Urban novel wild area identification, evaluation and management method

Abstract

The invention relates to the technical field of ecological civilization construction and urban green land planning, and discloses an identification, evaluation and management method for a novel urban wild area. According to the method, mining of ecological value and leisure potential of novel urban wastelands is taken as an entry point, weighted linear combination, a MaxEnt model, spatial autocorrelation and other methods and technologies are integrated, multi-source data are integrated, evaluation indexes are optimized, and a set of accurate identification, classification and evaluation of the novel urban wastelands is innovatively developed. And a management strategy based on an index evaluation result is formulated. According to the method provided by the invention, a comprehensive evaluation system is constructed and the spatial distribution heterogeneity is revealed by focusing on the specific scale of a city, so that a high-precision spatial decision tool is provided for urban ecological protection and leisure planning strategy making according to local conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological civilization construction and urban green space planning, and in particular to a method for identifying, evaluating and managing new urban wilderness areas. Background Art

[0002] With the acceleration of global urbanization, urban green spaces are decreasing, habitat fragmentation is increasing, and urban ecosystems are facing unprecedented pressures and challenges, with numerous adverse impacts on biodiversity and resident health. The planning, construction, and management of urban green space systems can effectively promote biodiversity conservation and meet residents' leisure and entertainment needs, becoming a key element in sustainable urban development. As an important component of urban green space systems, new urban wilderness areas (including traditional undeveloped land and brownfields caused by human disturbance) present a crucial opportunity to improve urban ecological networks, protect urban biodiversity, and address the intensifying human-land conflict in the Anthropocene. Identifying, assessing, and managing new urban wilderness areas can effectively respond to the requirements of building a beautiful China and an ecological civilization.

[0003] Wilderness possesses significant ecological, cultural, and social value. A series of laws and policy documents, including the National Park Law of the People's Republic of China, the National Park Spatial Layout Plan, and the Guiding Opinions on Establishing a Natural Conservation Area System with National Parks as the Core, highlight China's institutional support for wilderness development at the national and regional scales. These areas are assessed and protected through data methods such as habitat quality and human footprint. However, the value of emerging wilderness areas at the urban scale remains to be explored, and a scientific and effective method for identifying, assessing, and managing these emerging urban wilderness areas is lacking.

[0004] To build more sustainable urban green spaces, explore the potential value of new urban wilderness areas, further promote urban biodiversity conservation, and meet residents' leisure and entertainment needs, this paper has developed an innovative method for identifying, assessing, and managing new urban wilderness areas. This method integrates multi-source data and optimizes assessment indicators to accurately identify and classify new urban wilderness areas. Using weighted linear combinations, the MaxEnt model, and spatial autocorrelation methods, the method assesses the value status and recreational potential of new urban wilderness areas and analyzes spatial correlations. Management strategies based on these indicator assessments are then developed to promote the harmonious coexistence of ecological protection and recreational functions in urban environments, providing a scientific basis and practical strategies for urban planning and environmental management policies. Summary of the Invention

[0005] The purpose of the present invention is to solve the following problems:

[0006] In response to the scale limitations of existing wilderness assessment and management methods, which focus primarily on national and regional levels, and the lack of assessment indicators and implementation strategies at small and medium scales, this paper provides a method for identifying, assessing, and managing new urban wilderness areas. This method provides a scientific and effective means for identifying, assessing, and managing new wilderness areas at the urban scale, addressing the technical issues of incomplete definition of new wilderness concepts, insufficient utilization of their value, and incomplete planning responses in urban green space systems.

[0007] Using a wealth of basic data on the natural environment and socioeconomic factors, an evaluation index database was constructed. Urban wilderness value index indicators and urban outdoor recreation potential assessment indicators were extracted, leading to a multi-dimensional evaluation index system for the wilderness value and outdoor recreation potential of new urban wilderness areas. Urban wilderness value and outdoor recreation potential were assessed using the weighted linear combination (WLC) method and the MaxEnt model, identifying existing and potential new urban wilderness areas. Spatial autocorrelation analysis and geographically weighted regression analysis were used to identify the spatial heterogeneity and spatial correlation factors in the distribution of new urban wilderness areas. By analyzing key influencing factors and spatial dynamic networks, spatial planning and management strategies for new urban wilderness areas were proposed.

