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Festival studies is not only an important field of research for tourism or event industry but is also critical for an architectural and urban design field. Due to the amount of the progress done in this field by many inter- disciplinary areas, the subsequent corpus of research on festivals appears to be relatively disintegrated. Through a systematic literature review (SLR) from 2000 to 2021, this paper examined festivals studies from tourism to urban design disciplines. In this paper, descriptive and thematic analysis were carried out to analyse the state of existing research which gave in to the concept of festival studies. The purpose is to develop an SLR that intends to establish the knowhow on festival spaces and try to explore the advancement in the field and deliver potential research directions. The corpus thus helps us in establishing the topic which is least investigated and would need more research. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses the articles in the issue on topics including urban regeneration, covering multifunctional city centers, Dutch new town maturation, land use intensity in Egypt, and core city revitalization.

Advanced sensor technologies in geospatial sciences and engineering research have contributed to (1) geographic mapping and (2) understanding the geological, ecological, hydrological, and environmental characteristics of Earth surfaces. W. Zhu et al. in their paper entitled "Pre- and Postcollapse Ground Deformation Revealed by SAR Interferometry: A Case Study of Foshan (China) Ground Collapse" investigated the ground deformation in the vicinity of the collapsed sinkhole in the Foshan collapse event using multitemporal SAR interferometry. W. Zhu et al. in their paper entitled "Spatiotemporal Characterization of Land Subsidence in Guandu (China) Revealed by Multisensor InSAR Observations" investigated the time-series two-dimensional ground deformation in Guandu using the multisensor InSAR observation. [Extracted from the article]

Land use conflicts (LUCs) have become a significant global issue. Accurately identifying potential LUCs is crucial for mediating these conflicts, optimizing land use structure, and enhancing land use function. The necessary conditions of LUCs are land use multi-suitability (LUMS), land resource scarcity (LRS), and diversity of demands (DD). However, few studies have approached LUC identification from these three dimensions simultaneously. In addition, when assessing the diversity of demand, only human needs are considered and wildlife needs are ignored. In order to address this gap in the research, this paper constructs a novel framework for LUC identification and proposes an induction-oriented governance path. LUMS was evaluated from three aspects: construction suitability, cultivation suitability, and ecological suitability. LRS is measured from three dimensions: construction land, cultivated land, and ecological land scarcity. The DD is expanded into human and wildlife demand diversity. By analyzing the combination of LUMS, LRS, and DD, LUCs are classified using the potential LUC identification Rubik's cube model, and corresponding governance paths are suggested. In Shandong Province, potential LUCs are relatively high, with strong, medium, and weak conflicts accounting for 27.39%, 57.10%, and 13.06%, respectively. Potential strong conflicts are mainly distributed in the metropolitan suburbs and in the western plain of Shandong Province. Cultivated land is the main potential land use conflict space. The new framework of LUC identification proposed in this paper can effectively identify potential LUCs. Our research provides scientific reference for sustainable land use. [ABSTRACT FROM AUTHOR]

Land intensive use reflects the spatial structure, agglomeration characteristics, and internal mechanisms of urban economic, social, and ecological system development, significantly impacting urban resilience. Based on panel data from 287 cities in China from 2010 to 2020, this paper measures the levels of land intensive use and urban resilience, and empirically examines the impact mechanism of land intensive use on urban resilience through baseline regression and panel quantile regression. The results reveal that: (1) During the study period, China's urban land intensive use level has significantly improved. The land intensive use level shows a trend of "the strong become stronger, and the weak are always weak" and "high in the east and low in the west" spatial differentiation, while the urban resilience level showed a trend of accelerated "catching up" of low-resilience cities towards high-resilience cities and "high in the east and low in the west" spatial differentiation as well. (2) Land intensive use significantly promotes effect on urban resilience, and the effect depends on different conditions. (3) Among all dimensions of land intensive use, both land input intensity and land use benefits significantly promote urban resilience, while land use intensity shows an insignificant effect. (4) The impact of land intensive use on urban resilience demonstrates significant scale heterogeneity and geographic regional heterogeneity. Based on these findings, the paper proposes relevant policy suggestions for land intensive use aimed at improving urban resilience, offering guidance for promoting high-quality land use and sustainable urban resilience development. [ABSTRACT FROM AUTHOR]

The article discusses the growing interest in establishing smart cities based on the Internet of Things (IoT) and the challenges they face, particularly in terms of security. The article highlights a special issue of the IETE Journal of Research that focuses on IoT, sensor networks, and blockchain technology for smart cities and societies. The special issue contains 10 full papers that cover various topics such as urban land use change detection, air quality data imputation, steganographic approaches for secured communication, and agricultural applications using blockchain. The article expresses gratitude to the organizers and contributors of the special issue and hopes that it will be a valuable resource for researchers and engineers. [Extracted from the article]

Exploring the regional differences and convergence of urban land green use efficiency (ULGUE) is of great significance for urban green development and the goal of carbon neutrality. Based on the panel data of 282 prefecture-level cities in China from 2006 to 2019, this paper uses the SBM-Undesirable model to calculate ULGUE in China, then uses Dagum Gini coefficient decomposition, coefficient of variation and panel data model to reveal the regional differences and convergence of ULGUE. The results show that ULGUE in China increased from 0.441 to 0.555 in 2006–2019. The number of cities in the medium–low efficiency range is dominant, accounting for more than 54%. There are differences in ULGUE among the three major regions, which shows the distribution pattern of west > east > central. ULGUE in China has significant spatial agglomeration characteristics, and its overall, intra-regional and inter-regional differences show a downward trend. Over-variable density is the main source of the overall difference, with an average contribution rate of 49.71%. ULGUE across the country and in the three regions of east, central and west in China exist significant σ convergence, absolute β and conditions β convergence. The research results are helpful to objectively evaluate ULGUE in China, and provide decision-making reference for promoting the green, low-carbon and sustainable use of urban land and achieving the goal of carbon neutrality. [ABSTRACT FROM AUTHOR]

Urban land is the primary location for manufacturing and services, facilitating the expansion and interconnectedness of economic activities and factor flows to shape various urban land-use performances (ULUP). Exploring these spatial linkages of urban land-use performance can play a crucial role in fostering cohesive urban development. Taking 109 prefecture-level cities as research samples, this paper explores the characteristics and influencing factors of the spatial network associated with ULUP in the Yangtze River Economic Belt through modified gravity model, network analysis and QAP analysis. The analysis shows that ULUP has shown an N-shaped trend over the past two decades. It has formed a network of correlations, with Chongqing, Shanghai, and Wuhan emerging as central nodes. Notably, the correlation predominantly occurs between geographically adjacent cities, with weaker links between the Yangtze River Economic Belt's upstream, midstream, and downstream regions. The network can be divided into four distinct types: main inflow plate, main outflow plate, agent plate, and bidirectional spillover plate. Geographic location is the most significant factor influencing network formation, followed by resource mismatch, economic development, and openness. The study provides theoretical guidance and empirical support for improving the utilisation of urban land and promoting coordinated development. [ABSTRACT FROM AUTHOR]

These time-based travel models, Gilbert and Woodcock argue, aim to reduce urban dispersion by concentrating activities within the time-frames in contrast to automobile travel which disperses activity through travel speed. This is the second special issue arising from the 10th State of Australasian Cities Conference (SOAC) hosted by a consortium of Melbourne-based universities: RMIT University, the University of Melbourne, Monash University and Swinburne University of Technology. Kent's paper offers an important point of departure for travel research, such that familial travel has been often overlooked as a key multi-occupant trip component of overall travel demand, despite most travel surveys being conducted at the household scale. [Extracted from the article]

