A natural language interface can improve human-computer interaction with Geographic Information Systems (GIS). A prerequisite for this is the mapping of natural language expressions onto spatial queries. Previous mapping approaches, using, for example, fuzzy sets, failed because of the flexible and context-dependent use of spatial terms. Context changes the interpretation drastically. For example, the spatial relation "near" can be mapped onto distances ranging anywhere from kilometers to centimeters. We present a context-enriched semiotic triangle that allows us to distinguish between multiple interpretations. As formalization we introduce the notation of contextualized concepts that is tied to one context. One concept inherits multiple contextualized concepts such that multiple interpretations can be distinguished. The interpretation for one contextualized concept corresponds to the intention of the spatial term, and is used as input for a spatial query. To demonstrate our computational model, a next generation GIS is envisioned that maps the spatial relation "near" to spatial queries differently according to the influencing context.

The characterization of place and its representation in current Geographic Information System (GIS) has become a prominent research topic. This paper concentrates on places that are cognitive regions, and presents a computational framework to derive the geographic footprint of these regions. The main idea is to use Natural Language Processing (NLP) tools to identify unique geographic features from User Generated Content (UGC) sources consisting of textual descriptions of places. These features are used to detect on a map an initial area that the descriptions refer to. A semantic representation of this area is extracted from a GIS and passed over to a Machine Learning (ML) algorithm that locates other areas according to semantic similarity. As a case study, we employ the proposed framework to derive the geographic footprint of the historic center of Vienna and validate the results by comparing the derived region against a historical map of the city.

Recent years have witnessed a growing production of Volunteer Geographic Information (VGI). This led to the general availability of semantically rich datasets, allowing for novel ways to understand, analyze or generalize urban areas. This paper presents an approach that exploits this semantic richness to extract urban settings, i.e., conceptually-uniform geographic areas with respect to certain activities. We argue that urban settings are a more accurate way of generalizing cities, since it more closely models human sense-making of urban spaces. To this end, we formalized and implemented a semantic region growing algorithm-a modification of a standard image segmentation procedure. To evaluate our approach, shopping areas of two European capital cities (Vienna and London) were extracted from an OpenStreetMap dataset. Finally, we explored the use of our approach to search for urban settings (e.g., shopping areas) in one city, that are similar to a setting in another.

Data is spatial if it contains references to space. We can easily detect explicit references, for example coordinates, but we cannot detect whether data implicitly contains references to space, and whether it has properties of spatial data, if additional semantic information is missing. In this paper, we propose a graph model that meets typical properties of spatial data. We can, by the comparison of a graph representation of a data set to the graph model, decide whether the data set (implicitly or explicitly) has these typical properties of spatial data.

Geographic data is expensive to collect and maintain and sharing data is crucial for its effective use in urban planning at all levels. For a few hardly ever changing themes the simple distribution of copies of data is feasible, but for other data, access to “live” data and updating, sometimes even distributed updating, of the data is necessary. The organization of sharing data can be separated into three sets of issues: (1) Interpretation: how to understand the data, (2) Authorization: is a user permitted to use the data, and (3) Access: how to achieve effective and non-disturbing use and updating of data by several users? Solutions must take threats into account: hackers may try to steal or disturb the use of data, and the revelations of Snowden's documents only emphasize the danger of others reading data not intended for their eyes. Effective sharing geographic data without conflicts requires integrating results from different areas of computer science research, including at least: cryptography, computer security, database management, and computer networking.

An important aspect of personal information management (PIM) is the support of our prospective memory, that is, the memory of things to do in future. In particular, calendar-tools or todo-lists help us to keep track of plans and intended actions. Their pro-active capabilities to remind users in appropriate contexts remain limited. To achieve context-dependent and dynamic reminders, this work presents a (1) unifying semantic of various types of activities that allows for aggregation; and (2) a prospective memory formalization. Finally, we introduce the theoretical concept of alert-surfaces to enable context dependent reminders.

