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Spatial Effects (spatial + effects)
Selected AbstractsSTRUCTURAL COVARIATES OF U.S. COUNTY HOMICIDE RATES: INCORPORATING SPATIAL EFFECTS,CRIMINOLOGY, Issue 3 2001ROBERT D. BALLER Spatial analysis is statistically and substantively important for macrolevel criminological inquiry. Using county-level data for the decennial years in the 1960 to 1990 time period, we reexamine the impact of conventional structural covariates on homicide rates and explicitly model spatial effects. Important findings are: (1) homicide is strongly clustered in space; (2) this clustering cannot be completely explained by common measures of the structural similarity of neighboring counties; (3) noteworthy regional differences are observed in the effects of structural covariates on homicide rates; and (4) evidence consistent with a diffusion process for homicide is observed in the South throughout the 1960,1990 period. [source] SEXUAL SELECTION AND THE EVOLUTION OF COSTLY FEMALE PREFERENCES: SPATIAL EFFECTSEVOLUTION, Issue 3 2000Troy Day Abstract., Models of Fisher's runaway process show that if there is a cost to female preference, no preference or male trait exaggeration will evolve. Surprisingly, this is true no matter how small the cost, which reveals that these models of Fisher's process are structurally unstable (Bulmer 1989). Here a model of Fisher's runaway process is presented to demonstrate that costly female preference evolves very easily when space is explicitly included in the model. The only requirement is that the optimal male phenotype changes across the species' range. The model shows that the spatial average of the female preference and male trait reach an evolutionary equilibrium that is identical to those of nonspatial models, but that the preference and male trait can deviate greatly from these averages at any point in space. For example, if random mating results in the lowest cost to females, then at equilibrium the spatial average preference will be zero. Nevertheless, there will be some locations at which females prefer males with larger ornaments and others where they prefer males with smaller ornaments. Results also show that the structural instability of nonspatial models of Fisher's process is less of a problem in spatial models. In particular, many of the main qualitative features of cost-free spatial models of Fisher's process remain valid even when there are small costs of female preference. Finally, the model shows that abrupt changes in the optimal male phenotype across space can result in an amplification of this pattern when preference has a small cost, but it can also result in a pattern similar to reproductive character displacement. Which of these occurs depends on the magnitude of the cost of female preference. This suggests that some patterns of reproductive character displacement in nature might be explained simply by sexual selection rather than by hybrid dysgenesis and reinforcement. [source] INTEGRATED MODELING FOR WATERSHED MANAGEMENT: MULTIPLE OBJECTIVES AND SPATIAL EFFECTS,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 2 2002Stephen C. Newbold ABSTRACT: This paper presents an optimization framework for prioritizing sites for wetlands restoration on a watershed or landscape scale. The framework is designed for analyzing the potential environmental impacts of alternative management strategies while accounting for economic constraints, thereby aiding decision makers in explicitly considering multiple management objectives. The modeling strategy consists of two phases. First, relationships between the configuration of land use types in a watershed and valued ecosystem services are specified mathematically. Second, those functions are incorporated into a spatial optimization model that allows comparisons of the expected environmental impacts and economic costs of management strategies that change the configuration of land use in the watershed. By way of a stylized example, this paper develops the general structure of the framework, presents simulation results based on two production functions for ecosystem services, and discusses the potential utility of the methodology for watershed management. [source] Spatial Effects in Website Adoption by Firms in European RegionsGROWTH AND CHANGE, Issue 1 2009MARGARITA BILLON ABSTRACT The purpose of this paper is to provide empirical evidence on the neighboring effects of Internet adoption as measured by the percentage of firms with their own website in the European regions. This is the first study that explicitly analyzes the role played by spatial effects to explain website adoption for the European case. A set of instruments and techniques commonly used in the spatial econometrics framework is employed to test the hypothesis that proximity matters when explaining Internet adoption by firms. Results show that firms in physically adjacent regions register a similar degree of Internet adoption, confirming the presence in this context of positive spatial dependence. Nevertheless, the spatial effects detected are mainly constrained by national borders. Gross domestic product (GDP) per capita, population density, sectoral composition, and education are positively related to geographic distribution of Internet adoption in the enlarged European Union. In addition, regional disparities in Internet adoption were found to be less important than territorial inequalities in GDP per capita. [source] Spatial Effects within the Agricultural Land Market in Northern IrelandJOURNAL OF AGRICULTURAL ECONOMICS, Issue 1 2003Myles Patton The importance of dealing properly with spatial effects, such as spatial autocorrelation, in cross-sectional econometric estimation has become more widely recognised in recent years. Spatial autocorrelation is similar in many ways to serial correlation, but while the latter is ordered on a one-dimensional time axis, the former is ordered in two dimensions. The multi-directional nature of spatial dependence means that specialised techniques are needed for diagnostic testing and