1. Intro to advanced GIS and a review of basic GIS

2. Outlines About the class setting Materials to be covered and scheduled
Quick review of GIS basics
First lab (Lab 1)

3. What covered in introGIS
Geospatial Tech
GIS
GIS data
GIS data type
GIS data format

4. GIS a simplified view of Earth Two types of coordinate systems
Geographic coordinate system
Projected coordinate system
Conic,
cylindrical,
Azimuthal
Distortions (shape, size, distance, direction)
Two important things
Define
Project

5. Geographic Coordinate System
Unprojected

6. Projected Coordinate System

7. What is GIS ? A computer system for - collecting, - storing,
- manipulating,
- analyzing,
- displaying, and
- querying geographically related information.
GIS is a popular technology, but what exactly is it? What does it do? Basically, a geographic information system (GIS) is a computer-based tool for solving problems. A GIS integrates information in a way that helps us understand and find solutions to problems. Data about real-world objects is stored in a database and dynamically linked to an onscreen map, which displays the real-world objects. When the data in the database changes, the map updates to reflect the changes.
In general, people use a GIS for four main purposes: data creation, data display, analysis, and output. You can display objects according to the data in your database (this is a powerful feature that you'll appreciate later). GIS analysis tools allow you to do things like find out how far your best customers travel to visit your store, which land parcels are within a flood zone, and which soil type is best for growing a particular crop. Output options include cartographic-quality maps as well as reports, lists, and graphs.
Many different definitions of GIS have evolved in different areas and disciplines
the information is always geographic or spatial
Four components of any GIS: input; storage/retrieval; analysis; display
Used in a steadily growing number of fields/disciplines/projects
Origins lie (way back) in thematic maps; manual overlay
“GIS” refers to the application or the software; “doing GIS” refers to increasing number of things
Need understanding of GIScience to do GIS work.

8. In general GIS cover 3 components
Computer system
Hardware
Computer, plotter, printer, digitizer
Software and appropriate procedures
Spatially referenced or geographic data
People to carry out various management and analysis tasks

9. Geographic Data
Geospatial data tells you where it is and attribute data tells you what it is. Metadata describes both geospatial and attribute data.
In GIS, we call geographic data as GIS data or spatial data

10. 1. Geospatial data

11. Traditional method
To represent the geographic data is paper-based maps
Geology map
Topographic map
City street map (we still use it a lot)
...

12. Characteristics of spatial data
“mappable” characteristics:
Location (coordinate system, will be lectured later)
Size is calculated by the amount (length, area, perimeter) of the data
Shape is defined as shape (point, line, area) of the feature
Discrete or continuous
Spatial relationships

13. Discrete and continuous
Discrete data are distinct features that have definite boundaries and identities
A district, houses, towns, agricultural fields, rivers, highways, …
Continuous data has no define borders or distinctive values, instead, a transition from one value to another
Temperature, precipitation, elevation, ...

14. GIS: a simplified view of the real world
Points
Lines
Areas
Networks
A series of interconnecting lines
Road network
River network
Sewage network
Surfaces
Elevation surface
Temperature surface
Discrete features
Continuous features

15. Problems caused by the simplified features may still exist, but let’s live on it
Dynamic nature (not static)
Forest grow
River channel change
City expand or decline
Identification of discrete and continuous features
Road to be a line or a area?
Scale
Some may not fit to any type of features: fuzzy boundaries
Transition area between woodland and grassland
Lets do not worry about these problems now!!! Just keep in mind

16. Topology needed
A collection of numeric data which clearly describes adjacency, containment (coincidence), and connectivity between map features and which can be stored and manipulated by a computer.
A set of rules on how objects relate to each other
Major difference in file formats
Higher level objects have special topology rules

18. Two basic data models to represent these features
Raster spatial data model
Define space as an array of equally sized cells arranged in rows and columns. Each cell contains an attribute value and location coordinates
Individual cells as building blocks for creating images of point, line, area, network and surface
Continuous raster
Numeric values range smoothly from one location to another, for example, DEM, temperature, remote sensing images, etc.
Discrete raster
Relative few possible values to repeat themselves in adjacent cells, for example, land use, soil types, etc.
Vector spatial data model
Use x-, y- coordinates to represent point, line, area, network, surface
Point as a single coordinate pair, line and polygon as ordered lists of vertices, while attributes are associated with each features
Usually are discrete features

