Spatial Allocation Model Research Articles

Validation of spatially allocated small area estimates for 1880 Census demography

AbstractOBJECTIVEThis paper details the validation of a methodology which spatially allocates Census microdata to census tracts, based on known, aggregate tract population distributions. To protect confidentiality, public-use microdata contain no spatial identifiers other than the code indicating the Public Use Microdata Area (PUMA) in which the individual or household is located. Confirmatory information including the location of microdata households can only be obtained in a Census Research Data Center (CRDC). Due to restrictions in place at CRDCs, a systematic procedure for validating the spatial allocation methodology needs to be implemented prior to accessing CRDC data.METHODSThis study demonstrates and evaluates such an approach, using historical census data for which a 100% count of the full population is available at a fine spatial resolution. The approach described allows for testing of the behavior of a maximum entropy imputation and spatial allocation model under different specifications. The imputation and allocation is performed using a microdata sample of records drawn from the full 1880 Census enumeration and synthetic summary files created from the same source. The results of the allocation are then validated against the actual values from the 100% count of 1880.RESULTSThe results indicate that the validation procedure provides useful statistics, allowing an in-depth evaluation of the household allocation and identifying optimal configurations for model parameterization. This provides important insights as to how to design a validation procedure at a CRDC for spatial allocations using contemporary census data.(ProQuest: ... denotes formulae omitted.)1. IntroductionCensus public-use microdata possess an attribute richness which should make them tremendously useful to researchers interested in demographic small area estimation; however, they are underutilized, largely due to their coarse spatial resolution. The smallest identifiable geographic areas in Census microdata contain a minimum of 100,000 individuals, a restriction which may significantly compromise the geographic nature of a demographic study. Research which focuses on smaller geographic areas generally relies on a limited number of aggregate population characteristics provided by the Census Bureau in summary tables and cross-tabulations at the census tract or block group level. In order to better exploit the attribute richness of Census microdata at finer spatial scales, spatial allocation methods, which allocate microdata households to small areas and generate summary statistics for these smaller geographic units using the attributes of the allocated microdata households, may be used (Johnston and Pattie 1993; Ballas et al. 2005; Assuncao et al. 2005). Small area estimates, which contain extensive detail on the underlying population, are in great demand and are important to research on demographic and social processes such as migration, impoverishment, and human-environmental interactions.A persistent shortcoming in the use of such spatial allocation methods for deriving demographic small area estimates is the lack of confirmatory validation. There are often few, if any, sources against which to compare the estimated fine-scale population counts and the associated distributions of population characteristics. The main reason for the absence of fine-resolution comparison data is the confidentiality protection in census surveys, which precludes the release of confirmatory data. Although demographic estimates based on U.S. Census data and geographies may be validated at a Census Research Data Center (CRDC), the expense of accessing a CRDC and the necessary confidentiality restrictions in place at the CRDC mandate that the validation process, which is not trivial, be fully realized prior to its implementation at the CRDC.This article describes one procedure for validating demographic small area estimates derived from spatially allocated household microdata. …

Designing a connected nature reserve using a network flow theory approach

Establishing nature conservation reserves is an effective and widely accepted practice to protect biodiversity. In order to promote the effectiveness and efficiency of the reserve, spatial attributes of the reserve should be considered. Connectedness (contiguity) is one of these important spatial attributes. Currently in the biological literature there are only a few formal/exact optimization approaches to endogenously designing a connected nature reserve. This article adds a new approach by adapting a spatial unit allocation model to the reserve design problem. Using concepts from network flow theory, the model defines a sink site from which no flow directs out and ensures contiguity by specifying the outflow and inflow relationship of the potential sites. Computational performance of the model is tested using hypothetical problems with various sizes including up to 400 potential sites. Results show that the time needed to solve the problem to optimality increases exponentially both as number of potential sites increases and as species distribution gets more sparse. An empirical application involving 80 potential sites and 15 bird species in part of Fox River watershed, Illinois USA is presented. Factors influencing an IP model’s computational performance and potential extensions of the model were discussed.

