| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 444 | 86 | 78 | 13 |
The main research concerns discussed in the journal are Data mining, Artificial intelligence, Algorithm, Global Positioning System and Theoretical computer science. Topics in Data mining explored in the journal were investigated in conjunction with research in Spatial analysis, Search engine indexing and Noise (video). Spatial analysis and Grid are closely related fields of research discussed in it.
Artificial intelligence research featured in it incorporates concerns from various other topics such as Machine learning, Computer vision and Pattern recognition. The work tackled in ACM Transactions on Spatial Algorithms and Systems goes beyond the discipline of Algorithm as it also encompasses Boundary (topology). The Theoretical computer science study featured in the journal draws parallels with the field of Scalability.
The journal articles facilitate discussions on Algorithm, Artificial intelligence, Popularity, Data mining and Grid. While Algorithm is the focus of the journal papers, it also provides insights into the studies of Orientation (computer vision), Topology (electrical circuits) and Simple polygon. The works on Artificial intelligence tackled in the published articles bring together disciplines like Machine learning and Computer vision.
The journal mainly deals with areas of study such as Spatial analysis, Social media, Cluster analysis, Artificial intelligence and Theoretical computer science. Big spatial data studies in the realm of Spatial analysis interact with fields like Process (computing). The journal tackles studies in Geographic information system and the interrelated subject of Data mining to gain insights into Social media.
While Cluster analysis is the focus of the journal, it also provided insights into the studies of Divide and conquer algorithms, Topology (electrical circuits), Vehicle routing problem, Mathematical optimization and Fréchet distance. Machine learning, Cramér–Rao bound and Pattern recognition are some topics wherein Artificial intelligence research discussed in the journal have an impact. The study of Theoretical computer science encompasses disciplines such as Scalability, as well as fields such as Range (mathematics), k-nearest neighbors algorithm and Structure (category theory), all of which overlap with one another.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in ACM Transactions on Spatial Algorithms and Systems (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in ACM Transactions on Spatial Algorithms and Systems (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 0.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 14.29% were posted by at least one author from the top 10 institutions publishing in the journal. Another 14.29% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 42.86% of all publications and 28.57% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
The study and research of spatial algorithms and systems open doors to various career opportunities, helping individuals apply the knowledge acquired in practical scenarios. Many research students who indulge in this field end up working in multi-disciplinary areas, such as spatial analysis, artificial intelligence, data mining, etc. One of the popular career paths includes becoming a History teacher, as the study of these systems prove useful in understanding geographical and historical data much better.
If you are interested in pursuing a teaching career in this field, you may find help in this guide on how to be a history teacher in Wyoming. This guide provides in-depth information on the prerequisites and steps to building your career in teaching history at various educational levels, incorporating knowledge of spatial systems to give your students a unique learning experience. A career in the education sector not only allows you to share your knowledge but also encourages continuous learning and research in your field of interest.
Whether you intend to work in research or step into the education industry, the field of spatial algorithms can offer many opportunities for growth. All you need is a passion for learning and a committed mind to explore new avenues.
Xiaohui Huang;Pan He;Anand Rangarajan;Sanjay Ranka
(2020)Senzhang Wang;Jiannong Cao;Hao Chen;Hao Peng
(2020)Panagiotis Tampakis;Christos Doulkeridis;Nikos Pelekis;Yannis Theodoridis
(2020)Fan Zhou;Hantao Wu;Goce Trajcevski;Ashfaq Khokhar
(2020)Lijing Wang;Jiangzhuo Chen;Madhav Marathe
(2020)Erin Chambers;Brittany Terese Fasy;Yusu Wang;Carola Wenk
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