Modeling Boundaries of Influence among Positional Uncertainty Fields

Within a CIS environment, the proper use of information requires the identification of the uncertainty associated with it. As such, there has been a substantial amount of research dedicated to describing and quantifying spatial data uncertainty. Recent advances in sensor technology and image analysis techniques are making image-derived geospatial data increasingly popular. Along with development in sensor and image analysis technologies have come departures from conventional point-by-point measurements. Current advancements support the transition from traditional point measures to novel techniques that allow the extraction of complex objects as single entities (e.g., road outlines, buildings). As the methods of data extraction advance, so too must the methods of estimating the uncertainty associated with the data. Not only will object uncertainties be modeled, but the connections between these uncertainties will also be estimated. The current methods for determining spatial accuracy for lines and areas typically involve defining a zone of uncertainty around the measured line, within which the actual line exists with some probability. Yet within the research community, the proper shape of this \u27uncertainty band\u27 is a topic with much dissent. Less contemplated is the manner in which such areas of uncertainty interact and influence one another. The development of positional error models, from the epsilon band and error band to the rigorous G-band, has focused on statistical models for estimating independent line features. Yet these models are not suited to model the interactions between uncertainty fields of adjacent features. At some point, these distributed areas of uncertainty around the features will intersect and overlap one another. In such instances, a feature\u27s uncertainty zone is defined not only by its measurement, but also by the uncertainty associated with neighboring features. It is therefore useful to understand and model the interactions between adjacent uncertainty fields. This thesis presents an analysis of estimation and modeling techniques of spatial uncertainty, focusing on the interactions among fields of positional uncertainty for image-derived linear features. Such interactions are assumed to occur between linear features derived from varying methods and sources, allowing the application of an independent error model. A synthetic uncertainty map is derived for a set of linear and aerial features, containing distributed fields of uncertainty for individual features. These uncertainty fields are shown to be advantageous for communication and user understanding, as well as being conducive to a variety of image processing techniques. Such image techniques can combine overlapping uncertainty fields to model the interaction between them. Deformable contour models are used to extract sets of continuous uncertainty boundaries for linear features, and are subsequently applied to extract a boundary of influence shared by two uncertainty fields. These methods are then applied to a complex scene of uncertainties, modeling the interactions of multiple objects within the scene. The resulting boundary uncertainty representations are unique from the previous independent error models which do not take neighboring influences into account. By modeling the boundary of interaction among the uncertainties of neighboring features, a more integrated approach to error modeling and analysis can be developed for complex spatial scenes and datasets

Analytical modelling of positional and thematic uncertainties in the integration of remote sensing and geographical information systems

This paper describes three aspects of uncertainty in geographical information systems (GIS) and remote sensing. First, the positional uncertainty of an area object in a GIS is discussed as a function of positional uncertainties of line segments and boundary line features. Second, the thematic uncertainty of a classified remote sensing image is described using the probability vectors from a maximum likelihood classification. Third, the 'S-band' model is used to quantify uncertainties after combining GIS and remote sensing data.Department of Land Surveying and Geo-Informatic

A GPR-GPS-GIS-integrated, information-rich and error-aware system for detecting, locating and characterizing underground utilities

