613 research outputs found
The representation and management of evolving features in geospatial databases
Geographic features change over time, this change being the result of some
kind of event or occurrence. It has been a research challenge to represent
this data in a manner that reflects human perception. Most database systems used in geographic information systems (GIS) are relational, and change
is either captured by exhaustively storing all versions of data, or updates
replace previous versions. This stems from the inherent diffculty of modelling geographic objects in relational tables. This diffculty is compounded
when the necessary time dimension is introduced to model how those objects
evolve. There is little doubt that the object-oriented (OO) paradigm holds
signi cant advantages over the relational model when it comes to modelling
real-world entities and spatial data, and it is argued that this contention
is particularly true when it comes to spatio-temporal data. This thesis describes an object-oriented approach to the design of a conceptual model for representing spatio-temporal geographic data, called the Feature Evolution
Model (FEM), based on states and events. The model was used to implement a spatio-temporal database management system in Oracle Spatial, and
an interface prototype is described that was used to evaluate the system by
enabling querying and visualisation
An Exploratory Data Analysis Approach for Land Use-Transportation Interaction: The Design and Implementation of Transland Spatio-Temporal Data Model
Land use and transportation interaction is a complex and dynamic process. Many models have been used to study this interaction during the last several decades. Empirical studies suggest that land use and transportation patterns can be highly variable between geographic areas and at different spatial and temporal scales. Identifying these changes presents a major challenge. When we recognize that long-term changes could be affected by other factors such as population growth, economic development, and policy decisions, the challenge becomes even more overwhelming. Most existing land use and transportation interaction models are based on some prior theories and use mathematical or simulation approaches to study the problem. However, the literature also suggests that little consensus regarding the conclusions can be drawn from empirical studies that apply these models. There is a clear research need to develop alternative methods that will allow us to examine the land use and transportation patterns in more flexible ways and to help us identify potential improvements to the existing models.
This dissertation presents a spatio-temporal data model that offers exploratory data analysis capabilities to interactively examine the land use and transportation interaction at use-specified spatial and temporal scales. The spatio-temporal patterns and the summary statistics derived from this interactive exploratory analysis process can be used to help us evaluate the hypotheses and modify the structures used in the existing models. The results also can suggest additional analyses for a better understanding of land use and transportation interaction. This dissertation first introduces a conceptual framework for the spatio-temporal data model. Then, based on a systematic method for explorations of various data sets relevant to land use and transportation interaction, this dissertation details procedures of designing and implementing the spatio-temporal data model. Finally, the dissertation describes procedures of creating tools for generating the proposed spatio-temporal data model from existing snapshot GIS data sets and illustrate its use by means of exploratory data analysis.
Use of the spatio-temporal data model in this dissertation study makes it feasible to analyze spatio-temporal interaction patterns in a more effective and efficient way than the conventional snapshot GIS approach. Extending Sinton’s measurement framework into a spatio-temporal conceptual interaction framework, on the other hand, provides a systematic means of exploring land use and transportation interaction. Preliminary experiments of data collected for Dade County (Miami), Florida suggest that the spatio-temporal exploratory data analysis implemented for this dissertation can help transportation planners identify and visualize interaction patterns of land use and transportation by controlling the spatial, attribute, and temporal components. Although the identified interaction patterns do not necessarily lead to rules that can be applied to different areas, they do provide useful information for transportation modelers to re-evaluate the current model structure to validate the existing model parameter
Mobile objects and sensors within a video surveillance system: Spatio-temporal model and queries
International audienceThe videos recorded by video surveillance systems represent a key element in a police inquiry. Based on a spatio-temporal query specified by a victim, (e.g., the trajectory of the victim before and after the aggression) the human operators select the cameras that could contain relevant information and analyse the corresponding video contents. This task becomes cumbersome because of the huge volume of video contents and the cameras' mobility. This paper presents an approach, which assists the operator in his task and reduces the research space. We propose to model the cameras' network (fixed and mobile cameras) on top of the city's transportation network. We consider the video surveillance system as a multilayer geographic information system, where the cameras are situated into a distinct layer, which is added on top of the other layers (e.g., roads, transport) and is related to them by the location. The model is implemented in a spatio-temporal database. Our final goal is that based on a spatio-temporal query to automatically extract the list of cameras (fixed and mobile) concerned by the query. We propose to include this automatically computed relative position of the cameras as an extension of the standard ISO 22311
Мультиагентная система управления звуковой информацией
The multi-agent system for monitoring sound information (MAMS) in the environment is a set of agents for sound detection, analyzing, classification and transformation. The objective of this article is to propose a methodology from the detection of sound (specific agents) to the classification and decision making. In order to do the classification an agent must be implemented and regarding the situation it will generate a corresponding scenario. This agent will have as input, the sound, classification method, and the environment characteristics. Based on the information about environment the same sound can have different interpretation. MAMS implements the functions to ensure the required class of protection of people (working or living) and allows implementing an environmental safety system
Application of Geographic Information Systems
The importance of Geographic Information Systems (GIS) can hardly be overemphasized in today’s academic and professional arena. More professionals and academics have been using GIS than ever – urban & regional planners, civil engineers, geographers, spatial economists, sociologists, environmental scientists, criminal justice professionals, political scientists, and alike. As such, it is extremely important to understand the theories and applications of GIS in our teaching, professional work, and research. “The Application of Geographic Information Systems” presents research findings that explain GIS’s applications in different subfields of social sciences. With several case studies conducted in different parts of the world, the book blends together the theories of GIS and their practical implementations in different conditions. It deals with GIS’s application in the broad spectrum of geospatial analysis and modeling, water resources analysis, land use analysis, infrastructure network analysis like transportation and water distribution network, and such. The book is expected to be a useful source of knowledge to the users of GIS who envision its applications in their teaching and research. This easy-to-understand book is surely not the end in itself but a little contribution to toward our understanding of the rich and wonderful subject of GIS
Integration of a Contextual Observation System in a Multi-Process Architecture for Autonomous Vehicles
We propose a software layered architecture for autonomous vehicles whose efficiency is driven by pull-based acquisition of sensor data. This multiprocess software architecture, to be embedded into the control loop of these vehicles, includes a Belief-Desire-Intention agent that can consistently assist the achievement of intentions. Since driving on roads implies huge dynamic considerations, we tackle both reactivity and context awareness considerations on the execution loop of the vehicle. While the proposed architecture gradually offers 4 levels of reactivity, from arch-reflex to the deep modification of the previously built execution plan, the observation module concurrently exploits noise filtering and introduces frequency control to allow symbolic feature extraction while both fuzzy and first order logic management are used to enforce consistency and certainty over the context information properties. The presented use-case, the daily delivery of a network of pharmacy offices by an autonomous vehicle taking into account contextual (spatio-temporal) traffic features, shows the efficiency and the modularity of the architecture, as well as the scalability of the reaction levels
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