Introduction to nature of spatial data. Characteristics of space and of spatially-referenced events. Relevance and use of spatial pattern in other fields. Problems in measuring and interpreting spatial patterns in health. Possibilities of inference.
This lecture is an introduction to Geographic Information Systems (GIS) and spatial data handling.
The first hour of lecture focuses on many of the important issues involved in creating, manipulating, and analyzing spatial data. The focal point of this discussion will be GIS (Geographical Information Systems), which provide a vehicle for carrying out many of these functions:
The second hour of lecture deals with the role of spatial statistics in the analysis and creation of spatial data. Topics covered included quantification of spatial structure, and the construction of inferences from spatial data. Examples in population genetics and cancer epidemiology are presented.
Our attention, therefore, in this first lecture is on the very first stage of the course strategy: at the data collection/manipulation stage, and on the transition to maps created from the data; but it also looks forward to the stages at which some analysis will be required (particularly techniques of visualization).
This lecture deals with Exploratory Spatial Data Analysis (ESDA). Topics include the objectives of ESDA, its methods, both graphical and statistical, and the role of ESDA in hypothesis generation and testing. The lecture closes with a discussion of multiple testing and experimentwise error.
Exploratory SDA is just that: exploratory! This is the first step in making sense of the data you have collected/assembled. Some techniques are available in the GIS studied in the previous week, but we need to go outside GIS: the evolution from management and visualization to analysis has been slow for GIS, and so other software must be called into play.
Uses of space-time data to define outbreak clusters. Spatial patterns in monitoring for disease and directing intervention.
Public health surveillance is the ongoing systematic collection, analysis and interpretation of health event data with the objective of disease control and prevention. This lecture presents an introduction to issues in disease surveillance. The background of disease cluster investigations is presented, along with their role in public health. The basic cluster types are introduced and the cluster investigation guidelines of the Centers for Disease Control are described. The lecture closes with important issues such as `Texas Sharpshooter' sampling and whether disease causality may be inferred from cluster investigations.
There are three points in the course strategy where spatial statistics and models come into play: in the transition from data to thematic map (e.g. geostatistical techniques); in the exploration of spatial autocorrelation and clustering; and in the testing of a prediction based on theory, at the end of the process.
Spatial statistics are statistics calculated from spatial data, and differ from `classical' statistics (e.g. ANOVA, regression) in several ways. This lecture begins by identifying these differences, and then presents several spatial models. Spatial statistics are then developed as a special case of randomization tests, and issues of statistical inference with spatial data are discussed. Finally, the utility and limitations of empirical distributions are presented.
Spatial clustering and surveillance statistics will be discussed separately, as will geostatistical models.
Which disease models give rise to which patterns? Experimentalists gather the patterns, and then attempt to deduce the process (developing a model which they hope captures or reflects the process); theoreticians may attempt to develop a model, then find the data to verify their predictions. We will examine some specific models of contagion.
This lecture provides an overview of disease clustering methods. It opens with a discussion of the role of disease clustering in scientific inference, and then describes tests for temporal clustering, spatial clustering, and space-time interaction. These are presented within the framework of global, local and focused tests. Next, disease surveillance methods are described, and the lecture concludes with recommendations regarding hypothesis vs. data driven approaches.
Many spatial statistics are special cases of a flexible mathematical form called the Gamma product. This lecture describes the Gamma product and its constituent parts, including proximity metrics, data metrics, and spatial randomization procedures. Because it is so flexible, the Gamma product provides a ready means for creating `Designer' spatial statistics customized to specific requirements.
Dr. Jacquez will provide an introduction to compartmental models, which we can use to simulate or model a spatial process. He will begin with an introduction, and finish with a discussion of how spatial aspects can be modelled with compartmental models.
Geostatistical models are used essentially for three reasons:
Geostatistics, then, will be useful in moving from the data to thematic maps, in the analysis of spatial autocorrelation, and in creating maps for visualization and ESDA.
These techniques are notoriously complicated, however, so this will be a rather elementary and descriptive approach to geostatistical modelling. We will try to provide insight into the ideas via examples and many pictures, although there will be more math than you'll encounter in other modules.
Using knowledge of disease mechanisms to hypothesize underlying processes that produce observed patterns. Summarize various kinds of exposure and transmission. Develop hypotheses that might explain different time-space patterns. Compare direct and indirect contagious processes with different environmental exposures.
A special guest lecture by Dr. Uriel Kitron. The objective is to link process and pattern.
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