Simulation of urban system evolution in a synergetic modelling framework. The case of Attica, Greece

Spatial analysis and evolution simulation of such complex and dynamic systems as modern urban areas could greatly benefit from the synergy of methods and techniques that constitute the core of the fields of Information Technology and Artificial Intelligence. Additionally, if during the decision making process, a consistent methodology is applied and assisted by a user-friendly interface, premium and pragmatic solution strategies can be tested and evaluated. In such a framework, this paper presents both a prototype Decision Support System and a consorting spatio-temporal methodology, for modelling urban growth. Its main focus is on the analysis of current trends, the detection of the factors that mostly affect the evolution process and the examination of user-defined hypotheses regarding future states of the problem environment. According to the approach, a neural network model is formulated for a specific time intervals and each different group of spatial units, mainly based to the degree of their contiguity and spatial interaction. At this stage, fuzzy logic provides a precise image of spatial entities, further exploited in a twofold way. First, for the analysis and interpretation of up-to-date urban evolution and second, for the formulation of a robust spatial simulation model. It should be stressed, however, that the neural network model is not solely used to define future urban images, but also to evaluate the degree of influence that each variable as a significant of problem parameter, contributes to the final result. Thus, the formulation and the analysis of alternative planning scenarios are assisted. Both the proposed methodological framework and the prototype Decision Support System are utilized during the study of Attica, Greece?s principal prefecture and the definition of a twenty-year forecast. The variables considered and projected refer to population data derived from the 1961-1991 censuses and building uses aggregated in ten different categories. The final results are visualised through thematic maps in a GIS environment. Finally, the performance of the methodology is evaluated as well as directions for further improvements and enhancements are outlined. Keywords: Computational geography, Spatial modelling, Neural network models, Fuzzy logic.

Decentralisation: a One-to-many Relationship. The Case of Greece

There is no doubt that even a cursory examination of the indisputable efforts for decentralization in Greece, will reveal that the main problem lies within the ever expanding bureaucratic labyrinth of its political decision making processes and the underlying political and administrative system that generates and supports them. In short, this political system seems to strive for inclusiveness and broad acceptance, rather than theoretical consistency or elegance, setting the government incapable to play a truly coordinating role and exhibit the admistrative wisdom that it implies. Seeking in this respect, to accommodate new demands as they emerge by means, insofar as possible, that leave previous arrangements (programs and administrative regulations) undisturbed, which in turn involve the least possible disruption for public enterprises, as well as the least possible inconvenience and annoyance for institutions and individuals alike, who have built their life styles around the expectation of system stability. However, in terms of its administrative system, Greece has the same characteristics and problems with most of the Mediterranean European Countries. Consequently, the potential of future decentralization policies in a regional level, as they are expressed by either the elimination of regional disparities or the formulation of regional restructuring strategies, should be seen with respect to the extended political framework within which they have to be implemented. In our study, this is defined as the Mediterranean frontier. Within this framework, this paper reviews the historical course and critically presents the results of the proclaimed decentralization efforts in Greece. Specifically, certain political and socioeconomic indicators are utilized, in order both to micro evaluate the regional disparities within the country and to generate a parallel macro comparison with the other Mediterranean member states of the European Union.Decentralization; Greece

Defining Demographic Change in Locational Planning Problems

Since population, which actually represents demand in any organised or rearranged service system, is the final target and recipient of every planning strategy or policy action, the success of locational analysis and the locational planning process, is largely determined upon the decision makers' ability to estimate the study area's future population size and distribution. Such estimations can be demographically achieved through the analysis and extrapolation into the future of carefully measured birth, death and migration rates according to observed trends and tendencies of the relevant socio-economic factors that affect them. As opposed to traditional generalisations and recent practices, which deal with population as a whole, the approach presented in this paper focuses on each individual's attitude towards the issue of intended births, which when aggregated formulate a fertility rate. More specifically, certain socio-economic characteristics, based on questionnaire data, are analysed using discrete choice models in order to estimate the prospective family-size desires. In this respect, a birth-rate choice model is derived through the assessment of the expected number of children to be born in a household during a specific time period and with regard to its socio-economic identity. Moreover, modifications of the above characteristics generate alternative family-size scenarios and thus differing population forecasts, which in turn can lead to unforeseen solution strategies and thus a more sophisticated and pragmatic decision-making process when dealing with facility-location problems both in the public and the private sector.Locational Planning; Discrete Choice models; Population Projections; Greece

Assesing demand in stochastic locational planning problems: An Artificial Intelligence approach for emergency service systems.

The efficiency of emergency service systems is measured in terms of their ability to deploy units and personnel in a timely and effective manner upon an event’s occurrence. Aiming to exploit stochastic demand, spatial tracing and location analysis of emergency incidents are examined through the utilisation of Artificial Intelligence in two interacting levels. Firstly, spatio-temporal point pattern of demand is analysed by a new genetic algorithm. The proposed genetic algorithm interrelates sequential events formulating moving events and as a result, every demand point pattern is correlated both to previous and following events. Secondly, the approach provides the ability to predict, by means of neural networks optimised by genetic algorithms, how the pattern of demand will evolve and thus location of supplying centres and/or vehicles can be optimally defined. Neural networks provide the basis for a spatio-temporal clustering of demand, definition of the relevant centres, formulation of possible future states of the system and finally, definition of locational strategies for the improvement of the provided services.Locational planning; Point Pattern Analysis; Spatial analysis; Artificial Intelligence

Modeling urban evolution by identifying spatiotemporal patterns and applying methods of artificial intelligence.Case study: Athens, Greece.

While during the past decades, urban areas experience constant slow population growth, the spatial patterns they form, by means of their limits and borders, are rapidly changing in a complex way. Furthermore, urban areas continue to expand to the expense of "rural” intensifying urban sprawl. The main aim of this paper is the definition of the evolution of urban areas and more specifically, the specification of an urban model, which deals simultaneously with the modification of population and building use patterns. Classical theories define city geographic border, with the Aristotelian division of 0 or 1 and are called fiat geographic boundaries. But the edge of a city and the urbanization "degree" is something not easily distinguishable. Actually, the line that city ends and rural starts is vague. In this respect a synthetic spatio - temporal methodology is described which, through the adaptation of different computational methods aims to assist planners and decision makers to gain an insight in urban - rural transition. Fuzzy Logic and Neural Networks are recruited to provide a precise image of spatial entities, further exploited in a twofold way. First for analysis and interpretation of up - to - date urban evolution and second, for the formulation of a robust spatial simulation model, the theoretical background of which is that the spatial contiguity between members of the same or different groups is one of the key factors in their evolution. The paper finally presents the results of the model application in the prefecture of Attica in Greece, unveiling the role of the Athens Metropolitan Area to its current and future evolution, by illustrating maps of urban growth dynamics.urban growth; urban dynamics; neural networks; fuzzy logic; Greece; Athens

Locational planning on scenario-based networks

More and more frequently locational planners are faced with the problem of decision making under the condition of uncertainty. In this paper a methodological framework is presented for solving Location - Allocation problems, through the application of the multinomial logit model to data derived from the modification of the characteristics of a given network. The study differs from earlier work in two aspects: First, a utility junction, as a measure of relative attractiveness, is implemented, in order to assign realization probabilities to each alternative scenario. Second, the decisions are made through the utilization of two system - performance criteria. The expected loss and the minimax loss criterion of the optimal solution of each future scenario generated by the decision maker during the problem - solving procedure of th e approach

Locational planning on scenario-based networks

More and more frequently locational planners are faced with the problem of decision making under the condition of uncertainty. In this paper a methodological framework is presented for solving Location - Allocation problems, through the application of the multinomial logit model to data derived from the modification of the characteristics of a given network. The study differs from earlier work in two aspects: First, a utility junction, as a measure of relative attractiveness, is implemented, in order to assign realization probabilities to each alternative scenario. Second, the decisions are made through the utilization of two system - performance criteria. The expected loss and the minimax loss criterion of the optimal solution of each future scenario generated by the decision maker during the problem - solving procedure of th e approach

Redefinition of the Greek electoral districts through the application of a region-building algorithm

The main purpose of this paper is the formulation of a methodological approach for the definition of homogenous spatial clusters, taking into account both geographical and descriptive characteristics. The proposed methodology, is substantiated by SPiRAL (SPatial Integration and Redistricting ALgorithm), a constrained-based spatial clustering algorithm, whose successive steps focus on the analysis of the characteristics of the areas being integrated, the designation of the spatial clusters and the validity of a joining criterion. We applied the methodological approach and used SPiRAL to solve a realistic electoral redistricting problem. Namely, the redefinition of the electoral districts of the Prefecture of Lakonia in Greece. The results demonstrate an improved layout of the study area’s electoral map as far as the problem’s criteria and constraints are concerned (adjacency, population and size), justifying in this respect the perspectives and potential of our approach in the analysis and confrontation of similar problems

Supporting Locational Decision Making: regionalization of Service Delivery systems

In this paper we describe a case study of our attempt to provide decision support to a public agency, the Greek Ministry of Education, that is currently reorganizing the geo¬graphical configuration of its system. We treat the problem of determining the optimal number of catch¬ment areas, the school locations and the allocations of school children, as an interactive location-allocation problem. In our approach and during the problem-solving procedure, we deal with a set of predefined future scenarios, emphasizing on the uncertainty principles emerging from the notion of a popula¬tion being subject to elements of change. The scenarios are generated from an initial dataset, through the implementation of certain demographic assumptions, concerning fertility, mortality and migration rates. By linking demographic and locational models, and using the graphic capabilities of mapping software, we provide the basis for an integrated locational planning support system and thus a more pragmatic planning process. Furthermore, we assist the decision making process through the utilization of two solution - perform¬ance criteria. Namely, the expected loss and the minimax loss criterion of the optimal solution of each future scenario configur¬ation which is gener¬ated during the problem solving procedure

Supporting Locational Decision Making: regionalization of Service Delivery systems

In this paper we describe a case study of our attempt to provide decision support to a public agency, the Greek Ministry of Education, that is currently reorganizing the geographical configuration of its system. We treat the problem of determining the optimal number of catchment areas, the school locations and the allocations of school children, as an interactive location-allocation problem. In our approach and during the problem-solving procedure, we deal with a set of predefined future scenarios, emphasizing on the uncertainty principles emerging from the notion of a population being subject to elements of change. The scenarios are generated from an initial dataset, through the implementation of certain demographic assumptions, concerning fertility, mortality and migration rates. By linking demographic and locational models, and using the graphic capabilities of mapping software, we provide the basis for an integrated locational planning support system and thus a more pragmatic planning process. Furthermore, we assist the decision making process through the utilization of two solution - performance criteria. Namely, the expected loss and the minimax loss criterion of the optimal solution of each future scenario configuration which is generated during the problem solving procedure