19. DIGITAL SPATIAL DATA RASTER VECTOR Real World
This is an illustration of transferring a real world geographic area into the raster and vector formats. In the raster format the geographic area is parceled into numerous grid squares and the value that makes up the majority of the square dictates that that entire square gets the value. In the vector format all entities are classified into points, lines, or polygons.
Source: Defense Mapping School
National Imagery and Mapping Agency

20. Raster and Vector Data Models
Real World
600 1 2 3 4 5 6 7 8 9 10 1 B G
Trees
500 2 B G G 3 B
400 4 B G G
Trees
Y-AXIS 5 B G G
300 6 B
G G BK
House 7 B
200 8 B B
River 9 B
100 10 B
100
200
300
400
500
600
X-AXIS
Raster Representation
Vector Representation
Source: Defense Mapping School
National Imagery and Mapping Agency

21. Example: Discrete raster

22. Example: continuous raster
Xie et al. 2005

23. Raster
Real world
Vector
Heywood et al. 2006

24. Effects of changing resolution
Heywood et al. 2006

25. Vector – Advantages and Disadvantages
Good representation of reality
Compact data structure
Topology can be described in a network
Accurate graphics
Disadvantages
Complex data structures
Simulation may be difficult
Some spatial analysis is difficult or impossible to perform

26. Raster – Advantages and Disadvantages
Simple data structure
Easy overlay
Various kinds of spatial analysis
Uniform size and shape
Cheaper technology
Disadvantages
Large amount of data
Less “pretty”
Projection transformation is difficult
Different scales between layers can be a nightmare
May lose information due to generalization

27. GIS data formats (file formats)
Shapefiles
Coverages
TIN (e.g. elevation can be stored as TIN)
Triangulated Irregular Network
Grid (e.g. elevation can be stored as Grid)
Image (e.g. elevation can be stored as image, all remote sensing images)
Vector data
Raster data

28. Shape Files Nontopological Advantages no overhead to process topology
Disadvantages polygons are double digitized, no topologic data checking
At least 3 files .shp .shx .dbf

29. Coverages Original ArcInfo Format Directory With Several Files
Database Files are stored in the Info Directory
Uses Arc Node Topology
Containment (coincident)
Connectivity
Adjacency

30. Evolution of Vector Data Model
ESRI, Inc.
Arc/Info: coverages
ArcView: shapefiles
ArcGIS: geodatabase

31. Geodatabase components- vector data and table
Primary (basic) components
- feature classes,
- feature datasets,
- nonspatial tables.
complex components building on the basic components:
- topology,
- relationship classes,
- geometric networks

32. Geodatabase components- Raster data
Raster data referenced only in personal geodatabase
Raster data physically stored in multiuser geodatabse
Raster datasets and raster catalogs
A raster dataset is created from one or more individual rasters. When creating a raster dataset from multiple rasters, the data is mosaicked, or aggregated, into a single, seamless dataset in which areas of overlap have been removed. The input rasters must be contiguous (adjacent) and have the same properties, including the same coordinate system, cell size, and data format. For each raster dataset (.img, grid, JPEG, MrSID, TIFF), ArcGIS creates an ERDAS IMAGINE file (.img).
A raster catalog is defined as a table in the geodatabase which you can view like any other table in ArcCatalog. Each raster in the catalog is represented by a row in the table. It contains a collection of rasters that can be noncontiguous, stored in different formats, and have other different properties. In order to view all the rasters in the catalog, they must have the same coordinate system and a common geographic extent

34. 2. Attribute data
Attribute data is about “what” of a spatial data and is a list or table of data arranged as rows and columns
Rows are records (map features)
Each row represents a map feature, which has a unique label ID or object ID
Columns are fields (characteristics)
Intersection of a column and a row shows the values of attributes, such as color, ownership, magnitude, classification,…

36. examples

37. Relational database
A relational database is a collection of tables, also called relations, which can be connected to each other by keys.
A primary key represents one or more attributes whose values can uniquely identify a record in a table. Its counterpart in another table for the purpose of linkage is called a foreign key
Advantages
Each table in the database can be prepared, maintained, and edited separately from other tables
Efficient data management and processing, since linking tables query and/or analysis is often temporary