Assessing spatial uncertainties of land allocation using a scenario approach and sensitivity analysis: A study for land use in Europe

Land change model outcomes are vulnerable to multiple types of uncertainty, including uncertainty in input data, structural uncertainties in the model and uncertainties in model parameters. In coupled model systems the uncertainties propagate between the models. This paper assesses uncertainty of changes in future spatial allocation of agricultural land in Europe as they arise from a general equilibrium model coupled to a spatial land use allocation model. Two contrasting scenarios are used to capture some of the uncertainty in the development of typical combinations of economic, demographic and policy variables. The scenario storylines include different measurable assumptions concerning scenario specific drivers (variables) and parameters. Many of these assumptions are estimations and thus include a certain level of uncertainty regarding their true values. This leads to uncertainty within the scenario outcomes. In this study we have explored how uncertainty in national-level assumptions within the contrasting scenario assumptions translates into uncertainty in the location of changes in agricultural land use in Europe. The results indicate that uncertainty in coarse-scale assumptions does not translate into a homogeneous spread of the uncertainty within Europe. Some regions are more certain than others in facing specific land change trajectories irrespective of the uncertainty in the macro-level assumptions. The spatial spread of certain and more uncertain locations of land change is dependent on location conditions as well as on the overall scenario conditions. Translating macro-level uncertainties to uncertainties in spatial patterns of land change makes it possible to better understand and visualize the land change consequences of uncertainties in model input variables.

Markets of concentration permits: The case of manure policy

Concentration permits are regarded as an interesting policy tool for regulating emissions where, besides absolute amounts, also local concentration is important. However, effects of governance structure, trading system and possible policy interventions in the permits' allocation are not yet well analysed and understood. This paper explores in how far tradable fertilisation standards can be seen as a concentration permit trading (CPT) system which can be fine-tuned for further policy intervention. Indeed fertilisation standards such as obliged by the EU Nitrate Directive can be regarded as local nitrate emissions limits, and thus concentration permits. A multi-agent spatial allocation model is used to simulate the impact of defining the manure problem in terms of concentration permits rather than conventional emission permits. Impacts are simulated in terms of environmental performance and increased reallocation costs. The model is applied on the Flemish manure problem.

The novel H1N1 Influenza A global airline transmission and early warning without travel containments.

A novel influenza A (H1N1) has been spreading worldwide. Early studies implied that international air travels might be key cause of a severe potential pandemic without appropriate containments. In this study, early outbreaks in Mexico and some cities of United States were used to estimate the preliminary epidemic parameters by applying adjusted SEIR epidemiological model, indicating transmissibility infectivity of the virus. According to the findings, a new spatial allocation model totally based on the real-time airline data was established to assess the potential spreading of H1N1 from Mexico to the world. Our estimates find the basic reproductive number R0 of H1N1 is around 3.4, and the effective reproductive number fall sharply by effective containment strategies. The finding also implies Spain, Canada, France, Panama, Peru are the most possible country to be involved in severe endemic H1N1 spreading.

Generating plausible crop distribution maps for Sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach

Large gaps exist in our knowledge of the current geographic distribution and spatial patterns of performance of crops, and these gaps are unlikely to be filled. In addition, even the spatial scale of many sub-national statistical reporting units remains too coarse to capture patterns of spatial heterogeneity in crop production and performance that are likely important from a policy and investment planning perspective. To fill these spatial data gaps we have developed and applied a meso-scale model for the spatial disaggregation of crop production. Using a cross-entropy approach, our model makes plausible pixel-scale assessments of the spatial distribution of crop production within geopolitical units (e.g. countries or sub-national provinces and districts). The pixel-scale allocations are performed through the compilation and judicious fusion of relevant spatially-explicit data, including: production statistics, land use data, satellite imagery, biophysical crop “suitability” assessments, population density, and distance to urban centers, as wells as any prior knowledge about the spatial distribution of individual crops. The development, application and validation of a prior version of the model in Brazil strongly suggested that our spatial allocation approach shows considerable promise. This paper describes efforts to generate crop distribution maps for Sub-Saharan Africa for the year 2000 using this approach. Apart from the empirical challenge of applying the approach across many countries, the application includes three significant model improvements: (1) the ability to cope with production data sources that provided different degrees of spatial disaggregation for different crops within a single country; (2) the inclusion of a digital map of irrigation intensity as a new input layer; and (3) increased disaggregation of rainfed production systems. Applying the modified spatial allocation model we generated 5 min (approximately 10 km) resolution grid maps for the following 20 major crops across Sub-Saharan Africa: barley, dry beans, cassava, cocoa, coffee, cotton, cow peas, groundnuts, maize, millet, oil palm, plantain, potato, rice, sorghum, soybeans, sugar cane, sweet potato, wheat, and yam. The approach provides plausible results but also highlights the need for much more reliable input data for the region, especially with regard to sub-national production statistics and satellite-based estimates of cropland extent and intensity.

