1. Due Oct. 12th
Data Sources – a list of data files and their sources, an example of what I am looking for:
Data Source
Map of Bangor MEGIS
NG911 road file for Bangor MEGIS
Tax maps for Bangor City Hall, may need to digitize.
Addresses of Schools MEGIS
Questionaire results Need to conduct interviews
School quality rankings ?

2. Lecture 8.2

3. Lecture 8.2

4. The .mxd File
The MXD is a template, which tells ArcMap what data to load and how to display it.
MXD not only stores maps but also stores the symbology, layout, hyperlinks, toolbars added, etc. at the time of saving the map document.
Lecture 8.2

5. The .mxd Continued
An MXD can be distributed along with the data; hence whenever the MXD is opened the symbology, layout, order of layer always remains same.
Custom buttons or tools added to the MXD are also retained. The setting to show/hide ToolTips on toolbar can be saved in the MXD.
Lecture 8.2

6. The .mxd Continued
MXDs can be saved to be compatible with previous versions of ArcGIS software.
But it doe NOT contain the data.
Lecture 8.2

7. Lecture 8.2

8. Lecture 8.2

9. Ch. 8 Questions 1. What are the main components of a DBMS?
What are the primary functions of a DBMS?
4. What is a one-to-one relationship between tables? A many-to-one?
Lecture 8.2

10. Which single columns in the following table may serve as keys?
9. What is the primary reason that hybrid database models are used for spatial data?
Lecture 8.2

11. 10. Does an OR condition result in more, fewer, or the same number of records than the individual component parts? 11. Does an AND condition result in more, fewer, or the same number of records than the individual component parts?
Lecture 8.2

12. Smokers > 20% AND illiteracy <10 NOT (non-federal taxes > 9)
Illiteracy < 7 OR income > 22,000
Get more federal aid than paid in taxes AND non-federal taxes > 9
[firearm deaths < 10 AND income > 21,000] AND NOT {smokers > 20}
Lecture 8.2

13. The GIS Database
Chapter 8 – Part 2
Lecture 8.2

14. Reasons to Normalize a Database
There are three main reasons to normalize a database:
The first is to minimize duplicate data,
the second is to minimize or avoid data modification issues,
and the third is to simplify queries.
Lecture 8.2

15. Un-normalized Data
The first thing to notice is this table serves many purposes including:
Identifying the organization’s salespeople
Listing the sales offices and phone numbers
Associating a salesperson with an sales office
Showing each salesperson’s customers
As a DBA this raises a red flag.  In general I like to see tables that have one purpose
Lecture 8.2

16. Insert Anomaly
There are facts we cannot record until we know information for the entire row.  In our example we cannot record a new sales office until we also know the sales person.  Why?  Because in order to create the record, we need provide a primary key.  In our case this is the EmployeeID.
Lecture 8.2

17. Update Anomaly
The same information is recorded in multiple rows.  For instance if the office number changes, then there are multiple updates that need to be made.  If these updates are not successfully completed across all rows, then an inconsistency occurs.
Lecture 8.2

18. Deletion Anomaly
Deletion of a row can cause more than one set of facts to be removed.  For instance, if John Hunt retires, then deleting that row cause use to lose information about the New York office.
Lecture 8.2

19. Search and Sort Issues
In the SalesStaff table if you want to search for a specific customer such as Ford, you would have to write a query like
SELECT SalesOffice
FROM SalesStaff
WHERE
Customer1 = ‘Ford’ OR Customer2 = ‘Ford’ OR Customer3 = ‘Ford’
Lecture 8.2

20. Normalization Rules Begin with an unnormalized user view.
To put all tables in first normal form (1NF), remove repeating groups.
To put all tables in second normal form (2NF), remove partial dependencies.
To put all tables in third normal form (3NF) remove all transitive dependencies.
Lecture 8.2

21. 3421 Jones SIE 333 222 Aaaaa Bbbbb Holden Taylor 222 M A C 8725 Dow
Unnormalized Table
Student_ID
Name
Major
CRN
Title
Instructor
_Office
Grd
3421
Jones
SIE
333
222
Aaaaa
Bbbbb
Holden
Taylor
134 BD
222 M A C
8725
Dow
BIO
555
666
777
Ccccc
Dddd
Allen
Kemp
351 B
317 Nv B B- C+ …
Lecture 8.2

