Under the hood Issues in the specification and interpretation of spatial regression models

Anselin, Luc

doi:10.1016/s0169-5150(02)00077-4

Cited by 371 publications

(358 citation statements)

References 142 publications

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“…Although the basis for our study is microdata, the analysis is based on aggregated spatial data, which obviously entails disadvantages of ecological fallacies, spurious relations, and the modifiable areal unit problem (Anselin 2002). However, since our interest is in regional differences in partner choice behaviour and its potential explanatory factors on a regional level, we believe that our methodology is justified, although care is needed in the interpretation of results.…”

Section: Methodology Of the Spatial Data Analysismentioning

confidence: 99%

Explaining Spatial Homogamy. Compositional, Spatial and Regional Cultural Determinants of Regional Patterns of Spatial Homogamy in the Netherlands

Haandrikman

Wissen

Harmsen

2010

Appl. Spatial Analysis

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Spatial homogamy, or sharing a similarity in geographical origin, is an under-researched dimension in homogamy studies. In the Netherlands, people tend to choose spatially homogamous partners. Moreover, there is considerable regional variation in spatial homogamy, even when residential location and population density are controlled for. This study aims to explain the regional variation in spatial homogamy by means of a spatial regression. Three sets of explanations are taken into account: compositional effects, spatial determinants, and regional cultural differences. The data used consists of a unique geo-coded micro dataset on all new cohabiters in the Netherlands in 2004 (N=289,248), combined with other data from varying sources. In the spatial regression, the dependent variable is the standardized distance coefficient, based on the distance between partners before cohabitation, standardised for the average distance to other inhabitants. We find that especially educational, income and cultural differences contribute to the regional variation in spatial homogamy.

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Section: Methodology Of the Spatial Data Analysismentioning

confidence: 99%

Explaining Spatial Homogamy. Compositional, Spatial and Regional Cultural Determinants of Regional Patterns of Spatial Homogamy in the Netherlands

Haandrikman

Wissen

Harmsen

2010

Appl. Spatial Analysis

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“…Identification of these households has been made possible through the advancement in spatial analytical techniques; which has also enables spatial pattern of poverty (concentration of poverty rates and outliers) to be quantified [15], [16], [17]. References [18], [19] showed that poverty rate in nearby locations are likely to be similar to one another, or error for the model in one area or location is correlated with the error terms in its neighboring locations; hence the need to pay attention to the structure of spatial dependence or autocorrelation in our data.…”

Section: Review Of Previous Literaturementioning

confidence: 99%

The Landscape of Poverty in Nigeria: A Spatial Analysis Using Senatorial Districts- level Data

Sowunmi¹,

Akinyosoye²,

Okoruwa³

et al. 2012

ECONOMICS

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The study decomposes the Landscape of Poverty in Nigeria based on the significance of spatial contiguity using Senatorial Districts -level Data. The data used for the study were obtained fro m Nat ional Living Standard Survey and Core Welfare Indicators Questionnaire Survey conducted by National Bureau of Statistics in 2004 and 2006 respectively. Exp loratory spatial data analysis and spatial autocorrelation test were carried out on poverty incidence data. Average national poverty rate of the Senatorial Districts (SD) was 56.0%. Forty nine percent of the SD had poverty rate (PR) below the national average. The global Moran's I value obtained is strongly positive (0.6657), indicating that spillover of poverty exist among SD. The study revealed that 52% of the SD with significant spatial association had low PR neighboured by low PR SD (Low-Low), 41% of the SD with high PR were neighboured by high PR SD (High-High) and 7% of SDs with low PR were surrounded by high PR SD (Lo w-High). The mean PR in high-high and low-low SDs was 82.6% and 31.8% respectively. The study recommends that for a significant poverty reduction to be achieved in Nigeria, greater attention in terms of poverty allev iation strategies should be concentrated on the senatorial d istricts that constitute the hotspots of poverty.

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“…The parameter ρ is the spatial autoregressive or spatial lag parameter. The log-likelihood function for model (1) reads Anselin's (1988):…”

Section: The W-based Autoregressive Modelmentioning

confidence: 99%

“…The approach is based on a spatial weights matrix, usually denoted W, that accounts for spatial dependence or spill-over effects among the spatial units of observation. The selection of a spatial weights matrix is a crucial step in spatial modelling because it a priori imposes a model structure which affects estimates (Bhattacharjee and Jensen-Butler 2006;Anselin 2002;Fingleton 2003) and the substantive interpretation of the research findings (Hepple 1995).…”

Section: Introductionmentioning

confidence: 99%

“…To gain more insight into the quality of the estimators produced by both approaches, we carry out Monte Carlo simulations. The simulations are performed on the basis of Anselin's (1988) Columbus, Ohio, crime data set which was also used by Folmer and Oud (2008) for illustrative purposes. The performance of the approaches will be analyzed in terms of bias and root mean squared error (RMSE) for various values of spatial autocorrelation and kinds of weight matrices.…”

Section: Introductionmentioning

confidence: 99%

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W-based versus latent variables spatial autoregressive models: evidence from Monte Carlo simulations

2010

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In this paper, we compare by means of Monte Carlo simulations two approaches to take spatial autocorrelation into account: the classical spatial autoregressive model and the structural equations model with latent variables. The former accounts for spatial dependence and spillover effects in georeferenced data by means of a spatial weights matrix W. The latter represents spatial dependence and spillover effects by means of a latent variable in the structural (regression) model while the observed spatially lagged variables are related to the latent spatial dependence variable in the measurement model. The simulation results based on Anselin's Columbus, Ohio, crime data set show that the misspecified latent variables approach slightly trails the correctly specified classical approach in terms of bias and root mean squared error of the coefficient estimators. JEL Classification

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Under the hood Issues in the specification and interpretation of spatial regression models

Cited by 371 publications

References 142 publications

Explaining Spatial Homogamy. Compositional, Spatial and Regional Cultural Determinants of Regional Patterns of Spatial Homogamy in the Netherlands

Explaining Spatial Homogamy. Compositional, Spatial and Regional Cultural Determinants of Regional Patterns of Spatial Homogamy in the Netherlands

The Landscape of Poverty in Nigeria: A Spatial Analysis Using Senatorial Districts- level Data

W-based versus latent variables spatial autoregressive models: evidence from Monte Carlo simulations

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