According to the information of the Yuxi Housing and Urban-Rural Development Bureau, by the end of 2020, the total area of urban built-up area in the central city of Yuxi is 38.70 km², covering Hongjiu Road in the north, Dongfang Reservoir in the east, Subi Mountain in the south, and Huixi Road in the west. According to the Yuxi Green Space System Planning (2011–2030), the research scope is comprehensively defined, including Yudai Street, Fenghuang Street, Yuxing Street, and Liqi Street in Hongta District.

The DEM elevation data of the central city of Yuxi (built-up area) (Figure 1) were obtained by using open data and tools (Li et al., 2022) from the web such as Global Mapper, Geospatial Data Cloud, and ArcGIS, and were processed by bid projection conversion, area cropping, and raster output. The vector data of roads (Figure 2), water systems, and park green areas (Figure 3) in the study area were obtained using Open-Source Neighborhood Map (OSM) and used for layout evaluation after coordinate projection conversion and manual calibration.

POI data were mainly obtained through urban open-source data. They mainly include 4,575 housing prices, 4,284 catering services, 9,427 shopping facilities, 987 transportation facilities, 1,881 companies and enterprises, 4,081 living services, 561 sports and leisure service facilities, 899 science, education, and culture facilities, 3,740 public service facilities (including medical treatment), and 1,056 residential area data within the study area, a total of 31,491 pieces of data (Table 1).

The grid aggregation density analysis tool of QGIS was used to obtain statistics on the urban population’s outgoings and obtain the crowd activity heatmap (Figure 4). On this basis, the thermal area of crowd activities is divided into six levels, and the ArcGIS 10.8 software is used to calculate the thermal area of crowd activities at all levels using a grid calculator. Heat class II of crowd activity occupies the largest area, 9.16 km², accounting for up to 60.73%, followed by class I, with an area of 2.49 km², accounting for 16.54%, and the area of class V is the least, only 0.31 km², accounting for 2.04%. Figure 4shows how the thermal level of crowd activities decreases from the center of the city to the surrounding areas in space, and its area increases with the decrease of the thermal level. However, the farther away the thermal level is from the center of the city after reaching a certain level, the area also decreases, as shown in Figure 4.

After years of construction and development, the central city of Yuxi has gradually formed an overall urban pattern of “one center, two cores, and four clusters,” with the central city as the center and four clusters of Daying Street, Beicheng, Chunhe, and Yanhe in the periphery. As the central area of Hongta District, the central city basically forms a “centralized” overall layout with the old city as the core and gradually expands outward. The three areas included the central area of the old city as the main residential, commercial, and service comprehensive area; the eastern mountain area of tobacco industry production, new industrial park, as the main industrial, college, and secondary education area; and residential comprehensive area, the south and north of the residential, administrative, and commercial area, as the main urban new area.

However, it can be seen from Figure 4 that there are still two problems in Yuxi’s urban green space. The central city green system is not complete. As far as the whole urban area is concerned, a complete urban landscape green space system has not been formed yet, and there is no linear or ribbon-like connection between all kinds of green spaces. Although the background environment of mountains and the countryside around the city is good, it fails to form an organic whole. The combination of greening and urban culture is not high. The green investment in the core areas such as main entrances, urban trunk roads, and existing cultural facilities is insufficient, the theme is not prominent enough, and the cultural connotation is relatively weak. The distribution of public green space is uneven. From the perspective of urban outdoor hotspots, the heat and service pressure of the comprehensive area of living, trade, and service in the central area dominated by the old city are greater than those in other areas, indicating that the utilization rate of green space is higher, and the area of green space is smaller in this region. The plots show that public green space is mainly concentrated in the new urban areas, such as the north and southeast of the built-up area, while the central old city comprehensive area is obviously insufficient, and there is a big gap among all groups. The green space development in this area is difficult, the service radius of green space is insufficient, the building density is high, the traffic is crowded, and the ecological environment quality is declining.

