Analysis Of Spatial Structure Research Articles

Quantitative analysis and spatial distribution of landform spatial structure on Loess Plateau

The Loess Plateau is the largest gully geomorphic region in the world, characterized by the most intense soil erosion in a typical loess-covered area. Previous studies have focused on the terrain and texture of this region; however, there have been no systematic studies on the gully spatial structure of the Loess Plateau. Therefore, the present study investigated the characteristics and spatial distribution of landform spatial structure over the Loess Plateau. Specifically, gully weighted complex networks (GWCNs) were applied to simulate the gully spatial structure of loess landforms, and a series of quantitative indices were introduced to delineate these. Using 57 geomorphological units uniformly distributed across the Loess Plateau as test areas and six typical loess landforms as sample areas, GWCNs were constructed, and using these GWCNs, the spatial structure and internal mechanisms of typical loess landforms were explored. From a series of fresh insights, such as the regional scale-free distribution, homologous structure, tightness, community effect, connectivity, stability, and complexity, regular variations in quantitative indices delineate the spatial distribution of the characteristics of landform spatial structure over the plateau. Moreover, the spatial distribution of complex network indices exhibited strong spatial coupling with loess landforms. Overall, GWCNs could be effectively used for landform recognition and performed well. In conclusion, these experimental results suggest that introducing complex networks into landform studies can offer novel insights into landform quantitative analyses. The present work is of great significance, as it proposes a new methodology for describing the spatial structure and terrain features of landforms in quantitative analyses and furthers our understanding of landform genesis.

Metropolitan spatial structure analysis based on the identification of commuting zones with Nanjing City as an example

Commuting zone research is critical to the understanding of the operational rules of the metropolitan spatial structure and improving spatial performance. This study aims to identify the main commuting centers and zones by using cellular data with Nanjing City as the example. This study analyzes the operational features of the internal spatial structures of the city from two dimensions by merging multi-source data, namely, commuting centers and zones, thus achieving an understanding of the existing problems with the urban spatial structures and their internal causes. Results showed that the commuting zones of Nanjing are distributed in a pattern of “multiple commuting centers”, with Xinjiekou–Hunan Road and Hongwu Road–Chaotiangong–Shuangtang as the core, Mochou Lake as the main commuting area, and Dongshan and Jiangpu as the secondary commuting zones. Significant differences and similarities are discovered in our comparisons along the two dimensions of commuting zones and centers in terms of spatial structural factors, such as land use, transportation, and commuting in the city. The similarity is shown as a common declining trend in the values of all our indicators with the increase in the distance of commuting zones from the city center. However, the differences are significant in terms of the clustering features of the various parameters concerning commuting centers and zones. Specifically, four clustering patterns are discovered, namely, “monocentric clustering”, “circular monocentric clustering”, “polycentric clustering”, and “sparsely dotted distribution”. This study sheds light on the existing problems with the city's spatial structure and proposes some overall suggestions toward urban spatial structure improvement on the basis of these findings.

How is modern bedside teaching structured? A video analysis of learning content, social and spatial structures

BackgroundBedside teaching (BST) is an essential and traditional clinical teaching format. It has been subject to various impediments and has transformed over time. Besides a decrease in bedside time, there has also been a didactic diversification. In order to use time at the bedside effectively and understand the current design of BST, we here offer an evidence-based insight into how BST is practiced. This may serve as a basis for a refinement of its didactic design.MethodsIn the current study, we investigate the interrelationships between learning content and the social as well as spatial structures of BST. To this end, we have empirically analysed almost 80 hours of video material from a total of 36 BST sessions with good interrater reliability.ResultsBST lasted on average 125 min, most of which was spent in plenary and less than a third of the time at the patient’s bedside. History taking was primarily practiced at the bedside while case presentations, clinical reasoning and theoretical knowledge were largely taught away from the patient. Clinical examination took place to a similar extent in the patient’s room and in the theory room.ConclusionsEven though the filmed BSTs are not purely “bedside”, the teaching format investigated here is a typical example of undergraduate medical education. In order to maximize the teaching time available, a suitable learning space should be provided in addition to the bedside. Moreover, the clinical examination should be revised in its general sequence prior to the BST, and conscious decisions should be made regarding the social structure so as to optimize the potential of small groups and plenary sessions.

