Urban Planning Methods Research Articles

Optimal Location Method of Urban and Rural Emergency Shelter Based on Improved Ant Colony Algorithm

In order to realize the automatic location of urban and rural emergency shelters, the optimal location method of urban and rural emergency shelters based on the improved ant colony algorithm is proposed. Combined with the method of urban and rural land block grid space planning, the spatial grid planning of urban and rural distribution and the method of remote sensing image covering big data detection are adopted to extract the spatial grid geospatial detection model of urban and rural emergency shelters. The remote sensing images of urban and rural emergency shelters and the wireless sensor information sampling method of unmanned aerial vehicle (UAV) are adopted to establish the remote sensing images of urban and rural emergency shelters and the monitoring map database of UAV wireless sensor information. The improved ant colony optimization algorithm is adopted to optimize the path in the process of urban and rural emergency shelters’ site selection, and the overall geographical and geometric characteristics of urban and rural emergency shelters’ site selection are analyzed. The texture, color, shape and other characteristics related to the location of urban and rural emergency shelters are extracted, and the distributed optimization control method of dynamic ant colony is adopted to detect the distribution of geometric deformation characteristics and optimize the location of urban and rural emergency shelters. The location is realized by spatial enhancement of remote sensing dynamic information and the location of spatial feature points. The simulation results show that this method has a good positioning ability and a strong ability to optimize the location of emergency shelters in urban and rural areas.

THEORETICAL AND METHODOLOGICAL APPROACHES TO PROCESSES OF DEVELOPMENT OF URBAN SPACES: ZONING AND PLANNING

In the modern context of rapid urbanization and city growth, the issue of effective zoning and urban space planning is extremely relevant. Sustainable approaches to urban infrastructure development require the integration of environmental, social, and economic aspects. Current urban planning methods often fail to meet the requirements of sustainable development due to insufficient integration of new technologies, inadequate community involvement, and a lack of effective coordination between public and private institutions. The aim of the study is to develop theoretical and methodological approaches to urban space development through the improvement of zoning and planning methods. The objectives include analyzing international experience, assessing current planning methods, and developing recommendations for their adaptation to national conditions. The object of the study is the urban spaces of modern cities. The subject of the study is the approaches to zoning and urban space planning. The study employed methods of analysis, synthesis, systematic approach, comparative analysis, and expert assessments. These methodologies provided a comprehensive understanding of the strengths and limitations of existing urban planning practices. Key methodological aspects of effective zoning and urban planning, considering ecological, social, and technological challenges, have been identified, ensuring a balanced approach to development. The research emphasized the importance of integrating smart technologies and sustainable practices to enhance urban functionality. Additionally, recommendations for their application in Ukraine have been developed, highlighting the need for policy adjustments and strategic partnerships to implement modern urban planning solutions effectively.The research found that the adaptation of international experience, particularly the use of mixed zoning and smart technologies, will promote the sustainable development of urban spaces in Ukraine. Future research may focus on developing practical recommendations for the implementation of modern methods in Ukraine's urban planning policy.

Small Town Population Forecasting and Planning Method Based on Genetic Algorithm and BP Neural Network

It is very important to accurately predict the population pattern in the framework of spatial planning in the township development track. In this paper, the basic principle and application field of population forecasting method of urban spatial planning are deeply studied, and the applicability of BP neural network method of genetic evolution to predict population size is described. The study initially used genetic algorithms to refine the initial weights and structure of BP neural networks to improve their proficiency and generalization ability in the interpretation of demographic data. The empirical results show that the method produces superior predictive performance on multiple township demographic data sets, especially when trying to cope with complex population dynamics. In addition, when benchmarked against traditional forecasting models, the technology showed significant enhancements in the accuracy, stability, and adaptability of predictive models. These results suggest that combining GA-driven evolution with BP neural networks provides a more robust and precise tool for population prediction.

