An adaptive method for clustering spatio‐temporal events

The clustering of spatio‐temporal events has become one of the most important research branches of spatio‐temporal data mining. However, the discovery of clusters of spatio‐temporal events with different shapes and densities remains a challenging problem because of the subjectivity in the choice of two critical parameters: the spatio‐temporal window for estimating the density around each event, and the density threshold for evaluating the significance of clusters. To make the clustering of spatio‐temporal events objective, in this study these two parameters were adaptively generated from statistical information about the dataset. More precisely, the density threshold was statistically modeled as an adjusted significance level controlled by the cardinality and support domain of the dataset, and the appropriate sizes of spatio‐temporal windows for clustering were determined by the spatio‐temporal classification entropy and stability analysis. Experiments on both simulated and earthquake datasets were conducted, and the results show that the proposed method can identify clusters of different shapes and densities.     

Constructing a Near Real‐time Space‐time Cube to Depict Urban Ambient Air Pollution Scenario

This study adopts a near real‐time space‐time cube approach to portray a dynamic urban air pollution scenario across space and time. Originating from time geography, space‐time cubes provide an approach to integrate spatial and temporal air pollution information into a 3D space. The base of the cube represents the variation of air pollution in a 2D geographical space while the height represents time. This way, the changes of pollution over time can be described by the different component layers of the cube from the base up. The diurnal ambient ozone (O₃) pollution in Houston, Texas is modeled in this study using the space‐time air pollution cube. Two methods, land use regression (LUR) modeling and spatial interpolation, were applied to build the hourly component layers for the air pollution cube. It was found that the LUR modeling performed better than the spatial interpolation in predicting air pollution level. With the availability of real‐time air pollution data, this approach can be extended to produce real‐time air pollution cube is for more accurate air pollution measurement across space and time, which can provide important support to studies in epidemiology, health geography, and environmental regulation.     

A Space‐Time Raster GIS Data Model for Spatiotemporal Analysis of Vegetation Responses to a Freeze Event

Many past space‐time GIS data models viewed the world mainly from a spatial perspective. They attached a time stamp to each state of an entity or the entire area of study. This approach is less efficient for certain spatio‐temporal analyses that focus on how locations change over time, which require researchers to view each location from a temporal perspective. In this article, we present a data model to organize multi‐temporal remote sensing datasets and track their changes at the individual pixel level. This data model can also integrate raster datasets from heterogeneous sources under a unified framework. The proposed data model consists of several object classes under a hierarchical structure. Each object class is associated with specific properties and behaviors to facilitate efficient spatio‐temporal analyses. We apply this data model to a case study of analyzing the impact of the 2007 freeze in Knoxville, Tennessee. The characteristics of different vegetation clusters before, during, and after the 2007 freeze event are compared. Our findings indicate that the majority of the study area is impacted by this freeze event, and different vegetation types show different response patterns to this freeze.     

A Hybrid Geocoding Methodology for Spatio‐Temporal Data

As tools for collecting data continue to evolve and improve, the information available for research is expanding rapidly. Increasingly, this information is of a spatio‐temporal nature, which enables tracking of phenomena through both space and time. Despite the increasing availability of spatio‐temporal data, however, the methods for processing and analyzing these data are lacking. Existing geocoding techniques are no exception. Geocoding enables the geographic location of people and events to be known and tracked. However, geocoded information is highly generalized and subject to various interpolation errors. In addition, geocoding for spatio‐temporal data is especially challenging because of the inherent dynamism of associated data. This article presents a methodology for geocoding spatio‐temporal data in ArcGIS that utilizes several additional supporting procedures to enhance spatial accuracy, including the use of supplementary land use information, aerial photographs and local knowledge. This hybrid methodology allows for the tracking of phenomenon through space and over time. It is also able to account for reporting inconsistencies, which is a common feature of spatio‐temporal data. The utility of this methodology is demonstrated using an application to spatio‐temporal address records for a highly mobile group of convicted felons in Hamilton County, Ohio.     

Detection of clusters in traffic networks based on spatio‐temporal flow modeling

Spatio‐temporal clustering is a highly active research topic and a challenging issue in spatio‐temporal data mining. Many spatio‐temporal clustering methods have been designed for geo‐referenced time series. Under some special circumstances, such as monitoring traffic flow on roads, existing methods cannot handle the temporally dynamic and spatially heterogeneous correlations among road segments when detecting clusters. Therefore, this article develops a spatio‐temporal flow‐based approach to detect clusters in traffic networks. First, a spatio‐temporal flow process is modeled by combining network topology relations with real‐time traffic status. On this basis, spatio‐temporal neighborhoods are captured by considering traffic time‐series similarity in spatio‐temporal flows. Spatio‐temporal clusters are further formed by successive connection of spatio‐temporal neighbors. Experiments on traffic time series of central London's road network on both weekdays and weekends are performed to demonstrate the effectiveness and practicality of the proposed method.     

