Presence Of Spatial Dependence Research Articles

Testing Homoskedasticity in Spatial Panel Data Models

A robust test statistic for testing homoskedasticity in spatial panel data models that have entity and time fixed effects is introduced in a quasi maximum likelihood estimation setting. A size-correction approach is introduced to ensure that the score functions have an asymptotic distribution centered around zero in the local presence of certain nuisance parameters. The outer-product-of-martingale-difference (OPMD) method is used to formulate an estimator for the asymptotic variance of the score functions. The OPMD estimator and the adjusted score functions are used to formulate a computationally simple robust test statistic. The suggested test statistic does not require knowing the presence of spatial dependence in the outcome variable and/or the disturbance terms. The asymptotic distribution of the test statistic is established under the null and local alternative hypotheses. Through Monte Carlo simulations, the finite sample size and power properties of the proposed test statistic are investigated. Finally, two empirical applications are provided to illustrate the practical use of the proposed test statistic.

Effect of the measurement period and spatial dependence on the accuracy of urban freight trip generation models

Despite recent advancements in the Freight Trip Generation (FTG) modelling literature, there is a lack of understanding on the effect of the choice of a regression model, measurement period (daily/weekly FTG), and spatial dependence on model fit and freight-related policies. This study addresses these research gaps by developing non-spatial and spatial autoregressive multiple linear regression and count models for daily and weekly Freight Trip Production (FTP) and Freight Trip Attraction (FTA). We model Freight Shipments (FS) as FTP and Freight Deliveries (FD) as FTA. The results show that the best model for daily and weekly FTP is the spatial Zero-Inflated Negative Binomial (ZINB) model. The best daily and weekly FTA model is the non-spatial Negative Binomial (NB) model. The findings indicate the presence of spatial dependence in the best FTP model, while it is absent in the best FTA model. The elasticity analysis shows that daily models may lead to bias and inaccurate prediction of policy impacts. The study recommends using count models that capture more FTG characteristics with a week as the measurement period and consider spatial dependence, if present.

Semiparametric regression for spatial data via deep learning

In this work, we propose a deep learning-based method to perform semiparametric regression analysis for spatially dependent data. To be specific, we use a sparsely connected deep neural network with rectified linear unit (ReLU) activation function to estimate the unknown regression function that describes the relationship between response and covariates in the presence of spatial dependence. Under some mild conditions, the estimator is proven to be consistent, and the rate of convergence is determined by three factors: (1) the architecture of neural network class, (2) the smoothness and (intrinsic) dimension of true mean function, and (3) the magnitude of spatial dependence. Our method can handle well large data set owing to the stochastic gradient descent optimization algorithm. Simulation studies on synthetic data are conducted to assess the finite sample performance, the results of which indicate that the proposed method is capable of picking up the intricate relationship between response and covariates. Finally, a real data analysis is provided to demonstrate the validity and effectiveness of the proposed method.

The Quality of Regional Institutional Context and Museum Service Provision: Evidence from Italy

This study evaluates whether and how the services provided by Italian museums are influenced by the quality of the institutional context at the regional level. Institutional quality is measured by a range of indicators largely employed in the literature, such as the Institutional Quality Index (IQI), the European Quality of Government Index (EQI), and their components. Resorting to spatial autoregressive models, the presence of spatial dependence in museum service provision is also investigated. The analysis shows that the common institutional context is significant, especially for public museums, and it explains part of the spatial correlation among museums within regions.

Treatment Effects with Dyadic Data in the Presence of Spatial Dependence

Treatment Effects with Dyadic Data in the Presence of Spatial Dependence

Are family firms good neighbours? A spatial analysis of Italian technology-intensive firms

PurposeA burgeoning body of evidence points out the importance of spatial proximity in influencing firm efficiency besides internal characteristics. Nevertheless, the family status of the firm has been traditionally overlooked in that debate. Therefore, this study aims to investigate productivity spillovers stemming from the geographical closeness to innovators and family firms.Design/methodology/approachUsing secondary data on Italian technology-intensive manufacturing firms, the paper exploits spatial econometric models to estimate productivity spillovers across firms.FindingsAs regards the presence of spatial dependence, this study reveals that a firm's level of efficiency and productivity is influenced by that of nearby firms. Specifically, three main results emerge. First, spatial proximity to innovators is beneficial for the productivity of neighbouring firms. Second, closeness to family firms is a source of negative externalities for spatially proximate firms. However, and this is the third result, the adverse effect vanishes when the nearby family firms are also innovators.Research limitations/implicationsAs the study relies on cross-sectional data, future research should explore productivity spillovers in a longitudinal setting. Additionally, the channels through which productivity spillovers occur should be measured.Practical implicationsThe study highlights the importance of co-location for public policy initiatives to strengthen the competitiveness of firms and, indirectly, that of localities and regions. Moreover, the findings show the crucial role of innovation in mitigating the productivity gap between family and non-family firms.Social implicationsNotwithstanding the advent of the digital era, spatial proximity and localized social relationships are still a relevant factor affecting firms' performance.Originality/valueBy exploring the role of family firms in influencing the advantages of geographical proximity, this study contributes to the growing efforts to explore family enterprises across spatial settings.