Urban Planning Strategies for Coastal Cities in China in Response to Climate Change

Global climate change has increasingly posed a serious threat to coastal cities, rendering them particularly vulnerable to the adverse effects of rising sea levels, more frequent and intense extreme weather events, and the shifting patterns of climate. Traditional urban planning methods, which had been the cornerstone of city development, were found to be inadequate in addressing these escalating challenges. The previous studies often failed to account for the critical importance of integrating climate factors into the planning process, leading to vulnerabilities in urban infrastructure and design. This study meticulously examined the limitations of these conventional urban planning approaches and explored the potential for adapting urban planning strategies in Chinas coastal cities to better respond to the looming threat of climate change. By conducting a thorough analysis of the impacts of high temperatures, floods, and other climate-related events, the study proposed a comprehensive series of response measures tailored to the specific conditions of the time. These measures included the integration of green policies into urban planning to enhance environmental sustainability, the strategic selection of building materials aimed at mitigating urban heat, and the enhancement of water resource management systems to alleviate the urban heat island effect. Moreover, the study closely investigated existing climate response measures through detailed case studies in Xiamen and Dalian, two prominent coastal cities in China. The primary objective was to significantly enhance the resilience of these cities, effectively reduce the risks associated with climate change, and promote a path towards sustainable development. Ultimately, the goal was to create more livable, safer, and sustainable urban environments that would be better equipped to face the challenges posed by an unpredictable climate future.

Voluntary Planning and City Networks: A Systematic Bibliometric Review Addressing Current Issues for Sustainable and Climate-Responsive Planning

Over the years, energy saving and climate change have sparked a challenge in developing innovative spatial and urban planning tools, methods, and approaches for urban areas and territorial management. This challenge has pointed out a gap in the traditional planning framework in tackling energy and climate issues, highlighting the need for renovated spatial planning practices. The current effective measures carried out by the complex network of stakeholders (public and private) are fragmented, and the role of city networks in shaping decisions and interventions has emerged. In particular, among the approaches for urban and territorial planning and the development of climate-responsive measures, this research adopts a specific focus on voluntary planning. The authors, through a literature review and an analysis of nine city networks, investigated how this approach addresses energy and climate issues and how it is linked to the city networks in designing investments and interventions within the urban context. From the authors’ perspective, city networks are the operative component in designing local experiences and applications, contributing to achievement of the global energy and climate targets according to the current policies. In this context, successful results with respect to climate and energy challenges is expressed in the degree of the awareness and the commitment achieved by the local communities. Moreover, reaching the climate and energy goals is a representation of the outcome of the interaction between the (public or private) actors involved and the degree of a city’s climate-responsiveness, rather than from a normative constraint or an institutional framework.

Display in periodic specialist publications changes in the architecture and urban planning of Kharkiv during the beginning of industrial construction

The article contains a historiographic review of publications about the architecture and construction of Kharkiv in professional architectural journals of Ukraine during the radical turn in architectural and urban planning to the principles of modernism and accelerated deployment of industrial construction technologies. This article is a continuation of the publication in which the authors considered the Kharkiv professional press dedicated to the post-war reconstruction period (1947-1955) and the changes that took place in construction practice and in the project ideology of the 2nd half. 1950s. The article highlights the events of architectural life and the evolution of the views of architects and urban planners in the second most important city of Ukraine during the third quarter of the 20th century. To this day, this period remains insufficiently studied in Ukrainian architectural science, despite its enormous influence on the formation of the modern architectural appearance of Kharkiv. Professional periodicals, in particular publications in the magazine "Architecture and Construction" (since 1957 – "Construction and Architecture"), are one of the sources of research into Kharkiv architecture of this period. The article attempts, using the example of the architectural life of Kharkiv, reflected in these sources, to trace the essence of changes in conceptual approaches and architectural and urban planning practices of Ukrainian architecture during the period of the deployment of mass industrial construction. The presented overview of publications dedicated to Kharkiv clearly shows that in the period of the beginning of industrial construction, the focus of attention of articles was directed primarily to the process of construction according to typical projects. Kharkiv architects publish plans for construction on a new basis for residential areas of the city, analyze the results of the ongoing construction of micro-districts in Pavlovo Pole, the Selection Station area, Saltivka, noting achievements, problems and formulating plans for the future. An important topic of the publications is plans for the development of the architectural and spatial composition of the city, in particular the process of implementing an innovative method of urban planning – the focusing method.

Research on the Spatial-Temporal Evolution of Changsha’s Surface Urban Heat Island from the Perspective of Local Climate Zones