Current research on automated driving systems focuses on Level 4 automated driving (AD) in specific operational design Domains (ODD). Measurement data from customer fleet operation are commonly used to extract scenarios and ODD features (road infrastructure, etc.) for the testing of AD functions. To ensure data relevance for the vehicle use case, driving domain classification of the data is required. Generally, classification into urban, extra-urban and highway domains provides data with similar ODD features. Highway classification can be implemented using global navigation satellite system coordinates of the driving route, map-matching algorithms, and road classes stored in digital maps. However, the distinction between urban and extra-urban driving domains is more complex, as settlement taxonomies and administrative-level hierarchies are not globally consistent. Therefore, this paper presents a map-based method for driving domain classification. First, potential urban areas (PUA) are identified based on urban land-use density, which is determined based on land-use categories from OpenStreetMap (OSM) and then spatially smoothed by kernel density estimation. Subsequently, two road network scaling models are used to distinguish between urban and extra-urban domains for the PUA. Finally, statistics of ODD feature distribution are analysed for the classified urban and extra-urban areas. [ABSTRACT FROM AUTHOR]

This document discusses the applications of machine learning in national territory spatial planning. National territory spatial planning is important for solving issues such as human-land conflict and regional development imbalance. Machine learning technology is being used to improve national territory spatial planning, but there is still a need for further exploration and research in this area. The document highlights research papers that focus on topics such as urban spatial environmental monitoring, food security and agricultural development, and methodological innovation in territorial spatial planning. The authors note that there are challenges and difficulties that need to be addressed in the future, including data mining, small-scale research, and practical case studies. [Extracted from the article]

Land use planning studies are accumulating in unprecedented quantities, and have created a wide space for an extensive summary, the synthesis of fundamental developments, a sharpening of the focus of future study issues, and the dissemination of concise information among the academic community and the policy making environment. Nonetheless, the publication rate and content coverage of land use planning literature reviews have been relatively low. This study aims to contribute to the field by first assessing the effectiveness of the bibliometric method as a literature study method. It then summarizes the developments in the land use planning literature in general path building process, the planning context, and the development of methods. The study findings highlight that the bibliometric method tends to reward information carriage papers rather than original contributors. More than 85% of the time, published articles are cited for general information rather than their core research contribution, suggesting the incomprehensiveness of the bibliometric method in studying real knowledge development. In terms of the land use planning context, significant gaps are observed, particularly in urban land use, including the detachment of the general goal-oriented objective formulations from basic land use allocation theories and conceptual models. A serious concern also arises that the formulations of certain urban land use optimization objectives appear to contradict the original concept of a sustainable city. To address these gaps, this paper suggests several key research frontiers, including linking the basic land use allocation and utility theories to land use optimization, and a need to shift current urban land use planning/optimization approaches from spatial optimization, which changes land uses to meet flow resources, towards optimizing the flow of resources to fit the resilient nature of existing spatial configurations and physical establishments in the built environment. Additionally, evaluating the existing built environment for optimality should be prioritized rather than solely focusing on new developments. In terms of method development, the study suggests that advancing current loosely coupled methods into more integrated systems may be a promising frontier. In general, the paper strongly suggests the possible reiteration of the land use planning study landscape to make sustainable development discourse more concrete and to make the method development stage involve more integrated systems. Immediate research interventions may be the standardizing of land use planning studies through the development of protocols and standard benchmark problems. [ABSTRACT FROM AUTHOR]

The daily trips of urban residents in different transport modes reflect the urban mobility pattern and the latent spatial structure of the city, which is seldom addressed in the literature. A network-based cluster model is applied in this paper based on multimode transit big data. Thus, the urban spatial form can be observed at different levels according to the travel modes and distance, differing from traditional geographic structures. This paper will identify the urban mobility pattern and the corresponding spatial form by taking the Beijing metropolitan area as a study area. Origin–destination data at the traffic analysis zone level were collected, covering shared bike, bus, and metro. The results show that the metro trips reflect the single-center feature of Beijing. However, the multicenter development trend can be revealed by bus and shared-bike data. This paper finds that shared bike supports the establishment of subcenters as a new transport mode. The urban spatial form presents a complex feature of multiple levels and spatial diversity due to the comprehensive effect of multiple travel modes. The study results can provide a basis for the planning of transit network and urban equilibrium land use and development. [ABSTRACT FROM AUTHOR]

Surface waters are essential for meeting the needs of the world. In many regions, stream water quality is a major concern due to contamination from multiple sources. Stream water is also susceptible to climatic events and land-use practices influencing its catchment. Understanding the impact of such events on stream water quality is crucial for managing and protecting aquatic ecosystems and providing safe drinking water to communities that rely on these streams. Hence, monitoring and evaluating stream water quality holds significance in identifying potential hazards and implementing suitable management strategies. In this paper, a novel effort was made to determine the relative feature importance of a set of watershed characteristics (precipitation, temperature, urban land use, agricultural land use, and forest land-use factors) on four important water quality parameters (WQPs): fecal coliforms (FC), turbidity, pH, and conductivity of the Upper Green River watershed, Kentucky, USA. Random forest (RF), an ensemble learning method, was used to predict the WQPs from the causal parameters and determine the feature importance characteristics of the four WQPs previously mentioned. This model demonstrated that precipitation and temperature are the most influential factors on FC, turbidity, and pH. Forest land use and temperature are the two most important factors for conductivity. The novel feature importance factors of the RF model have likewise been confirmed for each WQP. In modeling stream WQPs, the developed the RF model outperformed the artificial neural network (ANN) model. Using the RF model, we obtain regression coefficients of (0.93, 0.74, and 0.94) for pH in training, testing, and overall. We obtain regression coefficients of (0.60, 0.64, and 0.61) using the ANN model. ⁠⁠⁠⁠⁠⁠⁠Overall, the RF model was more effective than the ANN model in modeling stream WQPs. The model identified precipitation and temperature as the most influential factors on FC, turbidity, and pH, while forest land use and temperature were the most important factors in determining conductivity. It is also found that land use factors are important to improve the accuracy of WQPs predictions from climate variables. The results of this study can be used by authorities to better understand and control pollution at the watershed scale. [ABSTRACT FROM AUTHOR]

In the context of accelerated urbanization, assessing the quality of the existing built environment plays a crucial role in urban renewal. In the existing research and use of deep learning models, most categories are urban construction areas, forest land, farmland, and other categories. These categories are not conducive to a more accurate analysis of the spatial distribution characteristics of urban green space, parking space, blue space, and square. A small sample of refined land use classification data for urban built-up areas was produced using remote sensing images. The large-scale remote sensing images were classified using deep learning models, with the objective of inferring the fine land category of each tile image. In this study, satellite remote sensing images of four cities, Handan, Shijiazhuang, Xingtai, and Tangshan, were acquired by Google Class 19 RGB three-channel satellite remote sensing images to establish a data set containing fourteen urban land use classifications. The convolutional neural network model EfficientNetV2 is used to train and validate the network framework that performs well on computer vision tasks and enables intelligent image classification of urban remote sensing images. The model classification effect is compared and analyzed through accuracy, precision, recall, and F1-score. The results show that the EfficientNetV2 model has a classification recognition accuracy of 84.56% on the constructed data set. The testing set accuracy increases sequentially after transfer learning. This paper verifies that the proposed research framework has good practicality and that the results of the land use classification are conducive to the fine-grained quantitative analysis of built-up environmental quality. [ABSTRACT FROM AUTHOR]

