Distinguishing extensive and intensive properties for meaningful geocomputation and mapping

A most fundamental and far-reaching trait of geographic information is the distinction between extensive and intensive properties. In common understanding, originating in Physics and Chemistry, extensive properties increase with the size of their supporting objects, while intensive properties are independent of this size. It has long been recognized that the decision whether analytical and cartographic measures can be meaningfully applied depends on whether an attribute is considered intensive or extensive. For example, the choice of a map type as well as the application of basic geocomputational operations, such as spatial intersections, aggregations or algebraic operations such as sums and weighted averages, strongly depend on this semantic distinction. So far, however, the distinction can only be drawn in the head of an analyst. We still lack practical ways of automation for composing GIS workflows and to scale up mapping and geocomputation over many data sources, e.g. in statistical portals. In this article, we test a machine-learning model that is capable of labeling extensive/ intensive region attributes with high accuracy based on simple characteristics extractable from geodata files. Furthermore, we propose an ontology pattern that captures central applicability constraints for automating data conversion and mapping using Semantic Web technology

THE DEVELOPMENT OF A HOLISTIC EXPERT SYSTEM FOR INTEGRATED COASTAL ZONE MANAGEMENT

Coastal data and information comprise a massive and complex resource, which is vital to the practice of Integrated Coastal Zone Management (ICZM), an increasingly important application. ICZM is just as complex, but uses the holistic paradigm to deal with the sophistication. The application domain and its resource require a tool of matching characteristics, which is facilitated by the current wide availability of high performance computing. An object-oriented expert system, COAMES, has been constructed to prove this concept. The application of expert systems to ICZM in particular has been flagged as a viable challenge and yet very few have taken it up. COAMES uses the Dempster- Shafer theory of evidence to reason with uncertainty and importantly introduces the power of ignorance and integration to model the holistic approach. In addition, object orientation enables a modular approach, embodied in the inference engine - knowledge base separation. Two case studies have been developed to test COAMES. In both case studies, knowledge has been successfully used to drive data and actions using metadata. Thus a holism of data, information and knowledge has been achieved. Also, a technological holism has been proved through the effective classification of landforms on the rapidly eroding Holderness coast. A holism across disciplines and CZM institutions has been effected by intelligent metadata management of a Fal Estuary dataset. Finally, the differing spatial and temporal scales that the two case studies operate at implicitly demonstrate a holism of scale, though explicit means of managing scale were suggested. In all cases the same knowledge structure was used to effectively manage and disseminate coastal data, information and knowledge

The Semantics of Extensive Quantities within Geographic Information

The next generation of Geographic Information Systems (GIS) is anticipated to automate some of the reasoning required for spatial analysis. An important step in the development of such systems is to gain a better understanding and corresponding modeling practice of when to apply arithmetic operations to quantities. The concept of extensivity plays an essential role in determining when quantities can be aggregated by summing them, and when this is not possible. This is of particular importance to geographic information systems, which serve to quantify phenomena across space and time. However, currently, multiple contrasting definitions of extensivity exist, and none of these suffice for handling the different practical cases occurring in geographic information. As a result, analysts predominantly rely on intuition and ad hoc reasoning to determine whether two quantities are additive. In this paper, we present a novel approach to formalizing the concept of extensivity. Though our notion as such is not restricted to quantifications occurring within geographic information, it is particularly useful for this purpose. Following the idea of spatio-temporal controls by Sinton, we define extensivity as a property of measurements of quantities with respect to a controlling quantity, such that a sum of the latter implies a sum of the former. In our algebraic definition of amounts and other quantities, we do away with some of the constraints that limit the usability of older approaches. By treating extensivity as a relation between amounts and other types of quantities, our definition offers the flexibility to relate a quantity to many domains of interest. We show how this new notion of extensivity can be used to classify the kinds of amounts in various examples of geographic information

