The Spatial Autocorrelation Analysis For Transport Accessibility In Selected Regions Of The European Union

The main purpose of this article is to assess and analyze the occurrence of spatial autocorrelation in connection with the transport accessibility (measured by density of a motorway network). The general hypothesis is: between European regions, there is a positive spatial autocorrelation in connection with the problems of transport accessibility. Research subjects are selected European regions at NUTS level 2. To evaluate the occurrence of spatial autocorrelation the classic Moran I statistic has been used

Modeling Spatial Autocorrelation in Spatial Interaction Data: A Comparison of Spatial Econometric and Spatial Filtering Specifications

The need to account for spatial autocorrelation is well known in spatial analysis. Many spatial statistics and spatial econometric texts detail the way spatial autocorrelation can be identified and modelled in the case of object and field data. The literature on spatial autocorrelation is much less developed in the case of spatial interaction data. The focus of interest in this paper is on the problem of spatial autocorrelation in a spatial interaction context. The paper aims to illustrate that eigenfunction-based spatial filtering offers a powerful methodology that can efficiently account for spatial autocorrelation effects within a Poisson spatial interaction model context that serves the purpose to identify and measure spatial separation effects to interregional knowledge spillovers as captured by patent citations among high-technology-firms in Europe.

More on the F-test under nonspherical disturbances

We show that the F-test can be both liberal and conservative in the context of a particular type of nonspherical behaviour induced by spatial autocorrelation, and that the conservative variant is more likely to occur for extreme values of the spatial autocorrelation parameter. In particular, it will wipe out the progressive one as the sample size increases. --F-test,spatial autocorrelation

Spatial Autocorrelation

The analysis of spatial distributions and the processes that produce and alter them is a central theme in geographic research and this volume is concerned with statistical methods for analyzing spatial distributions by measuring and testing for spatial autocorrelation. Spatial autocorrelation exists whenever a variable exhibits a regular pattern over space in which its values at a set of locations depend on values of the same variable at other locations. Spatial autocorrelation is present, for example, when similar values cluster together on a map. Spatial autocorrelation statistics make it possible to use formal statistical procedures to measure the dependence among nearby values in a spatial distribution, test hypotheses about geographically distributed variables, and develop statistical models of spatial patterns. Scientific Geography Series Editor: Grant Ian Thrall.https://researchrepository.wvu.edu/rri-web-book/1019/thumbnail.jp

Spatial Patterns of Poverty in Central-Java Province

overty is one of the key issues in development program of Indonesia government. Poverty can be caused by geographical factors, namely the natural conditions, such as climate, density of forest, etc. Therefore, poverty problem tend to be spatially dependent. Spatial dependence is the propensity for nearby locations to influence each other and to possess similar attributes. A measure of the similarity of attributes of locations is called spatial autocorrelation. Spatial autocorrelation measure and analyze the degree of dependency among observations in a geographic space This paper examines spatial patterns of poverty in Central Java Province with spatial autocorrelation using spatial analysis open source software. Through open source software OpenGeoDa, it can be shown that the poverty of certains districts in Central Java Province have significantly spatial autocorrelation and there are some spatial cluster poverty in Central Java which are spatial influenced by density of forest as geographical factor. Keywords : Spatial pattern, Poverty, Central-Java, Spatial Autocorrelatio

Testing for Network and Spatial Autocorrelation

Testing for dependence has been a well-established component of spatial statistical analyses for decades. In particular, several popular test statistics have desirable properties for testing for the presence of spatial autocorrelation in continuous variables. In this paper we propose two contributions to the literature on tests for autocorrelation. First, we propose a new test for autocorrelation in categorical variables. While some methods currently exist for assessing spatial autocorrelation in categorical variables, the most popular method is unwieldy, somewhat ad hoc, and fails to provide grounds for a single omnibus test. Second, we discuss the importance of testing for autocorrelation in data sampled from the nodes of a network, motivated by social network applications. We demonstrate that our proposed statistic for categorical variables can both be used in the spatial and network setting

Testing for Network and Spatial Autocorrelation

Testing for dependence has been a well-established component of spatial statistical analyses for decades. In particular, several popular test statistics have desirable properties for testing for the presence of spatial autocorrelation in continuous variables. In this paper we propose two contributions to the literature on tests for autocorrelation. First, we propose a new test for autocorrelation in categorical variables. While some methods currently exist for assessing spatial autocorrelation in categorical variables, the most popular method is unwieldy, somewhat ad hoc, and fails to provide grounds for a single omnibus test. Second, we discuss the importance of testing for autocorrelation in network, rather than spatial, data, motivated by applications in social network data. We demonstrate that existing tests for autocorrelation in spatial data for continuous variables and our new test for categorical variables can both be used in the network setting

Testing for Spatial Autocorrelation in a Fixed Effects Panel Data Model

This paper derives several Lagrange Multiplier statistics and the correspondinglikelihood ratio statistics to test for spatial autocorrelation in a fixed effectspanel data model. These tests allow discriminating between the two main typesof spatial autocorrelation which are relevant in empirical applications, namelyendogenous spatial lag versus spatially autocorrelated errors. In this paper, fivedifferent statistics are suggested. The first one, the joint test, detects the presenceof spatial autocorrelation whatever its type. Hence, it indicates whetherspecific econometric estimation methods should be implemented to account forthe spatial dimension. In case they need to be implemented, the other four testssupport the choice between the different specifications, i.e. endogenous spatiallag, spatially autocorrelated errors or both. The first two are simple hypothesistests as they detect one kind of spatial autocorrelation assuming the otherone is absent. The last two take into account the presence of one type of spatialautocorrelation when testing for the presence of the other one. We use themethodology developed in Lee and Yu (2008) to set up and estimate the generallikelihood function. Monte Carlo experiments show the good performance ofour tests. Finally, as an illustration, they are applied to the Feldstein-Horiokapuzzle. They indicate a misspecification of the investment-saving regressiondue to the omission of spatial autocorrelation. The traditional saving-retentioncoefficient is shown to be upward biased. In contrast our results favor capitalmobility.Testing ; Spatial ; Autocorrelation ; Fixed ; Effects ; Panel Data Model