Average Geodesic Distance Research Articles

An Analysis of Associations between Neighborhood Road Networks and Pedestrian Traffic Accidents : Focused on the TAAS (2012-2014) in Seoul, Korea

The complexity, connectivity, and continuity of road network structure affect the speed of vehicle and drivers‘ vision and driving behavior, which strongly determines pedestrian safety. Thus more objective and in-depth research is required to identify the association between road network and pedestrian safety. In this context, this study examines the relationship between neighborhood environment including road network structure and pedestrian safety using the TAAS (2012-2014) data. We utilize negative binominal regression model. Unlike previous studies, this study includes exposure variables such as pedestrian volume, population, and road congestion level and objective measures of road network structure such as average geodesic distance, network betweenness centrality, average clustering coefficient. Key finding and implications can be organized as follows. First, pedestrian traffic accidents on community living zone road is strongly associated to road network structure of neighborhood-level. Second, we confirm that pedestrian volume and traffic condition are important variables for pedestrian accidents. Third, pedestrian traffic accidents are strongly associated with commercial land use and public transportation systems including median exclusive bus station. This study suggests policy implications for neighborhood road network design in urban planning and design projects and for pedestrian safety.

Aggregate-related changes in network patterns of nematodes and ammonia oxidizers in an acidic soil

Nitrification plays a central role in global nitrogen cycle, which is affected by biological interaction between soil microfauna and microorganisms. However, the complexity of soil biotic communities made it difficult to reveal organizational principles of the community and the interactions among species. Here, we used the network analysis to decipher the interactions between nematodes and ammonia oxidizers within aggregate fractions under 10-year manure application, and examine their associations with soil variables and potential nitrification activity (PNA). Three aggregate fractions included large macroaggregates (>2000 μm, LA), small macroaggregates (250–2000 μm, SA), and inter-aggregate soil and space (<250 μm, IA). Aggregate factions showed a remarkable effect on association networks of nematodes and ammonia oxidizers. The average connectivity (avgK) and the number of edges in overall networks increased with increasing aggregate sizes, while the average geodesic distance (GD) followed the opposite trend. The LA network could be viewed as a better organized or a better operational soil food web with more functional interrelated members than the SA and IA networks. The modules related to PNA were significantly correlated and clustered together as meta-modules in networks of aggregate fractions. The role-shifts prevailed among the network members such as significant module memberships (MMs) and generalist/specialist operational taxonomic units (OTUs). A half of shared nodes were further identified as shared MMs, dominated by ammonia-oxidizing bacteria (AOB) especially for Nitrosospira cluster 3a and 10. Soil pH could explain partly the shift of module hubs in different networks, while grazing by bacterivores might account for three exclusively connecters related to Nitrososphaera clusters 1.1. The strongly coupled modules correlated positively to pH and total carbon (TC), regardless of aggregate fractions. The network analysis approach provided new insights into potential importance of network interactions between nematodes and ammonia oxidizers in soil nitrogen cycling.

Investigating the associations between road network structure and non-motorist accidents

Road networks channel traffic flow and can impact the volume and proximity of walking and bicycling. Therefore, the structure of road networks—the pattern by which roads are connected—can affect the safety of non-motorized road users. To understand the impact of roads’ structural features on pedestrian and bicyclist safety, this study analyzes the associations between road network structure and non-motorist-involved crashes using data from 321 census tracts in Alameda County, California. Average geodesic distance, network betweenness centrality, and an overall clustering coefficient were calculated to quantify the structure of road networks. Three statistical models were developed using the geographically weighted regression (GWR) technique for the three structural factors, in addition to other zonal factors including traffic behavior, land use, transportation facility, and demographic features. The results indicate that longer average geodesic distance, higher network betweenness centrality, and a larger overall clustering coefficient were related to fewer non-motorist-involved accidents. Thus, results suggest that: (1) if a network is more highly centered on major roads, there will be fewer non-motorist-involved crashes; (2) a network with a greater average number of intersections on the shortest path connecting each pair of roads tends to experience fewer crashes involving pedestrians and bicyclists; and (3) the more clustered road networks are into several sub-core networks, the lower the non-motorist crash count. The three structural measurements can reflect the configuration of a network so that it can be used in other network analyses. More information about the types of road network structures that are conducive to non-motorist traffic safety can help to guide the design of new networks and the retrofitting of existing networks. The estimation results of GWR models explain the spatial heterogeneity of correlations between explanatory factors and non-motorist crashes, which can support regional agencies in establishing local safety policies.

