Integrating ecological and recreational functions to optimize ecological security pattern in Fuzhou City

Study area

Fuzhou City, situated in the southeastern coastal region of China at the confluence of the Min River in eastern Fujian Province (118°6’37” to 119°27’16″E, 25°15’44” to 26°6’34″N), functions as the political, economic, and cultural center of Fujian Province (Fig. 1). The city oversees the administration of six counties and one county-level city, encompassing a total area of 11,968.53 km². The topography of Fuzhou is marked by a high western region and a low eastern region, with an average elevation of 84 m. The landscape is predominantly hilly (40.27%), followed by mountainous (32.41%) and plain areas (27.32%). Fuzhou experiences a subtropical monsoon climate, characterized by an average annual temperature of 21.2 °C. The city boasts an extensive and irregular coastline measuring 800.3 km, which includes a vast maritime area and numerous islands. Key water systems in Fuzhou comprise the Min River, Ao River, Dazhangxi River, and Longjiang River, which predominantly flow from east to west throughout the city.

As one of China’s national central cities, Fuzhou exhibits considerable economic strength, strategic location advantages, and substantial development potential. The city is characterized by diverse topography, abundant mountainous regions, high forest coverage, and a favorable ecological environment. Furthermore, Fuzhou serves as a cultural epicenter, being a significant origin of Min Yue culture, ancient architecture, and maritime culture, and possesses a rich historical and cultural legacy. As of 2023, Fuzhou’s permanent population stands at 8.469 million, with an urbanization rate of 73.91% and a regional GDP of 1.292847 trillion yuan. The city attracted 112.6263 million domestic and international tourists, generating total tourism revenue of 98.389 billion yuan, indicative of a robust recreational market. However, in light of evolving land use patterns and escalating recreational demands, Fuzhou’s ecological spaces are increasingly becoming fragmented⁴². According to the Fuzhou City Statistical Yearbook, in 2023, the forest area in Fuzhou was recorded at 7,307 km², and the wetland area at 563 km², reflecting decreases of 0.11% and 0.35%, respectively, compared to 2022. Additionally, the urban built-up area of Fuzhou expanded from 290.82 km² in 2017 to 410.02 km² in 2022. In this context, Fuzhou faces a significant challenge in reconciling rapid urban development with ecological preservation.

Data collection and process

The data employed in this research includes three distinct categories: environmental data, Point of Interest (POI) data, and online travel diary data.

The environmental data includes land use data for Fuzhou City in 2023, which was acquired from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences ( Additional data, such as administrative boundary vector data, Digital Elevation Model (DEM) data, and Normalized Difference Vegetation Index (NDVI) data for Fuzhou City, were sourced from the National Earth System Science Data Center ( Slope data was generated from the DEM of Fuzhou City and processed using ArcGIS 10.5. Road data was obtained from OpenStreetMap ( while river system data was collected via the Shuijingzhu map downloader (http://www.rivermap.cn).

For the POI data, this study utilizes the Amap Map Open Platform and the “POI Data Query” function provided by “XOmap” ( to extract a total of 401,452 POI records for Fuzhou City in 2023. Following a rigorous data screening and cleaning process, the final dataset comprised 6,487 valid POI records related to infrastructure and 5,928 valid POI records pertaining to recreational resources. Each record includes pertinent information such as the associated region, name, address, and geographical coordinates.

The online travel diary data encompasses both digital footprint data and web-based textual data. The outdoor travel application “foooooot” (www.foooooot.com), which is widely utilized in China, offers users a platform to document and share their outdoor recreational experiences⁴³. Utilizing the Octoparse data extraction tool, detailed information, including photographs and geographic locations shared by users in Fuzhou City from 2014 to 2023, was collected, resulting in a total of 1044 travelogues and 95,227 digital footprint points with geographic coordinates. Additionally, online text data was gathered by crawling travelogues themed focused on “Fuzhou City” from two prominent travel websites, Ctrip ( and Mafengwo ( also employing the Octoparse data extractor. These platforms are extensively used by Chinese tourists³³. A total of 1573 travelogues were collected from May 2019 to December 2023, comprising 573 from Ctrip and 1000 from Mafengwo. After conducting manual verification to excluded entries lacking location information or with ambiguous travel routes, 1510 valid travelogues were retained (542 from Ctrip and 968 from Mafengwo). An analysis of these valid travelogues identified 861 geographic locations as recreational flow nodes. The coordinates of each recreational flow node were extracted using the Baidu Coordinate Picker System, and the recreational routes and along with the visitation frequency of each recreational flow node were compiled into a binary matrix for subsequent analysis.

