Representation Of Spatial Data Research Articles

A development of spatiotemporal queries to analyze the simulation outcomes from a voxel automata model

The raster and vector spatial data models are the most commonly used in geographic information systems (GIS) practice but are insufficient for the representation of dynamic spatiotemporal phenomena that operates in multiple dimensions. Although numerous improvements to the spatial data models have been proposed and various prototype implementations have been developed in order to address this limitation, the problem has persisted. One of the proposals for spatial data representation is the geoatom data model which is a theoretical concept used for conceptualizing geographic information as it pertains to the four-dimensional (4D) space-time continuum. The objective of this study is to develop and apply the spatiotemporal queries in order to analyze and explore the evolution of the 4D geospatial process. The geo-atom data model and voxel automata have been used for the simulation of a dynamic 4D process of snow cover retreat in order to test the developed theoretical concepts. The developed queries for spatio-temporal analysis are volume, surface area, rate of change and temporal ordering. The data used have spatial, temporal and attribute components and represent the voxels units generated from the simulation of the process. Obtained results are the outcomes of various spatio-temporal queries that permit the analysis of the snow retreat process in 4D. This study contributes to conceptual and applied advancements in the field of 4D GIS and multidimensional analysis, and is relevant to geography, earth and environmental sciences, the disciplines where the phenomena need to be studied in 4D.

APPROACH TO CONSTRUCTING 3D VIRTUAL SCENE OF IRRIGATION AREA USING MULTI-SOURCE DATA

Abstract. For an irrigation area that is often complicated by various 3D artificial ground features and natural environment, disadvantages of traditional 2D GIS in spatial data representation, management, query, analysis and visualization is becoming more and more evident. Building a more realistic 3D virtual scene is thus especially urgent for irrigation area managers and decision makers, so that they can carry out various irrigational operations lively and intuitively. Based on previous researchers' achievements, a simple, practical and cost-effective approach was proposed in this study, by adopting3D geographic information system (3D GIS), remote sensing (RS) technology. Based on multi-source data such as Google Earth (GE) high-resolution remote sensing image, ASTER G-DEM, hydrological facility maps and so on, 3D terrain model and ground feature models were created interactively. Both of the models were then rendered with texture data and integrated under ArcGIS platform. A vivid, realistic 3D virtual scene of irrigation area that has a good visual effect and possesses primary GIS functions about data query and analysis was constructed.Yet, there is still a long way to go for establishing a true 3D GIS for the irrigation are: issues of this study were deeply discussed and future research direction was pointed out in the end of the paper.

Influence of volunteer and project characteristics on data quality of biological surveys.

Volunteer involvement in biological surveys is becoming common in conservation and ecology, prompting questions on the quality of data collected in such surveys. In a systematic review of the peer-reviewed literature on the quality of data collected by volunteers, we examined the characteristics of volunteers (e.g., age, prior knowledge) and projects (e.g., systematic vs. opportunistic monitoring schemes) that affect data quality with regards to standardization of sampling, accuracy and precision of data collection, spatial and temporal representation of data, and sample size. Most studies (70%, n = 71) focused on the act of data collection. The majority of assessments of volunteer characteristics (58%, n = 93) examined the effect of prior knowledge and experience on quality of the data collected, often by comparing volunteers with experts or professionals, who were usually assumed to collect higher quality data. However, when both groups' data were compared with the same accuracy standard, professional data were more accurate in only 4 of 7 cases. The few studies that measured precision of volunteer and professional data did not conclusively show that professional data were less variable than volunteer data. To improve data quality, studies recommended changes to survey protocols, volunteer training, statistical analyses, and project structure (e.g., volunteer recruitment and retention).

A Multi-Scale Fuzzy Spatial Analysis Framework for Large Data Based on IT2 FS

The geographical world is an intricate system that comprises the interaction of the Earth's atmosphere, hydrosphere, biosphere, lithosphere, and pedosphere. Existing technologies and systems can only store, represent, and analyze crisp or type-I fuzzy spatial data and obtain spatial knowledge on several discrete scales. However, these technologies are limited to multi-scale and high-order vagueness spatial data representation and analysis, particularly regarding the representation and acquisition of multi-scale knowledge. In this paper, the uncertainty in geographic information systems (GISs) and existing problems in classical spatial analysis methods are summarized. Innovative concepts, such as the scale aggregation model and scale polymorphism, are proposed. A multi-scale fuzzy spatial analysis framework based on an interval type-II fuzzy set is introduced, and critical points are highlighted, such as an interval type-II fuzzy geographical object model (the boundary model and metric methods for geometric properties), direction relations, topological relations, and overlap methods. An actual case based on a multi-scale regional debris-flow hazard assessment is used to confirm the validity of the theory proposed in this paper.

