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Article

Multiple Analysis of the Relationship between the Characteristics of Plant Landscape and the Spatiotemporal Aggregation of the Population

1
Department of Landscape Architecture, School of Horticulture and Forestry, Huazhong Agricultural University, Wuhan 430070, China
2
Suzhou Garden Design Institute, Suzhou 215002, China
3
School of Architecture, Nanjing Tech University, Nanjing 211816, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(10), 6254; https://0-doi-org.brum.beds.ac.uk/10.3390/su14106254
Submission received: 11 April 2022 / Revised: 10 May 2022 / Accepted: 11 May 2022 / Published: 20 May 2022

Abstract

:
The reformation and development of the education system in China have led to environmental upgrades in a great number of universities. Amid this improvement, plant landscapes hold an important role in improving the environment and highlighting the campus culture. However, due to the lack of in-depth exploration of the relationship between plant landscape characteristics and the spatiotemporal aggregation of the population in current research, the design methods of campus plant landscapes are not thoroughly studied. Therefore, the mutual improvement between landscaping and population activity has not been maximized. In this study, we collected 52 plant landscape units from Northwest A&F University as the research objects. We investigated the patterns of population aggregation on campus plant landscapes through quantitative analysis of the characteristics of plant landscapes and the temporal and spatial aggregation of people. Multiple regression analysis was used to explore the complex relationship between the characteristics of each landscape and the spatial-temporal agglomeration of people. Traditional survey questionnaires and field surveys, kernel density analysis, Python crawler technology, raincloud plots analysis, correlation analysis, principal component analysis, and other methods were used to further measure and analyze plant landscape characteristics under the influence of population density from the two levels of various characteristic elements and different landscape units, and explain the mechanism affecting population aggregation, striving to provide a theoretical basis and practical support for the sustainable development of the campus environment and landscape design methods.

1. Introduction

The contribution of the university campus to social, economic, academic, scientific, and technological fields at both the local and national levels has been extensively discussed in the previous literature [1,2,3,4,5]. Carree M. et al. claimed that universities are the most important producers of basic knowledge and that they export a great number of students every year as a favorable reflection of campus education achievements [6]. It includes not only the professional knowledge taught in class but also the potential impact of the campus environment on students. Meanwhile, young people are the main impetus for all new knowledge, policies, and concepts, which makes the topic of education a critical issue. Hence, the university campus is one of the keys to the implementation and dissemination of policies, regardless of the type of knowledge taught, inherent properties, resourcefulness, or the objective of education [7]. Plants occupy a critical position in the campus environmental atmosphere and cultural inheritance and have a significant impact on numerous aspects such as campus image, teacher and student cohesion, and university education development, especially in regard to the mental health of students. Studies show that the campus plant landscape produces prominent psychological effects, such as reducing stress, enhancing a sense of happiness, and alleviating depression [8,9,10]. Furthermore, it can improve students’ direct attention and performance [11,12,13]. On the contrary, campus plant landscapes with a poor level of effect have a certain negative impact on students [14]. This means that the quality of the campus plant landscape is crucial to the quality of life of teachers and students. Therefore, optimal planning of the campus plant landscape will be of great significance to the future of campus development.
As shown in the plant landscape designs of many Chinese colleges and universities, the designers blindly increase the species and quantity of plants and focus on large-scale plant cultivation. As a result, the plant landscape is just a decoration that can only be viewed from a distance. Teachers and students cannot step on the meadow, let alone move, communicate, read, or conduct other activities on the spacious lawn. Insufficient utilization of plants is a major loss for campus development, which is caused by lack of knowledge of the relationship between population and plant landscape. Hence, an important issue for the transformation and new construction of colleges and universities is in achieving the maximum benefit from the design of the campus plant landscape in practice. It has been found through the relevant literature that research on plant landscape and crowd aggregation on campuses is rarely conducted and that most previous research concentrates on aesthetic, ecological, and other aspects of evaluation. Furthermore, the methods adopted are limited [15,16,17] because the results of evaluation research fail to explore its internal mechanism from the external representation of the current design problems, it is difficult to achieve a qualitative breakthrough in the optimization of plant landscape design methods and they often stay in the solution stage of specific problems. Based on this, compared with the previous research paradigm of using questionnaires and surveys to obtain small sample data and a single mathematical statistical method to analyze the relationship of variables at a certain level, under the realistic background of increasing the availability of big data, we used Python crawling population activity density data, field survey and questionnaire distributions to obtain sample data to achieve an effective combination of big data and traditional small sample data, effectively avoiding the research limitations caused by a single data source. At the same time, we studied the multi-level synergy between crowd activity and plant landscape characteristics through different methods, analyzed the aggregation factors affecting campus users in different types, and explored the internal mechanism of plant landscape characteristics, which is of great value in realizing the optimization and improvement of plant landscape design to meet the needs of people and exploring the applicability of research methods. The main objectives are listed as following:
(1)
To measure the concentration of people in the study area.
(2)
To quantify the plant landscape characteristics in different areas.
(3)
To study the relationship between plant landscape characteristics and crowd aggregation.
(4)
To analyze the internal mechanism of campus users’ aggregation in different landscape units.

2. Literature Review

2.1. Spatiotemporal Aggregation of Crowds

Spatio-temporal aggregation of the crowd refers to the density of people in different spatial environments in a certain time. This index is intuitively expressed as the number of people engaged in various activities in the space. The physical environment provides people with a place for activities and has a certain impact on people’s activities. Therefore the inherent and external characteristics of the environment have a great influence on the concentration of space-time of crowds. At present, studies on the spatial-temporal aggregation of people mainly focus on urban streets and spaces with the purpose of exploring the relationship between human behavior and spatial environment [18,19]. Crowd aggregation reflects the vitality of space on a certain level, and by drawing on the understanding of space vitality by different scholars [20,21,22], crowd aggregation is defined as the degree of agglomeration of a large number of people participating in a series of regular or continuous activities. For the plant landscape, it is mainly reflected in the length of people’s stay, including rest, photography, and other occasional behaviors. People who are active in the plant landscape together influence the aggregation of the population, among which the spatial characteristics of the plant landscape may be the key factors affecting the flow of people. Therefore, this study selected the user’s thermal power as an index to measure the spatial-temporal aggregation of the population based on the quantitative analysis of the characteristics of the plant landscape. Meanwhile, studies have shown that spatial aggregation varies with different foci, and there can be night and day or peak and minimum aggregation in a certain area [23]. Different groups of activities may also show different characteristics of distribution. However, due to the specific group types of teachers and students in school, the effect of difference in activity group can be ignored. Therefore, we explored the relationship between uneven crowd concentration and landscape characteristics by analyzing the average activity time of the activity group during the week and weekend (7:00 a.m.–22:00 p.m.).