Urban land use multi-objective optimization aims to achieve greater economic, social, and environmental benefits by the rational allocation and planning of urban land resources in space. However, not only land use reconstruction, but renovation, which has been neglected in most studies, is the main optimization direction of urban land use. Meanwhile, urban land use optimization is subject to cost constraints, so as to obtain a more practical optimization scheme. Thus, this paper evaluated the renovation and reconstruction costs of urban land use and proposed a cost-heuristic genetic algorithm (CHGA). The algorithm determined the selection probability of candidate optimization cells by considering the renovation and reconstruction costs of urban land and integrated the renovation and reconstruction costs to determine the direction of optimization so that the optimization model can more practically simulate the actual situation of urban planning. The reliability of this model was validated through its application in Shenzhen, China, demonstrating that it can reduce the cost consumption of the optimization process by 35.86% at the expense of sacrificing a small amount of economic benefits (1.18%). The balance of benefits and costs enhances the applicability of the proposed land use optimization method in mature, developed areas where it is difficult to demolish buildings that are constrained by costs. [ABSTRACT FROM AUTHOR]

Making efforts to promote rationalized urban construction land change, distribution, allocation, and its performance is the core task of territory spatial planning and a complex issue that the government must face and solve. Based on the Boston Consulting Group matrix, a decoupling model, and a GIS tool, this paper constructs a new tool that integrates "dynamic analysis + performance evaluation + policy design" for urban construction land. We reached the following findings from an empirical study of Gansu, China: (1) Urban construction land shows diversified changes, where expansion is dominant and shrink cannot be ignored. (2) Most cities are in the non-ideal state of LH (Low-High) and LL (Low-Low), with a small number in the state of HH (High-High) and HL (High-Low). (3) Urban construction land change and population growth, economic development, and income increase are in a discordant relationship, mostly in strong negative decoupling and expansive negative decoupling. (4) The spatial heterogeneity of urban construction land change and its performance are at a high level, and they show a slow upward trend. Additionally, the cold and the hot spots show obvious spatial clustering characteristics, and the spatial pattern of different indexes is different to some extent. (5) It is suggested that in territory spatial planning Gansu should divide the space into four policy areas—incremental, inventory, a reduction development policy area, and a transformation leading policy area—to implement differentiated management policies and to form a new spatial governance system of "control by zoning and management by class". The change of urban construction land, characterized by dynamics and complexity, is a direct mapping of the urban growth process. The new tools constructed in this paper will help to reveal the laws of urban development and to improve the accuracy of territory spatial planning in the new era. They are of great theoretical significance and practical value for promoting high-quality and sustainable urban development. [ABSTRACT FROM AUTHOR]

In-depth research on the spatiotemporal patterns and evolution trend of urban land use carbon emission intensity (ULUCEI) can reveal the internal relationship between urban land use and carbon emissions, which is crucial for achieving carbon emission reduction and "double carbon" targets. This paper proposed a conceptual framework of ULUCEI; the methods of kernel density estimation (KDE), exploratory spatial data analysis (ESDA), and spatial Markov chains were adopted for exploring the spatiotemporal patterns and evolution trend of China's ULUCEI from 2000 to 2017. The following conclusions are drawn through research. (1) There was an increasing trend in ULUCEI in China from 0.102 in 2000 to 0.283 in 2017. From the regional perspective, the ULUCEI in the eastern region is markedly higher than that in the central and western regions. Moreover, the results of nuclear density estimation indicate that China's ULUCEI shows an obvious upward and polarized trend. (2) China's ULUCEI shows a positive spatial autocorrelation. The types of spatial agglomeration include "high-high" agglomeration, "high-low" polarization, "low-high" collapse, and "low-low" homogeneity, and there are obvious disparities in the distribution rules of cities with different spatial agglomeration forms. (3) China's ULUCEI presents strong stability and "club convergence" trend. Moreover, spatial factors significantly affect the dynamic transition of China's ULUCEI, and its effect on the shifting upwards gradually enhances with increasing lag type. This paper therefore suggests that policymakers should formulate differentiated urban land low-carbon use models and carbon emission reduction policies to reduce ULUCEI. [ABSTRACT FROM AUTHOR]

At present, the insufficient supply of land resources has seriously hindered the sustainable development of regional economy. Improving the urban green land use efficiency (UGLUE) has become a key issue on the road to sustainable development. As an important feature of economic development, industrial agglomeration has an impact on the UGLUE that cannot be ignored. This paper uses the Global Malmquist–Luenberger Index (GMLI) to measure UGLUE of 107 cities in the Yangtze River Economic Belt (YREB) from 2007 to 2016, and uses a dynamic panel model (DPM) to empirically analyze the effects of industrial specialization agglomeration and diversification agglomeration on UGLUE. On this basis, the heterogeneous impact of industrial agglomeration in different regions on UGLUE is further discussed. The results illustrate that: (1) The UGLUE shows a general downward trend. (2) Different modes of industrial agglomeration have different impacts on UGLUE. The impact of industrial specialization agglomeration on UGLUE was inverted U-shaped. Industrial diversification agglomeration has a positive effect on UGLUE. (3) The impact of industrial agglomeration in different regions on UGLUE is heterogeneous. The relationship between the industrial agglomeration and UGLUE in the YREB revealed in this paper will provide a reference for promoting UGLUE. [ABSTRACT FROM AUTHOR]

Urban land use provides essential information about how land is utilized within cities, which is critical for land planning, urban renewal, and early warnings for natural disasters. Although existing studies have utilized multi-source perception data to acquire land use information quickly and at low cost, and some have integrated urban morphological indicators to aid in land use identification, there is still a lack of systematic discussion in the literature regarding the potential of three-dimensional urban morphology to enhance identification effectiveness. Therefore, this paper aims to explore how urban three-dimensional morphology can be used to improve the identification of urban land use types. This study presents an innovative approach called the UMH–LUC model to enhance the accuracy of urban land use identification. The model first conducts a preliminary classification using points of interest (POI) data. It then improves the results with a dynamic reclassification based on floor area ratio (FAR) measurements and a variance reclassification using area and perimeter metrics. These methodologies leverage key urban morphological features to distinguish land use types more precisely. The model was validated in the Pearl River Delta urban agglomeration using random sampling, comparative analysis and case studies. Results demonstrate that the UMH–LUC model achieved an identification accuracy of 81.7% and a Kappa coefficient of 77.6%, representing an 11.9% improvement over a non-morphology-based approach. Moreover, the overall disagreement for UMH–LUC is 0.183, a reduction of 0.099 compared to LUC without urban morphology and 0.19 compared to EULUC-China. The model performed particularly well in identifying residential land, mixed-use areas and marginal lands. This confirms urban morphology's value in supporting low-cost, efficient land use mapping with applications for sustainable planning and management. [ABSTRACT FROM AUTHOR]

Image segmentation plays an important role in object-based classification. An optimal image segmentation should result in objects being internally homogeneous and, at the same time, distinct from one another. Strategies that assess the quality of image segmentation through intra- and inter-segment homogeneity metrics cannot always predict possible under- and over-segmentations of the image. Although the segmentation scale parameter determines the size of the image segments, it cannot synchronously guarantee that the produced image segments are internally homogeneous and spatially distinct from their neighbors. The majority of image segmentation assessment methods largely rely on a spatial autocorrelation measure that makes the global objective function fluctuate irregularly, resulting in the image variance increasing drastically toward the end of the segmentation. This paper relied on a series of image segmentations to test a more stable image variance measure based on the standard deviation model as well as a more robust hybrid spatial autocorrelation measure based on the current Moran's index and the spatial autocorrelation coefficient models. The results show that there is a positive and inversely proportional correlation between the inter-segment heterogeneity and the intra-segment homogeneity since the global heterogeneity measure increases with a decrease in the image variance measure. It was also found that medium-scale parameters produced better quality image segments when used with small color weights, while large-scale parameters produced good quality segments when used with large color factor weights. Moreover, with optimal segmentation parameters, the image autocorrelation measure stabilizes and follows a near horizontal fluctuation while the image variance drops to values very close to zero, preventing the heterogeneity function from fluctuating irregularly towards the end of the image segmentation process. [ABSTRACT FROM AUTHOR]

