Areas of Same Cardinal Direction

Cardinal directions, such as North, East, South, and West, are the foundation for qualitative spatial reasoning, a common field of GIS, Artificial Intelligence, and cognitive science. Such cardinal directions capture the relative spatial direction relation between a reference object and a target object, therefore, they are important search criteria in spatial databases. The projection-based model for such direction relations has been well investigated for point-like objects, yielding a relation algebra with strong inference power. The Direction Relation Matrix defines the simple region-to-region direction relations by approximating the reference object to a minimum bounding rectangle. Models that capture the direction between extended objects fall short when the two objects are close to each other. For instance, the forty-eight contiguous states of the US are colloquially considered to be South of Canada, yet they include regions that are to the North of some parts of Canada. This research considers the cardinal direction as a field that is distributed through space and may take on varying values depending on the location within a reference object. Therefore, the fundamental unit of space, the point, is used as a reference to form a point-based cardinal direction model. The model applies to capture the direction relation between point-to-region and region-to-region configurations. As such, the reference object is portioned into areas of same cardinal direction with respect to the target object. This thesis demonstrates there is a set of 106 cardinal point-to-region relations, which can be normalized by considering mirroring and 90° rotations, to a subset of 22 relations. The differentiating factor of the model is that a set of base relations defines the direction relation anywhere in the field, and the conceptual neighborhood graph of the base relations offers the opportunity to exploit the strong inference of point-based direction reasoning for simple regions of arbitrary shape. Considers the tiles and pockets of same cardinal direction, while a coarse model provides a union of all possible qualitative direction values between a reference region and a target region

Qualitative Spatial and Temporal Reasoning based on And/Or Linear Programming An approach to partially grounded qualitative spatial reasoning

Acting intelligently in dynamic environments involves anticipating surrounding processes, for example to foresee a dangerous situation or acceptable social behavior. Knowledge about spatial configurations and how they develop over time enables intelligent robots to safely navigate by reasoning about possible actions. The seamless connection of high-level deliberative processes to perception and action selection remains a challenge though. Moreover, an integration should allow the robot to build awareness of these processes as in reality there will be misunderstandings a robot should be able to respond to. My aim is to verify that actions selected by the robot do not violate navigation or safety regulations and thereby endanger the robot or others. Navigation rules specified qualitatively allow an autonomous agent to consistently combine all rules applicable in a context. Within this thesis, I develop a formal, symbolic representation of right-of-way-rules based on a qualitative spatial representation. This cumulative dissertation consists of 5 peer-reviewed papers and 1 manuscript under review. The contribution of this thesis is an approach to represent navigation patterns based on qualitative spatio-temporal representation and the development of corresponding effective sound reasoning techniques. The approach is based on a spatial logic in the sense of Aiello, Pratt-Hartmann, and van Benthem. This logic has clear spatial and temporal semantics and I demonstrate how it allows various navigation rules and social conventions to be represented. I demonstrate the applicability of the developed method in three different areas, an autonomous robotic system in an industrial setting, an autonomous sailing boat, and a robot that should act politely by adhering to social conventions. In all three settings, the navigation behavior is specified by logic formulas. Temporal reasoning is performed via model checking. An important aspect is that a logic symbol, such as \emph{turn left}, comprises a family of movement behaviors rather than a single pre-specified movement command. This enables to incorporate the current spatial context, the possible changing kinematics of the robotic system, and so on without changing a single formula. Additionally, I show that the developed approach can be integrated into various robotic software architectures. Further, an answer to three long standing questions in the field of qualitative spatial reasoning is presented. Using generalized linear programming as a unifying basis for reasoning, one can jointly reason about relations from different qualitative calculi. Also, concrete entities (fixed points, regions fixed in shape and/or position, etc.) can be mixed with free variables. In addition, a realization of qualitative spatial description can be calculated, i.e., a specific instance/example. All three features are important for applications but cannot be handled by other techniques. I advocate the use of And/Or trees to facilitate efficient reasoning and I show the feasibility of my approach. Last but not least, I investigate a fourth question, how to integrate And/Or trees with linear temporal logic, to enable spatio-temporal reasoning