Analysis of spatial form and structure of commercial pedestrian blocks based on Isovist and big data

Commercial pedestrian blocks play an indispensable role in urban life. With the development of urban commercial intensification, the construction of commercial pedestrian blocks has ushered in a period of rapid development. To explore the spatial form and structural features of commercial pedestrian blocks, we examined the Qingniwa-Tianjin Street commercial pedestrian block in Dalian, China. We developed a new quantitative research method, using Isovist-App software simulation, big data statistics and Arc GIS analysis methods to explore the spatial morphological structure characteristics of commercial districts. The results show that Tianjin Street’s pedestrian axis has low spatial permeability and monotonous browsing routes. This suggests that the outdoor pedestrian space design does not attract crowds to wander around and stroll. The overall centrality of the space was relatively high, and the spatial accessibility was good. The walking route provided a wide field of vision, stimulating people’s desire for exploration. The space is highly guided, and people can visit according to the designed route. There is a big difference in the spatial aggregation and dispersion of POI facilities. Transportation, sports, and leisure facilities are the most evenly distributed, while shopping and life service street facilities vary considerably. During holidays, the intensity and duration of crowd activities are the largest, followed by working days and weekends. The people in the block mainly gather in the western and southern commercial complexes, and the attractiveness of outdoor pedestrian streets is lower than that of commercial complexes. Node space B has pedestrian streets with varying spatial interfaces and inconsistent visual appeal. The results can be used to support the spatial form and structural renewal design of commercial pedestrian blocks. The methods presented in this study provide a quantitative approach for the spatial analysis of other functional areas in the city.

Study of regional air pollutant dispersion scenarios and atmospheric capacity changes based on spatial structure analysis of source intensity—the case of Hohhot, China

Study of regional air pollutant dispersion scenarios and atmospheric capacity changes based on spatial structure analysis of source intensity—the case of Hohhot, China

Three-Dimensional Quantitative Coherent Diffraction Imaging of Staphylococcus aureus Treated with Peptide-Mineralized Au-Cluster Probes.

Characterizing interactions between microbial cells and their specific inhibitory drugs is essential for developing effective drugs and understanding the therapeutic mechanism. Functional metal nanoclusters can be effective inhibitory agents against microorganisms according to various characterization methods, but quantitative three-dimensional (3D) spatial structural analysis of intact cells is lacking. Herein, using coherent X-ray diffraction imaging, we performed in situ 3D visualization of unstained Staphylococcus aureus cells treated with peptide-mineralized Au-cluster probes at a resolution of ∼47 nm. Subsequent 3D mass-density mapping and quantitative structural analyses of S. aureus in different degrees of destruction showed that the bacterial cell wall was damaged and cytoplasmic constituents were released from cells, confirming the significant antibacterial effects of the Au-cluster probe. This study provides a promising nondestructive approach for quantitative imaging and paves the way for further research into microbe-inhibitor drug interactions.

Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance

Macro-roughness elements such as boulders and bedrock outcrops, collectively referred to as large bed elements (LBEs), are key features influencing hydrodynamics and morphodynamics in mountain rivers. Where LBEs are abundant and account for a substantial portion of total flow resistance, existing geomorphic theory, previous physical experiments, and limited field observations support the theory that LBE configurations adjust to maximize flow resistance. However, methods to explicitly map individual features along entire river segments are lacking, thus limiting analysis of LBE spatial structure in boulder-bedded rivers. In addressing these gaps, this study sought to develop a procedure for mapping LBEs from 3D point-clouds, explore LBE spatial structure in a real boulder-bedded river, and test the hypothesis that LBE configurations were organized to maximize flow resistance. The mapping procedure applied a ground classification algorithm to produce a roughness surface model, from which LBEs were extracted by a marker controlled watershed algorithm. Implementing the procedure, 42,176 LBEs were mapped in 13.2-km of the mountainous Yuba River (Northern California). Scale and discharge-dependent LBE concentration and spacing metrics were quantified for multiple laterally and/or hierarchically nested spatial domains and classified to differentiate three flow-resistance based hydrodynamic regimes: isolated roughness, wake interference, and skimming flow. Of these regimes, wake interference corresponds to a state of maximum resistance, so hypothesis testing involved determining if this regime was dominant. Results confirmed 25 of 28 segment- and reach-scale LBE concentrations were in the wake interference regime. However, spacing metrics classified 24 of these same spatial domains in the skimming flow regime. Concentration metrics, which quantify LBE density in a given spatial area, differ from spacing metrics, which represent LBE proximity to one another. While comparison of segment and reach-scale regime classifications by each metric concluded concentration was superior to spacing for regime classification purposes, these disparities leave open questions about this extremal model of geomorphic adjustment. Lastly, lateral variability of metrics across the river corridor had implications for discharge-dependent resistance.

