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Predicting Ambulance Demand: a Spatio-Temporal Kernel Approach [article]

Zhengyi Zhou, David S. Matteson
2015 arXiv   pre-print
We propose a predictive method using spatio-temporal kernel density estimation (stKDE) to address these challenges, and provide spatial density predictions for ambulance demand in Toronto, Canada as it  ...  This allows us to draw out the most helpful historical data, and exploit spatio-temporal patterns in the data for accurate and fast predictions.  ...  The proposed method provides efficient estimation of the weight function, and offers customizable prediction to balance the tradeoffs between accuracy and computational cost.  ... 
arXiv:1507.00364v1 fatcat:ppmbjcska5cktifpwepd27rq5q

Predicting Ambulance Demand

Zhengyi Zhou, David S. Matteson
2015 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15  
We constrain the weights to capture weekly seasonality, and apply autoregressive priors on them to model location-specific patterns. Second, we propose a spatio-temporal kernel density estimator.  ...  For each prediction we build a kernel density estimator on a sparse set of most similar data (labeled data), and warp these kernels to a larger set of past data regardless of labels (point cloud).  ...  We propose to predict f u using a spatio-temporal weighted kernel density estimator.  ... 
doi:10.1145/2783258.2788570 dblp:conf/kdd/ZhouM15 fatcat:gdr7q4m2ubghvkviyfm5p7htcy

Modeling human location data with mixtures of kernel densities

Moshe Lichman, Padhraic Smyth
2014 Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '14  
Our experimental results indicate that the mixture-KDE method provides a useful and accurate methodology for capturing and predicting individual-level spatial patterns in the presence of noisy and sparse  ...  We investigate the application of kernel density estimation (KDE) to this problem using a mixture model approach that can interpolate between an individual's data and broader patterns in the population  ...  Acknowledgements This work was supported by a gift from the University Affairs Committee at Xerox Corporation, by the National Science Foundation under award IIS-1320527, and by Office of Naval Research  ... 
doi:10.1145/2623330.2623681 dblp:conf/kdd/LichmanS14 fatcat:adhy4z7h6fap3dmavmsit5uari

Non-stationary spatio-temporal point process modeling for high-resolution COVID-19 data [article]

Zheng Dong, Shixiang Zhu, Yao Xie, Jorge Mateu, Francisco J. Rodríguez-Cortés
2021 arXiv   pre-print
This paper is motivated by analyzing a high-resolution COVID-19 dataset in Cali, Colombia, that provides every confirmed case's exact location and time information, offering vital insights for the spatio-temporal  ...  The numerical results on real data demonstrate good predictive performances of our method compared to the state-of-the-art as well as its interpretable findings.  ...  Acknowledgements The authors are grateful to the Municipal Public Health Secretary of Cali, Valle del Cauca, Colombia for providing the COVID-19 data used in this paper.  ... 
arXiv:2109.09029v2 fatcat:ambxqdtmpremvctyrjpcugsn5u

Personalized Check-in Prediction Model Based On User's Dissimilarity and Regression

Chang Su, Qiuli Zhou, Xianzhong Xie, Dezheng Wu
2019 IEEE Access  
Meanwhile, the Hidden Markov model(HMM) is used to determine users' next check-in location by using time series feature (week-hour) and location sequence.  ...  In addition, by improving the kernel density estimation, we propose a multi-level hybrid kernel density estimation model, which is built based on the individual, city and region layers, and smoothes the  ...  EVALUATION METRICS AND BASELINE METHODS 1) EVALUATION METRICS We can get a list of prediction probability for each user by the model, each location corresponds to a probability value.  ... 
doi:10.1109/access.2019.2923435 fatcat:gmajmxcgjrfgjoir2temcggv2u

