Biotechnology is a branch of technology that studies plants and animals in a molecular perspective, thus involved on both health and environment. Geographical information systems (GIS) have the capability of organizing spatial databases from widely diverse perspectives. These are systems that compute spatial relationships and data that has been geo-referenced or located some where on the earth surface. The analysis of spatial information is supported by the first law of Geography where Waldo Tobler states, “everything is related to everything else, but near things are more related than distant things” (Camara et al., 2001). The preferred kind of application is based on extinct animals in a biodiversity study.
Researchers and scientists have turned their interest to this tool of technology. Camara et al. (2001) explains that GIS can be used to monitor endangered species, analyze vegetation distribution, oceanography and forest management. GIS has been and is being used in biodiversity studies. Biodiversity refers to the number and kind of species of plants and animals over a specific geographical region. The quantification and characterization for conservation of biodiversity at all levels is a key factor to how many species and population will survive and under what condition (Norris K. and Pain D., 2002).GIS is a suitable analysis and monitoring tool to integrate into the study of depleting species.
Geo-informational systems are new components that can add efficiency and reliability to the field of biotechnology. They are a great method of communicating information. This collectively involves gathering raw data, storing it and analyzing it to useful information for the user then presenting meaning compilations. Projects aimed at digitizing biodiversity information are an innovative way of improving understandability through simplified representations (Goodchild et al., 1991). Foody G. and Atkinson P. (2002) explain that such work will involve a team of ecologists, geologists and mathematicians to estimate characteristics from geospatial data and other attributes.
The system must have a geographical database where all the data is saved. The interactive phase will be through a user interface where data entry will be done. The usability of a system contributes to the user satisfaction and indirectly to the success of a product. The user interface should be easy to use and understand. Analysis on both the geographical and spatial information is carried out on the collected data on the database. After processing, the organized blocks of information and the relationships established are saved back into the database (Goodchild et al., 1991). The reporting capability of a geographical information system is utilized in interpreting the user’s query and presenting the relevant representations such as tables, graphs and maps. This is displayed on the user interface depending on the user and system parameters.
The data model is responsible for supporting a high level abstract view of the underlying data. It focuses on concepts rather than representations thus capable of having several representations for the same data. A data model should be open for sharing and use by other systems and databases. In this way, the system will support interoperability (Arctur and Zeiler, 2004). Designing a geographical information system involves building around a set of thematic layers that will tackle specified requirements. A thematic layer is a collection of common features like road network or satellite imagery for a certain date.
The suitable data model is the geo-relational model, also known as a dual architecture. It uses the relational database management systems (RDBMS) to store geographical information and other related data attributes. Wright (2003) states “this kind of model stores spatial and attribute data separately with spatial data in graphic files and attribute data in a relational database”. It cuts on costs since it utilizes database systems that already exist in the market. The risk involved is that the usual database systems are not designed for spatial data thus may result to inconsistencies in the database. The RDBMS does not offer any guarantee that the database holds absolute data integrity (Sauter et al, 2002).
Essential details to be gathered are on the ecosystem and functional interactions of the animal and its environs. Special variables and productivity measures need to be analyzed at different scales. This is necessary if any correlation is to be established between the variables in the study (Laszlo, 2003). A metadata template is essential in creating consistency in the database. Based on common geographical representations, vegetation, administrative boundaries and urban areas will be represented by polygons, well locations by points and roads by lines.
Datasets in GIS are collections of representations. In their book, Arctur & Zeiler illustrate that a transportation framework may represent multiple datasets, such as streets, bridges, railway and intersections. A raster dataset will be used in this application. It contains rasters that represent continuous geographic phenomena. A feature dataset contains related tables that hold both spatial information and attribute data but have one or more similar fields (rows). Spatial relationships represent features that touch, coincide, overlap or intersect each other, also called topology. Pre-defined data stored in metadata template can create consistency so that only variable unique data is added by the user. Relationships between datasets can be between the pre-defined static data or be user-defined attributes (Arctur & Zeiler, 2004).
The data sources to be drawn upon include aerial imagery from a plane or satellite. GPRS chips are necessary in this study to have real-time monitoring capability of the endangered animals. This will help record any empirical data on presence or absence in a specific patch of land, probably through migration of a herd. Another source will be existing themes on spatial and data attributes like administrative boundaries and vegetation coverage (Norris K. and Pain D., 2002). These can be accessed, might be free or commercial, from geographical and map organizations. The survey datasets, that capture measurements like slope and distance, are a reliable source of data. Drawing survey data from multiple surveys in the same region will improve accuracy on the spatial information.
