Data Mining - Concepts and Techniques.

1. Geographical Information: Society, Science, and Systems 2. Data models and axioms: Formal abstractions of reality 3. Geographical Data in the Computer 4. Data input, verification, storage and output 5. Creating continuous surfaces from point data 6. Optimal interpolation using geostatistics 7. The analysis of discrete entities in space 8. Spatial analysis using continuous fields 9. Errors and quality control 10. Error propagation in numerical modelling 11. Fuzzy sets and fuzzy geographical objects 12. Current issues and trends in GIS APPENDIX 1 GLOSSARY OF TERMS APPENDIX 2 A SELECTION OF WORLD WIDE WEB GEOGRAPHY AND GIS SERVERS APPENDIX 3 EXAMPLE DATA SETS

Principles of geographical information systems

This paper presents an overview of multi-agent system models of land-use/cover change (MAS/LUCC models). This special class of LUCC models combines a cellular landscape model with agent-based representations of decisionmaking, integrating the two components through specification of interdependencies and feedbacks between agents and their environment. The authors review alternative LUCC modeling techniques and discuss the ways in which MAS/LUCC models may overcome some important limitations of existing techniques. We briefly review ongoing MAS/LUCC modeling efforts in four research areas. We discuss the potential strengths of MAS/LUCC models and suggest that these strengths guide researchers in assessing the appropriate choice of model for their particular research question. We find that MAS/LUCC models are particularly well suited for representing complex spatial interactions under heterogeneous conditions and for modeling decentralized, autonomous decision making. We discuss a range of possible roles for MAS/LUCC models, from abstract models designed to derive stylized hypotheses to empirically detailed simulation models appropriate for scenario and policy analysis. We also discuss the challenge of validation and verification for MAS/LUCC models. Finally, we outline important challenges and open research questions in this new field. We conclude that, while significant challenges exist, these models offer a promising new tool for researchers whose goal is to create fine-scale models of LUCC phenomena that focus on human-environment interactions.

http://www.dpi.inpe.br/cursos/tutoriais/modelagem/referencias/agents_lucc_overview.pdf

Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review

http://staff.unila.ac.id/ekoefendi/files/2011/10/Malczewski_2004.pdf

GIS-based land-use suitability analysis: a critical overview

In this paper we describe a cellular automaton (CA) simulation model developed to predict urban growth as part of a project for estimating the regional and broader impact of urbanization on the San Francisco Bay area's climate. The rules of the model are more complex than those of a typical CA and involve the use of multiple data sources, including topography, road networks, and existing settlement distributions, and their modification over time. In addition, the control parameters of the model are allowed to self-modify: that is, the CA adapts itself to the circumstances it generates, in particular, during periods of rapid growth or stagnation. In addition, the model was written to allow the accumulation of probabilistic estimates based on Monte Carlo methods. Calibration of the model has been accomplished by the use of historical maps to compare model predictions of urbanization, based solely upon the distribution in year 1900, with observed data for years 1940, 1954, 1962, 1974, and 1990. The complexity of this model has made calibration a particularly demanding step. Lessons learned about the methods, measures, and strategies developed to calibrate the model may be of use in other environmental modeling contexts. With the calibration complete, the model is being used to generate a set of future scenarios for the San Francisco Bay area along with their probabilities based on the Monte Carlo version of the model. Animated dynamic mapping of the simulations will be used to allow visualization of the impact of future urban growth.

/pdf/a-self-modifying-cellular-automaton-model-of-historical-4znon8yqbj.pdf

A Self-Modifying Cellular Automaton Model of Historical Urbanization in the San Francisco Bay Area

The role of spatial metrics in the analysis and modeling of urban land use change

Cellular spaces have recently received a lot of attention in computer science and elsewhere as models capable of bridging the gap between disaggregate and aggregate description. Despite their obvious spatial interpretation, standard cell-space models are too constrained by their background conventions to be useful in realistic geographic applications. In this paper, a generalization of the cell-space principle is presented, based on discrete model theory, and then applied to a hypothetical but fairly complex problem of individual decision and large-scale urban change. The paper ends with a discussion of the wider import of this methodology, which has close links with, among other things, bifurcation theory, cognitive science modeling of individual decision and behavior, and other issues of actual or potential interest to geographers.

Cellular Worlds: A Framework for Modeling Micro—Macro Dynamics

Integration with geographic information systems (GIS) has helped move cellular automata (CA)-based urban and regional models from the realm of instructive metaphors to that of potentially useful qualitative forecasting tools. Such models can now be fully interactive for exploratory purposes and they can be based on actual data. New problems, however, arise as the formal integrity of the original CA framework is lost through successive relaxations of the assumptions and as the resulting complicated models become increasingly difficult to implement and understand. In this paper I propose that the theoretical problem can find a satisfactory answer in the notion of proximal space and the practical one can be handled successfully within the formalism of geo-algebra, a mathematical expression of proximal space which builds a bridge between GIS data and operations, on the one hand, and traditional robust classes of urban and regional models, on the other.

From Cellular Automata to Urban Models: New Principles for Model Development and Implementation:

Exploring the anchor-point hypothesis of spatial cognition

The ongoing debate in GIS regarding the relative merits of vector versus raster representations of spatial information is usually couched in technical terms Yet the technical question of the most appropriate data structure begs the philosophical question of the most appropriate conceptualization of geographic space The paper confronts this latter question in the context of the opposition between the “object” and “field” views of space I suggest that GIS can turn a rather dry debate into a source of insights regarding the nature of its subject matter by learning from how people actually experience and deal with the geographic world Human cognition indeed appears to make use of both the object and field views, but at different geographic scales, and for different purposes These observations suggest a list of desiderata for the next round of thinking about spatial representation in GIS

/pdf/people-manipulate-objects-but-cultivate-fields-beyond-the-xln74u1kmz.pdf

Helen Couclelis

Papers

The role of spatial metrics in the analysis and modeling of urban land use change

Cellular Worlds: A Framework for Modeling Micro—Macro Dynamics

From Cellular Automata to Urban Models: New Principles for Model Development and Implementation:

Exploring the anchor-point hypothesis of spatial cognition

People Manipulate Objects (but Cultivate Fields): Beyond the Raster-Vector Debate in GIS