Environmental public interest litigation (EPIL) is a significant part of the judicial system; it is aimed at strengthening judicial protections and safeguarding public interests. Based on the quasi-natural experimental setting of China's EPIL pilot project, this study examines the impact of EPIL on the country's urban land green use efficiency (ULGUE). The findings show that effectively implementing EPIL enhances ULGUE. Specifically, this policy has led to a 6.6% increase in ULGUE in pilot cities, and its impact has grown stronger over time. Mechanism analysis results show that EPIL mainly enhances ULGUE by strengthening environmental supervision and law enforcement, by increasing public participation in environmental governance, and by promoting green innovation and industrial structure upgrades. Furthermore, heterogeneity analysis revealed that the positive effects of this policy implementation are more pronounced in resource-based cities, cities with open environmental information, and cities with high marketization. This paper provides empirical evidence for the effectiveness of environmental governance via EPIL. [ABSTRACT FROM AUTHOR]

With the increasingly prominent issues of climate change and energy shortages, large-scale development of renewable and clean energy has become a common choice for countries around the world. Among them, onshore wind power plays an important role in the global energy transformation process due to its mature technology, safety, and efficiency advantages. However, because wind energy resources have the characteristics of low energy density, instability, and spatial dispersion, large-scale development of onshore wind power requires a large amount of land resources to support. To ensure the land resource demand for wind power development under the goal of 'carbon neutrality,' this paper conducted a quantitative study on the land occupation of onshore wind power in China. Firstly, based on the criterion of exclusivity, this paper reclassified the types of land occupation for wind farms from a spatio-temporal perspective. Secondly, based on the sample parameter method and the alpha shape algorithm, this paper calculated the land area of existing onshore wind farms in China categorically. Finally, considering the development trend of wind power technology, this paper systematically simulated and predicted the wind power land demand in different development scenarios under the 'carbon neutrality' goal. The results indicated that: (1) Based on the principle of exclusivity, the total land use area of wind farms could be divided into two parts in the spatial dimension: the direct impact area and the indirect impact area. The direct impact area could be divided into two parts in the temporal dimension: permanent land use and temporary land use. (2) In 2022, the land occupied by the wind turbine foundation, permanent land use, temporary land use, and direct impact area of China's onshore wind farms reached 48.28 km², 352.08 km², 1 234.86 km², and 1 638.81 km², respectively. (3) In 2022, the indirect impact area and total land use area of China's onshore wind farms reached 9.81x104 km ² and 9.99x104 km², respectively. The total land use area was almost equivalent to the total urban land use area of China. (4) Compared with the scenario of current technical level, under the scenario of technological progress and wind turbine upgradation, the various types of land use demand for wind power in China will have significantly decreased by 2060, and land use demand will have reached its peak by around 2050. The large-scale upgradation of wind turbines has great potential for saving land resources. Therefore, perfecting the land use standards of wind power construction, improving the land use efficiency of wind power development, and promoting the construction of offshore wind power are important paths for coordinating the sustainable use of wind energy resources and land resources. [ABSTRACT FROM AUTHOR]

Community green spaces (CGSs) constitute a crucial element of urban land use, playing a pivotal role in maintaining the stability of urban ecosystems and enhancing the overall quality of the urban environment. Through the post-occupancy evaluation (POE) of green spaces, we can gain insights into residents' actual needs and usage habits, providing scientific evidence for the planning, design, and management of green spaces. This ensures that CGSs better meet residents' needs and improve their quality of life. The POE of CGSs relies heavily on high-precision data support. However, the current POE system for CGSs faces challenges, such as limited data collection methods, incomplete indicator systems, and excessive manual involvement. To address these limitations in data collection, this study proposes a comprehensive, dynamically monitored, objective, and sustainable POE system for CGSs. This system incorporates a multi-dimensional perception system that integrates the Internet of Things (IoT) and sensors to collect data from various sources. It establishes an evaluation framework from the perspectives of policy guidance and usage needs for CGSs, utilizing neural network systems and artificial intelligence techniques to compute the evaluation results. Using the Jiayuan Sanli Community in Beijing as a case study, this paper demonstrates the feasibility of the proposed system. A comparison between the POE results obtained using the multi-dimensional perception technique and those obtained manually reveals an 87% improvement in the accuracy of the evaluation results based on the multi-dimensional perception system. This system bridges the gap between planning perspectives and user experiences, contributing significantly to future urban land planning and land policy formulation. [ABSTRACT FROM AUTHOR]

This paper examines how the zoning concepts and urban planning space standards adopted from colonial administration are implemented on the actual ground. Most cities in developing counties apply planning standards and concepts which are inherited from Europe or North America, Tanzania inclusive. Planning and design concepts that segregate residential, commercial and industrial activities and impose low densities that require large areas of urban land, and impose high transport and environmental costs and inhibit opportunities for local economic initiatives which can help lift communities out of poverty. Development control policies are also frequently based on imported or inherited norms, rather than traditional practices and local climatic conditions. Despite the existence of the inherited planning concepts and standards, however, little is known whether such concepts and standards are implemented on the actual ground and achieved. Goal and Objectives: The overall objective of this study is to examine whether the planning concepts and space standards inherited from colonial administration are implemented and achieved on the ground and the resulting effects on pedestrian mobility. Methodology: The study adopted descriptive research design of which data collection techniques involved case study, interviews, observations and surveys. A combination of methods were used during the fieldwork, including expert interviews to identify areas inside the case study block for in-depth survey, followed by intensive surveys, and use inventory and semi-structured interviews with residents. A total of 25 residents were purposely selected for in-depth interviews. Results: Fieldwork results reveal that the concept of zoning density plots according to incomes i.e. high, medium and low density plots to accommodate the low, middle and high income earners respectively is not fully implemented and it is outdated as it does not reflect the post-colonial reality. Likewise, the concepts of designating special land use per area such as pure residential, commercial, residential cum commercial, institutional or industrial areas, where no other uses are allowed are inappropriate today as they don't reflect the urban reality. The existing development in the study area seemed to be a kind of mixed use development. Violations of minimum setbacks and plot coverage in the study area were prevailing. The planned public open spaces or road reserves were mainly used for commercial, office, religious site and parking activities instead of green areas. [ABSTRACT FROM AUTHOR]

The paper by Zoncová et al. used CORINE land cover data for mapping extent and character of land cover changes in the Low Tatras National Park in Slovakia over the last 30 years (1990-2018). It is increasingly recognized that land use and land cover changes driven by anthropogenic pressures are increasingly impacting terrestrial and aquatic ecosystems and their services, human society, and human livelihoods and well-being. [Extracted from the article]

As a form of rapid mass transportation, urban rail systems have always been widely used to alleviate urban traffic congestion and reconstruct urban structures. Land use characteristics are indispensable to this system and correlate with urban ridership. Dock-less bicycle-sharing expands the station service coverage range because it integrates public transportation with an urban rail system to create a convenient travel model. Consequently, the land use pattern with dock-less bicycle-sharing is associated with urban rail ridership. This paper measures the correlation between land use and urban rail ridership based on the trajectory of dock-less bicycle-sharing, which precisely reflects the travel behavior of passengers along the trip chain. The specific relationship has been determined using the random forest model. This paper found that the land use pattern could better explain the egress ridership during morning peak hours. In particular, it could explain 48.46% of the urban rail ridership in terms of egress, but the explicability for the ingress ridership slightly decreased to 36.88%. This suggests that the land use pattern is related to urban rail ridership. However, the impact situation varies, so we should understand this relationship with greater care. [ABSTRACT FROM AUTHOR]

