Population Characterization in Location Modeling: Alternatives, Impacts, and Insights

2019 ◽  
pp. 57-72 ◽  
Author(s):  
Daoqin Tong ◽  
Wangshu Mu ◽  
Changfeng Li
Keyword(s):  
Location Modeling

scholarly journals What you should know about location modeling

2008 ◽  
Vol 55 (4) ◽  
pp. 283-294 ◽  
Author(s):  
Mark S. Daskin
Keyword(s):  
Location Modeling

Assembly Modeling for 3D Component Based on Polychromatic Sets

2011 ◽  
Vol 411 ◽  
pp. 388-392 ◽  
Author(s):  
Yun Long Li ◽  
Jian Min Gao ◽  
Lei Shi ◽  
Song Wang
Keyword(s):  
Information Matrix ◽  
Spatial Location ◽  
Assembly Sequence Planning ◽  
Assembly Model ◽  
Assembly Sequence ◽  
Assembly Modeling ◽  
Sequence Planning ◽  
Location Modeling ◽  
Dynamic Interference

In order to improve the efficiency of assembly modeling and provide a complete assembly model for assembly sequence planning (ASP), a method of assembly modeling based on polychromatic sets (PS) is proposed. Firstly, the assembly information of 3D Component is obtained by API function in SolidWorks. In addition, assembly incident matrix, information matrix and mapping matrix are built. On the basis of these matrixes, a method of location modeling is explored. The interference relation matrix is developed by judging the spatial location and analyzing dynamic interference relation among parts. Finally, a case study is given to verify the method.

scholarly journals Location Modeling of Final Palaeolithic Sites in Northern Germany

Geosciences ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 430 ◽  
Author(s):  
Wolfgang B. Hamer ◽  
Daniel Knitter ◽  
Sonja B. Grimm ◽  
Benjamin Serbe ◽  
Berit Valentin Eriksen ◽  
...  
Keyword(s):  
Expert Knowledge ◽  
Extended Period ◽  
Second Order ◽  
Order Effects ◽  
Interaction Patterns ◽  
Spatial Effects ◽  
Point Distribution ◽  
Location Modeling ◽  
Second Order Effects ◽  
The One

Location modeling, both inductive and deductive, is widely used in archaeology to predict or investigate the spatial distribution of sites. The commonality among these approaches is their consideration of only spatial effects of the first order (i.e., the interaction of the locations with the site characteristics). Second-order effects (i.e., the interaction of locations with each other) are rarely considered. We introduce a deductive approach to investigating such second-order effects using linguistic hypotheses about settling behavior in the Final Palaeolithic. A Poisson process was used to simulate a point distribution using expert knowledge of two distinct hunter–gatherer groups, namely, reindeer hunters and elk hunters. The modeled points and point densities were compared with the actual finds. The G-, F-, and K-function, which allow for the identification of second-order effects of varying intensity for different periods, were applied. The results reveal differences between the two investigated groups, with the reindeer hunters showing location-related interaction patterns, indicating a spatial memory of the preferred locations over an extended period of time. Overall, this paper shows that second-order effects occur in the geographical modeling of archaeological finds and should be taken into account by using approaches such as the one presented in this paper.

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