Whenever a person gets lost and there is no way to access stored spatial information, e.g. in the form of maps, they need to rely on the knowledge of other humans instead. This situation can be modelled as a communication setting where a person lacking spatial knowledge requests information from a knowledgeable source. The result are cognitive transactions in which information over various levels of detail (LoD) is negotiated. The overall goal is to agree on a shared spatial representation with equal semantics, i.e., common ground. We present a communication model that accounts for establishing common ground between two agents. The agents use a modified ”wayfinding choreme” language and special signals to negotiate the LoD. Findings of a case study were used to verify and refine our work.

Researching Cognitive and Linguistic Aspects of Geographic Space - Las Navas then and now.- Spatial Computing - How spatial structures replace computational effort.- The Cognitive Development of the Spatial Concepts NEXT, NEAR, AWAY and FAR.- From compasses and maps to mountains and territories: Experimental results on geographic cognitive categorization.- Prospects and Challenges of Landmarks in Navigation Services.- Landmarks and a hiking ontology to support wayfinding in a national park during different seasons.- Talking about Place Where It Matters.- Many to Many Mobile Maps.- Cognitive and linguistic ideas in geographic information semantics.- Spatial Relation Predicates In Topographic Feature Semantics.- The Egenhofer-Cohn Hypothesis-or, Topological Relativity?.- Twenty Years of Topological Logic.- Reasoning on Class Relations: an Overview.- Creating perceptually salient animated displays of spatiotemporal coordination in events.- Exploring and Reasoning about Perceptual Spaces for Theatre, New Media Installations and the Performing Arts.

Current personal information management (PIM) tools do not sufficiently recognize the spatio-temporal, hierarchical, or conceptual relations of tasks that constitute our plans. Using behavioral observation methods we analyzed people planning a trip to attend a conference taking place in a region they had little or no prior familiarity with. The resulting open-ended records were coded into higher-level segmentsand categories. These served as a basis for a cognitive engineering approach, to propose better design principles for spatio-temporally enabled PIM-tools.

3D city models are getting more important as a field of research and business and received an increasing amount of attention from both the scientific community and the professional field. 3D city models are one of the new tools for sustainable city development. Since development questions occur repeatedly, the used city models should be maintainable, i.e. the system should be kept up-to-date and not be created new for each decision. This is a challenge. Creating a model representing the current status of a city has been addressed in research literature. The major challenge is the vast amount of data to be collected, processed and visualised. However, keeping the resulting model up-to-date has not been discussed yet. Updating requires the introduction of a suitable concept of time in the model. This would then allow representing historic and current status of the city as well as future scenarios. Processes provide the connection between different points in time. Processes also change the appearance of the city and need to be represented in the model for change detection. In this paper, we discuss the challenges and show necessary properties for city models and systems maintaining them to reach a reasonable level of maintainability.

3D city models represent existing physical objects and their topological and functional relations. In everyday life the rights and responsibilities connected to these objects, primarily legally defined rights and obligations but also other socially and culturally established rights, are of importance. The rights and obligations are defined in various laws and it is often difficult to identify the rules applicable for a certain case. The existing 2D cadastres show civil law rights and obligations and plans to extend them to provide information about public law restrictions for land use are in several countries under way. It is tempting to design extensions to the 3D city models to provide information about legal rights in 3D. The paper analyses the different types of information that are needed to reduce conflicts and to facilitate decisions about land use. We identify the role 3D city models augmented with planning information in 3D can play, but do not advocate a general conversion from 2D to 3D for the legal cadastre. Space is not anisotropic and the up/down dimension is practically very different from the two dimensional plane - this difference must be respected when designing spatial information systems. The conclusions are: (1) continue the current regime for ownership of apartments, which is not ownership of a 3D volume, but co-ownership of a building with exclusive use of some rooms; such exclusive use rights could be shown in a 3D city model; (2) ownership of 3D volumes for complex and unusual building situations can be reported in a 3D city model, but are not required everywhere; (3) indicate restrictions for land use and building in 3D city models, with links to the legal sources.

