Spatial Network Analysis Research Articles

Towards large-scale analyses of settlement patterns in urbanizing landscapes—findings of first studies for India, Egypt, and China

Improved ability to assess and categorize the spatial characteristics of settlement patterns is required for a deeper understanding of how urbanization is driving land use and land cover transformation and its effects. Two approaches to the globally available settlement maps of the World Settlement Footprint 3D support a detailed assessment of spatial characteristics of settlement patterns in rural to urban landscapes and across scales: graph-based spatial network analysis and elements of fractal theory. Based on first comprehensive tests for the Punjab (India), the Nile Delta (Egypt) and the North China Plain, the results of our study suggest that the presented methods allow a quantitative and qualitative characterization and comparison of settlement patterns between different regions of the world. The approache allows to generate standardized baseline data for arbitrary regions in the world to analyze structuring principles of settlement hierarchies (e.g., self-organized fractal geometries) and their dependence on - or interaction with - cultural, political, socioeconomic, or environmental conditions.

Measuring Street Quality: A Human-Centered Exploration Based on Multi-Sourced Data and Classical Urban Design Theories

Advancements in analytical tools have facilitated numerous studies on perceived street quality. However, most have focused on limited aspects of street quality, failing to capture a comprehensive perception. This study introduces a quantitative approach to holistically measure street quality by integrating three key dimensions: visual perception, network accessibility, and functional diversity. Using Beijing and Shanghai as case studies, we employed artificial neural networks to analyze street view images and quantify the visual characteristics of streets. Additionally, street network accessibility was assessed through spatial design network analysis, and functional diversity was evaluated using the entropy of points of interest (POIs) data. The evaluation results were combined using the analytic hierarchy process. The reliability and accuracy of this method were validated through further testing. Our approach offers a human-centered, large-scale measurement framework, providing valuable insights for urban street renewal and design.

Spatio-temporal behaviour of cruise passengers at the destination: a big data approach using mobile location events

Purpose The purpose of this paper is to provide a deeper understanding of the spatio-temporal behaviour of cruise passengers at a given destination using the city of Hamburg as a use case. Design/methodology/approach Big data in the form of passive location events from smartphones were analysed to determine the spatio-temporal behaviour of cruise tourists. The data were filtered using a definition created specifically for determining the signals of cruise passengers and were analysed using spatial statistics and network analysis methods. Findings In Hamburg, cruise passengers have a relatively small area of activity that is concentrated in the city's core tourist area. Foreign cruise passengers are less spatially concentrated than German passengers. Foreign passengers generate most of their visits at the airport and in the city's shopping areas, while German passengers are most frequently seen at the railway station and at the city's maritime experience locations. Originality/value Although numerous publications have examined the spatio-temporal behaviour of cruise passengers at the destination, to the best of the authors’ knowledge, no studies have used big data sources for this purpose. This paper uses passive location events from smartphones to determine the routes taken by cruise tourists in Hamburg, shows the strengths and weaknesses of this data source and suggests how cruise tourists can be defined from the data.

Assessing the conditions to implement innovative activity of technoparks in the field of high technologies

During the restructuring of national technological system, the development of innovations is of particular importance. In this regard, it is important to analyze how economic entities and innovative infrastructure cope with changing internal and external conditions, such as the need for import substitution of high-tech products and replenishment of specialists’ outflow from the information technology sector. The key element of the innovation infrastructure is scientific and industrial technology parks. The article examines the features of the Russian model of technoparks, as well as the factors of their success and lag. The purpose of the study is to identify the factors contributing to and hindering the innovation activity of technopark residents. Using statistical data analysis and spatial network analysis, the paper studies the structure and features of the activities of 12 participants in the state program «Creation of high-tech technoparks in the Russian Federation». The study examines such aspects of technoparks’ activity as the specifics of management companies functioning, ownership structure and geographical localization of resident enterprises, the level of integration of scientific and educational organizations. It determines the results of technoparks activity in the context of long-term effects for the innovation development at regional level. The findings indicate insufficient elaboration of approaches to support collaborative activities between residents, which is an important condition for effective innovation exchange. The results of the study can be used in the development and adjustment of government programs to support the innovation sector.

