1. Fundamentals of Geographic Information System (GIS)
WMO/FAO Training Workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the SADC
Fundamentals of Geographic Information System (GIS)
Thelma A. Cinco
Senior Weather Specialist ,PAGASA Resource Person Philippines
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

2. Outline Overview of GIS Definition of GIS
Objectives and Potential of GIS
Components of GIS
Hardware,Software,Data,Method,Liveware
Functions/Task of GIS
Data Input:Data Model,
Data Management:Relational Database
Data quality,Map Scale & Accuracy/Errors
Data analysis: Queries & Spatial Analysis
Application of GIS
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

3. What is GIS? No consensus on the definition
However, there is consensus that GIS includes the following major elements:
hardware/software
database
applications/infrastructure
We are presently positioned at the beginning of the twenty first century
with the fast growing trends in computer technology information systems and
virtual world to obtain data about the physical and cultural worlds, and to
use these data to do research or to solve practical problems. The current digital
and analog electronic devices facilitate the inventory of resources and the rapid
execution of arithmetic or logical operations. These Information Systems are
undergoing much improvement and they are able to create, manipulate, store
and use spatial data much faster and at a rapid rate as compared to
conventional methods.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

4. What is GIS?
A GIS is a specific information system applied to geographic data and ;
is mainly referred to as a system of hardware, software, personnel and procedures;
designed to support capture, management, manipulation, analysis, modeling and display of spatially referenced data for solving complex planning and management problems [Burroughs, 1986; NCGIA, 1990].
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

5. Table 1-1. Alternative Names of GIS
Multipurpose geographical data system
Multipurpose input land use system
Computerized geographical information system System for handling natural resources inventory data
Geo-information system
Spatial information system
Land resource information system
Spatial data management and comprehensive analysis system
Planning information system
Resource information system
Natural resource management information system
Spatial data handling system
Geographically referenced information system
Environment information system
AGIS - Automated geographical information system
Multipurpose cadastre
Land information system
AM/FM - Automated mapping and facilities management
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

6. GIS OBJECTIVES Maximize the efficiency of planning and decision making
Provide efficient means for data distribution and handling
Elimination of redundant data base - minimize duplication
Capacity to integrate information from many sources
Complex analysis/query involving geographical referenced data to generate new information.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

7. Geographic Information System (GIS)
Powerful tools for addressing geographical /environmental issues
Allows us to arrange information about a location as a set of maps
Displaying information about one characteristic of the region
Needs a location reference system (such as latitude and longitude)
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

8. Potential for GIS
Once a GIS is implemented, the following benefits are expected.
geospatial data are better maintained in a standard format
revision and updating are easier
geospatial data and information are easier to search, analyze and represent
more value added product
geospatial data can be shared and exchanged freely
productivity of the staff is improved and more efficient
time and money are saved
better decisions can be made
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

9. The questions that a GIS is required to answer are mainly as follows :
What is at.....? (Locational question ; what exists at a particular location)
Where is it.....? (Conditional question ; which locations satisfy certain conditions)
How has it changed......? (Trendy question ; identifies geographic occurrence or trends that have changed or in the process of changing)
Which data are related .....? (Relational question : analyzes the spatial relationship between objects of geographic features)
What if ? (Model based question ; computers and displays an optimum path, a suitable land, risky area against disasters etc. based on model)
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

10. Components of GIS
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

11. Components of GIS :Hardware
Input devices (Keyboard, digitizing tablet, scanner)
Central Processing Unit (Pentium, AMD, Sun SPARC)
Storage devices (Tape drives, floppy drives, hard disks)
Output devices (Monitor, printer, plotter)
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

12. Sample of the hardware components
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

13. Components of GIS:Key software components
Tools for the input and manipulation of geographic information
A database management system (DBMS)
Tools that support geographic query, analysis, and visualization
A graphical user interface (GUI) for easy access to tools
Software components.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