Regional tourist heterogeneity in Spain: A dynamic spatial analysis

Spain is one of the most visited countries in the world. However, given that the number of tourist arrivals hugely differ among Spanish regions, notable tourist regional inequalities are perceived in this country. In this context, spatial econometric techniques can be used as an instrument to understand these regional disparities. In particular, this work analyses the existence of regional spillovers in Spain, while also identifying the degree of sensitivity of the different regions to fluctuations in the general trends of the domestic and foreign tourist arrivals. Our results reveal (i) that the tourist arrivals are persistent over time, (ii) the presence of spatial dependence between regions in terms of domestic and international tourism —the latter being greater— and (iii) the existence of heterogeneity between regions in terms of sensitivity to tourist arrivals. These findings may favor the design of a common strategy to promote the alleviation of current regional inequalities as well as better tourism strategies to face tourism changes, either as general trends or unexpected events.

Terrorism Determinants, Model Uncertainty and Space in Colombia

ABSTRACT This paper studies the determinants of terrorism at the sub-national level in Colombia during 2001–2014. In order to establish robust relationships, a Bayesian model averaging framework has been implemented using departmental data. We find that the violence suffered by this country is linked to economic factors, especially labor market outcomes. The results obtained are not significantly altered by the use of relative measures of terror, the specification of alternative parameters and model priors or the presence of spatial dependence. The main conclusion drawn from our analysis is that an appropriate strategy to fight against terrorism in similar contexts is to increase its opportunity cost. This might be achieved through the promotion of inclusive socioeconomic development, primarily in rural areas.

Swelter in the city: Urban greenery and its effects on temperature in Guwahati, India

Changing urban landscape with multistoried high rises, roads and pavements is continuously reducing urban green space. These structures result in high surface temperature variation within cities. To explore the relationship between surface temperature and normalized difference vegetation index (NDVI), this study estimates two models—geographically weighted regression (GWR) and a fixed effect panel data model in relation to the Guwahati Metropolitan Area (GMA), a secondary city in north east India. The results indicate the superiority of GWR regression in presence of spatial dependence. Panel data analysis shows that the densely populated urban areas in the GMA with less than 10 per cent greenery are 1°C warmer than the sub‐urban areas with 50 per cent greenery.

The Residential Solar PV in the Mid-Atlantic: A Spatial Panel Approach

This study examines residential solar photovoltaic (PV) adoption in mid-Atlantic counties over the 2005–2016 period. Using a spatial Durbin panel model, we find significant spatial dependence in residential solar PV adoption at the county level. In the presence of spatial dependence, major determinants of the solar adoptions are electricity rates and solar-related policy regulation. The combined direct and indirect effect of electricity rates on solar PV capacity is 5.37 percent increase for a 1 percent increase in electricity price. This spatial coefficient estimate is about twice as large as corresponding panel estimates. We also find that the regulation and incentives significantly boost solar PV demand, which explains about 38 percent variation in residential solar capacity.

A robust spatial autoregressive scalar-on-function regression with t-distribution

Modelling functional data in the presence of spatial dependence is of great practical importance as exemplified by applications in the fields of demography, economy and geography, and has received much attention recently. However, for the classical scalar-on-function regression (SoFR) with functional covariates and scalar responses, only a relatively few literature is dedicated to this relevant area, which merits further research. We propose a robust spatial autoregressive scalar-on-function regression by incorporating a spatial autoregressive parameter and a spatial weight matrix into the SoFR to accommodate spatial dependencies among individuals. The t-distribution assumption for the error terms makes our model more robust than the classical spatial autoregressive models under normal distributions. We estimate the model by firstly projecting the functional predictor onto a functional space spanned by an orthonormal functional basis and then presenting an expectation–maximization algorithm. Simulation studies show that our estimators are efficient, and are superior in the scenario with spatial correlation and heavy tailed error terms. A real weather dataset demonstrates the superiority of our model to the SoFR in the case of spatial dependence.

