Geographic Information Systems (GIS) are powerful tools that are revolutionizing how we understand and interact with our world. But what exactly is GIS? At its core, GIS is a computer system for capturing, storing, checking, and displaying data related to positions on Earth’s surface. This seemingly simple definition unlocks a vast potential for analysis and problem-solving across countless industries and disciplines. Understanding GIS starts with understanding the fundamental types of data it utilizes: geospatial data.
Geospatial data, often referred to as spatial data, is information about geographic locations and the features found there. This data comes in two primary forms: vector and raster, each uniquely suited to represent different types of geographic information.
Vector Data: Points, Lines, and Polygons
Vector data excels at representing discrete features, those with defined locations and boundaries. Imagine mapping out city infrastructure. Vector data allows you to represent:
- Points: Individual locations like fire hydrants, traffic signals, or specific addresses. Each point is defined by its precise coordinates.
- Lines: Linear features such as roads, rivers, power lines, or pipelines. Lines are created by connecting a series of points, defining paths and routes.
- Polygons: Areas or regions with closed boundaries, like buildings, parks, lakes, or administrative boundaries such as counties or districts. Polygons are formed by connecting lines to enclose an area.
Vector data is ideal for thematic mapping and applications requiring precise location and clear boundaries. For example, mapping land ownership, utility networks, or transportation infrastructure all benefit from the accuracy and clarity of vector data.
Raster Data: Grids and Continuous Surfaces
In contrast to vector data, raster data is best suited for representing continuous phenomena or data that varies smoothly across an area without sharp boundaries. Think of it as a grid of cells, where each cell holds a value representing a specific attribute. Common examples of raster data include:
- Elevation: Representing terrain height, where each cell’s value indicates the elevation at that location.
- Temperature: Mapping temperature variations across a region, with each cell showing the temperature value.
- Precipitation: Illustrating rainfall amounts, where cell values represent the amount of precipitation.
- Satellite Imagery and Aerial Photography: Images captured by satellites or aircraft, where each cell represents the brightness or color information of a specific area on the ground.
Raster data is perfect for modeling surfaces, analyzing environmental conditions, and working with imagery. Weather forecasting, environmental monitoring, and land cover analysis heavily rely on raster data.
Common Geospatial Data Formats
To effectively use geospatial data within GIS software, it’s crucial to understand the common file formats used to store and exchange this information. These formats ensure compatibility and allow for seamless data sharing between different GIS platforms. Here are some widely used geospatial data formats for both vector and raster data:
Vector Data Formats
- SHP (Shapefile): Developed by ESRI, the Shapefile format is a long-standing industry standard for vector data. Despite some limitations in handling complex geometries and attributes within a single file, its widespread compatibility makes it a staple in GIS. A shapefile actually comprises multiple files (.shp, .shx, .dbf, and optionally others like .prj), all essential for a complete dataset.
- GDB (Geodatabase): ESRI’s Geodatabase is a more advanced format designed to overcome the limitations of shapefiles. It’s a container for storing various types of geospatial data, including vector, raster, and tabular data. File Geodatabases (.gdb folders) are particularly common and are the native format for ESRI’s ArcGIS software, offering better performance and organizational capabilities compared to shapefiles.
- SDC (Smart Data Compression): Another ESRI format, SDC is a proprietary, highly compressed vector format. It’s primarily used to reduce file size, making it efficient for distributing large vector datasets, especially for use within the ArcGIS environment.
- ArcInfo Coverage: An older ESRI vector format, ArcInfo Coverages are less common today, having been largely superseded by Geodatabases. They are directory-based formats, lacking a single file extension, and are composed of multiple files within “workspace” folders.
- E00 (Arc Export or Interchange Format): Similar to ArcInfo Coverages, E00 files are legacy ESRI formats used for exchanging ArcInfo Coverages and Grids. They require import and conversion to be used in modern GIS software.
Raster Data Formats
- GeoTIFF: GeoTIFF is an extension of the widely used TIFF (Tagged Image File Format) image format specifically designed for geospatial raster data. It embeds georeferencing information (like coordinate system and geographic extent) directly within the TIFF header. This makes GeoTIFF an excellent format for exchanging raster data while preserving spatial context.
- DEM (Digital Elevation Model): DEM is a raster format specifically for representing elevation data. The USGS (United States Geological Survey) commonly uses DEM format. Unlike typical image raster formats, DEM cell values directly represent elevation values rather than color or brightness.
- ArcInfo Grid: Similar to ArcInfo Coverages for vector data, ArcInfo Grids are directory-based raster formats associated with older ESRI ArcInfo systems. They are also less frequently encountered now, replaced by more modern raster formats.
- BIL, BIP, BSQ (Band Interleaved by Line, Band Interleaved by Pixel, Band Sequential): These are raster formats commonly produced by remote sensing systems. They differ in how they organize spectral band information within the file, influencing how efficiently different software can access and process multi-band imagery data.
- LiDAR (.LAS): LiDAR (Light Detection and Ranging) is a remote sensing technology that uses laser pulses to measure distances and create detailed 3D point clouds. While raw LiDAR data is point-based and stored in formats like .LAS, LiDAR data is often processed to generate raster products like DEMs, combining both vector (point cloud) and raster aspects within the broader GIS context.
Conclusion
Understanding “What Is Gis” begins with grasping the fundamental concepts of geospatial data and its various formats. Whether you are working with vector representations of city streets or raster imagery of land cover, choosing the appropriate data format and understanding its characteristics is essential for effective GIS analysis and application. As GIS technology continues to evolve, a solid foundation in these data fundamentals will remain crucial for anyone working with geographic information.
