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Editorial. Spatial econometrics principles and challenges in Jean Paelinck’s research

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This special issue contains a selection of many excellent papers and posters presented at the Sixth ‘Jean Paelinck’ Seminar of Spatial Econometrics, which was held at the Universidad Autónoma de Madrid (UAM) in October, 2013. The collection of papers address some of the major concerns and progression lines proposed by professor Paelinck along his academic life for spatial econometrics: the treatment of spatial data and the need to specify and estimate appropriate econometric models in the realm of regional and urban science. The first two papers in this issue present two methods to detect spatial clusters (Rodero-Cosano, Salinas-Pérez, García-Alonso and Salvador-Carulla) and concentrations (Thomas-Agnan and Bonneu) using homogeneous micro-data and individual observations, respectively, to adequately deal with the Modifiable Areal Unit Problem. The following three articles focus on the specification of spatial effects –spatial autocorrelation and spatial heterogeneity– in more complex situations. Le Gallo and Chasco propose a nonparametric approach to deal with spatial effects when the model’s parametric structure is unknown, as it is the case in hedonic models with large databases. Ezcurra and Ríos derive a spatial Durbin panel data model from theory in order to model the spatio-temporal relationship between output volatily and regional growth. Finally, Díaz-Lanchas, Gallego, Llano and De la Mata take into account spatial complexity in trade flows introducing spatial dependence in a gravity model.

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... The paper by Le Gallo and Chasco (2015) is entitled 'Heterogeneity in perceptions of noise and air pollution: a spatial quantile approach on the city of Madrid'. The authors propose a flexible non-parametric approach to complex spatial interactions, avoiding the specification of a spatial weights matrix W and spatial lags when there is no theoretical basis for imposing such a spatial global parametric structure, as is the case in their hedonic model, which is estimated using a large database involving housing prices in Madrid. ...
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This virtual special issue marks International Women's Day 2021 by drawing together 15 papers published in Spatial Economic Analysis over the past decade by female authors and co-authors. It highlights the wide range of ground-breaking research by these authors and their collaborators, growth of female-authored publications over time, as well as the geographical and career-stage diversity of female authors within the field. The papers include agenda-setting directions, novel applications of econometric and spatial analysis techniques, and spatial modelling applied to policy-relevant research topics.
... The paper by Le Gallo and Chasco (2015) is entitled 'Heterogeneity in perceptions of noise and air pollution: a spatial quantile approach on the city of Madrid'. The authors propose a flexible non-parametric approach to complex spatial interactions, avoiding the specification of a spatial weights matrix W and spatial lags when there is no theoretical basis for imposing such a spatial global parametric structure, as is the case in their hedonic model, which is estimated using a large database involving housing prices in Madrid. ...
... The paper by Le Gallo and Chasco (2015) is entitled 'Heterogeneity in perceptions of noise and air pollution: a spatial quantile approach on the city of Madrid'. The authors propose a flexible non-parametric approach to complex spatial interactions, avoiding the specification of a spatial weights matrix W and spatial lags when there is no theoretical basis for imposing such a spatial global parametric structure, as is the case in their hedonic model, which is estimated using a large database involving housing prices in Madrid. ...
Article
Land use/land cover (LULC) is an important part of exploring the interaction between natural environment and human activities and achieving regional sustainable development. Based on the data of LULC types (cropland, forest land, grassland, built-up land, and unused land) from 1990 to 2015, we analysed the intensity and driving factors of land use/cover change (LUCC) in the Yarlung Zangbo River, Nyang Qu River, and Lhasa River (YNL) region, Qinghai-Tibet Plateau of China, using intensity analysis method, cross-linking table method, and spatial econometric model. The results showed that LUCC in the YNL region was nonstationary from 1990 to 2015, showing a change pattern with "fast-slow-fast" and "U-shaped". Built-up land showed a steady increase pattern, while cropland showed a steady decrease pattern. The gain of built-up land mainly came from the loss of cropland. The transition pattern of LUCC in the YNL region was relatively single and stable during 1990–2015. The transition pattern from cropland and forest land to built-up land was a systematic change process of tendency and the transition pattern from grassland and unused land to cropland was a systematic change process of avoidance. The transition process of LUCC was the result of the combined effect of natural environment and social economic development in the YNL region. This study reveals the impact of ecological environment problems caused by human activities on the land resource system and provides scientific support for the study of ecological environment change and sustainable development of the Qinghai-Tibet Plateau.
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The scan statistic is commonly used to test if a one dimensional point process is purely random, or if any clusters can be detected. Here it is simultaneously extended in three directions:(i) a spatial scan statistic for the detection of clusters in a multi-dimensional point process is proposed, (ii) the area of the scanning window is allowed to vary, and (iii) the baseline process may be any inhomogeneous Poisson process or Bernoulli process with intensity pro-portional to some known function. The main interest is in detecting clusters not explained by the baseline process. These methods are illustrated on an epidemiological data set, but there are other potential areas of application as well.