Accessibility in metropolitan transportation planning : visualizing a GIS-based measure for collaborative planning

Passed by the US Congress in 1995, the Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (SAFETEA-LU), requires Metropolitan Planning Organizations (MPOs) to further the trend of collaboration by employing visualization techniques for Transportation Improvement Programs (TIPs). In the first part of this two-part research, three New Jersey MPOs are investigated to understand how accessibility is considered by their organizations, how TIPs are evaluated, and how collaborative planning and visualization techniques are used to evaluate TIPs. In the second part of this study, a small segment of a MPO\u27s jurisdiction is selected to develop a visualization of the change in accessibility brought about by a TIP. Suitability analysis, a method commonly used for collaborative decision making in land use planning, is employed to develop the accessibility measure from service areas generated by ArcGIS Network Analyst. Service area values are calculated by a gravity- type model that decay inversely to network distance and network time and are dependent on the travel mode desires of the residents of the region. The resultant accessibility raster, a product of collaborative planning, is dependent on the physical characteristics of the region and the people residing there. This accessibility raster is used to visualize change in accessibility before and after a TIP. Zonal statistics may be applied on this raster to evaluate environmental justice concerns by MPOs

Methodological Issues of Spatial Agent-Based Models

Agent based modeling (ABM) is a standard tool that is useful across many disciplines. Despite widespread and mounting interest in ABM, even broader adoption has been hindered by a set of methodological challenges that run from issues around basic tools to the need for a more complete conceptual foundation for the approach. After several decades of progress, ABMs remain difficult to develop and use for many students, scholars, and policy makers. This difficulty holds especially true for models designed to represent spatial patterns and processes across a broad range of human, natural, and human-environment systems. In this paper, we describe the methodological challenges facing further development and use of spatial ABM (SABM) and suggest some potential solutions from multiple disciplines. We first define SABM to narrow our object of inquiry, and then explore how spatiality is a source of both advantages and challenges. We examine how time interacts with space in models and delve into issues of model development in general and modeling frameworks and tools specifically. We draw on lessons and insights from fields with a history of ABM contributions, including economics, ecology, geography, ecology, anthropology, and spatial science with the goal of identifying promising ways forward for this powerful means of modeling

Integrating case based reasoning and geographic information systems in a planing support system: Çeşme Peninsula study

Thesis (Doctoral)--Izmir Institute of Technology, City and Regional Planning, Izmir, 2009Includes bibliographical references (leaves: 110-121)Text in English; Abstract: Turkish and Englishxii, 140 leavesUrban and regional planning is experiencing fundamental changes on the use of of computer-based models in planning practice and education. However, with this increased use, .Geographic Information Systems. (GIS) or .Computer Aided Design.(CAD) alone cannot serve all of the needs of planning. Computational approaches should be modified to deal better with the imperatives of contemporary planning by using artificial intelligence techniques in city planning process.The main aim of this study is to develop an integrated .Planning Support System. (PSS) tool for supporting the planning process. In this research, .Case Based Reasoning. (CBR) .an artificial intelligence technique- and .Geographic Information Systems. (GIS) .geographic analysis, data management and visualization techniqueare used as a major PSS tools to build a .Case Based System. (CBS) for knowledge representation on an operational study. Other targets of the research are to discuss the benefits of CBR method in city planning domain and to demonstrate the feasibility and usefulness of this technique in a PSS. .Çeşme Peninsula. case study which applied under the desired methodology is presented as an experimental and operational stage of the thesis.This dissertation tried to find out whether an integrated model which employing CBR&GIS could support human decision making in a city planning task. While the CBS model met many of predefined goals of the thesis, both advantages and limitations have been realized from findings when applied to the complex domain such as city planning

