Urban Functions Research Articles

Identifying Urban functional regions: A multi-dimensional framework approach integrating metro smart card data and car-hailing data

Urban functions often diverge from initial planning due to changes driven by residents’ behaviors. Effective urban planning and renewal require accurately identifying urban functional regions based on residents’ behavior data (including activity and travel data). However, previous methods have primarily relied on either point of interest (POI) data or a single source of traffic data, and often ignore the combined influence of residents’ activities and travel behaviors. In this study, we introduce a novel framework that integrates multiple sources of traffic data (such as metro smart card data and car-hailing data) with POI data to identify urban functional regions. This approach is unique because it simultaneously considers two critical dimensions of residents’ behavior: travel and activity behaviors. By combining these dimensions, we extract a comprehensive set of characteristics, including travel time, travel flow, origin-destination patterns, activity types, and activity time, which are then aggregated at the regional level (i.e., traffic analysis zone). To process these characteristics, we use latent Dirichlet allocation (LDA) to extract high-level semantic features from each data type. Additionally, to handle the sparse data from metro smart cards, we employ a specialized clustering technique. The integration of diverse and complementary information from multiple data sources enables more accurate and nuanced identification of urban functional regions than single data source and k-means clustering algorithm, providing valuable insights for urban planners.

DenseNet-Transformer: A deep learning method for spatial–temporal traffic prediction in optical fronthaul network

The rapid evolution of 6G Radio Access Networks (RAN) towards virtualization and intelligence is driven by the widespread adoption of high-bandwidth services and the proliferation of mobile communications. Active Antenna Units (AAU) interface with Distributed Units (DU) over Passive Optical Networks (PON), serving as crucial bearers that transmit extensive data through the optical fronthaul network to the core network. However, the escalating demand for high-bandwidth services, coupled with the uneven spatial–temporal traffic patterns influenced by factors such as geographical location and urban functionalities across various base stations, poses significant challenges to the optical fronthaul network in terms of operational costs and resource allocation efficiency. To tackle these challenges, we introduce the DenseNet-Transformer spatial–temporal traffic prediction algorithm tailored specifically for the traffic characteristics of optical fronthaul networks. In DenseNet-Transformer, DenseNet captures spatial feature correlations among nearby traffic in adjacent regions, facilitating enhanced learning of traffic characteristics across distant areas through dense connections. The Transformer component learns both long and short-term temporal dependencies, enhancing the algorithm’s temporal prediction capabilities using multi-head attention mechanisms and positional encoding. We validate the effectiveness of DenseNet-Transformer through a series of ablation experiments and comparative tests against other algorithms under identical conditions. Experimental results on real datasets demonstrate that, in most scenarios, DenseNet-Transformer outperforms existing algorithms for time traffic prediction in both wireless and optical communication domains, as well as spatial–temporal prediction algorithms.

Improving real-time object detection in Internet-of-Things smart city traffic with YOLOv8-DSAF method

With the rise of global smart city construction, target detection technology plays a crucial role in optimizing urban functions and improving the quality of life. However, existing target detection technologies still have shortcomings in terms of accuracy, real-time performance, and adaptability. To address this challenge, this study proposes an innovative target detection model. Our model adopts the structure of YOLOv8-DSAF, comprising three key modules: depthwise separable convolution (DSConv), dual-path attention gate module (DPAG), and feature enhancement module (FEM). Firstly, DSConv technology optimizes computational complexity, enabling real-time target detection within limited hardware resources. Secondly, the DPAG module introduces a dual-channel attention mechanism, allowing the model to selectively focus on crucial areas, thereby improving detection accuracy in high-dynamic traffic scenarios. Finally, the FEM module highlights crucial features to prevent their loss, further enhancing detection accuracy. Additionally, we propose an Internet of Things smart city framework consisting of four main layers: the application domain, the Internet of Things infrastructure layer, the edge layer, and the cloud layer. The proposed algorithm utilizes the Internet of Things infrastructure layer, edge layer, and cloud layer to collect and process data in real-time, achieving faster response times. Experimental results on the KITTI V and Cityscapes datasets indicate that our model outperforms the YOLOv8 model. This suggests that in complex urban traffic scenarios, our model exhibits superior performance with higher detection accuracy and adaptability. We believe that this innovative model will significantly propel the development of smart cities and advance target detection technology.