38. Join and relate tables
Once tables are separated as relational tables, then two operations can be used to link those tables during query and analysis
Join, brings together two tables based on a common key.
Relate, connects two tables (based on keys) but keeps the tables separate.
Keys do not have to have the same name but must be of the same data type
Join
relate
Join
relate

39. The joined table
The joined table will only preserved within the map document-the tables remain separate on disk-and can be removed at any time

40. Related tables
The related table will only preserved within the map document-the tables remain separate on disk-and can be removed at any time

41. 3. metadata
Meta is defined as a change or transformation. Data is described as the factual information used as a basis for reasoning. Put these two definitions together and metadata would literally mean "factual information used as a basis for reasoning which describes a change or transformation."
In GIS, Metadata is data about the data. It consists of information that describes spatial data and is used to provide documentation for data products. Metadata is the who, what, when, where, why, and how about every facet of the spatial data.
According to the Federal Geographic Data Committee (FGDC), metadata is data about the content, quality, condition, and other characteristics of data.

42. Why use and create metadata
To help organize and maintain an organization's spatial data
- Employees may come and go but metadata can catalogue the changes and updates made to each spatial data set and how each employee implemented them
To provide information to other organizations and clearinghouses to facilitate data sharing and transfer
- It makes sense to share existing data sets rather than producing new ones if they are already available
To document the history of a spatial data set
- Metadata documents what changes have been made to each data set, such as changes in geographic projection, adding or deleting attributes, editing line intersections, or changing file formats. All of these could have an effect on data quality.

43. Metadata Should Include Data about
Date of data collected.
Date of coverage generated.
Bounding coordinates.
Processing steps.
Software used
RMSE, etc.
From where original data came.
Who did processing.
Projection
coordinate System
Datum
Units
Spatial scale
Attribute definitions
Who to contact for more information
Example of non-standard metadata: (source:
THIS COVERAGE HAS BEEN PROJECTED TO THE FOLLOWING PARAMETERS - September 2000:
Projection: UTM Zone: 12
Units: meters
Datum: NAD83 (previously NAD27)
Spheroid: GRS80 (previously Clarke 1866)
THIS DOCUMENT WAS CREATED AT ART, UNIVERSITY OF ARIZONA, September 14, 2001
NAME OF DATASET: QUADS DATA
TYPE: Vector-polygon
DESCRIPTION OF CONTENT: These are the boundary polygons of the four quadrangles for the Santa Rita Experimental Range in southeast Arizona.
FORMAT: Arc/Info
DATA SIZE: Approximate Megabytes: .017
HISTORY: This coverage was originally reselected out of the ART Arizona General Reference Library which contains data originally from ALRIS.
MAINTENANCE: NONE
COLUMN ITEM NAME WIDTH OUTPUT TYPE N.DEC
AREA F
PERIMETER F
QUADS# B
QUADS-ID B
QUAD I
NAME C –
USER ATTRIBUTES
QUAD -- the ALRIS 4 digit quad codes
NAME -- USGS quad sheet name
See an example of non-standard metadata (see)

44. Federal Geographic Data Committee’s (FGDC) Content Standard for Digital Geospatial Metadata (CSDGM)
The FGDC is developing the National Spatial Data Infrastructure (NSDI) in cooperation with organizations from State, local and tribal governments, the academic community, and the private sector. The NSDI encompasses policies, standards, and procedures for organizations to cooperatively produce and share geographic data.
The objectives of the CSDGM are to provide a common set of terminology and definitions for the documentation of digital geospatial data.

45. CSDGM (FGDC-STD-001-1998) Metadata = Identification_Information
Data_Quality_Information
Spatial_Data_Organization_Information
Spatial_Reference_Information
Entity_and_Attribute_Information
Distribution_Information
Metadata_Reference_Information
Identification Information – basic information about the data set. Examples include title, geographic area covered, currentness, and rules for acquiring or using the data.
Data Quality Information − an assessment of the quality of the data set. Examples include positional and attribute accuracy, completeness, consistency, sources of information, and methods used to produce the data.
Spatial Data Organization Information − the mechanism used to represent spatial information in the data set. Examples include the method used to represent spatial positions directly (such as raster or vector) and indirectly (such as street addresses or county codes) and the number of spatial objects in the data set.
Spatial Reference Information − description of the reference frame for, and means of encoding, coordinates in the data set. Examples include the name of and parameters for map projections or grid coordinate systems, horizontal and vertical datums, and the coordinate system resolution.
Entity and Attribute Information − information about the content of the data set, including the entity types and their attributes and the domains from which attribute values may be assigned. Examples include the names and definitions of features, attributes, and attribute values.
Distribution Information − information about obtaining the data set. Examples include contact information for the distributor, available formats, information about how to obtain data sets online or on physical media (such as cartridge tape or CD-ROM), and fees for the data.
Metadata Reference Information - information on the correctness of the metadata information, and the responsible party.
Connect to