Extraction of spatio‐temporal data about historical events from text documents

Often, we are faced with questions regarding past events and the answers are hidden in the historical text archives. The growing developments in geographic information retrieval and temporal information retrieval techniques have given new ways to explore digital text archives for spatio‐temporal data. The question is how to retrieve the answers from the text documents. This work contributes to a better understanding of spatio‐temporal information extraction from text documents. Natural language processing techniques were used to develop an information extraction approach using the GATE language processing software. The developed framework uses gazetteer matching, spatio‐temporal relationship extraction and pattern‐based rules to recognize and annotate elements in historical text documents. The extracted spatio‐temporal data is used as input for GIS studies on the time–geography context of the German–Herero resistance war of 1904 in Namibia. Related issues when analyzing the historical data in current GIS are discussed. Particularly problematic are movement data in small scale with poor temporal density and trajectories that are short or connect very distant locations.     

CAUSTA: Clifford Algebra‐based Unified Spatio‐Temporal Analysis

Introducing Clifford algebra as the mathematical foundation, a unified spatio‐temporal data model and hierarchical spatio‐temporal index are constructed by linking basic data objects, like pointclouds and Spatio‐Temporal Hyper Cubes of different dimensions, within the multivector structure of Clifford algebra. The transformation from geographic space into homogeneous and conformal space means that geometric, metric and many other kinds of operators of Clifford algebra can be implemented and we then design the shortest path, high‐dimensional Voronoi and unified spatial‐temporal process analyses with spacetime algebra. Tests with real world data suggest these traditional GIS analysis algorithms can be extended and constructed under Clifford Algebra framework, which can accommodate multiple dimensions. The prototype software system CAUSTA (Clifford Algebra based Unified Spatial‐Temporal Analysis) provides a useful tool for investigating and modeling the distribution characteristics and dynamic process of complex geographical phenomena under the unified spatio‐temporal structure.     

Spatio‐Temporal Building Population Estimation for Highly Urbanized Areas Using GIS

Detailed population information is crucial for the micro‐scale modeling and analysis of human behavior in urban areas. Since it is not available on the basis of individual persons, it has become necessary to derive data from aggregated census data. A variety of approaches have been published in the past, yet they are not entirely suitable for use in the micro‐scale context of highly urbanized areas, due mainly to their broad spatial scale and missing temporal scale. Here we introduce an enhanced approach for the spatio‐temporal estimation of building populations in highly urbanized areas. It builds upon other estimation methodologies, but extends them by introducing multiple usage categories and the temporal dimension. This allows for a more realistic representation of human activities in highly urbanized areas and the fact that populations change over time as a result of these activities. The model makes use of a variety of micro‐scale data sets to operationalize the activities and their spatio‐temporal representations. The outcome of the model provides estimated population figures for all buildings at each time step and thereby reveals spatio‐temporal behavior patterns. It can be used in a variety of applications concerning the implications of human behavior in urban areas.     

A UML‐based Representation of Spatio‐Temporal Evolution in Road Network Data

Geographic features change over time, this change being the result of some kind of event. Most database systems used in GIS are relational in nature, capturing change by exhaustively storing all versions of data, or updates replace previous versions. This stems from the inherent difficulty of modelling geographic objects and associated data in relational tables, and this is compounded when the necessary time dimension is introduced to represent how these objects evolve. This article describes an object‐oriented (OO) spatio‐temporal conceptual data model called the Feature Evolution Model (FEM), which can be used for the development of a spatio‐temporal database management system (STDBMS). Object versioning techniques developed in the fields of Computer Aided Design (CAD) and engineering design are utilized in the design. The model is defined using the Unified Modelling Language (UML), and exploits the expressiveness of OO technology by representing both geographic entities and events as objects. Further, the model overcomes the limitations inherent in relational approaches in representing aggregation of objects to form more complex, compound objects. A management object called the evolved feature maintains a temporally ordered list of references to features thus representing their evolution. The model is demonstrated by its application to road network data.     