Storm surge inundation hazards annually cause billions in economic losses globally, and millions of coastal residents live in danger. Properly understanding and assessing storm surge inundation are essential measures to guarantee the sustainable construction of coastal cities. In this paper, a differentiated urban risk semi-quantitative assessment method for storm surge inundation is proposed to evaluate the risk of storm surge hazard causing inundation to the coastal city. The Finite Volume Community Ocean Model (FVCOM) and the Jelesnianski model restore the historical storm surge cases to reveal hazards. The point of interest data and the urban land use and land cover data are utilized to assess the vulnerability of the coastal city, and a differentiated risk assessment method is proposed to evaluate the risks for urban facilities in the hazard. As an illustration, the method is utilized to assess storm surge Mangkhut in 2018 in Shenzhen, Guangdong Province, China. The vulnerability of the Shenzhen downtown area is assessed and designed as a map to visualize the strategic area. According to numerical simulation and inundation region mapping, the danger and the risk assessment map are made to intuitively present the distribution of the hazard-affected region. [ABSTRACT FROM AUTHOR]

The article focuses on advancing methods for evaluating, managing, and implementing technologies aimed at creating sustainable cities. Topics include the use of econometric models to assess sustainability, strategies for integrating global costs and benefits into urban governance decisions, and the effects of land use changes on urban resilience and health.

Rapid urban expansion places unprecedented pressure on urban land management. Optimization of urban spatial layout has become a theoretical proposition and a practical problem associated with urban expansion. Thus, by using the social value measurement model, equivalent factor method, and marginal benefit measurement model, in this paper, we analyze the land resource allocation mechanism, efficiency loss, and reasonable interval. The study results are as follows: (1) Theoretical analysis shows that there are three ranges of land use for urban expansion in China—ideal, moderate and extreme—and it is reasonable to control the amount of land used for urban expansion within the moderate range. (2) Value measurement reveals that the marginal social value and marginal ecological value of cultivated land in Xuzhou show an overall upward trend. From the perspective of the constant price in 2018, the marginal social value increased from CNY 4.91 million/hm2 in 2005 to CNY 14.31 million/hm2 in 2018, while the marginal ecological value increased from CNY 9513.60/hm2 in 2005 to CNY 13,629.70/hm2 in 2018. (3) Empirical analysis shows that when the marginal value of land as cultivated land and construction land is equal, the actual scale of occupied agricultural land is 51,887.23 hm2, of which 25,686.67 hm2 of cultivated land is converted into construction land, sacrificing the social value of farmland, and 12,281.70 hm2 of cultivated land is converted into construction land, ignoring the non-market value of land. Based on the perspective of the marginal principle, in this paper, we construct a model of urban expansion land use allocation and define reasonable intervals for urban expansion land use so as to provide a quantitative reference and decision-making basis for urban expansion governance. [ABSTRACT FROM AUTHOR]

Highly accurate urban vegetation extraction is important to supporting ecological and management planning in urban areas. However, achieving high-precision classification of urban vegetation is challenging due to dramatic land changes in cities, the complexity of land cover, and hill shading. Although convolutional neural networks (CNNs) have unique advantages in remote sensing image classification, they require a large amount of training sample data, making it difficult to adequately train the network to improve classification accuracy. Therefore, this paper proposed an urban vegetation classification method by combining the advantages of transfer learning, deep learning, and ensemble learning. First, three UNet++ networks (UNet++, VGG16-UNet++, and ResNet50-UNet++) were pre-trained using the open sample set of urban land use/land cover (LULC), and the deep features of Sentinel-2 images were extracted using the pre-trained three UNet++ networks. Subsequently, the optimal deep feature set was then selected by Relief-F and input into the Stacking algorithm for urban vegetation classification. The results showed that deeper features extracted by UNet++ networks were able to easily distinguish between different vegetation types compared to Sentinel-2 spectral features. The overall classification accuracy (OA) of UNet++ networks and the Stacking algorithm (UNS) was 92.74%, with a Kappa coefficient of 0.8905. The classification results of UNet++ networks and the Stacking algorithm improved by 2.34%, 1.8%, 2.29%, and 10.74% in OA compared to a single neural network (UNet++, VGG16-UNet++, and ResNet50-UNet++) and the Stacking algorithm, respectively. Furthermore, a comparative analysis of the method with common vegetation classification algorithms (RF, U-Net, and DeepLab V3+) indicated that the results of UNS were 11.31%, 9.38%, and 3.05% better in terms of OA, respectively. Generally, the method developed in this paper could accurately obtain urban vegetation information and provide a reference for research on urban vegetation classification. [ABSTRACT FROM AUTHOR]

Purpose: The absence of government intervention and market supervision cannot effectively promote green process innovation in manufacturing industries. As a new government regulation approach, environmental taxes provide a platform to internalize the externality of environmental pollution. This paper empirically investigates the impact of environmental taxes on green process innovation and the moderating effects of industry pollution heterogeneity and green credit. Design/methodology/approach: This research collects manufacturing industry data ranging from 2008 to 2020, resulting in a total of 351 observations. Time-individual, two-way fixed effect models are constructed to examine the hypotheses. Findings: The results indicate environmental taxes have an inverted-U effect on green process innovation in manufacturing industries. Implementation intensity of the current environmental taxes on China's manufacturing industries does not reach an inflection point. Further analysis suggests that environmental taxes exert influence on the inverted-U relationship with low-pollution industries displaying a steeper curvilinear pattern than high-pollution industries. Moreover, the analysis shows that green credit plays a moderating role in the inverted-U relationship, as low green credit provides more limited stimulus than high green credit in terms of the effect of environmental taxes on green process innovation. Research limitations/implications: This study offers empirical evidence to accommodate negative externalities of corporate production and provides new perspectives in nudging corporate green-process innovation. Originality/value: This paper verifies the effect of environmental taxes on green process innovation amid industry pollution heterogeneity by introducing an industrial-level analysis unit. This study improves the means by which environmental taxes are measured. Existing literature has narrowly used pollution discharge fees as a proxy for environmental taxes. The authors have summed up the taxes on vehicle and vessels, urban land use, urban maintenance and construction, vehicle purchases, waste gas, wastewater and solid waste to measure the effect of environmental taxes in this study. [ABSTRACT FROM AUTHOR]

There has been quite a debate to understand the scope and opportunities related to the fringe areas of urban cores and their associated areas. Indeed, planners envision fringe areas as 'great alternatives' to ease out housing pressure in cities. As many of the fast growing Asian cities, Bengaluru shows quite a rapid and haphazard development in terms of urban processes. The city has expanded rather comfortably since 1990s in a concentric pattern, but presently it is facing immense challenges in terms of land availability. City planners have struggled to find solutions, while taking measures for the decentralization of city functions in the case of fringe areas. This research highlights the physical set up and the existing land use components to understand the extension and development possibilities of the fringe region of Channapatna lying adjacent to the Bengaluru city. The study area is Channapatna, which is famous for its handmade wooden toy and doll making industries. The region is a fringe area that promises to be a great setting for residential and industrial activities. Nonetheless, the paper focuses on the current villages in the region, which are generally agriculture-oriented and thus the preservation of the land use and related conservation measures are needed in a single planning platform. The study describes the demographic parameters and discusses the current land use and future planning measures. Therefore, the focus is not on Bengaluru as the Indian software capital, but on Channapatna in Bengaluru fringe area, which has tremendously benefited from the city development efforts. As such, this development includes economic growth and sustainable planning measures that would promote the co-existence of the rural and urban land uses, safeguarding the interests of the local artisans and farmers while paving the way for urbanization. This paper highlights the demographic aspects of the villages considered for a comprehensive and cluster level planning of Channapatna with changing land uses showing its prospects in toy making industries and its nearness to the main city as a promising future of sustainable urban planning and development. [ABSTRACT FROM AUTHOR]