Spatio-temporal evolution characteristics and driving mechanisms of Urban–Agricultural–Ecological space in ecologically fragile areas: A case study of the upper reaches of the Yangtze River Economic Belt, China

Ecologically fragile areas, which are vulnerable to human disturbances, are facing development dilemmas due to the contradiction between economic development and environmental degradation. Examining the driving mechanism and future trends of land use transitions can inform territorial planning and land use policies to address the contradiction. However, research into the mechanisms governing the interaction between ecological and social factors driving land use transitions in ecologically fragile areas is currently limited. Considering the Upper Reaches of the Yangtze River Economic Belt (URYREB), a typical ecologically fragile area in China, as an example, this study discussed the spatio-temporal evolution characteristics and driving mechanisms of Urban-Agricultural-Ecological Space. The results indicated that ecological factors influence the direction, location, and scale of spatial evolution, while social factors dominate the direction and scale of spatial evolution. The interaction between ecological and social elements drives spatial evolution due to the endogenous development needs of the social-ecological system (SES), in conjunction with external policy guidance and economic development. In the future, urban function space may continue to expand and threaten surrounding agricultural function space, thus, corresponding land use policies are necessary to ensure food security. This study unveils the impact of multifactor interactions on land use transitions and offers insights for optimizing territorial space planning in ecologically fragile areas.

Quantifying urban spatial resilience using multi-criteria decision analysis (MCDA) and back propagation neural network (BPNN)

In the face of rapid urbanization and increasing environmental risks, building resilient cities has become a global priority. This study introduces a novel methodology for quantifying urban spatial resilience by integrating multi-criteria decision analysis (MCDA) and back propagation neural network (BPNN). The urban spatial resilience assessment system encompasses five critical criteria layers (urban spatial scale, structure, form, function, and network resilience) and 29 indicator layers. An empirical study of Yinchuan City, China, demonstrates the effectiveness of the MCDA-BPNN model in quantifying urban spatial resilience. The models identified significant disparities in resilience between central and peripheral urban zones, with central areas showing higher resilience scores compared to peripheral areas. This spatial pattern was most pronounced in structure, functional and network resilience. These results contribute to the field of urban environmental science by advancing the fundamental understanding of urban spatial resilience mechanisms and their spatial distribution. The study has significant implications for urban planners, policymakers, and risk management authorities, enabling data-driven decision-making and the development of targeted, resilience-oriented urban policies and strategies. The MCDA-BPNN approach offers a promising tool for comprehensive urban resilience assessment in diverse urban contexts.

Analyzing road network robustness in major Chinese cities through hierarchical stroke-based representations

ABSTRACT In this study, we explore the robustness of urban road networks, a crucial aspect of urban infrastructure whose failure can significantly disrupt urban functionality. Our approach offers a fresh perspective on network robustness by focusing on intrinsic street hierarchies within stroke-based representations across 20 major Chinese cities. These networks exhibit common complex properties, such as being scale-free and small-world. We determine street hierarchies based on the power law distribution of two key graph centrality measures: degree and betweenness. Our methodology involves a targeted attack strategy, sequentially removing roads from the highest to the lowest hierarchy in each urban street network. This process yields robustness profiles characterized by monotonic decreasing curves, which reflect changes in network connectivity and efficiency. The profiles not only provide valuable insights into urban structure and function, but also offer significant implications for practical road planning and urban resilience strategies.

