1. GIS Fundamentals/ Geographic Database Design

2. GIS Concepts Information cycle:
Data/Information/System/Information System
Geographic Information System
Main Components/Characteristics
Geographic Database
Data Modeling
Data Representation
Spatial Analysis
Implementing a GIS

3. Information Cycle
Territory
Data
GIS
DSS
Information
Decision

4. Data / Information
Information is the result of interpretation of relations existing between a certain number of single elements (called data).
Example:
The Museum located at 5th Avenue, NY, was built in 1898.
Data: Museum, address, year of construction.

5. System
A system is a set organized globally and comprising elements which coordinate for working towards doing a result.
Example: Water supply system
Elements: pipes, valves, hydrants, water meters, pumps, reservoirs, etc.

6. Information System (IS)
An Information System is a set organized globally and comprising elements (data, equipment, procedures, users) that coordinate for working towards doing a result (information).

7. GIS: “G” & “IS”
Definition: A GIS is a collection of computer hardware and software, geographic data, methods, and personnel assembled to capture, store, analyze and display geographically referenced information in order to resolve complex problems of management and planning.
A GIS is an IS with geographic data as input and geographic information as output
Geographic Data? Geographic Information?

8. Components of a GIS

9. GIS Input Output User Interface Models Other GIS Reports Maps
Geographic Data
Geographic Information
Input
GIS
Output
Reports
Maps
Photo. Products
Statistics
Input Data for models
Manipulation
Analysis
Maps
Census
Field Data
RS Data
Others
Data
Capture
Display
Storage
GIS Components
User
Interface
Models
Other GIS

10. GIS: Main Characteristics
Integration of Multiple data:
- Sources
- Scales
- Formats
Geographic Database
Spatial Analysis

11. Data from multiple sources-at multiple scales-in multiple formats
Census/ Tabular data
Maps
Picture & Multimedia
GPS/ air photos/ satellite images

12. Referencing map features: Coordinate systems & map projections
To integrate geographic data from many different sources, we need to use a consistent spatial referencing system for all data sets

13. The Latitude/Longitude reference system
latitude φ : angle from the equator to the parallel
longitude λ : angle from Greenwich meridian

14. Map Projections
Curved surface of the earth needs to be “flattened” to be presented on a map: Map Projection
Projections are classified according to which properties they preserve: area, shape, angles, distance
Some distortion is inevitable:
Less distortion if maps show only small areas, but large if the entire earth is shown
Projection is the method by which the curved surface is converted into a flat representation.
Projection is the method by which the curved surface is converted into a flat representation

15. UTM: Universal Transverse Mercator
Minimal distortions of area, angles, distance and shape at large and medium scales
Very popular for large and medium scale mapping (e.g., topographic maps)
Cylindrical projection with a central meridian that is specific to a standard UTM zone
60 zones around the world
One cartographic reference system that desreves more detailed discussion is the UTM system

16. Space as an indexing system

17. The concept of scale
Scale is the ratio between distances on a map and the corresponding distances on the earth’s surface
e.g., a scale of 1:100,000 means that 1 cm on the map corresponds to 100,000 cm or 1 km in the real world
Small scale: small fraction such as 1:10,000,000 shows only large features
Large scale: large fraction such as 1:25,000 shows great detail for a small area
“small scale” vs “large scale” often confused

18. Multi-scale The same feature represented in different scales.
Example: lake
large scale map of 1:25,000 may
show individual houses
smaller scale map of 1:500,000
shows only points representing
villages
Large scale
(1:25.000)
Small scale 1:

19. Multi-formats
Raster
Vector
Raster-Vector-Raster
DXF-DGN-etc.
Shapefile
KML
Etc.

20. Geographic Database Geographic Data Characteristics/Examples
Definitions:
Entity/Attribute/Dataset/Database
Data Modeling
Spatial representation
Vector/Raster
Topology

21. Descriptive Data vs Geographic Data
Descriptive attributes
Geographic Data:
Spatial attributes
Location
Form
Geographic location is the element that distinguishes geographic information from all other other types.

22. Geographic Data Characteristics :
Position:
explicit geographic reference
Cartesian coordinates :X,Y,Z
Geographic coordinates (lat, log)
implicit geographic reference
Address
Place-name
Etc.
Geometric Form:
ex: a polygon representing a parcel of land
Spatial data describes the location and shape of geographic features, and their relationship to other features, and
Descriptive data which characterizes the geographic features.

