de By 2004. GIS is computer-based system specially designed and implemented for two subtle but interrelate purposes: managing geospatial data and using these
data to solve spatial problems Lo and Yeung 2007. GIS is capable of assembling, storing, manipulating, and displaying map and database information.
GIS allows us to view, understand, question, interpret, and visualize data in a very informative way. Data management is more effective in order to support any
management in any sector by decision and policy makers Wicaksono et al. 2010. The construction and functions of GIS were explained by dividing them
into subsystems of input, processing, analysis, and output. GIS is made up of four components, namely, data, technology, application, and people. Geospatial data
record the locations and characteristics of natural features or human activities that occur on earth’s surface. GIS technology can be explained in terms of hardware
and software. The application component of GIS can be explained from three perspectives: areas of application, nature of application, and approaches of
implementation. The people component is defined as GIS users Lo and Yeung 2007.
Spatial analysis using GIS includes a wide range of operations. Typically they relate to analyses within or between layers of geographical data provided by
the GIS. In spatial analysis three different types of spatial data can be analyzed: point data, geostatistical data, and lattice data Pfeiffer et al. 1994. Point patterns
are the data set may consist of locations only, or it may be a marked point process, with data values associated with each location. Geostatistical data represents
continuous variation of a feature attribute such as rainfall or temperature Allepuz 2008. Lattice data represents discrete variation in space based on regular or
irregular units. These units can be for example farm or administrative boundaries Pfeiffer et al. 1994.
2.3 Spatial Analysis in Veterinary Epidemiology
Epidemiology is the study of disease in populations and of factors that determine its occurrence. Veterinary epidemiology involves observing animal
populations and making inferences from the observations Thrusfield 1986. Classic epidemiological analysis focused mainly on the animal dimension,
whereas time and space were usually explored using fairly basic methods. Transmission of an infectious agent requires direct or indirect contact between the
source of infection and the susceptible animal, which means that spatial proximity has to be considered as a key factor when determining the risk of infection for
individual animals or herds Durr and Gatrell 2004. The term of spatial epidemiology is defined as sub discipline of
epidemiology whose primary purpose is to describe and explain the spatial pattern of disease Durr and Gatrell 2004. Spatial epidemiology is the description and
analysis of the geographic, or spatial, variations in disease with respect to demographic, environmental, behavioral, socioeconomic, genetic, and infectious
risk factors Elliot and Wartenberg 2005. Some of the analytical tools used in spatial epidemiology include disease mapping, geographic correlation studies to
determine if spatial patterns are associated with particular risk factors, and disease cluster detection.
The use of the tools of GIS, spatial statistics and remote sensing is generally necessary for spatial epidemiology Durr and Gatrell 2004. Herbreteau
2006 explained that the purposes of spatial analysis in disease epidemiology are; the epidemiological surveillance, with disease mapping of reported incidences,
and further active surveillance, involving collection of animal health and animal population information; the explanatory understanding of animal population and
disease dynamics, by identifying patterns in the spatial-temporal distribution of diseases and identifying risk factors or causes of the diseases; and for the diseases
prevention, by predicting outbreaks and assisting in decision making. The objectives of spatial epidemiological analysis are the description of
spatial patterns, identification of disease clusters, and explanation or prediction of disease risk. The specific analytical objectives then lead to three groups of
analytical methods: visualization, exploration, and modeling. Visualization is probably the most commonly used spatial analysis method, resulting in maps that
describe spatial patterns. Exploration of spatial data involves the use of statistical methods to determine whether observed patterns are random in space. Modeling
introduces the concept of cause effect relationships using both spatial and non-
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