Biodiversity promotes urban ecosystem functioning

The proportion of people living in urban areas is growing globally. Understanding how to manage urban biodiversity, ecosystem functions, and ecosystem services is becoming more important. Biodiversity can increase ecosystem functioning in non‐urban systems. However, few studies have reviewed the relationship between biodiversity and ecosystem functioning in urban areas, which differ in species compositions, abiotic environments, food webs, and turnover rates. We reviewed evidence of biodiversity–ecosystem functioning relationships in urban environments and assessed factors that influence the relationship direction. Based on 70 studies, relationships between biodiversity and ecosystem functioning were more positive than negative in urban areas, especially for pollination and nutrient cycling and retention. Surprisingly, positive and negative relationships between biodiversity and biomass production and storage were equally not statistically different, perhaps due to extensive plant management in urban areas. The number of studies and geographic coverage of our review was still insufficient to provide a general predictive framework for when biodiversity positively impacts ecosystem functioning. We identify gaps and opportunities to improve urban biodiversity–ecosystem functioning research and discuss how our findings can improve urban green space management.

Advanced Integration of Urban Street Greenery and Pedestrian Flow: A Multidimensional Analysis in Chengdu’s Central Urban District

As urbanization accelerates, urban greenery, particularly street greenery, emerges as a vital strategy for enhancing residents’ quality of life, demanding attention for its alignment with pedestrian flows to foster sustainable urban development and ensure urban dwellers’ wellbeing. The advent of diverse urban data has significantly advanced this area of study. Focusing on Chengdu’s central urban district, this research assesses street greening metrics against pedestrian flow indicators, employing spatial autocorrelation techniques to investigate the interplay between street greenery and pedestrian flow over time and space. Our findings reveal a prevalent negative spatial autocorrelation between street greenery and pedestrian flow within the area, underscored by temporal disparities in greenery demands across various urban functions during weekdays versus weekends. This study innovatively incorporates mobile phone signal-based population heat maps into the mismatch analysis of street greenery for the first time, moving beyond the conventional static approach of space syntax topology in assessing pedestrian flow. By leveraging dynamic pedestrian flow data, it enriches our understanding of the disconnect between street greening plans and pedestrian circulation, highlighting the concept of urban flow and delving into the intricate nexus among time, space, and human activity. Moreover, this study meticulously examines multiple street usage scenarios, reflecting diverse behavior patterns, with the objective of providing nuanced and actionable strategies for urban renewal initiatives aimed at creating more inviting and sustainable urban habitats.

Urban Scaling Functions: Emission, Pollution and Health

Urban scaling is widely used to characterize the population dependence of city indicators including greenhouse gas emission. Here we analyze the population dependence of CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_2$$\\end{document} and PM2.5 emissions and concentrations across all European cities. Our analysis reveals considerable variations in emissions among cities of comparable population size which are not captured by conventional urban scaling. We thus characterize these fluctuations by multi-parameter scaling functions and multifractal spectral analysis. We find that the distribution of emissions and population is multifractal while that of air pollution is not, leading to non-trivial relations between emission and pollution in some large cities. We also analyze the impact of forests in curbing emission and the impact of air pollution on health. Our work provides a detailed picture of the fluctuations in the scaling of urban metabolism in Europe and suggests a general strategy that goes beyond conventional urban scaling laws.

Exploring factors influencing the agglomeration of implicit consumption space: A comparison with explicit consumption space

The implicit development of consumption space in buildings relying on the O2O business mode is a new form of urban renewal under experience economy and e-commerce. The concept of implicit consumption space emerged as a result. There is a lack of research on implicit consumption space and this study aims to fill this gap. Using POI data and spatial analysis methods, the paper studies the clustering characteristics and influencing factors of the explicit and implicit consumption space in the Old City of Nanjing to provide practical implications for urban renewal, and finds that the implicit consumption space grows fast and has strong clustering, and its layout basically follows the traditional commercial location theory. Commercial districts, office buildings and traffic accessibility are the common influencing factors for the distribution of explicit and implicit consumption spaces. Explicit and implicit consumption space influence each other in layout, while implicit consumption space has a stronger dependence on explicit consumption space. The influencing factors for the agglomeration of explicit and implicit consumption space have spatial variations. The implicit consumption space is an important path for the bottom-up renewal of the old city, and also provides an opportunity for urban function optimization, innovation, and entrepreneurship.

