FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2624056145> ?p ?o ?g. }

• W2624056145 abstract "GIScience 2016 Short Paper Proceedings An Algorithm for Empirically Informed Random Trajectory Generation Between Two Endpoints G. Technitis 1 , R. Weibel 1 , B. Kranstauber 2,3 , K. Safi 2,3 ! Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland Email: georgios.technitis@geo.uzh.ch ! Department of Migration and Immuno-ecology, Max Planck Institute for Ornithology, Am Obstberg 1, 78315 Radolfzell, Germany ! Department of Biology, University of Konstanz, Konstanz, Germany Abstract We present a method for enabling the researcher to create empirically informed, and thus realistic, random trajectories between two endpoints. The method used relies on empirical distribution functions, which define a dynamic drift expressed in a stepwise joint probability surface. We create random discrete time-step trajectories that connect spatiotemporal points while maintaining a predefined geometry, often based on real observed trajectory. The resulting trajectories can be used a)to generate null models for hypotheses testing, b)as a basis for resource selection models, through the integration of spatial context and c)to quantify space use intensity. 1.! Introduction Random trajectories have been increasingly used in movement ecology since their introduction in the early 1980s (Kareiva and Shigesada 1983), gaining significant popularity in the last two decades (Turchin 1998). A wide range of case studies have used the concept, addressing multiple questions related to movement and space use. The majority of the examples found in the literature, however, share one characteristic: the movement has only one restrictive point, the start. Consequently, the simulation is forced to start at a specific location, but can then move according to the set conditions in the given space. In the real world however, this is not always useful: when studying migration patterns (Codling et al. 2010), nest borrowing (Waldeck et al. 2008), or fusion of high and low frequency GPS points, etc. the ability to specify the ending point is crucial. Technitis et al. (2015) introduced RTG, an algorithm that enables the user to create randomly varying, possible trajectories between endpoints, based on principles of Time Geography. In this paper we substantially extend this algorithm. We present a methodology to connect two endpoints by generating empirically informed random trajectories, respecting characteristics of the moving object. Our approach is based on core theoretical concepts of Time Geography in combination with the Random Walk movement model, and most importantly, we use empirical data to inform our modelling process. 2.! Background Space-time prisms (STP) assist us in calculating the points accessible in space, given the time budget and the maximum speed of an agent (Kuijpers, et al. 2010). The calculated path space (in three dimensions defined by x,y and t), and more specifically its 2-D spatial projection, also known as potential path area (PPA), is a homogenous area within which the trajectory lies. The concept of the STP is very intuitive, although it accounts only for the maximum speed of the mover, gives no information regarding the preference of the mover within the given boundaries, and the result is an area, not an individual trajectory." @default.
• W2624056145 created "2017-06-15" @default.
• W2624056145 creator A5001388025 @default.
• W2624056145 creator A5004265743 @default.
• W2624056145 creator A5009195263 @default.
• W2624056145 creator A5035225429 @default.
• W2624056145 date "2016-01-01" @default.
• W2624056145 modified "2023-09-27" @default.
• W2624056145 title "An Algorithm for Empirically Informed Random Trajectory Generation Between Two Endpoints" @default.
• W2624056145 doi "https://doi.org/10.21433/b31194g0c634" @default.
• W2624056145 hasPublicationYear "2016" @default.
• W2624056145 type Work @default.
• W2624056145 sameAs 2624056145 @default.
• W2624056145 citedByCount "4" @default.
• W2624056145 countsByYear W26240561452018 @default.
• W2624056145 countsByYear W26240561452019 @default.
• W2624056145 countsByYear W26240561452021 @default.
• W2624056145 crossrefType "journal-article" @default.
• W2624056145 hasAuthorship W2624056145A5001388025 @default.
• W2624056145 hasAuthorship W2624056145A5004265743 @default.
• W2624056145 hasAuthorship W2624056145A5009195263 @default.
