The Elimination of Spurious Correlation due to Position in Time or Space

Student,

doi:10.2307/2331746

Cited by 83 publications

(14 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The easiest way to obtain a contour map of a single variable is to use inversesquare distance, which is but one case of moving average interpolation (Ripley 1981), or other such interpolation methods. The older method of trend-surface analysis (Student 1914), in which the variation of the variable of interest is expressed as a function of the geographic coordinates of the sampling locations, does not produce very accurate maps except in the most simple cases; it remains useful when ecologists want to remove a simple spatial structure, for instance a spatial trend or large-scale patches, from their data, either because they want to study finer scale spatial structures or because they hope that, after the spatial component is extracted, no significant spatial structure will be left in the data (see also the next section, The special case of gradients). The use oftrend-surface functions in spatial modeling is also discussed below (Eq.…”

Section: ) Estimation and Mappingmentioning

confidence: 99%

Spatial Autocorrelation: Trouble or New Paradigm?

Legendre¹

1993

Ecology

3,121

2,630

View full text Add to dashboard Cite

Abstract. Autocorrelation is a very general statistical property of ecological variables observed ac~oss geo~raphic spac~;its most common forms are patches and gradients. Spatial autocorrelation, whtch comes either from the physical forcing of environmental variables or from community processes, presents a problem for statistical testing because autocorrelated data ;iolate the assumption of independence of most standard statistical procedures. The paper discusses first how autocorrelation in ecological variables can be described and measured, with emphas~s o~ mapping ~echniques: Then, proper statistical testing in the presence_ of aut~correlation I~ bnefly di~cussed. Fmally, ways are presented of explicitly mtroducmg spatial structures. mto ecological models. Two approaches are proposed; in the raw-data. approa~h, the spatial structure takes the form of a polynomial of the x and y ~e~graphic coo:dmates of the sampling stations; in the matrix approach, the spatial structure IS mtroduced m the form of a geographic distance matrix among locations. These two approaches are compared in the concluding section. A table provides a list of computer programs available for spatial analysis.

show abstract

Section: ) Estimation and Mappingmentioning

confidence: 99%

Spatial Autocorrelation: Trouble or New Paradigm?

Legendre¹

1993

Ecology

3,121

2,630

View full text Add to dashboard Cite

show abstract

“…Because the data had a non-normal distribution, we computed Spearman correlations. When spatial autocorrelation exists, the degrees of freedom in the conventional correlation tests of the significance may be incorrect, which can lead to misestimation of significance of effects [47][48][49] because spatial autocorrelation is a violation of the independence assumption. If necessary, we used the Clifford and Richardson adjustment method to account for spatial autocorrelation in the Spearman correlation coefficients, with six spatial lags used in generating correlation matrices based on the row-standardized first-order Queen contiguity weights matrix.…”

Section: Spatial Analysesmentioning

confidence: 99%

The Geography of Recreational Open Space: Influence of Neighborhood Racial Composition and Neighborhood Poverty

et al. 2012

View full text Add to dashboard Cite

The geography of recreational open space might be inequitable in terms of minority neighborhood racial/ethnic composition and neighborhood poverty, perhaps due in part to residential segregation. This study evaluated the association between minority neighborhood racial/ethnic composition, neighborhood poverty, and recreational open space in Boston, Massachusetts (US). Across Boston census tracts, we computed percent non-Hispanic Black, percent Hispanic, and percent families in poverty as well as recreational open space density. We evaluated spatial autocorrelation in study variables and in the ordinary least squares (OLS) regression residuals via the Global Moran's I. We then computed Spearman correlations between the census tract socio-demographic characteristics and recreational open space density, including correlations adjusted for spatial autocorrelation. After this, we computed OLS regressions or spatial regressions as appropriate. Significant positive spatial autocorrelation was found for neighborhood sociodemographic characteristics (all pvalue00.001). We found marginally significant positive spatial autocorrelation in recreational open space (Global Moran's I00.082; pvalue00.053). However, we found no spatial autocorrelation in the OLS regression residuals, which indicated that spatial models were not appropriate. There was a negative correlation between census tract percent non-Hispanic Black and recreational open space density (r S 0−0.22; conventional pvalue00.005; spatially adjusted p value00.019) as well as a negative correlation between predominantly non-Hispanic Black census tracts (960 % non-Hispanic Black in a census tract) and recreational open space density (r S 0−0.23; conventional pvalue00.003; spatially adjusted p value00.007). In bivariate and multivariate OLS models, percent non-Hispanic Black in a census tract and predominantly Black census tracts were associated with decreased density of recreational open space (pvalueG0.001). Consistent with several previous studies in other geographic locales, we found that Black neighborhoods in Boston were less likely to have recreational open spaces, indicating the need for policy interventions promoting equitable access. Such interventions may contribute to reductions and disparities in obesity.

show abstract

“…We shall generalize the above statement for higher order differences when the trend is represented by sorne polynomial in t. THEOREM 1.1 (Student (1914)) , the mode1s Let {X(t)} and '{Y(t)} be two time series described by…”

Section: (P-l )2x(t)mentioning

confidence: 99%

Variate Difference Method

Siddiqui¹

2005

Van Nostrand's Scientific Encyclopedia

View full text Add to dashboard Cite

The Elimination of Spurious Correlation due to Position in Time or Space

Cited by 83 publications

References 0 publications

Spatial Autocorrelation: Trouble or New Paradigm?

Spatial Autocorrelation: Trouble or New Paradigm?

The Geography of Recreational Open Space: Influence of Neighborhood Racial Composition and Neighborhood Poverty

Variate Difference Method

Contact Info

Product

Resources

About