estimation purposes. This paper uses these specialised diagnostics to test for spatial effects within a hedonic pricing study of the agricultural land market. The tests indicate that spatial autocorrelation (in the form of spatial lag dependence) and spatially distinct sub-markets (or spatial heterogeneity) are present. Ignoring these effects in the estimation process is likely to lead to biased parameter estimates. Consequently, we re-specify the hedonic model to allow for these spatial effects. The presence of spatial lag dependence suggests that there is circularity of price setting within the agricultural land market. This means that agricultural land prices are not solely determined by the inherent characteristics of the land, but tend to reflect also the average local price per acre. [source] Some Simple Tests for Spatial Effects Around Putative Sources of Health RiskBIOMETRICAL JOURNAL, Issue 4 2007Andrew B. Lawson Abstract The need for tests dealing with different features of small area health data is less important with the increase in computation speed of computers and the access to MCMC methods. However there are many situations where exploratory testing could be useful and where MCMC methods are not readily useable or available. In this paper, a number of simple tests are derived for the logistic model for case events. This model assumes that a control disease is available and that the events have a binary label relating to case or control state. The tests are derived from likelihood considerations and Monte Carlo critical regions are examined. A simulated evaluation of the tests is presented in terms of Monte Carlo power. A data example is considered. (© 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] Experiments on the mechanism of tree and shrub establishment in temperate grassy woodlands: Seedling emergenceAUSTRAL ECOLOGY, Issue 4 2001Peter J. Clarke Abstract Field experiments were designed to examine tree and shrub seedling emergence in temperate grassy woodlands on the New England Tablelands. The effects of study sites, intensity of previous grazing, removal of ground cover by fire or clearing, burial of seeds and ant seed theft on seedling emergence were tested in two field experiments. Six tree and seven shrub species were used in the experiments and their cumulative emergence was compared with laboratory germination studies. All species used in field experiments had lower cumulative emergence than those in laboratory germination studies despite prolonged periods of above average rainfall before and after seeds were sown. Eucalypt species emerged faster in the field than the shrub species and generally attained higher cumulative emergence than the shrubs. Spatial effects of sites and patches within sites, and of previous grazing history did not strongly influence patterns of seedling emergence in most species. Ground and litter cover generally did not enhance or suppress the emergence of seedlings, although the removal of cover in recently grazed areas enhanced the emergence of some species. Burning enhanced the emergence of some tree and shrub species where plots had more fuel and intense fires, but this effect was not strong. Compared with other treatments, seedbed manipulations produced the strongest effects. In the absence of both invertebrate and vertebrate predators, seedling emergence was lower for surface-sown seed, compared with seed sown on scarified soil surfaces. Higher seedling emergence of buried seeds in the presence of invertebrate predators probably resulted from the combined effects of predator escape and enhanced moisture status of the germination environment. Some promotion of emergence was achieved for all species in most sown treatments probably as a result of a prolonged above average rainfall. In contrast, the natural recruitment of trees and shrubs was negligible in experimental plots, highlighting the importance of seed supply and dispersal as ultimate determinants of recruitment. [source] STRUCTURAL COVARIATES OF U.S. COUNTY HOMICIDE RATES: INCORPORATING SPATIAL EFFECTS,CRIMINOLOGY, Issue 3 2001ROBERT D. BALLER Spatial analysis is statistically and substantively important for macrolevel criminological inquiry. Using county-level data for the decennial years in the 1960 to 1990 time period, we reexamine the impact of conventional structural covariates on homicide rates and explicitly model spatial effects. Important findings are: (1) homicide is strongly clustered in space; (2) this clustering cannot be completely explained by common measures of the structural similarity of neighboring counties; (3) noteworthy regional differences are observed in the effects of structural covariates on homicide rates; and (4) evidence consistent with a diffusion process for homicide is observed in the South throughout the 1960,1990 period. [source] Contrasting spatial and temporal global change impacts on butterfly species richness during the 20th centuryECOGRAPHY, Issue 6 2006Peter White Regional patterns of species richness are often explained by models using temperature or measures habitat suitability. Generally, species richness is positively associated with temperature, and negatively associated with habitat degradation. While these models have been well tested across spatial scales, they have rarely been tested on a temporal scale , in part due to the difficulty in ascertaining accurate historical data at an appropriate resolution. In this study, we compared the results of temporal and spatial models, each incorporating two predictors of species richness: temperature, and human population density (as a surrogate of human-related habitat impacts). We found that the change in species richness from the early to late part of the 20th century was positively correlated with temperature change, and negatively correlated with human population density change. When we compared these results to two spatial models using contemporary and historic data, the spatial effects of temperature on butterfly richness were similar to its temporal effects, while the effect of human