From single-cell to spatial transcriptomics: decoding the glioma stem cell niche and its clinical implications.

Gliomas are aggressive brain tumors associated with a poor prognosis. Cancer stem cells (CSCs) play a significant role in tumor recurrence and resistance to therapy. This study aimed to identify and characterize glioma stem cells (GSCs), analyze their interactions with various cell types, and develop a prognostic signature. Single-cell RNA sequencing data from 44 primary glioma samples were analyzed to identify GSC populations. Spatial transcriptomics and gene regulatory network analyses were performed to investigate GSC localization and transcription factor activity. CellChat analysis was conducted to infer cell-cell communication patterns. A GSC signature (GSCS) was developed using machine learning algorithms applied to bulk RNA sequencing data from multiple cohorts. In vitro and in vivo experiments were conducted to validate the role of TUBA1C, a key gene within the signature. A distinct GSC population was identified, characterized by high proliferative potential and an enrichment of E2F1, E2F2, E2F7, and BRCA1 regulons. GSCs exhibited spatial proximity to myeloid-derived suppressor cells (MDSCs). CellChat analysis revealed an active MIF signaling pathway between GSCs and MDSCs. A 26-gene GSCS demonstrated superior performance compared to existing prognostic models. Knockdown of TUBA1C significantly inhibited glioma cell migration, and invasion in vitro, and reduced tumor growth in vivo. This study offers a comprehensive characterization of GSCs and their interactions with MDSCs, while presenting a robust GSCS. The findings offer new insights into glioma biology and identify potential therapeutic targets, particularly TUBA1C, aimed at improving patient outcomes.

Towards more equitable cooling services of urban parks: Linking cooling effect, accessibility and attractiveness

Global warming and rapid urbanization have caused frequent occurrences of heat waves and urban heat island effect, presenting a significant threat to health of urban residents. Researches have indicated that cooling services provided by parks are essential in alleviating impact of heat wave events and urban heat island effect. However, previous researches on park cooling services center around cooling effect, with a lack of exploration regarding the fairness of such services. To fill this gap, this study quantifies the level of equity in cooling services in 18 parks in the core area of Hangzhou. Through this study, we hope to clarify the current situation of fairness of cooling services in urban parks and provide fairer park cooling services through scientific and reasonable park layouts. This will alleviate the threat of rapid urbanization and climate change to urban residents, and make the urban environment develop in a more livable direction. We assessed the cooling effect using remote sensing and the ArcGIS platform to screen parks with cooling effect and to quantify their cooling service efficiency. We utilized spatial network analysis to quantify the accessibility and origin-destination matrix data to quantify the attractiveness to reflect the level of park cooling services. The results reveal that 18 parks exhibit a noticeable cooling effect, albeit with variations observed among parks. The percentage of urban parks with low accessibility is 77.80%, indicating that the distribution of accessible space presents an uneven status quo. In addition, 72.20% of parks have low attractiveness of cooling services, indicating that some parks have insufficient attractiveness of cooling services. Based on each indicator of cooling services, we categorize urban parks into four types based on supply and demand, and propose adaptive planning measures and intervention strategies to provide a reference for equitable distribution of cooling services in urban parks.

Spatial spillover effects and correlation network analysis of green construction development efficiency in China

Green construction has been implemented globally to address resource depletion and environmental degradation. The effectiveness of green construction development depends on the efficiency of resource utilization and the common progress of all regions. However, existing studies have paid limited attention to green construction development efficiency (GCDE) and its spatial correlation. This study uses the super-efficiency slacks-based measure (Super-SBM) model, spatial econometric models, and social network analysis (SNA) method to reveal the spatial distribution, spatial spillover effects, and spatial correlation of GCDE across 30 provinces in China. The results indicate significant disparities in GCDE among regions, with the eastern region exhibiting considerably higher GCDE than the western and northeastern regions. There is evidence of spatial spillover effects, and the spatial correlation of GCDE is strengthening. Provinces with high GCDE occupy central positions in the GCDE network and exert spillover effects on other provinces, while underdeveloped provinces have opportunities to benefit from regional cooperation. The findings provide policy insights for the implementation and enhancement of green construction development actions. Moreover, they provide Chinese experience for green construction practices in other areas of the world.

