An Agent-Based Variogram Modeller: Investigating Intelligent, Distributed-Component Geographical Information Systems

Geo-Information Science (GIScience) is the field of study that addresses substantive questions concerning the handling, analysis and visualisation of spatial data. Geo- Information Systems (GIS), including software, data acquisition and organisational arrangements, are the key technologies underpinning GIScience. A GIS is normally tailored to the service it is supposed to perform. However, there is often the need to do a function that might not be supported by the GIS tool being used. The normal solution in these circumstances is to go out and look for another tool that can do the service, and often an expert to use that tool. This is expensive, time consuming and certainly stressful to the geographical data analyses. On the other hand, GIS is often used in conjunction with other technologies to form a geocomputational environment. One of the complex tools in geocomputation is geostatistics. One of its functions is to provide the means to determine the extent of spatial dependencies within geographical data and processes. Spatial datasets are often large and complex. Currently Agent system are being integrated into GIS to offer flexibility and allow better data analysis. The theis will look into the current application of Agents in within the GIS community, determine if they are used to representing data, process or act a service. The thesis looks into proving the applicability of an agent-oriented paradigm as a service based GIS, having the possibility of providing greater interoperability and reducing resource requirements (human and tools). In particular, analysis was undertaken to determine the need to introduce enhanced features to agents, in order to maximise their effectiveness in GIS. This was achieved by addressing the software agent complexity in design and implementation for the GIS environment and by suggesting possible solutions to encountered problems. The software agent characteristics and features (which include the dynamic binding of plans to software agents in order to tackle the levels of complexity and range of contexts) were examined, as well as discussing current GIScience and the applications of agent technology to GIS, agents as entities, objects and processes. These concepts and their functionalities to GIS are then analysed and discussed. The extent of agent functionality, analysis of the gaps and the use these technologies to express a distributed service providing an agent-based GIS framework is then presented. Thus, a general agent-based framework for GIS and a novel agent-based architecture for a specific part of GIS, the variogram, to examine the applicability of the agent- oriented paradigm to GIS, was devised. An examination of the current mechanisms for constructing variograms, underlying processes and functions was undertaken, then these processes were embedded into a novel agent architecture for GIS. Once the successful software agent implementation had been achieved, the corresponding tool was tested and validated - internally for code errors and externally to determine its functional requirements and whether it enhances the GIS process of dealing with data. Thereafter, its compared with other known service based GIS agents and its advantages and disadvantages analysed

AN EVOLUTIONARY APPROACH TO BIBLIOGRAPHIC CLASSIFICATION

This dissertation is research in the domain of information science and specifically, the organization and representation of information. The research has implications for classification of scientific books, especially as dissemination of information becomes more rapid and science becomes more diverse due to increases in multi-, inter-, trans-disciplinary research, which focus on phenomena, in contrast to traditional library classification schemes based on disciplines.The literature review indicates 1) human socio-cultural groups have many of the same properties as biological species, 2) output from human socio-cultural groups can be and has been the subject of evolutionary relationship analyses (i.e., phylogenetics), 3) library and information science theorists believe the most favorable and scientific classification for information packages is one based on common origin, but 4) library and information science classification researchers have not demonstrated a book classification based on evolutionary relationships of common origin.The research project supports the assertion that a sensible book classification method can be developed using a contemporary biological classification approach based on common origin, which has not been applied to a collection of books until now. Using a sample from a collection of earth-science digitized books, the method developed includes a text-mining step to extract important terms, which were converted into a dataset for input into the second step—the phylogenetic analysis. Three classification trees were produced and are discussed. Parsimony analysis, in contrast to distance and likelihood analyses, produced a sensible book classification tree. Also included is a comparison with a classification tree based on a well-known contemporary library classification scheme (the Library of Congress Classification).Final discussions connect this research with knowledge organization and information retrieval, information needs beyond science, and this type of research in context of a unified science of cultural evolution

On Cells and Agents : Geosimulation of Urban Sprawl in Western Germany by Integrating Spatial and Non-Spatial Dynamics

