The Congressional Budget Process and the Aggregate Level of Spending

This paper analyzes whether the Congressional budget process (instituted in 1974) leads to lower aggregate spending than does the piece-meal appropriations process that preceded it. Previous theoretical analysis, using spatial models of legislator preferences, is inconclusive. This paper uses a model of interest group lobbying, where a legislature determines spending on a national public good and on subsidies to subsets of the population that belong to nationwide sector-specific interest groups. In the appropriations process, the Appropriations Committee proposes a budget, maximizing the joint welfare of voters and the interest groups, that leads to overspending on subsidies. In the budget process, a Budget Committee proposes an aggregate level of spending (the budget resolution); the Appropriations Committee then proposes a budget. If the lobby groups are not subject to a binding resource constraint, the two institutional structures lead to identical outcomes. With such a constraint, however, there is a free rider problem among the groups in lobbying the Budget Committee, as each group only obtains a small fraction of the benefits from increasing the aggregate budget. If the number of groups is sufficiently large, each takes the budget resolution as given, and lobbies only the Appropriations Committee. The main results are that aggregate spending is lower, and social welfare higher, under the budget process; however, provision of the public good is suboptimal. The paper also presents two extensions: the first endogenizes the enforcement of the budget resolution by incorporating the relevant procedural rules into the model. The second analyzes statutory budget rules that limit spending levels, but can be revised by a simple majority vote. In each case,the free rider problem prevents the groups from securing the required changes to procedural and budget rules.

Propuesta metodológica para la elaboración de un plan de ordenamiento territorial sustentable

El concepto “desarrollo sustentable" se ha consolidado en la academia, organismos internacionales e instituciones públicas que tienen como una preocupación central el bienestar colectivo o la calidad de vida de la población. También está presente en el discurso de partidos políticos, organizaciones no gubernamentales, movimientos sociales y otros actores de la sociedad civil, que buscan nuevas respuestas a distintos problemas que aquejan a la sociedad y que dicen relación con su estrategia de desarrollo. De esta forma, se trata de un concepto universalmente aceptado y legitimado, aunque su significado no siempre sea unívoco y no conlleve en todos los casos al mismo tipo de acciones. Su fortaleza, sin embargo, radica en su concepción ampliamente compartida como uno de los meta - objetivos de la sociedad. No obstante, tal vez una de las mayores debilidades del concepto sea su (todavía) baja aplicabilidad a la realidad. Como dice Reboratti (2000:202), “desarrollo sostenible es...una meta a alcanzar, una posibilidad que aparece en el futuro y que tal vez nunca alcanzaremos...", pero según reconoce el mismo autor, requiere de al menos un esfuerzo de planificación, que –según entendemos nosotros- ha de contar con herramientas específicas, que permitan encauzar en forma efectiva el desarrollo de un territorio hacia su sustentabilidad. En este contexto, resulta fundamental desarrollar una metodología de ordenamiento territorial que pueda conducir efectivamente a un desarrollo sustentable. Por cierto, es necesario que dicha metodología sea de fácil aplicación, de manera que se constituya en un apoyo eficiente y eficaz para las instituciones responsables de la planificación y administración del territorio. En virtud de lo anterior, el presente trabajo tiene como objetivo general exponer una metodología para la elaboración de un Plan de Ordenamiento Territorial, basada en el concepto de “sustentabilidad", que sea efectiva y simple en su aplicación. Los componentes centrales de esta propuesta metodológica son: (a) la integración de distintas herramientas de análisis para el diagnóstico evaluativo de un territorio, (b) la ponderación de todas las dimensiones de la sustentabilidad, (c) la proposición de instrumentos para el diseño de modelos espaciales que permitan encauzar el desarrollo de un territorio hacia su sustentabilidad, considerando el uso racional de los recursos naturales, la reducción de los riesgos de desastres y el mejoramiento de la calidad de vida de las generaciones presentes y futuras. La metodología propuesta ha sido aplicada a un caso de estudio de escala local, la comuna de San José de Maipo en Santiago de Chile, a través de un ejercicio docente desarrollado por los alumnos de la promoción 2007 en el Taller de Gestión Ambiental, del Magíster en Asentamientos Humanos y Medio Ambiente de la Pontificia Universidad Católica de Chile. El proceso metodológico propuesto considera básicamente dos subprocesos: el diagnóstico evaluativo del sistema territorial y el diseño de un modelo territorial, cuyo resultado es la elaboración de un Plan de Ordenamiento Territorial Sustentable para la comuna de San José de Maipo.