Research framework and methods

This research develops an ESP that harmonizes urban ecological conservation and recreational development by integrating the ecological and recreational functions of Fuzhou City. Initially, the MSPA model and landscape connectivity assessment are employed to identify the ecological source areas within Fuzhou City. Six resistance factors—namely elevation, slope, land use type, distance to river and roads, and vegetation—are selected to construct the ERS. Subsequently, the Linkage Mapper tool is utilized to extract ECs, ENs, EBPs and EPPs. Following this, a combination of kernel density analysis, raster overlay analysis, standard deviation ellipse analysis, and SNA is applied to identify recreational nodes. Six resistance factors—slope, land use type, distance to roads, infrastructure, digital footprints, and recreational resources—are employed to develop the RRS, with the Linkage Mapper tool again used to extract RCs.Ultimately, utilizing a trade-off matrix method, the relative significance of ecological and recreational functions within each unit is assessed to delineate functional zones and pinpoint strategic points. This process culminates in the reconstruction of the ESP for Fuzhou City, accompanied by recommendations for planning, development, and management strategies. Figure 2 provides a visual representation of the methods and procedures involved in the construction and optimization of the ESP in Fuzhou City, with a comprehensive explanation of each step detailed in the subsequent sections.

Construction of ecological security pattern

Identification of ESAs and ESPs

In this research, the MSPA model, along with landscape connectivity analysis, was employed to systematically identify and delineate ESAs within the designated study area. Due to the significant vegetation cover and well-established river systems in the study area, forests, grasslands, and water bodies were classified as foreground data, while other land use categories were categorized as background data. The analysis utilized the Guidos Toolbox software, configured with Foreground Connectivity set to 8, Edge Width set to 4, and both “Transition” and “Intext” options activated. A binary raster image of Fuzhou City for the year 2023 was subsequently analyzed. Following the importing of the results into ArcGIS 10.5, seven distinct landscape types were identified: Core Area, Edge Area, Perforation, Bridge Area, Branch, Loop Area, and Islet. Potential core patches with larger surface areas were extracted as preliminary data for the selection of ESAs, based on the landscape connectivity index. The landscape connectivity index of these potential core patches were computed using Conefor software. The dPC value serves as an indicator of the degree to which a particular landscape type facilitates or obstructs the diffusion of ecological flows. Patches were prioritized according to their dPC values, with those exhibiting a dPC value exceeding 0.2 being designated as ecological sources⁴⁴. The geometric centers of the ecological sources were calculated using ArcGIS 10.5, establishing them as ecological source points (ESPs).

Landscape connectivity is defined as the degree of ease or difficulty with which species can traverse between patches, thereby indicating the extent to which a region’s landscape supports species movement. Enhanced landscape connectivity is associated with increased ecosystem stability and spatial continuity⁴⁵. The following equations were utilized in the analysis:

Where n represents the total number of patches; a_i and a_j are the areas of patches i and j, respectively; A_l represents the cumulative area of the landscape elements; l_ij indicates the shortest distance between patches i and j ; p^*_ij reflects the maximum probability of species dispersal between patches i and j; PC_remove refers to the overall index following the removal of a patch. PC indicates the probability of connectivity, and dPC evaluates the significance of each patch in sustaining connectivity by quantifying the alterations in PC.

Construction of ERS

The development of a comprehensive ERSthrough the systematic screening and assessment of resistance factors enables a more accurate measurement of the impediments to ecological components during their movement, thereby aiding in the identification of potential ECs. In this research, resistance factors were selected based on prior studies and the specific environmental conditions of Fuzhou City, which included variables such as elevation, slope, land use type, distance to river and roads, and NDVI. The resistance values for each factor in this study are determined with reference to the values used in previous studies^15,44,46,47 (Table 1). Factors such as elevation, slope, NDVI, and land use type significantly affect the spatial distribution of species activities and resources availability. The distance to river system enhances biological activity, whereas roads serve as physical barriers to species migration, complicating movement for species located near these infrastructures. The weights assigned to each factor were established using the Analytical Hierarchy Process (AHP) within yaahp software, followed by a consistency assessment. The results for each factor were integrated with their respective weights to formulate a comprehensive ERS for Fuzhou City. The AHP is a multi-criteria, multi-objective decision-making method that combines qualitative and quantitative approaches, making it particularly effective for addressing complex problems that resist full quantification. This method decomposes complex decision problems into hierarchical structures, such as goals, criteria, and alternatives, and subsequently evaluates the relative significance of each indicator based on the experience and expertise of specialists in the field.

