This repository contains the data used in the empirical analysis of spatial spillover effects on Foreign Direct Investment (FDI) inflows across Chinese administrative divisions. The analysis employs two different model specifications: a balanced panel model and a generalized unbalanced (GU) model. Additionally, a spatial weight matrix file is provided, which is essential for modeling spatial dependencies.

Decomposition results of spatial effects.

Studies focusing on the effects of spatial processes versus environmental filtering on aquatic metacommunities have so far been focused almost entirely on relatively isolated systems, such as sets of different lakes or streams. In contrast, metacommunity patterns and underlying processes within a single aquatic system have received less attention. In this study, we aimed to examine how strongly variations in different diversity indices are affected by spatial processes (dispersal) versus local environmental conditions (species sorting) within a large lake system. Modern biodiversity research focuses on multiple diversity facets because different indices may be uncorrelated within and between facets, and they may thus describe different phenomena. We investigated the relationship of littoral macroinvertebrate diversity with environmental and spatial factors using 10 indices of species, functional and taxonomic diversity. Using spatial factors as proxies of dispersal, we decomposed variation in diversity indices into fractions attributable to environmental and spatial factors. Our results highlighted generally equal or higher importance of spatial processes in controlling the variation in diversity indices when compared to local environmental variables. Local environmental conditions accounted for higher proportion of variation only in a single index (i.e. taxonomic diversity). These findings suggest that the effects of high dispersal rates (mass effects) may override the influences of local environmental conditions (species sorting) on the diversity in highly-connected aquatic system, such as large lakes and marine coastal systems. Our results further suggest that biodiversity assessment and environmental monitoring in highly-connected systems cannot rely solely on the idea of environmental control. We hence recommend that the roles of both environmental and spatial processes should be integrated in basic and applied ecological research of aquatic systems.

The use of spatial econometric models in political science has steadily risen in recent years. However, the interpretation of these models has generally ignored the important substantive, and even spatial, nature of the estimated effects. This leaves many papers with a (non-spatial) interpretation of coefficients on the covariates and a brief discussion of the sign and strength of the spatial parameter. We introduce a general approach to interpreting spatial models and provide several avenues for an exposition of substantive spatial effects. Our approach can be generalized to most models in the spatial econometric taxonomy. Building on the example of the diffusion of democracy, we elucidate how our approach can be applied to modern political science problems.

The recent surge in studies analysing spatial dependence in political science has gone hand-in-hand with increased attention paid to the choice of estimation technique. In comparison,specification choice has been relatively neglected,even though it leads to equally, if not more, serious inference problems. In this article four specification issues are analysed. It is argued that to avoid biased estimates of the spatial effects, researchers need to consider carefully how to model temporal dynamics, common trends and common shocks, as well as how to account for spatial clustering and unobserved spatial heterogeneity.The remaining two specification issues relate to the weighting matrix employed for the creation of spatial effects: whether it should be row-standardised and what functional form to choose for this matrix.The importance of these specification issues is demonstrated by replicating Hays’ model of spatial dependence in international capital tax rate competition. Seemingly small changes to model specification have major impacts on the spatial effect estimates.It is recommended that spatial analysts develop their theories of spatial dependencies further to provide more guidance on the specification of the estimation model.In the absence of sufficiently developed theories,the robustness of results to specification changes needs to be demonstrated.

Replication Data for: The Localized and Spatial Effects of US Troop Deployments on Host-State Defense Spending

Estimated spatial coefficients and marginal effects for all models.

The study examines how the recent economic crisis and the related unfavourable economic features affect poverty. As economic crisis is usually associated with many economic and social problems, it tries to determine to what extent it influences poverty. The paper attempts to prove that economic recession contributes not only to the impoverishment of a significant section of society, but also increases the depth of poverty significantly. If the research supports this hypothesis, it is worth examining to what extent one percent economic growth or economic decline can decrease or increase the rate of the poor and the depth of poverty. Besides the effect of economic growth on the given area, the paper also analyses the effect of the economic growth of the neighbouring areas. The initial hypothesis states that the economic growth of the neighbouring regions can also alleviate poverty. As for spatial effects, spatial autocorrelation is examined in the average income level to reveal how the economic growth of the neighbouring areas affects a given region. The study examines Northern Hungary, one of the most backward regions in Hungary (based on GDP per capita). Eurostat (2010) reports this region is among the poorest twenty regions within the European Union (based on GDP per capita PPP, Northern Hungary is the 259th among the 271 regions of the European Union).