Distribution and Structure Analysis of Mountain Permafrost Landscape in Orulgan Ridge (Northeast Siberia) Using Google Earth Engine

An analysis of the landscape spatial structure and diversity in the mountain ranges of Northeast Siberia is essential to assess how tundra and boreal landscapes may respond to climate change and anthropogenic impacts in the vast mountainous permafrost of the Arctic regions. In addition, a precise landscape map is required for knowledge-based territorial planning and management. In this article, we aimed to explore and enhanced methods to analyse and map the permafrost landscape in Orulgan Ridge. The Google Earth Engine cloud platform was used to generate vegetation cover maps based on multi-fusion classification of Sentinel 2 MSI and Landsat 8 OLI time series data. Phenological features based on the monthly median values of time series Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), and Normalized Difference Moisture Index (NDMI) were used to recognize geobotanical units according to the hierarchical concept of permafrost landscapes by the Support Vector Machine (SVM) classifier. In addition, geomorphological variables of megarelief (mountains and river valleys) were identified using the GIS-based terrain analysis and landform classification of the ASTER GDEM scenes mosaic. The resulting environmental variables made it possible to categorize nine classes of mountain permafrost landscapes. The result obtained was compared with previous permafrost landscape maps, which revealed a significant difference in distribution and spatial structure of intrazonal valleys and mountain tundra landscapes. Analysis of the landscape structure revealed a significant distribution of classes of mountain Larix-sparse forests and tundra. Landscape diversity was described by six longitudinal and latitudinal landscape hypsometric profiles. River valleys allow boreal–taiga landscapes to move up to high-mountainous regions. The features of the landscape structure and diversity of the ridge are noted, which, along with the specific spatial organization of vegetation and relief, can be of key importance for environmental monitoring and the study of regional variability of climatic changes.

Optimized method of lines for non-linear waveguide problems

The Method of Lines is a semi-analytical versatile tool for the solution of partial differential equations. For the analysis of spatial complex linear waveguide structures, this method is combined with impedance/admittance and field transformation, as well as with finite differences. This paper extends this approach to the treatment of structures with non-linear dielectric materials. The non-linear generalized transmission line equations are derived. An iterative algorithm based on the impedance/admittance transformation with the field transformation obtains efficient and self-consistent solutions. For demonstration, a non-linear stripe waveguide is considered. The Kerr non-linearity was investigated, though the general case is treatable. As a criterion for the correctness of the algorithm, a second harmonic generation as well as a bidirectional, spatially, and temporally energy exchange between the harmonics was examined. The specific limiting factors for the algorithm were explored. The approach can be used for any spatial structure, including, for instance, photonic crystal waveguides and metamaterials.

Analysis of China's regional differences in water consumption based on spatial structural decomposition analysis model

Analyzing the factors that affect the spatial differences in production water consumption in China is necessary to implement its most stringent water resource management system effectively. Based on the input-output tables of 31 provinces and the water-consumption data of provinces by sectors in 2017, the water consumption-economy input-output tables of 31 provinces are constructed. The spatial structural decomposition analysis method is used to analyze the impact of technology level, economic scale, and regional characteristics on spatial differences in production water consumption. The final demand effect is then decomposed into final demand sectoral structural effect, final demand distribution structure effect, population-scale effect, and consumption-level effect. The results show that production water consumption depends on the economic scale and regional characteristics. Xinjiang, Jiangsu, Guangdong, Heilongjiang, and most provinces in the central region use more production water than the average level, while those in the Beijing-Tianjin region and most in the North-west region use less than average. The decomposition results show that the technical and the final demand effects are the main factors for the spatial differences. The impact of population-scale and consumption-level contribute the most to the final demand effect.

Integral Fire Protection Analysis of Complex Spatial Steel Structure Based on Optimized Gaussian Transformation Model.

