GIS Lecture 6 Notes PDF

Understanding Vector Data and Attributes ​ Vector Data: Composed of points, lines, or polygons. These objects are linked to tables storing information (attributes) about each object. ○​ Attribute Tables: The non-spatial data stored for each vector object. ​ Point At...

Understanding Vector Data and Attributes ​ Vector Data: Composed of points, lines, or polygons. These objects are linked to tables storing information (attributes) about each object. ○​ Attribute Tables: The non-spatial data stored for each vector object. ​ Point Attribute Table: Contains attributes for point features. ​ Line Attribute Table: Contains attributes for line features. ​ Polygon Attribute Table: Contains attributes for polygon features. ​ Table Structure: ○​ Rows (Records): Represent individual objects (e.g., a specific building or road). ○​ Columns (Fields): Store different types of information about the objects (e.g., name, size, type). ​ Relational Database: GIS links spatial data to attribute data, functioning as a spatial database. This relationship enables operations like: ○​ Attribute queries ○​ Attribute calculations ○​ Table joins Attribute Table Data Types ​ Numerical Data: ○​ Short Integer: Whole numbers with small ranges, used for categories or simple numeric data. ​ Example: Codes (1, 2, 3) or small ranges (-32,768 to 32,767). ○​ Long Integer: Whole numbers with larger ranges, used for longer codes or larger numeric data. ​ Example: ZIP codes or population counts. ○​ Float: Numbers with decimals, used for fractional calculations or precise measurements. ​ Example: Population density (123.45 people/sq. mile). ○​ Double: Higher-precision decimal numbers, used for calculations requiring greater accuracy. ​ Example: Geographic coordinates with high precision. ​ Text/String Data: ○​ Stores alphanumeric characters. ○​ Example: Building names ("Library"), categories ("Urban"), or hours ("08:00"). ○​ Numeric-like data (e.g., "12345") can also be stored as strings when arithmetic calculations are unnecessary. ​ Date/Time Data: ○​ Stores temporal information. ○​ Example: Event timestamps, trajectory data. Attribute Data Management Tasks 1.​ Attribute Calculations: ○​ Modify or compute new data within an attribute table using tools like the Field Calculator. ○​ Operations include arithmetic manipulations (e.g., converting feet to miles: Length_feet / 5280). 2.​ Attribute Queries: ○​ Automates record selection based on specific conditions. ○​ Utilizes Structured Query Language (SQL) to define criteria. ​ Example: State = 'Oklahoma' AND Population < 1000. ○​ Logical operators: ​ =, , =: Equality or comparison. ​ AND: Combines conditions (both must be true). ​ OR: Combines conditions (either can be true). ​ NOT: Excludes specific records. 3.​ Table Joins: ○​ Combines two tables based on a shared field (key field). ○​ Types: ​ Simple Join (1:1): Matches one record in Table A to one record in Table B. ​ Summarized Join (1:Many): Aggregates data from multiple records in Table B for each record in Table A. ○​ Direction matters: ​ Joining A → B vs. B → A can yield different results. ○​ Example: Joining income data to census tracts using a shared "tract ID" field. Practical Examples 1.​ Attribute Calculations: ○​ Field Shape_Length (in feet) → Convert to miles: ​ Formula: Shape_Length / 5280. 2.​ Attribute Queries: ○​ Find census tracts in Oklahoma with a population under 1000: ​ Query: State = 'Oklahoma' AND Population < 1000. 3.​ Table Joins: ○​ Scenario: A polygon attribute table lacks income data but has tract IDs. Income data is in another table with tract IDs. ○​ Solution: ​ Perform a table join using "tract ID" as the key field.  ​ Result: The income data is now appended to the polygon attribute table. Key Exam Takeaways 1.​ Understand Attribute Data Types: ○​ Know when to use integers (short vs. long), floats, doubles, strings, and date/time fields. 2.​ Know Table Operations: ○​ Attribute Calculations: Modify fields using mathematical or statistical operations. ○​ Attribute Queries: Retrieve specific records using SQL-like syntax. ○​ Table Joins: Combine data from multiple sources for comprehensive analysis. 3.​ Recognize Logical Operators: ○​ AND, OR, NOT are essential for constructing complex queries. 4.​ Consider Relationships in Joins: ○​ 1:1: Direct match between tables. ○​ 1:Many: Summarizes multiple matches into one. 5.​ Importance of Spatial Integration: ○​ Joining attribute data to spatial objects enhances the ability to perform spatial analysis and create meaningful visualizations.