Technology

Geographical Information Systems (GIS)

GIS has emerged as one of the most effective geospatial technologies for disaster risk reduction, mainly for its ability to provide a wide range of spatial data processing, analysis, modeling, and visualization. GIS provides key decision-support capabilities for disaster recovery, and addresses the need for timely access to location data sources for sharing accurate information among all decision-making levels involved in emergency management (local, provincial, and federal). The strong integration between the components of GIS—hardware, software, user, data, and procedures—has allowed for advanced modeling and analysis capabilities. The strength of GIS comes from the fact that it models spatial relationships, which are usually studied by considering simple features of objects, such as points, lines, and polygons.

GIS is an interdisciplinary science and utilizes input from many contributing disciplines that enhance GIS capabilities in providing data acquisition and verification, data compilation, data storage, data updates, manipulation, management and exchange, and retrieval and presentation. This includes remote sensing, photogrammetry, cartography, environmental modeling, spatial statistics, and surveying.

The connection between GIS and information and communications technology (ICT) has emerged in a term known as GeoICT, which is an enabling technology that stemmed from the integration of geospatial information and imaging technology with ICT. It is considered to be a core technology that forms the basis for spatial decision making, geocomputation, and location-based services (LBS).

GIS utilizes both spatial and nonspatial data and processes them to provide information; the spatial data comes in different data models and data structures. A data model is a digital representation of the real world to allow for its database realization. The dimensionality of GIS data allows abstraction of real-world phenomena, through spatial, temporal, and thematic dimensions.

The main data models are the vector data model and the raster data model. The fundamental premise in vector data is a point, from which lines, polygons, and areas are formed as connections between two points or more. Vector data is especially accurate and supports complex network analysis, can store more attributes, and does not require significant computing power. While raster models process faster, they require large computing storage and processing capabilities. In a raster data model, databases are formed from a series of pixels. The best example for raster data is satellite imagery. Both vector and raster data may represent location coordinates as well as the elevation of an area. Triangular Irregular Network (TIN) is the vector representation of elevation.

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