Urban mangroves at risk: Understanding organophosphate flame retardant pollution at different urban function zones in shenzhen

Organophosphate flame retardants (OPFRs) have become a ubiquitous environmental contaminant due to their extensive usage in various applications such as electronics, electrical equipment, building materials, automobiles, textiles, and cables. It have been detected in water bodies and sediment worldwide, indicating their global presence and potential environmental impact. Mangrove forests in the marine-terrestrial ecotone serve as the primary repository for terrestrial pollutants. However, the distribution of OPFRs and the underlying factors driving their presence are not yet well understood. In this study, three urban mangroves in Shenzhen were selected based on urban functional zoning: Shajing (SJ) mangrove, located in an industrial district; Futian (FT) mangrove and Baguang (BG) mangrove, located in the central business district and ecological preserve, respectively. This study revealed that the total concentrations of the seven OPFRs ranged from 30.0 to 418.3 ng L−1 in surface seawater and from 53.1 to 151.6 ng g−1 dry weight in sediment. Triethyl phosphate-TEP and tributyl phosphate-TnBP were detected in surface seawater from SJ and in all sediment samples. The dominant OPFRs varied between surface seawater (tris(1-chloro-2-propyl) phosphate-TCPP and triphenylphosphine oxide-TPPO) and sediments (TCPP and triphenyl phosphate-TPhP). Further analysis showed transfer of OPFRs was relatively weak in SJ but improved in BG and FT, suggesting varying degrees of contamination and accumulation across the different functional zones. Ecological risk assessment indicated that only TPHP exhibited moderate risk in surface seawater and sediment. This study provides a comprehensive understanding of the spatial distribution (surface seawater and sediment) of OPFRs in urban mangroves characterized by diverse functional zoning. It emphasizes the necessity of reducing OPFRs emissions into the environment and effectively managing OPFRs contamination in urban mangroves.

Ecosystem functions and services in urban stormwater ponds: Co‐producing knowledge for better management

Abstract Urban stormwater management ponds (SWMPs) are widely employed for stormwater control, but knowledge about their contributions to urban ecosystem function and service delivery remains unclear. We organized a workshop that brought together researchers, managers and students to assess and discuss current information on SWMP ecosystem function and services, identify perceived knowledge gaps and prioritize research needs, to advance understanding and management of SWMPs in Ontario, Canada. Workshop participants identified habitat provisioning and regulation of water quality and quantity as key ecosystem functions in SWMPs. They also recognized carbon sequestration, flood prevention, water purification, educational potential, human health promotion and community engagement as important ecosystem services provided by SWMPs. Despite the availability of engineering information and practitioner knowledge, workshop participants suggested that information on the impacts of maintenance operations, biological condition, water quality, costs and benefits and impact on surrounding landscape are important gaps that hinder a modern approach to design and management of SWMPs for multiple co‐benefits. Participants suggested current gaps can be tackled with a combination of continuous water‐quality monitoring, field, laboratory and mesocosm experiments. They also suggested that future SWMP studies take advantage of existing community and governmental databases using meta‐analyses to summarize knowledge and provide future directions. Practical implication: By linking knowledge gaps to management needs, this practice insight provides a road map that can be used to advance management of SWMPs in Ontario and elsewhere.

From Theory to Practice - Research of Education at the Faculty of Architecture and Urban Planning - Cluj-Napoca

This study investigates the integration of architectural education with public sector technology through a collaborationbetween the Faculty of Architecture and Urban Planning and Cluj County Council. Concentrating on the digitalizationof local urban plans, particularly in rural areas, it offers a practical case study for students to involve them in publicpractice. This partnership allows students to acquire skills in urban planning, territorial management, and compliancewith environmental and professional standards. The research studies how students attain expertise in spatialorganization, urban functionality, and the wider professional scope of architecture and urbanism. The studyemphasizes that real-world, hands-on experiences are indispensable for training future urban planners. It highlightsthe necessity of merging theoretical education with practical application, showcasing the value of collaborativeeducational models in architectural training, particularly in public sector contexts by introducing students and theurban actors of the county administration in a fruitful practice.