Locality-constrained sparse patch coding for 3D shape retrieval

3D shape retrieval is a fundamental task in many domains such as multimedia, graphics, CAD, and amusement. In this paper, we propose a 3D object retrieval approach by effectively utilizing low-level patches of 3D shapes, which are similar as superpixels in images. These patches are first obtained by means of stably over-segmenting 3D shape, and then we adopt five representative geometric features including shape diameter function, average geodesic distance, and heat kernel signature, to characterize these low-level patches. A large number of patches collected from shapes in a dataset are encoded into patch words by virtue of locality-constrained sparse coding under the consideration of local smooth sparsity. Input query is compared with 3D models in the dataset through probability distribution of patch words. Experiments reveal that the proposed method achieves comparable retrieval performance to state-of-the-art methods.

Scholarly interest in gamification: a citation network analysis

Purpose – Gamification has been studied in many areas, i.e. marketing, education, training, and psychology. There has been an increased interest in the topic of gamification among scholars in the past several years. The purpose of this paper is therefore to use citation network analysis and explore changes in scholarly interest in the topic of gamification. As a result, four study hypotheses were developed: H1a: the “other” category publications (books, proceedings, etc.) will have a significantly larger frequency when compared with “journal” publications; H1b: the trend line of the frequency of publications will most closely fit the S-curve of Adoption in the adoption of innovations or the spread of new ideas as postulated by Rogers (2003); H2a: there will be a negative correlation between graph density and the number of vertices (publications); and H2b: there will be a positive correlation between average geodesic distance (AVGD) and the number of vertices (publications). Design/methodology/approach – Data were collected from three searches for all published works that contained the word “gamification” in the titles of publication (the unit of analysis) from 2010 to 2013. The sampling was conducted via Google Scholar, amazon.com, and the academic library databases, i.e. EBSCO Search, JStor Scholarly Journal Archive, PsychArticles, and WorldCat. Data were analyzed using frequency counts and citation network. NodeXL is a highly structured workbook that includes multiple worksheets and computational functions necessary to store, represent, and analyze a network. Findings – All four hypotheses were supported; the “other” category accounted for a significantly larger number of publications with the word “gamification” in the title; the trend line of the frequency of publications will most closely fit the S-curve of Adoption in the adoption of innovations or the spread of new ideas as postulated by Rogers (2003); there was a negative correlation between graph density and the number of vertices (publications); and there was a positive correlation between AVGD and the number of vertices (publications). Research limitations/implications – It is highly improbable that a “pure” or “random” sample of publications could be collected because it is highly probable that there exists no known, i.e. identifiable and verifiable “true” population of works that include gamification in the title. Practical implications – The study findings have three major implications. The first takes in scholarly communication and the development of scientific knowledge. The findings imply that scholarly communication follows patterns similar to the adoption of innovation. The second implication deals with the topic known as “gamification.” The study findings imply that scholars believe gamification is worthy of serious study as the network of scholars studying gamification is increasing. The third implication of our study relates to the methods used to study scholarly communication. The study findings imply that network analysis can be used to understand how a new concept can be vetted by the scientific community. Originality/value – The citation network analysis of this study provided tangible evidence of how new concepts are vetted, i.e. adopted. Citation network studies thus offer promise for a deeper understanding of scholarly communication and the adoption of new research topics and fields of inquiry. In addition, the findings indicate that “gamification” is a potentially fruitful topic for scholars to continue to explore.

Geodesic spin contour for partial near-isometric matching

We propose a novel shape descriptor named geodesic spin contour. Geodesic spin contour is similar to the classical spin image, but it replaces spin coordinates with quantities invariant under isometric transformations: geodesic distance and average geodesic distance. This makes it applicable to matching near-isometric shapes. By choosing a small support length, one can obtain geodesic spin contours that describe the local geometry of the shape, and therefore are suitable for surfaces with missing data. Experimental results show that the geodesic spin contour provides an effective way to match features on near isometric shapes.