Visual Analytics Law Enforcement Toolkit

Abish Malik, Ross Maciejewski, Timothy F. Collins, David Ebert
2010 2010 IEEE International Conference on Technologies for Homeland Security (HST)  
In the spatial domain, we have implemented a kernel density estimation mapping technique that creates a color map of spatially distributed CTC events that allows analysts to quickly find and identify areas  ...  In the temporal domain, reports can be aggregated by day, week, month or year, allowing the analysts to visualize the CTC activities spatially over a period of time.  ...  ACKNOWLEDGMENTS The authors would like to thank the VACCINE Public Safety Coalition which includes the Tippecanoe County Police, West Lafayette Police, Lafayette Police and the Purdue Police for providing  ... 
doi:10.1109/ths.2010.5655057 fatcat:o5thpacfljg2ng2fuqgn2yqxvu

Crime Hotspot Detection and Monitoring Using Video Based Event Modeling and Mapping Techniques

Zou Beiji, Nurudeen Mohammed, Zhu Chengzhang, Zhao Rongchang
2017 International Journal of Computational Intelligence Systems  
In video analysis, crime indicator events are modelled using statistical distribution of semantic concepts. In crime prediction, a neuro-fuzzy method is used to model indicator events.  ...  In crime mapping, kernel density estimation is used to detect crime hotspots. This approach is tested in a simulated platform using violent scene detection (VSD) 2014 dataset.  ...  Acknowledgement This work is supported by the National Natural Science Foundation of China No.61573380.  ... 
doi:10.2991/ijcis.2017.10.1.64 fatcat:vy44mlqpvrem7nauakjfhnklpm

Proactive Spatiotemporal Resource Allocation and Predictive Visual Analytics for Community Policing and Law Enforcement

Abish Malik, Ross Maciejewski, Sherry Towers, Sean McCullough, David S. Ebert
2014 IEEE Transactions on Visualization and Computer Graphics  
We also present a novel kernel density estimation technique we have developed, in which the prediction process is influenced by the spatial correlation of recent incidents at nearby locations.  ...  In our approach, we provide analysts with a suite of natural scale templates and methods that enable them to focus and drill down to appropriate geospatial and temporal resolution levels.  ...  We incorporate this concept in a novel kernel density estimation method described in Section 4.2.2, where the kernel value at a given location depends on the locations of its k-nearest incidents.  ... 
doi:10.1109/tvcg.2014.2346926 pmid:26356900 fatcat:ar4def5sajelvm376uci6od72i

A Visual Analytics Approach to Understanding Spatiotemporal Hotspots

R. Maciejewski, S. Rudolph, R. Hafen, A. Abusalah, M. Yakout, M. Ouzzani, W.S. Cleveland, S.J. Grannis, D.S. Ebert
2010 IEEE Transactions on Visualization and Computer Graphics  
To facilitate the exploration of such data, advanced tool sets are needed that allow the user to interact with their data in a visual environment that provides direct analytic capability for finding data  ...  In this paper, we present a suite of tools designed to facilitate the exploration of spatiotemporal datasets.  ...  ACKNOWLEDGMENTS The authors would like to thank the Purdue University Student Health Center, the Indiana State Department of Health, and the Police Department of West Lafayette -Indiana for providing the  ... 
doi:10.1109/tvcg.2009.100 pmid:20075482 fatcat:w43rmb3blfbu3eqwftxj5fl7ee

Detecting Disease Outbreaks Using Local Spatiotemporal Methods

Yingqi Zhao, Donglin Zeng, Amy H. Herring, Amy Ising, Anna Waller, David Richardson, Michael R. Kosorok
2011 Biometrics  
Both daily residuals and AR model-based de-trended residuals are used for detecting abnormalities in the data given that either a large daily residual or an increasing temporal trend in the residuals signals  ...  We develop a model with relatively weak assumptions regarding the latent processes generating the observed data, ensuring a robust prediction of the spatiotemporal incidence surface.  ...  Acknowledgments This research was funded in part by a Gillings Innovation Laboratory grant from the University of North Carolina Gillings School of Global Public Health, and Grant no.  ... 
doi:10.1111/j.1541-0420.2011.01585.x pmid:21418049 pmcid:PMC3698245 fatcat:yoz3iarnv5c3pg2ohmtno5fvva