Underground utilities have proliferated throughout the years. The location and dimension of many underground utilities have not always been properly collected and documented, leading to utility conflicts and utility strikes, and thus resulting in property damages, project delays, cost overruns, environment pollutions, injuries and deaths. The underlying reasons are twofold. First, the reliable data regarding the location and dimension of underground utility are missing or incomplete. Existing methods to collect data are not efficient and effective. Second, positional uncertainties are inherent in the measured utility locations. An effective means is not yet available to visualize and communicate the inherent positional uncertainties associated with utility location data to end-users (e.g., excavator operator). To address the aforementioned problems, this research integrate ground penetrating radar (GPR), global positioning system (GPS) and geographic information system (GIS) to form a total 3G system to collect, inventory and visualize underground utility data. Furthermore, a 3D probabilistic error band is created to model and visualize the inherent positional uncertainties in utility data. ^ Three main challenges are addressed in this research. The first challenge is the interpretation of GPR and GPS raw data. A novel method is created in this research to simultaneously estimate the radius and buried depth of underground utilities using GPR scans and auxiliary GPS data. The proposed method was validated using GPR field scans obtained under various settings. It was found that this newly created method increases the accuracy of estimating the buried depth and radius of the buried utility under a general scanning condition. The second challenge is the geo-registration of detected utility locations. This challenge is addressed by integration of GPR, GPS and GIS. The newly created system takes advantages of GPR and GPS to detect and locate underground utilities in 3D and uses GIS for storing, updating, modeling, and visualizing collected utility data in a real world coordinate system. The third challenge is positional error/uncertainty assessment and modeling. The locational errors of GPR system are evaluated in different depth and soil conditions. Quantitative linkages between error magnitudes and its influencing factors (i.e., buried depths and soil conditions) are established. In order to handle the positional error of underground utilities, a prototype of 3D probabilistic error band is created and implemented in GIS environment. This makes the system error-aware and also paves the way to a more intelligent error-aware GIS. ^ To sum up, the newly created system is able to detect, locate and characterize underground utilities in an information-rich and error-aware manner

The importance of accurate road data for spatial applications in public health: customizing a road network

<p>Abstract</p> <p>Background</p> <p>Health researchers have increasingly adopted the use of geographic information systems (GIS) for analyzing environments in which people live and how those environments affect health. One aspect of this research that is often overlooked is the quality and detail of the road data and whether or not it is appropriate for the scale of analysis. Many readily available road datasets, both public domain and commercial, contain positional errors or generalizations that may not be compatible with highly accurate geospatial locations. This study examined the accuracy, completeness, and currency of four readily available public and commercial sources for road data (North Carolina Department of Transportation, StreetMap Pro, TIGER/Line 2000, TIGER/Line 2007) relative to a custom road dataset which we developed and used for comparison.</p> <p>Methods and Results</p> <p>A custom road network dataset was developed to examine associations between health behaviors and the environment among pregnant and postpartum women living in central North Carolina in the United States. Three analytical measures were developed to assess the comparative accuracy and utility of four publicly and commercially available road datasets and the custom dataset in relation to participants' residential locations over three time periods. The exclusion of road segments and positional errors in the four comparison road datasets resulted in between 5.9% and 64.4% of respondents lying farther than 15.24 meters from their nearest road, the distance of the threshold set by the project to facilitate spatial analysis. Agreement, using a Pearson's correlation coefficient, between the customized road dataset and the four comparison road datasets ranged from 0.01 to 0.82.</p> <p>Conclusion</p> <p>This study demonstrates the importance of examining available road datasets and assessing their completeness, accuracy, and currency for their particular study area. This paper serves as an example for assessing the feasibility of readily available commercial or public road datasets, and outlines the steps by which an improved custom dataset for a study area can be developed.</p

Bridging the Gap Between Traditional Metadata and the Requirements of an Academic SDI for Interdisciplinary Research

Metadata has long been understood as a fundamental component of any Spatial Data Infrastructure, providing information relating to discovery, evaluation and use of datasets and describing their quality. Having good metadata about a dataset is fundamental to using it correctly and to understanding the implications of issues such as missing data or incorrect attribution on the results obtained for any analysis carried out. Traditionally, spatial data was created by expert users (e.g. national mapping agencies), who created metadata for the data. Increasingly, however, data used in spatial analysis comes from multiple sources and could be captured or used by nonexpert users – for example academic researchers ‐ many of whom are from non‐GIS disciplinary backgrounds, not familiar with metadata and perhaps working in geographically dispersed teams. This paper examines the applicability of metadata in this academic context, using a multi‐national coastal/environmental project as a case study. The work to date highlights a number of suggestions for good practice, issues and research questions relevant to Academic SDI, particularly given the increased levels of research data sharing and reuse required by UK and EU funders

