Search results

The most sustainable forms of urban mobility are walking and cycling. These modes of transportation are the most environmental friendly, the most economically viable and the most socially inclusive and engaging modes of urban transportation. To measure and compare the effectiveness of alternative pedestrianization or cycling infrastructure plans, the authors need to measure the potential flows of pedestrians and cyclists. The paper aims to discuss this issue.

The authors have developed a computational methodology to predict walking and cycling flows and local centrality of streets, given a road centerline network and occupancy or population density data attributed to building plots.

The authors show the functionality of this model in a hypothetical grid network and a simulated setting in a real town. In addition, the authors show how this model can be validated using crowd-sensed data on human mobility trails. This methodology can be used in assessing sustainable urban mobility plans.

The main contribution of this paper is the generalization and adaptation of two network centrality models and a trip-distribution model for studying walking and cycling mobility.

Recently, the serviceability of the transportation infrastructure in urban areas has become crucial. Any impact of the hazardous conditions on the urban road network causes significant disruption to the functioning of the urban region, making the city’s resilience a point of concern. Thereby, the purpose of the study is to examine the city’s recovery capacity to absorb the impacts of adverse events like urban floods.

This study examines the road network resilience for an urban flood event for zones proposed by the Municipal Corporation to develop multiple central business districts. This study proposes a novel approach to measure the resilience of road networks in an urban region under floods caused due to heavy rainfall. A novel Road Network Resilience Index (RNRI) based on the serviceability of the road network during floods is proposed, estimated using Analytic Hierarchy Process - Multiple Criteria Evaluation (AHP-MCE) approaches by using the change in street centrality, impervious area and road network density. This study examines and analyses the resilience of road networks in two conditions: flood and nonflood conditions. Resilience was estimated for both the conditions at the city level and the decentralized zone level.

Based on RNRI values, this study identifies zones having a lower or higher resilience index. The central, southern and eastern zones have lower road network resilience and western and northern zones have high road network resilience.

The proposed methodology can be used to increase road network resilience within the city under flood conditions.

The previous literature on road network resilience concentrates on the physical properties of roads after flood events. This study demonstrates the use of nonstructural measures to improve the resilience of the road network by innovatively using the AHP-MCE approach and street centrality to measure the resilience of the road network.

In today’s digital era, data pertaining to scientific research have attracted considerable attention of researchers. Data of scientific publications can be modeled in the form of networks such as citation networks, co-citation networks, collaboration networks, and others. Identification and ranking of important nodes in such networks is useful in many applications, such as finding most influential papers, most productive researchers, pattern of citation, and many more. The paper aims to discuss this issue.

A number of methods are available in literature for node ranking, and K-shell decomposition is one such method. This method categorizes nodes in different groups based on their topological position. The shell number of a node provides useful insights about the node’s importance in the network. It has been found that shells produced by the K-shell method need to be further refined to quantify the influence of the nodes aptly. In this work, a method has been developed, which ranks nodes by taking the core(s) as the origin and second-order neighborhood of a node as its immediate sphere of influence.

It is found that the performance of the proposed technique is either comparable or better than other methods in terms of correctness and accuracy. In case of assigning different ranks to nodes, the performance of the proposed technique is far more superior to existing methods. The proposed method can be used to rank authors, research articles, and fields of research.

The proposed method ranks nodes by their global position in a network as well as their local sphere of information. It leads to better quantification of a node’s impact. This method is found to be better in terms of accuracy and correctness. In case of assigning different ranks to nodes, the performance of the proposed technique is far more superior to existing methods.

Networks linking tourist attractions or organizations are a major focus of tourism research. Despite extensive research on tourism networks, academic research on the spatial structure and formation of wine tourism networks is limited. This study aims to investigate the spatial structure and factors influencing the development of a network among Ningxia wineries, an emerging wine tourism destination in China.

This study uses social network analysis to uncover “what” the spatial structure of wine tourism networks looks like. Sixteen in-depth interviews were conducted among key stakeholders to explain the “why” of such structural characteristics.

