Towards development of fuzzy spatial datacubes : fundamental concepts with example for multidimensional coastal erosion risk assessment and representation

Les systèmes actuels de base de données géodécisionnels (GeoBI) ne tiennent généralement pas compte de l'incertitude liée à l'imprécision et le flou des objets; ils supposent que les objets ont une sémantique, une géométrie et une temporalité bien définies et précises. Un exemple de cela est la représentation des zones à risque par des polygones avec des limites bien définies. Ces polygones sont créés en utilisant des agrégations d'un ensemble d'unités spatiales définies sur soit des intérêts des organismes responsables ou les divisions de recensement national. Malgré la variation spatio-temporelle des multiples critères impliqués dans l’analyse du risque, chaque polygone a une valeur unique de risque attribué de façon homogène sur l'étendue du territoire. En réalité, la valeur du risque change progressivement d'un polygone à l'autre. Le passage d'une zone à l'autre n'est donc pas bien représenté avec les modèles d’objets bien définis (crisp). Cette thèse propose des concepts fondamentaux pour le développement d'une approche combinant le paradigme GeoBI et le concept flou de considérer la présence de l’incertitude spatiale dans la représentation des zones à risque. En fin de compte, nous supposons cela devrait améliorer l’analyse du risque. Pour ce faire, un cadre conceptuel est développé pour créer un model conceptuel d’une base de donnée multidimensionnelle avec une application pour l’analyse du risque d’érosion côtier. Ensuite, une approche de la représentation des risques fondée sur la logique floue est développée pour traiter l'incertitude spatiale inhérente liée à l'imprécision et le flou des objets. Pour cela, les fonctions d'appartenance floues sont définies en basant sur l’indice de vulnérabilité qui est un composant important du risque. Au lieu de déterminer les limites bien définies entre les zones à risque, l'approche proposée permet une transition en douceur d'une zone à une autre. Les valeurs d'appartenance de plusieurs indicateurs sont ensuite agrégées basées sur la formule des risques et les règles SI-ALORS de la logique floue pour représenter les zones à risque. Ensuite, les éléments clés d'un cube de données spatiales floues sont formalisés en combinant la théorie des ensembles flous et le paradigme de GeoBI. En plus, certains opérateurs d'agrégation spatiale floue sont présentés. En résumé, la principale contribution de cette thèse se réfère de la combinaison de la théorie des ensembles flous et le paradigme de GeoBI. Cela permet l’extraction de connaissances plus compréhensibles et appropriées avec le raisonnement humain à partir de données spatiales et non-spatiales. Pour ce faire, un cadre conceptuel a été proposé sur la base de paradigme GéoBI afin de développer un cube de données spatiale floue dans le system de Spatial Online Analytical Processing (SOLAP) pour évaluer le risque de l'érosion côtière. Cela nécessite d'abord d'élaborer un cadre pour concevoir le modèle conceptuel basé sur les paramètres de risque, d'autre part, de mettre en œuvre l’objet spatial flou dans une base de données spatiales multidimensionnelle, puis l'agrégation des objets spatiaux flous pour envisager à la représentation multi-échelle des zones à risque. Pour valider l'approche proposée, elle est appliquée à la région Perce (Est du Québec, Canada) comme une étude de cas.Current Geospatial Business Intelligence (GeoBI) systems typically do not take into account the uncertainty related to vagueness and fuzziness of objects; they assume that the objects have well-defined and exact semantics, geometry, and temporality. Representation of fuzzy zones by polygons with well-defined boundaries is an example of such approximation. This thesis uses an application in Coastal Erosion Risk Analysis (CERA) to illustrate the problems. CERA polygons are created using aggregations of a set of spatial units defined by either the stakeholders’ interests or national census divisions. Despite spatiotemporal variation of the multiple criteria involved in estimating the extent of coastal erosion risk, each polygon typically has a unique value of risk attributed homogeneously across its spatial extent. In reality, risk value changes gradually within polygons and when going from one polygon to another. Therefore, the transition from one zone to another is not properly represented with crisp object models. The main objective of the present thesis is to develop a new approach combining GeoBI paradigm and fuzzy concept to consider the presence of the spatial uncertainty in the representation of risk zones. Ultimately, we assume this should improve coastal erosion risk assessment. To do so, a comprehensive GeoBI-based conceptual framework is developed with an application for Coastal Erosion Risk Assessment (CERA). Then, a fuzzy-based risk representation approach is developed to handle the inherent spatial uncertainty related to vagueness and fuzziness of objects. Fuzzy membership functions are defined by an expert-based vulnerability index. Instead of determining well-defined boundaries between risk zones, the proposed approach permits a smooth transition from one zone to another. The membership values of multiple indicators (e.g. slop and elevation of region under study, infrastructures, houses, hydrology network and so on) are then aggregated based on risk formula and Fuzzy IF-THEN rules to represent risk zones. Also, the key elements of a fuzzy spatial datacube are formally defined by combining fuzzy set theory and GeoBI paradigm. In this regard, some operators of fuzzy spatial aggregation are also formally defined. The main contribution of this study is combining fuzzy set theory and GeoBI. This makes spatial knowledge discovery more understandable with human reasoning and perception. Hence, an analytical conceptual framework was proposed based on GeoBI paradigm to develop a fuzzy spatial datacube within Spatial Online Analytical Processing (SOLAP) to assess coastal erosion risk. This necessitates developing a framework to design a conceptual model based on risk parameters, implementing fuzzy spatial objects in a spatial multi-dimensional database, and aggregating fuzzy spatial objects to deal with multi-scale representation of risk zones. To validate the proposed approach, it is applied to Perce region (Eastern Quebec, Canada) as a case study