[0008] The present invention provides a method for identifying, assessing, and managing new urban wilderness areas, which can effectively identify existing and potential new urban wilderness areas and explore their spatial connections, thereby enhancing the scientific, standardized, and practical nature of the construction of a beautiful China and ecological civilization at the urban spatial scale.

[0009] To achieve the above objectives, a first aspect of the present invention provides a method for identifying, assessing, and managing new urban wilderness areas, the method comprising the following steps:

[0010] (1) Construct a multi-dimensional evaluation index database for new urban wilderness areas: collect a large amount of basic data on ecological environment and socio-economic aspects, and perform data preprocessing;

[0011] (2) Construct a multi-dimensional evaluation index system for the wilderness value and outdoor recreation potential of new urban wilderness areas: select evaluation factors from the urban wilderness value index and the urban outdoor recreation potential evaluation index;

[0012] (3) Construct a value potential assessment model for new urban wilderness areas: Use the weighted linear combination (WLC) method to assess the value of urban wilderness areas and the MaxEnt model to assess the urban outdoor recreation potential, identifying existing and potential new urban wilderness areas;

[0013] (4) Spatial analysis and spatial correlation of new urban wilderness areas: Identify the spatial heterogeneity and spatial correlation factors of the distribution of new urban wilderness areas through spatial autocorrelation analysis and geographically weighted regression analysis;

[0014] (5) Propose spatial planning and management strategies for new urban wilderness areas: By analyzing the resilience and dynamics of new urban wilderness areas, propose targeted spatial responses and management strategies to provide a scientific basis for protection and sustainable management.

[0015] Preferably, in step (1), the "collection of a large amount of basic data on ecological environment and social economy, and data preprocessing" specifically includes the following:

[0016] Collect data on land use types and assign naturalness values ​​based on expert evaluations of wilderness maps;

[0017] Population density data were collected and clipped to the study boundary;

[0018] Collect POI data and OSM data, and calculate Euclidean distances and perform kernel density analysis based on relevant indicators, including campsite accessibility, urban green space accessibility, public service infrastructure accessibility, water body accessibility, residential area accessibility, and urban road network accessibility;

[0019] Collect the Global Annual Artificial Impervious Area (GAIA) data and clip it to the study boundary;

[0020] Normalized difference vegetation index (NVDI) data were collected and clipped to the study boundary;

[0021] Collect elevation data and use the elevation to output slope data;

[0022] Nighttime light index data were collected and clipped to the study boundary;

[0023] Species distribution point data were collected and used to calculate the Shannon diversity index, habitat quality and wildlife viewing density.

[0024] Preferably, in step (2), the "evaluation factors are selected from the urban wilderness value index indicators", and the evaluation factors specifically include the following: biophysical naturalness, population density, residential area accessibility, residential area density, road network accessibility, road network density, public service facility accessibility, public service facility density, impervious surface index, and normalized vegetation index.

[0025] Preferably, in step (2), the "selection of evaluation factors from the urban outdoor leisure potential evaluation indicators" specifically includes the following:

[0026] Ecological environment assessment factors: elevation, slope, normalized difference vegetation index, Shannon diversity index, habitat quality, and water accessibility;

[0027] Socioeconomic assessment factors: night light index, population density, wildlife viewing activity density, normalized difference vegetation index, residential area density, residential area accessibility, road network accessibility, road network density, urban green space accessibility, and campsite accessibility.

