Discovery Of Spatio-temporal Patterns Research Articles

Urban streets profiling with coupled spatio-temporal characteristics and topological information from the biking perspective

Urban street profiling is the spatio-temporal pattern discovery of street-level urban areas, which plays a vital role in understanding urban structures and dynamics. Due to the natural topology and various geographic characteristics on the streets, it is necessary to combine multi-dimensional spatio-temporal information to understand different profiles of streets. This research aims to develop a street profiling framework according to the coupled characteristics of streets. At the start, a bidirected dual graph and a spatial weighted graph embedding method were used to solve the street representation. Then, the street profiles can be extracted by clustering embedding vectors of streets and feature importance analysis. As the case study, we employed the bike trajectories and street view images in Xiamen, China to depict the geographic attributes of streets. The results can reveal nine spatio-temporal street profiles from the biking perspective, including three spatial distribution patterns and two spatial semantic patterns. Urban streets in the study area show a significant hierarchical pattern because of locations and the spatial lags of the biking behaviors. Meanwhile, the spatio-temporal characteristics of biking behaviors are the main factors of street profiles, though the street environment attributes participate in over half the number of profile types. We further evaluated the profiling ability of the proposed framework and the importance of urban street profiles using coupled characteristics. Overall, this study explored the profiling method for coupling static and dynamic characteristics of urban streets. The profiling results also help understand street usage and experiences by bikers, which have a practical value on the human-oriented classification of streets and further urban development from a geographic view.

A multivariate hierarchical regionalization method to discovering spatiotemporal patterns

ABSTRACT In GIScience, the regionalization method is widely used for geographical data mining, spatiotemporal pattern discovery, and regional studies. An ideal regionalization method should consider spatial contiguity, temporal contiguity, and attribute similarity. Existing regionalization approaches mostly focus on spatial contiguity and attribute similarity while ignoring the temporal contiguity characteristics of geographic phenomena. We propose a multivariate spatiotemporal regionalization (STR) method that considers spatiotemporal contiguity and attribute similarity. We design a bottom – up unsupervised multivariate hierarchical clustering algorithm with constraints using spatiotemporal proximity rules, enabling the automatic regionalization of spatiotemporal data. To test the performance of the STR method, we applied it to a synthetic dataset and a real-world dataset (Chinese air pollutant data) and achieved ideal results. Such a method offers a spatiotemporal perspective to address regionalization or clustering problems, potentially supporting other applications in spatiotemporal data analysis, remote sensing, urban planning, and social science.

Heuristic Search Based Feature Selection and Discretive Self-Organized Map Clustering for Spatio-Temporal Pattern Discovery

Spatio-temporal pattern discovery is an essential one in data mining for predictive analytics. Since it manages both space and time information depending on their characteristics and the preferred applications performances. The predictive analytics uses the Spatio-temporal features to discover future outcomes. The several works have been done in the Spatio-temporal pattern discovery. But the accurate pattern discovery is the major challenges. In order to improve the accurate pattern discovery, Heuristic Best-First Search based Discretized Self-Organizing Feature Map (HBFS-DSOFM) Model is introduced. The HBFS-DSOFM model comprises two processes namely, Spatio-temporal feature selection and clustering. Initially, the Heuristic Best-First Search Algorithm is used for selecting the relevant Spatio-temporal features from the large dataset for pattern discovery. Best-first search explores a decision tree for selecting the relevant Spatio-temporal features through the maximum information gain value. After that, the Spatio-temporal data are clustered with the selected features by using Discretized Self-Organizing Feature Mapping Algorithm for Spatio-temporal pattern discovery. In Discretized Self-Organizing Feature Mapping, input spatio-temporal data is connected to the prototype neurons through the synaptic weight. For the clustering process, weights of the neurons (i.e. cluster) are initialized with random values. After that, the Manhattan distance is used to compute the distance between the input vector and cluster weight value. The gradient descent is applied to discover closest distance. The cluster whose weight is closest to the input data is grouped into the particular cluster. Then the weight of the cluster is updated with the previous weight value for grouping the entire data. This clustering process gets iterated until it satisfies termination condition. Finally, the outputs of Spatio-temporal data are combined to form a spatio-temporal pattern for efficient predictive analytics. Experimental evaluation is carried out for El Nino Dataset and taxi trajectory dataset using the factors such as time complexity, clustering accuracy, and false positive rate. The results confirm that the proposed HBFS-DSOFM model increases the Spatio-temporal pattern discovery in terms of high clustering accuracy with a less false positive rate as well as minimum time complexity. Based on the clarification, HBFS-DSOFM model is more efficient than the state-of-the-art methods.

Discovery of spatio-temporal patterns from location-based social networks

Location-based social networks (LBSNs) such as Twitter or Instagram are a good source for user spatio-temporal behaviour. These networks collect data from users in such a way that they can be seen as a set of collective and distributed sensors of a geographical area. A low rate sampling of user's location information can be obtained during large intervals of time that can be used to discover complex patterns, including mobility profiles, points of interest or unusual events. These patterns can be used as the elements of a knowledge base for different applications in different domains such as mobility route planning, touristic recommendation systems or city planning. The aim of this paper is twofold, first to analyse the frequent spatio-temporal patterns that users share when living and visiting a city. This behaviour is studied by means of frequent itemsets algorithms in order to establish some associations among visits that can be interpreted as interesting routes or spatio-temporal connections. Second, to analyse how the spatio-temporal behaviour of a large number of users can be segmented in different profiles. These behavioural profiles are obtained by means of clustering algorithms that show the different patterns of behaviour of visitors and citizens. The data analysed were obtained from the public data feeds of Twitter and Instagram within an area surrounding the cities of Barcelona and Milan for a period of several months. The analysis of these data shows that these kinds of algorithms can be successfully applied to data from any city (or general area) to discover useful patterns that can be interpreted on terms of singular places and areas and their temporal relationships.

Lupa espacio-temporal: Una herramienta para el análisis visual de trayectorias en una bodega de datos

<p>En este artículo se presenta una herramienta visual para facilitar el análisis de trayectorias y el descubrimiento de patrones espacio-temporales a partir de una bodega de datos de trayectorias (BDT). La herramienta propuesta, una lupa espacio-temporal, permite que el analista se enfoque en una determinada región donde han ocurrido varias trayectorias y permite detectar, según ciertos parámetros especificados por el analista a través de una interfaz gráfica, p. ej. la relación de cercanía de una trayectoria con otras o con los sitios a su alrededor. En el artículo se proponen y definen formalmente las relaciones de cercanía derivadas entre trayectorias y entre trayectorias y sitios. Una contribución adicional fue el enriquecimiento de un modelo de una BDT con el fin de permitir la formulación de consultas más expresivas y apoyar el aspecto de visualización de la herramienta propuesta. Aunque se requieren experimentos más exhaustivos, los resultados evidenciaron algunos patrones espacio-temporales que demuestran la conveniencia y la utilidad de la herramienta.</p>

EXPLORATION OF MASSIVE CRIME DATA SETS THROUGH DATA MINING TECHNIQUES

We incorporate two data mining techniques, clustering and association-rule mining, into a fruitful exploratory tool for the discovery of spatio-temporal patterns in data-rich environments. This tool is an autonomous pattern detector that efficiently and effectively reveals plausible cause–effect associations among many geographical layers. We present two methods for exploratory analysis and detail algorithms to explore massive databases. We illustrate the algorithms with real crime data sets.