Optimizing urban spatial morphology is one of the most effective methods for improving the urban thermal environment. Some studies have used the local climate zones (LCZ) classification system to examine the relationship between urban spatial morphology and Surface Urban Heat Islands (SUHIs). However, these studies often rely on single-time-point data, failing to consider the changes in urban space and the time-series LCZ mapping relationships. This study utilized remote sensing data from Landsat 5, 7, and 8–9 to retrieve land surface temperatures in Changsha from 2005 to 2020 using the Mono-Window Algorithm. The spatial-temporal evolution of the LCZ and the Surface Urban Heat Island Intensity (SUHII) was then examined and analyzed. This study aims to (1) propose a localized, long-time LCZ mapping method, (2) investigate the spatial-temporal relationship between the LCZ and the SUHII, and (3) develop a more convenient SUHI assessment method for urban planning and design. The results showed that the spatial-temporal evolution of the LCZ reflects the sequence of urban expansion. In terms of quantity, the number of built-type LCZs maintaining their original types is low, with each undergoing at least one type change. The open LCZs increased the most, followed by the sparse and the composite LCZs. Spatially, the LCZs experience reverse transitions due to urban expansion and quality improvements in central urban areas. Seasonal changes in the LCZ types and the SUHI vary, with differences not only among the LCZ types but also in building heights within the same type. The relative importance of the LCZ parameters also differs between seasons. The SUHI model constructed using Boosted Regression Trees (BRT) demonstrated high predictive accuracy, with R2 values of 0.911 for summer and 0.777 for winter. In practical case validation, the model explained 97.86% of the data for summer and 96.77% for winter. This study provides evidence-based planning recommendations to mitigate urban heat and create a comfortable built environment.

A Review of Urban Planning Approaches to Reduce Air Pollution Exposures.

With only 12% of the human population living in cities meeting the air quality standards set by the WHO guidelines, there is a critical need for coordinated strategies to meet the requirements of a healthy society. One pivotal mechanism for addressing societal expectations on air pollution and human health is to employ strategic modeling within the urban planning process. This review synthesizes research to inform coordinated strategies for a healthy society. Through strategic modeling in urban planning, we seek to uncover integrated solutions that mitigate air pollution, enhance public health, and create sustainable urban environments. Successful urban planning can help reduce air pollution by optimizing city design with regard to transportation systems. As one specific example, ventilation corridors i.e. aim to introduce natural wind into urban areas to improve thermal comfort and air quality, and they can be effective if well-designed and managed. However, physical barriers such as sound walls and vegetation must be carefully selected following design criteria with significant trade-offs that must be modeled quantitatively. These tradeoffs often involve balancing effectiveness, cost, aesthetics, and environmental impact. For instance, sound walls are highly effective at reducing noise, provide immediate impact, and are long-lasting. However, they are expensive to construct, visually unappealing, and may block views and sunlight. To address the costly issue of sound walls, a potential solution is implementing vegetation with a high leaf area index or leaf area density. This alternative is also an effective method for air pollution reduction with varying land-use potential. Ultimately, emission regulations are a key aspect of all such considerations. Given the broad range of developments, concerns, and considerations spanning city management, ventilation corridors, physical barriers, and transportation planning, this review aims to summarize the effect of a range of urban planning methods on air pollution considerations.

Evaluation and Division Methods of Coupled Spatial Units between Parks and Living Circles: A Case Study of Four National-Level New Areas

This study, based on the perspective of supply and demand coupling between parks and living circles, divides spatial units by introducing the Thiessen polygon method with urban parks as the core. Simultaneously, the coefficient of variation (CV) was used to evaluate the distribution characteristics of these spatial units. The suitability of the spatial units was comprehensively evaluated through the three indicators of park service support, functional concentration of living circle, and management and development level. The Node–Place (NP) model was used to quantify the coupling relationship between the park and the living circle, and the characteristics of the coupling units were deeply analyzed from the aspects of supply and demand services, coupling configuration relationship, development management, etc. The results are empirically applied to four national-level new areas, thereby providing a new evaluation method and perspective for urban planning and management.

