spatial data sources to explain or predict spatial patterns Pfeiffer et al. 2008. Figure 2 show the conceptual framework of spatial epidemiological data analysis.
Figure 2. Conceptual Framework of Spatial Epidemiological Data Analysis Pfeiffer et al. 2008
2.4 Methods for Spatial Analysis of Animal Disease
The objectives of spatial data analysis in animal disease are the description of spatial patterns, the identification of disease clusters and the explanation or
prediction of disease risk. Most currently available statistical methods will represent polygon data using the centroid point location together with any
associated attributes. A framework for the spatial analysis of epidemiological data includes of the following groups of analytical methods: data visualization,
exploratory analysis, and spatial modeling Durr and Gatrell 2004.
2.4.1 Data Visualization
The most commonly applied spatial analysis technique in research and surveillance of animal diseases is data visualization. This involves generating
maps to present the spatial and temporal patterns of disease occurrence, which are then used to develop hypotheses about possible cause – effect relationships Durr
and Gatrell 2004. There are three kind of spatial data type, i.e. point data, aggregated lattice data, and continuous geostatistical data.
The location of the disease occurrence is generally visualized using a point on the map. The visual analysis of point data includes the simple map display of
the point locations and the use of smoothing methods to generate surface representations of point density. Spatial smoothing can be achieved through
estimation of localized averages by using a spatial filter or by applying a mathematical function such as kernel smoothing Durr and Gatrell 2004.
Point maps are the simplest way to visualize disease event information when the locations of events are known. The oldest and most frequently used
method to visualize point data is to plot the locations of the study subjects using their Cartesian coordinates. Although point maps are the simplest way to visualize
disease event information when the locations of events are known, they present problems where there are large numbers of events or multiple events at the same
location. The use of different symbols to represent attribute values is one solution Stevenson 2009.
The process of aggregation involves summarizing a group of individual data points into single value to produce, for example, a total, mean, median, or
standard deviation. This summary statistic may then be assigned a spatial location, often a discrete area such as state, county, or some other administration region.
Disease counts can be expressed as a function of the population size to provide estimates of prevalence, incidence risk, or incidence rate per unit area. Choropleth
maps are the most commonly used means for visualizing data in this format. A choropleth map shows information by colouring each component area with colour,
providing an indication of the magnitude of the variable of interest Pfeiffer et al. 2008.
Spatially continuous data such as rainfall, humidity, air pollution, or soil mineral concentrations may be estimated at all possible locations within a region
of interest. In epidemiology continuous variables of the type cited above may be used as covariates for predicting disease risk. In the simplest situation, continuous
data may be summarized by area unit and plotted as a choropleth map Pfeiffer et al. 2008. Continuous data also can be visualized using interpolation that predicts
the values statistically at the grid-coordinates. The possible interpolate methods
are inverse distance weighting IDW, kernel smoothing, and kriging Allepuz 2008.
2.4.2 Exploratory Analysis