23. Example1: Parcel of land
Attribute (descriptive) Data
Landowner
Area
Etc.
Spatial data
Position
Located at 100 Nelson Mandela Ave
X= a; Y=b within system (X,Y)
Form
dimensions (sides and arcs, constituting a polygon)

24. Example 2: District Attribute (Descriptive) data: District-Code
District-Name
Population 1990
Population 2000
Population 2010
Spatial data:
Geographical Position
Polygon

25. Spatial entity
We use the term entity to refer to a phenomenon that can not be subdivided into like units.
Example: a house is not divisible into houses, but can be split into rooms.
Others: a lake, a statistical unit, a school, etc.
In database management systems, the collection of objects that share the same attributes.
An entity is referenced by a single identifier, perhaps a place-name, or just a code number
Each spatial entity has one or more attributes that identify what the entity is, describe it, or represent some magnitude associated with the entity. Indeed, the type of analysis you plan to do depends on the type of attributes you are working with.
Example: you can categorize roads by whether they are local roads, highways, etc; by their length; their width; their pavement; etc.

26. Attribute
Each spatial entity has one or more attributes that identify what the entity is, and describe it.
Example: you can categorize roads by whether they are local roads, highways, etc; by their length; their width; their pavement; etc.
The type of analysis you plan to do depends on the type of attributes you are working with.

27. Dataset
“A dataset is a single collection of values or objects without any particular requirement as to form of organization.”
Example: Streets, rivers, cities, etc.

28. Geographic Database
“A geographic database is a collection of spatial data and related descriptive data organized for efficient storage, manipulation and analysis by many users.”
It supports all the different types of data that can be used by a GIS such as:
Attribute tables
Geographic features
Satellite and aerial imagery
Surface modeling data
Survey measurements
Fundamentally, a GIS is based on a structured database that describes the world in geographic terms.

29. Data Modeling
Data Modeling is the process of defining (geographic features) to be included in the database, their attributes and relationships, and their internal representation in the Database. It involves the development of conceptual, logical and physical models of the geographic Database.
The outcomes include a Data Dictionary

30. Modeling Process Reality Modeling Geographic Database (data & treat.)
Abstracting the Real World
Reality
Modeling
(data & treat.)
We have real world as percieved by users > And we would build a physical data model
We insert an intermediate
step: the CDM
Geographic
Database

31. ANSI/SPARC: Study Group on Data Base Management Systems (1975)
Different users have different views of the world
“Real World”
External Model 1
External Model 2
External Model 3
Conceptual Model
Logical Model
Physical Model

32. Conceptual Model A synthesis of all external models (user’s views).
Schematic representations of phenomena and how they are related.
Information content of the database (not the physical storage) so that the same conceptual model may be appropriate for diverse physical implementations.
Therefore, the conceptual model is independent from technology.
The conceptual level corresponds to a synthesis of all external models.
Although an abstraction of the real world, the conceptual model is consisting of schematic representations of phenomena and how they are related.
The organization scheme created at this stage generally deals with only the information content of the database, not the physical storage, so that the same conceptual model may be appropriate for diverse physical implementations. Therefore, the conceptual model is independent from technology.

33. Conceptual Model (cont.)
Easy to read
Conceived for the analyst or designer
Objective representation of the reality, therefore independently from the selected GDB System
One conceptual model for the Database

34. Data Logical Model & Physical Model
We transform the conceptual model into a new modeling level which is more computing oriented: the logical model (Example: the Relational Database approach)
We transform the logical model into an internal model (physical model) which is concerned with the byte-level data structure of the database.
Whereas the logical model is concerned with tables and data records, the physical model deals with storage devices, file structure, access methods, and locations of data.