[0028] Preferably, in step (3), the “assessment of urban wilderness value by applying the weighted linear combination (WLC) method” specifically comprises the following steps:

[0029] S1: Expert consultation to determine indicator weights: We hired a number of urban planning, ecology, and landscape design experts to score the importance of urban wilderness value indicators and determine the weights of each indicator. The specific calculation method is as follows: Among them, W i Indicates the weight of the i-th indicator, IR i represents the importance score of the i-th indicator (the average of all expert scores); n represents the number of evaluation indicators;

[0030] S2: Calculate the Urban Wilderness Value Index (URV): Calculate the Urban Wilderness Value Index of different study units within the study area. The specific calculation method is as follows: Among them, Xi represents the value of the i-th indicator in the research unit, and Wi represents the weight of the i-th indicator;

[0031] S3: Classification and distribution mapping of the calculation results of the Urban Wilderness Value Index (URV): The calculation results of the Urban Wilderness Value Index (URV) are divided into five categories using the natural breakpoint method: high wilderness, relatively high wilderness, medium wilderness, relatively low wilderness and low wilderness, and distribution maps of different levels are generated in ArcGIS software.

[0032] Preferably, in step (3), the “assessment of urban outdoor recreation potential using the MaxEnt model” includes using two key data to analyze and predict the distribution of urban outdoor recreation potential, specifically including the following steps:

[0033] T1: Randomly sample and collect social media photo data and input them into the model. The photo data includes photos with geographic locations of hiking, cycling, and mountaineering.

[0034] T2: Capture campsite data within the study area and input it into the model;

[0035] T3: Classification and distribution mapping of the calculation results of urban outdoor recreation potential: The calculation results of urban outdoor recreation potential are divided into five categories through the natural breakpoint method: high potential, relatively high potential, medium potential, relatively low potential and low potential, and distribution maps of different levels are generated in ArcGIS software.

[0036] Preferably, in step (4), the “identifying the spatial heterogeneity and spatial correlation factors of the distribution of new urban wilderness areas through spatial autocorrelation analysis and geographically weighted regression analysis” specifically includes the following steps:

[0037] U1: Divide the research unit into grids as the basic analysis unit, construct a spatial weight matrix, and use GeoDa software to conduct global spatial autocorrelation analysis and local spatial autocorrelation analysis, including: univariate spatial autocorrelation analysis of urban wilderness areas, univariate spatial autocorrelation analysis of urban outdoor recreation potential areas, and bivariate spatial autocorrelation analysis of urban wilderness and recreation potential areas. Output the global spatial autocorrelation Moran index scatter plot and the local spatial autocorrelation LISA cluster map respectively to determine the "high-high", "low-low", "high-low", and "low-high" spatial clusters of wilderness value and recreation potential;

[0038] U2: Use the geographically weighted regression (GWR) model to analyze the spatial heterogeneity and related influencing factors of urban new wilderness areas. Specifically, the LISA clustering results obtained in the above step U1 are used as the dependent variable to measure the spatial clustering trend, and the influencing factors reflecting urban population pressure and the public's demand for accessibility to natural space are used as independent variables, including water body accessibility, wildlife viewing activity density, residential area accessibility, residential density, urban green space accessibility, and urban campsite accessibility. The local parameters of the model are estimated, and key wilderness and leisure indicators are identified. Then, the spatial heterogeneity and spatial correlation factors of the distribution of urban new wilderness areas are summarized.

[0039] Preferably, in step (5), the “proposing a spatial planning and management strategy for new urban wilderness areas” specifically includes the following steps:

[0040] V1: Based on the key wilderness and recreation indicators obtained in step U2 above, analyze the relationship between resident-nature interaction and biodiversity, and further summarize indicators and practical transformation paths to improve ecological quality and recreation resources while maintaining resident contact with nature;

[0041] V2: Based on the analysis of indicators and transformation pathways in step V1 above, propose an urban ecological function protection mechanism that combines natural maintenance and human participation to maintain and enhance the ecological value and recreational function of new urban wilderness areas;

[0042] V3: Based on the trends in ecological structure, spatial distribution, and interaction patterns between urban new wilderness areas and urban residents obtained in step U1 above, a dynamic network construction strategy for urban new wilderness space management is proposed to achieve ecological civilization in the urban environment;

[0043] V4: Based on the results of steps V1-V3 above, propose comprehensive specific planning and implementation measures for new urban wilderness areas that combine scientific management, human intervention, natural processes, and social participation.

