The Emergence of Urban Land Use Patterns Driven by Dispersion and Aggregation Mechanisms

Decraene, James; Monterola, Christopher; Lee, Gary Kee Khoon; Hung, Terence; Batty, Michael

doi:10.1371/journal.pone.0080309

Cited by 21 publications

(12 citation statements)

References 10 publications

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“…Urban land use models in the literature are typically built with relatively elaborate mechanisms [50,51]. If basic in the rules, the models typically refer to characteristics of cities such as the population or activity distributions [44][45][46].…”

Section: Modelling Land Usementioning

confidence: 99%

Comparing and modelling land use organization in cities

et al. 2015

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The advent of geolocated information and communication technologies opens the possibility of exploring how people use space in cities, bringing an important new tool for urban scientists and planners, especially for regions where data are scarce or not available. Here we apply a functional network approach to determine land use patterns from mobile phone records. The versatility of the method allows us to run a systematic comparison between Spanish cities of various sizes. The method detects four major land use types that correspond to different temporal patterns. The proportion of these types, their spatial organization and scaling show a strong similarity between all cities that breaks down at a very local scale, where land use mixing is specific to each urban area. Finally, we introduce a model inspired by Schelling's segregation, able to explain and reproduce these results with simple interaction rules between different land uses.

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Section: Modelling Land Usementioning

confidence: 99%

Comparing and modelling land use organization in cities

et al. 2015

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“…Moreover, cities have multiscale structures individually through fractal growth and collectively through size distributions. Their structural patterns have been modelled by scaling laws [1], archetypes of streets layouts [6,7] and land use [8]. Humangenerated data have been used to understand the dynamical behaviour of & 2017 The Author(s) Published by the Royal Society.…”

Section: Introductionmentioning

confidence: 99%

Global patterns of synchronization in human communications

Morales

Vaibhav

Benito

et al. 2017

J. R. Soc. Interface.

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Social media are transforming global communication and coordination and provide unprecedented opportunities for studying socio-technical domains. Here we study global dynamical patterns of communication on Twitter across many scales. Underlying the observed patterns is both the diurnal rotation of the Earth, day and night, and the synchrony required for contingency of actions between individuals. We find that urban areas show a cyclic contraction and expansion that resembles heartbeats linked to social rather than natural cycles. Different urban areas have characteristic signatures of daily collective activities. We show that the differences detected are consistent with a new emergent global synchrony that couples behaviour in distant regions across the world. Although local synchrony is the major force that shapes the collective behaviour in cities, a larger-scale synchronization is beginning to occur.

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“…To study the factors contributing to urban land use patterns, Decraene et al (2013) [38] developed a dynamic urban growth model that aims to construct artificial cities from the bottom-up using the measure of spatial entropy and index of dissimilarity. The study applies a CA model which relies on the range of influence of different land use sectors (residential, business and industrial).…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Algorithms for Urban Land Use Planning: A Review

Chaturvedi

Vries

2021

Urban Science

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Urbanization is persistent globally and has increasingly significant spatial and environmental consequences. It is especially challenging in developing countries due to the increasing pressure on the limited resources, and damage to the bio-physical environment. Traditional analytical methods of studying the urban land use dynamics associated with urbanization are static and tend to rely on top-down approaches, such as linear and mathematical modeling. These traditional approaches do not capture the nonlinear properties of land use change. New technologies, such as artificial intelligence (AI) and machine learning (ML) have made it possible to model and predict the nonlinear aspects of urban land dynamics. AI and ML are programmed to recognize patterns and carry out predictions, decision making and perform operations with speed and accuracy. Classification, analysis and modeling using earth observation-based data forms the basis for the geospatial support for land use planning. In the process of achieving higher accuracies in the classification of spatial data, ML algorithms are being developed and being improved to enhance the decision-making process. The purpose of the research is to bring out the various ML algorithms and statistical models that have been applied to study aspects of land use planning using earth observation-based data (EO). It intends to review their performance, functional requirements, interoperability requirements and for which research problems can they be applied best. The literature review revealed that random forest (RF), deep learning like convolutional neural network (CNN) and support vector machine (SVM) algorithms are best suited for classification and pattern analysis of earth observation-based data. GANs (generative adversarial networks) have been used to simulate urban patterns. Algorithms like cellular automata, spatial logistic regression and agent-based modeling have been used for studying urban growth, land use change and settlement pattern analysis. Most of the papers reviewed applied ML algorithms for classification of EO data and to study urban growth and land use change. It is observed that hybrid approaches have better performance in terms of accuracies, efficiency and computational cost.

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The Emergence of Urban Land Use Patterns Driven by Dispersion and Aggregation Mechanisms

Cited by 21 publications

References 10 publications

Comparing and modelling land use organization in cities

Comparing and modelling land use organization in cities

Global patterns of synchronization in human communications

Machine Learning Algorithms for Urban Land Use Planning: A Review

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