2.2. Measurement of Plant Landscape Characteristics and Crowd Space-Time Aggregation

We reviewed the currently available measurement methods of population space-time aggregation, such as GPS-based tracking records [24], handheld GPS technology [25,26], location analysis based on Wi-Fi hotspots [27], Bluetooth tracking [28], and so on. However, most of them were inapplicable to the measurement of crowd aggregation in the campus plant landscape. People carry devices deliberately, and GPS tracking technology provides a basis for accurate quantification of people’s optional behavior, which is more suitable for research needs. Based on the above analysis, we selected Tencent’s travel data to measure the population density of the selected sample nodes and used it as an indicator to measure the spatial-temporal aggregation of the population.
In the study of plant landscape features, a combination of data collection and processing methods is needed to explore the influence of different features. Interdisciplinarity has become a new growth point and a research frontier in science, and cross-discipline collaboration can provide new ideas to solve research problems, especially the research methods. Therefore, based on the humanities and social sciences, and computer science, this research has used grounded theory to analyze the characteristic elements of the landscape from the bottom up. We collected, processed, and analyzed data through computer programming, mainly using Python crawler technology, convolutional neural network, kernel density analysis, R language data visualization analysis, and SPSS multiple analysis, to make a more systematic and detailed objective analysis of the physical environment as a reference factor to guide the construction strategy of the plant landscape. Specifically, first, the grounded theory was used to analyze many factors closely related to the spatial characteristics of the plant landscape. Based on the results, it was summarized into three categories: ecology, aesthetics, and service. Secondly, the characteristic elements and the spatial-temporal agglomeration of the crowd were analyzed by multiple regression to assess the relationship between them, and the plant landscape elements irrelevant to the population density were eliminated. Finally, starting from the two levels of characteristic elements and landscape units, the correlation analysis and principal component analysis were conducted to analyze the characteristics of various elements related to thermal degree. Cluster analysis, kernel density analysis and raincloud plots analysis were used to analyze the characteristics of landscape units of different types and thermal degrees, which is constructive to comprehensively explore the internal mechanism of the relationship between them and provide more objective optimization strategies.

2.3. Research Gap

Based on a literature review, the limitations of previous research and the uniqueness of the current study are as follows. First, most studies on plant landscapes were mainly focused on the evaluation level, not on combining and digging out the internal relationship between crowd activities and plant landscape features. These limitations seriously hindered the improvement of the quality of plant landscapes. Second, the extraction of characteristic elements is usually based on expert inquiry. As this method is often preconceived and fails to fully and objectively grasp the needs of users, the results are often limited [29]. Grounded theory is a method of generating a theory from qualitative data through a rigorous and standardized analysis process from the bottom up without first setting up assumptions [30,31], which is more effective in understanding the real needs of users. Combining the bottom-up grounded theory with the top-down Delphi method can comprehensively cover and dig into the plant landscape characteristic elements that affect the degree of aggregation. Third, the sole use of traditional questionnaires often fails to measure the true results of crowd aggregation. To improve it, we used big data and advanced technical methods to obtain a more accurate crowd density in different periods which reflected the spatiotemporal aggregation characteristics of the population. Fourth, a large number of studies on the acquisition of green visual rate often make a rough judgement through the estimation of human eyes, and the results are too subjective and inaccurate. In recent years, convolution neural networks are widely used to accurately identify the green proportion in images through semantic segmentation [32]. Therefore, we used the scene map and semantic segmentation of each landscape unit as an advanced method of greening measurement to extract the actual visual greening index, i.e., GVI. Fifth, in order to better analyze the influential factors of degree of crowd aggregation, we conducted a multiple regression analysis on each element and its thermal intensity and investigated the internal relationship between plant landscape characteristics and crowd aggregation degree from the perspectives of characteristic elements and landscape units. Sixth, the visual analysis of a large amount of data can better help us analyze the results of the research. Commonly used data representation methods include box plots, scatter plots, violin plots, etc. A recent study showed that raincloud plots can better incorporate the above graphic approaches [33]. It is a combination of the original data points (rain) and the data distribution (clouds), and combines box plots, nuclear density plots, and scatter plots with the most concise visual images to show the kurtosis, median, maximum, minimum, upper quartile, and lower quartile of data distribution (Figure 1).

3. Study Area and Methods

3.1. Study Area

The research object was the south campus of Northwest A&F University, Yangling District, Xianyang City, Shanxi province. It is a typical agricultural university located in northwest China with coordinates of 110° E and 35.5° N, falling in the East Asian warm temperate zone with a semi-humid and semi-arid climate [34]. The campus occupies an area of about 80 hm2 and has 67 plant families, 128 genera, and 193 species with a greening rate of 49.02% [35,36]. After conducting a comprehensive survey on the characteristics of campus plant landscape and types of vegetation structure, we selected 52 typical plant landscape units of 20 m × 20 m under eight classes which were evenly distributed across the campus [37] (Table 1 and Figure 2). The plant landscape unit here refers to the area that mainly represents the plant landscape and can be used for vegetation investigation and sampling to limit the range.

