2000
DOI: 10.3233/sju-2000-17208
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Multiscalar analysis and map generalisation of discrete social phenomena: Statistical problems and political consequences

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Cited by 21 publications
(9 citation statements)
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“…None of the 377 Sardinian municipalities met these two conditions. To overcome this problem, we used a multiscalar smoothing method based on Gaussian neighborhood distribution (Grasland et al, 2000) that allows the study of the spatial concentration of centenarians as a continuum, and is not limited by the division of the Sardinian territory into 377 municipalities. The first step was to select the optimum scale in the Gaussian neighborhood distribution that would provide the best compromise between sufficiently large sample sizes and integrity of the information on the spatial distribution of centenarians.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…None of the 377 Sardinian municipalities met these two conditions. To overcome this problem, we used a multiscalar smoothing method based on Gaussian neighborhood distribution (Grasland et al, 2000) that allows the study of the spatial concentration of centenarians as a continuum, and is not limited by the division of the Sardinian territory into 377 municipalities. The first step was to select the optimum scale in the Gaussian neighborhood distribution that would provide the best compromise between sufficiently large sample sizes and integrity of the information on the spatial distribution of centenarians.…”
Section: Methodsmentioning
confidence: 99%
“…The great advantage of the Gaussian smoothing method (compared to a direct observation of municipality values or to an aggregation of municipalities) is that (i) the biases related to the shape and size of territorial divisions is reduced and that such a method allows for the selection of the best compromise between sample size of the neighborhoods and the variability of the spatial distribution, and (ii) it gives the possibility to test H 0 on a spatial sample of sufficient size (Tobler, 1969;Grasland et al, 2000).…”
Section: Methodsmentioning
confidence: 99%
“…We infer that property markets are discrete social data, similar to Tobler’s hypothesis [130]: a potential price for a specific location is a function of distance to nearby similar transactions, and also a function of the number of properties available, turnover and realized transactions. This method removes spatial bias, resolving the Modifiable Areal Unit Problem (MAUP), as demonstrated for demographic indices in Europe [127, 131]. We elaborate on Grasland’s framework for spatial analysis of social facts [129], based on Tobler’s first law of geography [132] and Stouffer’s intervening opportunities [133], justifying to use Stewart’s potential [128] for the spatial interpolation of social discrete data (details in S1 Methodological Appendix).…”
Section: Methodology: An Empirical Analysis Of Transactions In Paris mentioning
confidence: 99%
“…As stated by Grasland and Madelin [5], "The access to individual data is of course the ideal situation, not because the individual level is in all cases the most appropriate one to observe or model a phenomenon, but mainly because it gives the choice to observe information at all possible levels and for all forms of spatial partitions." Prominent advocates of the use of micro-data are Tobler [6,7], Grasland [8] as well as, and more specifically in the field of economic geography, Arbia [9][10][11]. They propose the use of continuous space in order to transcend the issues raised by discrete boundaries, or the use of the flexibility of aggregation provided by point-located micro-data.…”
Section: Open Accessmentioning
confidence: 99%