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Rectifying I: three point and continuous fit of the spatial autocorrelation metric, Moran’s I, to ideal form

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Abstract

Context

Interpreting spatial autocorrelation is complicated by differences in data type, spatial conformation, and contiguity definitions. Though lacking consistent meaning, Moran’s I is commonly reported, compared, and interpreted based on conceptual ideals. To provide consistent, logical, and intuitive meaning and enable broader synthetic work, a new approach to I is needed.

Objectives

We sought to standardize I and true it to conceptual ideals and existing intuition regarding regular correlations. We also wished to test performance of transformed metrics over a diversity of designed and empirical datasets.

Methods

We developed two means to rectify I. Both fit null distributions from data permutation to a target frame of [− 1, 0, 1], followed by projection of original I into this conformation. One method used three-point registration employing the distribution median and select tail percentiles. The other directly projected all I based on theory or cumulative frequencies reflecting the distribution of regular correlations. Repeatability and sensitivity of results were examined for varied permutation replication and framing parameter choices. Empirical and designed datasets were used to compare rectified to traditional metrics.

Results

Both rectification methods improved distributional characteristics of I. Three-point registration produced overly broad distributions with discontinuous peaks. Continuous projection fit the distribution for regular correlations precisely. Diverse case studies demonstrated failings of I and the clarity gained by rectification.

Conclusions

Rectified I enabled meaningful comparisons of spatial patterns for diverse data and landscape conditions. Preserving the intuitive value of Moran’s I while providing a theoretically sound and consistent approach for standardizing its values should foster sustained use.

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References

  • Anselin L (1988) Spatial econometrics: methods and models. Kluwer, Dordrecht

    Book  Google Scholar 

  • Anselin L (1995) Local indicators of spatial association—LISA. Geogr Anal 27:93–115

    Article  Google Scholar 

  • Chen Y (2013) New approaches for calculating Moran’s index of spatial autocorrelation. PLoS ONE 8:e68336

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Cliff AD, Ord JK (1969) The problem of spatial autocorrelation. In: Scott AJ (ed) Studies in regional science, vol 1. Pion, London, pp 25–55

    Google Scholar 

  • Cliff AD, Ord JK (1973) Spatial autocorrelation. Pion, London

    Google Scholar 

  • Cliff AD, Ord JK (1981) Spatial processes: models & applications. Pion, London

    Google Scholar 

  • Cozzarolo CS, Jenkins T, Toews DPL, Brelsford A, Christe P (2018) Prevalence and diversity of haemosporidian parasites in the yellow-rumped warbler hybrid zone. Ecol Evol 8:9834–9847

    Article  PubMed  PubMed Central  Google Scholar 

  • D’agostino RB, Belanger A, D’agostino RB Jr (1990) A suggestion for using powerful and informative tests of normality. Am Stat 44(4):316–321

    Google Scholar 

  • de Jong P, Sprenger C, van Veen F (1984) On extreme values of Moran’s I and Geary’s c. Geogr Anal 16:17–24

    Article  Google Scholar 

  • DeWitt TJ (2016) Expanding the phenotypic plasticity paradigm to broader views of trait space and ecological function. Curr Zool 62:463–473

    Article  PubMed  PubMed Central  Google Scholar 

  • Dokmanic I, Parhizkar R, Ranieri J, Vetterli M (2015) Euclidean distance matrices: essential theory, algorithms, and applications. IEEE Signal Process Mag 32:12–30

    Article  Google Scholar 

  • Durbin J, Watson GS (1950) Testing for serial correlation in least squares regression. Biometrika 37:409–428

    PubMed  CAS  Google Scholar 

  • Fortin M, Dale M (2005) Spatial analysis: a guide for ecologists. Cambridge University Press, New York

    Book  Google Scholar 

  • Fuentes JI, DeWitt TJ, Ioerger TR, Bishop MB (2020) Irescale: calculate and rectify Moran’s I. R package v. 2.3.0. https://cran.r-project.org/package=Irescale. Accessed 10 Dec 2020

  • Getis A (2010) Spatial autocorrelation. In: Fischer MM, Getis A (eds) Handbook of applied spatial analysis: software tools, methods and applications. Springer, Heidelberg

    Google Scholar 

  • Goldsmith GR, Allen ST, Braun S, Engbersen N, González-Quijano CR, Kirchner JW, Siegwolf RTW (2019) Spatial variation in throughfall, soil, and plant water isotopes in a temperate forest. Ecohydrology 12:e2059

    Google Scholar 

  • Goodchild MF (1988) Spatial autocorrelation. Concepts and techniques in modern geography. Geobooks, Norwich

    Google Scholar 

  • Greenwood JD (2015) A conceptual history of psychology: exploring the tangled web. Cambridge University Press, New York

    Book  Google Scholar 

  • Griffith DA (1987) Spatial autocorrelation: a primer. Association of American Geographers, Resource Publications in Geography, Washington DC