Scale challenges in inventory of forests aided by remote sensing

The impact of changing the scale of observation on information derived from forest inventories is the basis of scale-related research in forest inventory and analysis (FIA). Interactions between the scale of observation and observed heterogeneity in studied variables highlight a dependence on scale that affects measurements, estimates, and relationships between inventory data from terrestrial and remote sensing surveys. This doctoral research defines "scale" as the divisions of continuous space over which measurements are made, or hierarchies of discrete units of study/analysis in space. Therefore, the "scale of observation" (also known as support) refers to that integral of space over which statistics are computed and forest inventory variables regionalized. Given the ubiquitous nature of scale issues, a case study approach was undertaken in this research (Articles I-IV) with the goal to provide fundamental understanding of responses to the scale of observation for specific FIA variables. The studied forest inventory variables are; forest stand structural heterogeneity, forest cover proportion and tree species identities. Forest cover proportion (or simply forest area) and tree species are traditional and fundamental forest inventory variables commonly assessed over large areas using both terrestrial samples and remote sensing data whereas, forest stand structural heterogeneity is a contemporary FIA variable that is increasingly demanded in multi-resource inventories to inform management and conservation efforts as it is linked to biodiversity, productivity, ecosystem functioning and productivity, and used as auxiliary data in forest inventory. This research has two overall aims: 1. To improve the understanding of the association between the scale of observation and observed heterogeneity in inventory of forest stand structural heterogeneity, forest-cover proportions, and identification of tree species from a combination of terrestrial samples and remote sensing data. 2. To contribute knowledge to the estimation of scale-dependence in inventory of forest stand structural heterogeneity, forest-cover proportions, and identification of tree species from a combination of terrestrial samples and remote sensing data. Different scales of observation were considered across the four case studies encompassing individual leaf, crown-part or branch, single-tree crown, forest stand, landscape and global levels of analysis. Terrestrial and remote sensing data sets from a variety of temperate forests in Germany and France were utilized across case studies. In cases where no inventory data were available, synthetic data was simulated at different scales of observation. Heterogeneity in FIA variable estimates was monitored across scales of observation using estimators of variance and associated precision. As too much heterogeneity is hardly interpreted due to a low signal to noise ratio, object-based image analysis (OBIA) methods were used to manage heterogeneity in high resolution remote sensing data before evaluating scale dependence or scaling across observed scales. Similarly, ensemble classification techniques were applied to address methodological heterogeneity across classifiers in a case study on classification of two physically and spectrally similar Pinus species. Across case studies, a dependence on the scale of observation was determined by linking estimates of heterogeneity to their respective scales of observation using linear regression and a combination of geo-statistics and Monte-Carlo approaches. In order to address scale-dependence, thresholds to scale domains were identified so as to enable efficient observation of studied FIA variables and scaling approaches proposed to bridge observations across scales. For scaling, this research evaluated the potential of different regression techniques to map forest stand structural heterogeneity and tree species wall-to-wall from remote sensing data. In addition, radiative transfer modelling was evaluated in the transfer between leaf and crown hyperspectra, and a global sampling grid framework proposed to efficiently link different stages of survey sampling. This research shows that the scale of observation affected all studied FIA variables albeit to varying degrees, conditioned on the spatial structure and aggregation properties of the assessed FIA variable (i.e. whether the variable is extensive, intensive or scale-specific) and the method used in aggregation on support (e.g. mean, variance, quantile etc.). The scale of observation affected measurements or estimates of the studied FIA variables as well as relationships between spatially structured FIA variables. The scale of observation determined observed heterogeneity in FIA variables, affected parameter retrieval from radiative transfer models, and affected variable selection and performance of models linking terrestrial and remote sensing data. On the other hand, this research shows that it is possible to determine domains of scale dependence within which to efficiently observe the studied FIA variables and to bridge between scales of observation using various scaling methods. The findings of this doctoral research are relevant for the general understanding of scale issues in FIA. Research in Article I, for example, informs optimization of plot sizes for efficient inventory and mapping of forest structural heterogeneity, as well as for the design of natural resource inventories. Similarly, research in Article II is applicable in large area forest (or general land) cover monitoring from sampling by both visual interpretation of high resolution remote sensing imagery and terrestrial surveys. This research is also useful to determine observation design for efficient inventory of land cover. Research in Article III contributes in many contexts of remote sensing assisted inventory of forests especially in management and conservation planning, pest and diseases control and in the estimation of biomass. Lastly, research in Article IV highlights scale-related effects in passive optical remote sensing of forests currently understudied and can ultimately contribute to sensor calibration and modelling approaches