Intro to GIS PDF

Spatial Data Definition: Spatial data, also known as geospatial data, is information that has a geographic or spatial component. This means it is tied to specific locations on the Earth's surface. The two primary types of spatial data are **vector and raster data **in a GIS Examples: Coordinates...

Spatial Data Definition: Spatial data, also known as geospatial data, is information that has a geographic or spatial component. This means it is tied to specific locations on the Earth's surface. The two primary types of spatial data are **vector and raster data **in a GIS Examples: Coordinates on a map, locations of buildings, roads, rivers, etc. - Navigation maps like Google Maps. - Geolocation tags on social media posts. - Satellite images. Characteristics: Includes information about the location, shape, and relationships between different geographic features. For instance, spatial data can show how close two locations are or how they are connected. 1. Vectors are points, lines, and polygons - Vector data is not made up of a grid of pixels. Instead, vector graphics are comprised of vertices and paths. - The three basic symbol types for vector data are points, lines, and polygons (areas). - Because cartographers use these symbols to represent real-world features in maps, they often have to decide based on the level of detail on the map. Points are XY coordinates - Vector points are simply XY coordinates. Generally, they are latitude and longitude with a spatial reference frame. - When features are too small to be represented as polygons, points are used. - For example, you can't see city boundary lines on a global scale. In this case, maps often use points to display cities. - Points have zero dimensions Lines connect vertices  - Vector lines connect each vertex with paths. Basically, you're connecting the dots in a set order and it becomes a vector line with each dot representing a vertex. - Lines usually represent features that are linear in nature. For example, maps show rivers, roads, and pipelines as vector lines. Often, busier highways have thicker lines than abandoned roads. - On the other hand, networks are line data sets but they are often considered to be different. This is because linear networks are topologically connected elements. They consist of junctions and turns with connectivity. - If you were to find an optimal route using a traffic line network, it would follow set rules. For example, it can restrict turns and movement on one-way streets. Polygons connect vertices and close the path - When you join a set of vertices in a particular order and close it, this is now a vector polygon feature. When you create a polygon, the first and last coordinate pairs are the same. - Cartographers use polygons to show boundaries and they all have an area. For example, a building footprint has square footage and agricultural fields have acreage. Vector data advantages and disadvantages Advantage: - Because vector data have vertices and paths, this means that the graphical output is generally more aesthetically pleasing. Furthermore, it gives higher geographic accuracy because data isn't dependent on grid size. - Topology rules can help data integrity with vector data models. Not only that, network analysis and proximity operations use vector data structures. Disadvantage: - Continuous data is poorly stored and displayed as vectors. If you want to display continuous data as a vector, it would require substantial generalization. Although topology is useful for vector data, it is often processing intensive. Any feature edits require updates on topology. With a lot of features, vector manipulation algorithms are complex. 2. Raster Data  - Raster data is made up of pixels (also referred to as grid cells). They are usually regularly spaced and square, but they don't have to be. Rasters often look pixelated because each pixel has its own value or class - Raster data are composed of a number of individual cells. The fundamental strategy underlying the raster data model is the tessellation of a plane - Raster Types: Discrete vs Continuous - Each pixel value in a satellite image has a red, green, and blue value. Alternatively, each value in an elevation map represents a specific height. It could represent anything from rainfall to land cover. - Raster models are useful for storing data that varies continuously. For example, elevation surfaces, temperature, and lead contamination. Discrete Rasters have distinct values - Discrete rasters have distinct themes or categories. - For example, one grid cell represents a land cover class or a soil type. - In a discrete raster land cover/use map, you can distinguish each thematic class. Each class can be discretely defined where it begins and ends. - In other words, each land cover cell is definable and it fills the entire area of the cell. - Discrete data usually consists of integers to represent classes. For example, the value 1 might represent urban areas, the value 2 represents forest, and so on. - Continuous rasters (non-discrete) are grid cells with gradually changing data such as elevation, temperature, or an aerial photograph. - A continuous raster surface can be derived from a fixed registration point. For example, digital elevation models use sea level as a registration point. - Each cell represents a value above or below sea level. As another example, aspect cell values have fixed directions such as north, east, south, or west. - Phenomena can gradually vary along a continuous raster from a specific source. A raster depicting an oil spill can show how the fluid moves from high concentration to low concentration. At the source of the oil spill, concentration is higher and diffuses outwards with diminishing values as a function of distance. Raster data advantage and disadvantage Advantage: - A raster grid format is a data model for satellite data and other remote sensing data. For raster positions, it's simple to understand cell size. - Map algebra with raster data is usually quick and easy to perform. Overall, quantitative analysis is intuitive with discrete or continuous rasters. Disadvantage: - Because cell size contributes to graphic quality, it can have a pixelated look and feel. To illustrate, linear features and paths are difficult to display. - You cannot create network datasets or perform topology rules on rasters. Also, you don't have the flexibility with raster data attribute tables. - Raster datasets can become potentially very large because they record values for each cell in an image. As resolution increases, the size of the cell decreases. But this comes at a cost for speed of processing and data storage. Aspatial Data Definition: Aspatial data, also known as non-spatial data, does not have a direct geographic component. It refers to information that is not tied to a specific location. Examples: Attributes or characteristics of spatial features, such as the population of a city, the name of a building, or the type of vegetation in an area. - Text in books, articles, or social media posts. - Numerical data, like your age or the number of likes on a Facebook post. - Dates, like your birth date or the date of an event. Characteristics: This data provides descriptive information about spatial features but does not include location information