Urban Functional Zones Research Articles

Spatial coupling relationship between architectural landscape characteristics and urban heat island in different urban functional zones

The process of urbanization exacerbates the urban heat island phenomenon. In order to reasonably identify urban functional zones, and reveal the influence of buildings on land surface temperature (LST). Guiyang, a multi-mountainous city in China, was taken as the research area, Open Street Map (OSM) and land use type data were used to identify urban functional zones. Subsequently, the correlation between urban building morphology metrics and LST was analyzed using Pearson correlation and boosted regression tree (BRT) model. The results showed that: (1) There were significant differences in LST among different urban functional zones. (2) The relative contribution value of building density (BD) to land surface temperature was high, and it significantly exacerbated the urban heat island effect across all urban functional zones, with the maximum impact on LST reaching 2.5 °C in Utility zone. (3) Mean building height (MBH), Southerly wind speed (S_Wind speed) and Landscape shape index of building (LSI_Building) had the effect of alleviating thermal environment, and the mitigation degree of land surface temperature was the highest in Residential zone and Ecological zone, reaching 0.6 °C, 0.8 °C and 2.0 °C, respectively. This study enhances our understanding of the relationship between urban functions and LST, providing valuable insights for mitigating the urban thermal environment and promoting sustainable development.

Forecasting urban electric vehicle charging power demand based on travel trajectory simulation in the realistic urban street network

This paper presents a spatial-temporal urban charging power demand forecasting method based on EV travel trajectory simulation in the integrated urban street network and functional zones. Each EV trip is simulated via a travel trajectory based on the EV user’s travel behavior, which refers to the user’s decision-making processes regarding the choices of trip purpose, departure time, destination, trip route, parking time, and the time, location, and mode of EV charging. EV’s daily travel trajectory is built by merging the travel trajectories of the EV’s daily trips. The spatial-temporal slow and fast urban charging power demands are predicted using the simulated daily travel trajectories of available EVs in the urban area. The proposed simulation method is applied in the urban area of Campinas, Brazil. The results indicate that EVs’ daily travel trajectories are realistically produced to predict the daily charging load profiles at urban activity locations and functional zones.

How Does the 2D/3D Urban Morphology Affect the Urban Heat Island across Urban Functional Zones? A Case Study of Beijing, China

Studying driving factors of the urban heat island phenomenon is vital for enhancing urban ecological environments. Urban functional zones (UFZs), key for planning and management, have a substantial impact on the urban thermal environment through their two-dimensional (2D)/three-dimensional (3D) morphology. Despite prior research on land use and landscape patterns, understanding the effects of 2D/3D urban morphology in different UFZs is lacking. This study employs Landsat-8 remote sensing data to retrieve the land surface temperature (LST). A method combining supervised and unsupervised classification is proposed for UFZ mapping, utilizing multi-source geospatial data. Subsequently, parameters defining the 2D/3D urban morphology of UFZs are established. Finally, the Pearson correlation analysis and GeoDetector are used to analyze the driving factors. The results indicate the following: (1) In the Fifth Ring Road area of Beijing, the residential zones exhibit the highest LST, followed by the industrial zones. (2) In 2D urban morphology, the percentage of built-up landscape (built-PLAND) and Shannon’s diversity index (SHDI) are the main factors influencing LST. In 3D urban morphology, building density, the sky view factor (SVF), and the area-weighted mean shape index (shape index) are the main factors influencing LST. Therefore, low-density buildings with simple and dispersed shapes contribute to mitigating LST, while fragmented distributions of trees, grasslands, and water bodies also play important roles in alleviating LST. (3) In the interactive detection results, all UFZs show the highest interaction detection results with the built-PLAND. (4) Spatial variations are observed in the impact of different UFZs on LST. For instance, in the residential zones, industrial zones, green space zones, and public service zones, the SVF is negatively correlated with LST, while in the commercial zones, the SVF exhibits a positive correlation with LST.