Encouraging cycling through the improvement of streetscape perception: A bottom-up investigation into the relationship between street greening and bicycling volume

Enhancing neighborhood greenery quality proves to be an effective tactic in encouraging active travel among residents. From a cyclist's perspective, this study quantitatively evaluates street greening quality and its association with cycling behaviors. Utilizing shared bicycle usage data and SegNet-analyzed Baidu Street Views on Xiamen Island, we quantified comprehensive street greening quality. We then employed Spatial Design Network Analysis and a street greening quality rating scale to assess accessibility and aesthetics. Subsequently, models were developed using OLS, spatial autocorrelation, and Moran's I statistical methods, complemented by a spatial heterogeneity analysis of factors influencing shared bicycle riding behaviors through the Geographically Weighted Regression (GWR) model. Our study reveals that in arterial and commercial districts, the appeal and variety of street greening elements are more crucial than their quantity. These quality indicators exhibit a polycentric, continuous spatial distribution, aligning with the overall street layout. Beyond the visual impact of greenery on cycling behavior, factors like richness, openness, and safety of streets, along with diverse facilities, significantly influence cycling route preferences. In contrast, uniform street elements can disrupt or even discourage cycling. This research, by analyzing the relationship between street greening quality and cycling intensity, offers valuable insights for policymakers in prioritizing the transformation of streets into bike-friendly zones. Additionally, we propose a pragmatic, bottom-up approach to developing bicycle-friendly urban environments, indirectly fostering healthier travel habits among urban residents.

Machine learning for predictions of road traffic accidents and spatial network analysis for safe routing on accident and congestion-prone road networks

Road traffic accidents (RTAs) and the resulting traffic congestion are global concerns mainly in metropolitan environments. The need for road safety is directly correlated with the rapidly increasing impact of urbanization on infrastructure and day-to-day living. In this study, we have introduced an innovative approach integrating a Random Forest (RF) model, crash rates, and spatial network analysis to provide safe route recommendations for drivers aiming to reduce RTAs and congestion. Based on historical accident data from 2014–2019, the analysis of the RF model and crash rate served as a prediction of the likelihood and occurrence of RTAs. In applying the spatial network analysis, lower predicted crash counts from spatial joining were taken into consideration, as well as areas with lower crash rates that have had fewer incidents in the past. An alternative safe route and an optimum route that covers 32.27 km in 50.78 mins of travel time and 28.6 km in 41.58 mins of travel time were successfully identified, respectively. Having demonstrated 78 % predictive capability on the target variable, the RF model has proved its worth. Analyzing historically lower accident counts on segments leading to minimal crash rates validates the accuracy of the identified safe route. This advanced method significantly aids in improving traffic safety by making drivers and travelers aware of potentially high rates of accidents and traffic congestion on road segments. It assists travelers with their trip planning to anticipate potential risks and suggest safer alternate routes, making it a valuable contribution to the field.

Spatial network analysis and driving forces of urban carbon emission performance: Insights from Guangdong Province

As the primary contributor to carbon emissions, how cities enhance their carbon emission performance and mitigate emissions is crucial for achieving low-carbon urban environments in China. However, existing research often overlooks the spatial interconnectedness of carbon emission performance, neglecting reciprocal influences among cities. This study examines the network structure of carbon emission performance among Guangdong's cities from 1997 to 2019, using a super-efficient SBM model and social network analysis, and measures spatial impacts of network factors with the spatial Durbin model. Findings reveal that: (1) The overall network of carbon emission performance is relatively loose with minimal changes in connectivity and efficiency but shows significant local clustering. (2) Shenzhen, Guangzhou, and Zhuhai have high centrality, dominating carbon emission performance resources and acting as key transmission nodes, while most other regions have low centrality, indicating network polarization and potential vulnerabilities. (3) Enhancing a region's centrality, economic development, industrial structure, openness, and attraction of talent and technology can boost local carbon emission performance, but may also lead to the displacement of emissions to neighboring areas and outflow of low-carbon and innovative elements, negatively affecting surrounding regions through spatial spillover effects. This research advances regional carbon emission reduction strategies by highlighting the interplay between spatial networks and carbon emission performance, fostering synergies in reduction efforts.

