Modeling and Anomaly Detection for Event Occurrences Following an Inhomogeneous Spatio-Temporal Poisson Process
نویسندگان
چکیده
This paper presents a model that describes spatial variations of the intensity of events that occur at random geographical locations. An inhomogeneous Poisson process is used to model the intensity over a spatial region with multiplicative spatial and temporal covariate effects. Dynamic temporal effects are incorporated into the model allowing changes in the intensity structure over time. Additionally, anomaly detection in the event rates is developed based on exceedance probabilities. The methods are demonstrated on data of major crimes in Cincinnati during 2006.
منابع مشابه
Assessment of Neonate's Congenital Hypothyroidism Pattern Using Poisson Spatio-temporal Model in Disease Mapping under the Bayesian Paradigm during 2011-18 in Guilan, Iran
Background: Congenital Hypothyroidism (CH) is one of the reasons for mental retardation and defective growth in neonates. It can be treated if it is diagnosed early. The congenital hypothyroidism can be diagnosed using newborn screening in the first days after birth. Disease mapping helps to identify high-risk areas of the disease. This study aimed to evaluate the pattern of CH using the Poisso...
متن کاملSpatial and Temporal Reasoning for Ambient Intelligence Systems
Spatio-temporal outliers are occurrences that can reveal significant information about the phenomena under investigation. They are detected after comparing their non-spatial attributes with their spatio-temporal neighbours. One of the important definitions that need to be made is the spatio-temporal neighbourhood of an instance. There can be no universally applicable definition of the spatio-te...
متن کاملAnomaly Detection via Local Coordinate Factorization and Spatio-Temporal Pyramid
Anomaly detection, which aims to discover anomalous events, defined as having a low likelihood of occurrence, from surveillance videos, has attracted increasing interest and is still a challenge in computer vision community. In this paper, we propose an efficient anomaly detection approach which can perform both real-time and multi-scale detection. Our approach can handle the change of backgrou...
متن کاملDiscovering Sequential Patterns in Event-Based Spatio-Temporal Data by Means of Microclustering - Extended Report
In the paper, we consider the problem of discovering sequential patterns from event-based spatio-temporal data. The problem is defined as follows: for a set of event types F and for a dataset of events instances D (where each instance in D denotes an occurrence of a particular event type in considered spatio-temporal space), discover all sequential patterns defining the following relation betwe...
متن کاملMotion based Event Analysis
Motion is an important cue in videos that captures the dynamics of moving objects. It helps in effective analysis of various event related tasks such as human action recognition, anomaly detection, tracking, crowd behavior analysis, traffic monitoring, etc. Generally, accurate motion information is computed using various optical flow estimation techniques. On the other hand, coarse motion infor...
متن کامل