DOCSLIB.ORG

Understanding Functional Urban Centrality

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Understanding Functional Urban Centrality University College London UNDERSTANDING FUNCTIONAL URBAN CENTRALITY SPATIO-FUNCTIONAL INTERACTION AND ITS SOCIO- ECONOMIC IMPACT IN CENTRAL SHANGHAI A thesis submitted to the Bartlett Faculty of the Built Environment, University College London, in candidacy for the Degree of Doctorate of Philosophy Bartlett School of Architecture By Yao Shen London 2017 I, Yao Shen, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. 2017 To Liuchi and Simiao. ABSTRACT A deeper understanding of the structural characteristics of urban settings is a prerequisite to evaluating the effects of urban design and planning proposals more efficiently. This thesis aims at shaping a new, comprehensive approach to uncover the structure of cities through the investigation of a diachronic spatio-functional process and the socio-economic impacts of such a process. It proposes a spatial network-based framework, in which individual street segments, indexed by space syntax centrality measures, are utilised to develop a series of more complex urban function connectivity measures by an analysis of the spatial network and land-use patterns in tandem. The specific application of this approach in Central Shanghai is conducted with a threefold focus: firstly, to trace the evolutionary interdependence between the spatial grids and the land-use distribution; secondly, to explain the varying economic value of the spatio-functional relationship in the housing market; and thirdly, to capture the impact of the spatiol-functional interaction on the variation of co- presence. The outputs confirm that the centrality structures of the spatial network and the land-use distribution affect each other over time; however, certain degrees of inconsistency are observed, suggesting a distinct complementary relationship between these two systems, which is further validated by the improvement of the proposed model’s predictability of urban performance. The findings verify the hypothesis that urban spatio-functional synergy is a strong determinant of the formation of urban function regions, the delineation of housing submarkets, and the discrepancy of the spatial co-presence in the city. These results demonstrate that urban performance is directly affected by the way the spatial and functional structures of the city interact. Such findings support the proposition that understanding the complexities of the spatio-functional interaction in a morphological analysis can enhance the efficiency of urban design and planning interventions, which aim to improve socioeconomic conditions in cities. I CONTENT ABSTRACT I ACKNOWLEDGEMENT IX LIST OF TABLES X LIST OF FIGURES XIII CHAPTER 01 INTRODUCTION 1 1 The motivation 2 2 Research settings 5 2.1 Research aims 5 2.2 Research questions and hypothesis 8 2.3 Research scope 11 3 Structure of this thesis 13 CHAPTER 02 LITERATURE REVIEW 17 1 Introduction 18 2 Conceptualisation of centres 19 2.1 Recognition of centres 19 2.2 Nodality, flows and centrality 23 II 2.3 Dimensions and typologies of centrality/accessibility 30 3 Geographical centrality 34 3.1 Definitions of land-use systems 34 3.2 Interaction between land-use and transport systems 35 3.3 Typical measures and settings 37 3.4 Geographical accessibility as a socioeconomic indicator 40 3.5 Limitations 43 4 Configurational centrality 44 4.1 Definition of spatial configuration 44 4.2 Movement economy 47 4.3 Typical measures and settings 49 4.4 Configurational centrality as a socioeconomic indicator 50 4.5 Limitations 54 5 Combining geographical and configurational centralities 55 CHAPTER 03 THE METHODOLOGY 58 1 Introduction 59 2 The structure of research methodology 59 2.1 The research framework 59 2.2 Case study location 63 III 2.3 Data and method specification 65 3 Space syntax centrality– spatial accessibility measures 66 3.1 Basic models and concepts 66 3.2 Basic measures in the segmental model 68 3.3 Result examples and frequency statistics 69 4 Urban function connectivity – functional accessibility measures 72 4.1 Preliminary definition 72 4.2 The analytical procedures 75 4.3 Result examples and frequency statistics 83 5 Limitations 93 5.1 Other effects on the demand and supply sides 93 5.2 Distance decay 94 5.3 Bias of social media data 94 CHAPTER 04 URBAN EVOLUTION AS A SPATIO-FUNCTIONAL INTERACTION PROCESS 96 1 Introduction 97 2 Background 97 3 The framework and data 99 3.1 Research framework 99 3.2 Centrality computation 100 IV 3.3 Data 101 3.4 Identification of urban transformation in Central Shanghai 104 4 Empirical results 106 4.1 The spatial centrality process 106 4.2 The functional centrality process 114 4.3 The spatio-functional interaction process 124 5 Summary and discussion 131 CHAPTER 05 SPATIO-FUNCTIONAL INTERACTION AND HOUSE PRICE PATTERNS 134 1 Introduction 135 2 Background 135 3 The method 137 3.1 