22. StudentID Name Major 3421 Jones SIS 8725 Dow BIO …
Remove repeating groups – Creates two tables
Student Table
StudentID
Name
Major
3421
Jones
SIS
8725
Dow
BIO …
Lecture 8.2

23. 3421 333 Aaaaaa Holden 125 Bd A 222 Bbbbbb Taylor 222 M C 8725 555 B
Student_course Table
Student_ID
CRN
Title
Instructor
_Office
Grade
3421
333
Aaaaaa
Holden
125 Bd A
222
Bbbbbb
Taylor
222 M C
8725
555 B
666
Cccccc
Allen
351 B B-
777
Dddddd
Kemp
317 Nv C+
Lecture 8.2

24. 3421 333 A 222 C 8725 555 B 666 B- 777 C+ Remove partial dependencies.
This refers only to tables having a composite key.
You need to identify attributes that require only part of the composite key, and remove them.
Registration Table
Student_ID
CRN
Grade
3421
333 A
222 C
8725
555 B
666 B-
777 C+

25. 333 Aaaaaa Holden 125 Bd 222 Bbbbbb Taylor 222 M 555 666 Cccccc Allen
Course-Instructor Table
CRN
Title
Instructor
_Office
333
Aaaaaa
Holden
125 Bd
222
Bbbbbb
Taylor
222 M
555
666
Cccccc
Allen
351 B
777
Dddddd
Kemp
317 Nv
Lecture 8.2

26. 333 Aaaaaa Holden 222 Bbbbbb Taylor 555 666 Cccccc Allen 777 Dddddd
To put a table in third normal form remove transitive dependencies
A transitive dependency is an attribute that depends only on another attribute, not a key.
CRN
Title
Instructor
333
Aaaaaa
Holden
222
Bbbbbb
Taylor
555
666
Cccccc
Allen
777
Dddddd
Kemp
Course Table
Lecture 8.2

27. Holden 125 Bd Taylor 222 M Allen 351 B Kemp 317 Nv Instructor Table
_Office
Holden
125 Bd
Taylor
222 M
Allen
351 B
Kemp
317 Nv
Lecture 8.2

28. Student_ID Name Major CRN Title Instructor _Office Grade StudentID
Lecture 8.2

29. Lecture 8.2

30. When NOT to Normalize
Relates in large tables require large amounts of computer time to process.
If you have an application that speed is more important than database structure.
If you are creating a very simple data base that will be used for a short time and then discarded.
Lecture 8.2

31. Basic Spatial Analysis Ch. 9
Lecture 9

32. Input -> spatial operation -> output
Spatial data analysis
Input -> spatial operation -> output
Lecture 9

33. Input Scope The area or extent of the input data.
Local – “point” to “point”
Neighborhood – adjacent regions have input
Global – the entire input data layer may influence output
Lecture 9

34. Spatial Data Analysis Merge
Usually involves manipulations or calculation of coordinates or attribute variables with a various operators (tools), such as:
Measurement
Queries & Selection
Attribute
Location
Reclassification
Dissolve
Buffering
Overlay
Clip Erase Update
Intersect Identity
Union Symmetrical Difference
Merge
Network Analysis
Projects may contain both raster and vector data sets, but analysis is restricted to one data model of the other.
Lecture 9

35. Table Operations
Statistics – count, minimum, maximum, sum, mean, standard deviation and the number of null values, as well as a frequency distribution of the variable.
Summarize - summary statistics—include the count, average, minimum, and maximum values
Join – 1:1 or m:1, two tables become one, but it is not permanent.
Relate – all other relationships, tables remain separate.
Append – Tables must have the same columns, number and type
Lecture 9

36. Measurement:
Figure 6.4 Vector GIS measurements: (a) distance and (b) area
Lecture 9

37. Attribute Selection A query is a question to the database.
The database response is a table.
The ArcGIS database response is selected records. If the table is the feature table it also displays the selection on the map.
Selected records can be exported to form a new shapefile/feature class.
Lecture 9

38. Theme Name
SQL
Lecture 9

39. Spatial Selection (Select by Location)
Identifying features based on spatial criteria
Adjacency, connectivity, containment, arrangement
Lecture 9