Multi-dimensional equilibrium optimal allocation of water resources in Beijing-Tianjin-Hebei region through spatial association network analysis

The rapid social and economic development has led to problems such as over-exploitation of water resources, strong demand for physical water resources, and disequilibrium of water resources load. Based on the current situation of the water resources system in Beijing-Tianjin-Hebei region, this study proposed an input–output analysis-assessment-prediction-optimization framework. A water resources dissipation environment extended multi-regional input–output model was established, and the spatial equilibrium of water resources load was evaluated comprehensively. Furthermore, a multi-dimensional spatial equilibrium optimal allocation for water resources was built to identify key nodes and sectors. The primary findings were as follows: (1) Shijiazhuang and Tangshan were the main cities with net virtual water outflow, while Beijing and Baoding were the primary recipients of net virtual water inflow; (2) The overall score for the spatial equilibrium assessment of water resource load was low, with dimension scores predominantly ranging between 0.2 and 0.6; (3) The key transfer nodes were predominantly concentrated on the “outflow from Shijiazhuang” and the “inflow to Cangzhou and Langfang” nodes. This research conducted an in-depth exploration of the direction of water resource transfers, the actual stress conditions, and the spatial equilibrium of water resources within the Beijing-Tianjin-Hebei region. Finally, the future adjustment strategy for water-intensive industries at the urban agglomeration scale was obtained.

Spatial network analysis of green electricity efficiency dynamics in the Yellow River Basin cities

Improving the green electricity efficiency (GEE), is an important issue for China's high-quality economic development. This study presents a spatial correlation network of urban GEE in the Yellow River Basin from 2012 to 2021, constructed using an improved gravity model. Social network analysis and the quadratic assignment procedure method are employed to analyze the spatial correlation characteristics and influencing factors. The findings indicate that urban GEE in the Yellow River Basin exhibits complex and stable network characteristics. The spatial network analysis reveals that Jiayuguan City, Dongying City, Dingxi City, Zibo City, and Shizuishan City occupy central positions within the network. The results indicate that spatial adjacency, GDP per capita, industrial structure, and the level of science and technology expenditure are positively related to urban GEE, while environmental regulation and average temperature are negatively related. The findings of the study have led to policy recommendations aimed at enhancing urban GEE in the Yellow River Basin.

W47 - Using Spatial Social Network Analysis to Study the Journey to Overdose Deaths

W47 - Using Spatial Social Network Analysis to Study the Journey to Overdose Deaths

Mapping the structural evolution of intercity patent transfer constrained by space: a case study of China

ABSTRACT The patent transfer (PT) has been an essential means of technological catch-up and control for countries and regions. The development and diffusion of PT is influenced by space and network. Analyzing China’s patent transfer network (PTN) from 2001 to 2020 through spatial and complex network analysis, we found that the PTN became denser with significant Matthew effect, hierarchical and small-world properties. Beijing, Shanghai, and Shenzhen remained as national hubs, while new hubs like Suzhou, Guangzhou, Foshan, and Hangzhou, acting as intermediates, emerged. Network communities become more homogenised and geographically clustered due to the resonance effect. There was a shift from disperse hubs to spatial agglomerations, with the construction of long-distance shortcuts breaking spatial constraints on intercity patent transfer.

Evolution of spatial collaborative network in the Long March Cultural Tourism Belt: Characteristics, pattern, and mechanisms

AbstractThis research examines the evolution of the spatial collaborative network (SCN) within the Long March Cultural Tourism Belt in China, utilizing a mixed‐methods approach combining quantitative data analysis and GIS spatial analysis. Theoretically, it bridges national identity theory and political ideology using spatial network analysis; expanding understanding of how cultural heritage tourism contributes to national identity consolidation and the political narrative within a spatial framework. This contributes to the literature by demonstrating the role of SCNs in promoting cultural tourism's sustainable growth, addressing the call for methodological advances in cultural tourism research. In terms of the managerial side, it guides policymakers in leveraging SCNs for balanced cultural tourism growth through strategic planning and digital innovation. This research is distinguished by its comprehensive analysis of SCN evolution in a significant cultural tourism context, providing a model for global application.

