Geographically weighted summary statistics - a framework for localised exploratory data analysis

C. Brunsdon; Stewart Fotheringham; M. Charlton

doi:10.1016/S0198-9715(01)00009-6

Geographically weighted summary statistics - a framework for localised exploratory data analysis

C. Brunsdon, Stewart Fotheringham, M. Charlton

Research output: Contribution to journal › Article › peer-review

200 Scopus citations

Abstract

Geographical kernel weighting is proposed as a method for deriving local summary statistics from geographically weighted point data. These local statistics are then used to visualise geographical variation in the statistical distribution of variables of interest. Univariate and bivariate summary statistics are considered, for both moment-based and order-based approaches. Several aspects of visualisation are considered. Finally, an example based on house price data is presented.

Original language	English (US)
Pages (from-to)	501-524
Number of pages	24
Journal	Computers, Environment and Urban Systems
Volume	26
Issue number	6
DOIs	https://doi.org/10.1016/S0198-9715(01)00009-6
State	Published - Nov 2002
Externally published	Yes

Keywords

Geographical kernel weighting
Localised exploratory data analysis

ASJC Scopus subject areas

Ecological Modeling
General Environmental Science
Geography, Planning and Development

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10.1016/S0198-9715(01)00009-6

Cite this

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abstract = "Geographical kernel weighting is proposed as a method for deriving local summary statistics from geographically weighted point data. These local statistics are then used to visualise geographical variation in the statistical distribution of variables of interest. Univariate and bivariate summary statistics are considered, for both moment-based and order-based approaches. Several aspects of visualisation are considered. Finally, an example based on house price data is presented.",

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AU - Fotheringham, Stewart

AU - Charlton, M.

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AB - Geographical kernel weighting is proposed as a method for deriving local summary statistics from geographically weighted point data. These local statistics are then used to visualise geographical variation in the statistical distribution of variables of interest. Univariate and bivariate summary statistics are considered, for both moment-based and order-based approaches. Several aspects of visualisation are considered. Finally, an example based on house price data is presented.

KW - Geographical kernel weighting

KW - Localised exploratory data analysis

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ER -

Geographically weighted summary statistics - a framework for localised exploratory data analysis

Abstract

Keywords

ASJC Scopus subject areas

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