Integrated compression of vehicle spatio‐temporal trajectories under the road stroke network constraint

With fast growth of all kinds of trajectory datasets, how to effectively manage the trajectory data of moving objects has received a lot of attention. This study proposes a spatio‐temporal data integrated compression method of vehicle trajectories based on stroke paths coding compression under the road stroke network constraint. The road stroke network is first constructed according to the principle of continuous coherence in Gestalt psychology, and then two types of Huffman tree—a road strokes Huffman tree and a stroke paths Huffman tree—are built, based respectively on the importance function of road strokes and vehicle visiting frequency of stroke paths. After the vehicle trajectories are map matched to the spatial paths in the road network, the Huffman codes of the road strokes and stroke paths are used to compress the trajectory spatial paths. An opening window algorithm is used to simplify the trajectory temporal data depicted on a time–distance polyline by setting the maximum allowable speed difference as the threshold. Through analysis of the relative spatio‐temporal relationship between the preceding and latter feature tracking points, the spatio‐temporal data of the feature tracking points are all converted to binary codes together, accordingly achieving integrated compression of trajectory spatio‐temporal data. A series of comparative experiments between the proposed method and representative state‐of‐the‐art methods are carried out on a real massive taxi trajectory dataset from five aspects, and the experimental results indicate that our method has the highest compression ratio. Meanwhile, this method also has favorable performance in other aspects: compression and decompression time overhead, storage space overhead, and historical dataset training time overhead.     

A Spatio‐Temporal Graph Model for Marine Dune Dynamics Analysis and Representation

Defining a model for the representation and the analysis of spatio‐temporal dynamics remains an open domain in geographical information sciences. In this article we investigate a spatio‐temporal graph‐based model dedicated to managing and extracting sets of geographical entities related in space and time. The approach is based on spatial and temporal local relations between neighboring entities during consecutive times. The model allows us to extract sets of connected entities distant in time and space over long periods and large spaces. From GIS concepts and qualitative reasoning on space and time, we combine the graph model with a dedicated spatial database. It includes information on geometry and geomorphometric parameters, and on spatial and temporal relations. This allows us to extend classical measurements of spatial parameters, with comparisons of entities linked by complex relations in space and time. As a case study, we show how the model suggests an efficient representation of dunes dynamics on a nautical chart for safe navigation.     

A spatial‐temporal‐semantic approach for detecting local events using geo‐social media data

Social media networks allow users to post what they are involved in with location information in a real‐time manner. It is therefore possible to collect large amounts of information related to local events from existing social networks. Mining this abundant information can feed users and organizations with situational awareness to make responsive plans for ongoing events. Despite the fact that a number of studies have been conducted to detect local events using social media data, the event content is not efficiently summarized and/or the correlation between abnormal neighboring regions is not investigated. This article presents a spatial‐temporal‐semantic approach to local event detection using geo‐social media data. Geographical regularities are first measured to extract spatio‐temporal outliers, of which the corresponding tweet content is automatically summarized using the topic modeling method. The correlation between outliers is subsequently examined by investigating their spatial adjacency and semantic similarity. A case study on the 2014 Toronto International Film Festival (TIFF) is conducted using Twitter data to evaluate our approach. This reveals that up to 87% of the events detected are correctly identified compared with the official TIFF schedule. This work is beneficial for authorities to keep track of urban dynamics and helps build smart cities by providing new ways of detecting what is happening in them.     

Discovering Spatial Patterns in Origin‐Destination Mobility Data

Mobility and spatial interaction data have become increasingly available due to the wide adoption of location‐aware technologies. Examples of mobility data include human daily activities, vehicle trajectories, and animal movements, among others. In this article we focus on a special type of mobility data, i.e. origin‐destination pairs, and present a new approach to the discovery and understanding of spatio‐temporal patterns in the movements. Specifically, to extract information from complex connections among a large number of point locations, the approach involves two steps: (1) spatial clustering of massive GPS points to recognize potentially meaningful places; and (2) extraction and mapping of the flow measures of clusters to understand the spatial distribution and temporal trends of movements. We present a case study with a large dataset of taxi trajectories in Shenzhen, China to demonstrate and evaluate the methodology. The contribution of the research is two‐fold. First, it presents a new methodology for detecting location patterns and spatial structures embedded in origin‐destination movements. Second, the approach is scalable to large data sets and can summarize massive data to facilitate pattern extraction and understanding.     

A Hybrid Spatio-Temporal Data Model and Structure (HST-DMS) for Efficient Storage and Retrieval of Land Use Information

While the incorporation of geographical and environmental modeling with GIS requires software support for storage and retrieval of spatial information that changes over time, it continues to be an unresolved issue with modern GIS software. Two complementary approaches have been used to manage the spatial and temporal heterogeneity within datasets that use a field‐based representation of the world. Some researchers have proposed new data models that partition space into discrete elements on an as‐needed basis following each temporal event, while others have focused on eliminating duplication of repeated data elements present in spatio‐temporal information. It is proposed in this paper that both approaches have merit and can be combined to create a Hybrid Spatio‐Temporal Data Model and Structure (HST‐DMS) that efficiently supports spatio‐temporal data storage and querying. Specifically, Peuquet and Duan's (1995) Event‐based Spatio‐Temporal Data Model (ESTDM) and the Overlapping R‐tree (Guttman 1984, Tzourmanis et al. 2000) are utilized to create a prototype used to store information about urban expansion for the town of Carbondale, Illinois.     