Urban sprawl is one of the most challenging land-use and land-cover changes in Germany implicating numerous consequences for the anthropogenic and geobiophysical spheres. While the population and job growth rates of most urban areas stagnate or even decrease, the morphological growth of cities is ubiquitous. Against this backdrop, the quantitative and qualitative modeling of urban dynamics proves to be of central importance. Geosimulation models like cellular automata (CA) and multi-agent systems (MAS) treat cities as complex urban systems. While CA focus on their spatial dynamics, MAS are well-suited for capturing autonomous individual decision making. Yet both models are complementary in terms of their focus, status change, mobility, and representations. Hence, the coupling of CA and MAS is a useful way of integrating spatial pattern and non-spatial processes into one modeling infrastructure. The thesis at hand aims at a holistic geosimulation of the future urban sprawl in the Ruhr. This region is particularly challenging as it is characterized by two seemingly antagonistic processes: urban growth and urban shrinkage. Accordingly, a hybrid modeling approach is to be developed as a means of integrating the simulation power of CA and MAS. A modified version of SLEUTH (short for Slope, Land-use, Exclusion, Urban, Transport, and Hillshade) will function as the CA component. SLEUTH makes use of historic urban land-use data sets and growth coefficients for the purpose of modeling physical urban expansion. In order to enhance the simulation performance of the CA and to incorporate important driving forces of urban sprawl, SLEUTH is for the first time combined with support vector machines (SVM). The supported CA will be coupled with ReHoSh (Residential Mobility and the Housing Market of Shrinking City Systems). This MAS models population patterns, housing prices, and housing demand in shrinking regions. All dynamics are based on multiple interactions between different household groups as well as stakeholders of the housing market. Moreover, this thesis will elaborate on the most important driving factors, rates, and most probable locations of urban sprawl in the Ruhr as well as on the future migration tendencies of different household types and the price development in the housing market of a polycentric shrinking region. The results of SLEUTH and ReHoSh are loosely coupled for a spatial analysis in which the municipal differences that have emerged during the simulations are disaggregated. Subsequently, a concept is developed in order to integrate the CA and the MAS into one geosimulation approach. The thesis introduces semi-explicit urban weights as a possibility of assessing settlement-pattern dynamics and the regional housing market dynamics at the same time. The model combination of SLEUTH-SVM and ReHoSh is finally calibrated, validated, and implemented for simulating three different scenarios of individual housing preferences and their effects on the future urban pattern in the Ruhr. Applied to a digital petri dish, the generic urban growth elements of the Ruhr are being detected