3.2. Data Collection

The primary data of this study consisted of two parts: density data through Python crawl population, feature data through questionnaires, field plant records, and convolutional neural network.
(1)
Population density
We used Python programming to access the WeChat travel urban thermal map data interface, through a number of QQ numbers to obtain cookies on 23–24 May (no school days) and 25–26 May (school days), respectively. During the period from 9 a.m. to 9 p.m., thermal data collection was carried out, and a total of 6048 data points were collected. Data from teaching buildings, dormitory buildings and other buildings were excluded. Eventually, 3749 valid data points were obtained, which was written to csv, and then the distribution trajectory and focus of campus teachers and students on non-school days and school days over time (Equation (1)) were analyzed using the nuclear density in ArcGIS 10.4. Then, the spatial distribution characteristics of the population were analyzed by the thermal intensity value to show the mean of different time periods of each sample area [38] (Equation (2)).
f s = i = 1 n i h 2 k s c i h
In the formula, f(s) represents the kernel density calculation function at the spatial location s; ci is the core element; h is the distance attenuation threshold; n is the number of element points whose distance from the location s is less than or equal to h; and the k function represents the spatial weighting function.
HV = `Hi = ∑Hix/5
`Hi: Average heat intensity of plant landscape units i throughout the day; Hix: heat intensity of unit i at X-point moment; X = 1, 2, 3, 4, 5; i = 1, 2, 3, ⋯⋯, 52.
(2)
Features elements
Plant landscape characteristic elements refers to the indicators that describe the external and internal representation of plant landscapes contained in different plant landscape units. According to the data acquisition method of characteristic elements, they were divided into qualitative and quantitative elements. Qualitative elements were obtained by questionnaire and graphical measurement on the score criteria of 10 points, 8 points, 6 points, 4 points and 2 points. To conduct on-site questionnaire scoring on 52 plant landscape units, 58 landscape architecture students were organized.
The quantitative elements that characterize diversity were calculated using the Simpson index [34] (Equation (3)).
D = 1 P i 2
where, D is the diversity index, and Pi is the ratio of the number of individuals of the ith species in the selected plant landscape units to the total number of individuals.
The Green View Index (GVI) is the visibility of greenery from a specific position [39] and can be calculated as shown in the equation below [40] (Equation (4)). We used convolution neural network to segment the image semantically and calculate the green vision rate [32,41]. In the segmented image, the plants of each landscape unit were classified as green (Figure 3).
GVI = N u m b e r   o f   G r e e n   p i x e l s N u m b e r   o f   T o t a l   p i x e l s × 100 %
To make the qualitative and quantitative elements consistent in dimension, the calculation result of the quantitative elements was multiplied by 10 to obtain the score of each element [42].
(3)
Distance element
The distance from the main buildings was divided by determining the population density circle centered on the canteen, dormitory building, No. 3 teaching building, No. 1 teaching building, library and agricultural science building. The first central circle was drawn for the buffer zone with a radius of 50 m, and then concentric circles were drawn for each additional 50 m. The distance was within 50 m, 50–100 m, 100–150 m, 150–200 m, and more than 200 m. We drew the circle analysis diagram of the population density of the campus building service radius, giving scores of 10 points, 8 points, 6 points, 4 points, and 2 points, respectively, and obtained the corresponding circle level of 52 units (Figure 4). It should be noted that the higher the element score, the farther the distance.

3.3. Methods

Based on the analysis of the research content of each part, this study selected the methods suitable for solving the corresponding research problems for qualitative analysis and quantitative calculation. Although each method has advantages and disadvantages, this study mainly used its own advantages to study the contents of different levels. The optimization of method disadvantages was not considered as a key consideration in this study and could be ignored. Based on this, through the application of various analysis methods, we combined plant landscape features with the temporal and spatial aggregation of the population and conducted a multi-angle and multidimensional analysis of the influence of complex factors, including the construction of the plant landscape feature elements system, analysis of the relationship between the spatial-temporal aggregation of the population and the plant landscape characteristics, analysis of plant landscape element characteristics affecting population spatial-temporal aggregation, and analysis of plant landscape unit characteristics based on the spatial-temporal aggregation of the population of four steps (Figure 5).

3.3.1. Construction of Plant Landscape Characteristic Elements System

Grounded theory has the advantages of not giving subjective assumptions in advance and in-depth research from bottom to top. However, because its interviewees are oriented to the public, it has the disadvantage of insufficient analysis of plant landscape characteristics. The Delphi method is based on the subjective experience of experts, which can give the characteristic elements of plant landscape from a professional perspective, but it lacks consideration of the real needs of the population. The organic combination of the two methods can complement each other to build a more scientific and perfect element system. Therefore, based on previous studies, we conducted in-depth interviews with 300 teachers and students in the school and 30 experts in landscape architecture and related fields. We performed initial coding and main axis coding on more than 300 original sentences and invited experts to supplement them. Finally, 3 categories and 23 subcategory characteristic elements were obtained, including 5 quantitative elements and 18 qualitative elements (Figure 6).

3.3.2. Analysis of the Relationship between the Spatiotemporal Aggregation of the Population and the Characteristics of the Plant Landscape

Multiple regression analysis shows better advantages in exploring the relationship between a dependent variable and multiple independent variables. It is applicable to the analysis of population temporal and spatial aggregation and plant landscape characteristics. Therefore, in this study, the average thermal intensity of the temporal and spatial aggregation of the crowd on non-school days and school days was used as the dependent variable, and each feature element was the independent variable. We used SPSS 25.0 software to carry out multiple regression analysis between variables to analyze the influence of different characteristic elements on crowd aggregation. At the same time, the factors unrelated to the flow of people were eliminated to facilitate the subsequent in-depth analysis of the internal relationship between the two.

3.3.3. Analysis of Plant Landscape Elements Characteristics Affecting Population Spatial-Temporal Aggregation

Correlation analysis plays a significant advantage in identifying the close degree of correlation of multiple variable factors, while principal component analysis can convert a group of variables that may have correlation into a group of linearly uncorrelated variables through orthogonal transformation for the clear complex relationship of multi factor interleaving, which has the advantage of clearly interpreting the complex interleaving of multiple related factors. Therefore, based on the analysis of the direct relationship between plant landscape elements and crowd aggregation, we analyzed the internal action logic of influencing elements from the two levels of various elements related to human flow and comprehensive elements, so as to make the analysis and discussion of plant landscape characteristics more reliable. Among them, correlation analysis was used to analyze the correlation between the elements, and principal component analysis showed the comprehensive information of multiple intertwined individual elements so as to reflect the element performance characteristics of attractive plant landscapes.
(1)
Analysis of various elements
We used SPSS 25.0 software’s Pearson correlation analysis (CORR) to analyze the correlation between elements, explore the influence relationship between each element, and determine whether there is a certain degree of overlap in the information contained.
(2)
Comprehensive factor analysis
To separate the interweaving relationship between various elements, in order to transform many highly correlated variables into mutually independent or uncorrelated variables, SPSS 25.0 software factor analysis-dimensionality reduction was used in the principal component analysis.

