Spatiotemporal Object Database Approach to Dynamic Segmentation

Dynamic segmentation is commonly viewed as one of the most effective aspects of GIS for transportation applications. To date, much of this effort has focused on relational data models, with object data models receiving far less attention. This paper presents an object model that provides a natural representation of dynamically segmented features by extending the Object Database Management Group (ODMG) standard with a special mechanism, called parametric polymorphism. This mechanism supports the shifting of a conventional data type into a linear type to maintain knowledge about events (e.g. pavement condition, traffic volumes and traffic accidents) that change spatially along linear features. An associated object query language unavailable in current GIS packages is also provided for data analyses relevant to dynamic segmentation. The model and query language have been implemented using an object-oriented scripting language in a GIS environment.

In this paper, a multimodal video indexing and retrieval system, MMVIRS, is presented. MMVIRS models the auditory, visual, and textual sources of video collections from a semantic perspective. Besides multimodality, our model is constituted on semantic hierarchies that enable us to access the video from different semantic levels. MMVIRS has been implemented with data annotation, querying and browsing parts. In the annotation part, metadata information and video semantics are extracted in hierarchical ways. In the querying part, semantic queries, spatial queries, regional queries, spatio-temporal queries, and temporal queries have been processed over video collections using the proposed model. In the browsing parts, video collections are navigated using category information, visual and auditory hierarchies.

Summary 1. Linear features associated with transportation and riparian corridors are known to inhibit the mobility of birds and other wildlife, yet the factors contributing to their barrier effects are poorly understood. The diversity of roads in urban landscapes provides an opportunity for elucidating the relative importance of factors such as noise, traffic volume, gap width and adjacent vegetation on animal movement. 2. Using an avian mobbing call as a lure, we tested the willingness of forest songbirds to cross four types of linear features in the urban landscape of Calgary, Alberta, Canada: (1) roads of varying widths and traffic volumes, (2) conventional railways and light transit lines, (3) transportation bridges across riparian corridors, and (4) rivers. 3. Using mixed effects logistic regression, we found that the size of the gap in vegetation was the most important determinant of movement (P < 0·001). As the gap in vegetation exceeded 30 m, the likelihood of movement decreased dramatically and by 45 m, birds were only half as likely to move across gaps as they were to move an equivalent distance in continuous tree cover. Traffic volume also had a significant dampening effect on movement (odds ratio = 0·952 per 1000 vehicle per day increase; P < 0·001) and generally explained more variation in the data than noise levels. 4. Railroads proved to be the most permeable of the features we tested, probably owing to their relatively narrow width, which never exceeded 30 m. Surprisingly, rivers were less permeable than the anthropogenic linear features we tested, with a significant barrier effect evident even at widths <50 m. 5. The birds in our study showed a marked preference for flying over, rather than under, transportation bridges, particularly when adjacent vegetation was available. 6. Synthesis and applications. Our results suggest that linear features, both anthropogenic and natural, can significantly impede the movements of forest songbirds and that managing adjacent vegetation is a potentially effective way to mitigate these barrier effects in cities and other fragmented landscapes.

Roads and infrastructure strongly influenced on the environment. Therefore an effect of road network on hydrological conditions of watershed should be taken into account. Road networks have an effect on surface water flow and lead to direct and indirect changes of water circulation in the environment. Road networks appear to have increased contribution of impermeable areas and decreased infiltration and retention capabilities. This effect in increasing of flood waves peak and also expedite time to their occurrence. During storms or snow melting junctions of road and stream networks could be additional sources of water flowing directly from road surface or from ditch to the stream. In the paper we describe method which could help to find areas with high interaction between road networks and stream networks. Examined area of Kleszczewo community was divided into grid which consist of 92 square cells of 1 km side size. For each cell we described the interaction of road Inzynieria Ekologiczna