This dataset includes the code and data for reproducing the results shown on the paper "Effects of spatial resolution of terrain models on modelled discharge and soil loss in Oaxaca, Mexico".

We studied the relative effects of landscape configuration, environmental variables, forest age and spatial variables on estimated aboveground biomass (AGB) in Costa Rican secondary rain forests patches. We measured trees > 5 cm dbh in 24, 0.25 ha plots and estimated AGB for trees 5-24.9 cm dbh and for trees > 25 cm dbh using two allometric equations based on multispecies models using tree dbh and wood specific gravity. AGB averaged 87.3 Mg/ha for the 24 plots (not including remnant trees) and 123.4 Mg/ha including remnant trees (20 plots). There was no effect of forest age on AGB. Variation partitioning analysis showed that soils, climate, landscape configuration and space together explained 61% of tree AGB variance. When controlling for the effects of the other three variables, only soils remained significant. Soil properties, specifically K and Cu, had the strongest independent effect on AGB (variation partitioning, R2=0.17, p=0.0310), indicating that in this landscape, AGB variation in secondary forest patches is influenced by soil chemical properties. Elucidating the relative influence of soils in AGB variation is critical for understanding changes associated to land cover modification across neotropical landscapes, as it could have important consequences for land use planning since secondary forests are considered carbon sinks.

Aim: The influences of environmental and spatial processes on species composition have been at the center of metacommunity ecology. Conversely, the relative importance of these processes for species co-occurrences and taxonomic similarity has remained poorly understood. We hypothesized that at a subcontinental scale, shared environmental preference would be the major driver of co-occurrences across species groups. In contrast, co-occurrences due to shared dispersal history were more likely in dispersal-limited taxa. Finally, we tested whether taxa co-occurring due to similar responses to environmental and spatial processes were more taxonomically similar than expected by chance. Location: The conterminous United States Time Period: 1993-2019 Major taxa studied: Stream diatoms, insects, and fish Methods: We generated co-occurrence networks and developed methodology to determine the proportions of nodes and edges explained by pure environment alone (after accounting for space), pure space..., We obtained data from Passy et al., (2023), including diatoms, insects, and fish, collected from, respectively, 1698, 1700, and 1700 stream sites across the conterminous United States (Fig. S1). Sites were compiled from both the National Water-Quality Assessment (NAWQA) program of the US Geological Survey and the National Rivers and Streams Assessment (NRSA) of the US Environmental Protection Agency, which used similar collection methods (Moulton II et al., 2002; US Environmental Protection Agency, 2013). Streams were sampled between 1993 and 2019, with the majority of samples collected between 2007 and 2010. Diatoms and insects were collected from a predetermined area of substrate from May to September. Fish were sampled throughout the year using backpack electrofishing and seining. Community data consisted of counts of species or a lower taxonomic category in diatoms and fish, but genera in insects. To standardize the sampling effort, diatom counts were sampled down to 400 cells, and ..., , # Data from: Environmental and spatial effects on co-occurrence network size and taxonomic similarity in stream diatoms, insects, and fish

https://doi.org/10.5061/dryad.j3tx95xq6

Description of the data and file structure

We provide species by site matrices for 5 metacommunities of diatoms (species and genera), insects (genera), and fish (species and genera), in the files named “‘Taxon’ Dataset.csv†. We obtained these data from Passy et al., (2023), including diatoms, insects, and fish, collected from, respectively, 1698, 1700, and 1700 stream sites across the conterminous United States (Fig. S1). Sites were compiled from both the National Water-Quality Assessment (NAWQA) program of the US Geological Survey and the National Rivers and Streams Assessment (NRSA) of the US Environmental Protection Agency, which used similar collection methods (Moulton II et al., 2002; US Environmental Protection Agency, 2013). Streams were sampled betwee...