46. Metadata tools Metadata editors: - tkme / USGS
- ArcCatalog / ESRI
- SMMS / Intergraph
- FGDCMETA / Illinois State Geological Survey
- xtme / USGS
Metadata utilities (check compliance and export to text, HTML,XML, or SGML):
- mp / USGS
- MP batch / Intergraph
- ArcCatalog powered by mp/ ESRI
Metadata Server
- Isite / FGDC
- GeoConnect Geodata Management Server / Intergraph
- ArcIMS Metadata Server / ESRI
mp: Metadata Parser

47. 4. Geodatabase
Before geodatabase, in one GIS project, many GIS files (spatial data and nonspatial data) are stored separated. So for a large GIS project, the GIS files could be hundreds.
Within a geodatabase, all GIS files (spatial data and nonspatial data) in a project can be stored in one geodatabase, using the relational database management system (RDMS)

48. Types of geodatabases
personal
enterprise

49. Personal Geodatabase
The personal geodatabase is given a name of filename.mdb that is browsable and editable by the ArcGIS, and it can also be opened with Microsoft Access. It can be read by multiple people at the same time, but edited by only one person at a time. maximum size is 2 GB.

50. Multiuser Geodatabase
Multiuser (ArcSDE or enterprise) geodatabase are stored in IBM DB2, Informix, Oracle, or Microsoft SQL Server.
It can be edited through ArcSDE by many users at the same time, is suitable for large workgroups and enterprise GIS implementations. no limit of size. support raster data.

51. 3-tier ArcSDE client/server architecture with both
the ArcSDE and Oracle RDBMS running on the
same server, which minimizes network traffic
and client load while increasing the server load
compared to 2-tier system, in which the clients
directly connect to the RDBMS

52. Personal and Multiuser Geodatabase Comparison
source:

53. 5. Geometric transformation
projection and coordinate system is to project the 3D earth to 2D plane, so the 3D earth can be represented in different GIS data models (2D digital format) in a GIS system.
Geometric transformation is the process of using a set of control points and transformation equations to register a (2D) digitized map, a satellite image, or an air photograph onto a (2D) projected coordinate system.
In GIS, geometric transformation includes map-to-map transformation, image-to-map transformation, image-to-image transformation.
The root mean square (RMS) error is a quantitative measure of location accuracy that can determine the quality of a geometric transformation.
Image to map (or image) needs an additional step resampling to fill the cell values from the original image.

54. 6. Data accuracy and quality
Raster data quality
Geolocation accuracy
Estimation accuracy
Vector data quality
Location errors
Topographical errors
Location and value

55. 7. Vector data analysis
Vector data analysis uses the spatial features of point, line, and polygon as inputs.
The accuracy of analysis results depends on the accuracy of spatial features in terms of location and shape.
Topology can also be a factor for some vector data analyses such as buffering and overlay.
Pattern Analysis
Intersect
Union
Symmetrical difference
Identity
AND OR
XOR
AND OR
Point pattern: nearest neighbor, Ripley’s K-function
Moran’s I
G-Statistic

56. 8. Raster data analysis
Raster data analysis is based on cells and rasters.
Raster data analysis can be performed at the level of individual cells, or groups of cells, or cells within an entire raster.
Some raster data operations use a single raster, while others use two or more rasters.
Raster data analysis is also related to the type of cell value (numeric or categorical values) in the input raster(s).
Local, focal, zonal
Allocation and direction
Clip and mosaic
Aggregate and regiongroup
Map algebra

57. 9. Lab 1
Getting Started With the Geodatabase

60. COPY the result map of your last step to your home work

61. Copy your exam questions and result to your homework