3.3.4. Analysis of Plant Landscape Units Characteristics Based on the Spatial-Temporal Aggregation of the Population

(1)
Characteristic analysis of eight kinds of plant landscape units
Kernel density analysis is mainly used to calculate the unit density of the measured values of point and line elements in the specified neighborhood. It has the advantage of intuitively reflecting the distribution of discrete measured values in the continuous area. Therefore, this study used nuclear density analysis to study the change of population activity density on rest days and non-rest days, and the use of dominant landscape types. At the same time, the service radius of campus main buildings was determined by analyzing the buffer zone with a radius of 50 m. On this basis, the characteristics of 8 types of plant landscape units under the influence of different building distance were analyzed in combination with the raincloud plots.
(2)
Analysis of plant landscape characteristics of each unit
Cluster analysis can group the set of objects into different classes composed of similar objects. When analyzing the characteristics of each unit, it has the advantage of scientific classification to better compare and analyze the landscape characteristics of different population aggregation degrees. Therefore, based on thermal data, the plant landscape units were systematically clustered, and the R language was used to draw raincloud plots to analyze the characteristic elements of various landscape units. Based on the performance of each element, analysis of the characteristics of the plant landscape that affects the concentration of people in various units was conducted. The results showed the attraction of various landscapes to the flow of people in different time periods. Therefore, effective analysis of the importance of user behavior in the construction of different landscape types was achieved, and the relationship between plant landscape and crowd behavior over time were clarified. The changing characteristics are conducive to the construction of campus plant landscapes that meet the behavioral needs of the crowd.

4. Results

4.1. Analysis of the Relationship between the Spatiotemporal Aggregation of the Population and the Characteristics of the Plant Landscape

The results of the multiple regression models showed that the variance inflation factors (VIF) of all variables were <5, which indicated that there was no multicollinearity problem in the model (Table 2). The regression standardized residual graph and the normal distribution histogram had the same trend on the image, and the residual distribution followed the normal distribution (Figure 7 and Figure 8). The observed value of the F statistic of the model was 23.823 with a p-value of <0.001. At the significance level of 0.05, we drew the conclusion that there was a linear relationship between thermal power and various characteristic elements.
The analysis results of the ecological element variables showed that the plant species’ diversity, the diversity of life form structure, the phytoecommunity regionalism, and the plant landscape stability were important factors for one or more models. Among them, different independent variables had different magnitudes of effects on each dependent variable. For example, the different plant life forms of trees, shrubs, and grass are directly attractive to people who look from far away, and stable plant communities provide the user with a long-term decorative effect. When visited by people, native plants generally grow well and give the viewer a sense of belonging. Therefore, the above-mentioned elements affected the aggregation of teachers and students from different viewing angles and sensory experiences. On the contrary, other ecological elements had no significant impact to the selection of the crowd, which may be related to the population’s ecological cognition and their own needs.
The analysis results of the variables of aesthetic elements showed that the diversity of ornamental characteristics, the richness of seasonal changes and landscape layers, and the green visual ratio were significantly associated. The chronological changes of the plant landscape with the change of season, and the layering and artistry formed by the combination of trees, shrubs, and grass had greatly promoted the gathering of people. Meanwhile, the green index seen by the human eye affected the crowd’s choice of landscape on a certain level. For different units, teachers and students had low demand for spatial diversity, conception and culture, artistic configuration, and coordination with other landscape elements, thus it became difficult to influence the choices of teachers and students through the aesthetic effect on the environment. Therefore, the above four elements had little effect on the population density.
Among the service elements, the accessibility and exercise enthusiasm of teachers and students had a significant relationship with the degree of crowd gathering and had a marginally significant relationship with earthquake prevention, disaster reduction, and participation. The important role of the plant landscape was to provide a place for teachers and students to rest, enjoy recreation time, exercise, etc., and accessibility was the prerequisite for this role. The exercise enthusiasm of teachers and students means that the plant landscape could meet the needs for teachers and students to carry out activities on site, which was more related to the facilities in the landscape unit, the size of the activity space, and other factors. However, the impact of earthquake protection, disaster reduction, and participation on teachers and students was weak, mainly due to their low perception of earthquake protection and disaster reduction. At the same time, influenced by the characteristics of the plant landscape, teachers and students had a lower demand for participation than in other public landscapes. Therefore, accessibility, rich facilities, good earthquake protection, disaster reduction, and participation of the plant landscape had an important impact on the crowd activity aggregation, specifically with teachers and students tending to aggregate.
In summary, through the regression analysis of plant landscape characteristic elements and population spatial-temporal aggregation, excluding the elements that had no significant impact on population aggregation, 12 characteristic elements were identified in the final model, covering four levels of ecology, aesthetics and service on average (Figure 9).

4.2. Analysis of Plant Landscape Characteristics Elements Affecting Population Spatial-Temporal Aggregation

Through the results of multiple regression analyses, the plant landscape characteristic elements irrelevant to population aggregation were eliminated. Correlation analysis and principal component analysis were used to analyze the characteristics of various elements and comprehensive elements, and to explore the plant landscape characteristics of population spatial-temporal aggregation.

4.2.1. Analysis of Plant Landscape Characteristics Based on Population Time Aggregation

This study verified the overlap of the information contained between the elements through the correlation analysis between the characteristics of the elements. It was concluded that the elements that were related to the diversity of plant ornamental characteristics, diversity of plant life form structure, and earthquake prevention and disaster reduction were the most, followed by rich plant landscape layers, plant landscape accessibility, and phytoecommunity regionalism. The weakest relationship with various elements was the participation, the green visual ratio, the exercise enthusiasm of teachers and students (Figure 10A).
Figure 10B showed that there was almost a correlation between each element, and 33.33% of the influencing factors had a positively significant relationship, 36.36% had a negatively significant relationship, 6.06% had a marginally significant positive relationship, and 1.52% had a marginally significant negative relationship. The diversity of life form structure, the richness of landscape layers, and the diversity of ornamental characteristics were significantly positively correlated and had a high correlation coefficient. The diversity of life form structure, the diversified plant ornamental characteristics and plant landscape accessibility, the exercise enthusiasm of teachers and students all had a high negative correlation coefficient, which resulted in a significant negative correlation.
There was a certain correlation between the individual elements, and the information contained had a certain degree of overlap. It is necessary to further investigate the comprehensive information characteristics of the common performance of each element through principal component analysis.