BackgroundFood insecurity and vulnerability in Ethiopia are historical problems due to natural- and human-made disasters, which affect a wide range of areas at a higher magnitude with adverse effects on the overall health of households. In Ethiopia, the problem is wider with higher magnitude. Moreover, this geographical distribution of this challenge remains unexplored regarding the effects of cultures and shocks, despite previous case studies suggesting the effects of shocks and other factors. Hence, this study aims to assess the geographic distribution of corrected-food insecurity levels (FCSL) across zones and explore the comprehensive effects of diverse factors on each level of a household's food insecurity.MethodThis study analyzes three-term household-based panel data for years 2012, 2014, and 2016 with a total sample size of 11505 covering the all regional states of the country. An extended additive model, with empirical Bayes estimation by modeling both structured spatial effects using Markov random field or tensor product and unstructured effects using Gaussian, was adopted to assess the spatial distribution of FCSL across zones and to further explore the comprehensive effect of geographic, environmental, and socioeconomic factors on the locally adjusted measure.ResultDespite a chronological decline, a substantial portion of Ethiopian households remains food insecure (25%) and vulnerable (27.08%). The Markov random field (MRF) model is the best fit based on GVC, revealing that 90.04% of the total variation is explained by the spatial effects. Most of the northern and south-western areas and south-east and north-west areas are hot spot zones of food insecurity and vulnerability in the country. Moreover, factors such as education, urbanization, having a job, fertilizer usage in cropping, sanitation, and farming livestock and crops have a significant influence on reducing a household's probability of being at higher food insecurity levels (insecurity and vulnerability), whereas shocks occurrence and small land size ownership have worsened it.ConclusionChronically food insecure zones showed a strong cluster in the northern and south-western areas of the country, even though higher levels of household food insecurity in Ethiopia have shown a declining trend over the years. Therefore, in these areas, interventions addressing spatial structure factors, particularly urbanization, education, early marriage control, and job creation, along with controlling conflict and drought effect by food aid and selected coping strategies, and performing integrated farming by conserving land and the environment of zones can help to reduce a household's probability of being at higher food insecurity levels.

no abstract provided

Bølstad and Dinas (2017) propose a model of spatial voting, based on social identity theory, that suggests supporting a candidate/policy on the other side of the ideological spectrum has a disutility that is not accounted for by common spatial-models. Unfortunately, the data they use cannot speak directly to whether the disutility arises because individuals perceive their ideology as a social identity. We present the results of an experimental study that measures the norm against crossing the ideological spectrum; tests the cost of doing so, controlling for spatial effects; and demonstrates that this cost increases with the salience and strength of identity norms. By demonstrating the norm mechanism for the disutility of crossing the ideological spectrum, we provide strong support for B&D’s model.

This file includes the data used in the paper.

No description was included in this Dataset collected from the OSF

Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate “wall-to-wall” remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution. Forest Inventories used for AGB estimates in French GuianaThe xls file contains one readme and two datasets derived from two forest surveys : "inventaire papetier" inventory done by CTFT between 1974 and 1976 (coded "pap"), and "inventaire habitat" inventory done by ONF between 2006 and 2013 (coded "hab").The datasets include plots coordinates and areas, estimates of mean wood specific gravity (WSG) for each plots, and the number of trees per DBH classes for each plots.DataAGB.xlsx

All files published under CCBY Program and data leading to the paper Spatial frequency discrimination: effects of age, reward, and practice, published in Plos One. Data package contains Neurobs Presentation program, SPSS datafile and SPSS analyses file. Please contact C. van den Boomen for access to data, or any questions.

Social structure, limited dispersal and spatial heterogeneity in resources are ubiquitous in wild vertebrate populations. As a result, relatives share environments as well as genes, and environmental and genetic sources of similarity between individuals are potentially confounded. Quantitative genetic studies in the wild therefore typically account for easily captured shared environmental effects (e.g. parent, nest or region). Fine-scale spatial effects are likely to be just as important in wild vertebrates, but have been largely ignored. We used data from wild red deer to build 'animal models' to estimate additive genetic variance and heritability in four female traits (spring and rut home range size, offspring birth weight and lifetime breeding success). We then, separately, incorporated spatial autocorrelation and a matrix of home range overlap into these models to estimate the effect of location or shared habitat on phenotypic variation. These terms explained a substantial amount of variation in all traits and their inclusion resulted in reductions in heritability estimates, up to an order of magnitude up for home range size. Our results highlight the potential of multiple covariance matrices to dissect environmental, social and genetic contributions to phenotypic variation, and the importance of considering fine-scale spatial processes in quantitative genetic studies.

Decomposition of spatial effects of coupling coordination.