1. GIS Concepts ‣ What is a table? What is a table? ‣ Queries on tables Queries on tables ‣ Joining and relating tables Joining and relating tables ‣ Summary statistics Summary statistics ‣ Database storage concepts Database storage concepts

2. Table terminology Title Field Records Each field is specifically defined and established before any data can be entered. Field definitions control the type of data that can be stored in a field.

3. Types of tables ‣ Attribute table Stores attributes of map features Associated with a spatial data layer Has special fields for spatial information ‣ Standalone table Stores any tabular data Not associated with spatial data OID instead of FID

4. Database Management Systems (DBMS) ‣ Dedicated systems for managing many data tables ‣ Serve data for large organizations E.g., agencies, universities, companies, etc. ‣ Handles multi-user environments ‣ Provides enhanced security ‣ Often provide data management and analysis tools E.g., querying, reporting, graphing, etc. ‣ Examples: Oracle, SQL Server, etc.

5. Flat file DBMS ‣ Flat file Stores data as rows of information in files Simple and robust Inefficient for search and query Service calls Customers Electric usage Service personnel

6. Hierarchical DBMS ‣ Stores data in multiple tables ‣ Tables have defined parent- child relationships ‣ Pre-set hierarchy of table relationships designed for specific queries ‣ Very efficient for specific queries ‣ Range of queries limited by structure Service calls Customers Electric usage Service personnel Billing history Customer ID Employee ID Customer ID

7. Relational DBMS ‣ Stores data in multiple tables ‣ Table relationships are defined as needed ‣ Very flexible ‣ Ideal for open-ended applications when queries not known beforehand ‣ Most common type used in GIS applications Customers Electric usage Service calls Service personnel Billing history Customer ID Employee ID

8. Table Querying ‣ Queries select or extract records from a table based on specified conditions Students with GPA > 3.5? States with population > 1 million? Counties with greater population in 1990 than in 2000? Customers with last names beginning with “Mac” or “Mc”?

9. SQL: Structured Query Language ‣ Common query language ‣ Used in many DBMS environments ‣ Queries can be saved and reused ‣ Notes Nearly always case-sensitive Easy to learn

10. SQL Query Examples Some Valid Queries SELECT *FROM cities WHERE "POP1990" >= 500000 SELECT *FROM counties WHERE “BEEFCOW_92” < “BEEFCOW_87” SELECT *FROM parcels WHERE “LU-CODE” = 42 AND “VALUE” > 50000 SELECT *FROM rentals WHERE “RENT” > 700 AND “RENT” < 1500 Programs may have an interface to help users build SQL expressions In most databases, SQL expressions are case-sensitive “Smith” ≠ “SMITH”

11. Multiple Criteria Queries ‣ Multiple criteria queries using AND or OR “STATE” = ‘Alabama’ OR “STATE” = ‘Texas’ [Value] > 5000 AND [Value] < 10000 Note that the field name must be repeated for each condition

12. Searching for partial matches ‣ Sometimes you need to find one string within another rather than an exact match Find all customer names beginning with “Mac” or “Mc” Find all zip codes beginning with 0 ‣ Typically uses a “wildcard” character *Mac* or *Mc* 0*

13. The Like Operator (Depends on data file type) ‣ “NAME” LIKE ‘%(D)%’ Finds all of the (D) Democrats ‣ % is wildcard ‣ Ignores Don or Danforth ‣ “NAME” LIKE ‘%New %’ Would find New Hampshire and New York, but not Newcastle or Kennewick

14. Query results ‣ A set of selected records that meet the criteria ‣ Subsequent queries may use only selected records and ignore others ‣ One might… Export the selected records to a new table Calculate statistics on the selected records Calculate new values for the selected records Produce a report based on the selected records

15. Joining tables Destination tableSource table Common field

16. Join facts ‣ Tables must share a common field (key) ‣ Treats the two tables as a single table ‣ Temporary relationships between tables Original stored data is not affected Can be removed when no longer needed

17. One-to-one joins When each record in the destination table matches exactly one record in the source table, we call it a cardinality of one-to-one. Destination table (always imagine on the left) Source table (always imagine on the right)

18. Types of Cardinality ‣ One-to-one States to Governors Husbands to wives ‣ One-to-many States to cities Districts to schools Many to one Cities to states Schools to districts Many-to-many Students to classes Stores to customers In evaluating cardinality, always put the destination first. (Destination on the left)

19. Rule of Joining Each record in the destination table must match one and only one record in the source table. One to one Destination tableSource table Many to one

20. One to many (When a Relate is better) Destination tableSource table ? Violates the Rule of Joining Record to which to join to destination is ambiguous Must use a relate instead

21. Relates ‣ Similar to a join except that The tables remain separate Items selected in one table may be highlighted in the related table

22. Related tables Select the New England states Find reps of New England states