Management and Conflation of Multiple Representations within an Open Federation Platform

Building up spatial data infrastructures involves the task of dealing with heterogeneous data sources which often bear inconsistencies and contradictions, respectively. One main reason for those inconsistencies emerges from the fact that one and the same real world phenomenon is often stored in multiple representations within different databases. It is the special goal of this paper to describe how the problems arising from multiple representations can be dealt with in spatial data infrastructures, especially focusing on the concepts that have been developed within the Nexus project of the University of Stuttgart that is implementing an open, federated infrastructure for context-aware applications. A main part of this contribution consists of explaining the efforts which have been conducted in order to solve the conflicts that occur between multiple representations within conflation or merging processes to achieve consolidated views on the underlying data for the applications

A Geospatial Cyberinfrastructure for Urban Economic Analysis and Spatial Decision-Making

abstract: Urban economic modeling and effective spatial planning are critical tools towards achieving urban sustainability. However, in practice, many technical obstacles, such as information islands, poor documentation of data and lack of software platforms to facilitate virtual collaboration, are challenging the effectiveness of decision-making processes. In this paper, we report on our efforts to design and develop a geospatial cyberinfrastructure (GCI) for urban economic analysis and simulation. This GCI provides an operational graphic user interface, built upon a service-oriented architecture to allow (1) widespread sharing and seamless integration of distributed geospatial data; (2) an effective way to address the uncertainty and positional errors encountered in fusing data from diverse sources; (3) the decomposition of complex planning questions into atomic spatial analysis tasks and the generation of a web service chain to tackle such complex problems; and (4) capturing and representing provenance of geospatial data to trace its flow in the modeling task. The Greater Los Angeles Region serves as the test bed. We expect this work to contribute to effective spatial policy analysis and decision-making through the adoption of advanced GCI and to broaden the application coverage of GCI to include urban economic simulations

A flexible framework for assessing the quality of crowdsourced data

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.Crowdsourcing as a means of data collection has produced previously unavailable data assets and enriched existing ones, but its quality can be highly variable. This presents several challenges to potential end users that are concerned with the validation and quality assurance of the data collected. Being able to quantify the uncertainty, define and measure the different quality elements associated with crowdsourced data, and introduce means for dynamically assessing and improving it is the focus of this paper. We argue that the required quality assurance and quality control is dependent on the studied domain, the style of crowdsourcing and the goals of the study. We describe a framework for qualifying geolocated data collected from non-authoritative sources that enables assessment for specific case studies by creating a workflow supported by an ontological description of a range of choices. The top levels of this ontology describe seven pillars of quality checks and assessments that present a range of techniques to qualify, improve or reject data. Our generic operational framework allows for extension of this ontology to specific applied domains. This will facilitate quality assurance in real-time or for post-processing to validate data and produce quality metadata. It enables a system that dynamically optimises the usability value of the data captured. A case study illustrates this framework

Towards evidence-based, GIS-driven national spatial health information infrastructure and surveillance services in the United Kingdom

The term "Geographic Information Systems" (GIS) has been added to MeSH in 2003, a step reflecting the importance and growing use of GIS in health and healthcare research and practices. GIS have much more to offer than the obvious digital cartography (map) functions. From a community health perspective, GIS could potentially act as powerful evidence-based practice tools for early problem detection and solving. When properly used, GIS can: inform and educate (professionals and the public); empower decision-making at all levels; help in planning and tweaking clinically and cost-effective actions, in predicting outcomes before making any financial commitments and ascribing priorities in a climate of finite resources; change practices; and continually monitor and analyse changes, as well as sentinel events. Yet despite all these potentials for GIS, they remain under-utilised in the UK National Health Service (NHS). This paper has the following objectives: (1) to illustrate with practical, real-world scenarios and examples from the literature the different GIS methods and uses to improve community health and healthcare practices, e.g., for improving hospital bed availability, in community health and bioterrorism surveillance services, and in the latest SARS outbreak; (2) to discuss challenges and problems currently hindering the wide-scale adoption of GIS across the NHS; and (3) to identify the most important requirements and ingredients for addressing these challenges, and realising GIS potential within the NHS, guided by related initiatives worldwide. The ultimate goal is to illuminate the road towards implementing a comprehensive national, multi-agency spatio-temporal health information infrastructure functioning proactively in real time. The concepts and principles presented in this paper can be also applied in other countries, and on regional (e.g., European Union) and global levels