Land use and comprehensive land evaluation are essential. Based on fuzzy mathematics theory and biological heuristic algorithm, the land input-output benefits are evaluated comprehensively. This paper firstly selects six indicators from six aspects of land resource input, capital input, and economic output, and so on. Based on land input and output, this paper constructs the evaluation indicator system of comprehensive benefit for land use. Then, based on the theory of fuzzy mathematics, the improved particle swarm optimization (PSO) algorithm is used to identify the fuzzy density value of the evaluation index. Combined with the analytic hierarchy process (AHP), comprehensive evaluation method, through the combination of subjective and objective methods, is comprehensively evaluating land benefits. Finally, the Tobit model is constructed to further analyze the influencing factors of total factor productivity of urban land use and explore the influencing mechanism of government regulation, land opening, and other factors and land development. The research results of this paper can provide reference for future urban planning, land structure adjustment, land resource utilization and protection, food security, ecological security, economic security, and so on. [ABSTRACT FROM AUTHOR]

• A choice experiment was used to assess the preferences and willingness to pay for tangible and intangible industrial heritage. • The case study is the Baosteel steel factory, an industrial brownfield in Shanghai planned to be transformed into an arts and entertainment hub. • The results show that individuals tend to support selective rather than extensive preservation of physical attributes of the industrial sites. • Findings show there exist a demand for preserving and transmitting the intangible heritage of industrial culture along with adaptive reuse of the industrial sites. Industrial heritage has in recent years become an important component in land use and urban development policies. Yet, assessing the economic value associated with the preservation and reuse of industrial complexes remains an open question. In this paper, we apply a discrete choice experiment (DCE) method to assess the economic valuation of the Baosteel steel factory, an industrial brownfield in Shanghai planned to be transformed into an arts and entertainment hub. By eliciting the preferences of locals for the preservation and reuse of distinct attributes of the site, our findings suggest that the preservation of significant landmarks and the intangible component of the industrial site are the two most relevant attributes worth protection. Moreover, we add evidence on the relationship between respondents' economic preferences and their attitude toward the cultural value conveyed by industrial heritage by showing how distinct groups of respondents express different preferences for single attributes of the industrial heritage. The paper contributes to the heritage valuation literature by illustrating the implications and challenges of assessing the demand for industrial heritage rehabilitation projects. [ABSTRACT FROM AUTHOR]

The diversification of business forms leads to functional and spatial complexity in cities. The efficient determination of the complexity of an urban system is the basis for the scientific monitoring of the multi-functional aggregation within cities. Previous studies on the urban spatial structure were limited by the difficulty of collecting micro-data and the high time cost, and they focused on the macro-spatial structure, lacking fine-grained investigations of the micro-spatial structure. Additionally, high-resolution remote sensing images, which mainly rely on the textural characteristics of the spectrum of ground objects, cannot detect the social and economic functions of ground objects. Thus, it is difficult to meet the actual needs of urban planning and management. The purpose of this paper is to automatically identify the spatial heterogeneity and temporal variation of urban land use functions in the context of complex urban systems. The TF-IDF (term frequency–inverse document frequency) algorithm, a machine learning classification algorithm, and other methods are applied to identify the urban functions and distribution characteristics of the main urban area based on the POI (point of interest) data and urban form data. The results show the following: (1) From 2012 to 2022, all types of land use in Yiwu city grew at different rates, with logistics and warehousing space growing the fastest, which is in line with Yiwu's goal of building a national logistics center for trade and services. (2) The residential area has a spatial structure with a dense central circle and a scattered periphery extending from northeast to southwest and from east to west. (3) The commercial service sector shows clear spatial differentiation between the core and the periphery. The commercial functional areas of Niansanli, Houzhai, and Chengxi, where the number of commercial POIs is relatively small, are located at the intersection of the administrative subdistricts near the city center, indicating that the commercial economic activities of the downtown subdistrict have a certain spillover effect on adjacent subdistricts. (4) The public facilities of each subdistrict are generally located in the core of each subdistrict, which ensures better convenience and accessibility. (5) Industrial land with a large total area that is scattered and mixed with urban residential land gradually tends to be centralized, forming an industrial belt around the city. This study comprehensively considers the aggregation relationship between urban buildings and land use and improves the accuracy of land identification and functional zoning. [ABSTRACT FROM AUTHOR]

Against the backdrop of coordinated development and regional integration in the Guangdong–Hong Kong–Macao Greater Bay Area, China, the integration of Shenzhen and Dongguan has received increasing attention from both academia and the government. The cross-boundary area of the two cities has also shifted from being categorized as the development edge to becoming a key area, presenting more complex spatial and functional characteristics. This paper applies multi-source data to analyze the spatial characteristics and factors of influence in the Shenzhen–Dongguan cross-boundary region from the perspective of urban land use pattern evolution. The results showed that from 2008 to 2022, (1) the spatial structure presented a development trend from multi-core to axial, then to network, gradually breaking down the development barriers in the central region; (2) the function of regional land was transformed from a single type to a composite and mixed type, and the layout of commercial, public, green, and transportation land became more dispersed and balanced; (3) the cross-administrative boundaries of commercial land were exaggerated, and industrial land was widely transformed into composite industrial and commercial land, forming an integrated industrial belt; and (4) the overall increase in the mixing degree of land use functions indicated that the two regions were interconnected and integrated with each other, and the functional structure of the cross-boundary region was relatively more complete. [ABSTRACT FROM AUTHOR]

Multi-temporal urban scene classification and change analysis based on high resolution (HR) remote sensing imagery can provide reliable time-series information for the semantic interpretation of urban land use and the transitional relationships, which is important information for urban planning and sustainable development. However, there are still some difficulties encountered when applying the existing multi-temporal scene classification methods to analyze urban development in China due to the complex urban structure and scene shape. The main reasons for this can be summarized as follows: 1) the multi-temporal data labeling workload caused by the differences of the data distributions among multi-temporal images; and 2) the lack of practical socio-geographical urban unit boundaries resulting from the uniform grid based segmentation. In this paper, a multi-temporal scene classification framework based on a class-weighted deep adaptation network (CWDAN) is proposed. In the CWDAN framework, multi-temporal OpenStreetMap (OSM) road networks are introduced for the scene segmentation at the land parcel level, to build clear and meaningful geographic boundaries for the scene units. The problem of large scale difference of parcels is solved by area-weighted voting (AWV). In order to solve the problems of the high workload of multi-temporal data labeling in the cross-temporal scene classification task, a gradient reversal layer (GRL) is used in the proposed CWDAN to obtain deep features with invariance relative to the shift between the domains. A class-weighted fully connected layer is used to solve the problem of unbalanced proportion of different urban scene classes. Post-classification is finally performed to obtain the scene change information. Experiments with tri-temporal datasets in Chinese areas demonstrated that the proposed framework can obtain a significantly improved performance in the cross-temporal scene classification and change analysis task. [ABSTRACT FROM AUTHOR]

Formal urban land is market unable to supply adequate residential plot that could shoulder the prevailing demand from low- and middle-income people and hence, the aim of the paper was meant to explore the informal land buyers’ expectations of infrastructure and various related municipal services that they deprive themselves of. A mixed-method approach was used, combining a survey of 186 respondents in informal land markets, observation, and focus group discussions with municipal experts. Secondary data from formal land markets in Dilla municipality from 2017-2023 were analyzed using descriptive statistics. The result of the study revealed that the pushing factors of informal land market proliferation in Dilla city is due to the lack of formal land supply from Dilla municipality which is only 6.6% so that the remaining great majority (97.3%) of the study area residents expect at least one service (road, water, electricity, drainage, monitoring services) from Dilla municipality to improve deprivation of basic infrastructural services, notwithstanding the need of land monitoring services such as surveying and title deeds. The situations call for design a pro-poor land and housing policy, adequate compensation during expropriation of farmers for serviced supply of land, creating awareness about formal land markets, and working in collaboration with rural counterparts in basis of rural – urban collaboration frameworks. [ABSTRACT FROM AUTHOR]