Co-authorship research networks in public health in Santander

Although a good deal of research in public health has been performed, large inequalities still exist in health. It is necessary to know how knowledge is generated and disseminated to the public in order for research to reach decision-makers. To characterize public health research networks in Santander, Colombia. Analysis of social networks based on co-authorship of scientific publications by researchers living in Santander in 2012. Researchers were identified using a "snowball" technique. The publications search was conducted using national and international databases. The density and average geodesic distance of networks were calculated, as was the size, pairs, brokers and homophily of egocentric networks. There were 531 researchers. Most worked in epidemiology (77.59%), and in more than one thematic field. The network density was 0.0058 and the average geodesic distance was 4.418. Several indicators suggested that the most cohesive egocentric networks were those in which researches investigated more than in one knowledge area or in epidemiology. Homophily was lower for health systems, biostatistics and social and behavioral sciences, as well as private hospitals and the public university. The network structure suggests a growth phase in research and a predominance of epidemiology. Other public health areas need strengthening so as to better address the health needs of the state.

Redes de coautoría de investigación en salud pública en Santander

Introduction: Although a good deal of research in public health has been performed, large inequalities still exist in health. It is necessary to know how knowledge is generated and disseminated to the public in order for research to reach decision-makers.Objective: To characterize public health research networks in Santander, Colombia.Materials and methods: Analysis of social networks based on co-authorship of scientific publications by researchers living in Santander in 2012. Researchers were identified using a “snowball” technique. The publications search was conducted using national and international databases. The density and average geodesic distance of networks were calculated, as was the size, pairs, brokers and homophily of egocentric networks.Results: There were 531 researchers. Most worked in epidemiology (77.59%), and in more than one thematic field. The network density was 0.0058 and the average geodesic distance was 4.418. Several indicators suggested that the most cohesive egocentric networks were those in which researches investigated more than in one knowledge area or in epidemiology. Homophily was lower for health systems, biostatistics and social and behavioral sciences, as well as private hospitals and the public university.Conclusions: The network structure suggests a growth phase in research and a predominance of epidemiology. Other public health areas need strengthening so as to better address the health needs of the state.

Dolphins restructure social system after reduction of commercial fisheries

Although human activities are known to affect the social behaviour of group-living animals, the resilience of animals' social structure to disturbance is poorly understood. In the 1990s, bottlenose dolphins, Tursiops aduncus , in Moreton Bay, Australia, formed two distinct social communities (‘trawler’ and ‘nontrawler dolphins’) based on foraging interactions (or lack thereof) with commercial prawn trawlers. Members of the two communities almost never associated, despite overlapping home ranges. Since then, changes to fisheries legislation have substantially reduced trawling in Moreton Bay. We used association analyses and social network metrics to compare patterns of sociality among bottlenose dolphins across two periods: 1997–1999 (during trawling) and 2008–2010 (post trawling). Over this decade, their social network became less differentiated and more compact (average geodesic distance between individuals decreased), with significantly more and stronger associations between individuals (mean and maximum half-weight indices increased). The previously described partitioning into two communities has disappeared, with former ‘trawler’ and ‘nontrawler dolphins’ now dispersed over the entire social network and associating with each other. This restructuring suggests that although fisheries can influence the social behaviour of bottlenose dolphins, their social structure represents a complex adaptive system that is resilient to disturbance.