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Research in spatial econometrics has undergone a very intense increase in recent decades, both quantitatively and qualitatively. The indicators usually employed, such as published articles, handbooks and volumes edited and conferences and workshops organized, offer a robust image of this fast development. Another, and perhaps more important, aspect is the definite consolidation of this discipline as a fundamental tool for research in Economics. In this overall context, clearly optimistic, the problems associated with the specification of spatio-temporal models have attracted attention (Baltagi 2001). The scientific programs of recent international meetings, in regional economics and in spatial econometrics, with a growing increase in the number of sessions devoted to this topic is a good example for a tendency that, in our opinion, will continue in the near future. We need more techniques and more sophisticated tools to deal adequately with the larger and richer datasets that are currently available. Our contribution to this discussion is modest but enthusiastic. We would like to mention the series of Jean Paelinck Seminars that began in October 2004, with the objective of honoring the mastery of Jean Paelinck in the fields of regional economics and spatial econometrics [let us mention the very well-known textbook by Paelinck and Klaassen (1979)]. The first and second seminars were held at the University of Zaragoza and the third one at the Technical University of Cartagena, between the 10th and the 12th of October, 2008. Twenty-five papers were presented there, some of which appear in the special issue of Journal of Geographical Systems briefly introduced here.
Book
The purpose of models is not to fit the data but to sharpen the questions. S. Karlin, 11th R. A. Fisher Memorial Lecture, Royal Society, 20 April 1983 We are proud to offer this volume in honour of the remarkable career of the Father of Spatial Econometrics, Professor Jean Paelinck, presently of the Tinbergen Institute, Rotterdam. Not one to model solely for the sake of modelling, the above quotation nicely captures Professor Paelinck's unceasing quest for the best question for which an answer is needed. His FLEUR model has sharpened many spatial economics and spatial econometrics questions! Jean Paelinck, arguably, is the founder of modem spatial econometrics, penning the seminal introductory monograph on this topic, Spatial Econometrics, with Klaassen in 1979. In the General Address to the Dutch Statistical Association, on May 2, 1974, in Tilburg, "he coined the term [spatial econometrics] to designate a growing body of the regional science literature that dealt primarily with estimation and testing problems encountered in the implementation of multiregional econometric models" (Anselin, 1988, p. 7); he already had introduced this idea in his introductory report to the 1966 Annual Meeting of the Association de Science Regionale de Langue Fran~aise.
Book
1: Introduction.- 2: The Scope of Spatial Econometrics.- 3: The Formal Expression of Spatial Effects.- 4: A Typology of Spatial Econometric Models.- 5: Spatial Stochastic Processes: Terminology and General Properties.- 6: The Maximum Likelihood Approach to Spatial Process Models.- 7: Alternative Approaches to Inference in Spatial Process Models.- 8: Spatial Dependence in Regression Error Terms.- 9: Spatial Heterogeneity.- 10: Models in Space and Time.- 11: Problem Areas in Estimation and Testing for Spatial Process Models.- 12: Operational Issues and Empirical Applications.- 13: Model Validation and Specification Tests in Spatial Econometric Models.- 14: Model Selection in Spatial Econometric Models.- 15: Conclusions.- References.
Chapter
The following baker’s dozen of papers—constituting a unique document written by enthusiastic researchers and furnishing concrete evidence that Jean Paelinck is a very well appreciated friend of the scientific world—have been compiled as a lasting testimonial to the outstanding research, mentoring, teaching and service contributions of Jean Paelinck to science and society. Through his distinguished career and principal research accomplishments, Jean has established a legacy that should furnish a research endowment upon which regional science can draw for many years to come. Adapting a quote by Stephen Hawking, “Equations are just the boring part of [econometrics]. [Jean] attempt[s] to see things in terms of [space].” In writing to us, Professor Mignolet, who as a student first met Jean in 1975, provided an anecdote that exemplifies this very point: No sooner was I sitting in his office than he threw a string of equations on the blackboard.... His proposed solution method [was based on] the consider[ation] that a policy had not been implemented.
