ISSN 0016-7126 (Print)
ISSN 2587-8492 (Online)
Abstract:
GIS technology is increasingly being introduced into analytical activities due to a special focus on the relationship of observation objects in space. In addition, the growth of interest in geoinformation methods is facilitated by emergence of geographically structured data sets, including big social data, which, in addition to obvious advantages, are characterized by possible errors and incomplete facts, including address information. The aim of the work is to study the influence of the applied data geocoding algorithms and the weight matrices formed on their basis on the results of assessing the spatial autocorrelation of prices. The study was conducted on the prices of potato for 1952 settlements of the Russian Federation, in daily detail for the period from 01/01/2021 to 12/31/2024. The computational experiment is aimed at comparing alternative estimates of the global Moran`s index with testing various geocoding sources (Openstreetmap and Rosreestr) and approaches to constructing spatial weight matrices. The research showed that, in general, the geocoding source does not greatly affect the conclusion on presence/absence of spatial dependence of prices, but in the case of OSM API geocoding, higher index estimates were derived, with a corresponding proportion of statistically significant estimates and less variation in the values obtained. The choice of the weight matrix can influence the final estimates: lower values are resulted when using a matrix formed by the bandwidth. The outcomes highlight the importance of improving existing sources used for geocoding
Keywords:
The study was supported by grant No. 24-28-00774 from the Russian Science Foundation
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Citation:
| (2026) The impact of approaches to geocoding economic data on estimating the prices` spatial autocorrelation . Geodesy and cartography = Geodeziya i Kartografiya, 87(2), pp. 42-53. (In Russian). DOI: 10.22389/0016-7126-2026-1028-2-42-53 |