Human behavior may be one of the most challenging phenomena to model and validate. This paper proposes a method for automatically extracting and compiling evidence on human behavior determinants into a knowledge graph. The method (1) extracts associations of behavior determinants and choice options in relation to study groups and moderators from published studies using Natural Language Processing and Deep Learning, (2) synthesizes the extracted evidence into a knowledge graph, and (3) sub-selects the model components and relationships that are relevant and robust. The method can be used to either (4a) construct a structurally valid simulation model before proceeding with calibration or (4b) to validate the structure of existing simulation models. To demonstrate the feasibility of the method, we discuss an example implementation with mode of transport as behavior choice. We find that including non-frequently studied significant behavior determinants drastically improves the model's explanatory power in comparison to only including frequently studied variables. The paper serves as a proof-of-concept which can be reused, extended or adapted for various purposes.

The term homeomerosity refers to when a whole and its parts are the same kind of thing. For instance, a computer and its processor can both be classified as machines. Homeomerosity is a prerequisite for meaningful addition and subtraction. For example, adding the area sizes of two independent regions gives another area size, but adding an area size and a number of hours yields a number with a peculiar unit. In earlier work, homeomerosity has been formalized with respect to mereological parthood, but not in concurrence with a notion of class subsumption. Both are essential to homeomerosity, as a part can only be observed to be of the same kind as the whole if they are observed to be of some kinds in the first place. In this work, we use formal concept analysis to organize conceptual representations of parts and wholes in a shared contextual model. In our doing so, we show wholes and parts can be represented by sub-concepts of a concept with respect to which they are homeomerous.

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.

Spatial point tracks are of concern for an increasing number of analysts studying spatial behaviour patterns and environmental effects. Take an epidemiologist studying the behaviour of cyclists and how their health is affected by the city’s air quality. The accuracy of such analyses critically depends on the positional accuracy of the tracked points. This poses a serious privacy risk. Tracks easily reveal a person’s identity since the places visited function as fingerprints. Current obfuscation-based privacy protection methods, however, mostly rely on point quality reduction, such as spatial cloaking, grid masking or random noise, and thus render an obfuscated track less useful for exposure assessment. We introduce simulated crowding as a point quality preserving obfuscation principle that is based on adding fake points. We suggest two crowding strategies based on extending and masking a track to defend against inference attacks. We test them across various attack strategies and compare them to state-of-the-art obfuscation techniques both in terms of information loss and attack resilience. Results indicate that simulated crowding provides high resilience against home attacks under constantly low information loss.

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.

Currently, systems that let people search for opportunities to fulfill their spatio-temporal needs are built according to the conceptual model of service provider and consumer: After the providers make their needs publicly available, consumers use a specifically tailored query engine to find fitting offers. E.g., in carpooling, someone wants to fill an empty seat and to share costs (and publishes this offer), while another person wants to travel the same route. This model prevents the consuming side from making their needs available to the service providers and makes it hard to generalize, as query engines require rigid (often domain-specific) properties. Addressing this problem, we propose a generic model for publishing and processing complementary spatio-temporal needs. Our model uses a simulator to assess how well the collaboration between different entities would approximate their goals. To reuse existing concepts and embed the model into the emerging Semantic Web, everything is modeled in accordance with Linked Data principles.

How can data analysts identify spatio-temporal datasets that are suitable for their task? Answering this question is not only dependent on the aim of the analysis and the semantic contents of the data, but also on knowing whether the required data combinations and transformations, spatio-temporal analysis methods, charts and map visualizations are meaningfully applicable to the data. Operators need to assess whether they can meaningfully apply analytical operations to data to derive the information required. Answering this question in a general and computationally executable way is a crucial step on our way towards supporting data analysts and their research practice in e-Science. We propose an ontology design pattern for spatio-temporal information that enables to reason about the applicability of a number of fundamental classes of analyses in relation to given data, i.e., whether data sets can be compared, transformed, combined, and whether summary statistics can be applied to them. We demonstrate this ontology implemented in OWL through a set of corresponding SPARQL queries applied to meta-data of datasets from the AURIN portal.

Maintaining knowledge about the provenance of datasets, that is, about how they were obtained, is crucial for their further use. Contrary to what the overused metaphors of ‘data mining’ and ‘big data’ are implying, it is hardly possible to use data in a meaningful way if information about sources and types of conversions is discarded in the process of data gathering. A generative model of spatiotemporal information could not only help automating the description of derivation processes but also assessing the scope of a dataset’s future use by exploring possible transformations. Even though there are technical approaches to document data provenance, models for describing how spatiotemporal data are generated are still missing. To fill this gap, we introduce an algebra that models data generation and describes how datasets are derived, in terms of types of reference systems. We illustrate its versatility by applying it to a number of derivation scenarios, ranging from field aggregation to trajectory generation, and discuss its potential for retrieval, analysis support systems, as well as for assessing the space of meaningful computations.

The principle challenge for information semantics lies in the degrees of freedom to interpret symbols in terms of thoughts and experiences which leads to incompatible views on the world. Consequently, incompatible information ontologies and interpretations of the described data will remain. Even though there is usually a common experiential ground, it stays often unknown to users of semantically annotated data. This symbol grounding problem is a bottleneck of information semantics, which remains largely unsolved in ontological practice. In this paper, we suggest – in the spirit of Jeremy Bentham – to introduce formal primitives which are directly grounded in inter-subjective experience, and which serve to expose and construct complex qualities in information ontologies.

We propose a grounded ontological theory of channel networks to categorize features, such as junctions, in road network databases. The theory is grounded, because its primitives can be given an unambiguous interpretation into directly observable qualities of physical road networks, such as supported movements and their medium, connectedness of such media, and turnoff restrictions. The theory provides a very general approach to automatically annotate and integrate road network data from heterogeneous sources, because it rests on application-independent observation principles. We suggest that road network categories such as junctions and roads are based on locomotion affordances. Road network databases can be mapped into our channel network theory, so that instances of roads and junctions can be automatically categorized or checked for consistency by what they afford. In this paper, we introduce affordance-based definitions of a road network and a junction, and show that the definition of latter is satisfied by some of the most common junction types.

In this paper, we discuss an application of spatial data mining to predict pedestrian flow in extensive road networks using a large biased sample. Existing out-of-the-box techniques are not able to appropriately deal with its challenges and constraints, in particular with sample selection bias. For this purpose, we introduce s-knn-apriori, an efficient nearest neighbor based spatial mining algorithm that allows prior knowledge and deductive models to be included in a straightforward and easy way.