The rapid development of complex space steel structure buildings has brought new challenges to structural fire protection analysis and design. On the basis of analyzing the deficiencies of structural fire protection design in existing normative and performance-based design methods and based on the knowledge and research results of fire science, structural engineering, computer simulation, and other disciplines, this paper proposes an improved Gaussian transformation model and applied it to the overall fire performance analysis of this complex space steel structure. The proposed model is based on theoretical research and practical application of the simulation and analysis of the overall fire performance of complex spatial buildings. The coupled analysis model and system integration method of spatial overall structure fire and structure are established. The improved Gaussian transformation model is used to propose the integrity of complex spatial structures. It is a system analysis model for fire analysis. The experimental results show that the designed algorithm model can effectively realize the overall fire performance analysis of complex spatial steel structures and can provide a useful reference for China's building fire protection research, building fire protection design, fire rescue, emergency plan formulation, and other engineering practical applications.

Structural brain network measures in elderly patients with cerebral small vessel disease and depressive symptoms

ObjectivesTo investigate the relationship between diffusion tensor imaging (DTI) indicators and cerebral small vessel disease (CSVD) with depressive states, and to explore the underlying mechanisms of white matter damage in CSVD with depression.MethodA total of 115 elderly subjects were consecutively recruited from the neurology clinic, including 36 CSVD patients with depressive state (CSVD+D), 34 CSVD patients without depressive state (CSVD-D), and 45 controls. A detailed neuropsychological assessment and multimodal magnetic resonance imaging (MRI) were performed. Based on tract-based spatial statistics (TBSS) analysis and structural network analysis, differences between groups were compared, including white matter fiber indicators (fractional anisotropy and mean diffusivity) and structural brain network indicators (global efficiency, local efficiency and network strength), in order to explore the differences and correlations of DTI parameters among the three groups.ResultsThere were no significant differences in terms of CSVD burden scores and conventional imaging findings between the CSVD-D and CSVD+D groups. Group differences were found in DTI indicators (p < 0.05), after adjusting for age, gender, education level, and vascular risk factors (VRF), there were significant correlations between TBSS analysis indicators and depression, including: fractional anisotropy (FA) (r = − 0.291, p < 0.05), mean diffusivity (MD) (r = 0.297, p < 0.05), at the same time, between structural network indicators and depression also show significant correlations, including: local efficiency (ELocal) (r = − 0.278, p < 0.01) and network strength (r = − 0.403, p < 0.001).ConclusionsChanges in FA, MD values and structural network indicators in DTI parameters can predict the depressive state of CSVD to a certain extent, providing a more direct structural basis for the hypothesis of abnormal neural circuits in the pathogenesis of vascular-related depression. In addition, abnormal white matter alterations in subcortical neural circuits probably affect the microstructural function of brain connections, which may be a mechanism for the concomitant depressive symptoms in CSVD patients.

Urban spatial structure features in Qinling mountain area based on ecological network analysis-case study of Shangluo City

Since the 20th century, the world is facing two major problems: the serious deterioration of the ecological environment and the accelerated decline of living quality of people residing in the urban environment. Urban development policies, ecological patterns, production and consumption modes have an important impact on global environmental changes that threaten the future living of mankind. The focus of urban space construction has also turned to the fields of ecological security, energy development, resource management and urban ecological virtuous circulation. Investigating urban spatial structure features from the perspective of urban metabolic system is important for ecological security and resource management. Shangluo urban space in Qinling area is used as the research object and an analysis system of urban spatial structure based on ecological network analysis is proposed in this study. First, an ecological network model of urban space was constructed from the research perspective of ecological network analysis and the system metabolic level was evaluated using metabolic scale, intensity, and efficiency. Second, the system structure of urban metabolic network and its functional relations were analyzed. On this basis, interactive relationships among different components in the metabolic system were explored via symbiosis index and symbiotic, competitive, predatory, and control relations. Meanwhile, the ecological network analysis method is used to construct an analysis system for urban spatial structure in the Qinling mountain area. Finally, the analysis system of urban spatial structure in the Qinling mountain area is applied to examine the urban spatial structure of Shangluo, Qinling Mountains, China. Results showed that resource utilization efficiency of Shangluo City presents a rising trend and the city is located in a strong structural center in the regional space. Predatory and control relations played a dominant role among ecological relations in the urban metabolic structure, and the urban space presented a clear uniaxially extended spatial structure. Sensitivity of internal environment was extremely evident, and the natural spatial structure was an important basis for urban space development in Shangluo. The urban spatial structure analysis system in this research is constructed with the three aspects of “metabolic level”, “ecological relationship” and “compartment weight” of the spatial structure information, which solves the problem that the conventional spatial structure analysis system is difficult to quantify the ecological network information, and it provides a new idea to explain the internal component interaction mechanism and spatial structure composition of the urban system. The results of this study can provide not only useful reference for urban spatial structure analysis of underdeveloped ecological capital areas in China but also a basis for sustainable development planning of urban spaces.

