly cited papers (hot spots). The US maps, which we generate as examples in this study, point to four regions. These regions can be labelled as hot spots: around San Francisco, Los Angeles, Boston and Washington, DC. The empirical focus on institutional hot spots in a country using bibliometric data is of specific importance for science policy, because geospatial proximity is shown as an important factor for innovation processes.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.449
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.