With the help of the service pressure formula, the ratio of the number of POIs within the service range of each park to the park area was calculated, and the service pressure of each park was obtained (Figure 5). By referring to relevant research results and combining the population and facility density of various interest points in Yuxi City, the expert consultation method was adopted to divide the urban park service pressure in the central urban area of Yuxi City into five levels successively. The higher the level, the greater will be the service pressure. As shown in Figure 5, the service pressure level is inversely proportional to the area. The higher the pressure level, the smaller will be the area, and the lower the pressure level, the larger will be the area. The first-level area of service pressure is the largest, which is 7.68 km², accounting for 53.20% of the total area. The second is grade 2, with an area of 4.15 km², accounting for 28.75%. The smallest area is grade 5, accounting for only 1.89%, mainly distributed in the northeast of the central urban area (Table 2). Through the service pressure and cluster thermal analysis of the green space usage data in the central city of Yuxi City, it is found that the service level of the green space in the central city of Yuxi is strongly correlated with the type and geographical location of the urban green space (Figure 5).

Through comprehensive analysis, it is found that the use of comprehensive parks is affected by the type of recreation space, service facilities, and park transportation convenience. These parks are designed with diversified and characteristic recreation spaces and service entertainment facilities suitable for different users. The types of activities are also in line with the characteristics of the times and meet the recreation needs of modern people. For example, Nie Er Park and Nie Er Music Square (Yuhu area) have a large area of open squares, tree-lined walks, children’s playgrounds, beaches, and perfect surrounding commercial facilities.

The use heat and service pressure of special parks are greatly influenced by the attraction of the park theme. We can plan regular events to form characteristic recreation space and popular science education activities to attract many people. For example, the “Lantern Festival” in the landscape belt of the State River, the Memorial Park of Revolutionary Martyrs, the Ancient Kiln Site Park, Nie Er Residence, East Wind Playground, and other popular science and education theme activities have become the cultural symbols of these parks, which effectively enhance the attraction and uniqueness of the special parks. However, there is little correlation between the heat of famous special parks and the number of traffic facilities around them. In recent years, the proportion of floating population in Yuxi City has increased year by year, and foreign tourists occupy a certain proportion in the users of green space. The use of special parks is greatly affected by the popularity. Therefore, the attraction of well-known special parks to outsiders reduces the direct correlation between the use of green space and the convenience of public transport. However, special parks such as Gaolongtan Ecological Park, Qianguashan Park, Liuyin Park, Chaijiashan Mountain Park, and Wunaoshan Ecological Park, which are themed on mountain protection, water restoration, ecological recreation, and sports, are not well-known and mainly serve for the protection and restoration of the city’s ecological environment. Therefore, its entertainment facilities and accessibility are poor, not attractive, and space heat and service pressure are relatively low. Among the influencing factors of the frequency of community parks and street parks, the location of the park and its opening degree were significantly greater than the size of the park. Although the larger the scale is, the more favorable it is for the construction of recreation space, and the usage heat and service pressure are not positively correlated with the size of the park. Although the green spaces beside the streets, such as Triangle Park, Chess Park, and Jade Garden, are small in size, they occupy a favorable position, with a high degree of open use for citizens, convenient transportation links by foot and bicycle, large crowds of people around them, high heat value of outdoor activities, and higher service pressure and use frequency than other green spaces. Although the scale of community parks is relatively large, the use of heat is relatively low and the service pressure is small. Except for the high use rate of a small number of community parks that are open to the public, such as Dongfeng Square and Dongfeng Amusement Park, the service pressure and heat of large community parks, such as Outlet Square and Yuxi Dahe Phase III, are relatively low. Purple Maple Garden is mainly used by residents inside the community, and its location is relatively secluded, with low awareness of openness and public, and the pressure and heat of park service are extremely low. The analysis results are also consistent with the actual use in the survey. However, due to the lack of data on the elders and children in the POI data, the use evaluation of community parks has certain limitations.

