Empowering Urban Governance through Urban Science: Multi-scale Dynamics of Urban Systems Worldwide

• URL: http://arxiv.org/abs/2005.10007v1
• Date: Wed, 20 May 2020 12:47:40 GMT
• Title: Empowering Urban Governance through Urban Science: Multi-scale Dynamics of Urban Systems Worldwide
• Authors: Juste Raimbault and Eric Denis and Denise Pumain
• Abstract summary: The current science of cities can provide a useful foundation for future urban policies. International comparisons of the evolution of cities often produce uncertain results because national territorial frameworks are not always in strict correspondence with the dynamics of urban systems. We propose to provide various compositions of systems of cities to better take into account the dynamic networking of cities that go beyond regional and national territorial boundaries.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The current science of cities can provide a useful foundation for future urban policies, provided that these proposals have been validated by correct observations of the diversity of situations in the world. However, international comparisons of the evolution of cities often produce uncertain results because national territorial frameworks are not always in strict correspondence with the dynamics of urban systems. We propose to provide various compositions of systems of cities to better take into account the dynamic networking of cities that go beyond regional and national territorial boundaries. Different models conceived for explaining city size and urban growth distributions enable to establish a correspondence between urban trajectories when observed at the level of cities and systems of cities. We test the validity and representativeness of several dynamic models of complex urban systems and their variations across regions of the world, at the macroscopic scale of systems of cities. The originality of the approach is in considering spatial interaction and evolutionary path dependence as major features in the general behavior of urban entities. The models studied include diverse and complementary processes, such as economic exchanges, diffusion of innovations and physical network flows. Complex systems' dynamics is in principle unpredictable, but contextualizing it regarding demographic, income and resource components may help in minimizing the forecasting errors.

Related papers

• LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulation [60.920536939067524]
  We introduce LogiCity, the first simulator based on customizable first-order logic (FOL) for an urban-like environment with multiple dynamic agents. LogiCity models diverse urban elements using semantic and spatial concepts, such as IsAmbulance(X) and IsClose(X, Y) Key feature of LogiCity is its support for user-configurable abstractions, enabling customizable simulation complexities for logical reasoning.
  arXiv  Detail & Related papers  (2024-11-01T17:59:46Z)
• MetaUrban: An Embodied AI Simulation Platform for Urban Micromobility [52.0930915607703]
  Recent advances in Robotics and Embodied AI make public urban spaces no longer exclusive to humans. Micromobility enabled by AI for short-distance travel in public urban spaces plays a crucial component in the future transportation system. We present MetaUrban, a compositional simulation platform for the AI-driven urban micromobility research.
  arXiv  Detail & Related papers  (2024-07-11T17:56:49Z)
• Urban Generative Intelligence (UGI): A Foundational Platform for Agents in Embodied City Environment [32.53845672285722]
  Urban environments, characterized by their complex, multi-layered networks, face significant challenges in the face of rapid urbanization. Recent developments in big data, artificial intelligence, urban computing, and digital twins have laid the groundwork for sophisticated city modeling and simulation. This paper proposes Urban Generative Intelligence (UGI), a novel foundational platform integrating Large Language Models (LLMs) into urban systems.
  arXiv  Detail & Related papers  (2023-12-19T03:12:13Z)
• AI Agent as Urban Planner: Steering Stakeholder Dynamics in Urban Planning via Consensus-based Multi-Agent Reinforcement Learning [8.363841553742912]
  We introduce a Consensus-based Multi-Agent Reinforcement Learning framework for real-world land use readjustment. This framework serves participatory urban planning, allowing diverse intelligent agents as stakeholder representatives to vote for preferred land use types. By integrating Multi-Agent Reinforcement Learning, our framework ensures that participatory urban planning decisions are more dynamic and adaptive to evolving community needs.
  arXiv  Detail & Related papers  (2023-10-25T17:04:11Z)
• Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks [82.82866901799565]
  We build a new set of multimodal remote sensing benchmark datasets (including hyperspectral, multispectral, SAR) for the study purpose of the cross-city semantic segmentation task. Beyond the single city, we propose a high-resolution domain adaptation network, HighDAN, to promote the AI model's generalization ability from the multi-city environments. HighDAN is capable of retaining the spatially topological structure of the studied urban scene well in a parallel high-to-low resolution fusion fashion.
  arXiv  Detail & Related papers  (2023-09-26T23:55:39Z)
• Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark] [78.05103666987655]
  This work addresses challenges in accessing and utilizing diverse urban spatial-temporal datasets. We introduceatomic files, a unified storage format designed for urban spatial-temporal big data, and validate its effectiveness on 40 diverse datasets. We conduct extensive experiments using diverse models and datasets, establishing a performance leaderboard and identifying promising research directions.
  arXiv  Detail & Related papers  (2023-08-24T16:20:00Z)
• A Contextual Master-Slave Framework on Urban Region Graph for Urban Village Detection [68.84486900183853]
  We build an urban region graph (URG) to model the urban area in a hierarchically structured way. Then, we design a novel contextual master-slave framework to effectively detect the urban village from the URG. The proposed framework can learn to balance the generality and specificity for UV detection in an urban area.
  arXiv  Detail & Related papers  (2022-11-26T18:17:39Z)
• Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT) [0.0]
  We propose heterogeneous urban graph attention network (HUGAT) for learning the representations of urban regions. In our experiments on NYC data, HUGAT outperformed all the state-of-the-art models.
  arXiv  Detail & Related papers  (2022-02-18T04:59:20Z)
• Neural Embeddings of Urban Big Data Reveal Emergent Structures in Cities [7.148078723492643]
  We propose using a neural embedding model-graph neural network (GNN)- that leverages the heterogeneous features of urban areas. Using large-scale high-resolution mobility data sets from millions of aggregated and anonymized mobile phone users in 16 metropolitan counties in the United States, we demonstrate that our embeddings encode complex relationships among features related to urban components. We show that embeddings generated by a model trained on a different county can capture 50% to 60% of the emergent spatial structure in another county.
  arXiv  Detail & Related papers  (2021-10-24T07:13:14Z)
• Methodological Foundation of a Numerical Taxonomy of Urban Form [62.997667081978825]
  We present a method for numerical taxonomy of urban form derived from biological systematics. We derive homogeneous urban tissue types and, by determining overall morphological similarity between them, generate a hierarchical classification of urban form. After framing and presenting the method, we test it on two cities - Prague and Amsterdam.
  arXiv  Detail & Related papers  (2021-04-30T12:47:52Z)
• City limits in the age of smartphones and urban scaling [0.0]
  Urban planning still lacks appropriate standards to define city boundaries across urban systems. ICT provide the potential to portray more accurate descriptions of the urban systems. We apply computational techniques over a large volume of mobile phone records to define urban boundaries.
  arXiv  Detail & Related papers  (2020-05-06T17:31:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.