[0044] The method provided by the present invention has at least the following beneficial effects:

[0045] This paper focuses on the development of ecological civilization and urban green space planning in my country. Addressing the scale limitations of existing wilderness assessment and management methods, which primarily focus on national and regional levels, and the lack of assessment indicators and implementation strategies at small and medium scales, this paper innovatively develops a set of interpretable urban-scale identification, assessment, and management methods for emerging urban wilderness areas. Based on the national strategies of Beautiful China and ecological civilization, this paper integrates multidisciplinary techniques from urban and rural planning, landscape ecology, statistics, and computer science to efficiently identify, assess, and manage emerging urban wilderness areas. It integrates qualitative and quantitative multi-type analytical techniques to facilitate analysis of the potential value, spatial correlation, and key influencing factors of emerging urban wilderness areas. This method combines expert scoring with data-driven analysis, using weighted linear combinations to quantify the weights of different influencing factors and accurately construct an assessment formula. It also uses the MaxEnt model to analyze the spatiotemporal correlations between geotagged photos and leisure demand, accurately characterizing the outdoor recreation value of emerging urban wilderness areas across multiple dimensions. It also employs spatial autocorrelation and geographically weighted regression analysis to reveal the spatial distribution patterns of emerging urban wilderness areas and the differential mechanisms of different spatial units, accurately characterizing the spatial heterogeneity of their distribution. This invention provides key technical support for the identification, assessment and management of new urban wilderness in my country, and offers a high-precision spatial decision-making tool for the formulation of urban ecological protection and leisure planning strategies tailored to local conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a flow chart of a method for identifying, assessing and managing new urban wilderness areas according to the present invention;

[0047] Figure 2 is the calculation result of the Urban Wilderness Value Index (URV) of Shenzhen City in the embodiment of the present invention;

[0048] Figure 3 is the calculation result of Shenzhen's outdoor leisure potential in the embodiment of the present invention; DETAILED DESCRIPTION

[0049] The endpoints of the ranges and any values ​​disclosed herein are not limited to the precise ranges or values, and these ranges or values ​​should be understood to include values ​​close to these ranges or values. For numerical ranges, the endpoints of each range, the endpoints of each range and individual point values, and the individual point values ​​can be combined with each other to obtain one or more new numerical ranges, which should be considered to be specifically disclosed herein.

[0050] The present invention will be described in detail below through examples.

[0051] Example

[0052] This example is used to combine Figure 1 Provides a methodology for identifying, assessing, and managing emerging urban wilderness areas in Shenzhen, Guangdong Province, China. The methodology includes:

[0053] (1) Complete data processing: collect a large amount of basic data on ecological environment and socio-economic aspects, and perform data preprocessing to build a multi-dimensional evaluation index database for new urban wilderness areas.

[0054] In this embodiment, the following contents are specifically included:

[0055] Collect data on land use types in Shenzhen and assign naturalness values ​​based on expert evaluations of wilderness maps;

[0056] Collect population density data for Shenzhen and crop it to the research boundary of Shenzhen;

[0057] Shenzhen POI data and OSM data were collected, and Euclidean distances and kernel density analysis were performed based on these data. The relevant indicators included campsite accessibility, urban green space accessibility, public service infrastructure accessibility, water body accessibility, residential area accessibility, and urban road network accessibility.

[0058] Collect the Global Annual Artificial Impervious Area (GAIA) data for Shenzhen and clip it to the research boundary of Shenzhen;

[0059] Normalized Difference Vegetation Index (NVDI) data for Shenzhen City were collected and clipped to the research boundary of Shenzhen City;

[0060] Collect elevation data of Shenzhen City and use the elevation to output slope data;

[0061] Collect Shenzhen night light index data and crop it to the research boundary of Shenzhen;

[0062] Species distribution data were collected in Shenzhen and the Shannon diversity index, habitat quality and wildlife viewing density were calculated based on them.