Sintaxis espacial en el oppidum ibérico. Reflexiones sobre los modelos espaciales y sociales

En el presente trabajo realizamos un aproximación mediante el análisis basado en la sintaxis espacial aplicado al espacio doméstico de las sociedades de la edad del Hierro de la franja central de la Iberia Mediterránea, entre los siglos VI y III a. C. En una primera sección se introduce la importancia de la configuración de las casas para comprender la sociedad ibérica. A continuación seleccionamos algunos ejemplos de la mitad meridional del País Valenciano para realizar nuestro análisis. Se identifican de dos modelos espaciales claramente definidos. Por último se relacionan estos esquemas constructivos con los procesos de agregación de unidades familiares para constituir las comunidades ibéricas.

Modelling the influence of biotic factors on species distribution patterns

Biotic interactions can have large effects on species distributions yet their role in shaping species ranges is seldom explored due to historical difficulties in incorporating biotic factors into models without a priori knowledge on interspecific interactions. Improved SDMs, which account for biotic factors and do not require a priori knowledge on species interactions, are needed to fully understand species distributions. Here, we model the influence of abiotic and biotic factors on species distribution patterns and explore the robustness of distributions under future climate change. We fit hierarchical spatial models using Integrated Nested Laplace Approximation (INLA) for lagomorph species throughout Europe and test the predictive ability of models containing only abiotic factors against models containing abiotic and biotic factors. We account for residual spatial autocorrelation using a conditional autoregressive (CAR) model. Model outputs are used to estimate areas in which abiotic and biotic factors determine species’ ranges. INLA models containing both abiotic and biotic factors had substantially better predictive ability than models containing abiotic factors only, for all but one of the four species. In models containing abiotic and biotic factors, both appeared equally important as determinants of lagomorph ranges, but the influences were spatially heterogeneous. Parts of widespread lagomorph ranges highly influenced by biotic factors will be less robust to future changes in climate, whereas parts of more localised species ranges highly influenced by the environment may be less robust to future climate. SDMs that do not explicitly include biotic factors are potentially misleading and omit a very important source of variation. For the field of species distribution modelling to advance, biotic factors must be taken into account in order to improve the reliability of predicting species distribution patterns both presently and under future climate change.

Aplicación de la econometría espacial para el análisis de la miseria en los municipios del Departamento de Antioquia.

Este artículo comprueba la existencia de autocorrelación espacial al considerar la proporción de personas que viven en situación de miseria en los municipios del departamento de Antioquia, Colombia. Para ello se utiliza el test I de Moran y se propone un algoritmo para descartar la posibilidad de que la dependencia espacial sea espuria. Los resultados demuestran la necesidad de tener en cuenta la econometría espacial para determinar la asignación óptima del gasto social, destinado a intervenir en forma efectiva la situación de miseria en los municipios del departamento de Antioquia

Important omitted spatial variables in safety models: Understanding contributing crash causes at intersections