Peri-urban dynamics: assessing expansion patterns and influencing factors

BackgroundPeri-urbanization, the expansion of large metropolitan centers into adjacent peri-urban regions, is a growing concern due to land scarcity and escalating housing costs. These zones, a blend of rural and urban features, blur the line between urban and rural areas, creating new landscapes. This study examines historical, present, and potential growth trends in the peri-urban area surrounding Durgapur Municipal Corporation (DMC). Analytical techniques and spatial metrics are used to track development intensity changes over time, including built-up density, Shannon’s entropy, Landscape expansion index, Average Weighted Mean Expansion Index, Annual Built-Up Expansion Rate, Built-Up Expansion Intensity Index, and Built-Up Expansion Difference Index. Landscape indices like Patch Density, Edge Density, Landscape Shape Index, Largest Patch Index, Ratio of Open Space, and Area Weighted Mean Patch Fractal are used to understand fragmentation, connectivity, and spatial relationships. The Logistic Regression Model (LRM) is used to identify influencing factors and CA-Markov modeling for future built-up areas.ResultsBetween 1991 and 2001, built-up area in the region increased significantly, primarily due to urban development near industrial zones, roadways, and mining areas. The growth was primarily concentrated in the western sector and near National Highway-2 (NH-2). Urban sprawl was a continuous trend, with the highest built-up density in the South-South-East (SSE) direction from 1991 to 2011. Additionally, a key determinant of built-up development was the distance to the city core. By 2031, the built-up area is expected to concentrate in the western and southeast regions, reaching 177.90 km2.ConclusionsThis expansion is attributed to urban development near industrial zones, roadways, mining areas, and other infrastructure. The study identifies distance to the city center as a significant influencing factor for built-up development. The results emphasize the need for inclusive urban planning methods prioritizing sustainable development principles and prudent resource management for future growth and efficient management in the DMC’s peri-urban area.

AI-Driven Urban Planning: Enhancing Efficiency and Sustainability in Smart Cities

Urban planning in smart cities is increasingly leveraging artificial intelligence (AI) to enhance efficiency and sustainability. This article explores the integration of AI-driven technologies to optimize various aspects of urban development and management. Smart cities are characterized by their use of advanced technologies to improve quality of life, resource management, and infrastructure efficiency. Traditional urban planning methods often face challenges in adapting to rapid urbanization and dynamic environmental changes. AI presents opportunities to address these challenges by providing data-driven insights and predictive capabilities. This research employs a case study approach, analyzing the implementation of AI in urban planning processes across several smart cities globally. Key methodologies include data analytics, machine learning algorithms, and predictive modeling techniques applied to diverse urban datasets. The study evaluates how AI-driven decision support systems aid in infrastructure planning, traffic management, energy consumption optimization, and environmental sustainability. The findings demonstrate that AI-enabled urban planning significantly enhances efficiency and sustainability in smart cities. AI algorithms optimize traffic flow, reduce energy consumption through predictive maintenance of infrastructure, and facilitate adaptive urban design based on real-time data analytics. Moreover, AI-driven approaches improve decision-making processes by providing stakeholders with actionable insights for informed policy formulation and resource allocation. This article contributes to the evolving field of smart city technologies by showcasing the transformative potential of AI in urban planning. By harnessing AI capabilities, cities can effectively address complex urban challenges and pave the way for more resilient and sustainable urban environments.

Coupling Hyperbolic GCN with Graph Generation for Spatial Community Detection and Dynamic Evolution Analysis

Spatial community detection is a method that divides geographic spaces into several sub-regions based on spatial interactions, reflecting the regional spatial structure against the background of human mobility. In recent years, spatial community detection has attracted extensive research in the field of geographic information science. However, mining the community structures and their evolutionary patterns from spatial interaction data remains challenging. Most existing methods for spatial community detection rely on representing spatial interaction networks in Euclidean space, which results in significant distortion when modeling spatial interaction networks; since spatial community detection has no ground truth, this results in the detection and evaluation of communities being difficult. Furthermore, most methods usually ignore the dynamics of these spatial interaction networks, resulting in the dynamic evolution of spatial communities not being discussed in depth. Therefore, this study proposes a framework for community detection and evolutionary analysis for spatial interaction networks. Specifically, we construct a spatial interaction network based on network science theory, where geographic units serve as nodes and interaction relationships serve as edges. In order to fully learn the structural features of the spatial interaction network, we introduce a hyperbolic graph convolution module in the community detection phase to learn the spatial and non-spatial attributes of the spatial interaction network, obtain vector representations of the nodes, and optimize them based on a graph generation model to achieve the final community detection results. Considering the dynamics of spatial interactions, we analyze the evolution of the spatial community over time. Finally, using taxi trajectory data as an example, we conduct relevant experiments within the fifth ring road of Beijing. The empirical results validate the community detection capabilities of the proposed method, which can effectively describe the dynamic spatial structure of cities based on human mobility and provide an effective analytical method for urban spatial planning.

Urban Mobility Pattern Detection: Development of a Classification Algorithm Based on Machine Learning and GPS.