35. Several types of data organization
Hierarchical model
- Hierarchical relationships between data(parent- child)
Network Model
- Focus on connections (e.g. airline booking system)
Relational model
- Based on relations (tables)- True Relat. DBMS use SQL
Object-Oriented model
- Focus on Objects
Hierarchical: (Obselete).
Hierarchical model
- Hierarchical relationships between data (parent-child)
Network Model
- Focus on connections (e.g. airline booking systems)
- Adv.: fast, efficient; Disadv.: inflexible
Relational model
- Data is organized within Tables (files) and relationships expressed between tables and data elements
- True Relational DBMS use the Structured Query Language (SQL)
Object-Oriented model
- Focus on Objects: efficient storage and retrieval
- (Future + Web)
Network Model: technically obselete

36. Entity-relationship Formalism
Entity name
Attributes
ENTITY_NAME1
-attribute 1
-attribute 2 …
ENTITY_NAME2
-attribute 1
-attribute 2 …
0-N
0-1
Identifier (key-attribute)
Association (relationship)
Maximum cardinality
(indeterminable/any number)
(0,N) refers to the cardinality of the relationship. It means that , for example, at a minimum
Minimum cardinality
(0,N) refers to the cardinality of the relationship

37. An example of land parcels

38. The E/R diagram for land parcels
STREET
-name A B
SEGMENT
-number
PARCEL
-number
2-N
0-1
1-2
3-N
1-N
2-2
A: Streets have edges (segments)
B: parcels have boundaries (segments)
C: line have two endpoints
D: parcels have owners, and people own land. C D
2-N
1-N
POINT
-number
-x,y
LANDOWNER
-name
-date-of-birth

39. Data Tables

40. Data Dictionary Definition:
A data catalog that describes the contents of a database. Information is listed about each field in the attribute table and about the format, definitions and structures of the attribute tables.
A data dictionary is an essential component of metadata information.

41. Example Definition of entities
RAIL: way of communication and transportation
Definition of attributes
RAIL-ID: reference numbers for rail segments
RAIL_CLASS: single track, double track, electrified, etc.
RAIL_NAME: name for particular railway
Explanations for measurements of attributes (type of attribute values) or coding practices
RAIL-ID: INTEGER
RAIL-NAME: CHARACTER, LONG=30

42. Sample components of a digital EA map

43. EA database entities Street Number Name --- Admin. Unit AU AU_Pop. ---
EA-code
Area
Pop.
Buildings
Number
HHs
Etc.
Crew leader area
CL-code
Name
RO responsible
Landmark --
---

44. Example of Relations
EA entity can be linked to the entity crew leader area. The table for this entity could have attributes such as the name of the crew leader, the regional office responsible, contact information, and the crew leader code (CL code) as primary code, which is also present in the EA entity. R EA
EA-code
Area
Pop.
Crew leader area
CL-code
Name
RO responsible
1-1
1-N

45. Entity: Enumeration areas
Type (attributes)
EA-code Area Pop CL-code
… … …
Identifier

46. Components of a digital EA database
Boundary database

47. A Simpler Alternative
In many countries, EA map design may be simpler than in this example
Instead of a fully integrated digital base map in vector format, rasterized images of topographic maps may be used as a backdrop for EA boundaries
In some instances, map features may be more generalized, for instance by using only the centerlines for the streets and polygons for entire city blocks rather than for individual houses
This can include the use of free data as a baseline or starting point in the creation or updating of census related maps

48. Data Representation
Raster
Vector
Real World

49. Two Fundamental Types of Data
GIS work with two fundamentally different types of geographic information
Vector
Raster (or Grid)
Both types have unique advantages and disadvantages
A GIS should be able to handle both types

50. Vector vs Raster or Discrete vs Continuous
River
x1,y1
Raster-based analysis:
Area of analysis divided into squares of uniform size.
- Each cell characterizes the feature of interest within this area with a single value.
Vector data:
Coordiante-based data structure commonly used to represent linear features.
Each feature is represented as a list of ordered x,y coordinates
xn,yn

51. Raster Data
A raster image is a collection of grid cells - like a scanned map or picture
Raster data is extremely useful for continuous data representation
elevation
slope
modeling surfaces
Satellite imagery and aerial photos are commonly used raster data sets
Easier data structure
Certain analysis operations are more easily implemented
Good for representing continuous variation (vegetation, countour lines, etc.)