A survey of qualitative spatial representations

Representation and reasoning with qualitative spatial relations is an important problem in artificial intelligence and has wide applications in the fields of geographic information system, computer vision, autonomous robot navigation, natural language understanding, spatial databases and so on. The reasons for this interest in using qualitative spatial relations include cognitive comprehensibility, efficiency and computational facility. This paper summarizes progress in qualitative spatial representation by describing key calculi representing different types of spatial relationships. The paper concludes with a discussion of current research and glimpse of future work

Metsävaratiedon hallinta yli ajan ja mittakaavojen

During the last decades there has been a global shift in forest management from a focus solely on timber management to ecosystem management that endorses all aspects of forest functions: ecological, economic and social. This has resulted in a shift in paradigm from sustained yield to sustained diversity of values, goods and benefits obtained at the same time, introducing new temporal and spatial scales into forest resource management. The purpose of the present dissertation was to develop methods that would enable spatial and temporal scales to be introduced into the storage, processing, access and utilization of forest resource data. The methods developed are based on a conceptual view of a forest as a hierarchically nested collection of objects that can have a dynamically changing set of attributes. The temporal aspect of the methods consists of lifetime management for the objects and their attributes and of a temporal succession linking the objects together. Development of the forest resource data processing method concentrated on the extensibility and configurability of the data content and model calculations, allowing for a diverse set of processing operations to be executed using the same framework. The contribution of this dissertation to the utilisation of multi-scale forest resource data lies in the development of a reference data generation method to support forest inventory methods in approaching single-tree resolution.Perinteisesti metsävarojen hyödyntämisessä on keskitytty puuvarantoon, mutta viimeisimpinä vuosikymmeninä myös ekologiset, taloudelliset ja sosiaaliset ulottuvuudet ovat saaneet painoarvoa. Metsävarojen hallinnan kannalta tämä tarkoittaa uusien ajallisten ja tilallisten ulottuvuuksien lisäämistä osaksi toimintaa. Jotta voitaisiin arvioida, onko metsävarojen käyttö vastannut sille asetettuja tavoitteita, tulisi olla mahdollista seurata metsien muuttumista ajan myötä. Tämän tutkimuksen tavoitteena olikin kehittää tiedonhallinnallisia menetelmiä ajan ja eri mittakaavojen yhdistämiseksi osaksi metsävaratietojen hallintaa. Tutkimuksessa pyrittiin vastaamaan kysymykseen kuinka nykyhetken tiedot voitaisiin säilöä käytettävässä muodossa niiden muuttuessa historiatiedoiksi, toisin sanoen tutkimuksessa etsittiin menetelmiä säilyttää ajantasaisen puustotiedon lisäksi tieto metsän menneisyydestä. Lisäksi tutkittiin kuinka metsää voitaisiin tarkastella useammista näkökulmista samanaikaisesti, esimerkiksi puiden, puuryhmien, metsäkuvioiden tai suurempien alueiden tasolla, ja kuinka nämä aikaan ja paikkaan sidotut tiedonhallinnan tarpeet voitaisiin yhdistää. Menetelmäkehitys jakaantui neljään osaan: aika-paikkatiedon tallentamisen, laskennallisen käsittelyn, tiedonhaun ja hyödyntämisen menetelmiin. Kehitetyt tiedonhallinnan menetelmät perustuvat työssä kehitettyyn käsitteelliseen malliin, jossa metsä kuvataan joukkona hierarkisia kohteita. Toisin sanoen, kullakin kohteella voi olla joukko alikohteita, joilla taas voi olla omat alikohteensa ja niin edelleen. Esimerkkinä tästä toimii edellä mainittu suuralue-metsäkuvio-puuryhmä-puu hierarkia. Oleellista menetelmien kannalta oli mahdollistaa tietosisällön ja tiedon käsittelyyn käytettävien mallien mahdollisimman vapaa muokattavuus, sillä aika tuo väistämättä mukanaan muutoksia siinä, mitä metsästä mitataan ja miten mittaustietoa malleilla käsitellään. Monimittakaavaisen metsävaratiedon hyödyntämisen menetelmien osalta tässä väitöksessä kehitettiin menetelmä, jolla voidaan tuottaa hakkuukoneen tuottamasta mittaustiedosta kustannustehokkaasti yksittäisen puun tasoa lähestyvää maastotietoa kaukokartoitusmenetelmien käyttöön