Practicable methodologies for delivering comprehensive spatial soils information

This thesis is concerned with practicable methodologies for delivering comprehensive spatial soil information to end-users. There is a need for relevant spatial soil information to complement objective decision-making for addressing current problems associated with soil degradation; for modelling, monitoring and measurement of particular soil services; and for the general management of soil resources. These are real-world situations, which operate at spatial scales ranging from field to global scales. As such, comprehensive spatial soil information is tailored to meet the spatial scale specifications of the end user, and is of a nature that fully characterises the whole-soil profile with associated prediction uncertainties, and where possible, both the predictions and uncertainties have been independently validated. ‘Practicable’ is an idealistic pursuit but nonetheless necessary because of a need to equip land-holders, private-sector and non-governmental stakeholders and, governmental departments including soil mapping agencies with the necessary tools to ensure wide application of the methodologies to match the demand for relevant spatial soil information. Practicable methodologies are general and computationally efficient; can be applied to a wide range of soil attributes; can handle variable qualities of data; and are effective when working with very large datasets. In this thesis, delivering comprehensive spatial soil information relies on coupling legacy soil information (principally site observations made in the field) with Digital Soil Mapping (DSM) which comprises quantitative, state-of-the-art technologies for soil mapping. After the General Introduction, a review of the literature is given in Chapter 1 which describes the research context of the thesis. The review describes soil mapping first from a historical perspective and rudimentary efforts of mapping soils and then tracks the succession of advances that have been made towards the realisation of populated, digital spatial soil information databases where measures of prediction certainties are also expressed. From the findings of the review, in order to deliver comprehensive spatial soil information to end-users, new research was required to investigate: 1) a general method for digital soil mapping the whole-profile (effectively pseudo-3D) distribution of soil properties; 2) a general method for quantifying the total prediction uncertainties of the digital soil maps that describe the whole-profile distribution of soil properties; 3) a method for validating the whole-profile predictions of soil properties and the quantifications of their uncertainties; 4) a systematic framework for scale manipulations or upscaling and downscaling techniques for digital soil mapping as a means of generating soil information products tailored to the needs of soil information users. Chapters 2 to 6 set about investigating how we might go about doing these with a succession of practicable methodologies. Chapter 2 addressed the need for whole-profile mapping of soil property distribution. Equal-area spline depth functions coupled with DSM facilitated continuous mapping the lateral and vertical distribution of soil properties. The spline function is a useful tool for deriving the continuous variation of soil properties from soil profile and core observations and is also suitable to use for a number of different soil properties. Generally, mapping the continuous depth function of soil properties reveals that the accuracy of the models is highest at the soil surface but progressively decreases with increasing soil depth. Chapter 3 complements the investigations made in Chapter 2 where an empirical method of quantifying prediction uncertainties from DSM was devised. This method was applied for quantifying the uncertainties of whole-profile digital soil maps. Prediction uncertainty with the devised empirical method is expressed as a prediction interval of the underlying model errors. The method is practicable in the sense that it accounts for all sources of uncertainty and is computationally efficient. Furthermore the method is amenable in situations where complex spatial soil prediction functions such as regression kriging approaches are used. Proper evaluation of digital soil maps requires testing the predictions and the quantification of the prediction uncertainties. Chapter 4 devised two new criteria in which to properly evaluate digital soil maps when additional soil samples collected by probability sampling are used for validation. The first criterion addresses the accuracy of the predictions in the presence of uncertainties and is the spatial average of the statistical expectation of the Mean Square Error of a simulated random value (MSES). The second criterion addresses the quality of the uncertainties which is estimated as the total proportion of the study area where the (1-α)-prediction interval (PI) covers the true value (APCP). Ideally these criteria will be coupled with conventional measures of map quality so that objective decisions can be made about the reliability and subsequent suitability of a map for a given purpose. It was revealed in Chapter 4, that the quantifications of uncertainty are susceptible to bias as a result of using legacy soil data to construct spatial soil prediction functions. As a consequence, in addition to an increasing uncertainty with soil depth, there is increasing misspecification of the prediction uncertainties. Chapter 2, 3, and 4 thus represent a framework for delivering whole-soil profile predictions of soil properties and their uncertainties, where both have been assessed or validated across mapping domains at a range of spatial scales for addressing field, farm, regional, catchment, national, continental or global soil-related problems. The direction of Chapters 5 and 6 however addresses issues specifically related to tailoring spatial soil information to the scale specifications of the end-user through the use of scale manipulations on existing digital soil maps. What is proposed in Chapter 5 is a scaling framework that takes into account the scaling triplet of digital soil maps—extent, resolution, and support—and recommends pedometric methodologies for scale manipulation based on the scale entities of the source and destination maps. Upscaling and downscaling are descriptors for moving up to coarser or down to finer scales respectively but may be too general for DSM. Subsequently Fine-gridding and coarse-gridding are operations where the grid spacing changes but support remains unchanged. Deconvolution and convolution are operations where the support always changes, which may or may not involve changing the grid spacing. While disseveration and conflation operations occur when the support and grid size are equal and both are then changed equally and simultaneously. There is an increasing richness of data sources describing the physical distribution of the Earth’s resources with improved qualities and resolutions. To take advantage of this, Chapter 6 devises a novel procedure for downscaling, involving disseveration. The method attempts to maintain the mass balance of the fine scaled predictions with the available coarse scaled information, through an iterative algorithm which attempts to reconstruct the variation of a property at a prescribed fine scale through an empirical function using environmental or covariate information. One of the advantages associated with the devised method is that soil property uncertainties at the coarse scale can be incorporated into the downscaling algorithm. Finally Chapter 7 presents a synthesis of the investigations made in Chapters 2 to 6 and summarises the pertinent findings. Directly from the investigations carried out during this project there are opportunities for further work; both in terms of addressing shortcomings that were highlighted but not investigated in the thesis, and more generally for advancing digital soil mapping to an operational status and beyond