14. manifold
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

15. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

16. Components of GIS: GIS Data
Base Maps – include streets, highways, boundaries for census, postal, and political areas, rivers and lakes, parks and landmarks; place names
Environmental maps - include data related to the environment, weather, environmental risk, satellite imagery, topography, and natural resources.
Socio-economic data - include data related to census/demography, health care, real state, telecommunications, emergency preparedness, crime, business establishments, and transportation.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

17. Socio-economic Maps and Data Environmental Maps Other Maps and Data
Base Map
Socio-economic Maps
and Data
Environmental Maps
Other Maps and
Data
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

18. Components of GIS:Liveware
GIS Manager
GIS Specialist
GIS Technician
GIS Analyst
Computer Programmer
Data Encoder
Information Systems Analyst
Information Technology Officer
Engineer
Planning Officer
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

19. Components of GIS: Procedures/Methods
Well-designed plan
Business rules
Models and operating practices unique to each organization
When you are developing procedures and methods you need to that deal with spatial reasoning, the representation of space, and human
understanding of space:
Spatial reasoning - addresses the inference of spatial information from spatial
facts. It deals with the framework and models for space and time, and the
relationships that can be identified between objects in a spatio-temporal model
of real world phenomena.
Scientific methods for the representation of space are important for the
development of data models and data structures to represent objects in spatial
databases. Spatial databases are distinguished from standard databases by their
capability to store and manage data with an extent in space and time (spatial
data types).
The human understanding of space, influenced by language and culture,
plays an important role in how people design and use GIS.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

20. Functions/Tasks of GIS
Data Input
Data Management
Data Analysis and Manipulation
Data Display/Visualization
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

21. Functions/Tasks of GIS:Data Input
The procedure of encoding data into a computer-readable form and writing the data to the GIS database.
Data input includes three major steps (the latter two steps are also called data preprocessing):
Data capture
Editing and cleaning
Geo-coding
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

22. Functions/Tasks of GIS: Data Input
Keyboard entry
Manual digitizing (e.g., tablet, on-screen)
Scanning
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

23. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

24. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

25. Geographic Data Organized into Layers
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

26. Data Input :Data Sources for GIS
maps
aerial photos
satellite images
technical descriptions
GPS data
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

27. Data Input : Geographic Data Characteristics
Geographic data contains four integrated components, namely, location, attribute, spatial relationship and time.
Geographic data include those which are spatially referenced.
A GIS includes operations which support spatial analysis.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

28. Data Input: Geographic Data Characteristics
Spatial data and their attributes are linked (seamless)
By their geographic location
By unique identifiers
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

29. Data Input: Kinds of Data GIS Handles
Spatial data
usually translated into simple objects: points, lines, areas and grids (pixels).
represented as maps.
Example: a parcel of land
Attribute data (Non-spatial or Aspatial Data)
are descriptive information about specified spatial objects.
often have no direct information about the spatial location but can be linked to spatial objects they describe.
Usually organized in tables
Example: the owner of a parcel of land
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

30. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

31. Data Input: Kinds of Data GIS Handles
Identify if spatial(graphical) or non-spatial(textual)
This building
The color of this building
The people within this building
The name of this building
Rizal Park
Botswana Road
Gaborone
Population of Botswana
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

32. Data Input: Identifiers
Enable both spatial and attribute data to be stored separately but accessed together.
Identifiers are
Unique values - usually integers
Stored as part of the spatial data structure - as a numeric value (i.e., system-generated ID)
Stored as part of the attribute data structure - as a field in a table
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

33. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

34. Data Input: Data Model
Conversion of real world geographical variation into discrete objects is done through data models. It represents the linkage between the real world domain of geographic data and computer representation of these features.
Two major categories of spatial data representation in GIS: raster and vector.
Raster approach: cells
Vector approach: points, lines, and polygons
Data Model
Conversion of real world geographical variation into discrete objects is done
through data models. It represents the linkage between the real world domain
of geographic data and computer representation of these features. Data models
discussed here are for representing the spatial information.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

