Lecture 3 Part 1.Important Database Concepts Part 2. Queries

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Lecture 3 Part 1.Important Database Concepts Part 2. Queries Lecture slides by Austin Troy, University of Vermont, © 2008, except where noted How is Data Stored? People use number system with base 10 Each digit corresponds to 10 to some power

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## Lecture 3 Part 1.Important Database Concepts Part 2. Queries

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Lecture 3

Part 1.Important Database Concepts

Part 2. Queries

Lecture slides by Austin Troy, University of Vermont, © 2008, except where noted

Lecture materials by Austin Troy (c) 2008, except where noted

How is Data Stored?

• People use number system with base 10
• Each digit corresponds to 10 to some power
• Hence a number with 3 digits has 103 or 1000 possibilities
• Why are computer values so often in multiples of eight?
• Because computers use a base 8 system of storing numbers and values
• A byte is 8 “on-off switches” or bits
• Each switch/bit represents a binary number; one byte is 28 or 256 possibilities

Lecture materials by Austin Troy (c) 2008, except where noted

How do binary numbers translate to real numbers?

• Switch combinations determine base ten number based on the formula:
• N10= 2b-1+2b-2+…2b-b
• Where b= number of bits storing the number
• Hence the binary number
• 111111112 = 27*1+ 26 *1+ 25 *1+ 24 *1+ 23 *1+ 22 *1+ 21 *1+ 20 *1 = 25510
• And the binary number
• 111111102 = 27*1+ 26 *1+ 25 *1+ 24 *1+ 23 *1+ 22 *1+ 21 *1+ 20 *0 = 25410

Lecture materials by Austin Troy (c) 2008, except where noted

Another approach to coding numbers: ASCII (American Standard Computer Info Index) Based on Hexadecimal Numbering System

• 4 bit or base sixteen (24) system for representing numbers
• 0-9 =0-9 but 10-15= A,B,C,D,E,F
• Each digit represents up to 16 instead of 10
• So, the first digit in a two digit number xy= (16*x)+y
• Hence
• 21h= (16*2) +1 = 3310 = 001000012
• B2h= (16*11) + 2 =17810 = 101100102

Lecture materials by Austin Troy (c) 2008 except where noted

ASCII system provides standardized method for coding alphanumeric characters, and uses byte of 8 bits for each symbol. Those characters include everything you see on your keyboard and then some

• Standard character set is coded as hexadecimal numbers going from zero to FF (28).
• Example: Letter ‘A’ is 41h = 6510=010000012

Lecture materials by Austin Troy (c) 2008, except where noted

Number of Possible Values is fn of number of bits

• Number of possible values for a unit of data is an exponential function of the number of switches
• 28=256 eight bit data
• 216=65,536 sixteen bit data
• 232= 4,294,967,296 thirty two bit data

Lecture materials by Austin Troy (c) 2003, except where noted

Number of bits determines data types

• Examples of Integer data types
• Byte: 28 (0 to 255)
• Short Integer: 216 (ranges from –32,767 to +32,767 without decimals, the sixteenth bit determines sign)
• Long Integer: 232 (+/-2.147483e+09 )

Lecture materials by Austin Troy (c) 2003, except where noted

Floating point data types
• In this case the number can have a decimal, but the number of places is variable
• With this type of number the number of bits determines not just the number of possible magnitudes but also the level of precision of the decimal, represented as number of decimal places.
• Fewer bits in FP numbers can lead to rounding errors
• Two types of FP number
• Single Precision: Often 232
• Double Precision: Usually double the bits of single precision (i.e. 264)

Lecture materials by Austin Troy (c) 2003, except where noted

Other data types
• Currency (type of number with specific behaviors)
• Date (recognizes order in dates)
• String (text)
• When numbers are represented as text they have no numerical properties (e.g. zip codes)
• Boolean (yes, no)
• Object (e.g. pictures, bits of code, behaviors, multi-media, programs)

Lecture materials by Austin Troy (c) 2003, except where noted

Three database models
• Hierarchical
• Network
• Relational

Lecture materials by Austin Troy (c) 2003, except where noted

Hierarchical Database Model

A one-to-many method for storing data in a database that looks like a family tree with one root and a number of branches or subdivisions. Problem: linkages in the tables must be known before

