Centre de documentation
scientifique

Mapping areas of asynchronous‐temporal interaction in animal‐telemetry data / Brendan A. Hoover in Transactions in GIS, Vol 24 n° 3 (June 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 3 (June 2020) . - pp 573 - 586 Titre : Mapping areas of asynchronous‐temporal interaction in animal‐telemetry data Type de document : Article/Communication Auteurs : Brendan A. Hoover, Auteur ; Jennifer A. Miller, Auteur ; Jed A. Long, Auteur Année de publication : 2020 Article en page(s) : pp 573 - 586 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] comportement [Termes IGN] écologie [Termes IGN] habitat animal [Termes IGN] interaction spatiale [Termes IGN] maladie animale [Termes IGN] migration animale [Termes IGN] population animale [Termes IGN] Time-geography Résumé : (Auteur) Animal interactions are a crucial aspect of behavioral ecology that affect mating, territorial behavior, resource use, and disease spread. Commonly, animals will interact because of shared resources. Recent methods have used time geography to map landscape areas where interactions were possible. However, such methods do not identify areas of less direct interaction, like through smell or sight. These indirect or asynchronous interactions are also a crucial aspect of animal behavioral ecology and affect group behaviors such as leading/following hierarchies and joint resource use. Asynchronous interactions are difficult to map because they can occur in a synchronous space at asynchronous times, as well as in asynchronous spaces at a synchronous time. Here, we present a method termed the temporally asynchronous‐joint potential path area (ta‐jPPA) that maps areas of potential temporally asynchronous–spatially synchronous interactions. We used simulated data to statistically test ta‐jPPA and empirical data to demonstrate how ta‐jPPA can find patterns in habitat use. Numéro de notice : A2020-246 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12622 Date de publication en ligne : 05/05/2020 En ligne : https://doi.org/10.1111/tgis.12622 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95308 [article]

Kinematic interpolation of movement data / Jed A. Long in International journal of geographical information science IJGIS, vol 30 n° 5-6 (May - June 2016)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 30 n° 5-6 (May - June 2016) . - pp 854 - 868 Titre : Kinematic interpolation of movement data Type de document : Article/Communication Auteurs : Jed A. Long, Auteur Année de publication : 2016 Article en page(s) : pp 854 - 868 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] interpolation [Termes IGN] modèle conceptuel de données spatio-temporelles [Termes IGN] objet mobile [Termes IGN] positionnement cinématique [Termes IGN] requête spatiotemporelle Résumé : (Auteur) Mobile tracking technologies are facilitating the collection of increasingly large and detailed data sets on object movement. Movement data are collected by recording an object’s location at discrete time intervals. Often, of interest is to estimate the unknown position of the object at unrecorded time points to increase the temporal resolution of the data, to correct erroneous or missing data points, or to match the recorded times between multiple data sets. Estimating an object’s unknown location between known locations is termed path interpolation. This paper introduces a new method for path interpolation termed kinematic interpolation. Kinematic interpolation incorporates object kinematics (i.e. velocity and acceleration) into the interpolation process. Six empirical data sets (two types of correlated random walks, caribou, cyclist, hurricane and athlete tracking data) are used to compare kinematic interpolation to other interpolation algorithms. Results showed kinematic interpolation to be a suitable interpolation method with fast-moving objects (e.g. the cyclist, hurricane and athlete tracking data), while other algorithms performed best with the correlated random walk and caribou data. Several issues associated with path interpolation tasks are discussed along with potential applications where kinematic interpolation can be useful. Finally, code for performing path interpolation is provided (for each method compared within) using the statistical software R. Numéro de notice : A2016-286 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1081909 En ligne : https://doi.org/10.1080/13658816.2015.1081909 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80865 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2016032	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2016031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Measuring dynamic interaction in movement data / Jed A. Long in Transactions in GIS, vol 17 n° 1 (February 2013)  

Public [article]  inTransactions in GIS > vol 17 n° 1 (February 2013) . - pp 62 - 77 Titre : Measuring dynamic interaction in movement data Type de document : Article/Communication Auteurs : Jed A. Long, Auteur ; T. Nelson, Auteur Année de publication : 2013 Article en page(s) : pp 62 - 77 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] données localisées dynamiques [Termes IGN] interaction spatiale [Termes IGN] objet mobile Résumé : (Auteur) The emergence of technologies capable of storing detailed records of object locations has presented scientists and researchers with a wealth of data on object movement. Yet analytical methods for investigating more advanced research questions from such detailed movement datasets remain limited in scope and sophistication. Recent advances in the study of movement data has focused on characterizing types of dynamic interactions, such as single-file motion, while little progress has been made on quantifying the degree of such interactions. In this article, we introduce a new method for measuring dynamic interactions (termed DI) between pairs of moving objects. Simulated movement datasets are used to compare DI with an existing correlation statistic. Two applied examples, team sports and wildlife, are used to further demonstrate the value of the DI approach. The DI method is advantageous in that it measures interaction in both movement direction (termed azimuth) and displacement. Also, the DI approach can be applied at local, interval, episodal, and global levels of analysis. However the DI method is limited to situations where movements of two objects are recorded at simultaneous points in time. In conclusion, DI quantifies the level of dynamic interaction between two moving objects, allowing for more thorough investigation of processes affecting interactive moving objects. Numéro de notice : A2013-040 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/j.1467-9671.2012.01353.x Date de publication en ligne : 09/10/2012 En ligne : https://doi.org/10.1111/j.1467-9671.2012.01353.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32178 [article]