Advances in safety research—trying to improve the collective understanding of motor vehicle crash causation—rests upon the pursuit of numerous lines of inquiry. The research community has focused on analytical methods development (negative binomial specifications, simultaneous equations, etc.), on better experimental designs (before-after studies, comparison sites, etc.), on improving exposure measures, and on model specification improvements (additive terms, non-linear relations, etc.). One might think of different lines of inquiry in terms of ‘low lying fruit’—areas of inquiry that might provide significant improvements in understanding crash causation. It is the contention of this research that omitted variable bias caused by the exclusion of important variables is an important line of inquiry in safety research. In particular, spatially related variables are often difficult to collect and omitted from crash models—but offer significant ability to better understand contributing factors to crashes. This study—believed to represent a unique contribution to the safety literature—develops and examines the role of a sizeable set of spatial variables in intersection crash occurrence. In addition to commonly considered traffic and geometric variables, examined spatial factors include local influences of weather, sun glare, proximity to drinking establishments, and proximity to schools. The results indicate that inclusion of these factors results in significant improvement in model explanatory power, and the results also generally agree with expectation. The research illuminates the importance of spatial variables in safety research and also the negative consequences of their omissions.

Probability distributed time delays: integrating spatial effects into temporal models

Background In order to provide insights into the complex biochemical processes inside a cell, modelling approaches must find a balance between achieving an adequate representation of the physical phenomena and keeping the associated computational cost within reasonable limits. This issue is particularly stressed when spatial inhomogeneities have a significant effect on system's behaviour. In such cases, a spatially-resolved stochastic method can better portray the biological reality, but the corresponding computer simulations can in turn be prohibitively expensive. Results We present a method that incorporates spatial information by means of tailored, probability distributed time-delays. These distributions can be directly obtained by single in silico or a suitable set of in vitro experiments and are subsequently fed into a delay stochastic simulation algorithm (DSSA), achieving a good compromise between computational costs and a much more accurate representation of spatial processes such as molecular diffusion and translocation between cell compartments. Additionally, we present a novel alternative approach based on delay differential equations (DDE) that can be used in scenarios of high molecular concentrations and low noise propagation. Conclusions Our proposed methodologies accurately capture and incorporate certain spatial processes into temporal stochastic and deterministic simulations, increasing their accuracy at low computational costs. This is of particular importance given that time spans of cellular processes are generally larger (possibly by several orders of magnitude) than those achievable by current spatially-resolved stochastic simulators. Hence, our methodology allows users to explore cellular scenarios under the effects of diffusion and stochasticity in time spans that were, until now, simply unfeasible. Our methodologies are supported by theoretical considerations on the different modelling regimes, i.e. spatial vs. delay-temporal, as indicated by the corresponding Master Equations and presented elsewhere.

Environmental and economic evaluation of N fertilizer rates in a maize crop in Italy : a spatial and temporal analysis using crop models

Crop simulation models have the potential to assess the risk associated with the selection of a specific N fertilizer rate, by integrating the effects of soil-crop interactions on crop growth under different pedo-climatic and management conditions. The objective of this study was to simulate the environmental and economic impact (nitrate leaching and N2O emissions) of a spatially variable N fertilizer application in an irrigated maize field in Italy. The validated SALUS model was run with 5 nitrogen rates scenarios, 50, 100, 150, 200, and 250 kg N ha−1, with the latter being the N fertilization adopted by the farmer. The long-term (25 years) simulations were performed on two previously identified spatially and temporally stable zones, a high yielding and low yielding zone. The simulation results showed that N fertilizer rate can be reduced without affecting yield and net return. The marginal net return was on average higher for the high yield zone, with values ranging from 1550 to 2650 € ha−1 for the 200 N and 1485 to 2875 € ha−1 for the 250 N. N leaching varied between 16.4 and 19.3 kg N ha−1 for the 200 N and the 250 N in the high yield zone. In the low yield zone, the 250 N had a significantly higher N leaching. N2O emissions varied between 0.28 kg N2O ha−1 for the 50 kg N ha−1 rate to a maximum of 1.41 kg N2O ha−1 for the 250 kg N ha−1 rate.

