Graph deep learning model for network-based predictive hotspot mapping of sparse spatio-temporal events

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

[2]  Bani K. Mallick,et al.  ROADWAY TRAFFIC CRASH MAPPING: A SPACE-TIME MODELING APPROACH , 2003 .

[3]  Ken Pease,et al.  Prospective hot-spotting - The future of crime mapping? , 2004 .

[4]  B. Schölkopf,et al.  A Regularization Framework for Learning from Graph Data , 2004, ICML 2004.

[5]  Allyson M. Abrams,et al.  A model-adjusted space–time scan statistic with an application to syndromic surveillance , 2005, Epidemiology and Infection.

[6]  David Vere-Jones,et al.  Some models and procedures for space-time point processes , 2009, Environmental and Ecological Statistics.

[7]  Jun Yan,et al.  Kernel Density Estimation of traffic accidents in a network space , 2008, Comput. Environ. Urban Syst..

[8]  Elizabeth L. Wilmer,et al.  Markov Chains and Mixing Times , 2008 .

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

[10]  Toshiro Tango,et al.  International Journal of Health Geographics a Flexibly Shaped Space-time Scan Statistic for Disease Outbreak Detection and Monitoring , 2022 .

[11]  J. Ratcliffe Crime Mapping: Spatial and Temporal Challenges , 2010 .

[12]  David M. Hureau,et al.  The Relevance of Micro Places to Citywide Robbery Trends: A Longitudinal Analysis of Robbery Incidents at Street Corners and Block Faces in Boston , 2011 .

[13]  George E. Tita,et al.  Self-Exciting Point Process Modeling of Crime , 2011 .

[14]  R. Valentini,et al.  Predicting hot-spots of land use changes in Italy by ensemble forecasting , 2011 .

[15]  Gail M. Sullivan,et al.  Using Effect Size-or Why the P Value Is Not Enough. , 2012, Journal of graduate medical education.

[16]  Brian J. Reich,et al.  Evaluating temporally weighted kernel density methods for predicting the next event location in a series , 2012, Ann. GIS.

[17]  Berk Anbaroglu,et al.  Spatio-temporal clustering for non-recurrent traffic congestion detection on urban road networks , 2013 .

[18]  Pascal Frossard,et al.  The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains , 2012, IEEE Signal Processing Magazine.

[19]  Jiaqiu Wang,et al.  A Dynamic Spatial Weight Matrix and Localized Space–Time Autoregressive Integrated Moving Average for Network Modeling , 2014 .

[20]  Wenhao Huang,et al.  Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning , 2014, IEEE Transactions on Intelligent Transportation Systems.

[21]  M. D. Ugarte,et al.  On fitting spatio-temporal disease mapping models using approximate Bayesian inference , 2014, Statistical methods in medical research.

[22]  Narushige Shiode,et al.  Microscale Prediction of Near‐Future Crime Concentrations with Street‐Level Geosurveillance , 2014 .

[23]  George Mohler,et al.  Marked point process hotspot maps for homicide and gun crime prediction in Chicago , 2014 .

[24]  T. Cheng,et al.  Modifiable Temporal Unit Problem (MTUP) and Its Effect on Space-Time Cluster Detection , 2014, PloS one.

[25]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[26]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[27]  Elio Marchione,et al.  Event Networks and the Identification of Crime Pattern Motifs , 2015, PloS one.

[28]  Joan Bruna,et al.  Deep Convolutional Networks on Graph-Structured Data , 2015, ArXiv.

[29]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[30]  Tao Cheng,et al.  Novel evaluation metrics for sparse spatio-temporal point process hotspot predictions - a crime case study , 2016, Int. J. Geogr. Inf. Sci..

[31]  Shang-Hua Teng,et al.  Scalable Algorithms for Data and Network Analysis , 2016, Found. Trends Theor. Comput. Sci..

[32]  T. Poggio,et al.  Deep vs. shallow networks : An approximation theory perspective , 2016, ArXiv.

[33]  Xavier Bresson,et al.  Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.

[34]  Qingquan Li,et al.  A network Kernel Density Estimation for linear features in space–time analysis of big trace data , 2016, Int. J. Geogr. Inf. Sci..

[35]  Yann Dauphin,et al.  Predicting distributions with Linearizing Belief Networks , 2016, ICLR.

[36]  Bartosz Krawczyk,et al.  Learning from imbalanced data: open challenges and future directions , 2016, Progress in Artificial Intelligence.

[37]  Duo Zhang,et al.  Deep Learning for Real Time Crime Forecasting , 2017, ArXiv.

[38]  Xiqun Chen,et al.  Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach , 2017, ArXiv.

[39]  M. Brauer,et al.  High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data. , 2017, Environmental science & technology.

[40]  Mo Adepeju Modelling of sparse spatio-temporal point process (STPP) - An application in predictive policing , 2017 .

[41]  Tao Cheng,et al.  Developing an online cooperative police patrol routing strategy , 2017, Comput. Environ. Urban Syst..

[42]  Shane D. Johnson,et al.  Predictive Crime Mapping: Arbitrary Grids or Street Networks? , 2016, Journal of Quantitative Criminology.

[43]  Di Zhu,et al.  Street as a big geo-data assembly and analysis unit in urban studies: A case study using Beijing taxi data , 2017 .

[44]  Daniel J. Graham,et al.  Road traffic accident prediction modelling: a literature review , 2017 .

[45]  Jinjun Xiong,et al.  Large-scale short-term urban taxi demand forecasting using deep learning , 2018, 2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC).

[46]  M. Leitner,et al.  Laws of Geography , 2018 .

[47]  Zhanxing Zhu,et al.  Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting , 2017, IJCAI.

[48]  Cyrus Shahabi,et al.  Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting , 2017, ICLR.

[49]  Liang-Chih Yu,et al.  Grid-Based Crime Prediction Using Geographical Features , 2018, ISPRS Int. J. Geo Inf..

[50]  Qingchao Liu,et al.  Short‐Term Traffic Speed Forecasting Based on Attention Convolutional Neural Network for Arterials , 2018, Comput. Aided Civ. Infrastructure Eng..

[51]  Sheng Wu,et al.  Short-term traffic forecasting: An adaptive ST-KNN model that considers spatial heterogeneity , 2018, Comput. Environ. Urban Syst..

[52]  Jack Xin,et al.  Deep Learning for Real-Time Crime Forecasting and Its Ternarization , 2017, Chinese Annals of Mathematics, Series B.

[53]  Tao Cheng,et al.  Improving the Robustness and Accuracy of Crime Prediction with the Self-Exciting Point Process Through Isotropic Triggering , 2016, Applied Spatial Analysis and Policy.

[54]  Yang Zhang,et al.  Deep spatio-temporal residual neural networks for road-network-based data modeling , 2019, Int. J. Geogr. Inf. Sci..

[55]  Tao Cheng,et al.  A graph deep learning method for short‐term traffic forecasting on large road networks , 2019, Comput. Aided Civ. Infrastructure Eng..

[56]  Xinyue Ye,et al.  Designing efficient and balanced police patrol districts on an urban street network , 2018, Int. J. Geogr. Inf. Sci..

[57]  Ge Chen,et al.  A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes , 2019, Int. J. Geogr. Inf. Sci..