Research design 137 3.2 Indexing streets with spatial and functional context 138 3.3 Network-based mixed-scale hedonic model 140 3.4 Network-constrained clustering analysis 141 4 Study area, data and model specification 141 4.1 Study area 141 4.2 Asking house price data 142 V 4.3 Road network, POIs and social media check-in data 143 4.4 Variable selection 144 5 Empirical results 146 5.1 Preliminary findings 146 5.2 Regression results 150 5.3 Street-based submarket detection 154 5.4 Related implication 160 6 Summary and discussion 161 CHAPTER 06 SPATIO-FUNCTIONAL INTERACTION AND THE PATTERNS OF CO-PRESENCE 163 1 Introduction 164 2 Background 165 3 The method 166 3.1 The framework 166 3.2 Measuring physical co-presence in streets 167 3.3 Indexing the centralities of the spatial configuration 173 3.4 Regression analyses 173 4 The materials 174 4.1 Study area 174 4.2 Street network and checked-in POIs 174 VI 4.3 Trajectories in social media check-in data 175 4.4 Gate counts 177 5 Empirical results 177 5.1 Preliminary validation 177 5.2 Spatiotemporal co-presence patterns 179 5.3 The configurational logic of spatiotemporal co-presence 184 5.4 Modes of physical co-presence in streets 187 5.5 The configurational logic of co-presence modes 191 6 Summary and discussion 193 CHAPTER 07 DISCUSSION AND CONCLUSIONS 196 1 Introduction 197 2 Overview of findings for each chapter 198 3 A comprehensive spatio-functional interaction model 202 3.2 How does spatio-functional interaction effect urban performance? 202 3.3 How does spatial network interact with land-use system? 203 4 Potential contributions 207 4.1 Contributions to space syntax theory 207 4.2 Contributions to socioeconomic theory 208 4.3 Contributions to urban design and planning 209 VII 5 Limitations 210 5.1 Data availability 210 5.2 Generalisation for other cases 211 5.3 Variable structure and advanced models 211 6 Further research 212 6.1 Spatiotemporal urban function connectivity 212 6.2 Function visual graph analysis 213 6.3 Dynamic modelling of urban change 214 7 Closing words 216 REFERENCES 218 APPENDICES 248 VIII ACKNOWLEDGEMENTS This thesis is a summary of a long academic journey. It would not have been finished without the sincere help given by many gifted people. I own my first and deepest gratitude to my principle supervisor, Dr. Kayvan Karimi. Without his supervision and encouragement from the preliminary to the concluding stages, this research would not have been possible. His brilliant theoretical thoughts, immense advice, long-lasting patience through these years helped me grow the idea of this research and opened the door for my future academic life. I am grateful for the tremendous opportunities he provided to developing my teaching skills. My heartfelt thanks also go to my second supervisor, Professor Laura Vaughan, for her insightful comments on my research and academic writing, continuous encouragement and shared interests. My sincere thanks are also given to Tao Yang, Stephen Law, Chen Zhong, Ye Zhang, and Ying Long for their endless support on enhancing my study and their invaluable friendship in my daily life. My gratitude is extended to all of the talented staff and fellow students in Space Syntax Laboratory for their selfless sharing of experience. I am extremely grateful to the Chinese Scholarship Council for funding my doctoral research fully. This financial support reduced my family burden and enabled me to be a better pilot of my academic research. I would like to express my appreciation to Professor Michael Batty for offering me an opportunity to participate in an exciting project as a post-doctoral researcher and giving me much treasured supports. I am sincerely thankful to my family members for their warmth, love, support and sacrifice over years. I am deeply indebted to Liuchi and Simiao, my wife and son, my intimate partners in life, for staying by my side always. IX LIST OF TABLES CHAPTER 02 Table 2-1 39 Location-based accessibility measures (Geurs and Van Wee 2004) Table 2-2 50 Commonly used measures of space syntax centrality Table 2-3 56 Comparison between the spatial accessibility measures (geometric accessibility metrics) and the functional accessibility measures (geographical accessiblility metrics) in various aspects CHAPTER 03 Table 3-1 65 Data and method organisation in the analytical chapters Table 3-2 79 Classification of active land-uses in Central Shanghai according to the social media behaviour CHAPTER 04 Table 4-1 102 Historical POI classification and numbers in Central Shanghai Table 4-2 105 Descriptive statistics of stages in Central Shanghai’s urbanisation process Table
Load more

Recommended publications
  • Oracle Database 10G: Oracle Spatial Network Data Model