Generating Plausible Crop Distribution Maps for Sub-Saharan Africa Using a Spatial Allocation Model

Agricultural production statistics are fundamental parameters for agriculture policy research. Information on acreage and yields of important crops is critical for understanding trends within what is the most important economic sector of many developing countries. Sub-national data — i.e. data organized by administrative units such as regions or districts — enable the analysis of patterns within countries that may highlight important policy issues, such as the need to allocate resources to underproductive areas. However, collecting sub-national data is difficult for developing countries with limited resources. Even with great effort, and often only on broad regional scales, enormous data gaps exist and are unlikely to be filled. As a result, information is often only available at national or very broad sub-national levels (such as provinces). Such geographically coarse data are unable to reflect important variations within countries and are insufficient for the spatial analysis of production patterns and trends. To fill these spatial data gaps we developed a model to disaggregate production data from coarser to finer spatial units. Using a cross-entropy approach, our spatial allocation model attempts to make plausible allocations of crop production from large reporting units such as a country or state, into smaller spatial units organized as cells of a regularly-spaced grid. In addition to more detailed information, the organization of production information in geographic grids allows for greater analytical possibilities through geographic information systems. The allocation model works on the basis of available evidence of mapped indicators of agricultural production, which include farming systems, land cover, crop biophysical suitability surfaces, commodity prices and local market access. This article describes the generation of crop distribution maps for Sub-Saharan Africa for the year 2000 using the spatial allocation model and discusses the importance of such maps for development analysis and planning.

Efeitos do acordo entre o Mercosul e a União Européia sobre os mercados de grãos

Este estudo busca mensurar os efeitos do acordo entre o Mercosul e a União Européia (UE) sobre a produção, consumo e fluxos comerciais de arroz, milho, soja e trigo nas diversas regiões analisadas. Para avaliar os efeitos deste acordo utilizamos um modelo de alocação espacial apresentado como um Problema de Complementaridade Mista (PCM). Como resultado, o presente estudo identifica as variações no Excedente do Produtor (EP) nos cenários Mercosul-UE e Mercosul-UE ampliada considerando a eliminação das barreiras tarifárias e/ou subsídios. Em termos gerais, os maiores ganhos para os produtores de grãos dos países do Mercosul ocorrem no cenário Mercosul-UE com eliminação dos subsídios por parte dos países da UE. No entanto, salienta-se que, embora os ganhos totais para os países do Mercosul sejam positivos, o setor tritícola apresenta perdas significativas neste cenário.

Classification of Spatial Properties for Spatial Allocation Modeling

Given a set of spatial units, such as land parcels and grid cells, how to allocate subsets of it to activities of interest while satisfying certain criteria? Such a decision process is here called spatial allocation. Though many problems of spatial allocation share this generic construct, each may have a quite unique set of criteria and interpret even the same criteria in its own way. Such diversity makes it difficult to model spatial allocation problems in unambiguous terms that are amenable to algorithmic solution. This paper proposes a classification scheme for spatial properties that helps to address a variety of spatial properties in establishing spatial allocation criteria. The implication of the paper is that a number of spatial properties and spatial allocation criteria can be decomposed into a few kinds of primitive spatial properties and their relations.

