Modelling terrestrial route networks to understand inter-polity interactions (southern Etruria, 950-500 BC)

Prignano, Luce; Morer, Ignacio; Fulminante, Francesca; Lozano, Sergi

doi:10.48550/arxiv.1612.09321

Cited by 1 publication

(3 citation statements)

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“…Given a certain layout of the nodes in space, there are multiple connectivity patterns presenting approximately the same total length, L tot = i,j a ij d ij . Among the plethora of spatial network models available in the literature [4,19,[24][25][26][27], we selected the Minimum Spanning Tree (MST) [2], the Greedy Triangulation (GT) [28], the Equitable Efficiency Model (EEM) [29], and the Gastner-Newman model (GN) [19] as benchmarks, thus encompassing a wide spectrum of possibilities. Then, we built the MST, GT, EEM, and GN networks on random distributions of nodes in a unit square and compute their E glob and E loc .…”

Section: Global Local and Integrated Efficienciesmentioning

confidence: 99%

“…Latium Vetus/Southern Etruria: The networks of trails among settlements between 950 and 509 BC (Iron Age) in two regions of Italy, namely: Latium Vetus [33] and Southern Etruria [29]. Nodes represent settlements, while an edge denotes a direct route connecting them.…”

Section: Applicationsmentioning

confidence: 99%

“…In this section, we present the essential traits of the Equitable Efficiency Model (EEM) introduced by Prignano et al in [29]. EEM is a growth model which builds networks from empty and static spatial node layouts.…”

Section: Appendix C: Equitable Efficiency Modelmentioning

confidence: 99%

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Comparing spatial networks: A one-size-fits-all efficiency-driven approach

et al. 2020

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Spatial networks are a very powerful framework for studying a large variety of systems which can be found in a broad diversity of contexts: from transportation to biology, from epidemiology to communications, and migrations, to cite a few. Spatial networks can be defined by their total cost (generally understood as the total amount of resources needed for building or traveling their connections). Here, we address the issue of how to gauge and compare the quality of spatial network designs (i.e. efficiency vs. total cost) by proposing a two-step methodology. Firstly, we introduce a quality function to assess the overall performance of any network. Second, we propose an algorithm to estimate computationally the upper bound of our quality function for a given specific network. The smaller is the difference between such an upper bound and the empirical value, the higher we consider the design quality of the network under analysis to be. In order to avoid scalability limitations when applying this second step on large networks, we provide a universal expression to obtain an approximated upper bound to any network. Finally, we test the applicability of this analytic tool-set on spatial network datasets of different nature.

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