Groovy 70s TV

Action shows

Drama

Sitcoms

Welcome back Kotter

WKRP

Dukes of Hazzard

Dallas

Fantasy Island

CHIPs

Loni Anderson

Larry Hagman

Gabe Kaplan

Tom Wopat

Larry Wilcox

Ricardo Montalban

John Travolta

Lecture materials by Austin Troy (c) 2003, except where noted

Hierarchical Database Model

• Example where this model works well:
• plant and animal taxonomies
• Soil classification
• Works when: classes are totally mutually exclusive
• Problem with this model:
• Does not work when have entities that belong to several classes or do not have mutual exclusivity
• Think about the problems with Windows Explorer
• Example: classifying your music collection
• You may create classes like rock, jazz, classical, Latin, with folders for artists nested within
• However, an artist may do rock and Latin and jazz on the same album, or one song may be a combination

Lecture materials by Austin Troy (c) 2003, except where noted

Networked Database Model

A database design for storing information by linking all records that are related with a list of “pointers.” Problem: linkages in the tables must be known before. Not adaptable to change.

Action shows

Drama

Sitcoms

Three’s company

Love Boat

Dukes of Hazzard

Dallas

Fantasy Island

CHIPs

NBC

CBS

ABC

Lecture materials by Austin Troy (c) 2003, except where noted

Relational (Tabular) Database Model

• A design used in database systems in which relationships are created between one or more flat files or tables based on the idea
• that each pair of tables has a field in common, or “key”. In a relational database, the records are generally different in each table
• The advantages: each table can be prepared and maintained separately, tables can remain separate until a query requires connecting, or relating them, relationships can be one to one, one to many or many to one

Lecture materials by Austin Troy (c) 2003, except where noted

Records are the unit that the data are specific to

Fields, or columns, are attribute categories

Cells are where individual values of a record for a field are stored

fields

Headings: are the labels for the columns

records

cells

Lecture materials by Austin Troy (c) 2003, except where noted

Is a field that is common to two or more flat files; allows a query to be done across multiple tables or allows two tables to be joined

Flat file: professor info

Flat file: course info

Lecture materials by Austin Troy (c) 2003, except where noted

Join Tables

Based on the values of a field that can be found in both tables

The name of the field does not have to be the same

The data type has to be the same

In this case we have a one to one join; here the key is unique

Key

A

B

1

Key

C

4

1

10

2

5

1

2

Key

A

B

C

2

20

3

1

3

4

1

10

6

3

50

2

5

2

10

JOIN

3

3

6

50

Lecture materials by Austin Troy (c) 2003, except where noted

Join Tables

In this case we have a one to many join; here the key is not unique

Key

A

B

1

Key

C

4

1

10

2

5

1

1

Key

A

B

C

2

20

2

1

3

4

1

10

6

2

5

1

10

JOIN

2

3

6

20

Lecture materials by Austin Troy (c) 2003, except where noted

Relational (Tabular) Database Model: 70s TV example

• Now we can have various flat files (tables) with different record types and with various attributes specific to each record

Table 1- specific to actors

Table 2- specific to shows

*entirely guessed at- I am not responsible for mistaken TV trivia

Lecture materials by Austin Troy (c) 2003, except where noted

Relational (Tabular) Database Model

• This allows queries that go across tables, like which CBS lead actors were born before 1951? Answer: John Travolta and Larry Wilcox

It does this by combining information from the two tables, using common key fields

*entirely guessed at- I am not responsible for mistaken TV trivia

Lecture materials by Austin Troy (c) 2003, except where noted

Relational (Tabular) Database Model

• Object-relational databases can contain other objects as well, like images, video clips, executable files, sounds, links

Lecture materials by Austin Troy (c) 2003, except where noted

Relational Database: another example: property lot info

One-to-one relationship

Lecture materials by Austin Troy (c) 2003, except where noted

One-to-many relationship

In this case, several people co-own the same lot, so no longer one lot, one person

Lecture materials by Austin Troy (c) 2003, except where noted

Assuming each owner owned several parcels, we would structure the database differently

One-to-many relationship

Note: this table includes data pertinent only to Flores’ ownership of these properties

Lecture materials by Austin Troy (c) 2003, except where noted

Example

Here’s an example of a chart showing the relationships between flat files in a sample relational database for food suppliers* in Microsoft Access

* This comes from an MS ACCESS sample database

Lecture materials by Austin Troy (c) 2003, except where noted

* This comes from an MS ACCESS sample database

A real time RDBMS allows for realtime linking and embedding of tables based on common fields

Here we see all the orders for product ID 3; there is no need to include product ID in that sub-table

Lecture materials by Austin Troy (c) 2003, except where noted

* This comes from an MS ACCESS sample database

Part 2. Queries

Lecture materials by Austin Troy (c) 2003, except where noted

Queries

• This is how we ask questions of the data
• To ask queries, we use mathematical operators, like =, >, <
• To ask queries on multiple criteria, we use logical operators, like AND and OR
• Queries can simply select records or perform more advanced operations with those selections, such as make new tables, or summarize values by averages

Lecture materials by Austin Troy (c) 2003, except where noted

Queries in Arc GIS

• Arc GIS queries only select (highlight) records
• When a record is selected, so is its corresponding feature
• To summarize selected values, use the “statistics” function or “summarize” tool
• To create new values based on a query, use the “calculate” tool.

