Spatial Networks, Toxic Ecoscapes, and (In)visible Labor

2021 ◽  
pp. 64-107
Keyword(s):  
Spatial Networks

Patronage of urban commercial clusters: A network-based extension of the Huff model for balancing location and size

2017 ◽  
Vol 45 (3) ◽  
pp. 508-528 ◽  
Author(s):  
Andres Sevtsuk ◽  
Raul Kalvo
Keyword(s):  
Spatial Configuration ◽  
Analysis Tool ◽  
Spatial Networks ◽  
Urban Network ◽  
Household Level ◽  
New Town ◽  
New Towns ◽  
Public Facilities ◽  
User Access ◽  
Retail Demand

We introduce a version of the Huff retail expenditure model, where retail demand depends on households’ access to retail centers. Household-level survey data suggest that total retail visits in a system of retail centers depends on the relative location pattern of stores and customers. This dependence opens up an important question—could overall visits to retail centers be increased with a more efficient spatial configuration of centers in planned new towns? To answer this question, we implement the model as an Urban Network Analysis tool in Rhinoceros 3D, where facility patronage can be analyzed along spatial networks and apply it in the context of the Punggol New Town in Singapore. Using fixed household locations, we first test how estimated store visits are affected by the assumption of whether shoppers come from homes or visit shops en route to local public transit stations. We then explore how adjusting both the locations and sizes of commercial centers can maximize overall visits, using automated simulations to test a large number of scenarios. The results show that location and size adjustments to already planned retail centers in a town can yield a 10% increase in estimated store visits. The methodology and tools developed for this analysis can be extended to other context for planning and right-sizing retail developments and other public facilities so as to maximize both user access and facilities usage.

Disintegrating spatial networks based on region centrality

2021 ◽  
Vol 31 (6) ◽  
pp. 061101
Author(s):  
Zhi-Gang Wang ◽  
Ye Deng ◽  
Ze Wang ◽  
Jun Wu
Keyword(s):  
Spatial Networks

scholarly journals Towards Robust Representations of Spatial Networks Using Graph Neural Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6918
Author(s):  
Chidubem Iddianozie ◽  
Gavin McArdle
Keyword(s):  
Machine Learning ◽  
Model Performance ◽  
Network Models ◽  
Data Representation ◽  
Spatial Networks ◽  
Neural Network Models ◽  
Improve Model ◽  
Graph Neural Networks ◽  
Spatial Entities ◽  
Improve Model Performance

The effectiveness of a machine learning model is impacted by the data representation used. Consequently, it is crucial to investigate robust representations for efficient machine learning methods. In this paper, we explore the link between data representations and model performance for inference tasks on spatial networks. We argue that representations which explicitly encode the relations between spatial entities would improve model performance. Specifically, we consider homogeneous and heterogeneous representations of spatial networks. We recognise that the expressive nature of the heterogeneous representation may benefit spatial networks and could improve model performance on certain tasks. Thus, we carry out an empirical study using Graph Neural Network models for two inference tasks on spatial networks. Our results demonstrate that heterogeneous representations improves model performance for down-stream inference tasks on spatial networks.

scholarly journals Linking social and spatial networks to viral community phylogenetics reveals subtype‐specific transmission dynamics in African lions

2017 ◽  
Vol 86 (6) ◽  
pp. 1469-1482 ◽  
Author(s):  
Nicholas M. Fountain‐Jones ◽  
Craig Packer ◽  
Jennifer L. Troyer ◽  
Kimberly VanderWaal ◽  
Stacie Robinson ◽  
...  
Keyword(s):  
Transmission Dynamics ◽  
Spatial Networks ◽  
Community Phylogenetics ◽  
Viral Community ◽  
African Lions

scholarly journals Reconstructing Nanoscale Structures from Sequence Topology of Spatial Networks of Barcoded DNA

2018 ◽  
Vol 114 (3) ◽  
pp. 692a-693a
Author(s):  
Ian T. Hoffecker ◽  
Giulio Bernardinelli ◽  
Larsen Vornholz ◽  
Yunshi Yang ◽  
Björn Högberg
Keyword(s):  
Spatial Networks ◽  
Nanoscale Structures
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