Social networks, such as Facebook, Instagram, and Twitter, provide intuitive ways to share a variety of information including geotagged multimedia data within users' communities of interest (COI) or publicly in real-time. Real-time geotagged multimedia data can provide semantics to conventional spatial queries in order to enrich the user navigation experience. In this paper, we introduce a mechanism to process spatio-temporal queries by leveraging geotagged multimedia data such as images, audio, video, and text, in order to add semantics to the conventional queries. Our framework collects, stores, and spatially tags multimedia data shared by users through social networks or through our developed mobile application. The system then uses such data in order to enhance the conventional routing services by resolving existing usability issues and by providing semantics to the routes in terms of enriched points of interest while taking dynamic road conditions into account. A proof of concept of the system will be demonstrated with the following spatio-temporal queries on road networks: 1) multimedia-enhanced shortest path queries; 2) multimedia-enhanced k-nearest neighbor queries; and 3) multimedia-enhanced range queries. Finally, a novel technique for finding lost individuals using geotagged multimedia data is also introduced. The results are tailor-made to the users' smartphone bandwidth and resolution requirements

This study analyzed traffic congestion scenarios in underground roads diverging and merging sections and underground sections that run parallel to ground roads using a microscopic traffic simulation model. The appropriate ratio of the ramp inflow or outflow of traffic volume to the entry traffic volume at the starting section of the underground road network was analyzed. Furthermore, the effect of traffic operation such as adequate throughput, average traffic speed, occupancy, and volume versus capacity (V/C) ratios before the occurrence of traffic congestion in all three road networks was properly produced. To maintain a minimum level of service of all underground sections at the D–E level, the underground inflow traffic volume should be equal to approximately 70% of the 4000 pcph inflow traffic volume from an open road to the underground. The results of this study are a useful reference for establishing traffic control application standards to minimize underground traffic congestion. They also provide useful insights for underground road design.

The primary objective of this study is to analyze the national and provincial road network in the city of Constantine, employing a range of quantitative methods commonly used in transportation geography. The aim is to assess the current state of the city's road network, its adequacy, efficiency, and suitability in meeting the needs of the population. This evaluation will be conducted in terms of accessibility, connectivity between network components, and efficiency as measured by curvature and traffic volume. While transportation geography offers a variety of quantitative measurement techniques, this research will focus on four primary methods: (1) Network analysis based on the curvature index: This method will assess the efficiency of the road network by evaluating the degree of curvature of the road segments, (2) Network analysis based on daily traffic volume: This method will examine the capacity of the road network by measuring the volume of traffic on each road segment, (3) Network analysis based on connectivity and road dispersion: This method will assess the level of connectivity between different parts of the network and the overall distribution of roads within the study area, (4) Network analysis based on traffic volume density: This method will evaluate the concentration of traffic on the road network.

In video databases, each video contains temporal and spatial relationships between content objects. One of the well-known video indexing strategies is the 3D C-string strategy. However, it cannot deal with the condition that an object appears and then disappears for more than one time. To solve this problem, in this paper, we propose an indexing strategy, called temporal UID Matrix1. Based on the original 13 spatial relationships proposed by 2D C-string and our three new spatial relationships, we can derive the temporal relationships from the sequence of spatial relationships. Therefore, in our proposed strategy, although we build only index for spatial relationships, and we still can answer the video queries, i.e., spatial, temporal, and spatio-temporal queries. From our simulation study, we show that our proposed strategy is more efficient for video searching than the 3D C-string strategy.