This study introduces an innovative algorithm for classifying transportation modes. It categorizes modes such as walking, biking, tram, bus, taxi, and private vehicles based on data collected through sensors embedded in smartphones. The data include date, time, latitude, longitude, altitude, and speed, gathered using a mobile application specifically designed for this project. These data were collected through the smartphone's GPS to enhance the accuracy of the analysis. The stopping times of each transport mode, as well as the distance traveled and average speed, are analyzed to identify patterns and distinctive features. Conducted in Cuenca, Ecuador, the study aims to develop and validate an algorithm to enhance urban planning. It extracts significant features from mobility patterns, including speed, acceleration, and over-acceleration, and applies longitudinal dynamics to train the classification model. The classification algorithm relies on a decision tree model, achieving a high accuracy of 94.6% in validation and 94.9% in testing, demonstrating the effectiveness of the proposed approach. Additionally, the precision metric of 0.8938 signifies the model's ability to make correct positive predictions, with nearly 90% of positive instances correctly identified. Furthermore, the recall metric at 0.83084 highlights the model's capability to identify real positive instances within the dataset, capturing over 80% of positive instances. The calculated F1-score of 0.86117 indicates a harmonious balance between precision and recall, showcasing the models robust and well-rounded performance in classifying transport modes effectively. The study discusses the potential applications of this method in urban planning, transport management, public transport route optimization, and urban traffic monitoring. This research represents a preliminary stage in generating an origin-destination (OD) matrix to better understand how people move within the city.

Soundscape techniques applied to urban planning goals

The soundscape method is a powerful tool for understanding the perception of our acoustical environment. Urban planning is the technical and political process by which we manage and mold our cities. Smart growth is a movement within urban planning which emphasizes the livability of our surroundings. These disciplines all seek to shape the form of our communities to improve the quality of life for those who live, work, and play there. Yet, there has been little formal interaction between soundscaping and urban planning. It is imperative that soundscapers who wish to implement improved urban designs and interventions adopt the language and methods of urban planners in order to succeed and accomplish their mutual goals. Soundscape protocols must engage the urban planning process, including the comprehensive, or master, plan to achieve implementation on a city-wide scale. Several recent initiatives which illustrate the integration of soundscape techniques with urban planning methods are presented and discussed.

Research on Urban Building Parametric Modelling Method Based on CityEngine

With the advancement of technology and the development of cities, urban planning and management methods are also constantly improving. From paper-based assignments to modern digitization, new technologies have enabled more efficient design and management for cities. 3D modeling can used to simulate the urban environment, which can assist in urban planning and management. However, large-scale modeling cannot be achieved through existing modeling methods, and there are still some shortcomings in the maintenance of the model. Therefore, this article proposes a Computer Generated Architecture (CGA) parametric 3D modeling method based on CityEngine. Research on expanding and customizing modeling rules to create indoor and outdoor modeling rule templates for buildings and methods for generating urban 3D models have been carried out. The results have shown that the completed model can be displayed on different platforms thanks to parameterized modeling. The model can be modified easily and directly applied to the analysis and decision-making of urban planning schemes.

Enhancing Urban Landscape Design: A GAN-Based Approach for Rapid Color Rendering of Park Sketches

In urban ecological development, the effective planning and design of living spaces are crucial. Traditional color plan rendering methods, mainly using generative adversarial networks (GANs), rely heavily on edge extraction. This often leads to the loss of important details from hand-drawn drafts, significantly affecting the portrayal of the designer’s key concepts. This issue is especially critical in complex park planning. To address this, our study introduces a system based on conditional GANs. This system rapidly converts black-and-white park sketches into comprehensive color designs. We also employ a data augmentation strategy to enhance the quality of the output. The research reveals: (1) Our model efficiently produces designs suitable for industrial applications. (2) The GAN-based data augmentation improves the data volume, leading to enhanced rendering effects. (3) Our unique approach of direct rendering from sketches offers a novel method in urban planning and design. This study aims to enhance the rendering aspect of an intelligent workflow for landscape design. More efficient rendering techniques will reduce the iteration time of early design solutions and promote the iterative speed of designers’ thinking, thus improving the speed and efficiency of the whole design process.

Collaborative Intelligent Traffic Planning in the Internet of Vehicles.