4.2.2. Analysis of Plant Landscape Characteristics Based on Population Spatial Aggregation

Principal component analysis of the characteristic elements of the plant landscape showed that the contribution rate of the first four principal components reached 67.066%, which covered the main information of the original elements. Therefore, it was feasible to replace 12 initial variables with four principal components. Diversity of plant life form structure, diversified plant ornamental characteristics, earthquake prevention and disaster reduction, exercise enthusiasm of teachers and students, participation and rich plant landscape layers were higher in principal component 1, and most of them belonged to the information of aesthetics and service level, which fully demonstrated that aesthetics and service functions were in the campus the important role of plant landscape construction. In the principal component 2, factors such as the green visual ratio, the abundant plant seasonal changes, the rich plant landscape layers and other factors had obvious influence, and most of the information belonged to aesthetic characteristics. In the principal component 3, the plant species diversity, phytoecommunity regionalism and plant landscape stability occupied an absolute influential position. The first three principal components showed that there were some overlapping elements between the main structural features of aesthetics and service level, while the ecological level was clearly distinctive from the other two. The fourth principal component accounts for a small proportion, which reflected the importance of other individual elements, and was mainly based on ecological information. Therefore, the principal component analysis revealed that paying attention to environment aesthetics and strengthening the supply of service functions can better meet the use needs of teachers and students (Table 3).

4.3. Analysis of Plant Landscape Units Based on Population Spatial-Temporal Aggregation

4.3.1. Characteristics of Eight Types of Plant Landscape Units

According to daily observations, students’ outdoor activities are affected by class time to a certain extent. Therefore, we selected five representative points from two time types, namely non-school days and school days, to study the characteristics of eight types of plant landscape units.
Through the analysis of various landscape units on non-school days and school days, we found that the thermal intensity values of waterfront, multi-layer mixed, dense forest, grass and flower, and sketch landscapes on school days were generally low at each time period, and the crowd gathering degree of square and architectural plant landscape units near the teaching building was mainly high at 9 a.m. and 6 p.m. (Figure 11 and Figure 12). The no school days were mainly water landscapes. In the landscapes far away from dormitory buildings and teaching buildings, the flow of people had increased to a certain extent, and the volatility of crowd agglomeration had increased. The landscape, dominated by buildings and squares, had a high degree of crowd aggregation at 12 noon. The waterfront, grass and flower, sketch, sparse forest and multi-layer mixed landscapes were mainly highly aggregated around 3 p.m., and the degree of aggregation was low at other times, which was mainly due to the remote distribution of the above landscape types on the campus (Figure 12). As on school days, the dense forest landscape on campus was rarely used as a stopping place, lacking the facilitation of organic combination with user behavior, and the use efficiency of this unit was still low (Figure 12).

4.3.2. Analysis of Aggregation Degree Characteristics of Plant Landscape Units under the Influence of Building Distance

Through analysis of the population distribution on campus of teachers and students on non-school days and school days, we identified that the area, quantity, and location of the thermal intensity at all levels were constantly changing. However, in general, the utilization of space by people on school days and non-school days was mainly affected by class time and the distance of the plant landscape from teaching buildings and dormitory buildings, which are characterized by the high concentration of different space types. Aiming at the distance from the main buildings, we analyzed the plant landscape unit to investigate the characteristics of the transformation from surface dispersion to deep connection structure.
Ecologically, with the increase of distance, plant species diversity was gradually enriched, community regionality was enhanced, and the diversity of life form structure first increased and then decreased. The main reasons are that the landscape close to the building is mainly open and does not block the daylight and ventilation of the building, and its plant species richness is poor, while the plant landscape more than 200 m into campus mostly takes the form of dense forest as the isolation zone, resulting in a single life-style structure. Aesthetically, in the scores of each unit, except for the high concentration of green visual rate, the other elements showed a discrete state, the diversity of plant ornamental characteristics showed an obvious fluctuation trend, and the seasonal changes of plants were gradually obvious. The plant landscape nearest and farthest from the main buildings had a single level, and the landscape in the middle was matched with rich levels. In terms of service, the scores were mostly concentrated between 5–10 points. The change of distance made the accessibility strongly fluctuate, which increased first and then decreased with the sports vitality and participation of teachers and students. The scores of earthquake prevention and disaster reduction in each unit were low. It showed that the campus plant landscape was mainly service construction within 50–150 m, which generally lacks consideration of earthquake prevention and disaster reduction functions. In addition, the accessibility of plant landscape was quite different according to different location characteristics (Figure 13). In summary, the landscape close to the building generally had a single plant species and life-style structure, mostly with simple plant collocation, such as tree—grass, shrub—grass, etc. The ornamental construction of leaves, flowers and fruits was more prominent than the landscape by a long distance, which met the ornamental needs for teachers and students in the building.

4.3.3. Analysis of Plant Landscape Characteristics Based on Population Temporal and Spatial Aggregation

Based on the degree of spatial-temporal aggregation, 52 plant landscapes were clustered into 5 categories (Figure 14). For each type of unit, its plant landscape characteristics were analyzed through raincloud plots (Figure 15).
There were 6 units in category I, 26, 51, 31, 28, 29, and 1, respectively, with HV less than 5.54, and the thermal strength was very poor. Such landscape units generally had monotonous plant species, a single type of plant life form structure, too few native plants, poor growth and weak stability. The tree, shrub, and grass were not rich in layers, and the spatial experience was poor. They were mainly composed of trees and herbs, and the green space was severely trampled. The sports vitality of teachers and students was low, far from buildings and roads, and it is difficult to reach them quickly on school days.
There are two units in category II, 12 and 35, respectively, with a HV value of 6.65 and poor thermal power. Such landscape units were mostly characterized by insufficient tree, shrub and grass configuration, seasonal landscape mainly in spring, relatively singular ornamental characteristics focused on flower viewing, lack of ornament of other ornamental types, monotonous hierarchical structure, being far away from life and learning intensive areas and the inability to enter the sample land for activities. It resulted in a lack of vitality in the plant landscape, and thus difficulty in attracting teachers and students to visit.
There are a total of 14 units in category III, namely 9, 10, 39, 37, 2, 36, 30, 13, 32, 14, 45, 33, 46, and 38. The thermal power of this type of unit was moderate, and the HV value was between 7.00–7.96. The plant landscape units had the highest degree of dispersion in the richness of the plant landscape level, indicating that there were great differences in the configuration of trees, shrubs, and grasses in each unit and the median of the diversity of ornamental characteristics was low, reflecting the lack of diverse collocation of plant ornamental characteristics, which is a low value factor affecting the activities of teachers and students in the above landscape units.
There are 7 units in category IV, namely 17, 18, 19, 22, 52, 5, and 43. These units had good thermal power and HV values were all greater than 9.38. Plant landscapes were mostly located around water bodies. They were composed of tall trees and shrubs, closed dense plant communities combined with loose and open grasses and flowers. Native plants and exotic plants were matched, and the flowering period of trees, shrubs and grasses complemented each other, therefore enriching the spatial diversity and seasonal changes. The plant configuration simulated nature, combined with structures and water bodies, and had an attractive landscape art configuration and high participation. In addition, it was closer to major buildings than other types of landscapes.
The 23 units in category V are 48, 50, 25, 23, 21, 42, 16, 27, 41, 44, 15, 7, 24, 34, 47, 4, 6, 49, 20, 40, 11, 3, and 8, this category of units had good thermal power, HV values between 8.12–9.15, and several elements with a large median in the raincloud plots, factors such as plant landscape stability, participation, and green viewing rate were good, but factors such as life form structure, viewing characteristics, seasonal changes, rich levels and other elements were poor.
In general, with the changes in the concentration of landscape units in the five classification levels, the phytoecommunity regionalism, the diversity of ornamental characteristics, the accessibility of the landscape, and other factors varied greatly in different categories, which indicated that the above elements had a significant impact on the process of plant landscape construction, which are mutant elements. The plant landscape stability, green vision rate, earthquake prevention, disaster reduction, and the movement vitality of teachers and students changed little, which showed that in different types of units, the above four characteristic elements were in a state of stable development and belong to steady-state elements. The other characteristic elements change slowly and belong to the gradual change type.