[0063] (2) Complete the system construction: select evaluation factors from the urban wilderness value index indicators and the urban outdoor recreation potential evaluation indicators to construct a multi-dimensional evaluation index system for new urban wilderness areas.

[0064] In this embodiment, the following contents are specifically included:

[0065] The biophysical naturalness, population density, residential accessibility, residential density, road network accessibility, road network density, public service facility accessibility, public service facility density, impervious surface index, and normalized difference vegetation index of Shenzhen City were selected as evaluation factors for the urban wilderness value index.

[0066] Shenzhen's elevation, slope, normalized difference vegetation index, Shannon diversity index, habitat quality, and water accessibility were selected as ecological environment assessment factors in the evaluation factors for urban outdoor recreation potential;

[0067] Shenzhen's night light index, population density, wildlife viewing activity density, normalized vegetation index, residential area density, residential area accessibility, road network accessibility, road network density, urban green space accessibility, and campsite accessibility were selected as socioeconomic evaluation factors in the evaluation factors for urban outdoor recreation potential.

[0068] (3) Complete the value assessment: The urban wilderness value is assessed by applying the weighted linear combination (WLC) method, and the urban outdoor recreation potential is assessed by using the MaxEnt model. The existing and potential new urban wilderness areas are identified, and a value potential assessment model for new urban wilderness areas is constructed.

[0069] In this embodiment, the “assessment of urban wilderness value by applying the weighted linear combination (WLC) method” specifically includes the following steps:

[0070] S1: Expert consultation to determine indicator weights: 25 urban planning, ecology, and landscape design experts were hired to rate the importance of Shenzhen’s urban wilderness value indicators and determine the weights of each indicator. The specific calculation method is as follows: Among them, W i Indicates the weight of the i-th indicator, IR i represents the importance score of the i-th indicator (the average of the scores of 25 experts); n represents the number of evaluation indicators, n = 10;

[0071] S2: Calculate the Urban Wilderness Value Index (URV) of Shenzhen: Calculate the Urban Wilderness Value Index of different research units within the study area. The specific calculation method is as follows: Among them, Xi represents the value of the i-th indicator in the research unit, and Wi represents the weight of the i-th indicator;

[0072] S3: Classification and distribution mapping of the calculation results of the Urban Wilderness Value Index (URV) in Shenzhen: The calculation results of the Urban Wilderness Value Index (URV) were divided into five categories using the natural breakpoint method: high wilderness, relatively high wilderness, medium wilderness, relatively low wilderness and low wilderness, and the distribution maps of different levels were generated in ArcGIS software (such as Figure 2 The specific content of the result is described in detail in K1 below.

[0073] In this embodiment, the “assessment of urban outdoor recreation potential using the MaxEnt model” includes using two key data to analyze and predict the distribution of urban outdoor recreation potential in Shenzhen, specifically including the following steps:

[0074] T1: Based on random sampling of social media photo data from the "Six Feet" website, Shenzhen photo data was collected and input into the model. The photo data included geographically mapped photos of hiking, cycling, and mountaineering.

[0075] T2: Capture Shenzhen campsite data based on Baidu Maps and Ctrip and input it into the model;

[0076] T3: Classification and distribution mapping of the calculation results of Shenzhen's urban outdoor recreation potential: The calculation results of the urban outdoor recreation potential are divided into five categories using the natural breakpoint method: high potential, relatively high potential, medium potential, relatively low potential and low potential, and the distribution map results of different levels are generated in ArcGIS software (such as Figure 3 The specific content of the result is described in detail in K2 below.

[0077] (4) Complete factor analysis: Through spatial autocorrelation analysis and geographically weighted regression analysis, identify the spatial heterogeneity and spatial correlation factors of the distribution of new urban wilderness areas, and analyze the spatial analysis and spatial correlation of new urban wilderness areas.