• W2624056145 hasAuthorship W2624056145A5035225429 @default.
• W2624056145 hasBestOaLocation W26240561451 @default.
• W2624056145 hasConcept C11413529 @default.
• W2624056145 hasConcept C121332964 @default.
• W2624056145 hasConcept C1276947 @default.
• W2624056145 hasConcept C13662910 @default.
• W2624056145 hasConcept C149782125 @default.
• W2624056145 hasConcept C15744967 @default.
• W2624056145 hasConcept C166957645 @default.
• W2624056145 hasConcept C205649164 @default.
• W2624056145 hasConcept C2524010 @default.
• W2624056145 hasConcept C2779343474 @default.
• W2624056145 hasConcept C2780586970 @default.
• W2624056145 hasConcept C28719098 @default.
• W2624056145 hasConcept C33923547 @default.
• W2624056145 hasConcept C41008148 @default.
• W2624056145 hasConcept C42475967 @default.
• W2624056145 hasConcept C77805123 @default.
• W2624056145 hasConceptScore W2624056145C11413529 @default.
• W2624056145 hasConceptScore W2624056145C121332964 @default.
• W2624056145 hasConceptScore W2624056145C1276947 @default.
• W2624056145 hasConceptScore W2624056145C13662910 @default.
• W2624056145 hasConceptScore W2624056145C149782125 @default.
• W2624056145 hasConceptScore W2624056145C15744967 @default.
• W2624056145 hasConceptScore W2624056145C166957645 @default.
• W2624056145 hasConceptScore W2624056145C205649164 @default.
• W2624056145 hasConceptScore W2624056145C2524010 @default.
• W2624056145 hasConceptScore W2624056145C2779343474 @default.
• W2624056145 hasConceptScore W2624056145C2780586970 @default.
• W2624056145 hasConceptScore W2624056145C28719098 @default.
• W2624056145 hasConceptScore W2624056145C33923547 @default.
• W2624056145 hasConceptScore W2624056145C41008148 @default.
• W2624056145 hasConceptScore W2624056145C42475967 @default.
• W2624056145 hasConceptScore W2624056145C77805123 @default.
• W2624056145 hasLocation W26240561451 @default.
• W2624056145 hasLocation W26240561452 @default.
• W2624056145 hasLocation W26240561453 @default.
• W2624056145 hasLocation W26240561454 @default.
• W2624056145 hasOpenAccess W2624056145 @default.
• W2624056145 hasPrimaryLocation W26240561451 @default.
• W2624056145 hasRelatedWork W1524266066 @default.
• W2624056145 hasRelatedWork W2104445205 @default.
• W2624056145 hasRelatedWork W2115744809 @default.
• W2624056145 hasRelatedWork W2125819103 @default.
• W2624056145 hasRelatedWork W2160599504 @default.
• W2624056145 hasRelatedWork W2233865268 @default.
• W2624056145 hasRelatedWork W2331762730 @default.
• W2624056145 hasRelatedWork W2613307551 @default.
• W2624056145 hasRelatedWork W2613666590 @default.
• W2624056145 hasRelatedWork W2773875116 @default.
• W2624056145 hasRelatedWork W2796945618 @default.
• W2624056145 hasRelatedWork W2807199323 @default.
• W2624056145 hasRelatedWork W2906976759 @default.
• W2624056145 hasRelatedWork W2911359967 @default.
• W2624056145 hasRelatedWork W3089123516 @default.
• W2624056145 hasRelatedWork W3101310800 @default.
• W2624056145 hasRelatedWork W3139722015 @default.
• W2624056145 hasRelatedWork W3184471245 @default.
• W2624056145 hasRelatedWork W3190813577 @default.
• W2624056145 hasRelatedWork W3197822619 @default.
• W2624056145 isParatext "false" @default.
• W2624056145 isRetracted "false" @default.
• W2624056145 magId "2624056145" @default.
• W2624056145 workType "article" @default.