population density through time is the opposite of its spatial effect. More generally, the assumption that spatial patterns are equivalent to temporal ones when applying macroecological data to global change is clearly unreliable. [source] Environmental and spatial effects on the distribution of blue marlin (Makaira nigricans) as inferred from data for longline fisheries in the Pacific OceanFISHERIES OCEANOGRAPHY, Issue 6 2008NAN-JAY SU Abstract Blue marlin is distributed throughout tropical and temperate waters in the Pacific Ocean. However, the preference of this species for particular habitats may impact its vulnerability to being caught. The relationship between spatio-temporal patterns of blue marlin abundance and environmental factors is examined using generalized additive models fitted to catch and effort data from longline fisheries. The presence of blue marlin, and the catch rate given presence, are modeled separately. Latitude, longitude, and sea-surface temperature explain the greatest proportion of the deviance. Spatial distributions of relative density of blue marlin, based on combining the probability of presence and relative density given presence, indicate that there is seasonal variation in the distribution of blue marlin, and that the highest densities occur in the tropics. Seasonal patterns in the relative density of blue marlin appear to be related to shifts in SST. The distribution and relative abundance of blue marlin are sufficiently heterogeneous in space and time that the results of analyses of catch and effort data to identify ,hotspots' could be used as the basis for time-area management to reduce the amount of blue marlin bycaught in longline fisheries. [source] Functional biodiversity of macroinvertebrate assemblages along major ecological gradients of boreal headwater streamsFRESHWATER BIOLOGY, Issue 9 2005JANI HEINOArticle first published online: 3 AUG 200 Summary 1. Biodiversity,environment relationships are increasingly well-understood in the context of species richness and species composition, whereas other aspects of biodiversity, including variability in functional diversity (FD), have received rather little rigorous attention. For streams, most studies to date have examined either taxonomic assemblage patterns or have experimentally addressed the importance of species richness for ecosystem functioning. 2. I examined the relationships of the functional biodiversity of stream macroinvertebrates to major environmental and spatial gradients across 111 boreal headwater streams in Finland. Functional biodiversity encompassed functional richness (FR , the number of functional groups derived from a combination of functional feeding groups and habit trait groups), FD , the number of functional groups and division of individuals among these groups, and functional evenness (FE , the division of individuals among functional groups). Furthermore, functional structure (FS) comprised the composition and abundance of functional groups at each site. 3. FR increased with increasing pH, with additional variation related to moss cover, total nitrogen, water colour and substratum particle size. FD similarly increased with increasing pH and decreased with increasing canopy cover. FE decreased with increasing canopy cover and water colour. Significant variation in FS was attributable to pH, stream width, moss cover, substratum particle size, nitrogen, water colour with the dominant pattern in FS being related to the increase of shredder-sprawlers and the decrease of scraper-swimmers in acidic conditions. 4. In regression analysis and redundancy analysis, variation in functional biodiversity was not only related to local environmental factors, but a considerable proportion of variability was also attributable to spatial patterning of environmental variables and pure spatial gradients. For FR, 23.4% was related to pure environmental effects, 15.0% to shared environmental and spatial effects and 8.0% to spatial trends. For FD, 13.8% was attributable to environmental effects, 15.2% to shared environmental and spatial effects and 5% to spatial trends. For FE, 9.0% was related to environmental variables, 12.7% to shared effects of environmental and spatial variables and 4.5% to spatial variables. For FS, 13.5% was related to environmental effects, 16.9% to shared environmental and spatial effects and 15.4% to spatial trends. 5. Given that functional biodiversity should portray variability in ecosystem functioning, one might expect to find functionally rather differing ecosystems at the opposite ends of major environmental gradients (e.g. acidity, stream size). However, the degree to which variation in the functional biodiversity of stream macroinvertebrates truly portrays variability in ecosystem functioning is difficult to judge because species traits, such as feeding roles and habit traits, are themselves strongly affected by the habitat template. 6. If functional characteristics show strong responses to natural environmental gradients, they also are likely to do so to anthropogenic environmental changes, including changes in habitat structure, organic inputs and acidifying elements. However, given the considerable degree of spatial structure in functional biodiversity, one should not expect that only the local environment and anthropogenic changes therein are responsible for this variability. Rather, the spatial context, as well as natural variability along environmental gradients, should also be explicitly considered in applied research. [source] A Bayesian Dynamic Spatio-Temporal Interaction Model: An Application to Prostate Cancer IncidenceGEOGRAPHICAL ANALYSIS, Issue 1 2008Hoon Kim During the past three decades, prostate cancer incidence has changed substantially in the United States. A fully Bayesian hierarchical spatio-temporal interaction model is proposed to estimate prostate cancer incidence rates in the state of Iowa. We introduce random spatial effects to capture the local dependence among regions, random temporal effects to explain the nonlinearity of rates over time, and random