The results show that in an emerging wine tourism destination, popular tourist attractions enjoy high centrality and hold key positions in the wine tourism network. Small wineries exhibit high closeness centrality, and only one winery serves as a network broker. According to the stakeholders, the importance of network actors will increase as their economic and political importance increase, while small wineries that lack differentiation in the network may perish.

Local governments can implement the suggested measures for improving network connections, and wineries are advised to find suitable positions to improve the experiences of tourists.

This study pioneers the identification of the distinct structure and factors influencing the network of an emerging wine tourism destination, thus enriching the understanding of the interplay and roles of different actors.

The need for stakeholder theory has been widely highlighted in the literature to develop solid strategies for a large organization. However, there is still a lack of user-friendly visualization tools and no unique approach exists to identify and engage stakeholders. This paper aims to propose a general methodology to co-design the sustainability ecosystem at the local scale, to explore it and to assess the impact of a large organization within the identified ecosystem.

The methodology consists of two main processes: identifying an ontological map of the sustainability topics network and designing the local sustainability stakeholders ecosystem. Both processes are based on a nodes identification phase and a nodes prioritization phase. The identification phase was achieved by engaging 160 citizens, for the topics network and nearly 40 relevant stakeholders, for the stakeholders’ ecosystem, with a collaborative participatory mapping process. The prioritization phase was conducted because of three indicators, i.e. the closeness, the betweenness and the eigenvector centrality.

Betweenness centrality results to be the best indicator to assess the importance of a stakeholder with respect to the whole network, while eigenvector centrality highlights the quality of the already engaged stakeholders of an organization, as it mainly depends on the number of links of the first order neighbors. On the contrary, the closeness centrality, when applied to a small network, seems to be not appropriate to assess the centrality of a stakeholder.

This approach revealed some criticalities in the mapping process, as in the weighting link procedure. Further investigations are needed to generalize the approach to a dynamic one, to allow real-time mapping and to develop a robust interconnection among centrality degrees and the power, interest and legitimacy concept of stakeholder theory.

Obtained results for a case study, i.e. the position of the University of Turin Green Office within the City of Turin sustainability ecosystem, are discussed showing how social network analysis centrality degrees can be used to quantitatively assess the role of an organization within a stakeholders’ ecosystem.

Centrality analysis allows identifying emergent topics/stakeholders within a network of words/actors that, at a first sight, should not be considered by decision-makers and managers.

A new methodology for stakeholder identification and prioritization is proposed exploiting online data visualization tools, participatory mapping and social network analysis.

This study aims to analyze social networking and network centrality in the case of community enterprises, which the existing literature has not addressed. To do so, the authors use the survey of community enterprises from the One Tambon One Product (OTOP) entrepreneurship program of Nong Khai border province in Thailand as a case study.

Social network analysis (SNA) is a tool to study and understand the relationship patterns of units of analysis, which can be individual, household, community or production units, and how those units interact through social or economic activities. Network positions are important when identifying the centralization of links in a particular network. If a representative is close to the center of a network, it is possible that the production unit will be able to contact or coordinate with a greater number of other members of the cluster, create knowledge sharing, promote collaboration and then typically demonstrate greater performance.

The results show that overall, local government agencies play a critical role in the community enterprise relationship network, while private entities are the least active group. Enterprises in partnerships with external agencies are mostly cooperating with branding and marketing dimensions, followed by the design dimension. There is no cooperation regarding production and distribution. Most community enterprises have established at least a one-dimensional network of cooperation with external agencies; only five community enterprises have isolated nodes with no partnerships having been created within this group.

The study was limited by the surveys having been conducted in a single area and, therefore, can be used only as a case study for this area. Surveys in larger group sizes and in a wider range of areas would lead to results with greater applicability and reliability.

These results bring to mind policy proposals to increase the competitiveness of community enterprises through the development of social networks as follows: firstly, knowledge should be created with community enterprise operators to understand their supply chains and analyze the strengths and weaknesses and core competencies of their enterprises; and secondly, enterprises should be educated about which agencies can assist businesses at each stage in the value chain system and encouraged to ask for help in adding value at each stage of production.

