AniMoveMineR: Animal Behavior Exploratory Analysis Using Association Rules Mining
Environmental changes and major natural disasters are
most prevalent in the world due to the damage that humanity has
caused to nature and these damages directly affect the lives of
animals. Thus, the study of animal behavior and their interactions
with the environment can provide knowledge that guides researchers
and public agencies in preservation and conservation actions.
Exploratory analysis of animal movement can determine the patterns
of animal behavior and with technological advances the ability of
animals to be tracked and, consequently, behavioral studies have
been expanded. There is a lot of research on animal movement and
behavior, but we note that a proposal that combines resources and
allows for exploratory analysis of animal movement and provide
statistical measures on individual animal behavior and its interaction
with the environment is missing. The contribution of this paper is
to present the framework AniMoveMineR, a unified solution that
aggregates trajectory analysis and data mining techniques to explore
animal movement data and provide a first step in responding questions
about the animal individual behavior and their interactions with other
animals over time and space. We evaluated the framework through the
use of monitored jaguar data in the city of Miranda Pantanal, Brazil,
in order to verify if the use of AniMoveMineR allows to identify the
interaction level between these jaguars. The results were positive and
provided indications about the individual behavior of jaguars and
about which jaguars have the highest or lowest correlation.
[1] N. Phan, Mining Object Movement Patterns from Trajectory Data,
phdthesis, Universit Monpellier 2, France, 2013.
[2] A. Dekhtyar Lecture Notes on Data Science - DATA 301.,
California Polytechnic State University. 2016. Available from:
http://users.csc.calpoly.edu/ dekhtyar/DATA301-Spring2016/lectures/lec03.301.pdf.
[Accessed: 10-Feb-2020].
[3] M. Wikelski,R. Kays Movebank: archive, analysis and sharing
of animal movement data. Hosted by the Max Planck Institute
for Ornithology.Available from: www.movebank.org. Accessed in:
10/01/2016 (2016).
[4] S. Dodge et al., The environmental-data automated track annotation
(Env-DATA) system: linking animal tracks with environmental data,
Movement Ecology, vol. 1, p. 3, 2013, doi: 10.1186/2051-3933-1-3.
[5] H. Edelhoff, J. Signer, and N. Balkenhol, Path segmentation for
beginners: an overview of current methods for detecting changes in
animal movement patterns, Movement Ecology, vol. 4, no. 1, p. 21, Dec.
2016, doi: 10.1186/s40462-016-0086-5.
[6] T. Sippel, J. Holdsworth, T. Dennis, and J. Montgomery, Investigating
Behaviour and Population Dynamics of Striped Marlin (Kajikia audax)
from the Southwest Pacific Ocean with Satellite Tags, PLOS ONE, vol.
6, no. 6, p. e21087, Jun. 2011, doi: 10.1371/journal.pone.0021087.
[7] J. Zhao, C. Tian, F. Zhang, C. Xu, and S. Feng, Understanding
temporal and spatial travel patterns of individual passengers by mining
smart card data, in 2014 IEEE 17th International Conference on
Intelligent Transportation Systems (ITSC), 2014, pp. 29912997, doi:
10.1109/ITSC.2014.6958170.
[8] E. Gurarie, C. Bracis, M. Delgado, T. D. Meckley, I. Kojola, and C.
M. Wagner, What is the animal doing? Tools for exploring behavioural
structure in animal movements, J Anim Ecol, vol. 85, no. 1, pp. 6984,
Jan. 2016, doi: 10.1111/1365-2656.12379.
[9] M. Lavielle, Using Penalized Contrasts for the Change-point Problem,
Signal Process., vol. 85, no. 8, pp. 15011510, Aug. 2005, doi:
10.1016/j.sigpro.2005.01.012. [10] L. G. Torres, R. A. Orben, I. Tolkova, and D. R. Thompson,
Classification of Animal Movement Behavior through Residence in Space
and Time, PLOS ONE, vol. 12, no. 1, p. e0168513, Jan. 2017, doi:
10.1371/journal.pone.0168513.
[11] W. H. Burt, Territoriality and Home Range Concepts as Applied to
Mammals, J Mammal, vol. 24, no. 3, pp. 346352, Aug. 1943, doi:
10.2307/1374834.
[12] C. Calenge, The package adehabitat for the R software: A tool for the
analysis of space and habitat use by animals, Ecological Modelling, vol.
197, no. 3, pp. 516519, Aug. 2006, doi: 10.1016/j.ecolmodel.2006.03.017.
[13] D. D. Mari and S. Kotz, Correlation and Dependence. London: Imperial
College Press, 2001. ISBN: 978-1-86094-264-8.
[14] T. Cheng and J. Wang, Integrated Spatio-temporal Data Mining for
Forest Fire Prediction, Transactions in GIS, vol. 12, no. 5, pp. 591611,
2008, doi: 10.1111/j.1467-9671.2008.01117.x.
[15] G. M. Jacob and S. M. Idicula, Detection of flock movement in
spatio-temporal database using clustering techniques - An experience,
in 2012 International Conference on Data Science Engineering (ICDSE),
2012, pp. 6974, doi: 10.1109/ICDSE.2012.6282312.