Construction of ECs

The Linkage Mapper toolbox, an essential analytical instrument in circuit theory, was employed to simulate the stochastic dynamics of species migration and identify the least-cost pathways connecting ecological sources⁴⁷. Utilizing the Linkage Pathways tool within Linkage Mapper 2.0 and employing the ERS as foundational data, the least-cost paths for ecological and energy flows were calculated to elucidate the spatial distribution of ECs. The Centrality Mapper module facilitated the quantification of the significance of various corridors by identifying their centrality, subsequently categorizing these corridors into primary and secondary classifications through the application of the natural breaks method.

EPPs are defined as regions within ecological corridors that exhibit elevated current density. These regions are marked by concentrated voltage due to high resistance in the surrounding areas, resulting in robust currents that provide substantial ecological protection value⁶. The Pinchpoint Mapper tool within Linkage Mapper was utilized to identify these pinch points, employing the “All to one” mode. Following the establishment of corridor cost-weighted distance at 1000 m, 1500 m and 2000 m, it is empirical testing revealed that when the corridor cost-weighted distance exceeds 1500 m, the core positioning of the EPPs remains unaffected by variations in corridor width. Consequently, this study adopted a corridor cost-weighted distance of 1500 m. The cumulative current map was generated by aggregating the current across all Ecological Source Areas (ESAs), and the centrality values of each ESPs and ECs were calculated to identify the locations of EPPs.

EBPs are identified as areas that obstruct species migration and dispersal between ESPs. The restoration of these points is anticipated to enhance landscape integrity and connectivity while mitigating migration resistance. The Barrier Mapper tool in Linkage Mapper was utilized in “Maximum” mode, with a minimum search radius of 100 m and a maximum of 300 m, employing a step size of 100 m. A moving window approach was implemented to identify barrier points, wherein the central pixel values within the search window were substituted with the least-cost distance values between source patches. The enhancement in connectivity post-barrier removal was represented by the unit improvement in least-cost distance. Areas exhibiting high values were esignated as EBPs within the ECs.

Construction of RSP

Analyzing the spatiality of recreational resources and digital footprints

By integrating the spatial distribution of recreational resources and visitor preferences, a more focused ESP framework can be developed. Kernel Density Estimation (KDE) is utilized to examine the spatial density characteristics and distribution patterns within the study area, as well as to evaluate the effects on adjacent regions. KDE is employed to visualize recreational resources and digital footprints⁴⁸, represented by the following formula:

Where f(x) represents the value of kernel density analysis at position x; h signifies the bandwidth; N represents the number of points within the bandwidth distance from position x_i; And K is a spatially weighted kernel function.

The Standard Deviation Ellipse (SDE) is a traditional technique for analyzing directional distribution directions of spatial data, facilitating a quantitative assessment of the centrality, distribution, spatial configuration, and other directional attributes of recreational resources and digital footprints data within the study area¹⁵. This method is applied to investigate the directional changes of recreational resources and visitor digital footprints in Fuzhou City from 2014 to 2023.

Prior studies suggest that the grid overlay analysis method, which incorporates roads, digital footprints, and recreational resources, provides a theoretical foundation and practical framework for the scientific identification and assessment of recreational potential areas, as well as for the constructing RCs⁴⁹. ArcGIS 10.5 is employed to create buffers around the roads, digital footprints, and recreational resources data in Fuzhou City. Following raster processing, the raster calculator is utilized for overlay analysis, and the resultant raster data serves as the basis for selecting RNs in Fuzhou City.

Recreational flow analysis and RNs selection

In this research, SNA was utilized to develop a network representing recreational flow. Data matrices obtained from online travel diaries were analyzed using Gephi software, resulting in the establishment of a recreational flow network for Fuzhou City. RNs were identified and categorized based on weighted degree centrality, a metric that assesses the positioning and significance of nodes within the recreational flow network by indicating the degree of connectivity among them. This metric encompasses both inflow and outflow degrees. A higher centrality value for a node signifies its crucial role within the network, reflecting its greater attractiveness and influence in the recreational market. In studies of recreational flow network, this index proves advantages in pinpointing key nodes and their essential interconnections. Prior Research has indicates that nodes exhibiting elevated weighted degree centrality tend to fulfill more prominent functional roles and possess greater importance within the network³³. The formula for calculating this index is as follows:

where WD_i represents weighted degree centrality, DI_i is the diffusion index, w_ij represent the weights of the edges between nodes i and j in both directions, and w_ji represents the weights of the edges between nodes j and i in both directions.

