Rolf A. de By

A system of types and operators for handling vague spatial objects

Vagueness is often present in spatial phenomena. Representing and analysing vague spatial phenomena requires vague objects and operators, whereas current GIS and spatial databases can only handle crisp objects. This paper provides mathematical definitions for vague object types and operators. The object types that we propose are a set of simple types, a set of general types, and vague partitions. The simple types represent identifiable objects of a simple structure, i.e. not divisible into components. They are vague points, vague lines, and vague regions. The general types represent classes of simple type objects. They are vague multipoint, vague multiline, and vague multiregion. General types assure closure under set operators. Simple and general types are defined as fuzzy sets in ℝ2 satisfying specific properties that are expressed in terms of topological notions. These properties assure that set membership values change mostly gradually, allowing stepwise jumps. The type vague partition is a collection of vague multiregions that might intersect each other only at their transition boundaries. It allows for a soft classification of space. All types allow for both a finite and an infinite number of transition levels. They include crisp objects as special cases. We consider a standard set of operators on crisp objects and define them for vague objects. We provide definitions for operators returning spatial types. They are regularized fuzzy set operators: union, intersection, and difference; two operators from topology: boundary and frontier; and two operators on vague partitions: overlay and fusion. Other spatial operators, topological predicates and metric operators, are introduced giving their intuition and example definitions. All these operators include crisp operators as special cases. Types and operators provided in this paper form a model for a spatial data system that can handle vague information. The paper is illustrated with an application of vague objects in coastal erosion.

Aggregation and comparison of trajectories

Dealing with moving objects necessitates having available complete geographical traces for determining exact or possible locations that objects have had, have or will have. This is where trajectory determination plays an important role, and on which classification, aggregation and comparison methods must be built. The purpose of aggregation is to identify similar trajectories and to represent them by a single trajectory.Although much work has been done in similarity measurements for time series data, they mainly deal with one dimensional time series. On the other hand, they are good for short time series and in absence of noise, which is definitely not the case for moving objects. This paper describes different approaches to aggregate similar trajectories.

Enhancing Geo‐Service Chaining through Deep Service Descriptions

We demonstrate the integrated use of semantic and syntactic service descriptions, called deep service descriptions, for service chaining by combining two prototypes: one that deals with geoservice discovery abstract composition (called ‘GeoMatchMaker’), with one that supports concrete composition and execution of geoservices services (called ‘Integrated Component Designer’). Most other service chaining approaches confine themselves to handling either syntactic or semantic service descriptions. The proprietary formats of these descriptions hamper an effective integration of discovery, composition and execution of multiple services. In essence, service chaining should help a user by

Typed Sets as a Basis for Object-Oriented Database Schemas

The object-oriented data model TM is a language that is based on the formal theory of FM, a typed language with object-oriented features such as attributes and methods in the presence of subtyping. The general (typed) set constructs of FM allow one to deal with (database) constraints in TM.The paper describes the theory of FM, and discusses the role that set expressions may play in conceptual database schemas. Special attention is paid to the treatment of constraints, and a three-step specification approach is proposed. This approach results in the formal notion of database universe stated as an FM expression.

Interannual variability of NDVI and bird species diversity in Kenya

Abstract Species richness, or simply the number of species in a given area, is commonly used as an important indicator of biological diversity. Spatial variability in species richness has been postulated to depend upon environmental factors such as climate and climatic variability, which in turn may affect net primary productivity. The Advanced Very High Resolution Radiometer (AVHRR) derived Normalized Difference Vegetation Index (NDVI) has been shown to be correlated with climatic variables including rainfall, actual evapotranspiration and net primary productivity. To determine factors favoring high species richness, we examined the relationship between interannual NDVI variables and species richness of birds at a quarter degree scale (55 × 55 km). Results revealed a strong positive correlation between species richness and maximum average NDVI. Conversely, species richness showed negative correlation with standard deviation of maximum NDVI and the coefficient of variation. Though these relationships are indirect, they apparently operate through the green vegetation cover. Understanding such relationships can help in mapping and monitoring biological diversity, as well as in estimating changes in species richness in response to global climatic change.

Towards geosocial recommender systems

The usage of social networks sites (SNSs), such as Facebook, and geosocial networks (GSNs), such as Foursquare, has increased tremendously over the past years. The willingness of users to share their current locations and experiences facilitate the creation of geographical recommender systems based on user generated content (UGC). This idea has been used to create a substantial amount of geosocial recommender systems (GRSs), such as Gogobot, TripIt, and Trippy already, but can be applied to more complex scenarios, such as the recommendation of products with a strong binding to their region, such as real estate or vacation destinations. This extended form of GRS development requires advanced functionality for information collection (from the web, other social media and allowing the users to supply UGC inside the application), information enrichment (such as data quality assessment and advanced data analysis), and personalized recommendations. The creation of a toolset to cope with these challenges is the goal of this research project, for which the outline is presented in this paper.