Análise da dimensão espacial da pobreza em Minas Gerais

Dadas as disparidades regionais que caracterizam o estado de Minas Gerais, torna-se cada vez mais relevante investigar o comportamento da taxa de pobreza e a forma como os municípios pobres se distribuem no estado. Nesse sentido, o objetivo desse trabalho é investigar a distribuição espacial da pobreza no estado de Minas Gerais. Para isso, utilizou-se do método econométrico espacial para averiguar a presença de autocorrelação espacial. A partir da análise exploratória dos dados foi possível constatar a presença de dependência espacial entre as variáveis e a formação de clusters de pobreza. Por meio da regressão estimada pelo Método das Defasagens Espaciais foi possível ratificar a presença de dependência espacial, confirmada pela relevância da variável pobreza na vizinhança (variável pobreza defasada) no modelo. Portanto, conclui-se que a pobreza de um município é explicada, em parte, pela pobreza de seus municípios vizinhos.

A nonparametric spatial test to identify factors that shape a microbiome

The advent of high-throughput sequencing technologies has made data from DNA material readily available, leading to a surge of microbiome-related research establishing links between markers of microbiome health and specific outcomes. However, to harness the power of microbial communities we must understand not only how they affect us, but also how they can be influenced to improve outcomes. This area has been dominated by methods that reduce community composition to summary metrics, which can fail to fully exploit the complexity of community data. Recently, methods have been developed to model the abundance of taxa in a community, but they can be computationally intensive and do not account for spatial effects underlying microbial settlement. These spatial effects are particularly relevant in the microbiome setting because we expect communities that are close together to be more similar than those that are far apart. In this paper, we propose a flexible Bayesian spike-and-slab variable selection model for presence-absence indicators that accounts for spatial dependence and cross-dependence between taxa while reducing dimensionality in both directions. We show by simulation that in the presence of spatial dependence, popular distance-based hypothesis testing methods fail to preserve their advertised size, and the proposed method improves variable selection. Finally, we present an application of our method to an indoor fungal community found within homes across the contiguous United States.

Combining multiple data sources in species distribution models while accounting for spatial dependence and overfitting with combined penalized likelihood maximization

Abstract The increase in availability of species datasets means that approaches to species distribution modelling that incorporate multiple datasets are in greater demand. Recent methodological developments in this area have led to combined likelihood approaches, in which a log‐likelihood comprised of the sum of the log‐likelihood components of each data source is maximized. Often, these approaches make use of at least one presence‐only dataset and use the log‐likelihood of an inhomogeneous Poisson point process model in the combined likelihood construction. While these advancements have been shown to improve predictive performance, they do not currently address challenges in presence‐only modelling such as checking and correcting for violations of the independence assumption of a Poisson point process model or more general challenges in species distribution modelling such as overfitting. In this paper, we present an extension of the combined likelihood framework which accommodates alternative presence‐only likelihoods in the presence of spatial dependence as well as lasso‐type penalties to account for potential overfitting. We compare the proposed combined penalized likelihood approach to the standard combined likelihood approach via simulation and apply the method to modelling the distribution of the Eurasian lynx in the Jura Mountains in eastern France. The simulations show that the proposed combined penalized likelihood approach has better predictive performance than the standard approach when spatial dependence is present in the data. The lynx analysis shows that the predicted maps vary significantly between the model fitted with the proposed combined penalized approach accounting for spatial dependence and the model fitted with the standard combined likelihood. This work highlights the benefits of careful consideration of the presence‐only components of the combined likelihood formulation, and allows greater flexibility and ability to accommodate real datasets.

REDUCTION OF SAMPLE SIZE IN THE ANALYSIS OF SPATIAL VARIABILITY OF NONSTATIONARY SOIL CHEMICAL ATTRIBUTES

In the study of spatial variability of soil attributes, it is essential to define a sampling plan with adequate sample size. This study aimed to evaluate, through simulated data, the influence of parameters of the geostatistical model and sampling configuration on the optimization process, and resize and reduce the sample size of a sampling configuration of a commercial area composed of 102 points. For this, an optimization process called genetic algorithm (GA) was used to optimize the efficiency of the geostatistical model estimation based on the Fisher information matrix. The simulated data evidenced that the variation of the nugget effect or practical range did not significantly alter the sample size. GA was efficient in reducing the sample size, determining for soil chemical attributes a sample size between 30 and 40 points (29.41 to 39.22% of the initial sampling grid). The presence of spatial dependence was observed for all soil chemical attributes in the two sampling configurations (initial and optimized). The optimized sampling configuration evidenced an increase in trend intensity in the north direction and a more efficient estimation of parameters of the linear spatial regression model.

Inference for Income Mobility Measures in the Presence of Spatial Dependence

Income mobility measures provide convenient and concise ways to reveal the dynamic nature of regional income distributions. Statistical inference about these measures is important especially when it comes to a comparison of two regional income systems. Although the analytical sampling distributions of relevant estimators and test statistics have been asymptotically derived, their properties in small sample settings and in the presence of contemporaneous spatial dependence within a regional income system are underexplored. We approach these issues via a series of Monte Carlo experiments that require the proposal of a novel data generating process capable of generating spatially dependent time series given a transition probability matrix and a specified level of spatial dependence. Results suggest that when sample size is small, the mobility estimator is biased while spatial dependence inflates its asymptotic variance, raising the Type I error rate for a one-sample test. For the two-sample test of the difference in mobility between two regional economic systems, the size tends to become increasingly upward biased with stronger spatial dependence in either income system, which indicates that conclusions about differences in mobility between two different regional systems need to be drawn with caution as the presence of spatial dependence can lead to false positives. In light of this, we suggest adjustments for the critical values of relevant test statistics.