    Google Scholar 

  • Hotelling H (1953) New light on the correlation coefficient and its transforms. J R Stat Soc B 15:193–232

    Google Scholar 

  • Hurley JR, Cattell RB (1962) The procrustes program: producing direct rotation to test a hypothesized factor structure. Behav Sci 7:258–262

  • L’Herpiniere KL, O’Neill LG, Russell AF, Duursma DE, Griffith SC (2019) Unscrambling variation in avian eggshell colour and patterning in a continent-wide study. R Soc Open Sci 6:181269

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Kim D, DeWitt TJ, Costa CSB, Kupfer JA, McEwan RW, Stallins JA (2015) Beyond bivariate correlations: three-block partial least squares illustrated with vegetation, soil, and topography. Ecosphere 6:135

    Article  Google Scholar 

  • Li H, Calder CA, Cressie N (2007) Beyond Moran’s I: Testing for spatial dependence based on the spatial autoregressive model. Geogr Anal 39:357–375

    Article  Google Scholar 

  • Mantel N (1967) The detection of disease clustering and a generalized regression approach. Cancer Res 27:209–220

    PubMed  CAS  Google Scholar 

  • Marrot P, Garant D, Charmantier A (2015) Spatial autocorrelation in fitness affects the estimation of natural selection in the wild. Methods Ecol Evol 6:1474–1483

    Article  Google Scholar 

  • Möller AP, Jennions MD (2002) How much variance can be explained by ecologists and evolutionary biologist? Oecologia 132:492–500

    Article  PubMed  Google Scholar 

  • Moran PAP (1947) Some theorems on time series. Biometrika 34:281–291

    Article  PubMed  CAS  Google Scholar 

  • Moran PAP (1950) Notes on continuous stochastic phenomena. Biometrika 37:17–23

    Article  PubMed  CAS  Google Scholar 

  • Mukaka MM (2012) Statistics corner: a guide to appropriate use of correlation coefficient in medical research. Malawi Med J 24:69–71

    PubMed  PubMed Central  CAS  Google Scholar 

  • Pearson K (1924) Historical note on the origin of the normal curve of errors. Biometrika 16:402–404

    Article  Google Scholar 

  • Rahman NA (1968) A course in theoretical statistics. Charles Griffin and Company, Glasgow

    Google Scholar 

  • Ritters K (2019) Pattern metrics for a transdisciplinary landscape ecology. Landsc Ecol 34:2057–2063

    Article  Google Scholar 

  • Rogerson PA (1999) The detection of clusters using a spatial version of the chi-square goodness-of-fit statistic. Geogr Anal 31:130–147

    Article  Google Scholar 

  • Rohlf FJ, Sokal RR (1995) Statistical tables. Macmillan, New York

    Google Scholar 

  • Rosenthal R, Rubin DB (1986) Meta-analytic procedures for combining studies with multiple effect sizes. Psychol Bull 99:400–406

    Article  Google Scholar 

  • Sen A (1976) Large sample-size distribution of statistics used in testing for spatial correlation. Geogr Anal 8:175–184

    Article  Google Scholar 

  • Sokal RR, Rohlf FJ (1995) Biometry, 3rd edn. WH Freeman, San Francisco

    Google Scholar 

  • Thirey B, Hickman R (2015) Distribution of Euclidean distances between randomly distributed Gaussian points in n-space. arXiv preprint arXiv:1508.02238. https://arxiv.org/pdf/1508.02238.pdf. Accessed 20 Dec 2020

  • Tiefelsdorf M (1998) Some practical applications of Moran’s I’s exact conditional distribution. Pap Reg Sci 77:101–129

    Article  Google Scholar 

  • Tiefelsdorf M, Boots B (1997) A note on the extremities of local Moran’s Iis and their impact on global Moran’s I*. Geogr Anal 29:248–257

    Article  Google Scholar 

  • Tobler W (1970) A computer movie simulating urban growth in the Detroit region". Econ Geogr 46(Supplement):234–240

    Article  Google Scholar 

  • Upton GJ, Fingleton B (1985) Spatial data analysis by example, volume 1: Point pattern and quantitative data. Wiley, New York

    Google Scholar 

  • Waldhör T (1996) The spatial autocorrelation coefficient Moran’s I under heteroscedasticity. Stat Med 15:887–892

    Article  PubMed  Google Scholar 

Download references

Funding

This project was funded by Texas A&M University T3 (Triads for Tranformation) grant to TJD, MPB, and TRI.

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Correspondence to T. J. DeWitt.

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DeWitt, T.J., Fuentes, J.I., Ioerger, T.R. et al. Rectifying I: three point and continuous fit of the spatial autocorrelation metric, Moran’s I, to ideal form. Landscape Ecol 36, 2897–2918 (2021). https://doi.org/10.1007/s10980-021-01256-0

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