In this study, nodes with weighted degree centrality values exceeding the average were classified as strong functional nodes and identified as potential recreational nodes, whereas those with values below the average were categorized as weak functional nodes⁵⁰. Taking into account the specific characteristics of Fuzhou City, a thorough evaluation incorporating KDE, SED, grid overlay analysis, and the identification of potential RNs was conducted. Nodes exhibiting high weighted degree centrality, situated in areas characterized by elevated values in KDE and grid overlay analysis, were prioritized as RNs.

Construction of RRS and RCs

The construction of the RRS necessitated an examination of both natural geographical factors elements and socioeconomic factors, as well as insights into public preferences and behavioral patterns. This research, informed by prior studies^27,47,51 and the unique context of Fuzhou City, identified slope, land use type, distance to roads, infrastructure, digital footprints, and recreational resources as resistance factors, to which resistance coefficients were assigned accordingly. Slope and land use type significantly affect the spatial extent, comfort, and safety associated with recreational activities. Roads, as vital connections between various recreational areas, directly influence the spatial distribution and flow dynamics of the recreational network. Infrastructure plays a crucial role in facilitating recreational activities and ensuring their effective operation. The digital footprint serves as an indicator of recreational preferences and the intensity of engagement in recreational activities, while recreational resource constitute the foundation elements for the development of the recreational network and are pivotal in attracting tourists. The AHP was employed using the yaahp software to ascertain the weight of each factor (Table 2), followed by a consistency assessment. The results for each factor were subsequently integrated with their weights to formulate a comprehensive RRS for Fuzhou City. Using the Linkage Pathways tool within the Linkage Mapper 2.0 toolbox, and employing the RRS as input data, the least-cost paths for recreational flow were calculated to delineate the spatial distribution of recreational corridors. By quantifying the centrality of corridor flow, the corridors were further categorized based on their significance. In this study, the cost-weighted distance threshold for truncating recreational corridors was established at 1500 m.

Reconstruction of ecological security pattern

This study develops a tradeoff matrix to evaluate the relative significance of ecological and recreational functions across various units in Fuzhou City, based on their ecological and recreational function levels⁵². Utilizing the findings from the ERS and RRS analyses, the ecological and recreational functions of Fuzhou City were categorized into five levels, ranging from high to low (I, II, III, V), resulting in a total of 25 combinatorial outcomes (Fig. 3). The tradeoff matrix facilitates a comparative analysis of the ecological and recreational functions across different regions, leading to the classification of these outcomes into eight functional zones.

The Ecological Core Zone (ECZ) is identified as the central areas of the ecosystem, characterized by exceptionally high ecological value, sensitivity, and vulnerability. These regions typically encompass unique natural ecosystems, exhibit rich biodiversity, and provide essential ecological services, while also being particularly sensitive to anthropogenic impacts. Conversely, the Ecological Important Zone (EIZ) functions as a transitional buffer surrounding the core zone, primarily aimed at safeguarding the integrity and stability of ESAs. Although the ecological functions and biodiversity in these areas may be lower than those in core zones, they still possess considerable ecological significance.

The Recreational Core Zone (RCZ) is identified as the area with the highest concentration of recreational activities, supported by well-developed infrastructure. This zone is generally situated at the center of recreational spaces and serves as the primary venue for both residents and tourists. The Recreational Important Zone (RIZ) and the Recreational Development Zone (RDZ) are designed to complement and extend the functions of the core zone. Specifically, the RIZ, which encircles the core zone, possesses high recreational value and plays a vital strategic role in enhancing the regional recreational appeal for recreation, alleviating pressure on the core zone, and promoting a balanced spatial distribution of recreational activities. In contrast,,the RDZ is conducive to the development of outdoor recreational activities and the sustainable utilization of both natural and cultural resources.

The Eco-recreation Key Trade-off Zone (ER-KTZ) necessitates meticulous management to achieve a balance between ecological preservation and recreational activities. Although these areas exhibit high ecological value, their potential for recreational development is relatively constrained, necessitating rigorous planning and management to ensure a harmonious coexistence. In comparison, the Eco-recreation Secondary Trade-off Zone (ER-STZ) possesses moderate ecological value and recreational development potential, rendering the tradeoff between the two less critical, yet still requiring attention to their balance and optimization. Furthermore, the Elastic Development Zone (EDZ), characterized by lower ecological value and recreational potential, provides greater flexibility in reconciling ecological protection with recreational development. This zone allows for dynamic adjustments and optimizations based on actual ecological conditions and socioeconomic development needs, thereby exploring diverse pathways for recreational development while ensuring ecological security.

Strategic points are defined as critical locations within the ESP framework. In this study, the intersections of ECs and RCs are designated as strategic points, with intersections of primary corridors classified as major strategic points and intersections of secondary corridors classified as minor strategic points.