Hasil untuk "Cities. Urban geography"

D. Gubler

Dengue is the most important arboviral disease of humans with over half of the world’s population living in areas of risk. The frequency and magnitude of epidemic dengue have increased dramatically in the past 40 years as the viruses and the mosquito vectors have both expanded geographically in the tropical regions of the world. There are many factors that have contributed to this emergence of epidemic dengue, but only three have been the principal drivers: 1) urbanization, 2) globalization and 3) lack of effective mosquito control. The dengue viruses have fully adapted to a human-Aedes aegypti-human transmission cycle, in the large urban centers of the tropics, where crowded human populations live in intimate association with equally large mosquito populations. This setting provides the ideal home for maintenance of the viruses and the periodic generation of epidemic strains. These cities all have modern airports through which 10s of millions of passengers pass each year, providing the ideal mechanism for transportation of viruses to new cities, regions and continents where there is little or no effective mosquito control. The result is epidemic dengue. This paper discusses this unholy trinity of drivers, along with disease burden, prevention and control and prospects for the future.

A. Roy

M. Antrop

Yunqiang Liu, Jialing Zhu, Eldon Y. Li et al.

Abstract The contradiction between economic development and environmental protection has become a major concern in many developing countries. To resolve environmental issues, political and technical measures must be considered. However, because of geographical, climatic, and economic differences, ecological issues need to be resolved at the regional level. This study proposes a complex eco-efficiency (EE) system composed of multidimensional components with entropy flows for an economic region, the Yangtze River Economic Belt, in China. There were distinct disparities of eco-efficiency in urban cluster, with the higher efficiency in the central cities and the lower efficiency in the satellite cities. Based on the periodic characteristics of eco-efficiency, two distinct periods, 2008–2012 and 2013–2016, were found. The relationships among environmental regulation (ER), green technological innovation (GTI), and EE varied in different regions and periods because of the “innovative compensation”, “compliance cost”, and “energy rebound” effects. When GTI efficiently improved the EE, inappropriate ER weakened the marginal benefits of GTI. When an “energy rebound effect” occurred, moderate ER was found to assist in reducing the harmful influence of GTI. A “race to the top” phenomenon was found to be more likely in developed areas, while a “race to the bottom” effect was found in the western urban clusters. Differentiated sustainable environmental policies of integrating institutional and free-market approaches are provided.

C. Lee, Xiangdong Li, W. Shi et al.

A. Mcmichael, P. Wilkinson, R. Kovats et al.

M. Gandy

P. Crucitti, V. Latora, S. Porta

We study centrality in urban street patterns of different world cities represented as networks in geographical space. The results indicate that a spatial analysis based on a set of four centrality indices allows an extended visualization and characterization of the city structure. A hierarchical clustering analysis based on the distributions of centrality has a certain capacity to distinguish different classes of cities. In particular, self-organized cities exhibit scale-free properties similar to those found in nonspatial networks, while planned cities do not.

N. Brenner

L. Lees

Jindong Han, Yansong Ning, Zirui Yuan et al.

The long-standing vision of intelligent cities is to create efficient, livable, and sustainable urban environments using big data and artificial intelligence technologies. Recently, the advent of Large Language Models (LLMs) has opened new ways toward realizing this vision. With powerful semantic understanding and reasoning capabilities, LLMs can be deployed as intelligent agents capable of autonomously solving complex problems across domains. In this article, we focus on Urban LLM Agents, which are LLM-powered agents that are semi-embodied within the hybrid cyber-physical-social space of cities and used for system-level urban decision-making. First, we introduce the concept of urban LLM agents, discussing their unique capabilities and features. Second, we survey the current research landscape from the perspective of agent workflows, encompassing urban sensing, memory management, reasoning, execution, and learning. Third, we categorize the application domains of urban LLM agents into five groups: urban planning, transportation, environment, public safety, and urban society, presenting representative works in each group. Finally, we discuss trustworthiness and evaluation issues that are critical for real-world deployment, and identify several open problems for future research. This survey aims to establish a foundation for the emerging field of urban LLM agents and to provide a roadmap for advancing the intersection of LLMs and urban intelligence. A curated list of relevant papers and open-source resources is maintained and continuously updated at https://github.com/usail-hkust/Awesome-Urban-LLM-Agents.

Ruixing Zhang, Bo Wang, Tongyu Zhu et al.

Urban profiling aims to predict urban profiles in unknown regions and plays a critical role in economic and social censuses. Existing approaches typically follow a two-stage paradigm: first, learning representations of urban areas; second, performing downstream prediction via linear probing, which originates from the BERT era. Inspired by the development of GPT style models, recent studies have shown that novel self-supervised pretraining schemes can endow models with direct applicability to downstream tasks, thereby eliminating the need for task-specific fine-tuning. This is largely because GPT unifies the form of pretraining and inference through next-token prediction. However, urban data exhibit structural characteristics that differ fundamentally from language, making it challenging to design a one-stage model that unifies both pretraining and inference. In this work, we propose Urban In-Context Learning, a framework that unifies pretraining and inference via a masked autoencoding process over urban regions. To capture the distribution of urban profiles, we introduce the Urban Masked Diffusion Transformer, which enables each region' s prediction to be represented as a distribution rather than a deterministic value. Furthermore, to stabilize diffusion training, we propose the Urban Representation Alignment Mechanism, which regularizes the model's intermediate features by aligning them with those from classical urban profiling methods. Extensive experiments on three indicators across two cities demonstrate that our one-stage method consistently outperforms state-of-the-art two-stage approaches. Ablation studies and case studies further validate the effectiveness of each proposed module, particularly the use of diffusion modeling.

