Spring25-3-GRG460-DataModels

Page 1: Introduction to Environmental GIS

• Lecturer: Dr. Yuhao KangEmail: yuhao.kang@austin.utexas.eduGISense Lab, Department of Geography and the Environment, The University of Texas at Austin

• Course: GRG 460 - Environmental GIS

• Semester: Spring 2025

• Lecture 3

Page 2: Key Questions in GIS

• What is a GIS?

• Who is the “Father of GIS”?

• Who is the “Father of GIScience”?

• Name a few open-source GIS software.

Page 3: Important Announcements

• Labs: Use of QGIS accepted for assignments; points will be deducted if QGIS fails to perform specific tasks.

• Harry Ransom visit rescheduled to next Tuesday at 9:00 am.

• Notes: All slides will be posted after class. Focus should be on capturing key points as important concepts will be emphasized during the lecture.

Page 4: Learning Objectives

• Distinguish between:

  • Real world vs. Data models vs. Data structures

  • Spatial vs. Non-spatial data

  • Vector vs. Raster data

• Understand the four types of attributes.

Page 5: Data Models

• Purpose of Data Models:

  • Conceptualization of our world in a GIS context

  • Abstraction of phenomena and properties

  • Computer representation of those abstractions.

Page 6: Storage and Layering in GIS

• GIS typically stores data as layers.

  • Each layer organizes spatial and attribute data for a specific cartographic object (e.g., water, roads).

  • Often referred to as a thematic layer.

Page 7: Common Feature Types

• Points: Represent specific locations

• Lines: Represent linear features like roads

• Areas: Represent region features such as lakes or parks

• Gradients: Represent varying degrees, such as temperature or elevation

Page 8: Examples of Common Geographic Phenomena

• Red dots indicate locations of Flickr pictures.

• Blue dots indicate locations of Twitter tweets.

• White dots are locations posted to both platforms.

  • Source: Eric Fischer

Page 9: Common Geographic Features Representation

• Examples of geographic features include:

  • Rivers

  • Lakes (e.g., Lake Superior, Lake Michigan)

  • Cities (e.g., Toronto, Montreal, New York)

Page 10: Further Common Geographic Features

• Various geographic features of note:

  • Building footprints

  • Elevation representations

  • Land cover details

  • Parcel boundaries

  • Precipitation maps

Page 11: Data Types in GIS

• Data Types:

  • Age, Gender, Race and Ethnicity, Weight, Income...

  • Geographic location details (e.g., country, state, city)

  • Census data (tract/block information)

  • Geographic Coordinates (longitude, latitude)

Page 12: Geographic Data Components

• Geometry (Spatial Data):

  • Object’s spatial representation linked to real-world locations (points, lines, or polygons).

• Attribute (Aspatial Data):

  • Descriptive characteristics of the object.

Page 13: Visualizing Geographic Data

• Only spatial data is visualizable on maps.

Page 14: Key Data Models

• **Two common data models:

  • Vector

  • Raster**

  • Vector: Points, lines, areas

  • Raster: Grid-based representation

Page 15: Introduction to Vector Data

• Vector Data Structure:

  • Defined by coordinates and can represent features like points, lines, and polygons.

Page 16: Representation of Building Data in Vector Format

• Examples of buildings:

  • ID, Building Name, Floors, Roof Type

• Showcasing diversity in building structures and details.

Page 17: Conversion in Vector Data Representation

• Buildings can be represented as points or areas depending on context.

• Roads can similarly be represented as lines or areas.

Page 18: Multi-part vs. Singly-part Features

• Understanding storage for features with multiple parts:

  • Examples of multi-part and single-part features in GIS.

Page 19: RASTER DATA

• Introduction to raster data representation with fixed cell size and grid orientation.

Page 20: Raster Cells Representation

• Each raster cell has a value representing controlled attributes, critical for analysis.

Page 21: Mixed Pixel Problem in Rasters

• Cells that contain mixed data create challenges in categorizing land cover.

Page 22: Rules for Rasterizing Image Data

• Rule Overview:

  • Winner takes it all rule (majority categorization)

  • Center-of-cell rule (dominance position)

Page 23: Raster Resolution Importance

• Resolution impacts on raster accuracy:

  • Smaller cells yield higher accuracy.

Page 24: Relationship Between Rasters and Attributes

• Raster layers may be associated with attribute tables, forming many-to-one relationships.

Page 25: Metadata in GIS

• Role of Metadata:

  • Data about spatial data including content, source, lineage, methods, accuracy, etc.

Page 26: Vector vs. Raster Comparison

• Comparison of Vector and Raster data models:

  • Characteristics, storage requirements, analysis, and accessibility.

Page 27: Recommended Readings

• Suggested chapters in GIS Fundamentals for further understanding.