Multiscale Urban Functional Zone Recognition Based on Landmark Semantic Constraints

The classification of urban functional areas is important for understanding the characteristics of urban areas and optimizing the utilization of urban land resources. Existing related methods have improved accuracy. However, they neglect cognitive differences amongst humans in the different scales of regional functions. Moreover, how to build the correlations of cross-scale characteristics is still unresolved when realizing the classification of multiscale urban functional zones. To resolve these problems, a transportation analysis zone involving urban buildings as research units is created and these units are described by geometric and functional characteristics using multiple data sources. Then, a hierarchical clustering model is built for the recognition of urban functional areas at varying scales with landmark semantic constraints. In the experiments, Shanghai served as the study area, and multiscale zones were created using different levels of road networks considering the constraint correlation of the significance between cross-scale maps. The experiential results show the proposed method has excellent performance and optimizes the functional zone classification at different scales. This study not only enriches the multiscale urban functional area-recognition methods but also can be used in other aspects, like cartographic generalization or spatial analysis.

Investigating the performance of SDGSAT-1/GIU and NPP/VIIRS nighttime light data in representing nighttime vitality and its relationship with the built environment: A comparative study in Shanghai, China

In recent years, a substantial body of research has demonstrated the close relationship between the built environment and urban nighttime vitality. While scholars have established slightly varied evaluation systems for the built environment, most have utilized the open-source nighttime light (NTL) data (2012 ∼ ) from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite to characterize nighttime vitality. However, with the release of the high spatial resolution NTL data from Glimmer Imager for Urbanization (GIU) sensor on the Sustainable Development Goals Satellite 1 (SDGSAT-1) by the Chinese Academy of Sciences (CAS), more options are now available for indicating nighttime vitality. Yet, no studies have so far measured the representational capability of this new NTL data. In this study, we selected Shanghai, one of the most urbanized cities in Asia, as our research area. We utilized both GIU NTL data and VIIRS NTL data to represent nighttime vitality and collected multidimensional geospatial big data as indicators of the built environment. Various regression models were employed to explore the differences in explanatory power when using different NTL data. To obtain more reliable conclusions, we have also divided the study area according to urban functional zone categories and conducted experiments on both the overall scale (entire study area) and local scale (different urban functional zones). The results showed that regression models using GIU NTL data had higher R2 values both at overall scale(0.529 > 0.268) and local scale (0.704 > 0.641, 0.701 > 0.408, 0.537 > 0.512, 0.674 > 0.589), indicating stronger explanatory power of GIU NTL data. Furthermore, we found that the use of GIU NTL data revealed an amplification effect, where the impact of certain representative built environment factors (such as road density, building height, and vegetation coverage) on nighttime vitality was magnified, demonstrating more pronounced spatial heterogeneity. Our study investigated the performance of the new open-source SDGSAT1/GIU NTL data in representing nighttime vitality and shows its potential application in research on the relationship between the built environment and nighttime vitality. Additionally, our study also reveals the differences in the impact of the built environment on nighttime vitality across different urban functional zones. All these results will provide more valuable references for city managers aspiring to enhance the nighttime vitality and help them to make more rational planning plans.

An MIU-based deep embedded clustering model for urban functional zoning from remote sensing images and VGI data

Urban functional zoning offers valuable insights into urban morphology and sustainable development. However, the conventional fixed spatial units, such as blocks and grids, cannot easily capture the morphological characteristics inherent in functional union and separation during urban evolution. In this paper, by taking advantage of remote sensing images and geospatial big data, we propose a minimum identification unit (MIU)-based urban functional zoning model. This approach integrates the deep embedded clustering of buildings to generate the spatial unit segmentation, and then identifies the urban function by generating semantic vectors with the Word2Vec model. The effectiveness of the proposed method was tested in the city of Wuhan in China. The results highlight that MIUs provide a more flexible and suitable unit for segmenting urban functional zones compared to traditional street blocks. The proposed method is a feasible way to deal with the semantic redundancy of volunteered geographic information (VGI) data when identifying urban function, and the quality issue only has a significant impact on the minor functional types. Moreover, the building clustering results can effectively reveal the fine-scale urban structure, especially for the administration, manufacturing, and residential types. This demonstrates the potential of our approach in enhancing the understanding of urban morphology and supporting sustainable urban development.

Human Exposure to Ambient Atmospheric Microplastics in a Megacity: Spatiotemporal Variation and Associated Microorganism-Related Health Risk.