Virtual Vibrational Analytics of Reduced Graphene Oxide.

The digital twin concept lays the foundation of the virtual vibrational analytics suggested in the current paper. The latter presents extended virtual experiments aimed at determining the specific features of the optical spectra of the studied molecules that provide reliable express analysis of the body spatial structure and chemical content. Reduced graphene oxide was selected as the virtual experiment goal. A set of nanosize necklaced graphene molecules, based on the same graphene domain but differing by the necklace contents, were selected as the relevant DTs. As shown, the Raman spectra signatures contained information concerning the spatial structure of the graphene domains, while the molecule necklaces were responsible for the IR spectra. Suggested sets of general frequency kits facilitate the detailed chemical analysis. Express analysis of a shungite carbon, composed of rGO basic structural units, revealed the high ability of the approach.

Measuring urban nighttime vitality and its relationship with urban spatial structure: A data-driven approach

Nighttime vitality has garnered attention in recent years as an important indicator reflecting urban economy and quality of life. However, it is difficult to characterize this intangible issue. As a response, this study employed a data-driven approach to measure nighttime vitality and explored its relationships with urban spatial structure. Specifically, the data from Meituan.com—the largest Chinese shopping platform for local consumer products and retail services—were used to measure nighttime vitality based on a hierarchical weighting method. Multidimensional characteristics of the urban spatial structure were evaluated. Spatial regression models were conducted on the effect analysis of urban spatial structure on nighttime vitality. Relationship estimations were statistically significant with indicators, such as block functions, building density, interaction density, enclosure of locals, and the age structure of the main population. Our findings provide a more complete understanding of nighttime vitality, which is often overlooked in urban vitality studies. Insights derived from this study could help formulate spatial strategies to enhance nighttime vitality and quality of life.

Spatial Structure Dynamics and Maintenance of a Natural Mixed Forest

Spatial structure dynamics play a major role in understanding the mechanisms of forest structure and biodiversity formation. Recently, researches on the spatial structure dynamics utilizing multi-period data have been published. However, these studies only focused on comparative analyses of the spatial structure of multi-period living trees, without an in-depth analysis of the change processes. In this study, we propose a new comprehensive analysis method for dynamic change of the spatial structure at the individual level, which includes three processes (living trees’ flow, mortality process and recruitment process) that have not been considered in previous researches. Four spatial structural parameters (SSSPs, Uniform angle index, Mingling, Dominance and Crowding) and a natural spruce-fir-broadleaf mixed forest with two-phase data were taken as an example to find out the laws of the spatial structure dynamics. All types of dynamic change were named and their proportions were analyzed. The proportion of changes in the SSSPs of individuals was relatively high, even though the mean values of the stand did not change considerably. The five values (0, 0.25, 0.5, 0.75, 1) of the SSSPs are in mutual flow, and the flows are typically one-step, with three-steps and four-steps changes being uncommon. The processes of mortality and recruitment have a higher influence on the spatial structure than the flow of living trees. The dynamic change of spatial structure analysis method created in this study can capture more features not discovered in earlier approaches, as well as guiding forest management in some ways. Understanding the nuances of these changes is a critical part of reasonable spatial structure and biodiversity maintenance, and should be the focus of future research efforts.

KLFDAPC: a supervised machine learning approach for spatial genetic structure analysis.