Spatial considerations when monitoring reef fishes

Spatial dependence can obscure relationships between response and explanatory variables because of structuring within the residuals reducing variance and biasing coefficient estimates. Here, we highlight the influence of the spatial component, in the presence of spatial dependence, on abundance trends. This is illustrated using abundance data for a Critically Endangered reef fish, dageraad Chrysoblephus cristiceps, which were obtained from a long-term monitoring programme in the Tsitsikamma National Park marine protected area, South Africa. Correlograms illustrate distinct spatial structuring in the abundance data, and spatial variables were determined as more important than temporal variables when ranked according to predictive power using a random forest analysis. A generalised additive model (GAM) that did not account for spatial dependencies was compared to a generalised additive mixed model (GAMM) that incorporated a spatial residual correlation structure. Results derived from the spatially explicit GAMM differed considerably from the GAM lacking a spatial component, with the latter deemed to produce over-precise and partially biased abundance trends. The study emphasises the importance of space in accurately modelling abundance estimates, particularly temporal trends, and provides an introduction to the minimal statistical requirements necessary to address the violations associated with spatial autocorrelation.

Adjacency-Clustering for Identifying Defect Patterns and Yield Prediction in Integrated Circuit Manufacturing

Adjacency-clustering is a new concept of capturing phenomena in the presence of spatial dependencies, or Neighborhood Effect (NE). The technique is applied here to prediction problems in the presence of NE that arise in manufacturing system monitoring, quality control and yield prediction. This work is motivated by Integrated Circuit Manufacturing (ICM) process that involves multiple steps and is exceedingly expensive. Spatial variation of parameters across each wafer, where the circuits are positioned, result from equipment or process limitations, and a circuit is likely to be defective if its neighbors on the wafer are defective as well. The existence of this Neighborhood Effect, while recognized, is not well captured in traditional modeling approaches. The challenge is to extrapolate, from given samples, the patterns of the defects and predict accurately the yield of the process. The patterns are effectively identified using adjacency-clustering that is achieved with the graph-theoretic separation-deviation model, also known as the Markov Random Field (MRF) model. The use of the technique is shown to identify the defects’ patterns and provide dramatic improvements in the accuracy of yield prediction as compared to state-of-the-art methods.

Spatio-temporal analysis of the occurrence of human visceral leishmaniasis in Araçatuba, State of São Paulo, Brazil.

This study aimed to describe the occurrence of human visceral leishmaniasis in Araçatuba with regard to time and space and to identify high risk areas. We included all human visceral leishmaniasis autochthonous cases reported between 1999 and 2015. The incidence rates were calculated by sex, age, and year. The human visceral leishmaniasis cases were geocoded and grouped by urban census tracts, enabling the calculation of the incidence and mortality rates by census tracts. For the identification of high risk areas, we utilized the scan statistics and univariate Ripley's K-function. The incidence presented a cyclic pattern in 1999-2009, with peaks in 2002 and 2007 (30.1 and 19.6 cases per 100,000 inhabitant-years, respectively). In 2010-2015, the incidence remained relatively stable with about 2.0 cases per 100,000 inhabitant-years. The scan statistics detected two spatial clusters of high risk and three spatio-temporal clusters of high risk that lasted from 2001 to 2008. A spatial autocorrelation was observed in the human visceral leishmaniasis case point distribution in 1999-2009. No spatio-temporal clusters and no spatial autocorrelation in the case point pattern were identified in 2010-2015. We identified a changing pattern of human visceral leishmaniasis occurrence in Araçatuba: the first period (1999-2009) showed a cyclic pattern, clusters, and presence of spatial dependence in the case point distribution; the second period (2010-2015) showed the lowest rates of all historical series, stable incidence, and cases with a random distribution pattern.

Spatial Determinants of Financial Inclusion over Time

AbstractThis paper investigates the factors generating changes in the financial inclusion levels based on data from 120 countries for 2011 and 2014. After calculating the index of financial inclusion across countries, we find evidences on the presence of spatial dependencies by using the Global Moran's I and local indicators of spatial association analyses. Then, we employ spatial regression and spatial panel data models that control for spatial interdependence. The results show that social, banking and political factors play an important role in the determination of change in financial inclusion. We also find financial inclusion convergence among the countries. Copyright © 2018 John Wiley & Sons, Ltd.