[0078] In this embodiment, the following steps are specifically included:

[0079] U1: Shenzhen City is divided into 250M*250M grids as the basic analysis unit, and a spatial weight matrix is ​​constructed. GeoDa software is used to conduct global and local spatial autocorrelation analyses, including univariate spatial autocorrelation analysis of urban wilderness areas, univariate spatial autocorrelation analysis of urban outdoor recreation potential areas, and bivariate spatial autocorrelation analysis of urban wilderness and recreation potential areas. The global spatial autocorrelation Moran index scatter plot and local spatial autocorrelation LISA cluster map are output respectively to determine the "high-high", "low-low", "high-low", and "low-high" spatial cluster results of wilderness value and recreation potential. The spatial interaction relationship between urban wilderness quality and recreation value is understood through the "high-high", "high-low", and "low-high" spatial clusters. The specific content of the results is elaborated in K3 below.

[0080] U2: Use the geographically weighted regression (GWR) model to analyze the spatial heterogeneity and related influencing factors of the new urban wilderness in Shenzhen. Specifically, the LISA clustering results obtained in the above step U1 are used as the dependent variable to measure the spatial clustering trend, and the factors reflecting the urban population pressure and the public's demand for accessibility to natural space in Shenzhen are used as independent variables, including water accessibility, wildlife viewing activity density, residential area accessibility, residential density, urban green space accessibility, and urban campsite accessibility. The local parameters of the model are estimated, and key wilderness and leisure indicators are identified. The spatial heterogeneity and spatial correlation factors of the distribution of new urban wilderness areas are summarized. The specific content of this result is detailed in K4 below.

[0081] (5) Complete strategy formulation: By analyzing the resilience and dynamics of urban new wilderness areas, propose spatial planning and management strategies for urban new wilderness areas.

[0082] In this embodiment, the following contents are specifically included:

[0083] V1: Analyze the relationship between residents’ interactions with nature and biodiversity in Shenzhen, and further summarize indicators and practical transformation paths to improve ecological quality and recreational resources while maintaining residents’ contact with nature;

[0084] V2: Propose an urban ecological function protection mechanism in Shenzhen that combines nature conservation and human participation to maintain and enhance the ecological value and recreational function of new urban wilderness areas;

[0085] V3: Propose a dynamic network construction strategy for the management of new urban wilderness spaces in Shenzhen to achieve ecological civilization in the urban environment;

[0086] V4: Based on the results of steps V1-V3 above, a comprehensive proposal is made for the specific planning and implementation measures of Shenzhen’s new urban wilderness that combines scientific management, human intervention, natural processes, and social participation. The specific content of the results is elaborated in K5 below.

[0087] In summary, the following conclusions are drawn from this embodiment:

[0088] K1: In terms of the spatial distribution of urban wilderness value, the high wilderness area covers 693.14 square kilometers, accounting for about 36.0% of the urban area of ​​Shenzhen, mainly located in the central ecological backbone area and coastal protection areas.

[0089] K2: In terms of the spatial distribution of urban outdoor leisure potential, the area of ​​high leisure potential is 149.26 square kilometers, accounting for about 10.8% of Shenzhen’s urban area, including many natural mountainous areas and coastal areas.

[0090] K3: In terms of spatial correlation between urban wilderness and recreation potential, there is a “high-high” clustering in mountainous areas and nature reserves, and a “low-low” clustering in urbanized areas. The recreation potential values ​​show strong spatial autocorrelation, indicating that areas with high wilderness and recreation potential are usually less developed and ecologically rich, while rapidly urbanizing areas often have neither wilderness value nor recreation potential.

[0091] K4: In terms of key influencing factors of urban wilderness and recreational potential, water accessibility, population density and campsite accessibility are key factors affecting the recreational potential of Shenzhen's new urban wilderness areas, and are positively correlated with high recreational potential; the density of wildlife viewing activities has a positive impact on the recreational potential of the Northeast region, highlighting the contribution of ecologically diverse areas to recreational potential.