Microplastics are found in various human tissues and are considered harmful, raising concerns about human exposure to microplastics in the environment. Existing research has analyzed indoor and occupational scenarios, but long-term monitoring of ambient atmospheric microplastics (AMPs), especially in highly polluted urban regions, needs to be further investigated. This study estimated human environmental exposure to AMPs by considering inhalation, dust ingestion, and dermal exposure in three urban functional zones within a megacity. The annual exposure quantity was 7.37 × 104 items for children and 1.06 × 105 items for adults, comparable with the human microplastic consumption from food and water. Significant spatiotemporal differences were observed in the characteristics of AMPs that humans were exposed to, with wind speed and rainfall frequency mainly driving these changes. The annual human AMP exposure quantity in urban green land spaces, which were recognized as relatively low polluted zones, was comparable with that in public service zones and residential zones. Notably, significant positive correlations between the AMP characteristics and the pathogenicity of the airborne bacterial community were discovered. AMP size and immune-mediated disease risks brought by atmospheric microbes showed the most significant relationship, where Sphingomonas might act as the potential key mediator.

Multilevel effects of urban form and urban functional zones on housing prices: evidence from open-source big data

While the relationship between local housing prices and the urban form and distribution of urban functional zones in a single city is well-discussed, the conclusion is usually sensitive to a particular city context, and cross-city comparative study is limited. This study attempts to examine the influences of urban form and urban functional zone distribution on housing prices within and between cities after controlling the city-wide socio-economic and demographic differences. Based on multiple open-source big data, such as points-of-interest (POI) and historical housing transaction data, the hierarchical linear model is utilized to compare the housing market of 10 extra-large cities in China. Results indicate that the urban form and the urban functional zone distribution significantly influence housing prices after the socio-economic and demographic differences are controlled. For inter-city comparison, an urban form with high compactness, low centrality, low polycentricity, high density, and low dissimilarity in housing development is related to lower city-level housing prices. For intra-city, proximity to work centers, high-quality hospitals, and schools shows positive associations to housing prices.

Exploring the seasonal effects of urban morphology on land surface temperature in urban functional zones

Rapid urbanization intensifies the urban heat island (UHI) effect, leading to a multitude of urban challenges. It is important to conduct comprehensive research to understand the underlying mechanisms driving the UHI. Although previous studies have identified the significance of the effects of urban morphology on land surface temperature (LST), the variations in its effects on LST across seasons and different types of urban functional zone (UFZ) remains undefined. Therefore, we explored the seasonal effects of urban morphology, including landscape indicators, building morphology, and surface biophysical parameters, on LST in different types of UFZ by random forest (RF) regression model. Taking the central urban area of Guangzhou as an example, the results indicated that the impacts of urban morphology on LST were strongly dependent on the seasons and UFZs types. The effects of urban morphology on LST were similar across spring, autumn, and winter, with the building morphology showing substantial impacts in commercial and residential zones. In summer, the effects of surface biophysical parameters on LST were significant, especially in industrial, residential, and public service zones. In addition, warming and cooling factors varied across seasons and UFZs. Vegetation was the effective cooling factor during summer, with its fragmented and irregular distribution making a prominent cooling effect in commercial and residential zones, and its coverage crucial for reducing LST in industrial and public service zones. Building height was the dominant cooling factor in spring, autumn, and winter. We consider that these findings can enhance our understanding of the driving mechanism of UHI effect and offer precise references for urban planning in different seasons and UFZs to effectively mitigate the UHI effect.

Spatial Analysis of Creative Industries for Urban Functional Zones: A GIS-Based Comparative Study in Eastern European Regional Centres: Cluj-Napoca (Romania) and Pécs (Hungary)

Creative industries, by their nature of innovation through creativity, add value to any territory by giving it spatial functionality, and at the same time, they favour sustainable development through the sustainability of the urban centres in which they operate. This paper develops a GIS methodology based on the implementation of a spatial analysis model that captures, in a comparative way, the spatial functionality of two different urban centres—(Cluj-Napoca (Romania) and Pécs (Hungary))—in terms of spatial extent based on the impact of the spatial distribution of creative industries. The spatial analysis model is based on the management of alpha-numeric databases representing the addresses of the creative industries, based on geolocation, for the development of spatial analyses of pedestrian accessibility. The main aim is to identify the specific areas of 15 and 5 min model cities. The comparative analysis was carried out by implementing a deterministic methodology through which the areas with certain degrees of accessibility were analysed in relation to the total analysed area, with the main aim of standardising the scale of analysis due to the difference between the two urban centres, thus facilitating the use of qualitative boning in the final comparison process. The results obtained highlight the local behaviour, at the same time making it possible to conduct a realistic deterministic comparative analysis of the two urban centres, which are also identifiable from the point of view of local territorial functionality, thereby validating the GIS model implemented in the present study.