Geographic patterns of human genetic variation provide important insights into human evolution and disease. A commonly used tool to detect and describe them is principal component analysis (PCA) or the supervised linear discriminant analysis of principal components (DAPC). However, genetic features produced from both approaches could fail to correctly characterize population structure for complex scenarios involving admixture. In this study, we introduce Kernel Local Fisher Discriminant Analysis of Principal Components (KLFDAPC), a supervised non-linear approach for inferring individual geographic genetic structure that could rectify the limitations of these approaches by preserving the multimodal space of samples. We tested the power of KLFDAPC to infer population structure and to predict individual geographic origin using neural networks. Simulation results showed that KLFDAPC has higher discriminatory power than PCA and DAPC. The application of our method to empirical European and East Asian genome-wide genetic datasets indicated that the first two reduced features of KLFDAPC correctly recapitulated the geography of individuals and significantly improved the accuracy of predicting individual geographic origin when compared to PCA and DAPC. Therefore, KLFDAPC can be useful for geographic ancestry inference, design of genome scans and correction for spatial stratification in GWAS that link genes to adaptation or disease susceptibility.

Spatial structure analysis of tourism economy in Shaanxi province, China

This paper uses the modified gravity model to measure the intensity of tourism economic connection in 11 prefecture-level cities in Shanxi Province from 2016 to 2020. From the perspective of the social network, the density and core-periphery area of the tourism economic network’s spatial structure are explored. The research shows that: From 2016 to 2020, the overall tourism economic connection in Shanxi Province shows a growing trend, and the differentiation phenomenon between the central region and the southern and northern regions is more obvious. There is an unbalanced spatial structure of the tourism economy in all prefecture-level cities in Shanxi province. On the whole, the network density shows a growing trend. The various indexes of Taiyuan, the capital city of Shanxi Province, are obviously better than other cities’ indexes. The number of core areas of the tourism economy changes little. These areas are closely connected but the strengthening trend is not obvious. The relations within the periphery region are not strong, and the connection between the periphery region and the core region is also weak. The connection of the regional tourism economy is strongly dependent on tourism resources endowment and transportation accessibility. The spatial structure of the tourism economy network is under the great influence of policy suggestions and planning. This study provides a certain theoretical basis for the formulation of tourism economic development strategy in Shanxi Province. The limitation is that the impact of COVID-19 on tourism development has not been specifically analyzed.

Analysis of Spatial Structure in the Kashgar Metropolitan Area, China

Taking metropolitan areas as space carriers has become the engine of the Chinese government in its promotion of high-quality development, and this has also become an important measure by which to balance regional development. We used Zipf’s law and the gravity model to study the urban scale distribution characteristics of the Kashgar Metropolitan Area (KMA) in this paper. We also constructed a spatial structure judgment vector for the KMA and put forward the development objectives of different circles. The findings show the following: (1) large cities have a high primacy of development, while small and medium-sized cities are underdeveloped. At present, the KMA is a concentrated monocentric-pattern metropolitan area, with Kashgar City as its core city. (2) The urban built-up area of Kashgar City is expanding to the east and south, where it has broken through the administrative boundary and become integrated with the urban built-up area of Shule County. The spatial structure characteristics of the KMA have been further clarified. The KMA forms three circles: core, middle, and outer. (3) Tumxuk City, Bachu County, Yecheng County, Shache County, and other counties are far from the core city and cannot be connected with Kashgar, but they are closely related to the surrounding cities, forming the Bachu–Tumxuk Urban Group and the Shache–Zepu–Yecheng Urban Group. This study contributes to the understanding of the characteristics of urban scale distribution and the spatial structure of metropolitan areas in arid regions, as well as providing guidance for the formulation of policies for the development of different circles in the KMA.

Evaluation Index System of Economic and Social Development Pilot Area Based on Spatial Network Structure Analysis.

In order to improve the evaluation effect of the economic and social development pioneer area, this paper constructs the evaluation index system of the economic and social development pioneer area based on the spatial network structure analysis method and obtains an intelligent analysis system. Moreover, from the perspective of economic development information flow, this paper uses information flow direction analysis method and advantage flow analysis method to discuss the structural characteristics of urban economic development network in the economic belt, providing new methods and perspectives for the study of urban economic development flow. In addition, this paper attempts to propose a universal method for quantitative research on the “flow space” structure of urban economic development as the forward-looking content of urban economic development. According to the simulation test results, it can be seen that the evaluation index system of the economic and social development pilot area based on the analysis of the spatial network structure proposed in this paper has a good effect.