Constrained Best Euclidean Distance Embedding on a Sphere: A Matrix Optimization Approach

The problem of data representation on a sphere of unknown radius arises from various disciplines such as statistics (spatial data representation), psychology (constrained multidimensional scaling), and computer science (machine learning and pattern recognition). The best representation often needs to minimize a distance function of the data on a sphere as well as to satisfy some Euclidean distance constraints. It is those spherical and Euclidean distance constraints that present an enormous challenge to the existing algorithms. In this paper, we reformulate the problem as an Euclidean distance matrix optimization problem with a low rank constraint. We then propose an iterative algorithm that uses a quadratically convergent Newton-CG method at each step. We study fundamental issues including constraint nondegeneracy and the nonsingularity of generalized Jacobian that ensure the quadratic convergence of the Newton method. We use some classic examples from the spherical multidimensional scaling to demonstrat...

A Review On Accuracy and Uncertainty of Spatial Data and Analyses with special reference to Urban and Hydrological Modelling

Abstract. Data quality for GIS processing and analysis is becoming an increased concern due to the accelerated application of GIS technology for problem solving and decision making roles. Uncertainty in the geographic representation of the real world arises as these representations are incomplete. Identification of the sources of these uncertainties and the ways in which they operate in GIS based representations become crucial in any spatial data representation and geospatial analysis applied to any field of application. This paper reviews the articles on the various components of spatial data quality and various uncertainties inherent in them and special focus is paid to two fields of application such as Urban Simulation and Hydrological Modelling. Urban growth is a complicated process involving the spatio-temporal changes of all socio-economic and physical components at different scales. Cellular Automata (CA) model is one of the simulation models, which randomly selects potential cells for urbanisation and the transition rules evaluate the properties of the cell and its neighbour. Uncertainty arising from CA modelling is assessed mainly using sensitivity analysis including Monte Carlo simulation method. Likewise, the importance of hydrological uncertainty analysis has been emphasized in recent years and there is an urgent need to incorporate uncertainty estimation into water resources assessment procedures. The Soil and Water Assessment Tool (SWAT) is a continuous time watershed model to evaluate various impacts of land use management and climate on hydrology and water quality. Hydrological model uncertainties using SWAT model are dealt primarily by Generalized Likelihood Uncertainty Estimation (GLUE) method.

A Conceptual Modeling of spatio-temporal database to Estimate Runoff Changes in Urbanized Watersheds

Abstract. This paper presents the development of a conceptual model of a database that allows the monitoring of changes in watersheds over time and verifies the impact of these changes on runoff. The conceptual model was developed using ER modeling techniques. ER diagrams were constructed from the semantic analysis of the variables involved in the issue of runoff in watersheds using the Curve Number method of Natural Resource Conservation Service. The conceptual model was developed based on the concepts of states and events, and the use of time as a basis for organizing spatial data allowed to record the time and place of any changes. The model of representation of spatial data based on object was employed. Through the proposed conceptual model, it is possible to store the results of several simulations for a watershed, linking each simulation to a specific event and identifying which scenario is valid at the time. Thus, it is possible to identify quantitative changes related to runoff over time and relate them to the events that caused them and the entities involved in such events. The conceptual model supports the existence of alternate realities, allowing the simulation and comparison of past and future scenarios.

Web-Based Spatial Immunize-able Disease Surveillance System for Nigeria

The absence of spatial representation of health data often lead to poor decisions. Therefore, this paper introduces an ImDisease Surveillance System (IDSS) that can handle spatial health data using the web-based GIS capability. While ArcGIS was used to incorporate the GIS potentials; the WAMP (Window Apache MySQL and PHP) server was used to demonstrate the suitability of the system (application) to run on a local host as well as on the Internet. The health data used were randomly collected from local government areas and offices of the state ministry of health in Ekiti State as case study. Results from implementing the system showed that users were able to use the user interfaces to visualize various occurrences of immunize-able diseases; select the type of view they desired - either by local government or disease types; and graphically know the areas affected by diseases. These results - information when disseminated would assist in program planning and evaluation, and the formulation of research hypotheses for further research. We also believe that it will be useful in the deployment of health personnel and resources to address health related problems; and would also provide useful predictions.

