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Getis’s spatial filtering legacy: spatial autocorrelation mixtures in geospatial agricultural datasets

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  • Daniel A. Griffith

    (University of Texas at Dallas)

Abstract

The dual achievements of this paper are: establishing that the Getis spatial filtering technique can uncover latent PSA–NSA mixtures, and uncovering this very mixture property in geospatial agricultural datasets, acknowledging omitted variable complications attributable to its presence. This methodological extension derives from published comments by Getis himself, whereas this agricultural data category augments the existing set comprising georeferenced socio-economic/demographic and disease data. Puerto Rico space–time datasets—for milk, plantain, and sugarcane production—constitute the analyzed empirical specimens, adding consistency across sequential periods in time to the current repertoire of already recognized focal data features that include geographic resolution and scale as well as geographic landscape diversity. This paper also presents comparisons between the proposed novel Getis spatial filtering decomposition with both spatial autoregressive and Moran eigenvector spatial filtering specifications, credibly concluding that, to some degree, all are capable of identifying PSA–NSA mixtures in geotagged data. Its other prominent general conclusion is that PSA–NSA mixtures tend to be latent in geospatial agricultural datasets.

Suggested Citation

  • Daniel A. Griffith, 2023. "Getis’s spatial filtering legacy: spatial autocorrelation mixtures in geospatial agricultural datasets," Journal of Spatial Econometrics, Springer, vol. 4(1), pages 1-33, December.
  • Handle: RePEc:spr:jospat:v:4:y:2023:i:1:d:10.1007_s43071-023-00038-x
    DOI: 10.1007/s43071-023-00038-x
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    References listed on IDEAS

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    1. Rodolfo Metulini & Roberto Patuelli & Daniel A. Griffith, 2018. "A Spatial-Filtering Zero-Inflated Approach to the Estimation of the Gravity Model of Trade," Econometrics, MDPI, vol. 6(1), pages 1-15, February.
    2. Yanguang Chen, 2020. "New framework of Getis-Ord’s indexes associating spatial autocorrelation with interaction," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-25, July.
    3. Daniel A. Griffith, 2020. "A Family of Correlated Observations: From Independent to Strongly Interrelated Ones," Stats, MDPI, vol. 3(3), pages 1-19, June.
    4. Daniel A. Griffith, 2019. "Negative Spatial Autocorrelation: One of the Most Neglected Concepts in Spatial Statistics," Stats, MDPI, vol. 2(3), pages 1-28, August.
    5. D. A. Williams, 1982. "Extra‐Binomial Variation in Logistic Linear Models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(2), pages 144-148, June.
    6. Arthur Getis, 1990. "Screening For Spatial Dependence In Regression Analysis," Papers in Regional Science, Wiley Blackwell, vol. 69(1), pages 69-81, January.
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    More about this item

    Keywords

    Agriculture; Getis; Puerto Rico; Spatial autocorrelation; Spatial filtering;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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