Mapping the Pulse: Heterogeneous Impacts of Human-Scale Street Design on Urban Vibrancy in Four Chinese Metropolises

Urban vibrancy emerges from the complex interplay between individuals and public spaces. Recognizing the importance of capturing people’s experience and perception of streetscape and network configurations, this study investigates the relationship between human-scale street design and urban vibrancy in four Chinese metropolises: Nanjing, Wuhan, Xi’an, and Shenyang. Using a bottom-up metric framework with street view images and spatial design network analysis, this study assesses human-scale street design across five dimensions: reachability, betweenness, detours, aesthetics, and disorder. Multiscale geographically weighted regression is employed to examine how these design metrics spatially affect urban vibrancy during daytime and nighttime. The findings reveal the efficacy of enhancing street closeness and optimizing green spaces in promoting urban vibrancy. It further identifies mitigating strategies for reversing declines in urban vibrancy, such as addressing physical disorder and reducing street network severance. Additional analysis using a geographical detector explores the interactive effects of these elements, emphasizing the necessity of a comprehensive approach to urban planning. The comparative analysis across multiple cities amplifies the generalizability of the findings, offering actionable insights for adaptable, vibrancy-oriented urban planning.

Spatial network characteristics and economic effects of element flow in the Lanxi urban agglomeration.

The spatial characteristics of element flow and its spillover are important topics in economics, sociology, and geography, and significant to the promotion of the coordinated development of urban agglomerations. To study element flow in the Lanxi urban agglomeration and its effect to economic development, the spatial network characteristics and economic spillover effect were studied using the methods of spatial network analysis, the spatial Durbin model, and spatial effect decomposition. The results showed that (1) the scale of element flow in the Lanxi urban agglomeration is in an unbalanced distribution state, the scale of element flow in Lanzhou and Xining is higher than that in surrounding cities, and the connection between surrounding cities is also higher than that between other cities; (2) the network structure of element flow in the Lanxi urban agglomeration is relatively intensive, with Lanzhou and Xining as the center of element concentration, which indicates an obvious 'center periphery' structure, and gradually spreads from the core area to the surrounding areas; and (3) the element concentration level of the Lanxi urban agglomeration has a significant positive spillover effect, which plays a significant role in driving the development of surrounding cities. Other factors, such as the social consumption level, have significant direct effects, whereas the industrial structure and residents' income have significant direct and spillover effects, and are the main factors that affect the coordinated development of the regional economy.

Spatial Analysis of Intercity Migration Patterns of China’s Rural Population: Based on the Network Perspective

Since entering the 21st century, many developing countries around the world have begun to enter a stage of rapid urbanization; large-scale “rural-urban” population migration has become a typical social phenomenon in these countries. Against this backdrop, this paper aims to elucidate the spatial migration characteristics of rural populations and to discuss future rural–urban development strategies. For this purpose, this paper takes China as a case and employs methods such as spatial autocorrelation analysis, hotspot analysis, and network analysis to construct an intercity migration network of rural migrants and analyze its spatial characteristics and internal structure. The results indicate that the migration pattern of the rural population exhibits notable spatial clustering features. Cities in the eastern and central regions are, respectively, hotspots for the inflow and outflow of rural populations, with internal migration dominating in western cities and relatively inactive rural population movements in northeastern cities. Municipalities directly under the central government, sub-provincial cities, and provincial capitals show a significant tendency to attract rural populations, while prefecture-level and county-level cities mainly radiate rural populations outward. Cities nationwide form seven major clusters in the migration network, and these clusters exhibit distinct structural characteristics. Rural population migration is influenced by various factors. In the future, considerations should focus on the county as the primary unit, attracting rural populations for local employment, and promoting rural revitalization and agriculture development. The findings of this paper are of reference significance not only to China but also to many developing countries with similar national conditions in the world.