Urban Functional Zone Classification Using Light-Detection-and-Ranging Point Clouds, Aerial Images, and Point-of-Interest Data

Urban Functional Zones (UFZs) serve as the fundamental units of cities, making the classification and recognition of UFZs of paramount importance for urban planning and development. These differences between UFZs not only encompass geographical landscape disparities but also incorporate socio-economic information. Therefore, it is essential to extract high-precision two-dimensional (2D) and three-dimensional (3D) Urban Morphological Parameters (UMPs) and integrate socio-economic data for UFZ classification. In this study, we conducted UFZ classification using airborne LiDAR point clouds, aerial images, and point-of-interest (POI) data. Initially, we fused LiDAR and image data to obtain high-precision land cover distributions, building height models, and canopy height models, which served as accurate data sources for extracting 2D and 3D UMPs. Subsequently, we segmented city blocks based on road network data and extracted 2D UMPs, 3D UMPs, and POI Kernel Density Features (KDFs) for each city block. We designed six classification experiments based on features from single and multiple data sources. K-Nearest Neighbors (KNNs), random forest (RF), and eXtreme Gradient Boosting (XGBoost) were employed to classify UFZs. Furthermore, to address the potential data redundancy stemming from numerous input features, we implemented a feature optimization experiment. The results indicate that the experiment, which combined POI KDFs and 2D and 3D UMPs, achieved the highest classification accuracy. Three classifiers consistently exhibited superior performance, manifesting a substantial improvement in the best Overall Accuracy (OA) that ranged between 8.31% and 17.1% when compared to experiments that relied on single data sources. Among these, XGBoost outperformed the others with an OA of 84.56% and a kappa coefficient of 0.82. By conducting feature optimization on all 107 input features, the classification accuracy of all three classifiers exceeded 80%. Specifically, the OA for KNN improved by 10.46%. XGBoost maintained its leading performance, achieving an OA of 86.22% and a kappa coefficient of 0.84. An analysis of the variable importance proportion of 24 optimized features revealed the following order: 2D UMPs (46.46%) > 3D UMPs (32.51%) > POI KDFs (21.04%). This suggests that 2D UMPs contributed the most to classification, while a ranking of feature importance positions 3D UMPs in the lead, followed by 2D UMPs and POI KDFs. This highlights the critical role of 3D UMPs in classification, but it also emphasizes that the socio-economic information reflected by POI KDFs was essential for UFZ classification. Our research outcomes provide valuable insights for the rational planning and development of various UFZs in medium-sized cities, contributing to the overall functionality and quality of life for residents.

New insight into the spatiotemporal distribution and ecological risk assessment of endocrine-disrupting chemicals in the Minjiang and Tuojiang rivers: perspective of watershed landscape patterns.

This study evaluated the pollution characteristics, spatiotemporal distribution, and ecological risks of eight endocrine-disrupting chemicals (EDCs) in the Minjiang and Tuojiang rivers. Utilizing 3S technology (ArcGIS, remote sensing, GPS) and Fragstats, the research calculated eight landscape pattern indices related to land use types along the Minjiang river and established correlations between landscape factors and EDC distribution through stepwise multiple regression. The results indicated that bisphenol A (BPA) and nonylphenol (NP) were the most concerning EDCs, with detection frequencies of 97-100% and peak concentrations up to 63.35 ng L-1, primarily located in the middle and lower reaches of the Minjiang river and the upper reaches of the Tuojiang river. There was a significant correlation between the spatial distribution of pollutants and landscape patterns, where increased fragmentation, a higher number of patches, and complex patch shapes within a 10-kilometer buffer zone were associated with elevated levels of river pollution. By integrating four classical mathematical models to fit curves for acute and chronic toxicity data of BPA and NP, the findings suggested that BPA posed a higher ecological risk. This interdisciplinary research provided essential theoretical insights for investigating river pollution and its influencing factors, offering a new perspective on simultaneous river pollution control, urban functional zoning, and adjustment of watershed landscape spatial patterns from an urban planning standpoint.