Navigation-tools currently give us directions from location A to B. They help us with the physical process of moving from here to there. Tasks in general, are achieved by the subsequent determination and execution of sub-tasks until the goal is achieved. To help achieve the higher-ranking task, we commonly use so called “personal information management”-tools (PIM-tools). They offer possibilities to manage and organize information about errands that have personal or social implications. Such tasks are described in informal ways, todo-lists for example offer the storage of textual description of an errand, sometimes allowing geographic or temporal information to be added. The paper proposes a formalism that can produce instructions leading from A to the fulfilment of the “task”. Thus connecting the high-level task, that represents intentions, with the physical level of navigation.

Intersection computation is one of the fundamental operations of computational geometry. This paper presents an algorithm for intersection computation between two polygons (convex/nonconvex, with nonintersecting edges, and with or without holes). The approach is based on the decomposed representation of polygons, alternate hierarchical decomposition (AHD), that decomposes the nonconvex polygon into its convex components (convex hulls) arranged hierarchically in a tree data structure called convex hull tree (CHT). The overall approach involves three operations (1) intersection between two convex objects (2) intersection between a convex and a CHT (nonconvex object) and, (3) intersection between two CHTs (two nonconvex objects). This gives for (1) the basic operation of intersection computation between two convex hulls, for (2) the CHT traversal with basic operation in (I) and, for (3) the CHT traversal with operation in (2). Only the basic operation of intersection of two convex hulls is geometric (for which well known algorithms exist) and the other operations are repeated application of this by traversing tree structures.

The article presents a conceptual framework for computations with imprecise values. Typically, the treatment of imprecise values differs from the treatment of precise values. While precise computations use a single number to characterize a value, computations with imprecise values must deal with several numbers for each value. This results in significant changes in the program code because values are represented, e.g., by expectation and standard deviation and both values must be considered within the computations. It would be desirable to have a solution where only limited changes in very specific places of the code are necessary. The mathematical concept of lifting may lead to such a solution.

Observations and processing of data create data and their quality. Quantitative descriptors of data quality must be justified by the properties of the observation process. In this contribution two unavoidable sources of imperfections imperfection in the observation of physical properties are identified and their influences on data collections analyzed. These are, firstly, the random noise disturbing precise measurements; secondly, finiteness of observations—only a finite number of observations is possible and each of it averages properties over an extended area.These two unavoidable imperfections of the data collection process determine data quality. Rational data quality measures must be derived from them: Precision is the effect of noise in the measurement. The finiteness of observations leads to a novel formalized and quantifiable approach to level of detail.The customary description of a geographic data set by ‘scale’ seems to relate these two sources of imperfection in a single characteristic; the theory described here justifies this approach for static representation of geographic space and shows how to extend it for spatio-temporal data.

A systematic exploration of solutions to represent geometric objects shows that the combination of big integers for metric and convex polytopes for topological information is promising. The viability of this novel approach depends on the effective performance of geometric operations typical for GIS using big integers. We report an experiment to determine whether solutions using big integers are realistic for GIS geometry. Metric computations with big numbers are conceptually simpler and need no testing for approximation problems. Performance penalties in some cases are severe (but much less than what we expected), but we found that they should not cause noticeable effects for users. GEOMETRIC COMPUTATIONS WITH FINITE PRECISION ARITHMETIC Current commercial approaches in GIS use floating point numbers. These solutions are complex and therefore difficult to extend, whereas the theory based ones either require much effort when entering the data to produce the data structure or are based on complex algorithms; they seem not compelling for the designers of commercial GIS software. Geometric calculations in finite precision arithmetic can yield results that contradict geometric reasoning. Years ago, Franklin in a landmark paper (Franklin, 1984), has given instructive examples. Line intersections are most important in GIS (e.g., in overlay computations). Two lines AB and CD intersect at point P as shown in figure 1. We may find that P is neither on line AB nor on line CD.