Ontology of core concept data types for answering geo-analytical questions

In geographic information systems (GIS), analysts answer questions by designing workflows that transform a certain type of data into a certain type of goal. Semantic data types help constrain the application of computational methods to those that are meaningful for such a goal. This prevents pointless computations and helps analysts design effective workflows. Yet, to date it remains unclear which types would be needed in order to ease geo-analytical tasks. The data types and formats used in GIS still allow for huge amounts of syntactically possible but nonsensical method applications. Core concepts of spatial information and related geo-semantic distinctions have been proposed as abstractions to help analysts formulate analytic questions and to compute appropriate answers over geodata of different formats. In essence, core concepts reflect particular interpretations of data which imply that certain transformations are possible. However, core concepts usually remain implicit when operating on geodata, since a concept can be represented in a variety of forms. A central question therefore is: Which semantic types would be needed to capture this variety and its implications for geospatial analysis? In this article, we propose an ontology design pattern of core concept data types that help answer geo-analytical questions. Based on a scenario to compute a liveability atlas for Amsterdam, we show that diverse kinds of geo-analytical questions can be answered by this pattern in terms of valid, automatically constructible GIS workflows using standard sources

Geovisualization

Geovisualization involves the depiction of spatial data in an attempt to facilitate the interpretation of observational and simulated datasets through which Earth's surface and solid Earth processes may be understood. Numerous techniques can be applied to imagery, digital elevation models, and other geographic information system data layers to explore for patterns and depict landscape characteristics. Given the rapid proliferation of remotely sensed data and high-resolution digital elevation models, the focus is on the visualization of satellite imagery and terrain morphology, where manual human interpretation plays a fundamental role in the study of geomorphic processes and the mapping of landforms. A treatment of some techniques is provided that can be used to enhance satellite imagery and the visualization of the topography to improve landform identification as part of geomorphological mapping. Visual interaction with spatial data is an important part of exploring and understanding geomorphological datasets, and a variety of methods exist ranging across simple overlay, panning and zooming, 2.5D, 3D, and temporal analyses. Specific visualization outputs are also covered that focus on static and interactive methods of dissemination. Geomorphological mapping legends and the cartographic principles for map design are discussed, followed by details of dynamic web-based mapping systems that allow for greater immersive use by end users and the effective dissemination of data

A globally relevant change taxonomy and evidence-based change framework for land monitoring

A globally relevant and standardized taxonomy and framework for consistently describing land cover change based on evidence is presented, which makes use of structured land cover taxonomies and is underpinned by the Driver-Pressure-State�Impact-Response (DPSIR) framework. The Global Change Taxonomy currently lists 246 classes based on the notation ‘impact (pressure)’, with this encompassing the consequence of observed change and associated reason(s), and uses scale-independent terms that factor in time. Evidence for different impacts is gathered through temporal comparison (e.g., days, decades apart) of land cover classes constructed and described from Environmental Descriptors (EDs; state indicators) with pre-defined measurement units (e.g., m, %) or categories (e.g., species type). Evidence for pressures, whether abiotic, biotic or human-influenced, is similarly accumulated, but EDs often differ from those used to determine impacts. Each impact and pressure term is defined separately, allowing flexible combination into ‘impact (pressure)’ categories, and all are listed in an openly accessible glossary to ensure consistent use and common understanding. The taxonomy and framework are globally relevant and can reference EDs quantified on the ground, retrieved/classified remotely (from groundbased, airborne or spaceborne sensors) or predicted through modelling. By providing capacity to more consistently describe change processes—including land degradation, desertification and ecosystem restoration—the overall framework addresses a wide and diverse range of local to international needs including those relevant to policy, socioeconomics and land management. Actions in response to impacts and pressures and monitoring towards targets are also supported to assist future planning, including impact mitigation actions

Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond

A globally relevant change taxonomy and evidence-based change framework for land monitoring