Non-parametric smoothing of spatio-temporal point processes

Carlo Grillenzoni
2005 Journal of Statistical Planning and Inference  
A set of non-parametric tests, kernel density and regression estimators are proposed to study the space-time evolution of earthquakes.  ...  By this we mean changes in the spatial and temporal pattern of seismic occurrences.  ...  ARTICLE IN PRESS Density: Kernel density estimators can measure the intensity of a point process in space, namely the expected rate of occurrences per unit area.  ... 
doi:10.1016/j.jspi.2003.09.030 fatcat:woh4e7tsv5gx3h4ujj4zn6gixm

Criminal incident prediction using a point-pattern-based density model

Hua Liu, Donald E. Brown
2003 International Journal of Forecasting  
We use a point-pattern-based transition density model for space-time event prediction that relies on criminal preference discovery as observed in the features chosen for past crimes.  ...  Law enforcement agencies need crime forecasts to support their tactical operations; namely, predicted crime locations for next week based on data from the previous week.  ...  Taken together, In Section 3, we present a model of the transition 1 2 the locations, times, and features of all incidents are density as our approach for spatio-temporal event a realization of a marked  ... 
doi:10.1016/s0169-2070(03)00094-3 fatcat:6zt75zezarh7fj47xcyhewfcl4

Spatiotemporal Prediction of Ambulance Demand using Gaussian Process Regression [article]

Seth Nabarro, Tristan Fletcher, John Shawe-Taylor
2018 arXiv   pre-print
Here we present a novel method for ambulance demand prediction using Gaussian process regression (GPR) in time and geographic space.  ...  The method exhibits superior accuracy to MEDIC, a method which has been used in industry.  ...  Acknowledgements The authors would like to thank ER24 (South Africa's largest private emergency services operator) and Mediclinic, of which ER24 is a wholly owned subsidiary, for providing the data used  ... 
arXiv:1806.10873v1 fatcat:l5rmafyb4vavzeolixgtjobxfa

Large-Scale, Fine-Grained, Spatial, and Temporal Analysis, and Prediction of Mobile Phone Users' Distributions Based upon a Convolution Long Short-Term Model

Guangyuan Zhang, Xiaoping Rui, Stefan Poslad, Xianfeng Song, Yonglei Fan, Zixiang Ma
2019 Sensors  
Secondly, a convolutional long short-term memory (ConvLSTM) model was used in our study to predict mobile phone users' spatial and temporal distributions for the first time at such a fine-grained temporal  ...  The evaluation results show that the predicted people's mobility derived from the mobile phone users' density correlates much better with the actual density, both temporally and spatially, as compared  ...  Conflicts of Interest: The authors declare no conflicts of interest. Sensors 2019, 19, 2156  ... 
doi:10.3390/s19092156 fatcat:hs5ctcq6l5b3tjaejerlmbaoqy

Spatio-temporal multidimensional collective data analysis for providing comfortable living anytime and anywhere

Naonori Ueda, Futoshi Naya
2018 APSIPA Transactions on Signal and Information Processing  
We describe core technologies about smart data collection and spatio-temporal data analysis and prediction as well as a novel approach for real-time, proactive navigation in crowded environments such as  ...  In this paper, we discuss spatio-temporal multidimensional collective data analysis to create innovative services from such spatio-temporal data and describe the core technologies for the analysis.  ...  A set of kernel functions is trained using observed people-flow data for each time step for the spatial interpolation, and then the trained time-series kernel parameters are used for extrapolation.  ... 
doi:10.1017/atsip.2018.4 fatcat:5anb5fwe4vbnvkqtcgrmgdlika
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