[16] S. Brin, R. Motwani, J. D. Ullman, and S. Tsur, Dynamic
Itemset Counting and Implication Rules for Market Basket Data, in
Proceedings of the 1997 ACM SIGMOD International Conference on
Management of Data, New York, NY, USA, 1997, pp. 255264, doi:
10.1145/253260.253325.
[17] R. L. Plackett, Karl Pearson and the Chi-Squared Test, International
Statistical Review / Revue Internationale de Statistique, vol. 51, no. 1,
pp. 5972, 1983, doi: 10.2307/1402731.
[18] S. A. Alvarez, Chi-squared computation for association rules:
Preliminary results, 2003.
[19] C. Calenge and contributions from S. D. and M. Royer,adehabitatLT:
Analysis of Animal Movements. France, 2015.Available from:
https://cran.r-project.org/web/packages/adehabitatLT/index.html
[20] B. J. Worton, Kernel Methods for Estimating the Utilization Distribution
in Home-Range Studies, Ecology, vol. 70, no. 1, pp. 164168, 1989, doi:
10.2307/1938423.
[21] T. Vincenty, Direct and inverse solutions of geodesics on the ellipsoid
with application of nested equations., Survey Review, vol. 23, no. 176,
pp. 8893, Apr. 1975, doi: 10.1179/sre.1975.23.176.88.
[22] M. Hahsler, B. Gruen, K. Hornik,arules - A Computational Environment
for Mining Association Rules and Frequent Item Sets.Journal of Statistical
Software, doi: 10.18637/jss.v014.i15.
[23] A. C. Acock and G. R. Stavig, A Measure of Association for
Nonparametric Statistics, Social Forces, vol. 57, no. 4, pp. 13811386,
1979, doi: 10.2307/2577276.
[24] R. G. Morato et al., Jaguar movement database: a GPS-based movement
dataset of an apex predator in the Neotropics, Ecology, vol. 99, no. 7,
pp. 16911691, 2018, doi: 10.1002/ecy.2379.
[25] J. Manimaran and T. Velmurugan, Analysing the Quality of
Association Rules by Computing an Interestingness Measures, Indian
Journal of Science and Technology, vol. 8, no. 15, Jul. 2015, doi:
10.17485/ijst/2015/v8i15/76693.
[1] N. Phan, Mining Object Movement Patterns from Trajectory Data,
phdthesis, Universit Monpellier 2, France, 2013.
[2] A. Dekhtyar Lecture Notes on Data Science - DATA 301.,
California Polytechnic State University. 2016. Available from:
http://users.csc.calpoly.edu/ dekhtyar/DATA301-Spring2016/lectures/lec03.301.pdf.
[Accessed: 10-Feb-2020].
[3] M. Wikelski,R. Kays Movebank: archive, analysis and sharing
of animal movement data. Hosted by the Max Planck Institute
for Ornithology.Available from: www.movebank.org. Accessed in:
10/01/2016 (2016).
[4] S. Dodge et al., The environmental-data automated track annotation
(Env-DATA) system: linking animal tracks with environmental data,
Movement Ecology, vol. 1, p. 3, 2013, doi: 10.1186/2051-3933-1-3.
[5] H. Edelhoff, J. Signer, and N. Balkenhol, Path segmentation for
beginners: an overview of current methods for detecting changes in
animal movement patterns, Movement Ecology, vol. 4, no. 1, p. 21, Dec.
2016, doi: 10.1186/s40462-016-0086-5.
[6] T. Sippel, J. Holdsworth, T. Dennis, and J. Montgomery, Investigating
Behaviour and Population Dynamics of Striped Marlin (Kajikia audax)
from the Southwest Pacific Ocean with Satellite Tags, PLOS ONE, vol.
6, no. 6, p. e21087, Jun. 2011, doi: 10.1371/journal.pone.0021087.
[7] J. Zhao, C. Tian, F. Zhang, C. Xu, and S. Feng, Understanding
temporal and spatial travel patterns of individual passengers by mining
smart card data, in 2014 IEEE 17th International Conference on
Intelligent Transportation Systems (ITSC), 2014, pp. 29912997, doi:
10.1109/ITSC.2014.6958170.
[8] E. Gurarie, C. Bracis, M. Delgado, T. D. Meckley, I. Kojola, and C.
M. Wagner, What is the animal doing? Tools for exploring behavioural
structure in animal movements, J Anim Ecol, vol. 85, no. 1, pp. 6984,
Jan. 2016, doi: 10.1111/1365-2656.12379.
[9] M. Lavielle, Using Penalized Contrasts for the Change-point Problem,
Signal Process., vol. 85, no. 8, pp. 15011510, Aug. 2005, doi:
10.1016/j.sigpro.2005.01.012. [10] L. G. Torres, R. A. Orben, I. Tolkova, and D. R. Thompson,
Classification of Animal Movement Behavior through Residence in Space
and Time, PLOS ONE, vol. 12, no. 1, p. e0168513, Jan. 2017, doi:
10.1371/journal.pone.0168513.