A globally relevant and standardized taxonomy and framework for consistently describing land cover change based on evidence is presented, which makes use of structured land cover taxonomies and is underpinned by the Driver-Pressure-State-Impact-Response (DPSIR) framework. The Global Change Taxonomy currently lists 246 classes based on the notation 'impact (pressure)', with this encompassing the consequence of observed change and associated reason(s), and uses scale-independent terms that factor in time. Evidence for different impacts is gathered through temporal comparison (e.g., days, decades apart) of land cover classes constructed and described from Environmental Descriptors (EDs; state indicators) with pre-defined measurement units (e.g., m, %) or categories (e.g., species type). Evidence for pressures, whether abiotic, biotic or human-influenced, is similarly accumulated, but EDs often differ from those used to determine impacts. Each impact and pressure term is defined separately, allowing flexible combination into 'impact (pressure)' categories, and all are listed in an openly accessible glossary to ensure consistent use and common understanding. The taxonomy and framework are globally relevant and can reference EDs quantified on the ground, retrieved/classified remotely (from ground-based, airborne or spaceborne sensors) or predicted through modelling. By providing capacity to more consistently describe change processes-including land degradation, desertification and ecosystem restoration-the overall framework addresses a wide and diverse range of local to international needs including those relevant to policy, socioeconomics and land management. Actions in response to impacts and pressures and monitoring towards targets are also supported to assist future planning, including impact mitigation actions

A grammar for interpreting geo-analytical questions as concept transformations

Geographic Question Answering (GeoQA) systems can automatically answer questions phrased in natural language. Potentially this may enable data analysts to make use of geographic information without requiring any GIS skills. However, going beyond the retrieval of existing geographic facts on particular places remains a challenge. Current systems usually cannot handle geo-analytical questions that require GIS analysis procedures to arrive at answers. To enable geo-analytical QA, GeoQA systems need to interpret questions in terms of a transformation that can be implemented in a GIS workflow. To this end, we propose a novel approach to question parsing that interprets questions in terms of core concepts of spatial information and their functional roles in context-free grammar. The core concepts help model spatial information in questions independently from implementation formats, and their functional roles indicate how concepts are transformed and used in a workflow. Using our parser, geo-analytical questions can be converted into expressions of concept transformations corresponding to abstract GIS workflows. We developed our approach on a corpus of 309 GIS-related questions and tested it on an independent source of 134 test questions including workflows. The evaluation results show high precision and recall on a gold standard of concept transformations

Defining and delineating urban neighbourhoods: a case study of housing areas in Brent, North London

There is no geographical definition of the neighbourhood despite it being the chosen spatial scale for U.K. government policy tackling social injustice and rebuilding democracy, and the setting for the majority of life’s experiences where an individual’s lifelong welfare is largely determined. Consequently, resources are targeted sub-optimally with environmental and social dynamics largely undiagnosed. This has fuelled an urgent demand for revealing the nature of neighbourhoods, and how they can be identified on the ground and delineated on maps. Implementing a positive methodology I build upon the foremost theoretically-supported pedestrian-street network neighbourhood model, harnessing established theory, and present an integrative geographical theory of the neighbourhood and its practical manifestation to address the research problem. Using Grannis's empirical work as a benchmark I test the transferability of his methodology to the UK and the explanatory power of my housing area neighbourhood model, using correlation analysis, in two London case study areas, with positive results. Housing areas arise from the physical and built environments and are shown to explain social distributions better than other spatial units tested, and compare favourably with applied surrogate neighbourhoods. I then identify the datasets required to roll out the methodology for developing a practical, meaningful and bounded neighbourhood geography. Further analysis confirms the role of wealth as the great social and spatial segregator. The neighbourhood is re-conceptualised as an holistic and commonly understood entity, whilst the spatial precision introduced facilitates measurement and assessment for optimal service and resource provision, as well as monitoring and intervention. Presenting structural and social homophily as the geneses of social interaction patterns and the explanation for how space is transformed into place is a paradigm shift in our understanding of this fundamental geographical concept which promises to stimulate additional theoretical substantiation and development whilst providing a framework for phenomenological and ethnographical approaches