Incorporating spatial correlations into multispecies mean-field models

In biology, we frequently observe different species existing within the same environment. For example, there are many cell types in a tumour, or different animal species may occupy a given habitat. In modelling interactions between such species, we often make use of the mean field approximation, whereby spatial correlations between the locations of individuals are neglected. Whilst this approximation holds in certain situations, this is not always the case, and care must be taken to ensure the mean field approximation is only used in appropriate settings. In circumstances where the mean field approximation is unsuitable we need to include information on the spatial distributions of individuals, which is not a simple task. In this paper we provide a method that overcomes many of the failures of the mean field approximation for an on-lattice volume-excluding birth-death-movement process with multiple species. We explicitly take into account spatial information on the distribution of individuals by including partial differential equation descriptions of lattice site occupancy correlations. We demonstrate how to derive these equations for the multi-species case, and show results specific to a two-species problem. We compare averaged discrete results to both the mean field approximation and our improved method which incorporates spatial correlations. We note that the mean field approximation fails dramatically in some cases, predicting very different behaviour from that seen upon averaging multiple realisations of the discrete system. In contrast, our improved method provides excellent agreement with the averaged discrete behaviour in all cases, thus providing a more reliable modelling framework. Furthermore, our method is tractable as the resulting partial differential equations can be solved efficiently using standard numerical techniques.

Bayesian hierarchical models for analysing spatial point-based data at a grid level: A comparison of approaches

Spatial data are now prevalent in a wide range of fields including environmental and health science. This has led to the development of a range of approaches for analysing patterns in these data. In this paper, we compare several Bayesian hierarchical models for analysing point-based data based on the discretization of the study region, resulting in grid-based spatial data. The approaches considered include two parametric models and a semiparametric model. We highlight the methodology and computation for each approach. Two simulation studies are undertaken to compare the performance of these models for various structures of simulated point-based data which resemble environmental data. A case study of a real dataset is also conducted to demonstrate a practical application of the modelling approaches. Goodness-of-fit statistics are computed to compare estimates of the intensity functions. The deviance information criterion is also considered as an alternative model evaluation criterion. The results suggest that the adaptive Gaussian Markov random field model performs well for highly sparse point-based data where there are large variations or clustering across the space; whereas the discretized log Gaussian Cox process produces good fit in dense and clustered point-based data. One should generally consider the nature and structure of the point-based data in order to choose the appropriate method in modelling a discretized spatial point-based data.

Principles of goal-directed spatial robot navigation in biomimetic models

Mobile robots and animals alike must effectively navigate their environments in order to achieve their goals. For animals goal-directed navigation facilitates finding food, seeking shelter or migration; similarly robots perform goal-directed navigation to find a charging station, get out of the rain or guide a person to a destination. This similarity in tasks extends to the environment as well; increasingly, mobile robots are operating in the same underwater, ground and aerial environments that animals do. Yet despite these similarities, goal-directed navigation research in robotics and biology has proceeded largely in parallel, linked only by a small amount of interdisciplinary research spanning both areas. Most state-of-the-art robotic navigation systems employ a range of sensors, world representations and navigation algorithms that seem far removed from what we know of how animals navigate; their navigation systems are shaped by key principles of navigation in ‘real-world’ environments including dealing with uncertainty in sensing, landmark observation and world modelling. By contrast, biomimetic animal navigation models produce plausible animal navigation behaviour in a range of laboratory experimental navigation paradigms, typically without addressing many of these robotic navigation principles. In this paper, we attempt to link robotics and biology by reviewing the current state of the art in conventional and biomimetic goal-directed navigation models, focusing on the key principles of goal-oriented robotic navigation and the extent to which these principles have been adapted by biomimetic navigation models and why.

A comparative study of ordinary kriging and support vector machine models for the spatial variability of rock depth in Bangalore

In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 sq.km. In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, ordinary kriging and Support Vector Machine (SVM) models have been developed. In ordinary kriging, the knowledge of the semivariogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of Bangalore, where field measurements are not available. A cross validation (Q1 and Q2) analysis is also done for the developed ordinary kriging model. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing e-insensitive loss function has been used to predict the reduced level of rock from a large set of data. A comparison between ordinary kriging and SVM model demonstrates that the SVM is superior to ordinary kriging in predicting rock depth.