Public Space in the Metropolitan Area of Monterrey. Analyzing Parks: El Capitán, Clouthier, and La Silla

Urban parks have a significant impact on citizens' daily lives. Parks have become a visual attraction within the city, creatingopportunities for cultural enrichment and promoting diversity in social activities. It is well known that cities worldwidehave parks of different scales, with various urban equipment and functions. The present study was developed inMonterrey, México, where three different types of urban parks and their future projection were analyzed and compared.The article explores the sectors currently operating in these public spaces and which are best received by users. To thisend, surveys were carried out, and direct users were asked about the frequency of use of the parks. This methodologyallowed us to map the busiest places and make suggestions for the 2030 master plan of the municipalities. For the study,the parks selected were Río la Silla, Capitán, and Clouthier. The aim was to observe and document the effect that each ofthem has on the daily dynamics of Monterrey's public space.

On the Opportunities and Challenges of Foundation Models for GeoAI (Vision Paper)

Large pre-trained models, also known as foundation models (FMs), are trained in a task-agnostic manner on large-scale data and can be adapted to a wide range of downstream tasks by fine-tuning, few-shot, or even zero-shot learning. Despite their successes in language and vision tasks, we have not yet seen an attempt to develop foundation models for geospatial artificial intelligence (GeoAI). In this work, we explore the promises and challenges of developing multimodal foundation models for GeoAI. We first investigate the potential of many existing FMs by testing their performances on seven tasks across multiple geospatial domains, including Geospatial Semantics, Health Geography, Urban Geography, and Remote Sensing. Our results indicate that on several geospatial tasks that only involve text modality, such as toponym recognition, location description recognition, and US state-level/county-level dementia time series forecasting, the task-agnostic large learning models (LLMs) can outperform task-specific fully supervised models in a zero-shot or few-shot learning setting. However, on other geospatial tasks, especially tasks that involve multiple data modalities (e.g., POI-based urban function classification, street view image–based urban noise intensity classification, and remote sensing image scene classification), existing FMs still underperform task-specific models. Based on these observations, we propose that one of the major challenges of developing an FM for GeoAI is to address the multimodal nature of geospatial tasks. After discussing the distinct challenges of each geospatial data modality, we suggest the possibility of a multimodal FM that can reason over various types of geospatial data through geospatial alignments. We conclude this article by discussing the unique risks and challenges to developing such a model for GeoAI.

A Comparative Study of Topic Modelling Approaches for User-generated Point of Interest Data

This study aims to enhance urban planning and management by harnessing the power of machine learning (ML) andbig data. We focus on Urban Functional Zones (UFZs), the fundamental units for human socio-economic activities. Ourmethodology involves compiling Point of Interest (POI) data from various sources for comprehensive analysis. Weemploy various topic modeling approaches such as Latent Dirichlet Allocation (LDA), Latent Semantic Index (LSI),Hierarchical Dirichlet Process (HDP), and Top2Vec. Our principal results reveal significant differences in theperformance and coherence of these models on short text documents. Consequently, our major conclusion isidentifying the better-performing topic model for classifying UFZs from POI data. We also explore four textpreprocessing steps to optimize the performance of the topic models. This study contributes to the field by providinga nuanced understanding of UFZs, paving the way for future data-driven urban planning and management.

Understanding the Synergistic Effects of Walking Accessibility and the Built Environment on Street Vitality in High-Speed Railway Station Areas