[11] W. H. Burt, Territoriality and Home Range Concepts as Applied to
Mammals, J Mammal, vol. 24, no. 3, pp. 346352, Aug. 1943, doi:
10.2307/1374834.
[12] C. Calenge, The package adehabitat for the R software: A tool for the
analysis of space and habitat use by animals, Ecological Modelling, vol.
197, no. 3, pp. 516519, Aug. 2006, doi: 10.1016/j.ecolmodel.2006.03.017.
[13] D. D. Mari and S. Kotz, Correlation and Dependence. London: Imperial
College Press, 2001. ISBN: 978-1-86094-264-8.
[14] T. Cheng and J. Wang, Integrated Spatio-temporal Data Mining for
Forest Fire Prediction, Transactions in GIS, vol. 12, no. 5, pp. 591611,
2008, doi: 10.1111/j.1467-9671.2008.01117.x.
[15] G. M. Jacob and S. M. Idicula, Detection of flock movement in
spatio-temporal database using clustering techniques - An experience,
in 2012 International Conference on Data Science Engineering (ICDSE),
2012, pp. 6974, doi: 10.1109/ICDSE.2012.6282312.
[16] S. Brin, R. Motwani, J. D. Ullman, and S. Tsur, Dynamic
Itemset Counting and Implication Rules for Market Basket Data, in
Proceedings of the 1997 ACM SIGMOD International Conference on
Management of Data, New York, NY, USA, 1997, pp. 255264, doi:
10.1145/253260.253325.
[17] R. L. Plackett, Karl Pearson and the Chi-Squared Test, International
Statistical Review / Revue Internationale de Statistique, vol. 51, no. 1,
pp. 5972, 1983, doi: 10.2307/1402731.
[18] S. A. Alvarez, Chi-squared computation for association rules:
Preliminary results, 2003.
[19] C. Calenge and contributions from S. D. and M. Royer,adehabitatLT:
Analysis of Animal Movements. France, 2015.Available from:
https://cran.r-project.org/web/packages/adehabitatLT/index.html
[20] B. J. Worton, Kernel Methods for Estimating the Utilization Distribution
in Home-Range Studies, Ecology, vol. 70, no. 1, pp. 164168, 1989, doi:
10.2307/1938423.
[21] T. Vincenty, Direct and inverse solutions of geodesics on the ellipsoid
with application of nested equations., Survey Review, vol. 23, no. 176,
pp. 8893, Apr. 1975, doi: 10.1179/sre.1975.23.176.88.
[22] M. Hahsler, B. Gruen, K. Hornik,arules - A Computational Environment
for Mining Association Rules and Frequent Item Sets.Journal of Statistical
Software, doi: 10.18637/jss.v014.i15.
[23] A. C. Acock and G. R. Stavig, A Measure of Association for
Nonparametric Statistics, Social Forces, vol. 57, no. 4, pp. 13811386,
1979, doi: 10.2307/2577276.
[24] R. G. Morato et al., Jaguar movement database: a GPS-based movement
dataset of an apex predator in the Neotropics, Ecology, vol. 99, no. 7,
pp. 16911691, 2018, doi: 10.1002/ecy.2379.
[25] J. Manimaran and T. Velmurugan, Analysing the Quality of
Association Rules by Computing an Interestingness Measures, Indian
Journal of Science and Technology, vol. 8, no. 15, Jul. 2015, doi:
10.17485/ijst/2015/v8i15/76693.
@article{"International Journal of Information, Control and Computer Sciences:79865", author = "Suelane Garcia Fontes and Silvio Luiz Stanzani and Pedro L. Pizzigatti Corrła Ronaldo G. Morato", title = "AniMoveMineR: Animal Behavior Exploratory Analysis Using Association Rules Mining", abstract = "Environmental changes and major natural disasters are
most prevalent in the world due to the damage that humanity has
caused to nature and these damages directly affect the lives of
animals. Thus, the study of animal behavior and their interactions
with the environment can provide knowledge that guides researchers
and public agencies in preservation and conservation actions.
Exploratory analysis of animal movement can determine the patterns
of animal behavior and with technological advances the ability of
animals to be tracked and, consequently, behavioral studies have
been expanded. There is a lot of research on animal movement and
behavior, but we note that a proposal that combines resources and
allows for exploratory analysis of animal movement and provide
statistical measures on individual animal behavior and its interaction
with the environment is missing. The contribution of this paper is
to present the framework AniMoveMineR, a unified solution that
aggregates trajectory analysis and data mining techniques to explore
animal movement data and provide a first step in responding questions
about the animal individual behavior and their interactions with other
animals over time and space. We evaluated the framework through the
use of monitored jaguar data in the city of Miranda Pantanal, Brazil,
in order to verify if the use of AniMoveMineR allows to identify the
interaction level between these jaguars. The results were positive and
provided indications about the individual behavior of jaguars and
about which jaguars have the highest or lowest correlation.", keywords = "Data mining, data science, trajectory, animal behavior.", volume = "14", number = "9", pages = "316-8", }