Land allocation based on integrated GIS-optimization modeling at a watershed level

Results of a watershed optimization model that specifies the future amount of land for each land use type at a sub-area level are incorporated into a GIS-based spatial allocation model to provide specific location recommendations based on existing land use, slope, distance to surface water and conversion preference. Thus, more detailed interpretation and implementation of optimal development scenarios can be obtained. This integrated approach was applied in Lake Erhai basin, China. This prototype land allocation system combines the strength of optimization modeling and GIS. A decision maker knows where and what type of land use changes should be made to achieve an environmental and economic sustainable future. That is, the modeling results provide support for answering questions of “what should I do?” and “how do I do it?” in land use planning and decision-making.

Visualizing Alternative Models and their Objective Functions in the Solution of Optimal Location Problems

This paper presents an argument for introducing location-allocation theory to advanced undergraduate and beginning graduate students in a simplified continuous space environment that is relatively free of the distorting effects of networks and other aspects of more differentiated “real-world” environments. This approach can enable instructors to reinforce visually the role of different models and their objective functions. In this simplified setting, students can initially concentrate on the link between these mathematical programming techniques and the spatial nature of the problems being solved. In a short time, students can move into more advanced methods in more differentiated environments. A freeware program entitled NEWLAP was developed to facilitate this approach. This software features a variety of spatial allocation models and their associated constraints that can be applied on both the plane and the sphere. This paper outlines how this software can be used to show alternative solutions using different models on the same data set as well as application of the software in a “real world” problem on a global scale.

Two strategies for conservation planning in human-dominated landscapes

This paper discusses the modelling of two strategies for conservation planning in human-dominated landscapes: the enlargement of existing reserve sites and the allocation of stepping stones between these sites. We developed two spatial allocation models that plan new habitat according to these strategies, considering simple ecological rules of thumb to meet the requirements for population sustainability and the suitability of the land for competing land use. The model ENLARGE enlarges the existing sites. The model MENTOR modifies the spatial arrangement of patches by adding new patches that may act as stepping stones between reserve sites. These two models result in different habitat configurations. Both the allocation of stepping stones and the enlargement of existing sites provide a higher percentage of occupied habitat. These models may help to address the question under which conditions either the allocation of stepping stones or the enlargement of existing sites is preferred as strategy for conservation planning in human-dominated landscapes.

The importance of economic costs in the development of guidelines for spatial conservation management

The paper analyses the efficient spatial allocation of subsidies for biodiversity-enhancing land use. For this purpose a spatial allocation model for two different regions is developed which allows guidelines to be derived on how to achieve the highest possible ecological benefit within a given budget provided to support biodiversity-enhancing forms of land use. In contrast to other conservation biological models, our model includes more realistic economic assumptions. The difference from economic models dealing with the efficient spatial allocation of environmental goods is that the benefit function of our model is based on ecological theory. Therefore, our results differ from those obtained in monodisciplinary studies. In particular, with increasing marginal costs a more even allocation of resources becomes optimal.

A Spatial Allocation Model for the New England Fisheries

The objective of this study is to determine the impact of (1) the new 1984 maritime boundary between the United States and Canada in the Northwest Atlantic and (2) the availability of the Brooklyn, New York, port as an alternative landing site on the distribution of domestic and imported groundfish (cod, haddock, and founder) from their point of capture or entry to their final market. A deterministic nonlinear programming model of the New England groundfish fishery was developed. The model maximizes net consumer surplus to determine the competitive equilibrium spatial allocation of harvesting, landing, processing, pricing, and consumption patterns. The model incorporates retail demand functions for cod, haddock, and flatfish for five demand regions; steaming costs, fishing costs, and capacity constraints for two vessel classes; transportation costs; constraints on port and processing capacity; and supply constraints for nine possible ocean fishing areas. The model was specified for three different cases, ...