4.4. Construction of Relational Model Based on Multiple Analysis

According to the multiple analysis of different angles, we found that the relationship between population aggregation and plant landscape feature elements is complex and intertwined (Figure 16). In general, at the macro level, affected by the spatial distribution location of plant landscape, it presented different agglomeration characteristics in weekdays and weekends. Among them, the influence of landscape location on weekdays was higher. Secondly, the construction of campus plant landscapes should break the current situation of only focusing on ecology and aesthetics, strengthen the supply of service, and meet the needs of utilization of teachers and students. At the micro level, the types of plant landscape and the characteristic elements describing the plant landscape affected the spatial-temporal aggregation of people to varying degrees. Therefore, in the process of constructing the plant landscape system, considering the low frequency of dense forest landscape on campus, we should mainly arrange it on the periphery of the campus and divide the spatial functions, while other plant landscape types should be evenly distributed in different functional zones. In addition, we should also pay attention to the construction of different characteristic elements at the plant landscape aesthetics and service level. At the same time, the mutation and gradual change elements are optimized to avoid the problems of low crowd aggregation and poor efficiency of usage of a certain kind of landscape caused by the differential performance of various elements in different landscapes. Then guide the improvement of plant landscape quality, trigger a human sensory experience, and realize the increase in crowd aggregation.

5. Discussion

In this study, we combined the traditional field measurement, questionnaire distribution and big data crawling to obtain small samples of plant landscape characteristics and large samples of crowd activity density data, respectively. Through correlation analysis, principal component analysis and multiple regression analysis, we identified the relationship between plant landscape characteristics and population aggregation from various perspectives. As we hypothesized, the use of campus plant landscapes in different locations varied with many elements in ecology, aesthetics, and services. There was an interaction between these elements, which means that the relationship between different characteristic elements and population spatial-temporal aggregation is complex and not a simple direct correlation. Therefore, on the basis of discussing the relationship between landscape characteristics and population density, we investigated the internal mechanism of the two kinds of variables, which facilitates the in-depth understanding of the relationship between them.
Studies have shown that plant landscape features are related to many elements. In order to make the collected feature elements generalizable, we screened the elements in a bottom-up and top-down manner to obtain the elements at the three levels of ecology, aesthetics and service. At the same time, we found that there were differences in the sensitivity of the various elements at the level of population aggregation, which may be due to differences in functionality. The rich plant species and life form structure, stable plant landscape, and a reasonable mix of native plants are more attractive to teachers and students. The reason is that for teachers and students with low ecological awareness, these four elements intuitively represent the ecological situation. In addition, people’s preference towards plant landscapes depends on the comprehensive effects of different sceneries brought about by the change of seasons, the different decorative characteristics of leaves, flowers, and fruits, the rich community hierarchy, the high green viewing rate, reachability, participation, sports vitality and the site’s setting with regard to the function of earthquake prevention and disaster reduction. This result showed that in the plant landscape design, the comprehensive consideration of the above factors can maximize the functionality of the landscape, stimulate the vitality of the space, and reduce the amount of rarely visited landscape on campus. This is extremely important for university campuses as many universities have a certain amount of rarely visited landscapes. To make matters worse, maintenance and management are not in place for these landscapes. As a consequence, they have failed to beautify the environment and purify the air, and instead cause environmental pollution.
For the elements related to the flow of people, correlation analysis was used to explore the dependence between the elements. The results showed that the more abundant plant landscape with trees, shrubs and grasses was significantly associated to the multiple hierarchical structures that are matched with each other, diverse ornamental characteristics. This is because the diversity of life form structure overlaps with many relevant elements, and there is a positive effect between the information. At the same time, the rich plant life form structure and ornamental characteristics had a negative impact on the accessibility of the population and the sports vitality of teachers and students. This is because the selected landscape units are not large-scale green spaces. The landscape units with diverse arrangements of trees, shrubs, and grasses are mainly for decorative purposes. It is difficult to set up roads to enter, which seriously affects the development of various sports between teachers and students.
Due to the complex correlation between the characteristic elements, it is difficult to fully represent the characteristics of the plant landscape by using one or a few elements alone. Therefore, it is necessary to use the dimensionality reduction process of principal component analysis to interpret the comprehensive information of the obtained principal components. Interestingly, aesthetics and service-level elements often belong to the same main component, while ecology was obviously set apart. This indicated that aesthetics and service are more intertwined in the relationship of information coverage, while ecology can be better distinguished. In addition, it also reflects that aesthetics and service are more important than ecology in the construction of plant landscapes. This is also the problem of index selection and attribution in the process of existing plant evaluation research. The results of this study can be used as a theoretical support for the construction of plant landscape evaluation systems and the selection and classification of indicators in the future.
Affected by the temporal and spatial characteristics, the population aggregation degree has significant differences in the selection of plant landscape, and has an obvious relationship with non-school days, school days, and the distance of main buildings. Among them, under the influence of different times, the non-school days use was mainly waterfront, architecture, grass and flowers, sparse forest and multi-layer mixed plant landscapes, and the school days use was mainly the square and architectural areas. Under the influence of different building distances, although the plant landscape close to the building is far inferior to the landscape far away in terms of species diversity and life form structure diversity, teachers and students are still more inclined to have activities in it. This means that the design of plant landscapes should focus on the convenience of daily use of teachers and students, strengthen the analysis of the impact of time and space characteristics on the landscape, and choose living areas, learning areas and other places where teachers and students are active to build a variety of plant landscape types, which can increase the interest of outdoor activities among teachers and students, which is more meaningful than setting a beautiful plant landscape in a distant area. In addition, we also found that the utilization rate of dense forest plant landscape was low at all times, mainly due to the insecurity caused by its tightness. Therefore, such landscapes should be arranged on the edge of campus as protection or to divide the space of different functional areas. According to the clustering results of temporal and spatial aggregation degree, the plant landscape elements involved in different types of plant landscape sample plots showed different changes and development trends, which can be summarized as mutation type, steady-state type and gradual change type. In the future, plant landscape construction, and paying attention to the design of mutant elements will be of great significance in attracting more people.
To summarize, starting from the two levels of plant landscape elements and plant landscape units, analyzing the relationship between population temporal and spatial aggregation and plant landscape characteristics can clearly sort out the internal influence logic between elements and the characteristics of landscape units classified according to different criteria. In the design of plant landscape in the future, considering the influence of people’s temporal and spatial characteristics, we should focus on the regional design close to daily frequent activities such as living and learning areas. According to the positive and negative influence relationship between various elements related to the flow of people, give priority to the allocation of elements at the aesthetic and service levels. At the same time, pay special attention to the construction of mutant elements in different landscape units to avoid uneven population distribution caused by differential design.