Quality Assessment of the Canadian OpenStreetMap Road Networks

Volunteered geographic information (VGI) has been applied in many fields such as participatory planning, humanitarian relief and crisis management because of its cost-effectiveness. However, coverage and accuracy of VGI cannot be guaranteed. OpenStreetMap (OSM) is a popular VGI platform that allows users to create or edit maps using GPS-enabled devices or aerial imageries. The issue of geospatial data quality in OSM has become a trending research topic because of the large size of the dataset and the multiple channels of data access. The objective of this study is to examine the overall reliability of the Canadian OSM data. A systematic review is first presented to provide details on the quality evaluation process of OSM. A case study of London, Ontario is followed as an experimental analysis of completeness, positional accuracy and attribute accuracy of the OSM street networks. Next, a national study of the Canadian OSM data assesses the overall semantic accuracy and lineage in addition to the quality measures mentioned above. Results of the quality evaluation are compared with associated OSM provenance metadata to examine potential correlations. The Canadian OSM road networks were found to have comparable accuracy with the tested commercial database (DMTI). Although statistical analysis suggests that there are no significant relations between OSM accuracy and its editing history, the study presents the complex processes behind OSM contributions possibly influenced by data import and remote mapping. The findings of this thesis can potentially guide cartographic product selection for interested parties and offer a better understanding of future quality improvement in OSM

Development of a GIS-based method for sensor network deployment and coverage optimization

Au cours des dernières années, les réseaux de capteurs ont été de plus en plus utilisés dans différents contextes d’application allant de la surveillance de l’environnement au suivi des objets en mouvement, au développement des villes intelligentes et aux systèmes de transport intelligent, etc. Un réseau de capteurs est généralement constitué de nombreux dispositifs sans fil déployés dans une région d'intérêt. Une question fondamentale dans un réseau de capteurs est l'optimisation de sa couverture spatiale. La complexité de l'environnement de détection avec la présence de divers obstacles empêche la couverture optimale de plusieurs zones. Par conséquent, la position du capteur affecte la façon dont une région est couverte ainsi que le coût de construction du réseau. Pour un déploiement efficace d'un réseau de capteurs, plusieurs algorithmes d'optimisation ont été développés et appliqués au cours des dernières années. La plupart de ces algorithmes reposent souvent sur des modèles de capteurs et de réseaux simplifiés. En outre, ils ne considèrent pas certaines informations spatiales de l'environnement comme les modèles numériques de terrain, les infrastructures construites humaines et la présence de divers obstacles dans le processus d'optimisation. L'objectif global de cette thèse est d'améliorer les processus de déploiement des capteurs en intégrant des informations et des connaissances géospatiales dans les algorithmes d'optimisation. Pour ce faire, trois objectifs spécifiques sont définis. Tout d'abord, un cadre conceptuel est développé pour l'intégration de l'information contextuelle dans les processus de déploiement des réseaux de capteurs. Ensuite, sur la base du cadre proposé, un algorithme d'optimisation sensible au contexte local est développé. L'approche élargie est un algorithme local générique pour le déploiement du capteur qui a la capacité de prendre en considération de l'information spatiale, temporelle et thématique dans différents contextes d'applications. Ensuite, l'analyse de l'évaluation de la précision et de la propagation d'erreurs est effectuée afin de déterminer l'impact de l'exactitude des informations contextuelles sur la méthode d'optimisation du réseau de capteurs proposée. Dans cette thèse, l'information contextuelle a été intégrée aux méthodes d'optimisation locales pour le déploiement de réseaux de capteurs. L'algorithme développé est basé sur le diagramme de Voronoï pour la modélisation et la représentation de la structure géométrique des réseaux de capteurs. Dans l'approche proposée, les capteurs change leur emplacement en fonction des informations contextuelles locales (l'environnement physique, les informations de réseau et les caractéristiques des capteurs) visant à améliorer la couverture du réseau. La méthode proposée est implémentée dans MATLAB et est testée avec plusieurs jeux de données obtenus à partir des bases de données spatiales de la ville de Québec. Les résultats obtenus à partir de différentes études de cas montrent l'efficacité de notre approche.In recent years, sensor networks have been increasingly used for different applications ranging from environmental monitoring, tracking of moving objects, development of smart cities and smart transportation system, etc. A sensor network usually consists of numerous wireless devices deployed in a region of interest. A fundamental issue in a sensor network is the optimization of its spatial coverage. The complexity of the sensing environment with the presence of diverse obstacles results in several uncovered areas. Consequently, sensor placement affects how well a region is covered by sensors as well as the cost for constructing the network. For efficient deployment of a sensor network, several optimization algorithms are developed and applied in recent years. Most of these algorithms often rely on oversimplified sensor and network models. In addition, they do not consider spatial environmental information such as terrain models, human built infrastructures, and the presence of diverse obstacles in the optimization process. The global objective of this thesis is to improve sensor deployment processes by integrating geospatial information and knowledge in optimization algorithms. To achieve this objective three specific objectives are defined. First, a conceptual framework is developed for the integration of contextual information in sensor network deployment processes. Then, a local context-aware optimization algorithm is developed based on the proposed framework. The extended approach is a generic local algorithm for sensor deployment, which accepts spatial, temporal, and thematic contextual information in different situations. Next, an accuracy assessment and error propagation analysis is conducted to determine the impact of the accuracy of contextual information on the proposed sensor network optimization method. In this thesis, the contextual information has been integrated in to the local optimization methods for sensor network deployment. The extended algorithm is developed based on point Voronoi diagram in order to represent geometrical structure of sensor networks. In the proposed approach sensors change their location based on local contextual information (physical environment, network information and sensor characteristics) aiming to enhance the network coverage. The proposed method is implemented in MATLAB and tested with several data sets obtained from Quebec City spatial database. Obtained results from different case studies show the effectiveness of our approach