3-Dimensional Building Details from Aerial Photography for Internet Maps

This paper introduces the automated characterization of real estate (real property) for Internet mapping. It proposes a processing framework to achieve this task from vertical aerial photography and associated property information. A demonstration of the feasibility of an automated solution builds on test data from the Austrian City of Graz. Information is extracted from vertical aerial photography and various data products derived from that photography in the form of a true orthophoto, a dense digital surface model and digital terrain model, and a classification of land cover. Maps of cadastral property boundaries aid in defining real properties. Our goal is to develop a table for each property with descriptive numbers about the buildings, their dimensions, number of floors, number of windows, roof shapes, impervious surfaces, garages, sheds, vegetation, presence of a basement floor, and other descriptors of interest for each and every property of a city. From aerial sources, at a pixel size of 10 cm, we show that we have obtained positional accuracies in the range of a single pixel, an accuracy of areas in the 10% range, floor counts at an accuracy of 93% and window counts at 86% accuracy. We also introduce 3D point clouds of facades and their creation from vertical aerial photography, and how these point clouds can support the definition of complex facades

Automatic Geospatial Data Conflation Using Semantic Web Technologies

Duplicate geospatial data collections and maintenance are an extensive problem across Australia government organisations. This research examines how Semantic Web technologies can be used to automate the geospatial data conflation process. The research presents a new approach where generation of OWL ontologies based on output data models and presenting geospatial data as RDF triples serve as the basis for the solution and SWRL rules serve as the core to automate the geospatial data conflation processes

Developing tools and models for evaluating geospatial data integration of official and VGI data sources

PhD ThesisIn recent years, systems have been developed which enable users to produce, share and update information on the web effectively and freely as User Generated Content (UGC) data (including Volunteered Geographic Information (VGI)). Data quality assessment is a major concern for supporting the accurate and efficient spatial data integration required if VGI is to be used alongside official, formal, usually governmental datasets. This thesis aims to develop tools and models for the purpose of assessing such integration possibilities. Initially, in order to undertake this task, geometrical similarity of formal and informal data was examined. Geometrical analyses were performed by developing specific programme interfaces to assess the positional, linear and polygon shape similarity among reference field survey data (FS); official datasets such as data from Ordnance Survey (OS), UK and General Directorate for Survey (GDS), Iraq agencies; and VGI information such as OpenStreetMap (OSM) datasets. A discussion of the design and implementation of these tools and interfaces is presented. A methodology has been developed to assess such positional and shape similarity by applying different metrics and standard indices such as the National Standard for Spatial Data Accuracy (NSSDA) for positional quality; techniques such as buffering overlays for linear similarity; and application of moments invariant for polygon shape similarity evaluations. The results suggested that difficulties exist for any geometrical integration of OSM data with both bench mark FS and formal datasets, but that formal data is very close to reference datasets. An investigation was carried out into contributing factors such as data sources, feature types and number of data collectors that may affect the geometrical quality of OSM data and consequently affect the integration process of OSM datasets with FS, OS and GDS. Factorial designs were undertaken in this study in order to develop and implement an experiment to discover the effect of these factors individually and the interaction between each of them. The analysis found that data source is the most significant factor that affects the geometrical quality of OSM datasets, and that there are interactions among all these factors at different levels of interaction. This work also investigated the possibility of integrating feature classification of official datasets such as data from OS and GDS geospatial data agencies, and informal datasets such as OSM information. In this context, two different models were developed. The first set of analysis included the evaluation of semantic integration of corresponding feature classifications of compared datasets. The second model was concerned with assessing the ability of XML schema matching of feature classifications of tested datasets. This initially involved a tokenization process in order to split up into single words classifications that were composed of multiple words. Subsequently, encoding feature classifications as XML schema trees was undertaken. The semantic similarity, data type similarity and structural similarity were measured between the nodes of compared schema trees. Once these three similarities had been computed, a weighted combination technique has been adopted in order to obtain the overall similarity. The findings of both sets of analysis were not encouraging as far as the possibility of effectively integrating feature classifications of VGI datasets, such as OSM information, and formal datasets, such as OS and GDS datasets, is concerned.Ministry of Higher Education and Scientific Research, Republic of Iraq