spatio-temporal interactions. In addition, we introduce fixed age effects because most epidemiologic data are strongly related to age. We find that prostate cancer incidence in Iowa counties increased sharply over age while incidence rates increased initially, then decreased over time. We identify hot spots of high and low rates for age groups and time periods using disease mapping. [source] Spatial Effects in Website Adoption by Firms in European RegionsGROWTH AND CHANGE, Issue 1 2009MARGARITA BILLON ABSTRACT The purpose of this paper is to provide empirical evidence on the neighboring effects of Internet adoption as measured by the percentage of firms with their own website in the European regions. This is the first study that explicitly analyzes the role played by spatial effects to explain website adoption for the European case. A set of instruments and techniques commonly used in the spatial econometrics framework is employed to test the hypothesis that proximity matters when explaining Internet adoption by firms. Results show that firms in physically adjacent regions register a similar degree of Internet adoption, confirming the presence in this context of positive spatial dependence. Nevertheless, the spatial effects detected are mainly constrained by national borders. Gross domestic product (GDP) per capita, population density, sectoral composition, and education are positively related to geographic distribution of Internet adoption in the enlarged European Union. In addition, regional disparities in Internet adoption were found to be less important than territorial inequalities in GDP per capita. [source] Spatial Effects within the Agricultural Land Market in Northern IrelandJOURNAL OF AGRICULTURAL ECONOMICS, Issue 1 2003Myles Patton The importance of dealing properly with spatial effects, such as spatial autocorrelation, in cross-sectional econometric estimation has become more widely recognised in recent years. Spatial autocorrelation is similar in many ways to serial correlation, but while the latter is ordered on a one-dimensional time axis, the former is ordered in two dimensions. The multi-directional nature of spatial dependence means that specialised techniques are needed for diagnostic testing and estimation purposes. This paper uses these specialised diagnostics to test for spatial effects within a hedonic pricing study of the agricultural land market. The tests indicate that spatial autocorrelation (in the form of spatial lag dependence) and spatially distinct sub-markets (or spatial heterogeneity) are present. Ignoring these effects in the estimation process is likely to lead to biased parameter estimates. Consequently, we re-specify the hedonic model to allow for these spatial effects. The presence of spatial lag dependence suggests that there is circularity of price setting within the agricultural land market. This means that agricultural land prices are not solely determined by the inherent characteristics of the land, but tend to reflect also the average local price per acre. [source] Local environmental effects and spatial effects in macroecological studies using mapped abundance classes: the case of the rook Corvus frugilegus in ScotlandJOURNAL OF ANIMAL ECOLOGY, Issue 5 2006A. GIMONA Summary 1The study of the spatial pattern of species abundance is complicated by statistical problems, such as spatial autocorrelation of the abundance data, which lead to the confusion of environmental effects and dispersal. 2Atlas-derived data for the rook in Scotland are used as a case study to propose an approach for assessing the likely contribution of dispersal and local environmental effects, based on a Bayesian Conditional Autoregressive (CAR) approach. 3The availability of moist grasslands is a key factor explaining the spatial pattern of abundance. This is influenced by a combination of climatic and soil-related factors. A direct link to soil properties is for the first time reported for the wide-scale distribution of a bird species. In addition, for this species, dispersal seems to contribute significantly to the spatial pattern and produces a smoother than expected decline in abundance at the north-western edge of its distribution range. Areas where dispersal is most likely to be important are highlighted. 4The approach described can help ecologists make more efficient use of atlas data for the investigation of the structure of species abundance, and can highlight potential sink areas at the landscape and regional scale. 5Bayesian spatial models can deal with data autocorrelation in atlas-type data, while clearly communicating uncertainty through the estimation of the full posterior probability distribution of all parameters. [source] Simulating forest ecosystem response to climate warming incorporating spatial effects in north-eastern ChinaJOURNAL OF BIOGEOGRAPHY, Issue 12 2005Hong S. He Abstract Aim, Predictions of ecosystem responses to climate warming are often made using gap models, which are among the most effective tools for assessing the effects of climate change on forest composition and structure. Gap models do not generally account for broad-scale effects such as the spatial configuration of the simulated forest ecosystems, disturbance, and seed dispersal, which extend beyond the simulation plots and are important under changing climates. In this study we incorporate the broad-scale spatial effects (spatial configurations of the simulated forest ecosystems, seed dispersal and fire disturbance) in simulating forest responses to climate warming. We chose the Changbai Natural Reserve in China as our study area. Our aim is to reveal the spatial effects in simulating forest responses to climate warming and make new predictions by incorporating these effects in the Changbai Natural Reserve. Location, Changbai Natural Reserve, north-eastern China. Method, We used a coupled modelling approach that links a gap model with a spatially explicit landscape model. In our approach, the responses (establishment) of individual species to climate warming are simulated using a gap model (linkages) that has been utilized previously for making predictions in this region; and the spatial effects are simulated using a landscape model (LANDIS) that incorporates spatial configurations of the simulated forest ecosystems, seed dispersal and fire disturbance. We used the recent predictions of the Canadian Global Coupled Model (CGCM2) for the Changbai Mountain area (4.6 °C average annual temperature increase and little precipitation change). For the area encompassed by the simulation, we examined four major ecosystems distributed continuously from low to high elevations along the northern slope: hardwood forest, mixed Korean pine hardwood forest, spruce-fir forest, and sub-alpine forest. Results, The dominant effects of climate warming were evident on forest ecosystems in the low and high elevation areas, but not in the mid-elevation areas. This suggests that the forest ecosystems near the southern and northern ranges of their distributions will have the strongest response to climate warming. In the mid-elevation areas, environmental controls exerted the dominant influence on the dynamics of these forests (e.g. spruce-fir) and their resilience to climate warming was suggested by the fact that the fluctuations of species trajectories for these forests under the warming scenario paralleled those under the current climate scenario. Main conclusions, With the spatial effects incorporated, the disappearance of tree species in this region due to the climate warming would not be expected within the 300-year period covered by the simulation. Neither Korean pine nor spruce-fir was completely replaced by broadleaf species during the simulation period. Even for the sub-alpine forest, mountain birch did not become extinct under the climate warming scenario, although its occurrence was greatly reduced. However, the decreasing trends characterizing Korean pine, spruce, and fir indicate that in simulations beyond 300 years these species could eventually be replaced by broadleaf tree species. A complete forest transition would take much longer than the time periods predicted by the gap models. [source] Multivariate analysis of a fine-scale breeding bird atlas using a geographical information system and partial canonical correspondence analysis: environmental and spatial effectsJOURNAL OF BIOGEOGRAPHY, Issue 11 2004Nicolas Titeux Abstract Aim, To assess the relative roles of environment and space in driving bird species distribution and to identify relevant drivers of bird assemblage composition, in the case of a fine-scale bird atlas data set. Location, The study was carried out in southern Belgium using grid cells of 1 × 1 km, based on the distribution maps of the Oiseaux nicheurs de Famenne: Atlas de Lesse et Lomme which contains abundance for 103 bird species. Methods, Species found in < 10% or > 90% of the atlas cells were omitted from the bird data set for the analysis. Each cell was characterized by 59 landscape metrics, quantifying its composition and spatial patterns, using a Geographical Information System. Partial canonical correspondence analysis was used to partition the variance of bird species matrix into independent components: (a) ,pure' environmental variation, (b) spatially-structured environmental variation, (c) ,pure' spatial variation and (d) unexplained, non-spatial variation. Results, The variance partitioning method shows that the selected landscape metrics explain 27.5% of the variation, whilst ,pure' spatial and spatially-structured environmental variables explain only a weak percentage of the variation in the bird species matrix (2.5% and 4%, respectively). Avian community composition is primarily related to the degree of urbanization and the amount and composition of forested and open areas. These variables explain more than half of the variation for three species and over one-third of the variation for 12 species. Main conclusions, The results seem to indicate that the majority of explained variation in species assemblages is attributable to local environmental factors. At such a fine spatial resolution, however, the method does not seem to be appropriated for detecting and extracting the spatial variation of assemblages. Consequently, the large amount of unexplained variation is probably because of missing spatial structures and ,noise' in species abundance data. Furthermore, it is possible that other relevant environmental factors, that were not taken into account in this study and which may operate at different spatial scales, can drive bird assemblage structure. As a large proportion of ecological variation can be shared by environment and space, the applied partitioning method was found to be useful when analysing multispecific atlas data, but it needs improvement to factor out all-scale spatial components of this variation (the source of ,false correlation') and to bring out the ,pure' environmental variation for ecological interpretation. [source] Landmines and Local Community AdaptationJOURNAL OF CONTINGENCIES AND CRISIS MANAGEMENT, Issue 2 2002Aldo A. Benini Despite international mobilization for greater humanitarian mine action and despite considerable clearance achievements, the majority of mine-affected communities have not yet been involved in formal clearance activities. They adapt to the contamination largely by local means. The differing degree to which local adaptation is successful is now better understood as a result of the Global Landmine Survey, a multi-country survey project launched in the wake of the 1997 Ottawa treaty to ban anti-personnel mines. Socio-economic impact surveys have since been completed in several countries. In addition to landmines, the Global Landmine Survey records impacts also from unexploded ordnance (UXO). The ability to avoid mine incidents is used to measure adaptation success. We use a variant of Poisson regression models in order to identify community and contamination correlates of the number of recent landmine victims. We estimate separate models using data from the Yemen, Chad and Thailand surveys. We interpret them in a common