A REST Service for Triangulation of Point Sets Using Oriented Matroids

This paper describes the implementation of a prototype REST service for triangulation of point sets collected by mobile GPS receivers. The first objective of this paper is to test functionalities of an application, which exploits mobile devices' capabilities to get data associated with their spatial location. A triangulation of a set of points provides a mechanism through which it is possible to produce an accurate representation of spatial data. Such triangulation may be used for representing surfaces by Triangulated Irregular Networks (TINs), and for decomposing complex two-dimensional spatial objects into simpler geometries. The second objective of this paper is to promote the use of oriented matroids for finding alternative solutions to spatial data processing and analysis tasks. This study focused on the particular case of the calculation of triangulations based on oriented matroids. The prototype described in this paper used a wrapper to integrate and expose several tools previously implemented in C++.

Landscape Characterization Integrating Expert and Local Spatial Knowledge of Land and Forest Resources

In many developing countries, political documentation acknowledges the crucial elements of participation and spatiality for effective land use planning. However, operative approaches to spatial data inclusion and representation in participatory land management are often lacking. In this paper, we apply and develop an integrated landscape characterization approach to enhance spatial knowledge generation about the complex human-nature interactions in landscapes in the context of Zanzibar, Tanzania. We apply an integrated landscape conceptualization as a theoretical framework where the expert and local knowledge can meet in spatial context. The characterization is based on combining multiple data sources in GIS, and involves local communities and their local spatial knowledge since the beginning into the process. Focusing on the expected information needs for community forest management, our characterization integrates physical landscape features and retrospective landscape change data with place-specific community knowledge collected through participatory GIS techniques. The characterization is established in a map form consisting of four themes and their synthesis. The characterization maps are designed to support intuitive interpretation, express the inherently uncertain nature of the data, and accompanied by photographs to enhance communication. Visual interpretation of the characterization mediates information about the character of areas and places in the studied local landscape, depicting the role of forest resources as part of the landscape entity. We conclude that landscape characterization applied in GIS is a highly potential tool for participatory land and resource management, where spatial argumentation, stakeholder communication, and empowerment are critical issues.

GENERAL ANALYZING AND RESEARCH ON UNCERTAINTY OF MULTI-SCALE REPRESENTATION FOR STREET-BLOCK SETTLEMENT

Abstract. Analyzing and evaluating the reliability of multi-scale representation of spatial data are already becoming an important issue of the current digital cartography and GIS. Settlement place is the main content of maps. For this reason, studying on the uncertainty of multi-scale representation of settlement place is one of important contents of the uncertainty of multi-scale representation of spatial data. In this paper, uncertainty of multi-scale representation of street-block settlement was get comprehensive analysis and system research. This paper holds that map generalization is the essential cause leading to uncertainty of multi-scale representation of streetblock settlement. First, it is explored of essence and types of uncertainty on multi-scale representation of street-block settlement, and it divides these uncertainties into four large classes and seven subclasses. Second, among all kinds of uncertainties of multi-scale representation of street-block settlement, this paper mainly studies the uncertainty of settlement of street-block symbolic representation, and establishes the evaluation content and evaluation indexes and computing method of uncertainty of street-block and street network generalization and building generalization. The result can use for evaluating the good and bad of scale transfer methods and the uncertainty of products of multi-scale representation of street-block settlement.

Efficiently implementable algebra for distributed in-network spatial analysis

Existing sensor network query processors (SNQPs) have demonstrated that in-network processing is an effective and efficient means of interacting with wireless sensor networks (WSNs) for data collection tasks. Inspired by these findings, this article investigates the question as to whether spatial analysis over WSNs can be built upon established distributed query processing techniques, but, here, emphasis is on the spatial aspects of sensed data, which are not adequately addressed in the existing SNQPs. By spatial analysis, we mean the ability to detect topological relationships between spatially referenced entities (e.g. whether mist intersects a vineyard or is disjoint from it) and to derive representations grounded on such relationships (e.g. the geometrical extent of that part of a vineyard that is covered by mist). To support the efficient representation, querying and manipulation of spatial data, we use an algebraic approach. We revisit a previously proposed centralized spatial algebra comprising a set of spatial data types and a comprehensive collection of operations. We have redefined and re-conceptualized the algebra for distributed evaluation and shown that it can be efficiently implemented for in-network execution. This article provides rigorous, formal definitions of the spatial data types, points, lines and regions, together with spatial-valued and topological operations over them. The article shows how the algebra can be used to characterize complex and expressive topological relationships between spatial entities and spatial phenomena that, due to their dynamic, evolving nature, cannot be represented a priori.