Dynamic land use/cover change modelling

Landnutzungswandel ist eine komplexe Angelegenheit, die durch zahlreiche biophysikalische, sozioökonomische und wirtschaftliche Faktoren verursacht wird. Eine offensichtliche Art des Landnutzungswandels, die in den suburbanen Gebieten einer Metropole stattfindet, ist die Zersiedelung. Es gibt viele Modellierungstechniken, um dieses Phänomen zu studieren. Diese wurden seit den 1960iger Jahren entwickelt und finden weite Verbreitung. Einige dieser Modelle leiden unter dem Vernachlässigen signifikanter Variablen. Traditionelle Methoden wie etwa zellulare Automaten, Markow-Ketten-Modelle, zellulare Automaten-Markow-Modelle und logistische Regressionsmodelle, weisen inhärente Schwächen auf in Bezug auf menschliche Aktivitäten in der Umwelt. Das liegt daran, dass der Mensch der Hauptakteur in der Transformation der Umwelt ist und die suburbanen Gebiete durch Niederlassungspräferenzen und Lebensstil prägt. Das Hauptziel dieser Dissertation ist es, einige dieser traditionellen Techniken zu untersuchen, um ihre Vor- und Nachteile zu identifizieren. Diese Modelle werden miteinander verglichen, um ihre Funktionalität zu hinterfragen. Obwohl die Methodologie zur Evaluierung agentenbasierter Modelle unzureichend ist, wurde hier versucht, ein selbst-kalibriertes agentenbasiertes Modell für den Großraum Teheran zu erstellen. Einige Variablen, die in der Wirklichkeit die Zersiedelung im Studiengebiet kontrollieren, wurden durch Expertenwissen und ähnliche Studien extrahiert. Drei Hauptagenten, die mit der Ausbreitung von Städten zu tun haben, wurden definiert: Entwickler, Bewohner, Behörden. Jeder einzelne Agent beeinflusst Variablen; d.h. die Entscheidungen eines Agenten werden von einer Reihe realer Variablen beeinflusst. Das Verhalten der einzelnen Agenten wurde in einer GIS Umgebung kodiert und anschließend zusammengeführt, um einen Prototyp zur Simulation der Landnutzungsänderung zu erzeugen. Dieser Geosimulations-Prototyp ist in der Lage, die Quantität und die Lage von Landnutzungsänderungen insbesondere in der Umgebung von Teheran zu simulieren. Dieses agentenbasierte Modell zieht Nutzen aus der Stärke traditioneller Techniken wie etwa zellularen Automaten zur Änderungsallokation, Markow-Modellen zur Schätzung der Quantität der Änderung und einer Gewichtung der individuellen Faktoren. Eine detaillierte Diskussion der Implementierung der unterschiedlichen Methoden sowie eine Stärken-Schwächen-Analyse werden präsentiert und die Ergebnisse mit der tatsächlichen Situation verglichen, um die Modelle zu verifizieren. In dieser Arbeit wurden GIS Funktionen verwendet und zusätzliche Funktionen in Python programmiert. Diese Untersuchungen sollen Stadtplaner und Entscheidungsträger unterstützen, Städte und deren Ausbreitung zu simulieren.Land use/ cover change is a complex matter, which is caused by numerous biophysical, socio-economical and economic factors. An obvious form of land use change in the suburbs of the metropolis is defined as urban sprawl. There are a number of techniques to model this issue in order to investigate this topic. These models have been developed since the 1960s and are increasing in terms of quantity and popularity. Some of these models suffer from a lack of consideration of some significant variables. The traditional methods (e.g. Cellular Automata, the Markov Chain Model, the CA-Markov Model, and the Logistic Regression Model) have some inherent weaknesses in consideration of human activity in the environment. The particular significance of this problem is the fact that humans are the main actors in the transformation of the environment, and impact upon the suburbs due to their settlement preferences and lifestyle choices. The main aim of this thesis was to examine some of those traditional techniques in order to discover their considerable advantages and disadvantages. These models were compared against each other to challenge their functionality. Whereas there is a lack of methodology in evaluation of agent-based models, it was presumed to create a self-calibrated agent based model, by focussing on the Tehran metropolitan area. Some variables in reality control urban sprawl in the study area, which were extracted through the expert knowledge and similar studies. Three main agents, which deal with urban expansion, were defined: developers, residents, government. Each particular agent affects some variables, i.e. the agents‟ decisions are being influenced by a set of real variables. Agents‟ behaviours were coded in a GIS environment and, thereafter, the predefined agents were combined through a function to create a prototype for simulation of land change. This designed geosimulation prototype can simulate the quantity and location of changes specifically in the vicinity of the metropolis of Tehran. This customised agent-based model benefits from the strengths of traditional techniques; for instance, a Cellular Automata structure for change allocation, a Markov model for change quantity estimation and a weighting system to differentiate between the weights of the driving factors. A detailed discussion of each methodology implementation, and their weakness and strengths, is then presented, specifically comparing results with the reality to verify the models. In this research, we used only the GIS functionalities within GIS environments and the required functions were coded in the Python engine. This investigation will help urban planners and urban decision-makers to simulate cities and their movements over time

Adaptable Spatial Agent-Based Facility Location for Healthcare Coverage

Lack of access to healthcare is responsible for the world’s poverty, mortality and morbidity. Public healthcare facilities (HCFs) are expected to be located such that they can be reached within reasonable distances of the patients’ locations, while at the same time providing complete service coverage. However, complete service coverage is generally hampered by resource availability. Therefore, the Maximal Covering Location Problem (MCLP), seeks to locate HCFs such that as much population as possible is covered within a desired service distance. A consideration to the population not covered introduces a distance constraint that is greater than the desired service distance, beyond which no population should be. Existing approaches to the MCLP exogenously set the number of HCFs and the distance parameters, with further assumption of equal access to HCFs, infinite or equal capacity of HCFs and data availability. These models tackle the real-world system as static and do not address its intrinsic complexity that is characterised by unstable and diverse geographic, demographic and socio-economic factors that influence the spatial distribution of population and HCFs, resource management, the number of HCFs and proximity to HCFs. Static analysis incurs more expenditure in the analytical and decision-making process for every additional complexity and heterogeneity. This thesis is focused on addressing these limitations and simplifying the computationally intensive problems. A novel adaptable and flexible simulation-based meta-heuristic approach is employed to determine suitable locations for public HCFs by integrating Geographic Information Systems (GIS) with Agent-Based Models (ABM). Intelligent, adaptable and autonomous spatial and non-spatial agents are utilized to interact with each other and the geographic environment, while taking independent decisions governed by spatial rules, such as •containment, •adjacency, •proximity and •connectivity. Three concepts are introduced: assess the coverage of existing HCFs using travel-time along the road network and determine the different average values of the service distance; endogenously determine the number and suitable locations of HCFs by integrating capacity and locational suitability constraints for maximizing coverage within the prevailing service distance; endogenously determine the distance constraint as the maximum distance between the population not covered within the desired service distance and its closest facility. The models’ validations on existing algorithms produce comparable and better results. With confirmed transferability, the thesis is applied to Lagos State, Nigeria in a disaggregated analysis that reflects spatial heterogeneity, to provide improved service coverage for healthcare. The assessment of the existing health service coverage and spatial distribution reveals disparate accessibility and insufficiency of the HCFs whose locations do not factor in the spatial distribution of the population. Through the application of the simulation-based approach, a cost-effective complete health service coverage is achieved with new HCFs. The spatial pattern and autocorrelation analysis reveal the influence of population distribution and geographic phenomenon on HCF location. The relationship of selected HCFs with other spatial features indicates agents’ compliant with spatial association. This approach proves to be a better alternative in resource constrained systems. The adaptability and flexibility meet the global health coverage agenda, the desires of the decision maker and the population, in the support for public health service coverage. In addition, a general theory of the system for a better-informed decision and analytical knowledge is obtained