framework that includes variables from three domains: Pressure on resources, intensity of past conflict and communities' institutional endowments. Statistically significant associations occur in all three domains and in all the three countries studied. Physical correlates are the most strongly associated, pointing to a lasting deadly legacy of violent conflict, but also significant learning effects over time are present. Despite different measurements of institutional endowments, in each country one factor signifying greater local development is correlated with reductions in victims, whereas factors commonly associated with the presence of government officials do not contribute to local capacity to diminish the landmine problem. Strong spatial effects are manifest in clusters of communities with recent victims. Two policy consequences emerge. Firstly, given humanitarian funding limits, trade-offs between clearing contaminated land and creating alternative employment away from that land need to be studied more deeply; the Global Landmine Survey will need to reach out to other bodies of knowledge in development. Secondly, communities with similar contamination types and levels often form local clusters that are smaller than the administrative districts of the government and encourage tailored planning approaches for mine action. These call for novel coalitions that bring advocacy and grassroots NGOs together with local governments, agricultural and forestry departments and professional mine clearance and awareness education agencies. [source] Deforestation and land use change: sparse data environmentsAGRICULTURAL ECONOMICS, Issue 3 2002Gerald C. Nelson Abstract Understanding determinants of land use in developing countries has become a priority for researchers and policy makers with a wide range of interests. For the vast majority of these land use issues, the location of change is as important as its magnitude. This overview paper highlights new economic approaches to modeling land use determinants that combine non-traditional data sources with novel economic models and econometric techniques. A key feature is that location is central to the analysis. All data elements include an explicit location attribute, estimation techniques include the potential for complications from spatial effects, and results are location-specific. The paper reviews the theory underlying these models. Since this paper is intended to provide the potential new researcher with an introduction to the challenges of this analysis, we present an overview of how remotely-sensed data are collected and processed, describe key GIS concepts and identify sources of data for this type of econometric analysis. Finally, selected papers using these techniques are reviewed. [source] ESTIMATION AND HYPOTHESIS TESTING FOR NONPARAMETRIC HEDONIC HOUSE PRICE FUNCTIONSJOURNAL OF REGIONAL SCIENCE, Issue 3 2010Daniel P. McMillen ABSTRACT In contrast to the rigid structure of standard parametric hedonic analysis, nonparametric estimators control for misspecified spatial effects while using highly flexible functional forms. Despite these advantages, nonparametric procedures are still not used extensively for spatial data analysis due to perceived difficulties associated with estimation and hypothesis testing. We demonstrate that nonparametric estimation is feasible for large datasets with many independent variables, offering statistical tests of individual covariates and tests of model specification. We show that fixed parameterization of distance to the nearest rapid transit line is a misspecification and that pricing of access to this amenity varies across neighborhoods within Chicago. [source] THE FUTURE TRAJECTORY OF U.S. CO2 EMISSIONS: THE ROLE OF STATE VS.JOURNAL OF REGIONAL SCIENCE, Issue 1 2007AGGREGATE INFORMATION ABSTRACT This paper provides comparisons of a variety of time-series methods for short-run forecasts of the main greenhouse gas, carbon dioxide, for the United States, using a recently released state-level data set from 1960,2001. We test the out-of-sample performance of univariate and multivariate forecasting models by aggregating state-level forecasts versus forecasting the aggregate directly. We find evidence that forecasting the disaggregate series and accounting for spatial effects drastically improves forecasting performance under root mean squared forecast error loss. Based on the in-sample observations we attempt to explain the emergence of voluntary efforts by states to reduce greenhouse gas emissions. We find evidence that states with decreasing per capita emissions and a "greener" median voter are more likely to push toward voluntary cutbacks in emissions. [source] Bayesian geoadditive sample selection modelsJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 3 2010Manuel Wiesenfarth Summary., Sample selection models attempt to correct for non-randomly selected data in a two-model hierarchy where, on the first level, a binary selection equation determines whether a particular observation will be available for the second level, i.e. in the outcome equation. Ignoring the non-random selection mechanism that is induced by the selection equation may result in biased estimation of the coefficients in the outcome equation. In the application that motivated this research, we analyse relief supply in earthquake-affected communities in Pakistan, where the decision to deliver goods represents the dependent variable in the selection equation whereas factors that determine the amount of goods supplied are analysed in the outcome equation. In this application, the inclusion of spatial effects is necessary since the available covariate information on the community level is rather scarce. Moreover, the high temporal dynamics underlying the immediate delivery of relief supply after a natural disaster calls for non-linear, time varying effects. We propose a geoadditive sample selection model that allows us to address these issues in a general Bayesian framework with inference being based on Markov chain Monte Carlo simulation techniques. The