Fast Inference for the Latent Space Network Model Using a Case-Control Approximate Likelihood

Network models are widely used in social sciences and genome sciences. The latent space model proposed by Hoff et al. (2002), and extended by Handcock et al. (2007) to incorporate clustering, provides a visually interpretable model-based spatial representation of relational data and takes account of several intrinsic network properties. Due to the structure of the likelihood function of the latent space model, the computational cost is of order O(N 2), where N is the number of nodes. This makes it infeasible for large networks. In this article, we propose an approximation of the log-likelihood function. We adapt the case-control idea from epidemiology and construct a case-control log-likelihood, which is an unbiased estimator of the log-full likelihood. Replacing the full likelihood by the case-control likelihood in the Markov chain Monte Carlo estimation of the latent space model reduces the computational time from O(N 2) to O(N), making it feasible for large networks. We evaluate its performance using simulated and real data. We fit the model to a large protein–protein interaction data using the case-control likelihood and use the model fitted link probabilities to identify false positive links. Supplemental materials are available online.

Cloud Computing as an Innovation in GIS & SDI: Methodologies, Services, Issues and Deployment Techniques

Cloud computing is one of the main issues of interest to the scientific community of the spatial data. A cloud is referred to computing infrastructure for a representation of network. From the perspective of providers, the main characteristics of cloud computing is being dynamic, high power in computing and storage. Also cloud computing is a cost benefit and effective way for representation of web-based spatial data and complex analysis. Furthermore, cloud computing is a way to facilitate distributed computing and store different data. One of the main features of cloud computing is ability in powerful computing and dynamic storage with an affordable expense and secure web. In this paper we further investigate the methodologies, services, issues and deployed techniques also, about situation of cloud computing in the past, present and future is probed and some issues concerning the security is expressed. Undoubtedly cloud computing is vital for spatial data infrastructure and consequently the cloud computing is able to expand the interactions for spatial data infrastructure in the future.

Modelling and mapping third dimension in a spatial database

In this paper, an overview of phases for modelling and mapping third dimension of spatial objects in a database is presented based on a selected spatial database management system (DBMS). These phases include (1) defining a spatial reference system for representing three-dimensional (3D) objects with real-world coordinates, (2) geometric modelling of 3D objects in the database, (3) 3D spatial indexing for fast accessing/querying the 3D data, 3D spatial queries and representation of 3D data. Then, a case study is performed to assess needs, possibilities and potential limitations of 3D data modelling in the spatial database.

Effect of GIS Interpolation Techniques on the Accuracy of the Spatial Representation of Groundwater Monitoring Data in Gaza Strip

Effect of GIS Interpolation Techniques on the Accuracy of the Spatial Representation of Groundwater Monitoring Data in Gaza Strip

Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data.

Patterns of brain atrophy measured by magnetic resonance structural imaging have been utilized as significant biomarkers for diagnosis of Alzheimer's disease (AD). However, brain atrophy is variable across patients and is non-specific for AD in general. Thus, automatic methods for AD classification require a large number of structural data due to complex and variable patterns of brain atrophy. In this paper, we propose an incremental method for AD classification using cortical thickness data. We represent the cortical thickness data of a subject in terms of their spatial frequency components, employing the manifold harmonic transform. The basis functions for this transform are obtained from the eigenfunctions of the Laplace-Beltrami operator, which are dependent only on the geometry of a cortical surface but not on the cortical thickness defined on it. This facilitates individual subject classification based on incremental learning. In general, methods based on region-wise features poorly reflect the detailed spatial variation of cortical thickness, and those based on vertex-wise features are sensitive to noise. Adopting a vertex-wise cortical thickness representation, our method can still achieve robustness to noise by filtering out high frequency components of the cortical thickness data while reflecting their spatial variation. This compromise leads to high accuracy in AD classification. We utilized MR volumes provided by Alzheimer's Disease Neuroimaging Initiative (ADNI) to validate the performance of the method. Our method discriminated AD patients from Healthy Control (HC) subjects with 82% sensitivity and 93% specificity. It also discriminated Mild Cognitive Impairment (MCI) patients, who converted to AD within 18 months, from non-converted MCI subjects with 63% sensitivity and 76% specificity. Moreover, it showed that the entorhinal cortex was the most discriminative region for classification, which is consistent with previous pathological findings. In comparison with other classification methods, our method demonstrated high classification performance in both categories, which supports the discriminative power of our method in both AD diagnosis and AD prediction.