The high-speed railway (HSR) has profoundly influenced individuals’ lifestyles and travel behaviors. The development of HSR stations and their surrounding areas plays a critical role in urban growth, enhancing both transport efficiency and urban functionality. This study investigates the development of HSR station areas, with a particular focus on Shanghai Hongqiao station, emphasizing the enhancement of street vitality as essential for integrated urban development. Street vitality in station areas is closely associated with individuals’ activities and travel behaviors, influenced by walking accessibility and the built environment. Understanding these factors is crucial for improving the efficiency and attractiveness of HSR station areas. Although extensive research has examined the separate impacts of the built environment and walking accessibility on street vitality, a significant gap remains in comprehending their synergistic effects. This study employs GPS and point-of-interest (POI) data to analyze the stay time of HSR passengers in station areas. Utilizing machine learning algorithms and geographic information system (GIS) tools, this research models the impact of walking accessibility and the built environment on passengers’ stay time. The results indicate that passengers are more inclined to remain within areas accessible by a 7 min walk from the station. Furthermore, the synergistic effects of walking accessibility and the built environment can inform the spatial planning of various functions. These findings provide valuable insights for urban planners and policymakers aiming to enhance the development and efficiency of HSR station areas.

A Spatial Semantic Feature Extraction Method for Urban Functional Zones Based on POIs

Accurately extracting semantic features of urban functional zones is crucial for understanding urban functional zone types and urban functional spatial structures. Points of interest provide comprehensive information for extracting the semantic features of urban functional zones. Many researchers have used topic models of natural language processing to extract the semantic features of urban functional zones from points of interest, but topic models cannot consider the spatial features of points of interest, which leads to the extracted semantic features of urban functional zones being incomplete. To consider the spatial features of points of interest when extracting semantic features of urban functional zones, this paper improves the Latent Dirichlet Allocation topic model and proposes a spatial semantic feature extraction method for urban functional zones based on points of interest. In the proposed method, an assumption (that points of interest belonging to the same semantic feature are spatially correlated) is introduced into the generation process of urban functional zones, and then, Gibbs sampling is combined to carry out the parameter inference process. We apply the proposed method to a simulated dataset and the point of interest dataset for Chaoyang District, Beijing, and compare the semantic features extracted by the proposed method with those extracted by the Latent Dirichlet Allocation. The results show that the proposed method sufficiently considers the spatial features of points of interest and has a higher capability of extracting the semantic features of urban functional zones than the Latent Dirichlet Allocation.

KESADARAN HUKUM DI LINGKUNGAN PERUMAHAN KEC.SETU KAB.BEKASI

Law students and students at IBLAM Jakarta College of Law, Carrying out Real Work Lecture Activities or KKN as a form of community service. Implementation of Internship Activities in RW .014 Ciledug Village, District. Setu, Kab. Bekasi, West Java on Tuesday 23 April 2024. In this area, society has experienced very rapid development, resulting in a shift in urban functions to suburban areas. The next consequence is that suburban areas will experience a process of spatial transformation in the form of residential densification and social transformation. economy as a further impact of social transformation. We convey this Legal Awareness in Residential Environments with the aim of ordering and securing streets in residential environments so as not to disturb fellow road users, following government regulations based on Law Number 38 of 2004 concerning roads and the LLAJ Law, namely DKI Jakarta Regional Regulation 8/2007 concerning regulations making speed bumps and to provide driving comfort for road users

Urban functional zone mapping by coupling domain knowledge graphs and high‐resolution satellite images

AbstractTimely and accurate mapping of urban functional zones (UFZs) is crucial to urban planning and management. Although existing methods for identifying urban functions have made remarkable progress, they still suffer from limitations, such as high sample dependency, insufficient semantic relationship representation, and poor interpretability due to being data‐driven models. To bridge this gap between these methods and the way humans identify functional areas, a new framework that couples domain knowledge and remote sensing images was proposed for the mapping of UFZs. First, to model the concepts, attributes, and spatial and semantic relationships of urban functional objects, a UFZ knowledge graph (UFZ‐KG) was constructed to assist in the mapping of UFZs. Then, the contrastive language‐image pretraining model was adopted to encode jointly the semantic features of UFZ‐KG and the visual features of UFZ images. In this model, a nonlinear embedding module was designed to achieve semantic alignment of these two different modal features in shared space. The effectiveness of the proposed method was verified in three test areas in Shenzhen, China. Results demonstrate that the proposed method of coupling UFZ‐KG with satellite images significantly enhances the UFZ classification accuracy compared to the method that solely relies on image features. Furthermore, it exhibits good generalization performance.