model proposed is studied in simulations and applied to the relief supply data from Pakistan. [source] Plot shape effects on plant species diversity measurementsJOURNAL OF VEGETATION SCIENCE, Issue 2 2005Jon E. Keeley Abstract. Question: Do rectangular sample plots record more plant species than square plots as suggested by both empirical and theoretical studies? Location: Grasslands, shrublands and forests in the Mediterranean-climate region of California, USA. Methods: We compared three 0.1-ha sampling designs that differed in the shape and dispersion of 1-m2 and 100-m2 nested subplots. We duplicated an earlier study that compared the Whittaker sample design, which had square clustered subplots, with the modified Whittaker design, which had dispersed rectangular subplots. To sort out effects of dispersion from shape we used a third design that overlaid square subplots on the modified Whittaker design. Also, using data from published studies we extracted species richness values for 400-m2 subplots that were either square or 1:4 rectangles partially overlaid on each other from desert scrub in high and low rainfall years, chaparral, sage scrub, oak savanna and coniferous forests with and without fire. Results: We found that earlier empirical reports of more than 30% greater richness with rectangles were due to the confusion of shape effects with spatial effects, coupled with the use of cumulative number of species as the metric for comparison. Average species richness was not significantly different between square and 1:4 rectangular sample plots at either 1- or 100-m2. Pairwise comparisons showed no significant difference between square and rectangular samples in all but one vegetation type, and that one exhibited significantly greater richness with squares. Our three intensive study sites appear to exhibit some level of self-similarity at the scale of 400 m2, but, contrary to theoretical expectations, we could not detect plot shape effects on species richness at this scale. Conclusions: At the 0.1-ha scale or lower there is no evidence that plot shape has predictable effects on number of species recorded from sample plots. We hypothesize that for the mediterranean-climate vegetation types studied here, the primary reason that 1:4 rectangles do not sample greater species richness than squares is because species turnover varies along complex environmental gradients that are both parallel and perpendicular to the long axis of rectangular plots. Reports in the literature of much greater species richness recorded for highly elongated rectangular strips than for squares of the same area are not likely to be fair comparisons because of the dramatically different periphery/area ratio, which includes a much greater proportion of species that are using both above and below-ground niche space outside the sample area. [source] Do spatial effects play a role in the spatial distribution of desert-dwelling Acacia raddiana?JOURNAL OF VEGETATION SCIENCE, Issue 4 2000Kerstin Wiegand Abstract. We investigated the spatial pattern of A. raddiana in the Negev desert of Israel in order to gain insights into the factors and processes driving the dynamics of this species. Using a scale-dependent measure, the ring statistic, we analysed both patterns observed in the field and time series of spatial tree distributions produced by a simulation model. In the field, random spacing was the predominant pattern observed. However seedlings were clumped on small scales. We ran the model under two contrasting scenarios representing hypotheses that explain the clumping of seedlings and the random distribution of trees. One hypothesis is that there is spatial heterogeneity in seed distribution, germination and seedling mortality, but that these heterogeneities are not correlated with each other in space. The second hypothesis assumes a correlation between these heterogeneities leading to areas suitable for establishment. However, the suitability of the sites is temporally variable. Furthermore, the second hypothesis assumes density-dependent tree mortality due to competition. Both hypotheses lead to spatial distributions that are in qualitative agreement with the patterns observed in the field. Therefore, the classical view that a clumped seedling distribution and a random pattern of older trees is due to clumped regeneration and density-dependent mortality may not hold for Acacia trees in the Negev. [source] Geographical patterns of micro-organismal community structure: are diatoms ubiquitously distributed across boreal streams?OIKOS, Issue 1 2010Jani Heino A topic under intensive study in community ecology and biogeography is the degree to which microscopic, as well as macroscopic organisms, show spatially-structured variation in community characteristics. In general, unicellular microscopic organisms are regarded as ubiquitously distributed and, therefore, without a clear biogeographic signal. This view was summarized 75,years ago by Baas-Becking, who stated "everything is everywhere, but, the environment selects". Within the context of metacommunity theory, this hypothesis is congruent with the species sorting model. By using a broad-scale dataset on stream diatom communities and environmental predictor variables across most of Finland, our main aim was to test this hypothesis. Patterns of spatial autocorrelation were evaluated by Moran's I based correlograms, whereas partial regression analysis and partial redundancy analysis were used to quantify the relative importance of environmental and spatial factors on total species richness and on community composition, respectively. Significant patterns of spatial autocorrelation were found for all environmental variables, which also varied widely. Our main results were clear-cut. In general, pure spatial effects clearly overcame those of environmental effects, with the former explaining much more variation in species richness and community composition. Most likely, missing environmental variables cannot explain the higher predictive power of spatial variables, because we measured key