6. Conclusions

Despite some limitations, our study helped to understand the relationship between plant landscape characteristics on campus and crowd aggregation. In methodology, the combination of traditional questionnaire distribution, field measurement and big data mining, top-down and bottom-up factor screening, multi-relationship analysis overlay is an effective and precise method to analyze the relationship between landscape characteristics and crowd aggregation. The data representation using raincloud plots maximized the preservation of raw data and the direct visualization of mathematical statistical results. With this advantage, raincloud plots are expected to be important in data visualization. In addition, our findings are important for examining the complex relationship between plant landscape characteristics and population density from a more complete set of perspectives. These results showed that the reasons for the difference of crowd aggregation were influenced by the combination of time and spatial characteristics. We should optimize various landscape elements and strengthen the construction of ecological, aesthetic and service functions of the landscape near daily activities, so as to effectively promote the participation of teachers and students and improve the vitality of space.
The limitation of this study is that only one subject has been selected, without considering the cultural differences in plant landscape construction among states around the world, and the research method needs to be improved. In addition, for the application of convolutional neural networks, the image of plant landscape unit is not trained again, but the official training set is used directly. Although it meets the needs of this research, the recognition accuracy needs to be strengthened. In subsequent research, the prediction model will be based on main component and multiple regression analysis, and the accuracy of campus plant landscape detection models will be improved by selecting different regions and countries. At the same time, a large number of plant landscape images will be trained to build a convolution neural network model more suitable for plant landscape image recognition.

Author Contributions

Conceptualization, G.L. and J.S.; methodology, G.L. and J.S.; software, G.L. and Y.Z. (Yubin Zhang); validation, G.L., J.S. and M.Y.; formal analysis, G.L., X.Z. and Y.Z.; investigation, G.L. and Y.Z. (Yuxin Zhang); resources, G.L., J.S., W.W. and L.W.; data curation, G.L. and Y.Z. (Yubin Zhang); writing—original draft preparation, G.L. and J.S.; writing—review and editing, G.L. and J.S.; visualization, G.L.; supervision, J.S.; project administration, G.L. and J.S.; funding acquisition, J.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (51878339); The Fundamental Research Funds for the Central Universities (105-11042010016); Key Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province (2019SJZDA020); Project of the Social Science Fund of Jiangsu Province (19GLB006); Technology Research and Development Program of the Construction Department of Jiangsu Provincial (2018ZD303); Funded by Special Funds for Basic Scientific Research Business Expenses in Central Universities(2662021JC009); and Project of Teaching Studio of Huazhong Agricultural University.

Institutional Review Board Statement

According to institutional guidelines and national laws and regulations, since this study does not involve human clinical trials or animal experiments, we only conducted a questionnaire survey, therefore, no ethical approval is required.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The study did not report any data.