Analysis and visualisation of digital elevation data for catchment management

River catchments are an obvious scale for soil and water resources management, since their shape and characteristics control the pathways and fluxes of water and sediment. Digital Elevation Models (DEMs) are widely used to simulate overland water paths in hydrological models. However, all DEMs are approximations to some degree and it is widely recognised that their characteristics can vary according to attributes such as spatial resolution and data sources (e.g. contours, optical or radar imagery). As a consequence, it is important to assess the ‘fitness for purpose’ of different DEMs and evaluate how uncertainty in the terrain representation may propagate into hydrological derivatives. The overall aim of this research was to assess accuracies and uncertainties associated with seven different DEMs (ASTER GDEM1, SRTM, Landform Panorama (OS 50), Landform Profile (OS 10), LandMap, NEXTMap and Bluesky DTMs) and to explore the implications of their use in hydrological analysis and catchment management applications. The research focused on the Wensum catchment in Norfolk, UK. The research initially examined the accuracy of the seven DEMs and, subsequently, a subset of these (SRTM, OS 50, OS10, NEXTMap and Bluesky) were used to evaluate different techniques for determining an appropriate flow accumulation threshold to delineate channel networks in the study catchment. These results were then used to quantitatively compare the positional accuracy of drainage networks derived from different DEMs. The final part of the thesis conducted an assessment of soil erosion and diffuse pollution risk in the study catchment using NEXTMap and OS 50 data with SCIMAP and RUSLE modelling techniques. Findings from the research demonstrate that a number of nationally available DEMs in the UK are simply not ‘fit for purpose’ as far as local catchment management is concerned. Results indicate that DEM source and resolution have considerable influence on modelling of hydrological processes, suggesting that for a lowland catchment the availability of a high resolution DEM (5m or better) is a prerequisite for any reliable assessment of the consequences of implementing particular land management measures. Several conclusions can be made from the research. (1) From the collection of DEMs used in this study the NEXTMap 5m DTM was found to be the best for representing catchment topography and is likely to prove a superior product for similar applications in other lowland catchments across the UK. (2) It is important that error modelling techniques are more routinely employed by GIS users, particularly where the fitness for purpose of a data source is not well-established. (3) GIS modelling tools that can be used to test and trial alternative management options (e.g. for reducing soil erosion) are particularly helpful in simulating the effect of possible environmental improvement measures