factors that have previously been found to be the most important variables (e.g. pH, conductivity, colour, phosphorus, nitrogen) shaping the structure of diatom communities. Therefore, our results provided only limited support for the Baas-Becking hypothesis and the species sorting perspective of metacommunity theory. [source] Empirical growth models with spatial effects*PAPERS IN REGIONAL SCIENCE, Issue 2 2006Bernard Fingleton Spatial spillovers; regional productivity; spatial econometrics; EU regions Abstract., Recent contributions to the regional science literature have considered spatial effects in empirical growth specifications. In the case of spatial dependence, following theoretical arguments from new economic geography, and endogenous growth models, this phenomenon has been associated with the existence of externalities that cross regional borders. However, despite the general consensus that interactions or externalities are likely to be the major source of spatial dependence, they have been modelled in a rather ad hoc manner in most existing empirical studies. In contrast, we advocate basing the analysis on structural growth models which include externalities across economies, applying the appropriate spatial econometrics tools to test for their presence and estimate the magnitude of these externalities in the real world. [source] Integrated regional econometric+input-output modeling: Issues and opportunities,PAPERS IN REGIONAL SCIENCE, Issue 3 2000Sergio J. Rey Regional modeling; integrated; econometric; input-output Abstract. Recent research on integrated econometric+input-output modeling for regional economies is reviewed. The motivations for and the alternative methodological approaches to this type of analysis are examined. Particular attention is given to the issues arising from multiregional linkages and spatial effects in the implementation of these frameworks at the sub-national scale. The linkages between integrated modeling and spatial econometrics are outlined. Directions for future research on integrated econometric and input-output modeling are identified. [source] Geo-additive models of childhood undernutrition in three sub-Saharan African countriesPOPULATION, SPACE AND PLACE (PREVIOUSLY:-INT JOURNAL OF POPULATION GEOGRAPHY), Issue 5 2009Ngianga-Bakwin Kandala Abstract We investigate the geographical and socioeconomic determinants of childhood undernutrition in Malawi, Tanzania and Zambia, three neighbouring countries in southern Africa, using the 1992 Demographic and Health Surveys. In particular, we estimate models of undernutrition jointly for the three countries to explore regional patterns of undernutrition that transcend boundaries, while allowing for country-specific interactions. We use geo-additive regression models to flexibly model the effects of selected socioeconomic covariates and spatial effects. Inference is fully Bayesian based on recent Markov chain Monte Carlo techniques. While the socioeconomic determinants generally confirm findings from the literature, we find distinct residual spatial patterns that are not explained by the socioeconomic determinants. In particular, there appears to be a belt transcending boundaries and running from southern Tanzania to northeastern Zambia which exhibits much worse undernutrition. These findings have important implications for planning, as well as in the search for left-out variables that might account for these residual spatial patterns. Copyright © 2009 John Wiley & Sons, Ltd. [source] Contagion Effects and Ethnic Contribution NetworksAMERICAN JOURNAL OF POLITICAL SCIENCE, Issue 2 2003Wendy K. Tam Cho Many political behavior theories explicitly incorporate the idea that context matters in politics. Nonetheless, the concept of spatial dependence,in particular, that behavior in geographic units is somehow related to and affected by behavior in neighboring areas,is not extensively explored. The study of campaign finance is no exception. Research in this area concentrates on the attributes of the individual donor, leaving context underexplored. Concepts such as contribution networks, for instance, are not rigorously tested. This article reexamines the impact of conventional socio-demographic covariates on campaign donation behavior by ethnic contributors and explicitly models spatial effects. The spatial analysis reveals that patterns of campaign donations are geographically clustered (exhibiting both spatial dependence, implying a neighborhood effect, and spatial heterogeneity, implying a regional effect), and that this clustering cannot be explained completely by socio-economic and demographic variables. While socio-demographic characteristics are important components of the dynamic underlying campaign contributions, there is also evidence consistent with a contagion effect whereby ethnic contribution networks are fueling funds to candidate coffers. [source] Structured additive regression for overdispersed and zero-inflated count dataAPPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 4 2006Ludwig Fahrmeir Abstract In count data regression there can be several problems that prevent the use of the standard Poisson log-linear model: overdispersion, caused by unobserved heterogeneity or correlation, excess of zeros, non-linear effects of continuous covariates or of time scales, and spatial effects. We develop Bayesian count data models that can deal with these issues simultaneously and within a unified inferential approach. Models for overdispersed or zero-inflated data are combined with semiparametrically structured additive predictors, resulting in a rich class of count data regression models. Inference is fully Bayesian and is carried out by computationally efficient MCMC techniques. Simulation studies investigate performance, in particular how well different model components can be identified. Applications to patent data and to data from a car insurance illustrate the potential and, to some extent, limitations of our approach. Copyright © 2006 John Wiley & Sons, Ltd. [source] | |||||||||||||||||||||||||||||||||||||