Modeling urban morphology by unifying Diffusion-Limited Aggregation and Stochastic Gravitation

More than 30 years ago, Diffusion-Limited Aggregation (DLA) has been studied as mechanism to generate structures sharing similarities with real-world cities. Recently, a stochastic gravitation model (SGM) has been proposed for the same purpose but representing a completely different mechanism. Both...

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Main Authors: Diego Rybski, Yunfei Li, Stefan Born, Jürgen P. Kropp
Format: Article
Language:English
Published: Findings Press 2021-06-01
Series:Findings
Online Access:https://transportfindings.scholasticahq.com/article/22296-modeling-urban-morphology-by-unifying-diffusion-limited-aggregation-and-stochastic-gravitation.pdf
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spelling doaj-9495613c4eec4d9cbac03a5bf40b176c2021-06-03T23:20:53ZengFindings PressFindings2652-88002021-06-01Modeling urban morphology by unifying Diffusion-Limited Aggregation and Stochastic GravitationDiego RybskiYunfei LiStefan BornJürgen P. KroppMore than 30 years ago, Diffusion-Limited Aggregation (DLA) has been studied as mechanism to generate structures sharing similarities with real-world cities. Recently, a stochastic gravitation model (SGM) has been proposed for the same purpose but representing a completely different mechanism. Both approaches have advantages and disadvantages, while e.g. the dendrites emerging via DLA are visually very different from real-world cities, the SGM does not preserve undeveloped locations close to the city center. Here we propose a unification of both mechanisms, i.e. a particle moves randomly and turns into an urban site with a probability that depends on the proximity to already developed sites. We study the cluster size distributions of the structures generated by both models and find that SGM generates more balanced distributions. We also propose a way to assess to which extent the largest cluster is a primate city and find that in both models, beyond certain parameter value, the size of the largest cluster becomes inconsistent with being drawn from the same distribution of remaining clusters.https://transportfindings.scholasticahq.com/article/22296-modeling-urban-morphology-by-unifying-diffusion-limited-aggregation-and-stochastic-gravitation.pdf
collection DOAJ
language English
format Article
sources DOAJ
author Diego Rybski
Yunfei Li
Stefan Born
Jürgen P. Kropp
spellingShingle Diego Rybski
Yunfei Li
Stefan Born
Jürgen P. Kropp
Modeling urban morphology by unifying Diffusion-Limited Aggregation and Stochastic Gravitation
Findings
author_facet Diego Rybski
Yunfei Li
Stefan Born
Jürgen P. Kropp
author_sort Diego Rybski
title Modeling urban morphology by unifying Diffusion-Limited Aggregation and Stochastic Gravitation
title_short Modeling urban morphology by unifying Diffusion-Limited Aggregation and Stochastic Gravitation
title_full Modeling urban morphology by unifying Diffusion-Limited Aggregation and Stochastic Gravitation
title_fullStr Modeling urban morphology by unifying Diffusion-Limited Aggregation and Stochastic Gravitation
title_full_unstemmed Modeling urban morphology by unifying Diffusion-Limited Aggregation and Stochastic Gravitation
title_sort modeling urban morphology by unifying diffusion-limited aggregation and stochastic gravitation
publisher Findings Press
series Findings
issn 2652-8800
publishDate 2021-06-01
description More than 30 years ago, Diffusion-Limited Aggregation (DLA) has been studied as mechanism to generate structures sharing similarities with real-world cities. Recently, a stochastic gravitation model (SGM) has been proposed for the same purpose but representing a completely different mechanism. Both approaches have advantages and disadvantages, while e.g. the dendrites emerging via DLA are visually very different from real-world cities, the SGM does not preserve undeveloped locations close to the city center. Here we propose a unification of both mechanisms, i.e. a particle moves randomly and turns into an urban site with a probability that depends on the proximity to already developed sites. We study the cluster size distributions of the structures generated by both models and find that SGM generates more balanced distributions. We also propose a way to assess to which extent the largest cluster is a primate city and find that in both models, beyond certain parameter value, the size of the largest cluster becomes inconsistent with being drawn from the same distribution of remaining clusters.
url https://transportfindings.scholasticahq.com/article/22296-modeling-urban-morphology-by-unifying-diffusion-limited-aggregation-and-stochastic-gravitation.pdf
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