Modeling, Monitoring, and Visualizing Carbon Footprints at the Urban Neighborhood Scale

This paper presents a novel approach for calculating the carbon footprint in an urban region at the scale of individual neighborhoods. The approach is applied to Maricopa County, Arizona, for two different scenarios of growth. Our strategy for monitoring carbon footprints also involves a new form of visualization that goes beyond the two-dimensional thematic maps and provides better representation of multidimensional spatial data. Our monitoring system includes grid-based calculation and visualization of potential carbon dioxide emissions for future years under different policy and behavioral scenarios. To calculate a total carbon footprint value, we designate several varieties of relevant emissions such as housing expenditure and electricity. We develop datasets and models that allow for accurate calculation of the carbon footprint for Maricopa County. We provide emissions information for different types of housing units and compare the results for different future scenarios of household change. Finally, we visualize the results in three-dimensional representations that take into account the multidimensional dataset. We employ an intuitive approach in constructing three-dimensional surfaces of carbon footprints using B-Splines and Bezier techniques. This 3D representation allows for understanding and comparing multidimensional data using transparency, different layers, and superimposition techniques.

Spatial video and GIS

GIS elemental unit representations of spatial data are often defined in terms of points, lines and areas. However, another type of spatial data that is becoming frequently captured, but as yet is largely ignored in GIS, is that of video. While digital video recording is a commonly encountered medium in modern society and encompasses many forms, from simple personal camcorders through to sophisticated survey and surveillance systems, its geographical representation in a GIS has not been fully examined or realised. In the majority of cases the video footage is usually captured while the device and/or the objects being viewed are in motion. What is of particular interest is when video streams can be, or have been, associated with spatial data such as location and orientation to create geographically referenced videographic data, which, for simplicity, will be defined as spatial video. Fundamentally, the nature of video is to record space, so when spatial properties can be accurately acquired and associated with this footage, an important geographical element can be considered for integration and analysis within a GIS. Existing spatial video systems, both commercial and research, are predominantly used in survey or LBS roles and are usually bespoke and application specific. These systems do not model spatial video to any recognised standards that is generalised to be both data and platform independent. They do not support GIS integration and/or analysis from a purely spatial content perspective. A video-image/remote-sensing centric approach prevails where usage options range from simple visualisation interfaces to interactive computer vision systems. What has been largely overlooked is a spatial approach where the inherent geographical extent recorded in each video frame can be modelled and used in a geo-spatial analysis context. While this modelling approach has not been fully realised, it does exist in a GIS form based on Open Geospatial Consortium standards, where the spatial context of video is defined in a structure called a ViewCone. However, a ViewCone only defines a 2D model of the geographical extent of each frame and is restricted to a three-or-five sided polygon representation. Thus, this article examines the potential of modelling spatial video through the use of elemental data types within GIS; gives some examples of using this approach; describes some problems in using spatial video within GIS; and then demonstrates how these problems are being solved. This is done in three stages: Firstly, a detailed overview of spatial video in its current GIS role is provided – this is achieved through a complete introduction to the distinct elements of spatial video followed by a review of its use in both commercial and academic application areas. Secondly, a brief theoretical overview of an alternative GIS-constrained ViewCone data structure is given that defines a more flexible spatial video model for both 2D and 3D GIS analysis and visualisation. Thirdly, a selective sample of results is presented based on an implementation of this approach being applied to a constrained spatial video data source in a specific study area.

Overview of Study on Geo spatial Data Integration

Application of Geographical Information Systems (GIS) and multi resources geo spatial data promotes the appearance of digital geo spatial data integration in 1960's. But the weakness of study on geo spatial data integration hinders its own development. As a overview on geo spatial data integration, the paper focuses on fundamental concept, theory of data integration and problems in geo spatial data integration research currently. Geo spatial data is one kind of data used to represent, describe geographical processes and geographical phenomena. From the point view of geographical cognition, geo spatial data is abstraction of geographical entity controlled by geographical knowledge. Geo spatial data integration, as argued by many researchers, is the process during which multi resources and multi scale geo spatial data can be used in one uniform GIS software platform. The goals of geo spatial data integration, put simply, are creating seamless (including attribute seamless, temporal seamless and spatial seamless) dataset or database for certain application or general construction of geo spatial database. Study of geo spatial data integration mainly focus on data integration mechanism, error transfer, data quality controlling, multi scale, and representation of geo spatial data. Also many successful application of geo spatial data integration were carried out, but there are still many shortcomings of the fundamental researches on geo spatial data integration, such as integration principles, error propagation rules, multi scale data integration and data quality evaluation and controlling. The main problems about geo spatial data integration are short of general integration principles and rules, weak studying on geo spatial metadata, ignoring the applications of geo sciences principles and rules. Based on the analysis on geo spatial data integration, the authors list out some further research field on data integration such as network based data integration, data integration principles, metadata utility in data integration, geo sciences expert knowledge system and their application in geo spatial data integration.