Adapted K-Nearest Neighbors for Detecting Anomalies on Spatio–Temporal Traffic Flow

Outlier detection is an extensive research area, which has been intensively studied in several domains such as biological sciences, medical diagnosis, surveillance, and traffic anomaly detection. This paper explores advances in the outlier detection area by finding anomalies in spatio-temporal urban traffic flow. It proposes a new approach by considering the distribution of the flows in a given time interval. The flow distribution probability (FDP) databases are first constructed from the traffic flows by considering both spatial and temporal information. The outlier detection mechanism is then applied to the coming flow distribution probabilities, the inliers are stored to enrich the FDP databases, while the outliers are excluded from the FDP databases. Moreover, a k-nearest neighbor for distance-based outlier detection is investigated and adopted for FDP outlier detection. To validate the proposed framework, real data from Odense traffic flow case are evaluated at ten locations. The results reveal that the proposed framework is able to detect the real distribution of flow outliers. Another experiment has been carried out on Beijing data, the results show that our approach outperforms the baseline algorithms for high-urban traffic flow

Monitoring land use changes using geo-information : possibilities, methods and adapted techniques

Monitoring land use with geographical databases is widely used in decision-making. This report presents the possibilities, methods and adapted techniques using geo-information in monitoring land use changes. The municipality of Soest was chosen as study area and three national land use databases, viz. Top10Vector, CBS land use statistics and LGN, were used. The restrictions of geo-information for monitoring land use changes are indicated. New methods and adapted techniques improve the monitoring result considerably. Providers of geo-information, however, should coordinate on update frequencies, semantic content and spatial resolution to allow better possibilities of monitoring land use by combining data sets

Moving Object Trajectories Meta-Model And Spatio-Temporal Queries

In this paper, a general moving object trajectories framework is put forward to allow independent applications processing trajectories data benefit from a high level of interoperability, information sharing as well as an efficient answer for a wide range of complex trajectory queries. Our proposed meta-model is based on ontology and event approach, incorporates existing presentations of trajectory and integrates new patterns like space-time path to describe activities in geographical space-time. We introduce recursive Region of Interest concepts and deal mobile objects trajectories with diverse spatio-temporal sampling protocols and different sensors available that traditional data model alone are incapable for this purpose.Comment: International Journal of Database Management Systems (IJDMS) Vol.4, No.2, April 201

Inferring Unusual Crowd Events From Mobile Phone Call Detail Records

The pervasiveness and availability of mobile phone data offer the opportunity of discovering usable knowledge about crowd behaviors in urban environments. Cities can leverage such knowledge in order to provide better services (e.g., public transport planning, optimized resource allocation) and safer cities. Call Detail Record (CDR) data represents a practical data source to detect and monitor unusual events considering the high level of mobile phone penetration, compared with GPS equipped and open devices. In this paper, we provide a methodology that is able to detect unusual events from CDR data that typically has low accuracy in terms of space and time resolution. Moreover, we introduce a concept of unusual event that involves a large amount of people who expose an unusual mobility behavior. Our careful consideration of the issues that come from coarse-grained CDR data ultimately leads to a completely general framework that can detect unusual crowd events from CDR data effectively and efficiently. Through extensive experiments on real-world CDR data for a large city in Africa, we demonstrate that our method can detect unusual events with 16% higher recall and over 10 times higher precision, compared to state-of-the-art methods. We implement a visual analytics prototype system to help end users analyze detected unusual crowd events to best suit different application scenarios. To the best of our knowledge, this is the first work on the detection of unusual events from CDR data with considerations of its temporal and spatial sparseness and distinction between user unusual activities and daily routines.Comment: 18 pages, 6 figure

Visual mining of moving flock patterns in large spatio-temporal data sets using a frequent pattern approach

The popularity of tracking devices continues to contribute to increasing volumes of spatio-temporal data about moving objects. Current approaches in analysing these data are unable to capture collective behaviour and correlations among moving objects. An example of these types of patterns is moving flocks. This article develops an improved algorithm for mining such patterns following a frequent pattern discovery approach, a well-known task in traditional data mining. It uses transaction-based data representation of trajectories to generate a database that facilitates the application of scalable and efficient frequent pattern mining algorithms. Results were compared with an existing method (Basic Flock Evaluation or BFE) and are demonstrated for both synthetic and real data sets with a large number of trajectories. The results illustrate a significant performance increase. Furthermore, the improved algorithm has been embedded into a visual environment that allows manipulation of input parameters and interactive recomputation of the resulting flocks. To illustrate the visual environment a data set containing 30 years of tropical cyclone tracks with 6 hourly observations is used. The example illustrates how the visual environment facilitates exploration and verification of flocks by changing the input parameters and instantly showing the spatio-temporal distribution of the resulting flocks in the Space-Time Cube and interactively selecting

A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets

The term "outlier" can generally be defined as an observation that is significantly different from the other values in a data set. The outliers may be instances of error or indicate events. The task of outlier detection aims at identifying such outliers in order to improve the analysis of data and further discover interesting and useful knowledge about unusual events within numerous applications domains. In this paper, we report on contemporary unsupervised outlier detection techniques for multiple types of data sets and provide a comprehensive taxonomy framework and two decision trees to select the most suitable technique based on data set. Furthermore, we highlight the advantages, disadvantages and performance issues of each class of outlier detection techniques under this taxonomy framework