35. Raster Vector Real World
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

36. Raster Data Divides the entire study area into a regular grid of cells
Each cell contains a single value
Is space-filling since every location in the study area corresponds to a cell in the raster.
Raster data can be imagined as collection of cells organized like a matrix.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

37. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

38. Visual Representation
Database/Internal Structure
ncols 15
nrows 16
xllcenter .5
yllcenter .5
cellsize 1
RASTER DATA
Visual Representation
Data Summary
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

39. Vector Data Model Represented by lines, points, and polygons.
Fundamental primitive is a point
Points are stored as x,y coordinates and represent features as having no dimension.
Objects are created by connecting points with straight lines (or arcs)
Areas are defined by sets of lines
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

40. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

41. Visual Representation
Database/Internal Structure
Points
Lines
1 ( ),(3.1,2.7),(3.5, 1.3),
2 (2.7,2.9),
Polygons
1 ( ),(3.1,2.7),(3.5, 1.3), ( )
......
....
Vector Data
Data Summary
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

42. Format Issues
Most GIS applications can utilize both vector and raster formats, and/or they can convert between the two
Converting from vector to raster is easy;
Converting from raster to vector is difficult
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

43. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

44. Industry-Standard Formats
Standard vector GIS formats include:
DLG, shape files, TIGER files (preserve topology)
Standard raster GIS formats include:
.JPG, .TIF, GeoTIFF (georeferenced TIF image), other digital image formats, and DEMs (which are georeferenced)
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

45. Comparison of Raster and Vector Data Models.
RASTER MODEL VECTOR MODEL
Advantages: Advantages:
1.It is a simple data structure It provides a more compact data structure than the raster
model.
2.Overlay operations are easily and efficiently 2.It provides efficient encoding of topology, and as a result,
implemented more efficient implementation of operations that require
topological information, such as network analysis
3.High spatial variability is efficiently represented 3.The vector model is better suited to supporting graphics.
in a raster format that closely approximate hand-drawn maps.
4.The raster format is more or less required for efficient
manipulation and enhancement of digital images.
Disadvantages: Disadvantages:
1.The raster data structure is less compact It is a more complex data structure than a simple raster.
2.Topological relationships are more difficult to represent. 2.Overlay operations are more difficult to implement.
3.The output of graphics is less aesthetically pleasing 3.The representation of high spatial variability is inefficient.
because boundaries tend to have a blocky appearance 4.Manipulation and enhancement of digital images cannot
rather than the smooth lines of hand-drawn maps. This be effectively done in the vector domain.
can be overcome by using a very large number of cells,
but may result in unacceptably large files.
Source: Aronoff, S., Geographical Information Systems: A Management Perspective, WDL Publications, Ottawa.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

46. Vector Representation of Real World Objects
The representation of real world objects on a map as points, lines and polygons is known as abstraction.
In abstraction, the data are structured to be amenable to computer storage/retrieval and manipulation.
Abstraction is done based on the requirements of a specific application.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

47. Vector Representation of Real World Objects
Building
Represented by polygon in 1:500 cadastral map
Represented by point in 1:50,000 topographic map
Road
Represented by line in 1:50,000 topographic map
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

48. Layers and coverages
The common requirement to access data on the basis of one or more classes
has resulted in several GIS employing organizational schemes in which all data of a particular level of classification, such as roads, rivers or vegetation types are grouped into so called layers or coverages. The concept of layers is to be found in both vector and raster models. The layers can be combined with
each other in various ways to create new layers that are a function of the individual ones. The characteristic of each layer within a layer-based GIS is that all locations with each layer may be said to belong to a single Arial region
or cell, whether it be a polygon bounded by lines in vector system, or a grid cell in a Raster system. But it is possible for each region to have multiple attributes.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

49. Geodetic Control Network Digital Elevation Model Orthorectified Image
Roads
Water Features
Political Boundaries
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

50. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

51. Data Management
A collection of non-redundant data which can be shared by different application systems is known as a database.
Several layers of geographic data covering the same location are considered a database.
When data volumes become large, it is often best to use a database management system (DBMS) to help store, organize, and manage data
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

52. Data Management
A DBMS is nothing more than computer software for managing a database.
There are many different designs of DBMSs, but in GIS the relational design has been the most useful.
In the relational design, data are stored conceptually as a collection of tables.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

53. A DBMS contains Data definition language Data dictionary
Data-entry module
Data update module
Report generator
Query language
Data definition language -
Data dictionary -
Data-entry module -
Data update module -
Report generator -
Query language -
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

54. Data structure
Flat file (tabular) - data in a single table (no link between tables).
Hierarchical - keys for data retrieval are clearly defined (one-to-many relationship).
Network - constructed based on pointers and links between data records (many-to-many relationship).
Relational - normalized tables with common redundant fields for relational link (many-to-many relationship).
Data Models:
Flat file-
Hierarchical
Network
Relational
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

55. Hierarchical Data Structure
Database Structure
Hierarchical Data Structure
Relational Structure
Network System
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

56. Relational Model Widely used in GIS
Commonly used relational DBMS are as follows:
INFO - used in ARC/INFO
EMPRESS - used in System/9
ORACLE - used in ARC/INFO, GeoVision, etc.
SQL & ACCESS – used in PC-based GIS - Arcview
dBASE - used in pcARC/INFO and other PC-based GIS ARCVIEW.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

57. Relational Model
Each record has a set of attributes (fields or items).
The range of possible values (domain) is defined for each attribute.
Records of each type form a table or relation.
In a table, each row is a record or tuple and each column is an attribute or field.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

58. Relational Model
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

59. Relation
The degree of a relation is the number of attributes in the table.
A one-attribute table is a unary relation.
A two-attribute table is a binary relation.
A n-attribute table is an n-ary relation.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

60. The degree of a relation.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

61. Key
A key of a relation is a subset of attributes with the following properties:
unique identification: the value of a key is unique for each tuple.
non-redundancy: no attribute in the key can be discarded without destroying the key's uniqueness
A primary key is a combination of attributes (fields) whose values uniquely address each record in a relation.
A foreign key is an attribute in one relation (table) which can serve as a primary key into another table.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

62. Key
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

63. Foreign Key Primary Key
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

64. Normalization
It is a step-by-step process for converting data structures into a standard form (relational tables)
This standard form satisfies the ff. constraints:
Each entry in a table represents one data item (no repeating groups).
All items within each column are of the same kind.
Each column has a unique name.
All rows are unique (no duplicates).
The order of viewing the rows and columns does not affect the semantics of any function using the table.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

65. Normalization Elimination of anomalies in data structure
Update anomalies
Deletion anomalies
Insertion anomalies
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

66. Non-normalized relation.
Repeating
Group
What if this
group is
repeated in
10,000
records?
Non-normalized relation.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

67. Update Anomaly Changing the erodibility of Loamy Sand from 0.10 to
0.15.
What if there
are 10,000
records with
Loamy Sand?
Non-normalized relation.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

68. Deletion Anomaly Deleting all records with Loamy Sand. Should the
corresponding
entries under
Land System
be also
deleted?
Non-normalized relation.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

69. Insertion Anomaly Adding the Soil Type Clayey. What if there
are 10,000
records having
this Soil Type?
Non-normalized relation.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

70. SQL – Standard Query Language
Normalized relations.
SQL – Standard Query Language
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

71. Normalization Major Steps
remove repeating groups of information and move each group out into its own table
look at the information in terms of dependencies (separating out information that is not dependent on the table's Primary Key)
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

72. (using Standard Query Language or SQL)
Relational join
(using Standard Query Language or SQL)
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

73. Degree of Relationship
One-to-one relationship
A division has at most one chief and that a chief is a head of at most one division
One-to-many relationship
A division has many staff and a staff belongs to at most one division
Many-to-many relationship
An employee may be assigned to many projects and a project may have many employees assigned to it
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

