Identification and analysis of urban influential regions using spatial interaction networks

[1]  Michalis Vazirgiannis,et al.  Locating influential nodes in complex networks , 2016, Scientific Reports.

[2]  Min Wang,et al.  Mining Spatial-temporal Clusters from Geo-databases , 2006, ADMA.

[3]  P. Hall.  Looking Backward, Looking Forward: The City Region of the Mid-21st Century , 2009 .

[4]  Alexander Zipf,et al.  An exploration of the interaction between urban human activities and daily traffic conditions: A case study of Toronto, Canada , 2019, Cities.

[5]  Mark S. Granovetter.  The Strength of Weak Ties , 1973, American Journal of Sociology.

[6]  Mariano Sigman,et al.  A small world of weak ties provides optimal global integration of self-similar modules in functional brain networks , 2011, Proceedings of the National Academy of Sciences.

[7]  P. D. Straffing.  LINEAR ALGEBRA IN GEOGRAPHY, EIGENVECTORS OF NETWORKS , 1980 .

[8]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[9]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[10]  F. E. Grubbs.  Procedures for Detecting Outlying Observations in Samples , 1969 .

[11]  Lucas C Parra,et al.  Finding influential nodes for integration in brain networks using optimal percolation theory , 2018, Nature Communications.

[12]  Tao Jia,et al.  An empirical study on human mobility and its agent-based modeling , 2012 .

[13]  Tao Jia,et al.  Predicting Citywide Road Traffic Flow Using Deep Spatiotemporal Neural Networks , 2021, IEEE Transactions on Intelligent Transportation Systems.

[14]  C. Jones,et al.  Spatial economy and the geography of functional economic areas , 2017 .

[15]  V. Latora,et al.  Complex networks: Structure and dynamics , 2006 .

[16]  Bin Jiang,et al.  Zipf's law for all the natural cities in the United States: a geospatial perspective , 2010, Int. J. Geogr. Inf. Sci..

[17]  George Q. Huang,et al.  Efficiency and robustness of weighted air transport networks , 2019, Transportation Research Part E: Logistics and Transportation Review.

[18]  Bin Jiang,et al.  Exploring Human Activity Patterns Using Taxicab Static Points , 2012, ISPRS Int. J. Geo Inf..

[19]  Luciano da Fontoura Costa,et al.  The role of centrality for the identification of influential spreaders in complex networks , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  Zbigniew Smoreda,et al.  Delineating Geographical Regions with Networks of Human Interactions in an Extensive Set of Countries , 2013, PloS one.

[21]  Michael Batty,et al.  Multifractal to monofractal evolution of the London street network. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  Gert Sabidussi,et al.  The centrality index of a graph , 1966 .

[23]  Miroslav Marada,et al.  Delimitation of functional transport regions: understanding the transport flows patterns at the micro-regional level , 2017 .

[24]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[25]  Hernán A. Makse,et al.  Influence maximization in complex networks through optimal percolation , 2015, Nature.

[26]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[27]  Eric M. Delmelle,et al.  Visualizing the impact of space-time uncertainties on dengue fever patterns , 2014, Int. J. Geogr. Inf. Sci..

[28]  Haojie Zhu,et al.  A Spatio-Temporal Kernel Density Estimation Framework for Predictive Crime Hotspot Mapping and Evaluation , 2018, Applied Geography.

[29]  Xiang Li,et al.  Detecting and Analyzing Mobility Hotspots using Surface Networks , 2014, Trans. GIS.

[30]  W. Shi,et al.  Detecting the regional delineation from a network of social media user interactions with spatial constraint: A case study of Shenzhen, China , 2019, Physica A: Statistical Mechanics and its Applications.

[31]  S. Strogatz,et al.  Redrawing the Map of Great Britain from a Network of Human Interactions , 2010, PloS one.

[32]  Tomoki Nakaya,et al.  Visualising Crime Clusters in a Space‐time Cube: An Exploratory Data‐analysis Approach Using Space‐time Kernel Density Estimation and Scan Statistics , 2010, Trans. GIS.

[33]  Weiren Shi,et al.  Evaluating the importance of nodes in complex networks , 2016 .

[34]  T. Killingback,et al.  Attack Robustness and Centrality of Complex Networks , 2013, PloS one.

[35]  Shehroz S. Khan,et al.  Spatiotemporal clustering: a review , 2019, Artificial Intelligence Review.

[36]  E. Bullmore,et al.  Hubs of brain functional networks are radically reorganized in comatose patients , 2012, Proceedings of the National Academy of Sciences.

[37]  Albert-László Barabási,et al.  Error and attack tolerance of complex networks , 2000, Nature.

[38]  David S. Ebert,et al.  Proactive Spatiotemporal Resource Allocation and Predictive Visual Analytics for Community Policing and Law Enforcement , 2014, IEEE Transactions on Visualization and Computer Graphics.

[39]  Lev Muchnik,et al.  Identifying influential spreaders in complex networks , 2010, 1001.5285.

[40]  Lisa Tompson,et al.  The Utility of Hotspot Mapping for Predicting Spatial Patterns of Crime , 2008 .

[41]  Sanjay Garg,et al.  Development and validation of OPTICS based spatio-temporal clustering technique , 2016, Inf. Sci..