    Oracle Database 10g: Oracle Spatial Network Data Model An Oracle Technical White Paper May 2005 Oracle Spatial Network Data Model Table of Contents Introduction ....................................................................................................... 3 Design Goals and Architecture ....................................................................... 4 Major Steps Using the Network Data Model................................................ 5 Network Modeling........................................................................................ 5 Network Analysis.......................................................................................... 6 Network Modeling ............................................................................................ 6 Metadata and Schema ....................................................................................... 6 Network Java Representation and API.......................................................... 7 Network Creation Using SQL and PL/SQL ................................................ 8 Creating a Logical Network......................................................................... 9 Creating a Spatial Network.......................................................................... 9 Creating an SDO Geometry Network ................................................ 10 Creating an LRS Geometry Network.................................................. 10 Creating a Topology Geometry Network........................................... 11 Network Creation
    [Show full text]
  • A Network-Of-Networks Percolation Analysis of Cascading Failures in Spatially Co-Located Road-Sewer Infrastructure Networks
    Physica A 538 (2020) 122971 Contents lists available at ScienceDirect Physica A journal homepage: www.elsevier.com/locate/physa A network-of-networks percolation analysis of cascading failures in spatially co-located road-sewer infrastructure networks ∗ ∗ Shangjia Dong a, , Haizhong Wang a, , Alireza Mostafizi a, Xuan Song b a School of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97331, United States of America b Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China article info a b s t r a c t Article history: This paper presents a network-of-networks analysis framework of interdependent crit- Received 26 September 2018 ical infrastructure systems, with a focus on the co-located road-sewer network. The Received in revised form 28 May 2019 constructed interdependency considers two types of node dynamics: co-located and Available online 27 September 2019 multiple-to-one dependency, with different robustness metrics based on their function Keywords: logic. The objectives of this paper are twofold: (1) to characterize the impact of the Co-located road-sewer network interdependency on networks' robustness performance, and (2) to unveil the critical Network-of-networks percolation transition threshold of the interdependent road-sewer network. The results Percolation modeling show that (1) road and sewer networks are mutually interdependent and are vulnerable Infrastructure interdependency to the cascading failures initiated by sewer system disruption; (2) the network robust- Cascading failure ness decreases as the number of initial failure sources increases in the localized failure scenarios, but the rate declines as the number of failures increase; and (3) the sewer network contains two types of links: zero exposure and severe exposure to liquefaction, and therefore, it leads to a two-phase percolation transition subject to the probabilistic liquefaction-induced failures.
    [Show full text]
  • Planarity As a Driver of Spatial Network Structure
    DOI: http://dx.doi.org/10.7551/978-0-262-33027-5-ch075 Planarity as a driver of Spatial Network structure Garvin Haslett and Markus Brede Institute for Complex Systems Simulation, School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, United Kingdom [email protected] Downloaded from http://direct.mit.edu/isal/proceedings-pdf/ecal2015/27/423/1903775/978-0-262-33027-5-ch075.pdf by guest on 23 September 2021 Abstract include city science (Cardillo et al., 2006; Xie and Levin- son, 2007; Jiang, 2007; Barthelemy´ and Flammini, 2008; In this paper we introduce a new model of spatial network growth in which nodes are placed at randomly selected lo- Masucci et al., 2009; Chan et al., 2011; Courtat et al., 2011; cations in space over time, forming new connections to old Strano et al., 2012; Levinson, 2012; Rui et al., 2013; Gud- nodes subject to the constraint that edges do not cross. The mundsson and Mohajeri, 2013), electronic circuits (i Can- resulting network has a power law degree distribution, high cho et al., 2001; Bassett et al., 2010; Miralles et al., 2010; clustering and the small world property. We argue that these Tan et al., 2014), wireless networks (Huson and Sen, 1995; characteristics are a consequence of two features of our mech- anism, growth and planarity conservation. We further pro- Lotker and Peleg, 2010), leaf venation (Corson, 2010; Kat- pose that our model can be understood as a variant of ran- ifori et al., 2010), navigability (Kleinberg, 2000; Lee and dom Apollonian growth.
