lctools-package: Local and Geographically Weighted Spatial Statistics Tools

In lctools: Local and Geographically Weighted Spatial Statistics Tools

Local and Geographically Weighted Spatial Statistics Tools

Description

Provides researchers and educators with easy-to-learn, user friendly tools for calculating key spatial statistics and for applying simple as well as advanced methods of spatial analysis on real data. These include: Local Pearson and Geographically Weighted Pearson Correlation Coefficients; Spatial Inequality Measures (Gini coefficient, Spatial Gini, Location Quotient (LQ) and Focal Location Quotient); Spatial Autocorrelation indices (Global and Local Moran's I); several Geographically Weighted Regression techniques, including the Geographically Weighted Zero-Inflated Poisson Regression; tools for computing variables used in Spatial Interaction Models; and other spatial analysis tools (other geographically weighted statistics). The local correlation tools were originally developed to test for local multicollinearity among the explanatory variables of local regression models and can also be used to examine the local association between pairs of variables. The package also contains functions for measuring the significance of each statistic calculated, mainly based on Monte Carlo simulations, and comes with two example datasets, one of which is a spatial data frame referring to the municipalities of Greece. Methods are described in Kalogirou (2012) <doi:10.1007/s10037-011-0061-y>, Kalogirou (2016) <doi:10.1111/gean.12092> and Rey and Smith (2013) <doi:10.1007/s12076-012-0086-z>.

Details

Package:	lctools
Type:	Package
Version:	0.3
Date:	2026-06-30
License:	GPL (>= 2)

Note

Acknowledgement: I am grateful to the University of Luxembourg and would like to personally thank Ass. Professor Geoffrey Caruso, Professor Markus Hesse and Professor Christian Schulz for their support during my research visit at the Institute of Geography and Spatial Planning (Sept. 2013 - Feb. 2014) where this package was originally developed.

Author(s)

Stamatis Kalogirou

Maintainer: Stamatis Kalogirou <[e-mail]>

References

Hope, A.C.A. (1968) A Simplified Monte Carlo Significance Test Procedure, Journal of the Royal Statistical Society. Series B (Methodological), 30 (3), pp. 582 - 598.

Kalogirou, S. (2003) The Statistical Analysis and Modelling of Internal Migration Flows within England and Wales, PhD Thesis, School of Geography, Politics and Sociology, University of Newcastle upon Tyne, UK. https://theses.ncl.ac.uk/jspui/handle/10443/204

Kalogirou, S. (2012) Testing local versions of correlation coefficients, Review of Regional Research - Jahrbuch fur Regionalwissenschaft, 32(1), pp. 45-61, doi: 10.1007/s10037-011-0061-y. https://link.springer.com/article/10.1007/s10037-011-0061-y

Kalogirou, S. (2013) Testing geographically weighted multicollinearity diagnostics, GISRUK 2013, Department of Geography and Planning, School of Environmental Sciences, University of Liverpool, Liverpool, UK, 3-5 April 2013.

Kalogirou, S. (2015) Spatial Analysis: Methodology and Applications with R. [ebook] Athens: Hellenic Academic Libraries Link. ISBN: 978-960-603-285-1 (in Greek). https://www.openarchives.gr/aggregator-openarchives/edm/kallipos/000065-11419_5029?language=en

Kalogirou, S. (2016) Destination Choice of Athenians: an application of geographically weighted versions of standard and zero inflated Poisson spatial interaction models, Geographical Analysis, 48(2),pp. 191-230. DOI: 10.1111/gean.12092 https://onlinelibrary.wiley.com/doi/abs/10.1111/gean.12092

Rey, S.J., Smith, R.J. (2013) A spatial decomposition of the Gini coefficient, Letters in Spatial and Resource Sciences, 6 (2), pp. 55-70.

lctools documentation built on July 9, 2026, 9:07 a.m.