74. One-to-one Relationship1
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

75. One-to-many Relationship1 N
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

76. Determine if the following tables are normalized or not.
A. Health_Facility (HF_Code, Health Facility Name, Class)
HF_Code
Health Facility Name
Class
043425H0001
Marinduque Provincial Hospital
HOSPITAL
043412H0001
Sta. Cruz Provincial Hospital
043412R0001
Brgy. Morales Brgy. Clinic
RHU
B. Health_Facility (HF_Code, Health Facility Name, Equipment_Code, Equipment)
HF_Code
Health Facility Name
EQUIPMENT_CODE
EQUIPMENT
043425H0001
Marinduque Provincial Hospital E1
X-Ray Machine
043412H0001
Sta. Cruz Provincial Hospital E2
Computer
043412R0001
Brgy. Morales Brgy. Clinic E3
Air Conditioner
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

77. Determine if the following tables are normalized or not.
C. Health_Facility (HF_Code, Health Facility Name, Catchment Population)
HF_Code
Health Facility Name
Catchment Population
043425H0001
Marinduque Provincial Hospital
13,980
043412H0001
Sta. Cruz Provincial Hospital
17,490
043412R0001
Brgy. Morales Brgy. Clinic
3,459
D. Available_HW (HF_Code, Health Facility Name, HealthWorker_ID, Type_of_HW)
HF_Code
Health Facility Name
HW_ID
Type_of_HW
043425H0001
Marinduque Provincial Hospital
Doctor
043412H0001
Sta. Cruz Provincial Hospital
Nurse
043412R0001
Brgy. Morales Brgy. Clinic
Medical Technologist
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

78. Topology
The mathematical procedure for explicitly defining relationships between spatial objects.
Topology expresses different types of spatial relationships: (3 major topological concepts)
arcs connect to each other at nodes (connectivity)
arcs that connect to surround an area define a polygon (area definition)
arcs have direction and left and right sides (contiguity)
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

79. An arc in vector GIS.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

80. Example of "built" topology (ARC/INFO)
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

81. Data Manipulation
GIS data need to undergo transformation before they can be integrated, displayed or analyzed.
same scale, coordinate system, format, etc.
A temporary transformation for display purposes or a permanent one required for analysis
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

82. GIS Data Quality
As a result of increased mapping capabilities using GIS, two new and very significant mapping issues arose .
Map Scale
Accuracy/Error
Uncertainties and errors are intrinsic to spatial data and need to be
addressed properly, not sweeping away the users by high quality color
outputs. Data accuracy is often grouped according to thematic accuracy,
positional accuracy and temporal accuracy occurring at various stages in spatial
data handling.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

83. Map scale
The major concern in collecting/using spatial data any mapping purpose is scale
The ratio of distance on a map over the corresponding distance on the ground.
Scale is represented as 1: M or 1/M, where M is called the scale denominator. The Larger the scale, the more the detail described by the map and with higher accuracy.
Most of the available GIS data are collected at the common scale such as 1:50000 or 1:24000.
It is possible to change the scale in GIS but not advised (important detail are missing)
Use of multi scale/multi resolution data
US geological survey provides two dataset at two resolution(NIMA-100 m and USGS-30 m).Using above two sources result in different maps.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

84. Map Scale
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

85. Data Quality: Accuracy/Error
The closeness of measurements or estimates by computation to true values.
Accuracy is generally represented by standard deviation of errors, that is difference between measurements and the true value.
xi: error in measurements
n : number of measurements
In GIS, errors result from the map itself, map digitizing and coordinate
transformation, which will sum up to about 0.5 mm on the map.
Error associated with spatial information can be user errors, measurement error and processing error.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