Devashish Khulbe, Alexander Belyi, Stanislav Sobolevsky

Urban socioeconomic modeling has predominantly concentrated on extensive location and neighborhood-based features, relying on the localized population footprint. However, networks in urban systems are common, and many urban modeling methods don't account for network-based effects. In this study, we propose using commute information records from the census as a reliable and comprehensive source to construct mobility networks across cities. Leveraging deep learning architectures, we employ these commute networks across U.S. metro areas for socioeconomic modeling. We show that mobility network structures provide significant predictive performance without considering any node features. Consequently, we use mobility networks to present a supervised learning framework to model a city's socioeconomic indicator directly, combining Graph Neural Network and Vanilla Neural Network models to learn all parameters in a single learning pipeline. Our experiments in 12 major U.S. cities show the proposed model outperforms previous conventional machine learning models. This work provides urban researchers methods to incorporate network effects in urban modeling and informs stakeholders of wider network-based effects in urban policymaking and planning.

Nathalia A. Loureiro, Camilo R. Neto, Jack Sutton et al.

Inter-city interactions are critical for the transmission of infectious diseases, yet their effects on the scaling of disease cases remain largely underexplored. Here, we use the commuting network as a proxy for inter-city interactions, integrating it with a general scaling framework to describe the incidence of seven infectious diseases across Brazilian cities as a function of population size and the number of commuters. Our models significantly outperform traditional urban scaling approaches, revealing that the relationship between disease cases and a combination of population and commuters varies across diseases and is influenced by both factors. Although most cities exhibit a less-than-proportional increase in disease cases with changes in population and commuters, more-than-proportional responses are also observed across all diseases. Notably, in some small and isolated cities, proportional rises in population and commuters correlate with a reduction in disease cases. These findings suggest that such towns may experience improved health outcomes and socioeconomic conditions as they grow and become more connected. However, as growth and connectivity continue, these gains diminish, eventually giving way to challenges typical of larger urban areas - such as socioeconomic inequality and overcrowding - that facilitate the spread of infectious diseases. Our study underscores the interconnected roles of population size and commuter dynamics in disease incidence while highlighting that changes in population size exert a greater influence on disease cases than variations in the number of commuters.

Jun Yin, Jing Zhong, Peilin Li et al.

Urban cultures and architectural styles vary significantly across cities due to geographical, chronological, historical, and socio-political factors. Understanding these differences is essential for anticipating how cities may evolve in the future. As representative cases of historical continuity and modern innovation in China, Beijing and Shenzhen offer valuable perspectives for exploring the transformation of urban streetscapes. However, conventional approaches to urban cultural studies often rely on expert interpretation and historical documentation, which are difficult to standardize across different contexts. To address this, we propose a multimodal research framework based on vision-language models, enabling automated and scalable analysis of urban streetscape style differences. This approach enhances the objectivity and data-driven nature of urban form research. The contributions of this study are as follows: First, we construct UrbanDiffBench, a curated dataset of urban streetscapes containing architectural images from different periods and regions. Second, we develop UrbanSense, the first vision-language-model-based framework for urban streetscape analysis, enabling the quantitative generation and comparison of urban style representations. Third, experimental results show that Over 80% of generated descriptions pass the t-test (p less than 0.05). High Phi scores (0.912 for cities, 0.833 for periods) from subjective evaluations confirm the method's ability to capture subtle stylistic differences. These results highlight the method's potential to quantify and interpret urban style evolution, offering a scientifically grounded lens for future design.

Pushkin Kachroo, Samarth Y. Bhatia, Gopal R. Patil

Abstract The study presents the model of dynamic urban boundary evolution using the Eikonal equation arising in the wave Partial Differential Equation (PDE) using Huygens principle. It is shown how the mathematical model may be used in analyzing urban morphology through the formulation of wave equations that can be solved to represent the development of an urban area. The technique uses the urban boundary obtained through satellite data and estimates the parameters used in the ordinary differential equations (ODE) that arise when solving the eikonal PDE using the method of characteristics. The proposed mathematical framework is implemented for a small study area to demonstrate its functionality. The boundary of the study area for three years is extracted using satellite remote sensing data. The initial year boundary is discretized into fifty subdivisions. The Huygens principle is used to determine the parameters - the rate of change in magnitude and direction of urban boundaries at these selected boundary points. Once the parameters are estimated, they are related to various potential urban boundary expansion drivers using neural networks. The calibrated model shows a high degree of accuracy and may be used to further interpolate or extrapolate the boundary data.

Benjamin Büttner

Buchrezension

Amin Ullah, Guilin Qi, Saddam Hussain et al.

Smart cities stand as pivotal components in the ongoing pursuit of elevating urban living standards, facilitating the rapid expansion of urban areas while efficiently managing resources through sustainable and scalable innovations. In this regard, as emerging technologies like Artificial Intelligence (AI), the Internet of Things (IoT), big data analytics, and fog and edge computing have become increasingly prevalent, smart city applications grapple with various challenges, including the potential for unauthorized disclosure of confidential and sensitive data. The seamless integration of emerging technologies has played a vital role in sustaining the dynamic pace of their development. This paper explores the substantial potential and applications of Deep Learning (DL), Federated Learning (FL), IoT, Blockchain, Natural Language Processing (NLP), and large language models (LLMs) in optimizing ICT processes within smart cities. We aim to spotlight the vast potential of these technologies as foundational elements that technically strengthen the realization and advancement of smart cities, underscoring their significance in driving innovation within this transformative urban milieu. Our discourse culminates with an exploration of the formidable challenges that DL, FL, IoT, Blockchain, NLP, and LLMs face within these contexts, and we offer insights into potential future directions.

Tore Sager