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1.
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. 相似文献
2.
Seung-Hyun Jeong Norman W. Paton Alvaro A. A. Fernandes Tony Griffiths 《Transactions in GIS》2005,9(2):129-156
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. 相似文献
3.
Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting
Traffic forecasting is a challenging problem due to the complexity of jointly modeling spatio‐temporal dependencies at different scales. Recently, several hybrid deep learning models have been developed to capture such dependencies. These approaches typically utilize convolutional neural networks or graph neural networks (GNNs) to model spatial dependency and leverage recurrent neural networks (RNNs) to learn temporal dependency. However, RNNs are only able to capture sequential information in the time series, while being incapable of modeling their periodicity (e.g., weekly patterns). Moreover, RNNs are difficult to parallelize, making training and prediction less efficient. In this work we propose a novel deep learning architecture called Traffic Transformer to capture the continuity and periodicity of time series and to model spatial dependency. Our work takes inspiration from Google’s Transformer framework for machine translation. We conduct extensive experiments on two real‐world traffic data sets, and the results demonstrate that our model outperforms baseline models by a substantial margin. 相似文献
4.
Piotr Jankowski Natalia Andrienko Gennady Andrienko Slava Kisilevich 《Transactions in GIS》2010,14(6):833-852
This article presents a geovisual analytics approach to discovering people's preferences for landmarks and movement patterns from photos posted on the Flickr website. The approach combines an exploratory spatio‐temporal analysis of geographic coordinates and dates representing locations and time of taking photos with basic thematic information available through the Google Maps Web mapping service, and interpretation of the analyzed area. The article describes data aggregation and filtering techniques to reduce the size of the dataset and focuses on information addressing research questions. The results of analysis for the Seattle metropolitan area help to distinguish between sites that are occasionally popular among the photographers and can be considered as potential attractions from sites that are regularly visited and already known as city landmarks. The analysis of photographers' movements across the metropolitan area shows that most photographers' itineraries are short and highly localized. 相似文献
5.
Yan Shi Min Deng Jianya Gong Chang‐Tien Lu Xuexi Yang Huimin Liu 《Transactions in GIS》2019,23(2):312-333
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. 相似文献
6.
In the integrated framework presented in this article, a geographical information system serves as a shell to integrate spatial, dynamic and stochastic perspectives of biological invasions in a coherent workflow. A decision tree model stratifies the landscape with respect to spatial susceptibility and builds the spatial biophysical structure for the simulation of species invasion. A dynamic diffusion model evaluates the temporal changes of species to provide the dynamic parameters for the simulation. A simulation model derived from percolation theory simulates species invasion by combining spatial and dynamic information, and explicitly representing spatio‐temporal patterns of invasions within the GIS‐percolation environment. The simulation illustrates the great influence of spatial structure and connectivity of landscape on the diffusion of the species. 相似文献
7.
A Space‐Time Raster GIS Data Model for Spatiotemporal Analysis of Vegetation Responses to a Freeze Event 
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下载免费PDF全文 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. 相似文献
8.
Urban event detection with big data of taxi OD trips: A time series decomposition approach 下载免费PDF全文
下载免费PDF全文 Big urban mobility data, such as taxi trips, cell phone records, and geo‐social media check‐ins, offer great opportunities for analyzing the dynamics, events, and spatiotemporal trends of the urban social landscape. In this article, we present a new approach to the detection of urban events based on location‐specific time series decomposition and outlier detection. The approach first extracts long‐term temporal trends and seasonal periodicity patterns. Events are defined as anomalies that deviate significantly from the prediction with the discovered temporal patterns, i.e., trend and periodicity. Specifically, we adopt the STL approach, i.e., seasonal and trend decomposition using LOESS (locally weighted scatterplot smoothing), to decompose the time series for each location into three components: long‐term trend, seasonal periodicity, and the remainder. Events are extracted from the remainder component for each location with an outlier detection method. We analyze over a billion taxi trips for over seven years in Manhattan (New York City) to detect and map urban events at different temporal resolutions. Results show that the approach is effective and robust in detecting events and revealing urban dynamics with both holistic understandings and location‐specific interpretations. 相似文献
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10.
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. 相似文献
11.
Mining Association Rules in Spatio-Temporal Data: An Analysis of Urban Socioeconomic and Land Cover Change 总被引：8，自引：0，他引：8
This research demonstrates the application of association rule mining to spatio‐temporal data. Association rule mining seeks to discover associations among transactions encoded in a database. An association rule takes the form A→B where A (the antecedent) and B (the consequent) are sets of predicates. A spatio‐temporal association rule occurs when there is a spatio‐temporal relationship in the antecedent or consequent of the rule. As a case study, association rule mining is used to explore the spatial and temporal relationships among a set of variables that characterize socioeconomic and land cover change in the Denver, Colorado, USA region from 1970–1990. Geographic Information Systems (GIS)‐based data pre‐processing is used to integrate diverse data sets, extract spatio‐temporal relationships, classify numeric data into ordinal categories, and encode spatio‐temporal relationship data in tabular format for use by conventional (non‐spatio‐temporal) association rule mining software. Multiple level association rule mining is supported by the development of a hierarchical classification scheme (concept hierarchy) for each variable. Further research in spatio‐temporal association rule mining should address issues of data integration, data classification, the representation and calculation of spatial relationships, and strategies for finding ‘interesting’ rules. 相似文献
12.