Scale in remote sensing and its impact on landscape ecology

The use of remotely sensed derived thematic data has become ubiquitous in landscape ecology. Remote sensing data has the potential to describe broad scale landscape patterns and relate them to ecological processes such as species persistence and distribution. However, these datasets are being used without considering the spatial uncertainty that is ever present in remote sensing data. Maps derived from remote sensing data will vary in the extent, patchiness and accuracy of their landcover classes predominantly due to the inter-relationships of a number of scale dependent factors such as pixel size, minimum mappable unit and categorical resolution. Furthermore, the effect of these factors on landcover classification is more pronounced in fragmented environments which are spatially complex, with habitat patches varying in size from roadside reserves (~10m2) to large vegetation remnants contained within national parks (100km2). This thesis investigated the interaction and the relative importance of scale dependent factors on the characterisation of landscape pattern and ecological analysis using real and synthetic landcover data and simulated species-environment models. Several key findings emerged from this research. Firstly, it found that mapping error was highest when the scale of the feature and the raster grid coincided. Ecologically important landscape elements such as small and linear vegetation patches of similar scales to the raster grid had lower classification accuracies, and were less likely to be extracted than larger more compact features. Secondly, this thesis showed that at coarser scales, subtle levels of patchiness declined. Small patches either aggregated into larger patches or completely disappeared. Thirdly, it demonstrated that the ability to identify the scale at which a species interacts with the environment, using multi-scale species-environment models is affected by the scale of the remote sensing data. In conclusion, this thesis quantified the impact of scale on the classification of landcover maps and demonstrated how spatial uncertainty in the characterisation of landscape pattern can impact on ecological analysis. Without the incorporation of uncertainty arising from scale, ecological analyses using remote sensing data will continue to produce results with unquantified uncertainties, which may result in poor and/or ineffective management decisions

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

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

An Evolutionary Approach to Adaptive Image Analysis for Retrieving and Long-term Monitoring Historical Land Use from Spatiotemporally Heterogeneous Map Sources

Land use changes have become a major contributor to the anthropogenic global change. The ongoing dispersion and concentration of the human species, being at their orders unprecedented, have indisputably altered Earth’s surface and atmosphere. The effects are so salient and irreversible that a new geological epoch, following the interglacial Holocene, has been announced: the Anthropocene. While its onset is by some scholars dated back to the Neolithic revolution, it is commonly referred to the late 18th century. The rapid development since the industrial revolution and its implications gave rise to an increasing awareness of the extensive anthropogenic land change and led to an urgent need for sustainable strategies for land use and land management. By preserving of landscape and settlement patterns at discrete points in time, archival geospatial data sources such as remote sensing imagery and historical geotopographic maps, in particular, could give evidence of the dynamic land use change during this crucial period. In this context, this thesis set out to explore the potentials of retrospective geoinformation for monitoring, communicating, modeling and eventually understanding the complex and gradually evolving processes of land cover and land use change. Currently, large amounts of geospatial data sources such as archival maps are being worldwide made online accessible by libraries and national mapping agencies. Despite their abundance and relevance, the usage of historical land use and land cover information in research is still often hindered by the laborious visual interpretation, limiting the temporal and spatial coverage of studies. Thus, the core of the thesis is dedicated to the computational acquisition of geoinformation from archival map sources by means of digital image analysis. Based on a comprehensive review of literature as well as the data and proposed algorithms, two major challenges for long-term retrospective information acquisition and change detection were identified: first, the diversity of geographical entity representations over space and time, and second, the uncertainty inherent to both the data source itself and its utilization for land change detection. To address the former challenge, image segmentation is considered a global non-linear optimization problem. The segmentation methods and parameters are adjusted using a metaheuristic, evolutionary approach. For preserving adaptability in high level image analysis, a hybrid model- and data-driven strategy, combining a knowledge-based and a neural net classifier, is recommended. To address the second challenge, a probabilistic object- and field-based change detection approach for modeling the positional, thematic, and temporal uncertainty adherent to both data and processing, is developed. Experimental results indicate the suitability of the methodology in support of land change monitoring. In conclusion, potentials of application and directions for further research are given

A new trajectory for spatial data infrastructure evolution in the developing world