Applying Social Network Analysis to Analyze a Web-Based Community

this paper deals with a very renowned website (that is Book-Crossing) from two angles: The first angle focuses on the direct relations between users and books. Many things can be inferred from this part of analysis such as who is more interested in book reading than others and why? Which books are most popular and which users are most active and why? The task requires the use of certain social network analysis measures (e.g. degree centrality). What does it mean when two users like the same book? Is it the same when other two users have one thousand books in common? Who is more likely to be a friend of whom and why? Are there specific people in the community who are more qualified to establish large circles of social relations? These questions (and of course others) were answered through the other part of the analysis, which will take us to probe the potential social relations between users in this community. Although these relationships do not exist explicitly, they can be inferred with the help of affiliation network analysis and techniques such as m-slice. Book-Crossing dataset, which covered four weeks of users' activities during 2004, has always been the focus of investigation for researchers interested in discovering patterns of users' preferences in order to offer the most possible accurate recommendations. However; the implicit social relationships among users that emerge (when putting users in groups based on similarity in book preferences) did not gain the same amount of attention. This could be due to the importance recommender systems attain these days (as compared to other research fields) as a result to the rapid spread of e-commerce websites that seek to market their products online. Certain social network analysis software, namely Pajek, was used to explore different structural aspects of this community such as brokerage roles, triadic constraints and levels of cohesion. Some overall statistics were also obtained such as network density, average geodesic distance and average degree.

การแปลงรูปหลายเหลี่ยมสามมิติให้เป็นรูปเรขาคณิตปฐมฐานด้วยเรบกราฟ (3D Polygonal Mesh to Primitive Conversion Using Reeb Graph)

บทคัดย่อ ในโลกเสมือนการแทนวัตถุสามมิติจะใช้เรขาคณิตปฐมฐานสามมิติ เช่น ลูกบาศก์ ทรงกลม ทรงกระบอก เป็นต้นซึ่งไม่เหมือนกับโปรแกรมที่ใช้งานทั่วไปที่ใช้การแทนวัตถุสามมิติแบบเมช ดังนั้นการแปลงวัตถุสามมิติแบบเมชเป็นปฐมฐานจะทำให้ลดภาระและค่าใช้จ่ายในการสร้างวัตถุสามมิติขึ้นมาใหม่ในโลกเสมือน งานวิจัยนี้ได้ใช้เรบกราฟโดยมีระยะทางจีออเดสิกเฉลี่ยเป็นมอร์สฟังก์ชันในการแปลงวัตถุสามมิติแบบเมชเป็นปฐมฐานเพื่อให้วัตถุสามมิติที่แทนด้วยปฐมฐานนั้นมีรูปร่างเหมือนกับวัตถุสามมิติต้นฉบับ แล้วจึงนำไปเปรียบเทียบว่าปฐมฐานใดเหมาะสมที่สุด ผลการทดลองวัดความเหมือนด้วยระยะทางเฮาส์ดอร์ฟ จากวัตถุสามมิติ 380 ชิ้น 19 ประเภทของมหาวิทยาลัยปริ๊นซ์ตั้นสรุปว่าวัตถุสามมิติที่แทนด้วยปฐมฐานมีรูปร่างเหมือนกับวัตถุสามมิติต้นฉบับโดยวัตถุสามมิติที่มีลักษณะนูนจะเหมือนต้นฉบับมากกว่าวัตถุสามมิติที่มีลักษณะเว้า ABSTRACT In Virtual world 3D objects are represented by primitives, such as cube sphere cylinder, but in general programs use polygonal mesh. Hence, 3D polygonal mesh to primitive conversion can reduce cost and time to create new 3D object in Virtual world. We preserve shape of 3D object by using, average geodesic distance (AGD) as a Morse function in, Reeb graph. Then compare all defined fitting primitive and choose the best one. The results were measured by Hausdorff distance with the originals of 380 models 19 types from Princeton University show that primitive represented objects have the same shape as originals. The convex objects have less Hausdorff distance than the concave objects.

Möbius Transformations For Global Intrinsic Symmetry Analysis

AbstractThe goal of our work is to develop an algorithm for automatic and robust detection of global intrinsic symmetries in 3D surface meshes. Our approach is based on two core observations. First, symmetry invariant point sets can be detected robustly using critical points of the Average Geodesic Distance (AGD) function. Second, intrinsic symmetries are self‐isometries of surfaces and as such are contained in the low dimensional group of Möbius transformations. Based on these observations, we propose an algorithm that: 1) generates a set of symmetric points by detecting critical points of the AGD function, 2) enumerates small subsets of those feature points to generate candidate Möbius transformations, and 3) selects among those candidate Möbius transformations the one(s) that best map the surface onto itself. The main advantages of this algorithm stem from the stability of the AGD in predicting potential symmetric point features and the low dimensionality of the Möbius group for enumerating potential self‐mappings. During experiments with a benchmark set of meshes augmented with human‐specified symmetric correspondences, we find that the algorithm is able to find intrinsic symmetries for a wide variety of object types with moderate deviations from perfect symmetry.