Lecture materials by Austin Troy (c) 2003, except where noted

Queries

Here’s an example of a simple query in Arc GIS

PRICE > 250000. This highlights all records (houses) in the specified layer with a sales price greater that \$250,000

Lecture materials by Austin Troy (c) 2003, except where noted

Queries

That results in the following selection on the map

Lecture materials by Austin Troy (c) 2003, except where noted

Queries

And it also selects the corresponding records in the attribute table

Lecture materials by Austin Troy (c) 2003, except where noted

Queries

Here’s an example with a polygon layer; I’m querying for census tracts over 8000 people in population.

Lecture materials by Austin Troy (c) 2003, except where noted

Queries: multiple criteria

Now let’s add a criteria; let’s say we’re looking for big population tracts (>8,000) with a high rate of population change (> 3% annual). Note the use of the AND operator. Note also that a subset of the last selection was selected

Lecture materials by Austin Troy (c) 2003, except where noted

Queries: Select From Set vs. New Set

We did the previous selection by clicking Using the “create a new selection” method.

We could have done the same thing by doing the first query (pop>8000), clicking “Apply,” then, without clearing that selection, typing in a new query for the second condition (popchng97 > 3) and choosing the “Select From Selection” method instead

Lecture materials by Austin Troy (c) 2003, except where noted

Three query methods in Arc GIS

New Selection: Creates a new query from scratch

Add to Current Selection: Used when there is already a group of records/features selection; it is equivalent to the OR operator and widens the selection by introducing a criterion that is equivalent to the first one

Select from Current Selection: Used when there is already a of records/features selection; selects a subset from the originally selected set; equivalent to the AND operator

Lecture materials by Austin Troy (c) 2003, except where noted

Queries: OR operator

Here’s a query where we use the OR operator to select either tracts greater than 8000 population OR with a growth rate greater than 3%; results in many more records selected; can also do the same thing by doing one query using “new selection” then another, using “add to current selection”

Lecture materials by Austin Troy (c) 2003, except where noted

Queries: Strings

Queries can also be made on text strings, but it is imperative to put the values in quotes. Here we query for both BLM and Parks and Rec land.

Lecture materials by Austin Troy (c) 2003, except where noted

Queries: Strings and numbers

String and number queries can be combined. For example, let’s say we’re looking for land for a suburban park and our criteria are that we need areas whose land use is classed as agricultural and that are bigger than 500,000 square feet.

Lecture materials by Austin Troy (c) 2003, except where noted

Queries: Strings and numbers

Results in:

Lecture materials by Austin Troy (c) 2003, except where noted

Queries: Strings and numbers

Whereas if our query asks for agricultural land use without the area criterion, we get:

Lecture materials by Austin Troy (c) 2003, except where noted

So what can Arc GIS do with queries?

• A query selects records; once selected you can:
• Look at the selection
• Requery the selection
• Do stats on the selection
• Create new fields that recategorize the selection by an an attribute field
• Create new fields by doing calculations across several fields
• Create a shapefile from the selection

Lecture materials by Austin Troy (c) 2003, except where noted

Examples

Let’s query high unemployment census tracts in LA

Lecture materials by Austin Troy (c) 2003, except where noted

Now let’s do “statistics” to determine the population in those areas. Answer: almost 5 million people live in tracts with 6%+ unemployment (see Sum). We can also see that there are 844 tracts meeting that description (see Count)

Lecture materials by Austin Troy (c) 2003, except where noted

Another thing we can do is convert the selection to a either a new shapefile or geodatabase feature class

Right click and then click Data>>export data

Lecture materials by Austin Troy (c) 2003, except where noted

Now, let’s say we wanted to prioritize inner city areas for urban redevelopment projects:

• Let’s query based on unemployment and home value
• Based on these we’ll create a new field that classes all tracts into High, Medium and Low priority areas
• Tracts with median home value < \$100,000 and un-employment > 12% are “High”

Lecture materials by Austin Troy (c) 2003, except where noted

To reclassify, we create a new field, “priority”, activate the field heading and use the field calculator to set all selected records to “high”

Note: we must uses quotes with a text field

Lecture materials by Austin Troy (c) 2003, except where noted

Now we would set criteria for “medium” and “low” based on unemployment and home value. These would probably be more complex queries because we’re querying for records, say, between 8 and 12% unemployment and between \$100,000 and \$150,000 median value.

