135Accesses
Abstract
This paper proposes a quantitative approach to specify potentially hazardous asteroids using clustering tools to group a set of near-Earth asteroids (NEAs). The data pool adopted in the study contains a number of distinct indices characterizing\(\sim 25,000\) NEAs. The hierarchical clustering (HC) and multidimensional scaling (MDS) algorithmic techniques are adopted for generating two- and three-dimensional graphical representations reflecting the main features of the NEAs. These techniques provide useful computational visualization tools for extracting information embedded in data sets having multidimensional nature. The structure of the loci, given by the emerging clusters and patterns, leads to a deeper understanding of the problem. The HC and MDS rely on the selection of adequate metrics for comparing the objects in the data set. Therefore, a pool of prototype distances are tested and a number of numerical experiments reveal that the Clark distance characterizes more assertively the NEA data set. The overlapped clusters and the pattern of a curved polyhedron that emerge in the MDS charts reveal that some PHAs may be overlooked with standard classifications of NEAs based merely on some scalar index, such as the case of the perihelion distance\(q<1.3\) AU. Furthermore, it is observed that the MDS is superior in performance to the HC since it takes advantage of three-dimensional representations. In fact, three-dimensional plots require iterative operations of rotation, shifting and magnification for achieving an efficient visualization, but such procedures are straightforward with present day computational resources. This strategy allows the adoption of advanced scientific data processing and visualization techniques that lead to ‘maps’ close to our understanding of the NEA impact risk.
This is a preview of subscription content,log in via an institution to check access.
Access this article
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
Price includes VAT (Japan)
Instant access to the full article PDF.














Similar content being viewed by others
References
Binzel RP, Lupishko DF, Di Martino M, Whitheley RJ, Hahn GJ. Physical properties of near-earth objects. In: Asteroids III. Arizona: University of Arizona Press; 2002. p. 205–18.
Masiero JR, et al. Asteroid diameters and albedos from NEOWISE reactivation mission years four and five. AAS Planet Sci J. 2020;1:10.
Granvik M, et al. Super-catastrophic disruption of asteroids at small perihilion distances. Nature. 2016;530:303–6.
Bottke WF, et al. Debiased orbital and absolute magnitude distribution of the near-earth objects. Icarus. 2002;156(2):399–433.
Granvik M, et al. Debiased orbit and absolute-magnitude distributions for near-Earth objects. Icarus. 2018;312:181–207.
de Elía GC, Brunini A. Collisional and dynamical evolution of the main belt and NEA population. Astron Astrophys. 2007;466:1159–77.
Cibulková H, Brož M, Benavidez PG. A six-part collisional model of the main asteroid belt. Icarus. 2014;241:358–72.
Zain PS, de Elía GC, Di Sisto RP. New multi-part collisional model of the main belt: the contribution to near-Earth asteroids. Astron Astrophys. 2020;639:A9.
Jedicke R, Bolin B, Granvik M, Beshore E. A fast method for quantifying observational selection effects in asteroid surveys. Icarus. 2016;266:173–88.
Larson S, et al. The Catalina Sky survey for NEOs. Bull Am Astron Soc. 1998;30:1037.
Jedicke R, Metcalfe TS. The orbital and absolute magnitude distributions of main belt asteroids. Icarus. 1998;131:245–60.
Drummond JD. The D discriminant and near-earth asteroid streams. Icarus. 2000;146(2):453–75.
Fu H, Jedicke R, Durda DD, Fevig R, Scotti JV. Identifying near earth object families. Icarus. 2005;178(2):434–49.
Schunová E, et al. Searching for the first near-Earth object family. Icarus. 2012;220:1050–63.
Jopek TJ. The near earth asteroid associations. Proc Int Astron Union. 2012;10(H16):474–5.
de la Fuente Marcos C, de la Fuente Marcos R. Far from random: dynamical groupings among the NEO population. Month Not Roy Astron Soc. 2016;456(3):2946–56.
Zappalà V, Cellino A, Farinella P, Knezevic Z. Asteroid families. I-Identification by hierarchical clustering and reliability assessment. Astron J. 1990;100(6):2030–46.
Hartigan JA. Clustering algorithms. New York: John Wiley & Sons; 1975.
Cha S. Taxonomy of nominal type histogram distance measures. In: Proceedings of the American Conference on Applied Mathematics. Harvard, MA, USA; 2008. p. 325–30.
Baggaley WJ, Galligan DP. Cluster analysis of the meteoroid orbit population. Planet Space Sci. 1997;45:865.
Galligan DP. A direct search for significant meteoroid stream presence within an orbital data set. Mon Not R Astron Soc. 2003;340:893.
Zappalà V, Bendjoya P, Cellino A, Farinella P, Froeschle C. Asteroid families: search of a 12487-asteroid sample using two different clustering techniques. Icarus. 1995;116:291.
Bendjoya P, Zappalà V. Asteroids III. Tucson: University of Arizona Press; 2002. p. 613.
Carruba V, et al. A multi-domain approach to asteroid families identification. Mon Not R Astron Soc. 2013;433:2075–96.
Masiero JR, et al. Asteroid family identification using the hierarchical clustering method and WISE/NEOWISE physical properties. Astrophys J. 2013;770:7.
Jopek TJ. The orbital clusters among the near-Earth asteroids. Mon Not R Astron Soc. 2020;494(1):680–93.
