This paper assesses the degree of spatial socio-economic diversity exhibited across Canada by using 1996 Census of Population data aggregated at the Census division level. The study is based on a range of commonly used & understood demographic, social, & economic variables. A factor analysis is conducted to identify underlying dimensions that characterize each Census division across Canada. Six factors are identified that account for about 78 percent of the variance in the data set: labour force & economic attributes, remote & agro-rural attributes, demographic & labour force attributes, complex manufacturing versus primary production employment attributes, traditional manufacturing versus government employment attributes, and demographic dynamics. The spatial distribution of these factors reveal regional differences as well as differences between Census division types.
This in turn has raised a number of questions The utility of factor analysis stems from a about the structure and characteristics of reduction of the complexity due to the variety of homogeneous regions and the way in which these measures and indicators that a researcher often should be identified. [...] This bulletin assesses the degree of spatial These dimensions provide a perspective on the diversity across rural and urban Canada in terms relative position of the CD with respect to the of a number of demographic, social and economic dimension identified. [...] The use and interpretation of the results of this study cannot be stretched beyond the scope of the research itself. [...] The results of the factor analysis, then, clearly depend on the nature of the variables used in the computation. [...] For analogous septile categories, the closer are the values of the two tables, the better the factor captures the variability of the indicator.
This book is designed to introduce doctoral and graduate students to the process of conducting scientific research in the social sciences, business, education, public health, and related disciplines. It is a one-stop, comprehensive, and compact source for foundational concepts in behavioral research, and can serve as a stand-alone text or as a supplement to research readings in any doctoral seminar or research methods class. This book is currently used as a research text at universities on six continents and will shortly be available in nine different languages.
Rural and Small Town Canada examines the economic and social reality of rural and small town Canada today. Emphasis is placed on labour markets, the well-being of people, economic diversity, and the environment. This book provides a wealth of information not available elsewhere. Much of the analysis is based on unpublished tabulations derived from Statistics Canada's vast databases. This work is an invaluable resource for all those interested in the future of rural Canada.
This volume presents new evidence on the size of the health gap between different groups of people in Britain. It asks whether the government is concerned enough about reducing inequalities and highlights living conditions in the worst areas.
Applied Spatial Data Analysis with R, second edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. Compared to the first edition, the second edition covers the more systematic approach towards handling spatial data in R, as well as a number of important and widely used CRAN packages that have appeared since the first edition. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information science and geoinformatics, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003.
