Data Queries Raster & Vector Data Models Ming-Chun Lee

Types of GIS Data Describes Objects In Terms Of: Spatial Data Absolute X,Y Coordinates – Locations Size, and Shape Attributes Characteristics associated with Geography We can categorize GIS data into three different types: First type is Spatial Data, include location, size, and shape, Absolute X,Y Coordinates – Locations Size, and Shape For example, like land parcels, cone with locations, different sizes and shapes Second one is non-spatial attributes: Non-spatial Attributes Characteristics Unrelated to Geography For example, the owners’ names of those parcels, an owner can own multiple properties, the name is one of the attribute associate with parcels, but no really location specific Topology Spatial Relationships Between Features How each feature relate with each other, like which one is connected to which one, which one is on the left of which one, those kinds of relations

GIS Associates Spatial and Attribute Data in a Geo-Referenced Database ARC INFO 1 3 5 ID AREA PERIM CLASS LANDUSE 1 260210 20688 100 Single Family 2 18210 20688 100 Multi Family 3 260210 20688 100 Commercial 4 258876 16880 200 Forest 5 3008 88 500 Water 2 4 GIS Associates Spatial and Attribute Data in a Geo-Referenced Database

Data models Data models: Formats in which geographic data is stored and managed. Vector Data Models (Features) Points Lines Polygons Raster Data Models (Surfaces)

Raster and Vector Data Models Raster Representation Vector Representation line polygon point Let get started Quick comparison between two different data models Raster is represented by a grid of cells, Vector is represented by point and line and polygon features Point,

Raster Data Models Raster Data Model Location is referenced by a grid cell in a rectangular array or matrix Attribute is represented as a single value for that grid cell Let’s take a closer look at the two data models: First look at Raster: the map on the bottom shows the reality In raster data the entire area of the map is subdivided into a grid of tiny cells. A value is stored in each of these cells to represent the nature of whatever is present at the corresponding location on the ground.

Raster Data Models

Raster Data Models Raster Data Model Typical data sources: Images from remote sensing (LANDSAT, SPOT) Elevation data from USGS Best for continuous data: Elevation Temperature

Raster Data Model Uses Elevation Temperature Noise Levels Air Quality Distance or Accessibility Surfaces Probabilities (e.g. flooding, liquefaction) Photographic Images

Vector Data Models Vector Data Model Location is referenced by x,y coordinates, which can be linked to form lines and polygons Attributes referenced through unique ID number to tables In vector data the features are recorded one by one, with shape being defined by the numerical values of the pairs of xy coordinates. A point is defined by a single pair of coordinate values. A line is defined by a sequence of coordinate pairs defining the points through which the line is drawn. An area is defined in a similar way, only with the first and last points joined to make a complete enclosure.

Features Geographic objects that have different shapes are represented as features Geographic objects that have different shapes are represented as features

Features Points are a pair of x,y coordinates

Features Lines are sets of coordinates that define a shape

Features Polygons are sets of coordinates defining boundaries that enclose areas. Polygons are sets of coordinates defining boundaries that enclose areas.

Vector Data Models Vector Data Model Typical data sources: DIME (Dual Independent Map Encoding) and TIGER (Topologically Integrated Geographic Encoding and Referencing file) files from US Census DLG (Digital Line Graph) from USGS for streams, roads, etc. Best for features with discrete boundaries: Soil Type Land Use DIME file (Dual Independent Map Encoding) introduced for the 1970 US Census and used again in 1980; replaced by TIGER in 1990 TIGER File (Topologically Integrated Geographic Encoding and Referencing file) introduced for 1990 Census to eliminate inconsistencies between census products USGS DLG (Digital Line Graph) file showing roads, streets, rivers,

Vector Data Model Uses Land Use or Zoning Classifications Polygons Transportation Networks Points and Polylines Hydrology Polylines for Rivers, Polygons for Water Bodies Utilities Points for Facilities, Polylines for Pipelines & Wires

Raster Advantages & Disadvantages Easy to Understand the Data Model Easy to Analyze Low Computing Requirements Compatible with Remote Sensing Sources Efficient for Continuous Data Easy to Use in Modeling Raster Disadvantages Spatial Inaccuracies Common Low Resolution, Relative to Vector Data Imprecise Locational Data Requires All Cells to be Coded Not As Efficient for Discrete Data or Features

Vector Advantages & Disadvantages More Readable as a Map Higher Resolution than Raster Data Can Have High Spatial Accuracy Can Have Storage Advantages Can Be Topological Efficient for Discrete Data or Features Vector Disadvantages Difficult to Manage Data Storage High Computing Requirements Complex to Perform Overlay and Modeling Operations Inefficient for Continuous Data

Basic Spatial Analysis Operations Data Review Tasks: Viewing Spatial Data Viewing Attribute Data Selecting Data Interactively Selecting Data by Data Query Measurement Data Manipulation Tasks: Extract a Subset Dissolves Reclassification

Viewing Spatial Data

Viewing Attribute Data: The Identify Tool

Viewing Attribute Data: The Attribute Table

Selecting Data Interactively

Identifying specific features Query Identifying specific features Where is parcel No. 2945? Identifying features based on conditions Find all trees with DBH > 35 cm

Selecting Data by Data Query

Measurement: Distance

Measurement: Area & Perimeter

Extract a Subset For Any Current Selection from a Data Layer, Creates a New Data Layer with Only the Selected Features

Extract a Subset

Dissolve Combine Features that Share Values of a Particular Attribute

Dissolve

Reclassification