Millions of users share their experiences, thoughts, and interests online, through social media sites (e.g., Twitter, Flickr, YouTube). As a result, these sites host a substantial number of user-contributed documents (e.g., textual messages, photographs, videos) for a wide variety of events (e.g., concerts, political demonstrations, earthquakes). In this dissertation, we present techniques for leveraging the wealth of available social media documents to identify and characterize events of different types and scale. By automatically identifying and characterizing events and their associated user-contributed social media documents, we can ultimately offer substantial improvements in browsing and search quality for event content. To understand the types of events that exist in social media, we first characterize a large set of events using their associated social media documents. Specifically, we develop a taxonomy of events in social media, identify important dimensions along which they can be categorized, and determine the key distinguishing features that can be derived from their associated documents. We quantitatively examine the computed features for different categories of events, and establish that significant differences can be detected across categories. Importantly, we observe differences between events and other non-event content that exists in social media. We use these observations to inform our event identification techniques. To identify events in social media, we follow two possible scenarios. In one scenario, we do not have any information about the events that are re ected in the data. In this scenario, we use an online clustering framework to identify these unknown events and their associated social media documents. To distinguish between event and non-event content, we develop event classification techniques that rely on a rich family of aggregate cluster statistics, including temporal, social, topical, and platform-centric characteristics. In addition, to tailor the clustering framework to the social media domain, we develop similarity metric learning techniques for social media documents, exploiting the variety of document context features, both textual and non-textual. In our alternative event identification scenario, the events of interest are known, through user-contributed event aggregation platforms (e.g., Last.fm events, EventBrite, Facebook events). In this scenario, we can identify social media documents for the known events by exploiting known event features, such as the event title, venue, and time. While this event information is generally helpful and easy to collect, it is often noisy and ambiguous. To address this challenge, we develop query formulation strategies for retrieving event content on different social media sites. Specifically, we propose a two-step query formulation approach, with a first step that uses highly specific queries aimed at achieving high-precision results, and a second step that builds on these high-precision results, using term extraction and frequency analysis, with the goal of improving recall. Importantly, we demonstrate how event-related documents from one social media site can be used to enhance the identification of documents for the event on another social media site, thus contributing to the diversity of information that we identify. The number of social media documents that our techniques identify for each event is potentially large. To avoid overwhelming users with unmanageable volumes of event information, we design techniques for selecting a subset of documents from the total number of documents that we identify for each event. Specifically, we aim to select high-quality, relevant documents that re ect useful event information. For this content selection task, we experiment with several centrality-based techniques that consider the similarity of each event-related document to the central theme of its associated event and to other social media documents that correspond to the same event. We then evaluate both the relative and overall user satisfaction with the selected social media documents for each event. The existing tools to find and organize social media event content are extremely limited. This dissertation presents robust ways to organize and filter this noisy but powerful event information. With our event identification, characterization, and content selection techniques, we provide new opportunities for exploring and interacting with a diverse set of social media documents that re ect timely and revealing event content. Overall, the work presented in this dissertation provides an essential methodology for organizing social media documents that re ect event information, towards improved browsing and search for social media event data.

When an enterprise engages in electronic commerce (EC, also called e‐commerce), the Information Systems (IS) function must provide the organization with specialized communications and information processing capabilities for this method of commerce. These capabilities are reflected in the Information Technology (IT) infrastructure, applications, operations, and support staff, which are the responsibility of the IS function. This article defines e‐commerce types (B2C, B2B, B2G, C2C, G2C, and G2B), transactions (from the perspective of transaction phases such as pre‐purchase intentions, purchase consummation, and post‐purchase intentions), and applications (describe, publish, advertise, sell, and provide service), and also identifies e‐commerce management issues (from both the buyer's and seller's perspectives).

Spatiotemporal topological semantic information has attracted a lot of attention recently when exploring high‐level human movement features in geographic contexts. Although topological semantics between a trajectory and individual spatial objects have been studied extensively, few studies have focused on movements in the specific context of spatial infrastructures, such as road networks. This study proposes a topological trajectory model that integrates the topological semantics of trajectory data related to road networks and regions in a unified framework. The proposed model considers key points during a trajectory and establishes a topological intersection model to describe key points’ semantics. With the topological semantics of the trajectory–road network obtained, the road network’s semantic relation is derived directly from the topological relation between road networks and regions. This model can intuitively express the semantic relations of the three elements, trajectory, road network, and region, which helps spatial queries and analyses of network constrained movements. A case study of real traffic data shows that more detailed semantic information can be obtained based on this model.

