STING: A Statistical Information Grid Approach to Spatial Data Mining
Impact in
Classified as
- Journal
- Very Large Data Bases
In The Last Decade
doi.org/w487780 →Countries where authors are citing STING: A Statistical Information Grid Approach to Spatial Data Mining
This map shows the geographic impact of STING: A Statistical Information Grid Approach to Spatial Data Mining. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by STING: A Statistical Information Grid Approach to Spatial Data Mining with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites STING: A Statistical Information Grid Approach to Spatial Data Mining more than expected).
Fields of papers citing STING: A Statistical Information Grid Approach to Spatial Data Mining
This network shows the impact of STING: A Statistical Information Grid Approach to Spatial Data Mining. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the STING: A Statistical Information Grid Approach to Spatial Data Mining.
About STING: A Statistical Information Grid Approach to Spatial Data Mining
This paper, published in 1997, received 802 indexed citations . Written by Wei Wang, Jiong Yang and Richard R. Muntz covering the research area of Signal Processing, Artificial Intelligence and Information Systems. It is primarily cited by scholars working on Artificial Intelligence (561 citations), Signal Processing (415 citations), Information Systems (240 citations), Computer Vision and Pattern Recognition (157 citations) and Statistical and Nonlinear Physics (102 citations). Published in Very Large Data Bases.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/w487780.