Urbanity: automated modelling and analysis of multidimensional networks in cities

[1]  A. Heppenstall,et al.  Modeling agent decision and behavior in the light of data science and artificial intelligence , 2023, Environ. Model. Softw..

[2]  Andres Sevtsuk,et al.  Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery , 2023, Comput. Environ. Urban Syst..

[3]  F. Biljecki,et al.  Quality of crowdsourced geospatial building information: A global assessment of OpenStreetMap attributes , 2023, Building and Environment.

[4]  L. Kaack,et al.  EUBUCCO v0.1: European building stock characteristics in a common and open database for 200+ million individual buildings , 2023, Scientific Data.

[5]  B. Giles-Corti,et al.  Policy relevant health related liveability indicator datasets for addresses in Australia’s 21 largest cities , 2023, Scientific Data.

[6]  Y. Turgut,et al.  A framework proposal for machine learning-driven agent-based models through a case study analysis , 2022, Simul. Model. Pract. Theory.

[7]  F. Biljecki,et al.  A comprehensive framework for evaluating the quality of street view imagery , 2022, Int. J. Appl. Earth Obs. Geoinformation.

[8]  F. Biljecki,et al.  Incorporating networks in semantic understanding of streetscapes: Contextualising active mobility decisions , 2022, Environment and Planning B: Urban Analytics and City Science.

[9]  M. Batty.  Integrating space syntax with spatial interaction , 2022, Urban Informatics.

[10]  M. Batty,et al.  Prospective for urban informatics , 2022, Urban informatics.

[11]  F. Biljecki,et al.  Free and open source urbanism: Software for urban planning practice , 2022, Comput. Environ. Urban Syst..

[12]  B. Chen,et al.  Transit Voronoi diagrams in multi-mode public transport networks , 2022, Comput. Environ. Urban Syst..

[13]  M. Batty.  The conundrum of ‘form follows function’ , 2022, Environment and Planning B: Urban Analytics and City Science.

[14]  F. Biljecki,et al.  A review of spatially-explicit GeoAI applications in Urban Geography , 2022, Int. J. Appl. Earth Obs. Geoinformation.

[15]  F. Biljecki,et al.  Global Building Morphology Indicators , 2022, Comput. Environ. Urban Syst..

[16]  Sergio J. Rey,et al.  Open Source Software for Spatial Data Science , 2022, Geographical Analysis.

[17]  M. Timme,et al.  Demand-driven design of bicycle infrastructure networks for improved urban bikeability , 2022, Nature Computational Science.

[18]  Rui Cao,et al.  Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction , 2022, Comput. Environ. Urban Syst..

[19]  M. Adams,et al.  Using open data and open-source software to develop spatial indicators of urban design and transport features for achieving healthy and sustainable cities , 2022, The Lancet. Global health.

[20]  F. Chirigati.  Gauging urban development with neural networks , 2022, Nature Computational Science.

[21]  M. Brovelli,et al.  Assessing OSM building completeness using population data , 2022, Int. J. Geogr. Inf. Sci..

[22]  K. Lane,et al.  Impacts of the choice of distance measurement method on estimates of access to point-based resources , 2022, Journal of Exposure Science & Environmental Epidemiology.

[23]  M. Batty.  Mumford’s recurring challenge: What is a city? , 2022, Environment and Planning B: Urban Analytics and City Science.

[24]  C. Ratti,et al.  The effect of co-location on human communication networks , 2022, Nature Computational Science.

[25]  A. Schwing,et al.  Masked-attention Mask Transformer for Universal Image Segmentation , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Mei Qi Lim,et al.  Semantic 3D City Database — An enabler for a dynamic geospatial knowledge graph , 2021 .

[27]  K. Janowicz,et al.  A review of location encoding for GeoAI: methods and applications , 2021, Int. J. Geogr. Inf. Sci..

[28]  Alec Kirkley.  Spatial regionalization based on optimal information compression , 2021, Communications Physics.

[29]  H. V. Ribeiro,et al.  Commuting network effect on urban wealth scaling , 2021, Scientific Reports.

[30]  M. Karsai,et al.  Universal patterns of long-distance commuting and social assortativity in cities , 2021, Scientific Reports.

[31]  M. Felsberg,et al.  Graph Representation Learning for Road Type Classification , 2021, Pattern Recognit..