    [Show full text]
  • Arxiv:2006.02870V1 [Cs.SI] 4 Jun 2020
    The why, how, and when of representations for complex systems Leo Torres Ann S. Blevins [email protected] [email protected] Network Science Institute, Department of Bioengineering, Northeastern University University of Pennsylvania Danielle S. Bassett Tina Eliassi-Rad [email protected] [email protected] Department of Bioengineering, Network Science Institute and University of Pennsylvania Khoury College of Computer Sciences, Northeastern University June 5, 2020 arXiv:2006.02870v1 [cs.SI] 4 Jun 2020 1 Contents 1 Introduction 4 1.1 Definitions . .5 2 Dependencies by the system, for the system 6 2.1 Subset dependencies . .7 2.2 Temporal dependencies . .8 2.3 Spatial dependencies . 10 2.4 External sources of dependencies . 11 3 Formal representations of complex systems 12 3.1 Graphs . 13 3.2 Simplicial Complexes . 13 3.3 Hypergraphs . 15 3.4 Variations . 15 3.5 Encoding system dependencies . 18 4 Mathematical relationships between formalisms 21 5 Methods suitable for each representation 24 5.1 Methods for graphs . 24 5.2 Methods for simplicial complexes . 25 5.3 Methods for hypergraphs . 27 5.4 Methods and dependencies . 28 6 Examples 29 6.1 Coauthorship . 29 6.2 Email communications . 32 7 Applications 35 8 Discussion and Conclusion 36 9 Acknowledgments 38 10 Citation diversity statement 38 2 Abstract Complex systems thinking is applied to a wide variety of domains, from neuroscience to computer science and economics. The wide variety of implementations has resulted in two key challenges: the progenation of many domain-specific strategies that are seldom revisited or questioned, and the siloing of ideas within a domain due to inconsistency of complex systems language.
    [Show full text]
  • Query Processing in Spatial Network Databases
    Query Processing in Spatial Network Databases Dimitris Papadias Jun Zhang Department of Computer Science Department of Computer Science Hong Kong University of Science and Technology Hong Kong University of Science and Technology Clearwater Bay, Hong Kong Clearwater Bay, Hong Kong [email protected] [email protected] Nikos Mamoulis Yufei Tao Department of Computer Science and Information Systems Department of Computer Science University of Hong Kong City University of Hong Kong Pokfulam Road, Hong Kong Tat Chee Avenue, Hong Kong [email protected] [email protected] than their Euclidean distance. Previous work on spatial Abstract network databases (SNDB) is scarce and too restrictive Despite the importance of spatial networks in for emerging applications such as mobile computing and real-life applications, most of the spatial database location-based commerce. This necessitates the literature focuses on Euclidean spaces. In this development of novel and comprehensive query paper we propose an architecture that integrates processing methods for SNDB. network and Euclidean information, capturing Every conventional spatial query type (e.g., nearest pragmatic constraints. Based on this architecture, neighbors, range search, spatial joins and closest pairs) we develop a Euclidean restriction and a has a counterpart in SNDB. Consider, for instance, the network expansion framework that take road network of Figure 1.1, where the rectangles advantage of location and connectivity to correspond to hotels. If a user at location q poses the efficiently prune the search space. These range query "find the hotels within a 15km range", the frameworks are successfully applied to the most result will contain a, b and c (the numbers in the figure popular spatial queries, namely nearest correspond to network distance).
    [Show full text]
  • Urban Street Network Analysis in a Computational Notebook
    Urban Street Network Analysis in a Computational Notebook Geoff Boeing Department of Urban Planning and Spatial Analysis Sol Price School of Public Policy University of Southern California [email protected] Abstract: Computational notebooks offer researchers, practitioners, students, and educators the ability to interactively conduct analytics and disseminate re- producible workflows that weave together code, visuals, and narratives. This article explores the potential of computational notebooks in urban analytics and planning, demonstrating their utility through a case study of OSMnx and its tu- torials repository. OSMnx is a Python package for working with OpenStreetMap data and modeling, analyzing, and visualizing street networks anywhere in the world. Its official demos and tutorials are distributed as open-source Jupyter notebooks on GitHub. This article showcases this resource by documenting the repository and demonstrating OSMnx interactively through a synoptic tutorial adapted from the repository. It illustrates how to download urban data and model street networks for various study sites, compute network indicators, vi- sualize street centrality, calculate routes, and work with other spatial data such as building footprints and points of interest. Computational notebooks help in- troduce methods to new users and help researchers reach broader audiences in- terested in learning from, adapting, and remixing their work. Due to their utility and versatility, the ongoing adoption of computational notebooks in urban plan- ning, analytics, and related geocomputation disciplines should continue into the future.1 1 Introduction A traditional academic and professional divide has long existed between code creators and code users. The former would develop software tools and workflows for professional or research ap- plications, which the latter would then use to conduct analyses or answer scientific questions.
