GIS Fundamentals/ Geographic Database Design

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.

8
Components of a GIS
9
GIS Components
Geographic Data
Geographic Information
Input
Output
GIS

• Reports
• Maps
• Photo. Products
• Statistics
• Input Data for models

Manipulation Analysis

• Maps
• Census
• Field Data
• RS Data
• Others

Data Capture
Display
Storage
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 f angle from the equator to the
  parallel
• longitude ? angle from Greenwich meridian

14
Map Projections

• Curved surface of the earth needs to be
  flattened to be presented on a map
• Projection is the method by which the curved
  surface is converted into a flat representation

15
Map Projections (Cont.)

• We can think of a projection as a light source
  located inside the globe which projects the
  features on the earths surface onto a flat map
• Point p on the globe becomes point p on the map

16
Distortion in Map Projections

• Some distortion is inevitable
• Less distortion if maps show only small areas,
  but large if the entire earth is shown
• Projections are classified according to which
  properties they preserve area, shape, angles,
  distance

17
Compromise projections

• Do not preserve any property, but represent a
  good compromise between the different objectives
• e.g., Robinsons projection for the World

18
Compromise projections
19
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)

20
UTM

• Cylindrical projection with a central meridian
  that is specific to a standard UTM zone
• 60 zones around the world

21
Space as an indexing system
22
The concept of scale

• scale is the ratio between distances on a map and
  the corresponding distances on the earths
  surface
• e.g., a scale of 1100,000 means that 1cm on the
  map corresponds to 100,000 cm or 1 km in the real
  world

23
The concept of scale

• scale is essentially a ratio or representative
  fraction
• small scale small fraction such as 110,000,000
  shows only large features
• large scale large fraction such as 125,000
  shows great detail for a small area
• small scale versus large scale often confused

24
Multi-scales

• The same feature represented in different scales.
• Example lake

Large scale (125.000)
Small scale 1500.000
25
Multi-formats

• Raster
• Vector
• Raster-Vector-Raster
• DXF-DGN-etc.
• Shapefile
• KML
• Etc.

26
Geographic Database

• Geographic Data
• Characteristics
• Examples
• Geographic Dataset
• Geographic Database Concepts
• Spatial entity
• Data Modeling

27
Descriptive Data vs Geographic Data

• General Data
• Descriptive attributes
• Geographic Data
• Descriptive attributes
• Spatial attributes
• Location
• Form

28
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

29
Example1 Parcel of land

• Attribute (descriptive) Data
• Landowner
• Area
• Etc.
• Spatial data
• Position
• Located at 100 Nelson Mandela Ave
• X a Yb within system (X,Y)
• Form
• dimensions (sides and arcs, constituting a
  polygon)

30
Example 2 District

• Attribute (Descriptive) data
• District-Code
• District-Name
• Population 1990
• Population 2000
• Population 2010
• Spatial data
• Geographical Position
• Polygon

31
Geographic Database

• Definition
• Components
• Spatial Entity/Attribute/Dataset
• Data Modeling/Data Dictionary
• Spatial Representation
• Vector/Raster
• Topology
• Standard Spatial Operations

32
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

33
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.

34
Dataset

• A dataset is a single collection of values or
  objects without any particular requirement as to
  form of organization.

35
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

36
Data Modeling

• Data Approach
• Modeling Process
• Entity/Relationship Approach
• Example

37
Modeling Process
Abstracting the Real World
Reality
Modeling
(data treat.)
Geographic Database
38
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
39
Conceptual Model

• A synthesis of all external models (users
  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.

40
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

41
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.

42
Several types of data organization

• Hierarchical model
• - Hierarchical relationships between data
  (parent- child)
• Network Model
• - Focus on connections
• Relational model
• - Based on relations (tables)
• Object-Oriented model
• - Focus on Objects

43
Entity-relationship Formalism
Entity
Entity name
Attributes
ENTITY_NAME
-attribute 1 -attribute 2
ENTITY_NAME
-attribute 1 -attribute 2
0-N
0-1
Identifier (key-attribute)
Maximum cardinality
Association (relationship)
Minimum cardinality
44
An example of land parcels
45
The E/R diagram for land parcels
STREET
-name
A
B
PARCEL
-number
SEGMENT
-number
2-N
0-1
3-N
1-2
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
46
Data Tables
47
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.

