Spatial Data Analysis: Theory and Practice
Spatial Data Analysis: Theory and Practice, first published in 2003, provides a broad ranging treatment of the field of spatial data analysis. It begins with an overview of spatial data analysis and the importance of location (place, context and space) in scientific and policy related research. Covering fundamental problems concerning how attributes in geographical space are represented to the latest methods of exploratory spatial data analysis and spatial modeling, it is designed to take the reader through the key areas that underpin the analysis of spatial data, providing a platform from which to view and critically appreciate many of the key areas of the field. Parts of the text are accessible to undergraduate and master's level students, but it also contains sufficient challenging material that it will be of interest to geographers, social and economic scientists, environmental scientists and statisticians, whose research takes them into the area of spatial analysis.
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scientific and policy context
The nature of spatial data
Obtaining spatial data through sampling
implications for spatial data analysis
b Methods for data from a continuous surface
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aggregation approach area-level areal associated attribute values autoregressive average Bayesian boundary boxplots census chapter choropleth maps clusters computed counts covariance Cressie data matrix data set data values defined denotes deprivation described disease distance distance bands distribution ecological effects ESDA estimate example exploratory Figure function geographical geostatistics graph Haining identifying individual individual-level inference interpolation kriging level of measurement linear Markov property mean measure methods missing values missing-data neighbourhood neighbours null hypothesis observed parameters partition pattern pixels plot Poisson population prediction error predictors problem properties random rates refer regression model relationships relative risk response variable scale scatterplot semi-variogram smoothing spatial analysis spatial autocorrelation spatial correlation spatial data analysis spatial dependence spatial framework spatial modelling spatial objects spatial units spatial variation spatially lagged spatially structured specified statistical subsets tion trend surface types variance vector visualization weights whilst WinBUGS zone