The complex world of the small brain

Since Stanley Milgram's six degrees of separation, the interest in the topological structure of network graphs and implications for their functional role experienced a dramatic surge. 40 years later, small-world and scale-free properties, with the latter being generally viewed as a crucial prerequisite for complex dynamical behaviours, are identified as a unifying feature of many real-world networks. However, the study and characterisation of complexity at the level of neuronal populations such as cortical microcircuits, large-scale functional networks or, ultimately, the whole brain still remains a technically and mathematically difficult and, therefore, widely unsolved task. Moreover, recent research shows that small-world and scale-free connectivity are just two out of a vast plethora of applicable graph-theoretical measures to yield a more accurate characterisation of the networks structural or functional properties. In this contribution we provide a detailed comparative characterisation of publicly available brain networks. The latter include structural areal connectivity graphs from the cat cortex, macaque and macaque visual cortex, as well as cellular networks of C. elegans and corresponding subnetworks formed by chemical synapses and gap junctions only. Graph theoretical tools applied include node degree and correlation, edge distance, clustering and cycle, entropy, hierarchical, centrality, spectral and complexity measures, as well as the study of subgraphs and fractal properties. Moreover, extensions of these measures incorporating weight and spatial information are proposed and applied to graphs where such data were available. Our analysis shows that, first, in agreement with numerous previous studies, all investigated systems exhibit small-world properties (i.e. small average geodesic distance and high clustering coefficient) when relational graphs are considered. Second, for many other measures, marked differences (e.g. for efficiency and vulnerability) between the investigated networks were found, thus revealing a rich universe of structural qualities. We argue that the latter forces a re-evaluation of the question about structural prerequisites for functionally complex dynamical behaviours. Third, the incorporation of weight and spatial information qualitatively alters some conclusions drawn from the analysis of corresponding relational graphs, thus arguing for a careful re-evaluation of real-world networks in the context weighted and spatial graph theory. In summary, our study suggests that a deeper understanding of the functional dynamics and role, and possible differences in the latter, of neural and brain networks necessitate their detailed structural characterisation beyond small-world and scale-free qualities. Moreover, a detailed graph-theoretical characterisation of structural and functional brain networks will allow to constraint developmental mechanisms which lead to the preference of specific network topologies over others. Finally, studying structural and functional patterns on the global scale with the full weight of graph theory could provide an alternative way to argue for complexity as an emergent quality of brain networks which goes beyond a pure description of the wealth of structural and functional properties observed in isolated neural systems.

Pose-Oblivious Shape Signature

A 3D shape signature is a compact representation for some essence of a shape. Shape signatures are commonly utilized as a fast indexing mechanism for shape retrieval. Effective shape signatures capture some global geometric properties which are scale, translation, and rotation invariant. In this paper, we introduce an effective shape signature which is also pose-oblivious. This means that the signature is also insensitive to transformations which change the pose of a 3D shape such as skeletal articulations. Although some topology-based matching methods can be considered pose-oblivious as well, our new signature retains the simplicity and speed of signature indexing. Moreover, contrary to topology-based methods, the new signature is also insensitive to the topology change of the shape, allowing us to match similar shapes with different genus. Our shape signature is a 2D histogram which is a combination of the distribution of two scalar functions defined on the boundary surface of the 3D shape. The first is a definition of a novel function called the local-diameter function. This function measures the diameter of the 3D shape in the neighborhood of each vertex. The histogram of this function is an informative measure of the shape which is insensitive to pose changes. The second is the centricity function that measures the average geodesic distance from one vertex to all other vertices on the mesh. We evaluate and compare a number of methods for measuring the similarity between two signatures, and demonstrate the effectiveness of our pose-oblivious shape signature within a 3D search engine application for different databases containing hundreds of models.