One of the products will be a geographical map, which is a representation of the area of study with colored display for distribution and intensity of the species. Rigaux et al. (2002) in the discussion on representation of data stated that descriptive text can be placed on the map through labeling and annotation. Event patterns for occurrences such as migration and deaths can also be represented in graphs. Pictorial representations improve the understanding of analysts and learners alike. Tabular representations are also helpful in showing numbers (counts) and dependencies. These tabular representations will be utilized to come up with database reports as one of the application’s products.
Analysis in geo-databases involves a variety of phenomena. It incorporates thematic data (such as vegetation type), continuous data (such as elevation surfaces) and pictures (like photo-maps and pictures of buildings and other features). Essic (2010) states that data analysis must be within the parameters set for the study. These comprise of age limits, data accuracy and scale, storage space, attribute availability and restrictions applied.
Visualizations in GIS can either be abstract or cartographic. Manipulation is employed to provide multiple views of the database in abstract, while geometric objects and symbols are manipulated to produce spatial data representations. Geographical databases handle more data compared to normal databases. This means that geographical systems need bigger buffers when manipulating data for the user interface (Chapman, 2005).
Spatial analysis of data is to be employed in determining any relationships between the animal species aspects and the environment. One case that is necessary to analyze is the kind of climate, the animal is inclined to live in. This is a definite determinant of the animal distribution because of skin cover and type of vegetation. The human intrusion into a forest area may contribute to death rates through activities like poaching. Poaching involves non-compliance with a legal harvest, bans and restrictions in hunting animals (Magelah Peter, 2007 Oct 5). This analysis will involve both geographical and socio-economic data to identify any dependency patterns in the study.
Composite structures are those spatial features that are better represented as composites of points, lines and polygons. Geographical information can be represented in form of 2-points (x for latitude, y for longitude), a polygon, an image, samples or regular grid. An area is represented as a polygon when there are several points and the last point is the same as the first. Samples are represented as (x,y,z) where x and y specify the geographical location and z is the value of the studied phenomenon in that location. The samples are usually associated with field surveys, for instance when there are many instances in the same location (Rigaux et al., 2002). A regular grid is a set of elements in a matrix that is associated with a geographical area. Arctur and Zeiler (2004) stated that imagery of surfaces can be done by triangular irregular networks (TINs) to support the map graphics.
Documentation is essential for every project or new application. It improves the understanding of non-technical and future users. Any developments or adjustments done later will depend on the documentation to create a basis of modification (Arctur & Zeiler, 2004). It is easier to manage features of the system only when they are understood in-depth.
GIS, like other technologies invading the operations of various organizations, has faced resistance at different levels. The restructuring associated with old systems in any application or procedure requires revenue and time. The management is bound to be reluctant to dedicate the organization’s resources in a technical under-taking. A GIS system has to be built up (customized) within an organization. Foody G. and Atkinson P. (2002) point out that data inaccuracy can either be based on precision or biasness from different sources, adding to limitations of GIS.
On the other hand, the benefits of integrating computer technology with geographical studies are overwhelming and interesting. The measurable benefits of GIS can be expressed as gains in efficiency in terms of time saved, increased income through improved products and services and reductions in costs (Bernharnsen, 2002). Other indirect returns include better decision making, better presentations of projects and more precise analysis. Bernharnsen (2002) states that “If we can express the contents of a map or image in digital form, the power of the computer opens an enormous range of possibilities for communication, analysis, modeling and accurate decision making.” (p.2).
References
Arctur D., Zeiler M. (2004) Designing geodatabases: case studies in GIS data modeling. New York: ESRI Press.
Bernhardsen Tor (2002) Geographical Information Systems: An Introduction. 3rd edition. New York: John Wiley & Sons.
Camara G., Monteiro A., Carvalho M. (2001) Spatial Analysis and GIS: A Primer. New York: ESRI Press.
Chapman, A. D. (2005) Uses of Primary Species-Occurrence Data, version 1.0. Report for the Global Biodiversity Information Facility, Copenhagen.
Essic J. (2010) Geospatial data services. North Carolina Sate University: DH Hill Library, Research and Information Services.
Foody G., Atkinson P. (2002) Uncertainity of remote sensing and GIS. New York: John Wiley & Sons.
Goodchild F., Rhind W., Maguire, J. (1991) Geographical Information Systems Vol. 1. Essex, England: Longman Scientific and Technical.
Nagy Laszlo, G. Grabherr (2003) Alpine biodiversity in Europe: Ecological Studies 167. Berlin: Springer-Verlag.
Norris K., Pain D. (2002) Conserving Bird Biodiversity: General principles and their application. Cambridge, UK: Cambridge University Press.
Rigaux P., Scholl M., Voisard A. (2002) Spatial databases: with application to GIS. USA: Elsier Science.
Sauter C., Boeschen S., Klestova Z. (2002) Biotechnology and its acceptance. Brazil: EPSO Conference.
Wright J. D. (2002) Undersea with GIS. New York: ESRI Press.