By Colette Cauvin, Francisco Escobar, Aziz Serradj
This sequence in 3 volumes considers maps as structures due to a couple of successive changes and phases built-in in a logical reasoning and an order of selections. Volume 2 specializes in the effect of the quantitative revolution, in part regarding the appearance of the pc age, on thematic cartography.
Chapter 1 From the outline to the Generalization of an characteristic Variable Z (pages 7–75):
Chapter 2 Generalization of Thematic Attributes (pages 77–148):
Chapter three Modeling Thematic Attributes: Generalizable Cartographic offerings (pages 149–214):
Chapter four Cartographic changes of place (pages 223–276):
Chapter five Taking a 3rd size under consideration, Transformation of exhibit (pages 277–349):
Read or Download Cartography and the Impact of the Quantitative Revolution, Volume 2 PDF
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Additional info for Cartography and the Impact of the Quantitative Revolution, Volume 2
The horizontal axis corresponds to different discrete values (or modalities of the variable) and the vertical axis shows the total number of occurrences (or the frequency) of each modality. For each value Zi of the variable there is a vertical segment (a bar) whose length is proportional to the number of occurrences ni, or to the frequency fi of the value Zi5. 4 All the graphical representations in this chapter are constructed with the statistical data from Luxembourg. 5 It is important to mention that ni represents the absolute frequency while fi stands for the relative frequency.
7 This chapter is based on our work Discrétisation et représentation cartographique, written in collaboration [CAU 87a]. 1. From data to the basic rules On the one hand, performing a correct discretization requires certain preliminary actions to be performed which are intended as data preparation for a better understanding of the phenomenon Z. On the other hand, discretization requires knowledge of the rules in order to avoid aberrations during the construction of the classes. 1. Data preparation This step essentially consists of converting the data into a standard form and making a number of graphical representations such as were described in the part on exploratory data analysis.
Kimmerling [KIM 75], J. C. Muller and J. Honsacker [MUL 78c], J. C. Muller [MUL 79] and M. Peterson [PET 79b]. They produced very interesting results. It is obvious that if we work manually and want the differences between the degrees of gray to remain perceptible, this limits us to six or seven degrees as a maximum. A computer and a good laser printer make it possible to obtain, in theory, 8 It is important to remember that these methods are intended for finding the optimal number of classes for constructing various plots in statistics.