    [Show full text]
  • Experimenting with Spatial Networks to Save Time When Experimenting, It Can Be Useful to Create a Subset of the Airport Dataset
    Experimenting with Spatial Networks To save time when experimenting, it can be useful to create a subset of the airport dataset, for instance keeping only the airports of larger degree. Note also that when computing deterrence function, it is sometimes useful to work with random samples instead of computing distances between all pairs of nodes. This dataset is small enough however to compute all values in less than 1 minute on an average personal computer. To the best of my knowledge, there is no good library to work with spatial networks. networkx has functions to generate random spatial graphs (called geometric graphs https://networkx.org/ documentation/stable/reference/generators.html), but they are not designed to fit deterrence functions, and are not adapted to work with geographical coordinates. 1. Deterrence Function on the airport dataset When studying a network with spatial information, a first step to check if it can indeed be considered as a spatial network is to compute its deterrence function. (a) We will need to discretize distances using bins(similar to a histogram). Choose b bins, for instance every 500 km from 0km to 10,000km. Write a function which, provided a list of distances, return a list with b values, the number of values in each bin. A convenient way is to use numpy.digitize and collections.Counter . (b) Write a function which compute the distance between two positions on Earth in km. You can use function haversine from the package of the same name( pip install haversine ). To obtain standard latitude and longitude from the data in the airport dataset, you need to divide latitude and longitude values by 3600.
    [Show full text]
  • Osmnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks
    OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks Geoff Boeing Email: [email protected] Department of City and Regional Planning University of California, Berkeley May 2017 Abstract Urban scholars have studied street networks in various ways, but there are data availability and consistency limitations to the current urban planning/street network analysis literature. To address these challenges, this article presents OSMnx, a new tool to make the collection of data and creation and analysis of street networks simple, consistent, automatable and sound from the perspectives of graph theory, transportation, and urban design. OSMnx contributes five significant capabilities for researchers and practitioners: first, the automated downloading of political boundaries and building footprints; second, the tailored and automated downloading and constructing of street network data from OpenStreetMap; third, the algorithmic correction of network topology; fourth, the ability to save street networks to disk as shapefiles, GraphML, or SVG files; and fifth, the ability to analyze street networks, including calculating routes, projecting and visualizing networks, and calculating metric and topological measures. These measures include those common in urban design and transportation studies, as well as advanced measures of the structure and topology of the network. Finally, this article presents a simple case study using OSMnx to construct and analyze street networks in Portland, Oregon. This is a preprint. Download the published article at: http://geoffboeing.com/publications/osmnx-complex- street-networks/ Cite as: Boeing, G. 2017. “OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks.” Computers, Environment and Urban Systems 65, 126-139. doi:10.1016/j.compenvurbsys.2017.05.004 BOEING 1.
    [Show full text]
  • Spatial Science and Network Science: Review and Outcomes of a Complex Relationship César Ducruet, Laurent Beauguitte
    Spatial science and network science: Review and outcomes of a complex relationship César Ducruet, Laurent Beauguitte To cite this version: César Ducruet, Laurent Beauguitte. Spatial science and network science: Review and outcomes of a complex relationship. Networks and Spatial Economics, Springer Verlag, 2014, 14 (3-4), pp.297-316. 10.1007/s11067-013-9222-6. halshs-01093664 HAL Id: halshs-01093664 https://halshs.archives-ouvertes.fr/halshs-01093664 Submitted on 10 Dec 2014 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Spatial science and network science: Review and outcomes of a complex relationship DUCRUET César1 French National Centre for Scientific Research (CNRS) UMR 8054 Géographie-Cités, Paris, France Email: [email protected] BEAUGUITTE Laurent UMR 6266 IDEES, Rouen, France Email: [email protected] Pre-final version of the paper published in Networks and Spatial Economics, Vol. 14, Issue 3-4, pp. 297-316. Abstract For decades, the spatial approach to network analysis has principally focused on planar and technical networks from a classic graph theory perspective. Reference to models and methods developed by other disciplines on non-planar networks, such as sociology and physics, is recent, limited, and dispersed.