[32]  Alexander G. Schwing,et al.  Per-Pixel Classification is Not All You Need for Semantic Segmentation , 2021, NeurIPS.

[33]  Gareth Simons.  The cityseer Python package for pedestrian-scale network-based urban analysis , 2021, Environment and Planning B: Urban Analytics and City Science.

[34]  Sergio J. Rey,et al.  spaghetti: spatial network analysis in PySAL , 2021, J. Open Source Softw..

[35]  S. Dhakal,et al.  Future urban expansion and local climate zone changes in relation to land surface temperature: Case of Bangkok Metropolitan Administration, Thailand , 2021 .

[36]  Fahad Shahbaz Khan,et al.  Transformers in Vision: A Survey , 2021, ACM Comput. Surv..

[37]  Satish V. Ukkusuri,et al.  Quantifying spatial homogeneity of urban road networks via graph neural networks , 2021, ArXiv.

[38]  Geoff Boeing,et al.  GIS and Computational Notebooks , 2021, Geographic Information Science & Technology Body of Knowledge.

[39]  S. Lai.  Planning within Complex Urban Systems , 2020 .

[40]  M. Marselle,et al.  Urban street tree biodiversity and antidepressant prescriptions , 2020, Scientific Reports.

[41]  M. Batty,et al.  Smart cities, big data and urban policy: Towards urban analytics for the long run , 2020, Cities.

[42]  Pratyush Tripathy,et al.  An open-source tool to extract natural continuity and hierarchy of urban street networks , 2020, Environment and Planning B: Urban Analytics and City Science.

[43]  Belinda Yuen,et al.  The associations between older adults’ daily travel pattern and objective and perceived built environment: A study of three neighbourhoods in Singapore , 2020 .

[44]  M. San Miguel,et al.  Emergence of complex structures from nonlinear interactions and noise in coevolving networks , 2020, Scientific Reports.

[45]  M. Batty,et al.  Data-driven urban management: Mapping the landscape , 2019 .

[46]  Krzysztof Janowicz,et al.  GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond , 2019, Int. J. Geogr. Inf. Sci..

[47]  S. Dhakal,et al.  Physical and non-physical factors driving urban heat island: Case of Bangkok Metropolitan Administration, Thailand. , 2019, Journal of environmental management.

[48]  Riccardo Di Clemente,et al.  Inequality is rising where social network segregation interacts with urban topology , 2019, Nature Communications.

[49]  Mark Gahegan,et al.  Fourth paradigm GIScience? Prospects for automated discovery and explanation from data , 2019, Int. J. Geogr. Inf. Sci..

[50]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  Reconstructing commuters network using machine learning and urban indicators , 2019, Scientific Reports.

[51]  Camilla Perrone.  ‘Downtown Is for People’: The street-level approach in Jane Jacobs' legacy and its resonance in the planning debate within the complexity theory of cities , 2019, Cities.

[52]  Luis Álvarez-León,et al.  Every single street? Rethinking full coverage across street‐level imagery platforms , 2019, Trans. GIS.

[53]  Daniel Arribas-Bel,et al.  Geographic Data Science , 2019, Geographical Analysis.

[54]  Jan Eric Lenssen,et al.  Fast Graph Representation Learning with PyTorch Geometric , 2019, ArXiv.

[55]  George Grekousis,et al.  Artificial neural networks and deep learning in urban geography: A systematic review and meta-analysis , 2019, Comput. Environ. Urban Syst..

[56]  Dieter Pfoser,et al.  Using OpenStreetMap point-of-interest data to model urban change—A feasibility study , 2019, PloS one.

[57]  Martin Tomko,et al.  Street Network Studies: from Networks to Models and their Representations , 2018, Networks and Spatial Economics.

[58]  Achilleas Psyllidis,et al.  Urban Analytics , 2018, Int. J. Geogr. Inf. Sci..

[59]  Andres Sevtsuk,et al.  Patronage of urban commercial clusters: A network-based extension of the Huff model for balancing location and size , 2018 .

[60]  Xianming Liu,et al.  Mapping the world population one building at a time , 2017, ArXiv.

[61]  Stephen Glackin,et al.  Planning support systems for smart cities , 2017 .

[62]  M. Delgado-Rodríguez,et al.  Systematic review and meta-analysis. , 2017, Medicina intensiva.