48
Example Census GIS database

• - Basic elements
• Entity administrative or census units
• enumeration areas
• Entity type / Relations
• Components of a digital spatial census database
• Boundary database
• Geographic attribute tables
• Census data tables

49
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
Crew leader area
CL-code Name RO responsible
EA
EA-code Area Pop.
1-N
1-1
50
Entity Enumeration areas
Type (attributes)
EA-code Area Pop. CL-code
50101 28.5 988 78 50102 20.2 708 78 50103 18.1 590 78 50104 22.4 812 78 50201 19.3 677 79 50202 17.6 907 79 50203 25.7 879 79 50204 26.8 591 79
Identifier
51
Components of a digital spatial census database
52
Data Representation
Raster
Vector
Real World
53
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

54
Vector vs Raster or Discrete vs Continuous
Raster
Vector
River





x1,y1
xn,yn
55
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

56
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

57
Raster-Vector conversion (vectorization)
58
Vector to Raster Conversion Polygons
b
a
c
59
Vector to Raster Conversion Lines
60
Raster to Vector Conversion Polygons
61
Raster to Vector Conversion Polygons
62
Vector data image (raster)
63
Vector Points, lines, polygons

• Set of geometric primitives

points
lines
polygons
y
node
vertex
x
64
Vector Structure

• Spaghetti
• Topology
• Network
• (graph)

65
Spaghetti File
No Topology raw file or spagehetti
file Lines not connected have no intelligence
66
Example of Spaghetti data structure
6
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
1 2 3 4 5 6
67
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?

68
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
69
Example of Topological data structure
Node X Y Lines I 1 4 1,2,4 II 4 4 4,5,6 III 6 4 1,3,5 IV 4 1 2,3,6
1
Poly Lines A 1,4,5 B 2,4,6 C 3,5,6
6
A
5
I
II
III
4
4
5
3
From To Left Right Line Node Node Poly Poly 1 I III O A 2 I IV B O 3 III IV O C 4 I II A B 5 II III A C 6 II IV C B
2
B
C
3
6
IV
1
2
1 2 3 4 5 6
O outside polygon
70
Encoding Topology (not) CAD
71
Encoding Topology GIS
72
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
73
Comparison (cont.)
Disavantages
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
74
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

75
TIGER Data Polygon
Counties
MCDs
Census Tracts
Block Groups
Zip Codes
Cities
Voting Districts
76
TIGER Data Line
Streams
Streets
Railroads
77
TIGER Data Point
Key Locations
Landmarks
Place Names
Zip4 Centroids
78
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
  its useful to integrate raster and vector

79
Spatial Analysis Query

• select features by their attributes
• find all districts with literacy rates lt 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

80
Examples
What is at?
Features that meet a set of criteria
81
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

82
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?

83
Spatial relationships

• Logical connections between spatial objects
  represented by points, lines and polygons
• e.g.,
• - point-in-polygon
• - line-line
• - polygon-polygon

84
Spatial Operations

• adjacent to
• connected to
• near to
• intersects with
• within
• overlaps
• etc.

85
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

86
is nearest to Thiessen Polygons
87
is near to Buffer Operations

• Point buffer
• Affected area around a polluting facility
• Catchment area of a water source

88
Buffer Operations

• Line buffer
• How many people live near the polluted river?
• What is the area impacted by highway noise

89
Buffet Operations

• Polygon buffer
• Area around a reservoir where development should
  not be permitted

90
is within point in polygon

• Which of the cholera cases are within the
  containment area

91

• 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.
92
overlaps Polygon overlay
93
Polygon Overlay
94
Data Layers
95
Spatial aggregation

• Example of Spatial aggregation
• fusion of many provinces constituting an economic
  region

96
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
97
GIS capabilities Visualization
98
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
99
GISEnables 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

100
GISHelps 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

101
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.

102
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

103
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

104
Contact Information Demographic Statistics
Section UN Statistics Division New York
globalcensus2010_at_un.org