Upton, G. and B. Fingleton:. Spatial Data Analysis bei Example. Volume 1: Point Pattern and Quantitative Data. J. Wiley & Sons, Chichester/New York/Brisbane/Toronto/Singapore 1985. X, 410 S., L. 32.95

1986 ◽  
Vol 28 (6) ◽  
pp. 664-664
Author(s):  
D. Stoyan
Keyword(s):  
New York ◽  
Data Analysis ◽  
Spatial Data ◽  
Quantitative Data ◽  
Spatial Data Analysis ◽  
Point Pattern

Spatial Data Analysis by Example. Vol. 1. Point Pattern and Quantitative Data.

1986 ◽  
Vol 74 (1) ◽  
pp. 313
Author(s):  
S. Openshaw ◽  
G. Upton ◽  
B. Fingleton
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Quantitative Data ◽  
Spatial Data Analysis ◽  
Point Pattern

Spatial Data Analysis by Example: Volume 1: Point Pattern and Quantitative Data

Technometrics ◽  
1987 ◽  
Vol 29 (1) ◽  
pp. 114
Author(s):  
Noel Cressie ◽  
Graham Upton ◽  
Bernard Fingleton
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Quantitative Data ◽  
Spatial Data Analysis ◽  
Point Pattern

Spatial Data Analysis by Example, Vol. 1: Point Pattern and Quantitative Data.

Biometrics ◽  
1986 ◽  
Vol 42 (4) ◽  
pp. 1001
Author(s):  
J. K. Ord ◽  
G. Upton ◽  
B. Fingleton
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Quantitative Data ◽  
Spatial Data Analysis ◽  
Point Pattern

Spatial Data Analysis by Example, Volume 1: Point Pattern and Quantitative Data

1987 ◽  
Vol 82 (399) ◽  
pp. 957
Author(s):  
J. Richard Alldredge ◽  
Graham Upton ◽  
Bernard Fingleton
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Quantitative Data ◽  
Spatial Data Analysis ◽  
Point Pattern

scholarly journals Progress in the R ecosystem for representing and handling spatial data

2020 ◽  
Author(s):  
Roger S. Bivand
Keyword(s):  
New York ◽  
Data Analysis ◽  
Open Source ◽  
Spatial Data ◽  
Spatial Data Analysis ◽  
Data Handling ◽  
Good Match ◽  
R Programming Language ◽  
R Packages ◽  
R Programming

Abstract Twenty years have passed since Bivand and Gebhardt (J Geogr Syst 2(3):307–317, 2000. 10.1007/PL00011460) indicated that there was a good match between the then nascent open-source R programming language and environment and the needs of researchers analysing spatial data. Recalling the development of classes for spatial data presented in book form in Bivand et al. (Applied spatial data analysis with R. Springer, New York, 2008, Applied spatial data analysis with R, 2nd edn. Springer, New York, 2013), it is important to present the progress now occurring in representation of spatial data, and possible consequences for spatial data handling and the statistical analysis of spatial data. Beyond this, it is imperative to discuss the relationships between R-spatial software and the larger open-source geospatial software community on whose work R packages crucially depend.

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