The hierarchical structure model is established according to the required evaluation method. When constructing the judgment matrix, the index quantification table was distributed to 20 industry experts. A total of 20 quantified index tables were recovered with a recovery rate of 100%. After hierarchical single sorting, feature vector calculation, consistency test, and other steps, the weight table of impact factors was obtained (Table 3), which was transformed into the score table of the GIS evaluation index (Table 4). The natural breakpoint method is a univariate classification method based on cluster analysis. By calculating data breakpoints between classes to determine a certain number of grades, the difference between classes can be minimized and the difference between classes can be maximized. The main advantage of this method is to distinguish similar values in the data most effectively (Cai et al., 2021). Therefore, the demand evaluation value of park green space was obtained through GIS calculation, and it was divided into nine levels (Figure 6). The ArcGIS 10.8 software was used to obtain statistics on the demand area of green space at all levels, and it was found that the demand for green space in Yuxi City was mainly concentrated in level 1, with an area of 29.62 km², accounting for 69.68% of the total demand, mainly including the peripheral and sporadic distribution in the central urban area. The next is grade 4, with an area of 4.45 km², accounting for 10.46%. The third is level 2, with an area of 4.26 km², accounting for 10.02%. The area of other demand levels is relatively low and has little difference.

As shown in Figure 6, regions with higher demand levels occupy smaller areas, while regions with lower demand levels occupy larger areas. In fact, ideally, the area with a higher rating should have denser and better-served community parks and recreation parks and be closer or more accessible to large regional or city-wide parks. The highest demand for parks in central urban areas is along Yuxing Road, Zhongwei area, to public rental housing. Another high point is the old railway station area and rainbow community, Bailong Road in Liqi Town, Kangjing Road line area, which is also relatively high demand. Most people in Yuxi live and work in this area, which not only brings traffic pressure but also produces high-pressure and high-demand areas in terms of park services (Figure 6).

Urban big data, especially urban open data, provide a scientific basis for quantitative research on the precise placement of urban green space and the precise efficiency of resources (Wu and Peng, 2015; Chen S. S. et al., 2021). Therefore, it is urgent to explore the optimization path of the spatial layout of urban green space based on the support of urban big data (Ahas et al., 2010; Gao et al., 2021), so as to construct fine planning, management and regulation of urban green space, and efficient ecological space creation. Big data have the characteristics of huge data volume, comprehensive data coverage, and complex data types (Xue et al., 2020a; Jinwon et al., 2022; Liu et al., 2022). Therefore, when using big data to conduct relevant research, selecting appropriate data is crucial for the smooth development of the research (Xue et al., 2020b; Sim et al., 2020). This study innovatively used POI data to analyze the intensity of crowd activities and then evaluate and optimize the green space. The data are easy to obtain, the processing is relatively simple, and the results are also easy to visualize and visually present, which can avoid many influences brought by traditional planning, but there are still some problems and challenges.

On one hand, the existing big data acquisition and processing technology in the field of spatial planning is not yet mature, and there are technical blind spots in data validity, redundancy processing, stripping (Liu et al., 2022), and other aspects, and the lack of a perfect data security system (Ahas et al., 2010). At the same time, due to interests and confidentiality, it is difficult to achieve data integration and sharing between government departments and enterprises in a short period of time, which limits the breadth and depth of big data acquisition for planning and compilation (Huang et al., 2021). On the other hand, big data are more scientific in the simulation and analysis of the correlation of factors, but there are still imprecision problems in the study of causes and mechanisms (Zhang L. et al., 2022). How to integrate with traditional data analysis to avoid similar problems needs further exploration. In addition, the existing green space system planning results based on big data are still at the theoretical and methodological level, and a complete technical system has not yet been formed (Zabelskyte et al., 2022). How to promote in the industry to guide practical work and in what form to participate in the national statutory planning system will also become a challenge for future research.

But nonetheless, from the perspective of the data used, compared with the traditional spatial layout model, this study obtains the data of bus station density, housing price, cultural and entertainment facilities, office facilities, commercial service facilities, public facilities, and residential areas based on Baidu Map API, which has higher efficiency, lower cost, and more targeted experimental data. From the perspective of research methods, based on the principle of the original park layout planning, this paper introduces urban open data to quantitatively analyze the service level of urban parks and the evaluation of park green space needs from two aspects, providing a new perspective for the study of the spatial layout of urban green space.