Incorporating Neighborhood Effects into Spatial Allocation Models

In this paper the authors are concerned with the explicit introduction of endogenous spatial externalities into gravity-type allocation models. The discussion focuses on the locational choice by households, which is taken to be affected by the spatial distribution of all other actors. A stochastic linear function of utility is assumed, in which the arguments include, as endogenous variables, land rents and the level of externalities. A multinomial logit model of demand for land is defined for each zone and each population group and is imbedded in a long-run equilibrium model of the land market. The equation system which results has multiple solutions which can be found by use of a simple iterative algorithm. By means of numerical examples it is shown that positive spatial externalities result in the spatial concentration of sensitive households, whereas negative externalities lead to their dispersal to the periphery. Combined effects may result in different asymmetric local equilibria depending upon the initial solution used in the algorithm.

Spatial and temporal price and allocation modeling: Some extensions

This paper delineated the conditions under which linear complimentarity programming is applicable in the area of spatial and temporal price and allocation modeling. Two examples are offered which are not solvable using the previous methodology available.

A note on spatial and temporal price and allocation modeling: Quadratic programming or linear complementary programming?

This paper presents a clarification of the specific conditions under which the linear complementary programming (LCP) formulation, instead of the quadratic programming (QP) formulation, is applicable in such areas as spatial and temporal price and allocation modeling. An important condition for the use of the LCP formulation is that the coefficient matrix of the demand and/or supply functions is asymmetric. Dynamic formulations can be treated as a LCP but it is demonstrated that the problem can be reformulated in a standard QP format.

A note on the Solution of spatial Price and Allocation Models

Models of spatial and temporal price and allocation have enjoyed widespread acceptance in agricultural economics and continue to be employed, refined and extended in the literature. In this journal, for instance, contributions have been made by Yaron et al. [16], Ghosh [2], Baumes and McCarl [1] and Jenson and Piedrahita [4]. It is the purpose of this note to draw attention to a versatile and computationally efficient nonlinear programming system called MINOS, as yet little known outside the operations research community, for the solution of nonlinear price and allocation models — including the widely applied special class of quadratic programming models. This software system has several features which very strongly commend its use to the practitioner and have already led to its adoption for solving nonlinear models by Manne [7] and Rowse [10], among others. A skeletal outline of several of the principal advantages of the MINOS system follows and an example of its use in solving a small nonlinear model is provided.

Analysis of a World Grain Reserve Plan Proposed under a New International Wheat Agreement

Analysis of a World Grain Reserve Plan Proposed under a New International Wheat Agreement

A Modeling Approach to Flows of Funds in Localized Financial Markets

tered simultaneously increases in loan demand and changes in competition and saver behavior that have increased the cost and reduced the supply of loanable funds obtained as deposits from local sources. To evaluate the impact of such changes in the competitive environment of the banking industry or in regulations that affect banking structure, it is essential to determine the impact of those changes on the flow of funds in local markets. Yet the determinants of and factors influencing funds flows within and between local markets are not well understood. Numerous studies have been completed, for example, on the implications for competition and funds flow of changes in bank structure regulations (Mote, Guttentag and Herman). Most of these studies have been descriptive, with limited recognition of either the supply of and demand for funds in various markets or the transactions costs of management constraints that would impede or encourage funds flows between markets and regions. In addition, one of the striking characteristics of these studies is the inconsistency of their results. For example, some analysts indicate that changes in branch banking or holding company regulations result in a decline in lending activity for farmers, and thus a flow of funds from rural to urban areas (Lee and Reichert, Sullivan). Others indicate that changes in bank structure result in increased lending activity with local borrowers, including farmers (Snider, Rosenblum, and Eisenbeis). Spatial allocation models that explicitly incorp rate supply and demand functions have been used widely in the agricultural sector to analyze commodity flows between various markets and geographic regions as well as the implications of government intervention on such flows (Heady and Srivastava). The purpose of this article is to examine the potential for use of spatial allocation modeling techiques to evaluate the flow of funds within and between local financial markets. The unique structural dimensions of the methodology will be reviewed, data needs for related empirical work will be discussed, and an evaluation will be presented.