Exhaustible resources flows in a spatial context

2018 ◽  
Vol 11 (1) ◽  
pp. 71-83
Author(s):  
Octave Keutiben
2006 ◽  
Author(s):  
Nobutaka Endo ◽  
Walter R. Boot ◽  
Arthur F. Kramer ◽  
Alejandro Lleras ◽  
Takatsune Kumada

2019 ◽  
Vol 34 (2) ◽  
pp. 251-261 ◽  
Author(s):  
Elizabeth Ankudowich ◽  
Stamatoula Pasvanis ◽  
M. Natasha Rajah

2010 ◽  
Author(s):  
Christopher J. Harris ◽  
Sam Howison ◽  
Ronnie Sircar

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Ellen Soward ◽  
Jianling Li

AbstractMost cities in the United States rely on zoning to address important planning-related issues within their jurisdictions. Planners often use GIS tools to analyze these issues in a spatial context. ESRI’s ArcGIS Urban software seeks to provide the planning profession with a GIS-based solution for various challenges, including zoning’s impacts on the built environment and housing capacity.This research explores the use of ArcGIS Urban for assessing the existing zoning and comprehensive plans in meeting the projected residential growth in the near future using the City of Arlington, Texas as a case study. The exploration provides examples and lessons for how ArcGIS Urban might be used by planners to accomplish their tasks and highlights the capabilities and limitations of ArcGIS Urban in its current stand. The paper is concluded with some suggestions for future studies.


2020 ◽  
Vol 16 (4) ◽  
pp. 271-289
Author(s):  
Nathan Sandholtz ◽  
Jacob Mortensen ◽  
Luke Bornn

AbstractEvery shot in basketball has an opportunity cost; one player’s shot eliminates all potential opportunities from their teammates for that play. For this reason, player-shot efficiency should ultimately be considered relative to the lineup. This aspect of efficiency—the optimal way to allocate shots within a lineup—is the focus of our paper. Allocative efficiency should be considered in a spatial context since the distribution of shot attempts within a lineup is highly dependent on court location. We propose a new metric for spatial allocative efficiency by comparing a player’s field goal percentage (FG%) to their field goal attempt (FGA) rate in context of both their four teammates on the court and the spatial distribution of their shots. Leveraging publicly available data provided by the National Basketball Association (NBA), we estimate player FG% at every location in the offensive half court using a Bayesian hierarchical model. Then, by ordering a lineup’s estimated FG%s and pairing these rankings with the lineup’s empirical FGA rate rankings, we detect areas where the lineup exhibits inefficient shot allocation. Lastly, we analyze the impact that sub-optimal shot allocation has on a team’s overall offensive potential, demonstrating that inefficient shot allocation correlates with reduced scoring.


Sign in / Sign up

Export Citation Format

Share Document