Note: AND is used three times, with two parenthetical clauses

Lecture materials by Austin Troy (c) 2003, except where noted

Now, for the third class our task is easier—we just select everything that has not been selected yet. To do this we query for “priority”= ‘’ where those two marks after the equals sign are single quote marks. By putting empty quote marks, you’re querying for records with no values in them for that field. Now you’d set all those fields equal to “low.”

Lecture materials by Austin Troy (c) 2003, except where noted

Now we can make a category map showing us that classification based, which is based on two attributes—median value and unemployment

Lecture materials by Austin Troy (c) 2003, except where noted

Another example:

This time, let’s take a vegetation layer and query for stands with crown fire potential; because there are several classes we have to query for all

Lecture materials by Austin Troy (c) 2003, except where noted

Then let’s calculate a fire hazard index for selected polygons equal to .5(rate of spread * flame length)

We’ll create a new field, “fireindex” (floating point) and set all selected polygons equal to that calculation

Lecture materials by Austin Troy (c) 2003, except where noted

Then, for all other polygons without crown fire potential, a different equation can be used, say .38(Rate of spread * Flame length). But first we have to take the inverse of the selection by using the “switch selection” function

Then we can do the new calculation on the new selection

Lecture materials by Austin Troy (c) 2003, except where noted

Now we can plot out the map of fire index, plotted out using graduated color (quantity) mapping

Lecture materials by Austin Troy (c) 2003, except where noted

Access and Arc GIS queries

You can do all these queries and much much more in MS Access, which is a relational DBMS.

This can be done because a geodatabase is an MS Access file (.MDB)

There are six basic queries you can do in Access:

Select, cross-tab, make table, update,

append, delete

Lecture materials by Austin Troy (c) 2003, except where noted

Access Queries

• Select: the most general purpose and versatile query—creates a new temporary table; used for getting summary statistics for a field, or breaking down summary statistics by category
• Cross-tab: for summarizing statistics across two factors (row and column)
• Make table: for creating a new, stand-alone data table from a query
• Update query: this is where we fill a field (could be an empty field) in an existing table with new values, either equal to a constant, to values in another field or to an operation using values from another field; can use Where criteria on this
• Append/delete queries: query that defines rows to append to or delete from a table; append queries usually require another table.

Lecture materials by Austin Troy (c) 2003, except where noted

Access Queries

• Queries can be used to:
• Summarize information stored in one or many tables (e.g. sales by year, sales by category, sales by saleperson, sales by date, orders by date, orders by product type, orders by zip code)
• Create new fields using simple or complex expressions, with the option of using criteria to specify which records will be filled in for that field
• Derive averages, maxima, minima, sums, standard deviations, and counts for values in fields, with or without criteria
• Derive those same things for categories within a field
• Summarize and ask questions of attribute data stored in different tables

Lecture materials by Austin Troy (c) 2003, except where noted

Access Queries

• Example of query run to get sums of sales values across product categories:

Lecture materials by Austin Troy (c) 2003, except where noted

Relational attribute queries

Here’s a an Access select query; note how it queries across various linked tables

This one asks for a summary of sales by category and product name for the dates between 1/1/1997 and 12/31/1997

Lecture materials by Austin Troy (c) 2003, except where noted

Queries can be used to return statistics: here we get the mean price from a database of housing sales

Lecture materials by Austin Troy (c) 2003, except where noted

And here we summarize mean price by zip code

Lecture materials by Austin Troy (c) 2003, except where noted

Remember the food database?

Lecture materials by Austin Troy (c) 2003, except where noted

This simple select query yields a summary table of sales by category for a given year period: generates a mean value for each category

relates

criteria

Lecture materials by Austin Troy (c) 2003, except where noted

This select query perform a math operation: it multiplies price and quantity, times a discount and delivers a table of order subtotals

Lecture materials by Austin Troy (c) 2003, except where noted

Here we sort sales by product and city

operation

criteria

Lecture materials by Austin Troy (c) 2003, except where noted

Here we sort sales by city only

Lecture materials by Austin Troy (c) 2003, except where noted