Storage and Manipulation of Vague Spatial Objects Using Existing GIS Functionality

We collect and store data to derive information and make judgments about a world of our interest. Ideally, they should indicate in a unique and certain way which possible world corresponds to the actual world [17]. Imperfection arises when this is not possible. Imprecision is a type of imperfection that is often encountered. Data are imprecise if we cannot precisely define the actual world, i.e. several worlds satisfy the data. A specific type of imprecision is vagueness [17; 22], which is the focus of this study. A concept is vague if objects exist that cannot be classified either to the concept or to its complement. Vagueness arises in the presence of borderline cases [18]. It is often present in collected spatial information, such as forest inventories, or geological, soil, and vegetation maps. Soil or vegetation classes are such that they cannot be defined sharply. The change from one class to another is gradual. This is in confliict with current geographical information systems (GIS) which assume that spatial objects are precisely defined, sharp objects, using points, lines, and polygons as representations.

Automated semantic trajectory annotation with indoor point-of-interest visits in urban areas

User trajectories contain a wealth of implicit information. The places that people visit, provide us with information about their preferences and needs. Furthermore, it provides us with information about the popularity of places, for example at which time of the year or day these places are frequently visited. The potential for behavioral analysis of trajectories is widely discussed in literature, but all of these methods need a pre-processing step: the geometric trajectory data needs to be transformed into a semantic collection or sequence of visited points-of-interest that is more suitable for data mining. Especially indoor activities in urban areas are challenging to detect from raw trajectory data. In this paper, we propose a new algorithm for the automated detection of visited points-of-interest. This algorithm extracts the actual visited points-of-interest well, both in terms of precision and recall, even for the challenging urban indoor activity detection. We demonstrate the strength of the algorithm by comparing it to three existing and widely used algorithms, using annotated trajectory data, collected through an experiment with students in the city of Hengelo, The Netherlands. Our algorithm, which combines multiple trajectory pre-processing techniques from existing work with several novel ones, shows significant improvements.

A geo-information system approach for forest fire likelihood based on causative and anti-causative factors

Innumerable forest fire spread models exist for taking a decision, but far less focus is on the real causative factors which initiate/ignite fire in an area. It has been observed that the majority of the forest fires in India are initiated due to anthropogenic factors. In this study, we develop a geo-information system approach for management of forest fire in Mudumalai Wildlife Sanctuary, Tamil Nadu, India, with the objective to develop a forest fire likelihood model, integrating GIS and knowledge-based approach for predicting fire-sensitive initiation areas considering major causative and anti-causative factors. Amongst the various causative factors investigated, it was found that wildlife-dependent factor (antler collection and poaching) contributed significantly to fire occurrence followed by management-dependent factors (uncontrolled tourism and grazing), with much less influence of demographic factors. Similarly, anti-causative factor (stationing of anti-poaching/ fire camps) was considered as quite significant. The likelihood model so developed, envisaging various factors and flammability, accounted for different scenarios as a result of pair-wise comparison on an ordinal scale in a knowledge matrix. The inferential statistics computed indicated the robustness of the model and its insensitivity to moderate changes. It makes it possible for this forest fire likelihood model to predict and prevent a forest fire in an effective and scientific manner because it can assume forest fire likelihood in real time and present in proper time.

Digital earth applications in the twenty-first century

In these early years of the twenty-first century, we must look at how the truly cross-cutting information technology supports other innovations, and how it will fundamentally change the information positions of government, private sector and the scientific domain as well as the citizen. In those positions, location will be a prominent linking pin. The classical top-down system architectures of information exchange will be diluted by peer-to-peer and bottom-up channels, forcing us to rethink their designs. We should not only focus on better architectures, but need to attend to a different economy of information exchange, in which the ‘client’ is not only the information sink, but has become an important source as well. The laws of this rising ‘infoconomy’ have yet to be settled on. This special issue on ‘Digital Earth Applications: Technological design and organizational strategies’ brings together a number of papers that shed light on this future information ecosystem in which location-specific information will be exchanged between stakeholders. The introduction presents a framework that combines geoinformation streams and organisations brokering between government, science, private sector and citizens. This novel framework helps us improve the appreciation of those papers.