A Multisource Dynamic Fusion Network for Urban Functional Zone Identification on Remote Sensing, POI, and Building Footprint

A Multisource Dynamic Fusion Network for Urban Functional Zone Identification on Remote Sensing, POI, and Building Footprint

Classification of Urban Functional Zones Based on Multiple Data Sources

With the rapid development of cities, more and more people are flocking to them. Therefore, cities require good planning to meet the needs of all urban residents. The first step in planning a city is to classify the functional areas of the city. This article introduces a method for classifying urban functional areas in the context of rapid urban population growth, aiming to plan cities better and meet population needs. Based on remote sensing, social perception, and multi-source data, corresponding classification methods were explored, and their roles in urban functional area classification were analyzed. Research has found that focusing remote sensing images on local areas, utilizing social perception data uploaded by passersby, and complimentary use of multi-source data can all improve classification accuracy. The exploration of these methods helps to classify urban functional areas more accurately, providing a better basis for future urban development planning and alleviating the pressure brought by urban population growth.

Drivers of Land Surface Temperatures From the Perspective of Urban Functional Zones

Drivers of Land Surface Temperatures From the Perspective of Urban Functional Zones

Evolution and zoning of spatial ecosystem functional stability in the southern hilly region of China: a “structure–function” perspective

IntroductionA series of significant ecological construction projects in the southern hilly region have brought about substantial changes to the ecological status and comprehensive zoning of the region. Hunan Province, with its strategic significance, was chosen as a representative research subject in the southern hilly region.MethodsWe conducted a dynamic evaluation of the ecological status change and comprehensive zoning of Hunan Province from a structure–function perspective by applying the transfer matrix, Theil-Sen, Mann-Kendall, and ecosystem service trade-offs or synergies methods. The research goal was to integrate and harmonize structural complexity and functional diversification, providing valuable insights for optimizing both ecological background and territorial background.ResultsThe main results are as follows: (1) The structural changes of ecosystems were mainly concentrated in settlement and Cropland ecosystems. While the area of bulti-up ecosystems has increased significantly, from 1.34% in 2000 to 2.72% in 2020. Cropland ecosystems marking a decrease of 1.39%, with a continued conversion of Cropland into construction land ecosystems. (2) Ecosystem function changes have introduced instability. Over time, NPP exhibited an oscillating trend of increase followed by a decrease. Spatially, there was a sharp decline in peripheral building land, and the regions of declining NPP displayed a lateral U-shaped distribution. (3) The overall trend in ecosystem service changes was positive. Quantitatively, GP and CS experienced an ascending-then-decreasing pattern, while HQ showed a weak decline and WY increased annually. At the county scale, there was noticeable spatial heterogeneity. Human socio-economic activities and environmental protection policies exert a significant impact on the ecological conditions within the study area. (4) Regarding the national territory space function partition, urban functional zones have primarily catered to residential functions and were mainly distributed in the Changsha-Zhuzhou-Xiangtan area.DiscussionEcological functional zones were mainly centered on ecological functions, and most were located in the mountainous areas of western and southern Hunan Province. The agricultural function areas were mainly to serve production functions and were mainly located in the Dongting Lake Plain, the nearshore plain of the mainstream and tributary systems of the rivers of Xiang, Zi, Yuan, and Li. In general, the changes in ecosystem structure and function in the study area reflect changes in ecological conditions. In the future, ecosystem diversity, stability, and sustainability should be improved from an integrated structure–function perspective.