A wireless sensor network consists of many sensors that collect and transmit physical or environmental conditions at different locations to a server continuously. Many researches mainly focus on processing continuous queries on real-time data stream. However, they do not concern the problem of storing the historical data, which is mandatory to the historical queries. In this paper, we propose a system architecture for handling and storing sensor data stream in real-time to support the spatial and/or temporal queries besides continuous queries. We exploit a segment-based method to store the sensor data stream and reduce the managed tuples without any loss of information, which lead to the improvement of the accuracy of query results. In addition, we offer a method to reduce the cost of join operations in processing spatiotemporal queries by filtering out the list of irrelevant sensors from the query range before making the join operation. We then present a design of the system architecture for processing spatial and/or temporal queries. Finally, we implement a climate monitoring application system based on our proposed system architecture.

In this paper, we propose a system architecture for handling and storing sensor data stream in real-time to support the spatial and/or temporal queries besides continuous queries. We exploit a segment-based method to store the sensor data stream and reduce the managed tuples without any loss of information, which lead to the improvement of the accuracy of query results. In addition, we offer a method to reduce the cost of join operations in processing spatiotemporal queries by filtering out the list of irrelevant sensors from the query range before making the join operation. We then present a design of the system architecture for processing spatial and/or temporal queries. Finally, we implement a climate monitoring application system.KeywordsWireless Sensor NetworkSensor Data StreamSpatiotemporal Data StreamContinuous QueryHistorical Query

Understanding the interplay between urban form, traffic volume, and air quality is significant for urban planning and environmental sustainability. However, limited progress has been made in bringing effective urban planning strategies to help control traffic demand and resulting air pollutants. Therefore, this study aims to investigate the interrelation between urban form, traffic volume, and air quality with a spatiotemporal stratified method. The method extracts and preprocesses traffic volume data in spatial (polluted and unpolluted zones) and temporal (periods in holidays and workdays) dimensions. Three decision tree models (random forest, random tree, M5 model tree) and two comparison models (multiple linear regression, artificial neural network) are used to examine the relationships. The final results show that the spatiotemporal stratification approach effectively reveals the interrelations, and the random forest model outperforms the other models. Specifically, highly aggregated roads and industrial areas are more associated with traffic volume in polluted zones. The dominance of waterway and vegetation shows a strong association with traffic volume in unpolluted zones. The degree of association also varies significantly between workdays and holidays. Our spatiotemporal stratified approach reveals heterogeneous relationships between urban form, traffic volume, and air quality and provides insightful references on sustainable urban development.

A steady growth in traffic volumes in industrialized countries with dense human populations is expected, especially on minor roads. As a consequence, the fragmentation of wildlife populations will increase dramatically. In human-dominated landscapes, typically minor roads occur in high densities, and animals encounter them frequently. Traffic calming is a new approach to mitigate negative impacts by reducing traffic volumes and speeds on minor roads at a regional scale. This leads to a distinction between roads with low volumes as being part of the traffic-calmed area, whereas roads with bundled traffic are located around this area. Within the traffic-calmed area, volumes and speeds can be decreased substantially; this is predicted to decrease the disturbance and mortality risk for animals. Thus far, data on the effects of traffic calming on wildlife population persistence remain scarce. Using metapopulation theory, we derived a model to estimate thresholds in the size of traffic-calmed areas and traffic volumes that may allow persistent populations. Our model suggests that traffic calming largely increases the persistence of roe deer in a landscape with a dense road network. Our modeling results show trade-offs between traffic volume on roads within the traffic-calmed area and both the area of habitat available for this species in the traffic-calmed area and the size of the traffic-calmed area. These results suggest ways to mitigate the fragmentation of wildlife habitat by road networks and their expected traffic volumes