Extracting Agent-Based Models of Human Transportation Patterns

Due to their cheap development costs and ease of deployment, surveys and questionnaires are useful tools for gathering information about the activity patterns of a large group and can serve as a valuable supplement to tracking studies done with mobile devices. However in raw form, general survey data is not necessarily useful for answering predictive questions about the behavior of a large social system. In this paper, we describe a method for generating agent activity profiles from survey data for an agent-based model (ABM) of transportation patterns of 47,000 students on a university campus. We compare the performance of our agent-based model against a Markov Chain Monte Carlo (MCMC) simulation based directly on the distributions fitted from the survey data. A comparison of our simulation results against an independently collected dataset reveals that our ABM can be used to accurately forecast parking behavior over the semester and is significantly more accurate than the MCMC estimator. © 2012 IEEE

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

Survey of Image Processing Techniques for Brain Pathology Diagnosis: Challenges and Opportunities

In recent years, a number of new products introduced to the global market combine intelligent robotics, artificial intelligence and smart interfaces to provide powerful tools to support professional decision making. However, while brain disease diagnosis from the brain scan images is supported by imaging robotics, the data analysis to form a medical diagnosis is performed solely by highly trained medical professionals. Recent advances in medical imaging techniques, artificial intelligence, machine learning and computer vision present new opportunities to build intelligent decision support tools to aid the diagnostic process, increase the disease detection accuracy, reduce error, automate the monitoring of patient's recovery, and discover new knowledge about the disease cause and its treatment. This article introduces the topic of medical diagnosis of brain diseases from the MRI based images. We describe existing, multi-modal imaging techniques of the brain's soft tissue and describe in detail how are the resulting images are analyzed by a radiologist to form a diagnosis. Several comparisons between the best results of classifying natural scenes and medical image analysis illustrate the challenges of applying existing image processing techniques to the medical image analysis domain. The survey of medical image processing methods also identified several knowledge gaps, the need for automation of image processing analysis, and the identification of the brain structures in the medical images that differentiate healthy tissue from a pathology. This survey is grounded in the cases of brain tumor analysis and the traumatic brain injury diagnoses, as these two case studies illustrate the vastly different approaches needed to define, extract, and synthesize meaningful information from multiple MRI image sets for a diagnosis. Finally, the article summarizes artificial intelligence frameworks that are built as multi-stage, hybrid, hierarchical information processing work-flows and the benefits of applying these models for medical diagnosis to build intelligent physician's aids with knowledge transparency, expert knowledge embedding, and increased analytical quality

Integrated design of transport infrastructure and public spaces considering human behavior: A review of state-of-the-art methods and tools

In order to achieve holistic urban plans incorporating transport infrastructure, public space and the behavior of people in these spaces, integration of urban design and computer modeling is a promising way to provide both qualitative and quantitative support to decision-makers. This paper describes a systematic literature review following a four-part framework. Firstly, to understand the relationship of elements of transport, spaces, and humans, we review policy and urban design strategies for promoting positive interactions. Secondly, we present an overview of the integration methods and strategies used in urban design and policy discourses. Afterward, metrics and approaches for evaluating the effectiveness of integrated plan alternatives are reviewed. Finally, this paper gives a review of state-of-the-art tools with a focus on seven computer simulation paradigms. This article explores mechanisms underlying the complex system of transport, spaces, and humans from a multidisciplinary perspective to provide an integrated toolkit for designers, planners, modelers and decision-makers with the current methods and their challenges