86. Error: User error Age of data - Reliability decreases with age.
Map scale Non-availability of data on a proper
scale or Use of data at different scales
Density of observation - Sparsely distributed data set is less
reliable
Relevance of data results from indirect or derived data layers as input into GIS
Data inaccuracy Positional, elevation, minimum,
mapable unit etc.
Inaccuracy of contents - Attributes are erroneously attached
Accessibility Military restrictions, inter-agency rivalry, privacy laws, and economic factors may restrict data availability or the level of accuracy in the data
Scale When wrong scale is applied.
Coverage When full coverage is not available for the area of interest. (users are forced to to choose between full coverage with obsolete data or with only partial coverage.
Relevance Errors results from indirect or derived data layers as input into GIS.
Density of observation An insufficient number of observations may not provide the level of resolution required to perform spatial analysis and determine the patterns.
Format Conversion of scale, projection, changing from raster to vector format, and resolution size of pixels are examples of possible areas for format error.
Accessibility Military restrictions, inter-agency rivalry, privacy laws, and economic factors may restrict data availability or the level of accuracy in the data.
Cost Extensive and reliable data is often quite expensive to obtain or convert.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

87. Error: Measurement Error
Error associated with variations in data set.
Error due to limitations and/or quality of instrument.
Error due to limitation in collecting data in the field.(human skill)
Error due to natural conditions
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

88. Error: Processing Error
Processing error includes factors such as precision, interpolation,generalization, data conversion, digitization and other methodical operations.
Error due to combining data layers
Error due to registration
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

89. Data Analysis Perform queries:
Who owns the hospital on the corner?
How far is it between two places?
Where is the site suitable for building new hospital?
How big is the area serviced by the hospital?
GIS provides both simple point-and-click query capabilities and sophisticated analysis tools
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

90. Data Analysis
Perform analysis and modeling to look for patterns and trends and to undertake "what if" scenarios.
If a new factory is built here, how will the residents’ health be affected?
Given a series of environmental data, where will malaria most likely break out?
If all the factories near a wetland accidentally release chemicals into the river at the same time, how long would it take for a damaging amount of pollutant to enter the wetland reserve?
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

91. GIS Queries Attribute Query Spatial Query
Combination of Attribute Query and Spatial Query
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

92. Spatial Overlay
An operation that merges the features of two coverage layers into a new layer and relationally joins their feature attribute table.
When overlay occurs, spatial relationships between objects are updated for the new, combined map.
In some circumstances, the result may be information about relationships (new attributes) for the old maps rather than the creation of new objects.
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

93. Spatial Analysis
GIS operational procedure and analytical tasks that are particularly useful for spatial analysis include:
Single layer operations
Multi layer operations/ Topological overlay
Geometric modeling
Calculating the distance between geographic features
Calculating area, length and perimeter
Geometric buffers.
Network analysis
Surface analysis
Raster/Grid analysis
Whether it is effective utilization of natural resources or sustainable
development or natural disaster management, selecting the best site for waste
disposal, optimum route alignment or local problems have a geographical
component; geoinformatics will give you power to create maps, integrate
information, visualize scenarios, solve complicated problems, present powerful ideas, and develop effective solutions like never before. In brief it can be described as a supporting tool for decision-making process. Map making and geographic analysis are not new, but a GIS performs these tasks better and faster than do the old manual methods. Today, GIS is a multibillion-dollar industry employing hundreds of thousands of people worldwide.
GIS is used to perform a variety of Spatial analysis, including overlaying
combinations of features and recording resultant conditions, analyzing flows
or other characteristics of networks; proximity analysis (i.e. buffet zoning) and
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

94. GIS Applications in Natural Resource Management
Agricultural development
Land evaluation analysis
Change detection of vegetated areas
Analysis of deforestation and associated environmental hazards
Monitoring vegetation health
Mapping percentage vegetation cover for the management of land
Crop acreage and production estimation
Wasteland mapping
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

95. References:
Fundamentals of GIS by P.L.N. Raju, Geoinformatics Division Indian Institute of Remote Sensing, Dehra Dun
Bobby Crisostomo, NAMRIA, Philippines
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

96. THANK YOU
Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)