How does urban heat island differ across urban functional zones? Insights from 2D/3D urban morphology using geospatial big data

The deteriorating urban thermal environment poses a huge impediment to sustainable urban development, which is closely related to the urban morphology under different urban functional zones (UFZs). By integrating remote sensing and geospatial big data, this work aims to reveal the divergent mechanism behind Urban Heat Island (UHI) across UFZs taking insights from 2D/3D urban morphology. The Minimum Spanning Tree (MST) indicator depicting 3D building distribution was introduced and integrated with the classic indicators. Their impacts on UHI were measured by ensemble learning and SHapley Additive exPlanations (SHAP) model. Taking Wuhan as the study area, the eighteen 2D/3D urban morphology indicators affecting UHI in different locations and UFZs were extensively examined and compared. The results reveal that: 1) The impact of 2D/3D urban morphology indicators on UHI significantly varies across different UFZs, with dominance in transportation zones exhibiting opposite polarity compared to the other types; 2) positive impacts have a decreasing trend from the urban center to the edge, while negative impacts exhibit opposite trend; 3) XGBoost outperforms other classic methods in interpreting the impact of urban morphology on UHI for all UFZ types. The findings improving knowledge of UHI across UFZs will provide a practical guide for urban planning.

EXPLORING URBAN FUNCTIONAL ZONES BASED ON MULTI-SOURCE SEMANTIC KNOWLEDGE AND CROSS MODAL NETWORK

Abstract. The refined identification of urban functional zones can provide important basic data and decision-making basis for the formulation of urban spatial development planning, effective relaxation of urban spatial development planning, effective relaxation of urban functions, and optimal allocation of resource space. The multi-source spatiotemporal data represented by multi-source geographic data, social perception data, and thematic data have been widely used in various fields, providing new data sources for the refined identification of urban functional areas. However, there are significant differences in the data generation sources, collection methods, and storage organization formats of multi-source data. In this paper, we propose a method for exploring urban functional zones based on Multi-source Semantic knowledge and deep Coupling Model (MSCM). Our approach integrates information from multiple sources and incorporates the semantics of urban functional zones into a knowledge graph, enabling effective fusion and mining of multi-source data.This method can improve the credibility and precision of the results, providing a richer research perspective for refined urban functional zoning. The results of this paper have important theoretical value and practical significance for the construction of identification, labelling, and monitoring tools for engineering smart cities.

Explore the spatial pattern of carbon emissions in urban functional zones: a case study of Pudong, Shanghai, China.

It is crucial for the development of carbon reduction strategies to accurately examine the spatial distribution of carbon emissions. Limited by data availability and lack of industry segmentation, previous studies attempting to model spatial carbon emissions still suffer from significant uncertainty. Taking Pudong New Area as an example, with the help of multi-source data, this paper proposed a research framework for the amount calculation and spatial distribution simulation of its CO2 emissions at the scale of urban functional zones (UFZs). The methods used in this study were based on mapping relations among the locations of geographic entities and data of multiple sources, using the coefficient method recommended by the Intergovernmental Panel on Climate Change (IPCC) to calculate emissions. The results showed that the emission intensity of industrial zones and transport zones was much higher than that of other UFZs. In addition, Moran's I test indicated that there was a positive spatial autocorrelation in high emission zones, especially located in industrial zones. The spatial analysis of CO2 emissions at the UFZ scale deepened the consideration of spatial heterogeneity, which could contribute to the management of low carbon city and the optimal implementation of energy allocation.

Multi-scale multi-temporal landscape pattern analysis using high spatial urban scene classification

ABSTRACTUrban landscape pattern analysis is conducive to understanding the land cover distribution and changes and promoting urban spatial layout rationality and sustainable development. Previous research has often used low and medium spatial resolution data to classify urban land cover at the pixel level and then analyse landscape pattern indices. However, the underlying semantic information provided by pixel classification needs to consider the spatial correlation of surface features, and it is challenging to obtain high-level semantic information on urban functional areas and analyse the relationship between human activities and land use. Leveraging the abundant spatial characteristics in high-resolution remote sensing images, high spatial urban scene classification can infer the feature categories and spatial relationships between features to realize higher-level image interpretation and obtain regional urban scene details. And it contributes to urban planning and decision-making, better meeting the different demands. Therefore, this study aims at a novel methodological framework for landscape pattern analysis of cross-temporal multi-scale urban scene classification. The results imply that: (1) The high-resolution images can make up for the lack of information on urban functional zones in low and medium resolution images; (2) Landscape pattern analysis based on scene classification can analyse urban spatial relationships more rationally than traditional research; and (3) With the continuous advancement of urbanization, the urban functional zoning becomes more apparent, and the dominant industries have changed significantly from industry to commerce and services. This study expands the analytical application range of landscape ecology and remote sensing technology and provides new inspirations for urban planning, environmental monitoring, ecological protection.