52. Vector Data Vector data are stored as a series of x,y coordinates
Good for discrete data representation
points: wells, town centroids
lines: roads, rivers, contours
polygons: enumeration areas,
districts, town boundaries, building footprints

53. Raster-Vector conversion (“vectorization”)
Computer algorithms are used to convert data of one type to the other:
Vector --- Raster is trivial
Raster ---- Vector is harder

54. Vector data + image (raster)

55. Vector: Points, lines, polygons
Set of geometric primitives:
points
lines
polygons y
node
vertex x

56. II I Vector Structure Spaghetti Topology Network (graph)
On retrouve dans le modèle vecteur, plusieurs niveaux sémantiques: du plus structuré (topo) vers le plus simple (spaghetti)
Modèle spaghetti
Modèle réseau (graphe quelconque)
Modèle topologique

57. Spaghetti File No Topology = raw file or ‘spagehetti file’
Lines not connected; have no ‘intelligence’

58. Example of “Spaghetti” data structure6
Poly coordinates
A (1,4), (1,6), (6,6), (6,4), (4,4), (1,4)
B (1,4), (4,4), (4,1), (1,1), (1,4)
C (4,4), (6,4), (6,1), (4,1), (4,4) A 5 4 3 B C 2 1

59. Topology
Data structure in which each point, line and piece or whole of a polygon :
“knows” where it is
“knows” what is around it
“understands” its environment
“knows” how to get around
Helps answer the question what is where?

60. Topology: Spatial Relationships
Left Polygon = A
Right Polygon = B
Node 1 = Chains A,B,C
Chain A is connected to chains B & C
Polygon B Contained within polygon A
Adjacency
Connectivity
Containment

61. Example of Topological data structure
Node X Y Lines
I ,2,4
II ,5,6
III ,3,5
IV ,3,6 1
Poly Lines
A ,4,5
B ,4,6
C ,5,6 6 A 5 I II
III 4 4 5 3
From To Left Right
Line Node Node Poly Poly
I III O A
I IV B O
III IV O C
I II A B
II III A C
II IV C B 2 B C 3 6 IV 1 2
O = “outside” polygon

62. Encoding Topology (not): CAD

63. Encoding Topology: GIS

64. Comparison Advantages: Spaghetti Topology Set of independent objects
Representation of heterogonous objects within the same model
Appropriate to CAD
Pre-calculation of topological relations
Maintenance of topological constraints
correspondence with exchange formats

65. …cont. Disadvantages: Spaghetti Topology
Spatial Relationships calculated
Risk of incoherence (duplication of common boundaries)
High cost of up-to-date
Many levels of indirections for complex objects
Maintenance

66. Some well known Topological models
TIGER: Topologically Integrated Geographic Encoding and Referencing (Census Bureau of the USA)
Line is the principal element to which are related points and area features
ARC/INFO model: ESRI
Point, Line, Polygon

67. Zip Codes Cities Voting Districts Census Tracts Counties MCD’s
TIGER Data: Polygon
Zip Codes
Cities
Voting Districts
Census Tracts
Counties
MCD’s
Block Groups

68. TIGER Data: Line
Railroads
Streams
Streets

69. TIGER Data: Point
Zip+4 Centroids
Place Names
Landmarks
Key Locations

70. Recapitulation on spatial models
Transformations between models:
“vectorization” of raster images (costly)
topology toward spaghetti (easy)
spaghetti toward topology (possible but costly)
The vector model most used, essentially topology; it’s useful to integrate raster and vector

71. Spatial Analysis: Query
select features by their attributes:
“find all districts with literacy rates < 60%”
select features by geographic relationships
“find all family planning clinics within this district”
combined attributes/geographic queries
“find all villages within 10km of a health facility that have high child mortality”
Query operations are based on the SQL (Structured Query Language) concept

72. Examples:
What is at…?
Features that meet a set of criteria

73. Spatial Analysis (cont.)
Buffer: find all settlements that are more than 10km from a health clinic
Point-in-polygon operations: identify for all villages into which vegetation zone they fall
Polygon overlay: combine administrative records with health district data
Network operations: find the shortest route from village to hospital

74. Modeling/Geoprocessing
modeling: identify or predict a process that has created or will create a certain spatial pattern
diffusion: how is the epidemic spreading in the province?
interaction: where do people migrate to?
what-if scenarios: if the dam is built, how many people will be displaced?