VGDI – Advancing the Concept: Volunteered Geo‐Dynamic Information and its Benefits for Population Dynamics Modeling

The concept of Volunteered Geographic Information (VGI) has progressed from being an exotic prospect to making a profound impact on GIScience and geography in general, as initially anticipated. However, while massive and manifold data is continuously produced voluntarily and applications are built for information and knowledge extraction, the initially introduced concept of VGI lacks certain methodological perspectives in this regard which have not been fully elaborated. In this article we highlight and discuss an important gap in this concept, i.e. the lack of formal acknowledgment of temporal aspects. By coining the proposed advanced framework ‘Volunteered Geo‐Dynamic Information’ (VGDI), we attempt to lay the ground for full conceptual and applied spatio‐temporal integration. To illustrate that integrative approach of VGDI and its benefits, we describe the potential impact on the field of dynamic population distribution modeling. While traditional approaches in that domain rely on survey‐based data and statistics as well as static geographic information, the use of VGDI enables a dynamic setup. Foursquare venue and user check‐in data are presented for a test site in Lisbon, Portugal. Two core modules of spatio‐temporal population assessment are thereby addressed, namely time use profiling and target zone characterization, motivated by the potential integration in existing population dynamics frameworks such as the DynaPop model.     

Heterogeneous Space–Time Artificial Neural Networks for Space–Time Series Prediction

Space–time series prediction plays a key role in the domain of geographic data mining and knowledge discovery. In general, the existing methods of space–time series prediction can be divided into two main categories: statistical machine learning methods. Comparatively, machine leaning methods have obvious advantages with respect to handling nonlinear problems. However, space–time dependence and the heterogeneity of space–time data are not well addressed by the existing machine learning methods. Because of this limitation, an accurate prediction of a space–time series is still a challenging problem. Therefore, in this study, both space–time dependence and heterogeneity are incorporated into the feedback artificial neural network, and heterogeneous space–time artificial neural networks (HSTANNs) are developed for space–time series prediction. First, to handle spatial heterogeneity, space–time series clustering is used to divide the study area into a set of homogeneous sub‐areas. Then, a space–time autocorrelation analysis is employed to explore the space–time dependence structure of the dataset. Finally, a HSTANN is established for each sub‐area. Further, HSTANNs are applied to predict the concentrations of fine particulate matter (PM_2.5) in Beijing–Tianjin–Hebei. The experimental results show that when compared with other methods, the accuracy of the forecasting results is considerably improved by using HSTANNs.     

An Experimental Performance Evaluation of Spatio-Temporal Join Strategies

Many applications capture, or make use of, spatial data that changes over time. This requirement for effective and efficient spatio‐temporal data management has given rise to a range of research activities relating to spatio‐temporal data management. Such work has sought to understand, for example, the requirements of different categories of application, and the modelling facilities that are most effective for these applications. However, at present, there are few systems with fully integrated support for spatio‐temporal data, and thus developers must often construct custom solutions for their applications. Developers of both bespoke solutions and of generic spatio‐temporal platforms will often need to support the fusion of large spatio‐temporal data sets. Supporting such requests in a database setting involves the use of join operations with both spatial and temporal conditions – spatio‐temporal joins. However, there has been little work to date on spatio‐temporal join algorithms or their evaluation. This paper presents an evaluation of several approaches to the implementation of spatio‐temporal joins that build upon widely available indexing techniques. The evaluation explores how several algorithms perform for databases with different spatial and temporal characteristics, with a view to helping developers of generic infrastructures or custom solutions in the selection and development of appropriate spatio‐temporal join strategies.     

The Effect of Data Inaccuracy on Tests of Space‐Time Interaction

Space‐time event data are often subject to deficiencies in: (1) locational accuracy; (2), temporal accuracy; and (3) completeness. This work explores how these failings in the quality of input data may affect the results of global space‐time interaction tests. While previous work has partially investigated the impact of locational inaccuracy on the results of these tests, more work remains. The impacts of temporal inaccuracy and incomplete data reporting on the results of these tests remain completely unexplored. This study examines the influence of these problems individually and collectively, using a series of simulations. Findings demonstrate that even in cases of slight inaccuracy or underreporting, the consequences on results are potentially severe. Although the study is couched in terms of data inaccuracy, its relevance to situations where inaccuracy is replaced with uncertainty is self‐evident.