[0092] K5: The resilience and dynamism of new urban wilderness areas are key attributes for coping with urbanization pressures and maintaining the health of urban ecosystems. We should actively develop wilderness recreation networks that adapt to environmental changes and dynamic social needs, and adopt strategies that integrate ecological and recreational values ​​into urban planning and management, including rewilding design, biodiversity enhancement strategies, and sustainable recreation planning, as well as enhance public participation and community governance.

[0093] The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited thereto. Within the technical concept of the present invention, various simple variations of the technical solution of the present invention may be made, including combining the various technical features in any other appropriate manner. These simple variations and combinations should also be regarded as disclosed in the present invention and fall within the scope of protection of the present invention.

Claims

1. A method for identifying, assessing and managing new urban wilderness areas, characterized by: The method comprises the following steps: (1) Construct a multi-dimensional evaluation index database for new urban wilderness areas: collect a large amount of basic data on ecological environment and socio-economic aspects, and perform data preprocessing; (2) Construct a multi-dimensional evaluation index system for the wilderness value and outdoor recreation potential of new urban wilderness areas: select evaluation factors from the urban wilderness value index and the urban outdoor recreation potential evaluation index; (3) Construct a value potential assessment model for new urban wilderness areas: Use the weighted linear combination (WLC) method to assess the value of urban wilderness areas and the MaxEnt model to assess the urban outdoor recreation potential, identifying existing and potential new urban wilderness areas; (4) Spatial analysis and spatial correlation of new urban wilderness areas: Identify the spatial heterogeneity and spatial correlation factors of the distribution of new urban wilderness areas through spatial autocorrelation analysis and geographically weighted regression analysis; (5) Propose spatial planning and management strategies for new urban wilderness areas: By analyzing the resilience and dynamics of new urban wilderness areas, propose targeted spatial responses and management strategies to provide a scientific basis for protection and sustainable management.

2. The method according to claim 1, wherein In step (1), the "collection of a large amount of basic data on ecological environment and social economy, and data preprocessing" specifically includes the following: Collect data on land use types and assign naturalness values ​​based on expert evaluations of wilderness maps; Population density data were collected and clipped to the study boundary; Collect POI data and OSM data, and calculate Euclidean distances and perform kernel density analysis based on relevant indicators, including campsite accessibility, urban green space accessibility, public service infrastructure accessibility, water body accessibility, residential area accessibility, and urban road network accessibility; Collect the Global Annual Artificial Impervious Area (GAIA) data and clip it to the study boundary; Normalized difference vegetation index (NVDI) data were collected and clipped to the study boundary; Collect elevation data and use the elevation to output slope data; Nighttime light index data were collected and clipped to the study boundary; Species distribution point data were collected and used to calculate the Shannon diversity index, habitat quality and wildlife viewing density.

3. The method according to claim 1, wherein In step (2), the "selection of evaluation factors from the urban wilderness value index" specifically includes the following: Biophysical naturalness, population density, residential accessibility, residential density, road network accessibility, road network density, public service facility accessibility, public service facility density, impervious surface index, and normalized difference vegetation index.

4. The method according to claim 1, wherein In step (2), the "selection of evaluation factors from the urban outdoor leisure potential evaluation indicators" specifically includes the following: Ecological environment assessment factors: elevation, slope, normalized difference vegetation index, Shannon diversity index, habitat quality, and water accessibility; Socioeconomic assessment factors: night light index, population density, wildlife viewing activity density, normalized difference vegetation index, residential area density, residential area accessibility, road network accessibility, road network density, urban green space accessibility, and campsite accessibility.