Konstantin Greger 《Transactions in GIS》2015,19(1):129-150
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. 相似文献
13.
随着云计算技术的不断发展,大数据与信息化时代的优势越来越突出,应用越来越广泛。时空信息平台作为智慧城市建设的重要内容,管理海量基础地理信息数据,是智慧城市建设的基础。因此,海量数据的管理成为时空信息平台设计的关键。以智慧唐山建设为例,结合云计算技术,探讨时空信息平台数据库的构建,针对云平台基础地理信息数据体系、云平台数据库体系架构以及云平台数据库管理系统等方面进行设计,明确基于云计算时空信息平台数据库建设内容,探讨适用于智慧城市建设的时空信息云平台解决方案。 相似文献
14.
Zhixian Yan Jose Macedo Christine Parent Stefano Spaccapietra 《Transactions in GIS》2008,12(Z1):75-91
Many real world applications today are built on analyses of movement and related features. Examples of such applications include transportation management, urban planning, tourism services, and animal migration monitoring, among others. Recent database modeling and management research prototypes have the capability to store and manipulate movement data in terms of point or region geometries that evolve over time (moving point or moving and deforming region). This captures the spatio‐temporal trace left by a moving object, but ignores its links with non‐geometric information that enable a semantic interpretation of the movement of moving objects. The concept of trajectory has been introduced to express a more semantic understanding of movement, taking it closer to the perception of applications. This article describes a framework for a semantics‐oriented structuring, modeling and querying of trajectory data. The framework relies on the definition of trajectory‐related ontologies, addressing domain‐independent and application‐specific geometric and semantic facets. Last we briefly discuss how the proposed approach has been applied for a traffic management application. 相似文献
15.
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. 相似文献
16.
Knowing the locations and spatio‐temporal paths of individual mice is an essential prerequisite for behavioral analysis of large groups of laboratory mice. Traditional observations have been carried out by trained humans who are able to distinguish more than fifty behavioral patterns. This is a tedious labor limited with respect to length of observation time. In this paper we propose a tracking solution based on the integration of GIS and RFID (Radio Frequency Identification) technology to automatically collect 24h/7days of movement data. Appropriate cage design and antenna placement are discussed. A software solution is presented to facilitate the recording (JerryTS), visualization, and analysis (TOM) of mice movements. 相似文献
17.
For an effective interpretation of spatio‐temporal patterns of crime clusters/hotspots, we explore the possibility of three‐dimensional mapping of crime events in a space‐time cube with the aid of space‐time variants of kernel density estimation and scan statistics. Using the crime occurrence dataset of snatch‐and‐run offences in Kyoto City from 2003 to 2004, we confirm that the proposed methodology enables simultaneous visualisation of the geographical extent and duration of crime clusters, by which stable and transient space‐time crime clusters can be intuitively differentiated. Also, the combined use of the two statistical techniques revealed temporal inter‐cluster associations showing that transient clusters alternatively appeared in a pair of hotspot regions, suggesting a new type of “displacement” phenomenon of crime. Highlighting the complementary aspects of the two space‐time statistical approaches, we conclude that combining these approaches in a space‐time cube display is particularly valuable for a spatio‐temporal exploratory data analysis of clusters to extract new knowledge of crime epidemiology from a data set of space‐time crime events. 相似文献
18.
Spatio‐temporal prediction and forecasting of land surface temperature (LST) are relevant. However, several factors limit their usage, such as missing pixels, line drops, and cloud cover in satellite images. Being measured close to the Earth's surface, LST is mainly influenced by the land use/land cover (LULC) distribution of the terrain. This article presents a spatio‐temporal interpolation method which semantically models LULC information for the analysis of LST. The proposed spatio‐temporal semantic kriging (ST‐SemK) approach is presented in two variants: non‐separable ST‐SemK (ST‐SemKNSep) and separable ST‐SemK (ST‐SemKSep). Empirical studies have been carried out with derived Landsat 7 ETM+ satellite images of LST for two spatial regions: Kolkata, India and Dallas, Texas, U.S. It has been observed that semantically enhanced spatio‐temporal modeling by ST‐SemK yields more accurate prediction results than spatio‐temporal ordinary kriging and other existing methods. 相似文献
19.
Linwang Yuan Zhaoyuan Yu Shaofei Chen Wen Luo Yongjun Wang Guonian Lü 《Transactions in GIS》2010,14(Z1):59-83
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. 相似文献
20.
We used GIS‐based techniques and inductive‐spatial modelling to analyse spatial interactions between predator and prey populations, a unique example of an extension to an existing ecological approach. The approach identifies spatial predictors of predation and is demonstrated for braided riverbed habitat in the Mackenzie Basin, New Zealand. This habitat is highly modified and is the focus of intensive management because of the presence of introduced mammalian predators and their native prey (primarily nests of ground‐nesting native birds that are extremely vulnerable to predators). Native prey are currently in decline due to predation; GIS techniques therefore have the potential to augment conventional ecological techniques and management practices. Predation risk to nests of ground‐nesting birds was interpolated across an area using inverse distance weighting. A linear regression model for spatio‐temporal predictors of nest predation was deter‐mined using a predation risk surface and three separate surfaces representing predator abundance, prey abundance, and habitat structure. These measures can enhance the non‐spatial results from conventional ecological inquiry and help solve pest management problems affecting the natural environment. 相似文献