A “OTOP to Business Networking” platform for community and private enterprises should be created with projects/activities that offer venues to exchange business learning and opportunities. Holding meetings among people in a variety of business sectors may help inspire entrepreneurs to innovate to further their businesses, which can lead to networking businesses conducting activities across community enterprises and the private sector for the mutual benefit of producing merchandise for large customers and markets. Learning systems, production techniques and cooperation should be created, as well as opportunities to increase market share and profitability, if this platform is successful.

This paper is the first study, to the best of the authors’ knowledge, to utilize SNA to examine the use of social networking among community enterprises participating in Thailand’s OTOP entrepreneurship program in Nong Khai province. The results show that overall, local government agencies play a critical role in the community enterprise relationship network, while private entities are the least active group. Therefore, the government can play an important role in helping to develop a network of community enterprises with external entities at each stage of the value chain to enhance the competitiveness of each enterprise.

This paper presents a critical review of the economic network analysis methods and their applications to maritime transport. A network can be presented in terms of its structure, topology, characteristics as well as the connectivity with different measures such as density, degree distribution, centrality (degree, betweenness, closeness, eigenvector and strength), clustering coefficient, average shortest path length and assortative. Various models such as the random graph model, block model, and ERGM can be used to analyse and explore the formation of a network and interaction between nodes. The review of the existing theories and models has found that, while these models are rather computationally intensive, they are based on some rather restrictive assumption on network formation and relationship between ports in the network at the local and global levels that require further investigation. Based on the review, a conceptual framework for maritime transport network research is developed, and the applications for future research are also discussed.

This paper presents a critical review of the economic network analysis methods and their applications to maritime transport. A network can be presented in terms of its structure, topology, characteristics as well as the connectivity with different measures such as density, degree distribution, centrality (degree, betweenness, closeness, eigenvector and strength), clustering coefficient, average shortest path length and assortative. Various models such as the random graph model, block model, and ERGM can be used to analyse and explore the formation of a network and interaction between nodes. The review of the existing theories and models has found that, while these models are rather computationally intensive, they are based on some rather restrictive assumption on network formation and relationship between ports in the network at the local and global levels that require further investigation. Based on the review, a conceptual framework for maritime transport network research is developed, and the applications for future research are also discussed.

The increased complexity of water distribution networks (WDNs) emphasizes the importance of studying the relationship between topology and vulnerability of these networks. However, the few existing studies on this subject measure the vulnerability at a specific location and ignore to quantify the vulnerability as a whole. The purpose of this paper is to fill this gap by extending the topological vulnerability analysis further to the global level.

This paper introduces a two-step procedure. In the first step, this work evaluates the degree of influence of a node by employing graph theory quantities. In the second step, information entropy is used as a tool to quantify the global vulnerability of WDNs.

The vulnerability analysis results showed that a network with uniformly distributed centrality values exhibits a lower drop in performance in the case of partial failure of its components and therefore is less vulnerable. In other words, the failure of a highly central node leads to a significant loss of performance in the network.

The vulnerability analysis method, developed in this work, provides a decision support tool to implement a cost-effective maintenance strategy, which relies on identifying and prioritizing the vulnerabilities, thereby reducing expenditures on maintenance activities.

By situating the research in the entropy theory context, for the first time, this paper demonstrates how heterogeneity and homogeneity of centrality values measured by the information entropy can be interpreted in terms of the network vulnerability.

Search engine users are faced with long lists of search results, each entry being of a varying degree of relevance. Often users' expectations based on the short text of a search result hold false expectations about the linked web page. This leads users to skip relevant information, missing valuable insights, and click on irrelevant web pages wasting time. The purpose of this paper is to propose a new summary generation technique, ReClose, which combines query‐independent and query‐biased summary techniques to improve the accuracy of users' expectations.

The authors tested the effectiveness of ReClose summaries against Google summaries by surveying 34 participants. Participants were randomly assigned to use one type of summary approach. Summary effectiveness was judged based on the accuracy of each user's expectations.

It was found that individuals using ReClose summaries showed a 10 per cent increase in the expectation accuracy over individuals using Google summaries, and therefore better user satisfaction.

The survey demonstrates the effectiveness of using ReClose summaries to improve the accuracy of user expectations.

This paper presents a novel summary generation technique called ReClose, a new approach to summary evaluation and improvements upon previously proposed summary generation techniques.