[63]  Pietro Liò,et al.  Graph Attention Networks , 2017, ICLR.

[64]  Peter Kontschieder,et al.  The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[65]  Adam Millard-Ball,et al.  The world’s user-generated road map is more than 80% complete , 2017, PloS one.

[66]  M. Charlton,et al.  More bark than bytes? Reflections on 21+ years of geocomputation , 2017 .

[67]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[68]  Jure Leskovec,et al.  Inductive Representation Learning on Large Graphs , 2017, NIPS.

[69]  A. Daniere,et al.  The Dilemmas of Equity Planning in the Global South: A Comparative View from Bangkok and Medellín , 2017 .

[70]  Hartwig H. Hochmair,et al.  User Contribution Patterns and Completeness Evaluation of Mapillary, a Crowdsourced Street Level Photo Service , 2016, Trans. GIS.

[71]  Geoff Boeing,et al.  OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks , 2016, Comput. Environ. Urban Syst..

[72]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[73]  Sybil Derrible,et al.  A protocol to convert spatial polyline data to network formats and applications to world urban road networks , 2016, Scientific Data.

[74]  Shenghao Xu,et al.  Supplementary Information , 2014, States at War, Volume 3.

[75]  Sascha Ossowski,et al.  Dynamic coordination of ambulances for emergency medical assistance services , 2014, Knowl. Based Syst..

[76]  Pascal Neis,et al.  Quality assessment for building footprints data on OpenStreetMap , 2014, Int. J. Geogr. Inf. Sci..

[77]  Pascal Neis,et al.  Recent Developments and Future Trends in Volunteered Geographic Information Research: The Case of OpenStreetMap , 2014, Future Internet.

[78]  Marc Barthelemy,et al.  Self-organization versus top-down planning in the evolution of a city , 2013, Scientific Reports.

[79]  L. Bettencourt,et al.  Supplementary Materials for The Origins of Scaling in Cities , 2013 .

[80]  A. Sia,et al.  Perspectives on five decades of the urban greening of Singapore , 2013 .

[81]  R. Comunian.  Rethinking the Creative City , 2011 .

[82]  christian bason,et al.  Mapping the landscape , 2010 .

[83]  Marc Barthelemy,et al.  Spatial Networks , 2010, Encyclopedia of Social Network Analysis and Mining.

[84]  M. Haklay.  How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets , 2010 .

[85]  D. Reisman.  The Singapore Experience , 2010 .

[86]  Jure Leskovec,et al.  Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters , 2008, Internet Math..

[87]  Michael Batty,et al.  Cities and complexity - understanding cities with cellular automata, agent-based models, and fractals , 2007 .

[88]  Margot P. C. Weijnen,et al.  Innovation in networked infrastructures: coping with complexity , 2006, Int. J. Crit. Infrastructures.

[89]  M. Newman,et al.  Mixing patterns in networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[90]  Belinda Yuen.  Creating the Garden City: The Singapore Experience , 1996 .

[91]  H. Baxter Williams,et al.  A Survey , 1992 .

[92]  W. Tobler.  A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .

[93]  J. Jacobs.  The Death and Life of Great American Cities , 1962 .

[94]  J. Vanneste.  REPRINTS , 1962, The Lancet.

[95]  J. Plamenatz.  Interests , 1954 .

[96]  Paul Waddell,et al.  A Generalized Computational Framework for Accessibility : From the Pedestrian to the Metropolitan Scale , 2012 .

[97]  Michael Batty,et al.  WORKING PAPERS SERIES Cities as Complex Systems : Scaling , Interactions , Networks , Dynamics and Urban Morphologies , 2008 .

[98]  B. Strom,et al.  In clarification. , 2007, Pharmacoepidemiology and drug safety.

[99]  Atsuyuki Okabe,et al.  SANET: A Toolbox for Spatial Analysis on a Network , 2006 .

[100]  Alyson C. Flournoy,et al.  Coping with complexity , 2006, Nature.

[101]  Atsuyuki Okabe,et al.  A Toolbox for Spatial Analysis on a Network , 2005 .

[102]  W. Arveson.  Methods and Applications , 2002 .

[103]  A Turner,et al.  Depthmap: a program to perform visibility graph analysis , 2001 .

[104]  W. Whyte.  The social life of small urban spaces , 1980 .