40. Adjacency depends on the algorithm used
(the same is true for all spatial operations)
Lecture 9

41. Touch the boundary of
Lecture 9

42. Share a line segment with
Lecture 9

43. Spatial Selection Allows us to know which roads connect.
Identifying features based on spatial criteria
Adjacency, connectivity, containment, arrangement
Allows us to know which roads connect.
Analyze transportation networks.
Which river or streams flow into another.
Analyze electrical networks.
Lecture 9

44. Spatial Selection Identifying features based on spatial criteria
Adjacency, connectivity, containment, arrangement
Lecture 9

45. Selection based on spatial and non-spatial attributes
Lecture 9

46. Spatial data analysis: Reclassification
An assignment of a class or value based on the attributes or geography of an object
Example:
Parcels
Reclassified
By size
Lecture 9

47. Spatial data analysis: Reclassification
Lecture 9

48. Reclassify in ArcGIS
Lecture 9

49. Lecture 9

50. Natural Breaks (Jenks)
Natural Breaks classes are based on natural groupings inherent in the data.
Class breaks are identified that best group similar values and that maximize the differences between classes.
The features are divided into classes whose boundaries are set where there are relatively big differences in the data values.
Natural breaks are data-specific classifications and not useful for comparing multiple maps built from different underlying information.
    
    
Lecture 9
From ArcGIS 10 Help

51. Natural Breaks
Lecture 9

52. Lecture 9

53. Equal Interval
Equal interval divides the range of attribute values into equal-sized subranges.
This allows you to specify the number of intervals, and ArcGIS will automatically determine the class breaks based on the value range. For example, if you specify three classes for a field whose values range from 0 to 300, ArcGIS will create three classes with ranges of 0–100, 101–200, and 201–300.
Equal interval is best applied to familiar data ranges, such as percentages and temperature.
This method emphasizes the amount of an attribute value relative to other values. For example, it will show that a store is part of the group of stores that make up the top one-third of all sales.
Lecture 9
From ArcGIS 10 Help

54. Equal Interval
Lecture 9

55. Lecture 9

56. Quantile Each class contains an equal number of features.
A quantile classification is well suited to linearly distributed data.
Quantile assigns the same number of data values to each class.
There are no empty classes or classes with too few or too many values.
Lecture 9
From ArcGIS 10 Help

57. Quantile
Lecture 9

58. Lecture 9

59. Standard Deviation
The Standard deviation classification method shows you how much a feature's attribute value varies from the mean.
ArcMap calculates the mean and standard deviation. Class breaks are created with equal value ranges that are a proportion of the standard deviation—usually at intervals of 1,½, ⅓, or ¼ standard deviations using mean values and the standard deviations from the mean.
A two-color ramp helps emphasize values above the mean and values below the mean.
Lecture 9
From ArcGIS 10 Help

60. Standard Deviation
Lecture 9

61. Lecture 9

62. Geometric Interval
The geometrical interval classification scheme creates class breaks based on class intervals that have a geometrical series. The geometrical coefficient in this classifier can change once (to its inverse) to optimize the class ranges.
The algorithm creates geometrical intervals by minimizing the square sum of elements per class. This ensures that each class range has approximately the same number of values with each class and that the change between intervals is fairly consistent.
This algorithm was specifically designed to accommodate continuous data. It produces a result that is visually appealing and cartographically comprehensive. It minimizes variance within classes and can even work reasonably well on data that is not normally distributed.
In mathematics, a geometric series is a series with a constant ratio between successive terms. For example, the series
½+1/4+1/8+1/16…
is geometric, because each successive term can be obtained by multiplying the previous term by 1/2.
Lecture 9
From ArcGIS 10 Help

63. Geometric Interval
Lecture 9

64. Lecture 9

65. Table Normalization Assignment
Put the following information into 3NF.
Sales Order
Widget Company
200 N Main Street
Bangor, Maine 04401
Customer Number: Sales Order Number: 400
Customer Name: XYZ Company Sales Order Date: Oct. 3, 2017
Customer Address: 400 College Ave. Clerk Number: 210
Orono, Maine Clerk Name: Carmen San Diego
04469
Item
Ordered
Description
Quantity
Unit Price
Total
800
807
925
Widget small
Thingamajigger
thingabob 40 30 15
9.99
23.50
17.32
399.60
750.00
259.80
Order Total

66. Assignment Finish reading chapter 8. Problems: 17, 18, 20
Begin reading chapter 9.