Includes abstract.Includes bibliographical references (leaves 107-113).Spatial Data is a key resource in the development of cities. There is a lot of socio-economic potential that is locked away in spatial data holdings and this potential is unlocked by making the datasets widely available for use. Spatial Data Infrastructures (SDIs) have served this primary purpose; to make data accessible through the use of web based technologies. However, SDIs have not had their anticipated impact at local levels of governance. They have traditionally served as platforms that facilitate access to raw spatial datasets. They have not fully facilitated for the use of these datasets and therefore have attracted minimal attention from decision makers and users. This research suggests a new trajectory for SDI evolution; a trajectory that will allow them to evolve into more relevant platforms for confronting the urban crisis in developing nations and thereby ensuring that they have the societal impact that they are intended to. The research explores the characteristics of the mainstream efforts to counter urban crises in the developing world to determine how the new SDI should be re-conceptualised to more adequately assist in responding to the urban crisis. This leads to the incorporation of Evidence Based Practice (EBP) into SDI through the use of urban indicators and knowledge creation processes to reflect on the pressing societal issues. From the new SDI concept, an architectural design is implemented as a “proof of concept”. At the heart of this new concept is the SDIs ability to provide access to more than just raw spatial datasets but useful information products that are based on these data. This proves that EBP can be incorporated into SDI to make them more efficient in responding to the urban problems in developing nation and consequently more relevant Information Infrastructures for urban decision makers

Points of comparison : what indicating gestures tell us about the origins of signs in San Juan Quiahije Chatino sign language

New languages emerge under rare conditions, when deaf children who cannot access the vocal-auditory language(s) used around them invent visual-manual communication systems of their own. Such homesign or family sign systems have simple structures but nevertheless show the hallmarks of language, including a stable lexicon of signs composed of meaningful, recombinable elements. Prior research has found that many of these elements are invented by signers, though some are adapted from the gestural input received from hearing interlocutors. The current project returns to this claim, examining the influence of gestures on the structure of two emerging family sign languages used in a rural, indigenous community in Oaxaca, Mexico. It focuses on foundational, visually accessible ‘indicating gestures’ such as pointing that direct the addressees attention to a region in physical space. Three linked studies were performed to investigate whether indicating gestures have internal structure that is accessible to deaf signers, and whether such structure is incorporated into their emerging languages. In the first, the spontaneous, speech-linked indicating gestures of hearing people were examined for internal structure. They were found to comprise three recombinable elements that, through systematic modulations in form, convey information about the direction and distance of targets. A second study looked for a relationship between the form of indicating gestures and the features of the speech that accompanies them. No such relationship was found, suggesting that the meaningful modulation of the gesture features occurs independently from speech. The final study compared the forms and meanings of two deaf signers’ indicating gestures with those of the hearing participants. Signers were found to use the direction and elbow height features, but not the handshape features, from the conventional indicating system. These findings reveal that indicating gestures, often described as holistic, non-composite signals, in fact exhibit an internal structure that can be incorporated into an emerging signed language. Interestingly, they also reveal that not all features of gestures—even ones that exhibit clear patterning—will be adopted by signers, perhaps because gesture features must be both systematically patterned and visually iconic for signers to interpret them as meaningful.Linguistic

Improving Region-Scale Landslide Inventory and Susceptibility Mapping

Landslides are a natural hazard that have major societal, economic, and environmental impacts. The understanding of landslides begins with the observation that they are a spatial problem. Spatial relationships have historically been explored with maps, and landslides are no different. Such maps range in focus from inventory maps, which identify where landslides have occurred, to susceptibility maps, which identify where landslides might occur. The current state of landslide inventory mapping consists of trained geologists scouring the terrain, either on the ground or using remotely sensed imagery or Light Detection and Ranging (lidar). Lidar is particularly advantageous due to its high resolution and ability to reveal geomorphologic features in vegetated areas. Inventories produced in these ways are typically of very high quality, but time consuming to produce. Susceptibility maps often relate landslide causative factors determined using an inventory map to provide categorical descriptions or probabilities of landslide likelihood. Among all susceptibility maps there is a shortcoming in the treatment of the mechanical properties of landslides. Mechanical properties, such as soil unit weight, pore water pressures, rupture surface geometry, and shear strength, are fundamental to detailed landslide studies of individual landslides, but are either omitted or overly simplified in susceptibility analysis. To improve the processes of landslide mapping so that their products may be used to better understand and predict landslide activity, we improved the capability of an existing semi-automated method for producing landslide inventories with a new approach that can be implemented rapidly across diverse terrain. We then sought to improve susceptibility mapping by modeling three-dimensional geometry of landslide rupture surfaces across entire inventories. Finally, we performed forensic analyses using the rupture surface geometry to reliably characterize soil strength at a regional scale. Our results present a preliminary, time- and cost-efficient framework for investigating landslides on a regional scale and represent the first true synthesis of geomorphologic and geotechnical principles