Book
Part 1. Non-standard spatial statistics.- 1. Introduction: spatial statistics, - 2. Individual versus ecological analyses.- 3. Statistical models for spatial data: some linkages and communalities.- 4. Frequency distributions for simulated spatially autorcorrelated random variable.- 5. Understanding correlations among spatial random variables.- 6. Spatially structured random effects: a comparison of three popular specifications.- 7. Spatial filter versus conventional spatial model specifications: some comparisons.- 8. The role of spatial of autocorrelation in prioritizing sites within a geographic landscape.- 9. General spatial statistics conclusions.- 10. References: spatial statistics (Part 1) Part 2. Non-standard spatial econometrics.- 11. Introduction: spatial econometrics.- 12. Mixed linear-logarithmetic specification for Lotka-Volterra models with endogenously generated SDLS-variables.- 13. Selecting spatial regimes by threshold analysis.- 14. Finite automata.- 15 Learning from residuals.- 16. Verhulst and Poisson distributions.- 17. QUARLIREG: qualitative regression and its application to spatial data.- 18. Filtering complexity for observational errors and spatial bias.- 19. General spatial econometrics conclusions.- 20. References: spatial econometrics (Part 2).
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ABSTRACT Though standard spatial econometric models may be useful for specification testing, they rely heavily on a parametric structure that is highly sensitive to model misspecification. The commonly used spatial AR model is a form of spatial smoothing with a structure that closely resembles a semiparametric model. Nonparametric and semiparametric models are generally a preferable approach for more descriptive spatial analysis. Estimated population density functions illustrate the differences between the spatial AR model and nonparametric approaches to data smoothing. A series of Monte Carlo experiments demonstrates that nonparametric predicted values and marginal effect estimates are much more accurate then spatial AR models when the contiguity matrix is misspecified.
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The statistics Gi(d) and Gi*(d), introduced in Getis and Ord (1992) for the study of local pattern in spatial data, are extended and their properties further explored. In particular, nonbinary weights are allowed and the statistics are related to Moran's autocorrelation statistic, I. The correlations between nearby values of the statistics are derived and verified by simulation. A Bonferroni criterion is used to approximate significance levels when testing extreme values from the set of statistics. An example of the use of the statistics is given using spatial-temporal data on the AIDS epidemic centering on San Francisco. Results indicate that in recent years the disease is intensifying in the counties surrounding the city.
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The capabilities for visualization, rapid data retrieval, and manipulation in geographic information systems (GIS) have created the need for new techniques of exploratory data analysis that focus on the “spatial” aspects of the data. The identification of local patterns of spatial association is an important concern in this respect. In this paper, I outline a new general class of local indicators of spatial association (LISA) and show how they allow for the decomposition of global indicators, such as Moran's I, into the contribution of each observation. The LISA statistics serve two purposes. On one hand, they may be interpreted as indicators of local pockets of nonstationarity, or hot spots, similar to the Gi and G*i statistics of Getis and Ord (1992). On the other hand, they may be used to assess the influence of individual locations on the magnitude of the global statistic and to identify “outliers,” as in Anselin's Moran scatterplot (1993a). An initial evaluation of the properties of a LISA statistic is carried out for the local Moran, which is applied in a study of the spatial pattern of conflict for African countries and in a number of Monte Carlo simulations.
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The spatial aggregation problem – also termed the modifiable areal unit problem – has attracted regular attention in spatial statistics and econometrics. In this study econometric aggregation analysis is used to investigate the formal composition of meso-areal parameters given micro-areal underlying relations with spatial dependence. Impact on stochastic terms (possible meso-areal spatial autocorrelation) is also studied. Finally consequences for meso-areal estimation are derived, the general finding having been that spatial aggregation leads to meso-region specific parameter values, with the estimation problems this implies.
Article
In this paper, I give a personal view on the development of the field of spatial econometrics during the past 30 years. I argue that it has moved from the margins to the mainstream of applied econometrics and social science methodology. I distinguish three broad phases in the development, which I refer to as preconditions, take off and maturity. For each of these phases I describe the main methodological focus and list major contributions. I conclude with some speculations about future directions. Copyright (c) 2010 the author(s). Journal compilation (c) 2010 RSAI.
Article
To study the detailed location patterns of industries, and particularly the tendency for industries to cluster relative to overall manufacturing, we develop distance-based tests of localization. In contrast to previous studies, our approach allows us to assess the statistical significance of departures from randomness. In addition, we treat space as continuous instead of using an arbitrary collection of geographical units. This avoids problems relating to scale and borders. We apply these tests to an exhaustive U.K. data-set. For four-digit industries, we find that (i) 52% of them are localized at a 5% confidence level, (ii) localization mostly takes place at small scales below 50 km, (iii) the degree of localization is very skewed, and (iv) industries follow broad sectoral patterns with respect to localization. Depending on the industry, smaller establishments can be the main drivers of both localization and dispersion. Three-digit sectors show similar patterns of localization at small scales as well as a tendency to localize at medium scales.
New Models explaining regional Evolution in Europe Contributions to an international conference in honour of Professor Introduction, Cybergeo: European Journal of Geography (on-line), Dossiers, La science régionale: Journées d'études en l'honneur de Jean Paelinck, document 41
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Spatial econometrics: More light than shadows
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