With the increasing number of urban vehicles, as well as the current situation of non-intelligent traffic control systems, spatiotemporal non-uniform traffic resource occupation, and limited traffic planning and design, existing urban traffic planning methods cannot effectively solve problems such as frequent traffic congestion and uncontrollable commuting time for residents. In order to solve the above problems, this paper first constructs a multi-queue, multi-server queuing model based on the server vacation and a multi-hop cascaded queuing model from the perspective of local intersections and global commuting paths. We analyze the theoretical changes in passage delay costs at local intersections and on global commuting paths as a function of traffic flow and the random duration of traffic signals. On this basis, this article proposes a collaborative intelligent traffic planning algorithm based on artificial intelligence, which utilizes traffic sensors to dynamically perceive traffic congestion status and collaboratively plans the optimal duration of traffic signals and the optimal driving path of vehicles from both local and global perspectives, thereby maximizing the on-time arrival ratio of vehicles while ensuring the required commuting delay. The simulation results show that the proposed method can increase the on-time arrival ratio of vehicles by at least 20% compared to contrast methods while meeting the requirements relating to commuting delays. This verifies that our method can provide support for the improvement in efficiency in future Internet of vehicles.

Municipal and Urban Renewal Development Index System: A Data-Driven Digital Analysis Framework

Urban renewal planning and development are vital for enhancing the living quality of city residents. However, such improvement activities are often expensive, time-consuming, and in need of standardization. The convergence of remote sensing technologies, social big data, and artificial intelligence solutions has created unprecedented opportunities for comprehensive digital planning and analysis in urban renewal development and management. However, fast interdisciplinary development imposes some challenges because the data collected and the solutions built are defined piece by piece and require further fusion and integration of knowledge, evaluation standards, systematic analyses, and new methodologies. To address these challenges, we propose a municipal and urban renewal development index (MUDI) system with data modeling and mathematical analysis models. The MUDI system is applied and studied in three circumstances: (1) at regional level, 337 cities are selected in China to demonstrate the MUDI system’s comparable analysis capabilities on a large scale across cities; (2) at city level, 285 residential communities are selected in Xiamen to demonstrate the use of remote sensing data as key MUDIs for a temporal urban land change analysis; and (3) at the level of residential neighborhoods’ urban renewal practices, Xiamen’s Yingping District is selected to demonstrate the MUDI system’s project management capabilities. We find that the MUDI system is highly effective in municipal and urban data model building through the abstraction and summation of grid-based satellite and social big data. Secondly, the MUDI system enables comprehension of the high dimensionality and complexity of multisource datasets for municipal and urban renewal development. Thirdly, the system is applied to enable the use of the newly developed UMAP algorithm, a model based on Riemannian geometry and algebraic topology, and the carrying out of a principal component analysis for the key dimensions and an index correlation analysis. Fourthly, various artificial intelligence-driven algorithms can be developed for urban renewal analyses based on the MUDIs. The MUDI system is a new and effective method for urban renewal planning and management that can be flexibly extended and applied to various cities and urban districts.

Fire Safety in Smart Cities in Hungary With Regard to Urban Planning

Today, smart city principles are emerging as a highly relevant method in contemporary urban planning and development. In line with international trends, nations almost without exception have smart city strategies. The assessment system supporting smart development strategies for EU municipalities defines complex indicators in six thematic sub-systems: smart mobility, smart environment, smart people, smart living conditions, smart governance, smart economy. One monitoring aspect of the smart living subsystem is safety. The smart cities of the future will be made up of a network of smart buildings, which will fundamentally determine fire safety based on prevention. In this monitoring theme, the researchers will show how the future of fire safety - a cornerstone of safety- will evolve in smart buildings of smart cities. The researchers will describe the potential of building a smart fire safety network using data generated by building information modelling. They will show how BIM-based complex digital fire safety can be integrated into the fire safety network based on the new digital “one urban one plan” regulation in Hungary. The authors show that their method is suitable for the expansion of a smart cities database based on a digital urban plan.

A Study on the Ephemerality of Public Space in Traditional Village from a Cognitive Perspective

Based on the design method of urban and rural planning, we combine the theories and methods of geography and habitat environment disciplines, and introduce the "schema" theory of psychology to construct the cognitive maps of residents and tourists. Using Zishan Village in Hanwang Town, Xuzhou City as an empirical case, we extracted regional characteristics and explored the cognitive evaluation of residents and tourists, and analyzed the research data in a qualitative and quantitative way. The satellite tracking 3D track function was used to record the travel routes and shooting points of the sample population, and the collected data were overlaid to analyze the overlap of the routes and shooting points, so as to derive the interest points of residents and tourists. The data were summarized in tabular form and imported into SPSS for cross-tabulation analysis and chi-square test. Questionnaire interviews were used to collect the personal feelings of residents and tourists from a qualitative perspective. The cognitive maps of residents and tourists were constructed and their differences were explored to visualize the regional characteristics of traditional village public space and to identify effective landscape design and public space construction, which can provide reference and basis for village construction.