Davison ML. Multidimensional scaling. New York: Wiley; 1983. p. 85.
Cox TF, Cox MA. Multidimensional scaling. Boca Raton: CRC Press; 2000.
Borg I, Groenen PJ. Modern multidimensional scaling: theory and applications. NewYork: Springer-Verlag; 2005.
Saeed N, Nam H, Haq MIU, Saqib DBM. A survey on multidimensional scaling. ACM Comput Surv. 2018;51(3):47.
Banda JM, Anrgyk R. Usage of dissimilarity measures and multidimensional scaling for large scale solar data analysis. In Proceedings of the 2010 conference on Intelligent Data Understanding. December 1-3, 2010.
Tenreiro Machado J, Hamid MS. Multidimensional scaling analysis of the solar system objects. Commun Nonlinear Sci Numer Simul. 2019;79:104923.
Hamid Mehdipour S, Tenreiro Machado J. Cluster analysis of the large natural satellites in the solar system. Appl Math Model. 2021;89(2):1268–78.
Jiang I-G, Yeh L-C, Hung W-L, Yang M-S. Data analysis on the extra-solar planets using robust clustering. Mon Not R Astron Soc. 2006;370:1379.
Cil I. Consumption universes based supermarket layout through association rule mining and multidimensional scaling. Expert Syst Appl. 2012;39(10):8611–25.
Corten R. Visualization of social networks in Stata using multidimensional scaling. Stata J. 2011;11(1):52.
Machado JT, Lopes AM. Multidimensional scaling analysis of soccer dynamics. Appl Math Model. 2017;45:642–52.
Lopes AM, Tenreiro Machado JA, Pinto CM, Galhano AM. Fractional dynamics and MDS visualization of earthquake phenomena. Comput Math Appl. 2013;66(5):647–58.
Tenreiro Machado JA, Galhano A, Cao Labora D. A clustering perspective of the Collatz conjecture. Mathematics. 2021;9(4):314.
Tenreiro Machado J, Luchko Y. Multidimensional scaling and visualization of patterns in distribution of nontrivial zeros of the zeta-function. Commun Nonlinear Sci Numer Simul. 2021;102:105924.
Tzagarakis C, Jerde TA, Lewis SM, Uǧurbil K, Georgopoulos AP. Cerebral cortical mechanisms of copying geometrical shapes: a multidimensional scaling analysis of fMRI patterns of activation. Exp Brain Res. 2009;4(3):369–80.
Lopes AM, Andrade JP, Tenreiro Machado JA. Multidimensional scaling analysis of virus diseases. Comp Methods Programs Biomed. 2016;131:97–110.
Tenreiro Machado JA, Lopes AM. The persistence of memory. Nonlinear Dyn. 2014;79(1):63–82.
Tenreiro Machado J, Lopes AM. A computational perspective of the periodic table of elements. Commun Nonlinear Sci Numer Simul. 2019;78:104883.
Lopes AM, Tenreiro Machado JA. Fractional-order sensing and control: embedding the nonlinear dynamics of robot manipulators into the multidimensional scaling method. Sensors. 2021;21(22):7736.
Deza MM, Deza E. Encyclopedia of Distances. Berlin, Heidelberg: Springer-Verlag; 2009.
Cha S-H. Measures between probability density functions. Int J Math Models Methods Appl Sci. 2007;1(4):300–7.
Tenreiro Machado JA, Rocha-Neves JM, Andrade JP. Computational analysis of the SARS-CoV-2 and other viruses based on the Kolmogorov’s complexity and Shannon’s information theories. Nonlinear Dyn. 2020;101(3):1731–50.
Borg I, Groenen PJ. Modeling asymmetric data. New York: Springer-Verlag; 2005. p. 495–518.
Lopes AM, Tenreiro Machado JA. Entropy analysis of industrial accident data series. ASME J Comput Nonlinear Dyn. 2016;11(3):0310061–7.
Lopes AM, Tenreiro Machado JA. Multidimensional scaling analysis of generalized mean discrete-time fractional order controllers. Commun Nonlinear Sci Numer Simul. 2021;95:105657.
Lopes AM, Tenreiro Machado JA. Modeling and visualizing competitiveness in soccer leagues. Appl Math Model. 2021;92:136–48.
Lopes AM, Tenreiro Machado JA. Multidimensional scaling and visualization of patterns in global large-scale accidents. Chaos Solitons Fract. 2022;157:111951.
Author information
Authors and Affiliations
Department of Electrical Engineering, Institute of Engineering of Polytechnic of Porto, Porto, Portugal
J. A. Tenreiro Machado
Department of Physics, College of Basic Sciences, Lahijan Branch, Islamic Azad University, P. O. Box 1616, Lahijan, Iran
S. Hamid Mehdipour
- J. A. Tenreiro Machado
You can also search for this author inPubMed Google Scholar
- S. Hamid Mehdipour
You can also search for this author inPubMed Google Scholar
Corresponding author
Correspondence toS. Hamid Mehdipour.
Ethics declarations
Conflict of interest
The authors have no financial or proprietary interests in any material discussed in this article.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Machado, J.A.T., Mehdipour, S.H. Multidimensional Analysis of Near-Earth Asteroids.SN COMPUT. SCI.3, 207 (2022). https://doi.org/10.1007/s42979-022-01103-2
Received:
Accepted:
Published:
Share this article
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative