The Experts below are selected from a list of 243 Experts worldwide ranked by ideXlab platform
Manfred M. Fischer - One of the best experts on this subject based on the ideXlab platform.
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Spatial regression based model specifications for exogenous and endogenous Spatial Interaction
ERSA conference papers, 2014Co-Authors: Manfred M. Fischer, James P. LesageAbstract:Spatial Interaction models represent a class of models that are used for modelling origin-destination flow data. The focus of this paper is on the log-normal version of the model. In this context, we consider Spatial econometric specifications that can be used to accommodate two types of dependence scenarios, one involving endogenous Interaction and the other exogenous Interaction. These model specifications replace the conventional assumption of independence between origin-destination flows with formal approaches that allow for two different types of Spatial dependence in magnitudes. Endogenous Interaction reflects situations where there is a reaction to feedback regarding flow magnitudes from regions neighbouring origin and destination regions. This type of Interaction can be modelled using specifications proposed by LeSage and Pace (2008) who use Spatial lags of the dependent variable to quantify the magnitude and extent of the feedback effects, hence the term endogenous Interaction. Exogenous Interaction represents a situation where spillovers arise from nearby (or perhaps even distant) regions, and these need to be taken into account when modelling observed variations in flows across the network of regions. In contrast to endogenous Interaction, these contextual effects do not generate reactions to the spillovers, leading to a model specification that can be interpreted without considering changes in the long-run equilibrium state of the system of flows. As in the case of social networks, contextual effects are modelled using Spatial lags of the explanatory variables that represent characteristics of neighbouring (or more generally connected) regions, but not Spatial lags of the dependent variable, hence the term exogenous Interaction. In addition to setting forth expressions for the true partial derivatives of non-Spatial and endogenous Spatial Interaction models and associated scalar summary measures from Thomas-Agnan and LeSage (2014), we propose new scalar summary measures for the exogenous Spatial Interaction specification introduced here. An illustration applies the exogenous Spatial Interaction model to a flow matrix of teacher movements between 67 school districts in the state of Florida.
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neural Spatial Interaction models network training model complexity and generalization performance
International Conference on Computational Science and Its Applications, 2013Co-Authors: Manfred M. FischerAbstract:Spatial Interaction models approximate mean Interaction frequencies between origin and destination locations by using origin-specific, destination-specific and Spatial separation information. The focus is on models that are based on the theory of feedforward neural networks. This contribution considers the functional form of neural Spatial Interaction models, including the specification of the activation functions, and discusses the problem of network training within a maximum likelihood framework that involves the solution of a non-linear optimization problem. This requires the evaluation of the log-likelihood function with respect to the network parameters. Overfitting is a problem that is likely to occur in neural Spatial Interaction models. To avoid this problem the contribution recommends controlling the model complexity either by regularization or by early stopping in network training. A bootstrapping pairs approach with replacement may be adopted to evaluate the generalization performance of the models.
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Spatial Interaction Models and Spatial Dependence
Spatial Data Analysis, 2011Co-Authors: Manfred M. Fischer, Jinfeng WangAbstract:Spatial Interaction models of the types discussed in the previous chapter take the view that inclusion of a Spatial separation function between origin and destination locations is adequate to capture any Spatial dependence in the sample data. LeSage and Pace (J Reg Sci 48(5):941–967, 2008), and Fischer and Griffith (J Reg Sci 48(5):969–989, 2008) provide theoretical as well as an empirical motivation that this may not be adequate to model potentially rich patterns that can arise from Spatial dependence. In this chapter we consider three approaches to deal with Spatial dependence in origin–destination flows. Two approaches incorporate Spatial correlation structures into the independence (log-normal) Spatial Interaction model. The first specifies a (first order) Spatial autoregressive process that governs the Spatial Interaction variable (see LeSage and Pace (J Reg Sci 48(5):941–967, 2008)). The second approach deals with Spatial dependence by specifying a Spatial process for the disturbance terms, structured to follow a (first order) Spatial autoregressive process. In this framework, the Spatial dependence resides in the disturbance process (see Fischer and Griffith (J Reg Sci 48(5):969–989, 2008)). A final approach relies on using a Spatial filtering methodology developed by Griffith (Spatial autocorrelation and Spatial filtering, Springer, Berlin, Heidelberg and New York, 2003) for area data, and leads to eigenfunction based Spatial filtering specifications of both the log-normal and the Poisson Spatial Interaction model versions (see Fischer and Griffith (J Reg Sci 48(5):969–989, 2008)).
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Principles of Neural Spatial Interaction Modeling
Advances in Spatial Science, 2009Co-Authors: Manfred M. FischerAbstract:This chapter is intended as a convenient resource for regional scientists interested in a statistical view of the neural Spatial Interaction modelling approach. We view neural Spatial Interaction models as an example of non-parametric estimation that makes few, if any, a priori assumptions about the nature of the data-generating process to approximate the true, but unknown Spatial Interaction function of interest. We limit the scope of this chapter to unconstrained Spatial Interaction and use appropriate statistical arguments to gain important insights into the problems and properties of this modelling approach that may be useful for those interested in application development. The remainder of this chapter is structured as follows. The next section introduces the class of neural Spatial Interaction models of interest, and sets forth the context in which Spatial Interaction modelling will be considered. The sections that follow present important components of a methodology for neural Spatial Interaction modelling.
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Spatial Interaction and Spatial Autocorrelation
Perspectives on Spatial Data Analysis, 2008Co-Authors: Manfred M. Fischer, Martin Reismann, Thomas ScherngellAbstract:The objective is to combine insights from two research traditions, Spatial Interaction modelling and Spatial autocorrelation modelling, to deal with the issue of Spatial autocorrelation in Spatial Interaction data analysis. First, the problem is addressed from an exploratory perspective for which a generalisation of the Getis–Ord G statistic is presented. This statistic may yield interesting insights into the processes that give rise to Spatial association between residual flows. Second, the log-additive Spatial Interaction model is extended to Spatial econometric origin-destination flow models consistent with an error structure that reflects origin, destination or origin-destination autoregressive Spatial dependence. The models are formally equivalent to conventional Spatial regression models. But they differ in terms of the data analysed and the way in which the Spatial weights matrix is defined.
Martin Reismann - One of the best experts on this subject based on the ideXlab platform.
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Spatial Interaction and Spatial Autocorrelation
Perspectives on Spatial Data Analysis, 2008Co-Authors: Manfred M. Fischer, Martin Reismann, Thomas ScherngellAbstract:The objective is to combine insights from two research traditions, Spatial Interaction modelling and Spatial autocorrelation modelling, to deal with the issue of Spatial autocorrelation in Spatial Interaction data analysis. First, the problem is addressed from an exploratory perspective for which a generalisation of the Getis–Ord G statistic is presented. This statistic may yield interesting insights into the processes that give rise to Spatial association between residual flows. Second, the log-additive Spatial Interaction model is extended to Spatial econometric origin-destination flow models consistent with an error structure that reflects origin, destination or origin-destination autoregressive Spatial dependence. The models are formally equivalent to conventional Spatial regression models. But they differ in terms of the data analysed and the way in which the Spatial weights matrix is defined.
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A methodology for neural Spatial Interaction modelling
Research Papers in Economics, 2002Co-Authors: Manfred M. Fischer, Martin ReismannAbstract:This paper presents a methodology for neural Spatial Interaction modelling. Particular emphasis is laid on design, estimation and performance issues in both cases, unconstrained and singly constrained Spatial Interaction. Families of classical neural network models, but also less classical ones such as product unit neural network models are considered. Some novel classes of product unit and summation unit models are presented for the case of origin or destination constrained Spatial Interaction flows. The models are based on a modular connectionist architecture that may be viewed as a linked collection of functionally independent neural modules with identical feedforward topologies, operating under supervised learning algorithms. Parameter estimation is viewed as Maximum Likelihood (ML) learning. The nonconvex nature of the loss function makes the Alopex procedure, a global search procedure, an attractive and appropriate optimising scheme for ML learning. A benchmark comparison against the classical gravity models illustrates the superiority of both, the unconstrained and the origin constrained, neural network model versions in terms of generalization performance measured by Kullback and Leibler`s information criterion. Hereby, the authors make use of the bootstrapping pairs approach to overcome the largely neglected problem of sensitivity to the specific splitting of the data into training, internal validation and testing data sets, and to get a better statistical picture of prediction variability of the models. Keywords: Neural Spatial Interaction models, origin constrained or destination constrained Spatial Interaction, product unit network, Alopex procedure, boostrapping, benchmark performance tests.
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A Methodology for Neural Spatial Interaction Modeling
Geographical Analysis, 2002Co-Authors: Manfred M. Fischer, Martin ReismannAbstract:This paper attempts to develop a mathematically rigid and unified framework for neural Spatial Interaction modeling. Families of classical neural network models, but also less classical ones such as product unit neural network ones are considered for the cases of unconstrained and singly constrained Spatial Interaction flows. Current practice appears to suffer from least squares and normality assumptions that ignore the true integer nature of the flows and approximate a discrete-valued process by an almost certainly misrepresentative continuous distribution. To overcome this deficiency we suggest a more suitable estimation approach, maximum likelihood estimation under more realistic distributional assumptions of Poisson processes, and utilize a global search procedure, called Alopex, to solve the maximum likelihood estimation problem. To identify the transition from underfitting to overfitting we split the data into training, internal validation and test sets. The bootstrapping pairs approach with replacement is adopted to combine the purity of data splitting with the power of a resampling procedure to overcome the generally neglected issue of fixed data splitting and the problem of scarce data. In addition, the approach has power to provide a better statistical picture of the prediction variability, Finally, a benchmark comparison against the classical gravity models illustrates the superiority of both, the unconstrained and the origin constrained neural network model versions in terms of generalization performance measured by Kullback and Leibler's information criterion.
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Neural Network Modelling of Constrained Spatial Interaction Flows
Research Papers in Economics, 2001Co-Authors: Manfred M. Fischer, Martin ReismannAbstract:Fundamental to regional science is the subject of Spatial Interaction. GeoComputation - a new research paradigm that represents the convergence of the disciplines of computer science, geographic information science, mathematics and statistics - has brought many scholars back to Spatial Interaction modeling. Neural Spatial Interaction modeling represents a clear break with traditional methods used for explicating Spatial Interaction. Neural Spatial Interaction models are termed neural in the sense that they are based on neurocomputing. They are clearly related to conventional unconstrained Spatial Interaction models of the gravity type, and under commonly met conditions they can be understood as a special class of general feedforward neural network models with a single hidden layer and sigmoidal transfer functions (Fischer 1998). These models have been used to model journey-to-work flows and telecommunications traffic (Fischer and Gopal 1994, Openshaw 1993). They appear to provide superior levels of performance when compared with unconstrained conventional models. In many practical situations, however, we have - in addition to the Spatial Interaction data itself - some information about various accounting constraints on the predicted flows. In principle, there are two ways to incorporate accounting constraints in neural Spatial Interaction modeling. The required constraint properties can be built into the post-processing stage, or they can be built directly into the model structure. While the first way is relatively straightforward, it suffers from the disadvantage of being inefficient. It will also result in a model which does not inherently respect the constraints. Thus we follow the second way. In this paper we present a novel class of neural Spatial Interaction models that incorporate origin-specific constraints into the model structure using product units rather than summation units at the hidden layer and softmax output units at the output layer. Product unit neural networks are powerful because of their ability to handle higher order combinations of inputs. But parameter estimation by standard techniques such as the gradient descent technique may be difficult. The performance of this novel class of Spatial Interaction models will be demonstrated by using the Austrian interregional traffic data and the conventional singly constrained Spatial Interaction model of the gravity type as benchmark. References Fischer M M (1998) Computational neural networks: A new paradigm for Spatial analysis Environment and Planning A 30 (10): 1873-1891 Fischer M M, Gopal S (1994) Artificial neural networks: A new approach to modelling interregional telecommunciation flows, Journal of Regional Science 34(4): 503-527 Openshaw S (1993) Modelling Spatial Interaction using a neural net. In Fischer MM, Nijkamp P (eds) Geographical information systems, Spatial modelling, and policy evaluation, pp. 147-164. Springer, Berlin
Zhen You - One of the best experts on this subject based on the ideXlab platform.
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measuring directional urban Spatial Interaction in china a migration perspective
PLOS ONE, 2017Co-Authors: Zhiming Feng, Zhen YouAbstract:The study of urban Spatial Interaction is closely linked to that of economic geography, urban planning, regional development, and so on. Currently, this topic is generating a great deal of interest among researchers who are striving to find accurate ways to measure urban Spatial Interaction. Classical Spatial Interaction models lack theoretical guidance and require complicated parameter-adjusting processes. The radiation model, however, as proposed by Simini et al. with rigorous formula derivation, can simulate directional urban Spatial Interaction. We applied the radiation model in China to simulate the directional migration number among 337 nationwide research units, comprising 4 municipalities and 333 prefecture-level cities. We then analyzed the overall situation in Chinese cities, the Interaction intensity hierarchy, and the prime urban agglomerations from the perspective of migration. This was done to ascertain China’s urban Spatial Interaction and regional development from 2000 to 2010 to reveal ground realities.
Zhiming Feng - One of the best experts on this subject based on the ideXlab platform.
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measuring directional urban Spatial Interaction in china a migration perspective
PLOS ONE, 2017Co-Authors: Zhiming Feng, Zhen YouAbstract:The study of urban Spatial Interaction is closely linked to that of economic geography, urban planning, regional development, and so on. Currently, this topic is generating a great deal of interest among researchers who are striving to find accurate ways to measure urban Spatial Interaction. Classical Spatial Interaction models lack theoretical guidance and require complicated parameter-adjusting processes. The radiation model, however, as proposed by Simini et al. with rigorous formula derivation, can simulate directional urban Spatial Interaction. We applied the radiation model in China to simulate the directional migration number among 337 nationwide research units, comprising 4 municipalities and 333 prefecture-level cities. We then analyzed the overall situation in Chinese cities, the Interaction intensity hierarchy, and the prime urban agglomerations from the perspective of migration. This was done to ascertain China’s urban Spatial Interaction and regional development from 2000 to 2010 to reveal ground realities.
Aura Reggiani - One of the best experts on this subject based on the ideXlab platform.
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Accessibility and Spatial Interaction - Accessibility and Spatial Interaction
2014Co-Authors: Ana Condeço-melhorado, Aura Reggiani, Javier GutiérrezAbstract:Contents: 1. Accessibility and Spatial Interaction: An Introduction Ana Condeco-Melhorado, Aura Reggiani, Javier Gutierrez PART I: ADVANCES IN MODELING ACCESSIBILITY AND Spatial Interaction 2. Novel Methods for the Estimation of Cost/Distance Decay in Potential Accessibility Models John Osth, Aura Reggiani, Giacomo Galiazzo 3. Transport Networks and Accessibility: Complex Spatial Interactions David Philip Mcarthur, Inge Thorsen, Jan Uboe 4. High Resolution Accessibility Computations Thomas W. Nicolai, Kai Nagel 5. Sensing 'Socio-Spatio' Interaction and Accessibility from Location-Sharing Services Data Laurie A. Schintler, Rajendra Kulkarni, Kingsley Haynes, Roger Stough PART 2: THE SOCIAL AND Spatial DIMENSION OF ACCESSIBILITY 6. Spatial Organisation and Accessibility: A Study of US Counties Andrea De Montis, Simone Caschili And Daniele Trogu 7. Border Effect and Market Potential: The Case of the European Union Maria Henar Salas-Olmedo, Ana Condeco-Melhorado, Javier Gutierrez 8. Mapping Transport Disadvantages of Elderly People in Relation to the Health Services: Contribution of Geographic Information Systems Vitor Ribeiro, Paula Remoaldo, Javier Gutierrez PART 3: ACCESSIBILITY AS A DRIVER OF Spatial Interaction 9. Productivity and Accessibility of Road Transportation Infrastructure in Spain. A Spatial Econometric Approach Pelayo Arbues, Matias Mayor, Jose Banos 10. Location, Accessibility and Firm-Level Productivity in Spain Adelheid Holl 11. Accessibility: An Underused Analytical and Empirical Tool in Spatial Economics. Urban Grasjo, Charlie Karlsson
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Analysis of Dynamic Spatial Interaction Models by Means of Optimal Control
Geographical Analysis, 2010Co-Authors: Peter Nijkamp, Aura ReggianiAbstract:This paper deals with the design of general classes of dynamic Spatial Interaction models. On the basis of a general (well-behaved) multiperiod objective function and of a dynamic model representing the evolution of a Spatial Interaction system, an optimal control model is constructed. Particular attention is given to the equilibrium and stability conditions. It turns out that it is possible to identify steady-state solutions for a dynamic Spatial Interaction model. Furthermore, it can also be demonstrated that the entropy model is a specific case of this Spatial Interaction system. A simple illustration for urban dynamics is given as well.
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Spatial Interaction Models: From the Gravity to the Neural Network Approach
Urban Dynamics and Growth: Advances in Urban Economics, 2005Co-Authors: Manfred M. Fischer, Aura ReggianiAbstract:Abstract Spatial Interaction models describe and predict Spatial flows of people, commodities, capital and information. They are one of the oldest and most widely used of all social science models. This chapter provides a coherent state-of-the-art overview of the field that has witnessed the progression from gravity models to entropy maximising and random utility maximising models and finally to models based on neurocomputing principles that represent the most recent innovation in the design of Spatial Interaction models.
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Entropy Theory and Spatial Interaction Analysis
Interaction Evolution and Chaos in Space, 1992Co-Authors: Peter Nijkamp, Aura ReggianiAbstract:In Chapter 1 the need for a behavioural, social science based underpinning of Spatial Interaction models has been outlined. It turned out that in the history of these models several attempts have been made to offer a plausible description of behavioural backgrounds implicitly present in these models. Such behavioural notions appeared to be particularly interesting from a macro-systems viewpoint. Individual motives (or utility functions) played a much less important role. Since the 1970’s two main streams in Spatial Interaction research can be distinguished, the first one more macro-oriented and based on the entropy concept and the second one more micro-oriented and based on discrete choice models. In the present chapter the nature and use of the entropy concept will be described. This framework will be extended, in Chapter 3, to a utility interpretation offered by optimization models. Next, Chapter 4 will be devoted to a further exploration of the relationships between discrete choice models and Spatial Interaction analysis.
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Accessibility and Spatial Interaction: an introduction
Accessibility and Spatial Interaction, 1Co-Authors: Ana Condeço-melhorado, Aura Reggiani, Javier GutiérrezAbstract:The concept of accessibility is linked to the level of opportunities available for Spatial Interaction (flows of people, goods or information) between a set of locations, through a physical and/or digital transport infrastructure network. Accessibility has proved to be a crucial tool for understanding the framework of sustainability policy in light of best practice planning and decision-making processes. Methods such as cost–benefit analysis, multi-criteria analysis and risk analysis can benefit greatly from embedding accessibility results. This book presents a cohesive collection of recent studies, modeling and discussing Spatial Interaction by means of accessibility indicators