Spatial Network Analysis of Coupling Coordination between Digital Financial Inclusion and Common Prosperity in the Yangtze River Delta Urban Agglomeration

Digital financial inclusion and common prosperity are pivotal elements in promoting the sustainable socioeconomic development of China. This study introduces a novel Multi-Criteria Decision Analysis (MCDA) method to evaluate the Common Prosperity Index (CPI). Using this index, alongside the Digital Financial Inclusion Index (DFII) released by Peking University, it examines the evolution of the coupling coordination relationship between digital financial inclusion and common prosperity within the Yangtze River Delta (YRD) urban agglomeration from 2011 to 2021. By integrating gravity models and social network analysis, in this paper, we thoroughly investigate the spatiotemporal evolution characteristics of the spatial network of this coupling coordination relationship. The results indicate that both the DFII and CPI generally exhibit an upward trend, but the decline in the coupling degree reflects a weakened interaction strength between them. Specifically, Anhui significantly lags behind Jiangsu, Zhejiang, and Shanghai in the development of digital financial inclusion and common prosperity, indicating regional development imbalances. Furthermore, the strength of spatial connections in city coupling coordination has significantly increased, with Nanjing’s siphon effect on cities in Anhui becoming markedly stronger, and the number of core cities in the network increasing, which demonstrates a geographical proximity feature in network development. Additionally, the overall network characteristics are transitioning towards higher density and “small-world” properties, suggesting a trend toward network stabilization. The disparity in centrality among cities has decreased, with an overall enhancement in centrality, where the spatial spillover effects from core areas such as Hangzhou-Ningbo, Nanjing-Changzhou, and Shanghai-Suzhou-Wuxi significantly promote the development of peripheral cities. Based on these findings, this paper proposes policy recommendations for the sustainable development of digital financial inclusion and common prosperity in the YRD region.

Abstract 6171: Evolution of the spatial myeloid-lymphoid engagement in glioblastoma under temozolomide treatment

Abstract The current standard of care for glioblastoma (GBM), encompassing surgery, chemotherapy, and radiation, fails to extend patient survival beyond ~12-15 months. Even advanced immunotherapies, such as checkpoint blockade and chimeric antigen receptor-armored T-cell therapies, are ineffective in GBM due to tissue-specific niche-dependent escape strategies employed by glioma cells. Immune effectiveness, a finely regulated spatial and context-dependent process, underpins such a dismal state of the present therapeutic options. Herein, we applied CO-Detection by indEXing (CODEX), a state-of-the-art multiplex imaging platform, to elucidate the immune cell networks of the tumor microenvironment. By harnessing computational tools on multiplexed regions of interest across whole-slide images, we characterize the immune repertoire of GBMs by studying intratumor heterogeneity across space-histological territories- and time, matching pre- and post-treatment tissue sections of GBMs from seven patients under treatment (Stupp protocol). We aimed to assess changes in the interactions between the two main compartments of the immune system, myeloid and lymphoid, pre- and post-treatment using spatial statistics. We designed a computational workflow for the georeferencing, classification, and phenotyping individual cells of CODEX images across 105 regions of interest, representative of a mosaic of histologically defined cellular tumor (CT) or infiltrating tumor. We defined six major immune phenotypes and evaluated their spatial association with Cd45-Nestin+Olig2+ glioma cells. We modeled the GBM ecosystem and the cells’ spatial coexistence as spatial point processes that allowed the projection of coexistence networks. After stringent quality control, we detected and phenotyped 2.3M cells. Despite interpatient heterogeneity, some patients maintained an even immune composition, while others showed an enrichment in microglia, particularly in CT. Spatial network analyses revealed an increased coexistence between myeloid and lymphoid cells after treatments despite T cells being canonically considered moderately abundant. Quantitatively, the immune meta-network displayed higher edge density and lower modularity post-treatment compared with pre-treatment, reflecting increased lymphoid-myeloid engagement. In summary, we detected an increased spatial myeloid-lymphoid engagement in GBM undergoing chemo-radiation treatments. Spatiotemporal rearrangement of tumor-immune interactions indicates mechanisms implicated in disease recurrence and resistance to standard treatment, opening the frontiers for developing new targeted immunotherapies. Citation Format: Simon P. Castillo, Afrooz Jahedi, Pravesh Gupta, Jason T. Huse, Kasthuri Kannan, Yinyin Yuan, Krishna P. Bhat. Evolution of the spatial myeloid-lymphoid engagement in glioblastoma under temozolomide treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6171.