5. The method according to claim 1, wherein In step (3), the "assessment of urban wilderness value by applying the weighted linear combination (WLC) method" specifically includes the following steps: S1: Expert consultation to determine indicator weights: We hired a number of urban planning, ecology, and landscape design experts to score the importance of urban wilderness value indicators and determine the weights of each indicator. The specific calculation method is as follows: Among them, W i Indicates the weight of the i-th indicator, IR i represents the importance score of the i-th indicator (the average of all expert scores); n represents the number of evaluation indicators; S2: Calculate the Urban Wilderness Value Index (URV): Calculate the Urban Wilderness Value Index of different study units within the study area. The specific calculation method is as follows: Among them, Xi represents the value of the i-th indicator in the research unit, and Wi represents the weight of the i-th indicator; S3: Classification and distribution mapping of the calculation results of the Urban Wilderness Value Index (URV): The calculation results of the Urban Wilderness Value Index (URV) are divided into five categories using the natural breakpoint method: high wilderness, relatively high wilderness, medium wilderness, relatively low wilderness and low wilderness, and distribution maps of different levels are generated in ArcGIS software.

6. The method according to claim 1, wherein In step (3), the "assessment of urban outdoor recreation potential using the MaxEnt model" includes using two key data to analyze and predict the distribution of urban outdoor recreation potential, specifically including the following steps: T1: Randomly sample and collect social media photo data and input them into the model. The photo data includes photos with geographic locations of hiking, cycling, and mountaineering. T2: Capture campsite data within the study area and input it into the model; T3: Classification and distribution mapping of the calculation results of urban outdoor recreation potential: The calculation results of urban outdoor recreation potential are divided into five categories through the natural breakpoint method: high potential, relatively high potential, medium potential, relatively low potential and low potential, and distribution maps of different levels are generated in ArcGIS software.

7. The method according to claim 1, wherein In step (4), the "identification of spatial heterogeneity and spatial correlation factors of the distribution of new urban wilderness areas through spatial autocorrelation analysis and geographically weighted regression analysis" specifically includes the following steps: U1: Divide the research unit into grids as the basic analysis unit, construct a spatial weight matrix, and use GeoDa software to conduct global and local spatial autocorrelation analyses, including univariate spatial autocorrelation analysis of urban wilderness areas, univariate spatial autocorrelation analysis of urban outdoor recreation potential areas, and bivariate spatial autocorrelation analysis of urban wilderness and recreation potential areas. The global spatial autocorrelation Moran index scatter plot and local spatial autocorrelation LISA cluster map are output respectively to determine the "high-high", "low-low", "high-low", and "low-high" spatial clusters of wilderness value and recreation potential; U2: Use the geographically weighted regression (GWR) model to analyze the spatial heterogeneity and related influencing factors of urban new wilderness areas. Specifically, the LISA clustering results obtained in the above step U1 are used as the dependent variable to measure the spatial clustering trend, and the influencing factors reflecting urban population pressure and the public's demand for accessibility to natural space are used as independent variables, including water body accessibility, wildlife viewing activity density, residential area accessibility, residential density, urban green space accessibility, and urban campsite accessibility. The local parameters of the model are estimated, and key wilderness and leisure indicators are identified. Then, the spatial heterogeneity and spatial correlation factors of the distribution of urban new wilderness areas are summarized.

8. The method according to claim 1, wherein in, In step (5), the “proposing a spatial planning and management strategy for new urban wilderness areas” specifically includes the following steps: V1: Based on the key wilderness and recreation indicators obtained in step U2 above, analyze the relationship between resident-nature interaction and biodiversity, and further summarize indicators and practical transformation paths to improve ecological quality and recreation resources while maintaining resident contact with nature; V2: Based on the analysis of indicators and transformation pathways in step V1 above, propose an urban ecological function protection mechanism that combines natural maintenance and human participation to maintain and enhance the ecological value and recreational function of new urban wilderness areas; V3: Based on the trends in ecological structure, spatial distribution, and interaction patterns between urban new wilderness areas and urban residents obtained in step U1 above, a dynamic network construction strategy for urban new wilderness space management is proposed to achieve ecological civilization in the urban environment; V4: Based on the results of steps V1-V3 above, propose comprehensive specific planning and implementation measures for new urban wilderness areas that combine scientific management, human intervention, natural processes, and social participation.

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