    [Show full text]
  • Improved Graph Neural Networks for Spatial Networks Using Structure-Aware Sampling
    International Journal of Geo-Information Article Improved Graph Neural Networks for Spatial Networks Using Structure-Aware Sampling Chidubem Iddianozie * and Gavin McArdle School of Computer Science, University College Dublin, Dublin 4, Ireland; [email protected] * Correspondence: [email protected] Received: 30 September 2020; Accepted: 11 November 2020; Published: 13 November 2020 Abstract: Graph Neural Networks (GNNs) have received wide acclaim in recent times due to their performance on inference tasks for unstructured data. Typically, GNNs operate by exploiting local structural information in graphs and disregarding their global structure. This is influenced by assumptions of homophily and unbiased class distributions. As a result, this could impede model performance on noisy real-world graphs such as spatial graphs where these assumptions may not be sufficiently held. In this article, we study the problem of graph learning on spatial graphs. Particularly, we focus on transductive learning methods for the imbalanced case. Given the nature of these graphs, we hypothesize that taking the global structure of the graph into account when aggregating local information would be beneficial especially with respect to generalisability. Thus, we propose a novel approach to training GNNs for these type of graphs. We achieve this through a sampling technique: Structure-Aware Sampling (SAS), which leverages the intra-class and global-geodesic distances between nodes. We model the problem as a node classification one for street networks with high variance between class sizes. We evaluate our approach using large real-world graphs against state-of-the-art methods. In the majority of cases, our approach outperforms traditional methods by up to a mean F1-score of 20%.
    [Show full text]
  • A Centrality Measure for Urban Networks Based on the Eigenvector Centrality Concept
    This is a repository copy of A Centrality Measure for Urban Networks Based on the Eigenvector Centrality Concept.. White Rose Research Online URL for this paper: https://eprints.whiterose.ac.uk/120372/ Version: Accepted Version Article: Agryzkov, Taras, Tortosa, Leandro, Vicent, Jose et al. (1 more author) (2017) A Centrality Measure for Urban Networks Based on the Eigenvector Centrality Concept. Environment and Planning B: Urban Analytics and City Science. ISSN 2399-8083 https://doi.org/10.1177/2399808317724444 Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ Journal Title A Centrality Measure for Urban XX(X):1–15 c The Author(s) 2015 Reprints and permission: Networks Based on the Eigenvector sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/ToBeAssigned Centrality Concept www.sagepub.com/ Taras Agryzkov1 Leandro Tortosa1 Jose´ F. Vicent1 and Richard Wilson2 Abstract A massive amount of information as geo-referenced data is now emerging from the digitization of contemporary cities.
    [Show full text]
  • A Geospatial Analysis Framework for Fine Scale Urban Infrastructure Networks
    The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-4, 2018 ISPRS TC IV Mid-term Symposium “3D Spatial Information Science – The Engine of Change”, 1–5 October 2018, Delft, The Netherlands A geospatial analysis framework for fine scale urban infrastructure networks Q. Ji1, *, S. Barr1, P. James1, D. Fairbairn1 1School of Engineering, Newcastle University, Newcastle upon Tyne, UK – (q.ji2, stuart.barr, phil.james, david.fairbairn)@ncl.ac.uk Commission IV, WG IV/7 KEY WORDS: Infrastructure Network, Fine Spatial Scale, Spatial Heuristics, Database System ABSTRACT: Understanding the spatial connectivity of urban infrastructure networks that connect assets to buildings is important for the fine-scale spatial analysis and modelling of the resource flows within cities. However, rarely are spatially explicit representations of infrastructure networks available for such analysis. Further, an appropriate database system is the core of development of an infrastructure assets information and management platform, capable of handling the wide range of data for infrastructure system modelling and analysis. In this paper, we develop a geospatial simulation and analysis framework, which is capable of generating fine-scale urban infrastructure networks and storing the network instances in a hybrid database system for further modelling and analysis needs. We demonstrate the use of this platform by simulating the entire-city electricity distribution network for the city of Newcastle upon Tyne. Validation
    [Show full text]