Acknowledgments

The authors express heartfelt thanks to all anonymous college students from Northwest A&F University who were willing to participate in the questionnaire.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Example of raincloud plots.
Figure 1. Example of raincloud plots.
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Figure 2. Plant landscape plot distribution [34].
Figure 2. Plant landscape plot distribution [34].
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Figure 3. An example of semantic segmentation of a scene image (A,B) original image, (C,D) semantic segmentation image.
Figure 3. An example of semantic segmentation of a scene image (A,B) original image, (C,D) semantic segmentation image.
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Figure 4. Buffer zone map of main buildings.
Figure 4. Buffer zone map of main buildings.
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Figure 5. Method flow chart.
Figure 5. Method flow chart.
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Figure 6. Elements system construction process.
Figure 6. Elements system construction process.
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Figure 7. Histogram of regression standardization.
Figure 7. Histogram of regression standardization.
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Figure 8. Normal P-P of regression standardization residuals.
Figure 8. Normal P-P of regression standardization residuals.
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Figure 9. Factors affecting population spatial-temporal aggregation.
Figure 9. Factors affecting population spatial-temporal aggregation.
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Figure 10. The correlation between features of various elements. (A) Correlation network of various elements, (B) Correlation heat map of various elements. Note: * and ** indicate significant correlation at the 0.05 level and extremely significant correlation at the 0.01 level, respectively.
Figure 10. The correlation between features of various elements. (A) Correlation network of various elements, (B) Correlation heat map of various elements. Note: * and ** indicate significant correlation at the 0.05 level and extremely significant correlation at the 0.01 level, respectively.
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Figure 11. Heat map of the distribution of crowd activities on no school days and school days.
Figure 11. Heat map of the distribution of crowd activities on no school days and school days.
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Figure 12. Raincloud plots of thermal intensity of eight types of landscape units on no school days and school days. (A) Raincloud plots of various landscapes thermal degrees on no school days, (B) Raincloud plots of various landscapes thermal degrees on school days.
Figure 12. Raincloud plots of thermal intensity of eight types of landscape units on no school days and school days. (A) Raincloud plots of various landscapes thermal degrees on no school days, (B) Raincloud plots of various landscapes thermal degrees on school days.
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Figure 13. Landscape elements characteristics of different main building distances. (A) Ecological level, (B) Aesthetic level, (C) Service level.
Figure 13. Landscape elements characteristics of different main building distances. (A) Ecological level, (B) Aesthetic level, (C) Service level.
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Figure 14. Cluster analysis of thermal intensity results of 52 plant landscape plots.
Figure 14. Cluster analysis of thermal intensity results of 52 plant landscape plots.
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Figure 15. Landscape elements characteristics of different clustering categories. (A) Ecological level, (B) Aesthetic level, (C) Service level.
Figure 15. Landscape elements characteristics of different clustering categories. (A) Ecological level, (B) Aesthetic level, (C) Service level.
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Figure 16. Relationship model based on multiple analysis.
Figure 16. Relationship model based on multiple analysis.
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Table 1. Plant landscape units’ type and characteristics.
Table 1. Plant landscape units’ type and characteristics.
Plant Landscape Units No.Dominant Landscape TypeCharacteristics
7, 8, 14, 15, 18, 19WaterfrontIt focuses on recreational activities, with a high greening rate.
1, 4, 9, 17, 32, 33Small ornamentIt focuses on appreciation and rest, with a high greening rate.
3, 27, 28, 29, 47, 51, 52SquareIt focuses on distribution, rest, activity, and passage, with capacious space yet low greening rate.
12, 20, 26, 37, 38, 45, 46, 48ArchitectureIt focuses on the softening of architecture borders, appreciation, and environment improvement, with closed green space and general greening rate.
21, 22, 36, 44, 49, 50Grass and FlowerIt focuses on appreciation, with a high ratio of grasses and flowers, very few tree and shrub species, as well as capacious space.
2, 10, 24, 25, 30, 31, 42Open ForestIt focuses on quiet recreation activities such as forest sightseeing and rest, with sparse woods forming the under-crown space.
23, 39, 40, 41, 43Dense ForestIt focuses on recreational activities and dense plants build a private and serene environment.
5, 6, 11, 13, 16, 34, 35Multilayered MixedIt focuses on recreational activities, with multiple layers and abundant vertical structures.
Table 2. Multiple Regression Analysis.
Table 2. Multiple Regression Analysis.
VariablesUnstationalized CoefficientStandardized CoefficientstSignificanceCollinear Statistics
BStandard ErrorBetaToleranceVIF
(Constant)9.4590.306 30.8650.000
Plant species diversity0.0400.0110.0703.5800.000 ***0.7481.337
Diversity of plant life form structure–0.0910.015−0.142−5.8940.000 ***0.4962.015
Harmony between phytoecommunity and habitat−0.0020.018−0.003−0.1210.9040.6411.560
Plant landscape stability−0.0300.013−0.041−2.3760.018 **0.9481.055
Flora and fauna symbiosis−0.0080.014−0.011−0.5980.5500.9181.090
Phytoecommunity regionalism0.0980.0120.1457.8800.000 ***0.8411.189
Diversified plant ornamental characteristics−0.1110.014−0.178−8.1880.000 ***0.6081.645
Abundant plant seasonal changes0.0430.0140.0592.9500.003 ***0.7231.383
Rich plant landscape layers0.0760.0140.1195.4510.000 ***0.5971.675
Green visual ratio−0.1610.017−0.179−9.4760.000 ***0.8041.244
Artistic quality of plant arrangement−0.0110.020−0.011−0.5410.5880.6381.567
Coordination between plant landscape and other landscape elements0.0120.0200.0130.5770.5640.5751.740
Plant landscape space diversity−0.0260.017−0.031−1.5380.1240.6941.442
Plant landscape accessibility−0.1260.014−0.171−8.8890.000 ***0.7741.293
Plant landscape conception and culture−0.0080.018−0.009−0.4280.6680.6381.567
Degree of willingness to stay−0.0070.016−0.009−0.4170.6760.6661.501
Earthquake prevention and disaster reduction0.0300.0170.0361.7400.082 *0.6811.468
Science popularization and education−0.0240.018−0.029−1.3330.1830.6211.609
Safety0.0080.0180.0090.4470.6550.6761.479
Exercise enthusiasm of teachers and students0.0400.0130.0533.0050.003 ***0.9141.095
Participation0.0210.0100.0372.1040.035 **0.9321.072
Note: * <0.1; ** <0.05; *** <0.01.
Table 3. Principal component analysis.
Table 3. Principal component analysis.
Feature Features1234
Plant species diversity−0.0050.185−0.3700.008
Diversity of plant life form structure−0.235−0.0110.165−0.107
Plant landscape stability0.0500.1320.3140.554
Phytoecommunity regionalism−0.0100.028−0.3670.550
Diversified plant ornamental characteristics−0.228−0.048−0.011−0.362
Abundant plant seasonal changes−0.1190.290−0.147−0.011
Rich plant landscape layers−0.1920.2510.117−0.032
Green visual ratio−0.0140.3650.2320.165
Plant landscape accessibility0.1620.098−0.212−0.182
Earthquake prevention and disaster reduction0.203−0.1750.1850.056
Exercise enthusiasm of teachers and students0.2020.237−0.052−0.246
Participation0.1960.2070.156−0.344
Eigenvalues3.2502.1061.6881.004
Contribution rate (%)27.08317.54714.0668.371
Cumulative contribution rate (%)27.08344.63058.69567.066
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Liu, G.; Shao, J.; Zhang, Y.; Yang, M.; Zhang, X.; Wan, W.; Zhang, Y.; Wang, L. Multiple Analysis of the Relationship between the Characteristics of Plant Landscape and the Spatiotemporal Aggregation of the Population. Sustainability 2022, 14, 6254. https://0-doi-org.brum.beds.ac.uk/10.3390/su14106254

AMA Style

Liu G, Shao J, Zhang Y, Yang M, Zhang X, Wan W, Zhang Y, Wang L. Multiple Analysis of the Relationship between the Characteristics of Plant Landscape and the Spatiotemporal Aggregation of the Population. Sustainability. 2022; 14(10):6254. https://0-doi-org.brum.beds.ac.uk/10.3390/su14106254

Chicago/Turabian Style

Liu, Guan, Jizhong Shao, Yubin Zhang, Minge Yang, Xiaosi Zhang, Wentao Wan, Yuxin Zhang, and Linjie Wang. 2022. "Multiple Analysis of the Relationship between the Characteristics of Plant Landscape and the Spatiotemporal Aggregation of the Population" Sustainability 14, no. 10: 6254. https://0-doi-org.brum.beds.ac.uk/10.3390/su14106254

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