75. Spatial relationships
Logical connections between spatial objects represented by points, lines and polygons
e.g.,
- point-in-polygon
- line-line
- polygon-polygon
Fundamental Questions addressed by GIS
What is in a particular place?
Find the position of some object
What is the area of, the distance from specific object?
Find patterns- by looking at the distribution of features on the map instead of just an individual feature
Combination of data layers
Point in polygon operation
Line in polygon operation
Polygon overlay

76. Spatial Operations “adjacent to” “connected to” “near to”
“intersects with”
“within”
“overlaps”
etc.

77. “is nearest to” Point/point
Which family planning clinic is closest to the village?
Point/line
Which road is nearest to the village
Same with other combinations of spatial features

78. “is nearest to”: Thiessen Polygons

79. “is near to”: Buffer Operations
Point buffer
Affected area around a polluting facility
Catchment area of a water source

80. Buffer Operations Line buffer
How many people live near the polluted river?
What is the area impacted by highway noise

81. Buffet Operations Polygon buffer
Area around a reservoir where development should not be permitted

82. “ is within”: point in polygon
Which of the cholera cases are within the containment area

83. Problem: We may have a set of point coordinates representing clusters from a demographic survey and we would like to combine the survey information with data from the census that is available by enumeration areas.
Solution:
“Point-in-Polygon” operation will identify for each point the EA area into which it falls and will attach the census data to the attribute record of that survey point.

84. Polygon Overlay

85. “overlaps”: Polygon overlay

86. Data Layers
A GIS combines layers of information about a place to give you a better understanding of that place. What layers of information you combine depends on your purpose.
GIS uses geography, or space, as the common key element between datasets. Information is linked only if it relates to the same geographic area.
- Data Layers: space as an indexing system.
- A GIS combines layers of information about a place to give you a better understanding of that place. What layers of information you combine depends on your purpose.

87. Spatial aggregation Example of Spatial aggregation:
fusion of many provinces constituting an economic region

88. Spatial data transformation: interpolation
Example 1: Based on a set of station precipitation surface estimates, we can create a raster surface that shows rainfall in the entire region
13.5
20.1
26.0
27.2
12.7
15.9
24.5
26.1

89. GIS capabilities:
Visualization

90. Implementing a GIS Consider the strategic purpose
Plan for the planning
Determine technology requirements
Determine the end products
Define the system scope
Create a data design
Choose a data model
Determine system requirements
Analyze benefits and costs
Make an implementation plan
Source: Thinking About GIS, Third Edition Geographic Information System Planning for Managers

91. GIS: Enables us to handle very large amounts of data
Example: census data
– thousands of EAs
– hundreds of variables
– many complementary data layers
(roads, rivers, public facilities)
Example: remote sensing
– satellites send huge amounts of data
that need to be processed, interpreted
and stored

92. GIS: Helps to make data re-usable and useful to many more users
Census geography
– EA maps do not have to be redrawn
every time, only updated
– census information can be used for
many more applications
– data sharing among agencies

93. In Conclusion GIS for inventory/visualization
GIS creates maps from data pulled from databases anytime to any scale for anyone
GIS for database management
GIS for spatial analysis/modeling
GIS a tool to query, analyze, and map data in support of the decision making process.
GIS can be viewed three ways:
The Database View
The Map View
The Model View
Improve Organizational Integration
A GIS can link data sets together by common locational data, such as addresses, which help departments and agencies share their data. By creating a common database, data can be collected once and used many times.

94. What is Not GIS GPS – Global Positioning System …not just software!
…not just for making maps!
Maps are an input data to and a “product” of a GIS
A way to visualize the analysis

95. Literature related to Census Mapping & GIS
US National Research Council:
Tools and Methods for Estimating
Populations At Risk
David Martin (1996)
Geographic Information Systems:
Socioeconomic Applications
Longley and al, Wiley (2005)
Geographic Information Systems and
Science, second edition
ESRI Press:
Unlocking the Census with GIS
Mapping the Census 2000

96. Contact Information:
Demographic Statistics Section
UN Statistics Division
New York

97. Compromise projections
Comoromise projections: Do not preserve any proprety but reprsent a good compromise between the different objectives. E.g. Robibson’s projection of the world

98. Vector to Raster Conversion: Polygonsb a c

99. Vector to Raster Conversion: Lines

100. Raster to Vector Conversion: Polygons

101. Raster to Vector Conversion: Polygons