In the study of urban sustainable development, accurate classification of land use has become an important basis for monitoring urban dynamic changes. Hence it is necessary to develop the appropriate recognition model for urban-rural land use. Although deep learning algorithms have become a research hotspot in image classification tasks in recent years, and many good results have been achieved. But other machine learning algorithms are not going away. Compared deep learning with machine learning, there are some advantages and disadvantages in data dependence, hardware dependence, feature processing, problem solving methods, execution time, and interpretability, etc. Especially in the classification for remote sensing images, the continuous research and development of traditional machine learning algorithms is still of great significance. In this paper, the performances of several SLFN-based classification algorithms were studied and compared, including ELM, RBF K-ELM, mixed K-ELM, A-ELM and SVM. Extreme Learning Machine (ELM) is a new algorithm for single-hidden-layer feedforward neural network (SLFN). It has simple structure, fast speed and is easy to train. In some applications, however, standard ELM is prone to be overfitting and its performance will be affected seriously when outliers exist. In order to explore the performance of ELM and its improved algorithm for urban-rural land use classification, comparative experiments between three improved ELM algorithms (RBF K-ELM, mixed K-ELM and A-ELM), ELM and SVM with image data from several study areas were performed, and the classification accuracy and efficiency were analysed. The results show that the three improved ELM algorithms perform better than the standard ELM and SVM both in overall accuracy and Kappa coefficient. However, it is worth noting that the computation efficiency of RBF K-ELM and mixed K-ELM decreases greatly with larger image, the time cost is much more than other algorithms. Compared with other algorithms, A-ELM has the advantages of higher Overall Accuracy and less classification time. [ABSTRACT FROM AUTHOR]

Global climate change and rapid urbanization have placed enormous pressure on the urban ecological environment worldwide. Urban green spaces, which are an important component of urban ecosystems, can maintain ecological and environmental sustainability and benefits, including biodiversity conservation and carbon sequestration. However, land use changes across urban landscapes, especially in plain urban areas with high development pressure, have significantly impacted the carbon sequestration efficiency of urban green spaces. Nevertheless, research examining the impact of land use change and development pressure on urban green spaces and carbon sequestration is relatively scarce. Understanding the carbon sequestration efficiency of urban green spaces and its determining factors will help predict future carbon capture trends within urban ecosystems and formulate more targeted sustainable urban planning and management strategies to improve urban carbon sink efficiency and achieve the goal of carbon neutrality. Therefore, to understand the factors affecting the carbon sequestration efficiency of urban green spaces, this paper used an integrated framework that combined the Carnegie–Ames–Stanford approach (CASA) model, landscape pattern index, multiple linear regression, and Markov–FLUS model. The study explored the impact of urban land use and land cover changes on carbon sequestration within the plain urban areas of Beijing at street scale. The results showed that, at street scale, there was a significant positive and negative correlation between the landscape pattern index and net primary productivity (NPP). In addition, the green spaces located in areas with more complex landscape structures had better carbon sequestration benefits. In addition, multiscenario carbon sequestration efficiency prediction suggested that the sustainable development (SD) scenario could achieve a positive increment of overall NPP. In contrast, the business-as-usual development (BD), the fast development (FD), and the low development (LD) scenarios showed a downward trend in NPP. This paper also proposed strategies for optimizing and enhancing green spaces within urban plain areas. Based on the strategies, the results guide decision making for sustainable urban green space planning that maintains the ecological, economic, and social integrity of urban landscapes during urbanization. [ABSTRACT FROM AUTHOR]

Under the background of global urbanization, the continuous expansion and extensive utilization of urban and rural construction land has caused a large amount of arable land to be occupied, which seriously threatens national food security. This paper describes the spatio-temporal patterns of urban and rural construction land expansion and its occupation of arable land by using the urban and rural construction land expansion intensity, the urban and rural construction land expansion intensity difference index, and geo-detector model. It also explores the mechanisms through which the arable land was occupied. Results showed that construction land in both urban and rural areas expanded over the period 2009–2018 despite a large number of rural and urban migrants, and the major contributor to the rapid urbanization in China. This dual expansion could mainly be attributed to the tendency of these migrants to keep or even enlarge their rural construction land, which also resulted in a severer arable land loss than that caused by the expansion of urban construction land. Second, the rate of arable land occupied by urban and rural construction land in Henan province has been gradually slowing down, whereas the expansion of rural construction land is most dependent on arable land occupation. Third, according to the geo-detector model, the relationship between urbanization level and arable land occupied by urban and rural construction was the strongest, followed by the growth rate of fixed asset investment and the proportion of secondary and tertiary industries in GDP. [ABSTRACT FROM AUTHOR]

Against the background of increasingly uncontrolled urbanization, neglect of informal settlements in urban land use and socioeconomic development planning process, this paper evaluates the spatial dynamics and socioeconomic-cum housing environment of the informal migrant settlement of Sabo, Ibadan. Geo-spatial data (Landsat imageries) were obtained from United States Geological Survey and Google earth pro to generate land use maps and analyze its dynamics between 1975 and 2015. The socioeconomic-cum-housing data were obtained through questionnaire administration to 280 sampled residents and or business operators using a multistage sampling procedure involving both random and accidental sampling, alongside direct observations. Resulting data were presented using tables, charts and pictures. The paper observes the changing land use types and pattern, particularly the general increase in residential (76%) and exponential increase in commercial land uses (172.7%) and the shrinking of open space/vegetation land uses, together with the concomitant environmental issues, especially in the housing environment. It also confirms the concentration of migrants in the area and their socioeconomic importance to the larger city, as well as recommends effective integration of the informal settlement into the city formal planning and environmental management framework. [ABSTRACT FROM AUTHOR]

Context and background Presently, one of the major challenges confronting the growth of rapidly urbanizing cities is the fact that, cities are growing in unsustainable form which is largely market-led growth and suffers from informal land market distortions. From a conventional point of view, planning aims at achieving compact growth. At the moment, The pervasive knowledge gap especially inadequacies in theoretical premises and polices on city forms and have contributed to mixed forms in many cities of the urbanizing world. Extensive literature expounding on practical problems related to ineffective planning interventions is also scanty. Goal and Objectives: This paper is an attempt to exploring the implications of urban land markets on the emerging city form, it aims at examining theoretical premises with regard to influence of urban land markets on city form transformation in rapidly urbanizing cities context. Methodology This paper drew empirical evidences from four (4) settlements located in broad acre policy areas in Dodoma National Capital City. Mixed approach was adopted in which both, qualitative and quantitative data were collected. Data were collected using official and household interviews, focus group discussions, measurements and observations. Results The study findings reveal that informal land buying and selling, informal plot subdivision and densification, informal change of land uses and unregulated land price inflations have contributed to the consolidated city form on areas closer to public services and Central Business District (CBD). The findings further reveal that there is sprawling city form in areas far from public services, infrastructure and CBD. As a way forward it is recommended that, there is a need to revisit policies, legislation and guidelines governing city planning towards agglomeration of settlement centres and provision of services and infrastructure. It is also recommended that there is a need to redevelop areas along main roads and identify areas for infill development and establish special guidelines advocating compact city development. [ABSTRACT FROM AUTHOR]

The structure of the urban road network affects the mode of urban development and the position of urban social and economic activities, families, and employment centers. Therefore, it is necessary to consider the supply inequality of urban road network and its impact on urban development so as to form a sustainable and fair urban development model in cities. Based on the fractal analysis, this paper seeks the evaluation index of urban road network layout and internal structure characteristics to guide the optimization and adjustment of the urban road network, which is of great significance for guiding urban land use, effectively utilizing geospatial space, and promoting sustainable urban development, and attempts to apply the fractal analysis concept to evaluate the urban road network in Harbin, China. It is found that there is a good relationship between the length of the urban road network and the build-up area. Therefore, fractal analysis is used to reasonably determine the spatial demand of incremental road networks, considering the impact of road network increase on development mode. This paper argues that fractal analysis is an auspicious tool for the evaluation of urban road networks, including solving the problem of supply inequality in urban road networks, which is very suitable for the gradual construction of road networks in urban situations, especially in the development environment of road networks with scarce resources. [ABSTRACT FROM AUTHOR]

With the rapid development of urbanization and the sharp increase in population, urban land is becoming increasingly scarce. The efficient and reasonable development of the underground space is a crucial way to solve the problem of urban diseases, and comprehensive evaluation of urban underground space resources is an important basic task to achieve reasonable planning of the underground space. Adopting Xianyang city as an example, in this paper, we comprehensively evaluated the underground space resources in the main urban area and established evaluation models for the amount of resources available for development, development difficulty, potential value, and comprehensive quality of the underground space. Evaluation indicators, including urban environmental constraints, geological conditions, socioeconomic conditions and many other factors, were determined. With the use of the method of item-by-item elimination of restrictive elements and the analytic hierarchy process for determining the weight of each evaluation index, GIS technology was used to calculate and evaluate the underground space resources (0–30 m) in the main urban area of Xianyang city that could be reasonably developed, as well as the corresponding development difficulty and potential value, and we obtained the underground space that could be reasonably developed under different types of land use in the main urban area of Xianyang city on the basis of the resource quantity and comprehensive quality evaluation results. The results showed that in terms of quantity, the amount of underground space available for development in the main urban area of Xianyang city accounts for approximately 25.11% of the total development amount, and the underground space that could be developed and utilized is approximately 82.3 km2. The underground space resources that could be developed within a 30 m depth interval in the main urban area reached 2.465 billion m3, accounting for approximately 79.5% of the total shallow underground space resources, and the potential for development and utilization is enormous. In terms of the comprehensive quality, the highest comprehensive quality level of shallow underground resources is located in the core areas along Renmin Road, Weiyang Road, and Century Avenue, with an area of 21.52 km2, and the highest comprehensive quality level of subshallow underground resources is located along Renmin Road and Weiyang Road, with an area of 4.37 km2. The evaluation results could provide high reference value for urban development planning and underground space development and utilization in Xianyang. [ABSTRACT FROM AUTHOR]

The Guangdong‐Hong Kong‐Macao Greater Bay Area (GBA), a cluster of world‐class cities, is undergoing rapid urbanization. However, the heterogeneity of the urban thermal environment resulting from the diversity of urban forms is not yet fully understood. This paper assesses the heterogeneity of the urban heat island (UHI) effect in the GBA using the coupled Weather Research and Forecasting (WRF) model/multi‐layer urban canopy and building energy model (BEP/BEM), with high‐resolution local climate zone (LCZ) map as urban land use/land cover data. The average UHI intensity is found to peak at 1.8 ± 0.4°C in the evening, when the average UHI intensity of LCZ 2 can reach a maximum of 2.4 ± 0.58°C. Properly setting air‐conditioning temperatures can effectively prevent the enhancement of the UHI phenomenon at night by the anthropogenic heat (AH) released from air‐conditioning. The UHI‐induced local circulations and enhanced surface roughness inhibit the penetration of sea breezes inland, and surface wind speed decreases in all LCZs, with a maximum change of more than 0.8 m s−1. However, the increased thermal difference between land and sea leads to enhanced sea breezes offshore, especially in the Pearl River estuary. In addition, a series of sensitivity experiments have been conducted in this paper on initial and boundary conditions, building drag coefficients and urban fractions, which paves the way for further analyzing urban climate in GBA using WRF model and LCZs. Plain Language Summary: With the rapid urbanization of the world, the demand for functional buildings has increased. Along with the diversification of urban forms, the differences in the thermal environment within cities are becoming more and more significant. This study therefore provides an in‐depth study of the urban thermal environment in the Guangdong‐Hong Kong‐Macao Greater Bay Area (GBA) based on numerical simulation and local climate zones (LCZs). It was found that the urban heat island (UHI) intensity in different urban forms has obvious differences, and may vary by 1°C. However, the daily variation trends are similar, all showing a stronger UHI intensity at night than during the day, and reasonable setting of air‐conditioning temperature can effectively mitigate the UHI intensity at night. The UHI‐induced local circulations and enhanced surface roughness weaken the surface wind speed and inhibit the penetration of sea breeze inland, but enhance the sea breezes offshore, especially in the Pearl River estuary. This study provides references for urban planning and future sustainable development, especially for areas located along the coast that are undergoing rapid development. In addition, a series of sensitivity experiments on initial and boundary conditions, building drag coefficients and urban fractions provide useful suggestions for numerical model configuration in the GBA. Key Points: Tests of initial and boundary conditions, building drag coefficient and urban fractions provide recommendations for Weather Research and Forecasting configurationUrban heat island (UHI) varies between different local climate zones, but all peak in evening, and proper setting of air‐conditioning temperatures can mitigate UHI at nightUrbanization weakens surface wind speeds and inhibits the penetration of sea breezes inland, but strengthens the sea breezes offshore [ABSTRACT FROM AUTHOR]

With most of the world population living in cities, there is an increasing complexity that is accompanying this process. In addition to the growing number of agents interfering in the planning process and in cities evolution, it is important to note that until today urban policy fails to understand its dynamics. Complexity sciences offers an open system methodology to approach urban systems. However, it is not a recent trend, but it has taken time to be part of the urban planning process. Complexity sciences are based on a wide range of approaches that aim to reflect back our understanding of the world. In this paper we intend to set the grounds for discussion about a new emerging city science based on complexity sciences methods. Context and background This paper will explore the contribution of complexity sciences in city sciences. It starts by defining complexity sciences and tracing the evolution of urban theories until nowadays. It argues that a new science of cities based on complexity is required to understand urban systems and be able to guide urban policy choices. In fact complexity sciences applied to urban studies has challenged many misconceptions and generated counterintuitive assumptions that has helped build a new understanding of cities dynamics Goal and Objectives: This contribution is aimed at setting a common ground for urban planning professionals and academics about complexity sciences to set a debate on the importance of opening cities to complexity sciences. Methodology: In this paper we review the evolution of urban planning theories and trends chronologically and identify the tendency towards complexity. We then define complexity sciences based on reviewing main contributions in the field and end by presenting examples and applications of complexity sciences in understanding urban phenomena. Results: Major findings include the eminence of shifting our approach to urban planning from a reductionist point of view to a transdisciplinary approach that is based on system thinking and oriented towards complexity sciences. [ABSTRACT FROM AUTHOR]

High-quality urban land-use maps are essential for grasping the dynamics and scale of urban land use, predicting future environmental trends and changes, and allocating national land resources. This paper proposes a multisample "voting mechanism" based on multisource data and random forests to achieve fine mapping of urban land use. First, Zhengzhou City was selected as the study area. Based on full integration of multisource features, random forests were used to perform the preliminary classification of multiple samples. Finally, the preliminary classification results were filtered according to the "voting mechanism" to achieve high-precision urban land-use classification mapping. The results showed that the overall classification accuracy of Level I features increased by 5.66% and 14.32% and that the overall classification accuracy of Level II features increased by 9.02% and 12.46%, respectively, compared with the classification results of other strategies. Therefore, this method can significantly reduce the influence of mixed distribution of land types and improve the accuracy of urban land-use classification at a fine scale. [ABSTRACT FROM AUTHOR]