GIS Fundamentals/
Geographic Database Design
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Information Cycle
Territory
Data
GIS
Information
DSS
Decision
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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.
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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.
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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).
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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.
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Geographic Data
Geographic Information
Input
Output
GIS
•Maps
•Census
•Field Data
•RS Data
•Others
Data
Capture
Manipulation
Analysis
Display
Storage
• Reports
• Maps
• Photo.
Products
• Statistics
• Input Data
for models
GIS Components
Other GIS
User
Interface
Models
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
GIS: Main Characteristics
•
Integration of Multiple data:
•
•
•
- Sources
- Scales
- Formats
•
Geographic Database
•
Spatial Analysis
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Data from multiple sources-at multiple scales-in
multiple formats
Census/
Tabular
data
Maps
Picture &
Multimedia
GPS/ air photos/
satellite images
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
The Latitude/Longitude reference system
•
latitude φ : angle from
the equator to the
parallel
•
longitude λ : angle
from Greenwich
meridian
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Map Projections (Cont.)
•
We can think of a projection as a
light source located inside the globe
which projects the features on the
earth’s surface onto a flat map
•
Point p on the globe becomes point p
on the map
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Compromise projections
•
Do not preserve any property, but represent a good
compromise between the different objectives
•
e.g., Robinson’s projection for the World
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Compromise projections
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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)
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
UTM
•
Cylindrical projection with a
central meridian that is specific
to a standard UTM zone
•
60 zones around the world
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Space as an indexing system
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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 1cm on the map
corresponds to 100,000 cm or 1 km in the real world
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
The concept of scale
•
scale is essentially a ratio or representative fraction
•
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” versus “large scale” often confused
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Multi-scales
•
The same feature represented in different
scales.
• Example: lake
Large scale
Small scale
(1:25.000)
1:500.000
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Multi-formats
•
•
•
•
•
•
•
Raster
Vector
Raster-VectorRaster
DXF-DGN-etc.
Shapefile
KML
Etc.
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Geographic Database
•
Geographic Data
•
Characteristics
•
Examples
•
Geographic Dataset
•
Geographic Database Concepts
•
Spatial entity
•
Data Modeling
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Descriptive Data vs Geographic Data
•
General Data:
• Descriptive attributes
•
Geographic Data:
• Descriptive attributes
• Spatial attributes
• Location
• Form
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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)
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Example 2: District
•
Attribute (Descriptive) data:
• District-Code
• District-Name
• Population 1990
• Population 2000
• Population 2010
•
Spatial data:
• Geographical Position
• Polygon
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Geographic Database
•
•
Definition
Components:
• Spatial Entity/Attribute/Dataset
• Data Modeling/Data Dictionary
• Spatial Representation
• Vector/Raster
• Topology
• Standard Spatial Operations
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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 placename, or just a code number
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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.
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Dataset
“A dataset is a single collection of values or objects without
any particular requirement as to form of organization.”
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Data Modeling
•
Data Approach
•
Modeling Process
•
Entity/Relationship Approach
•
Example
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Modeling Process
Abstracting the Real World
Reality
Modeling
(data & treat.)
Geographic
Database
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
ANSI/SPARC: Study
Group on Data Base
Management
Systems (1975)
External Model 1
“Real
World”
Different users have different
views of the world
External Model 2
External Model 3
Conceptual Model
Logical Model
Physical Model
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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.
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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.
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Entity-relationship Formalism
Entity
Entity name
Attributes
ENTITY_NAME
ENTITY_NAME
-attribute 1
-attribute 2
…
-attribute 1
-attribute 2
…
0-N
Identifier
(key-attribute)
0-1
Maximum cardinality
Association
(relationship)
Minimum cardinality
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
An example of land parcels
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
The E/R diagram for land parcels
STREET
A
-name
2-N
B
SEGMENT
0-1
A: Streets have edges
(segments)
B: parcels have boundaries
(segments)
C: line have two endpoints
D: parcels have owners, and
people own land.
PARCEL
-number
-number
1-2
3-N
2-2
1-N
C
D
2-N
POINT
-number
-x,y
1-N
LANDOWNER
-name
-date-of-birth
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Data Tables
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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.
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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.
1-1
Crew leader area
1-N
CL-code
Name
RO responsible
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Entity: Enumeration areas
Type (attributes)
EA-code
Area
Pop.
50101
50102
50103
50104
50201
50202
50203
50204
…
28.5
20.2
18.1
22.4
19.3
17.6
25.7
26.8
…
988
708
590
812
677
907
879
591
…
CL-code
78
78
78
78
79
79
79
79
Identifier
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Components of a digital spatial census database
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Data Representation
Raster
Vector
Real World
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Vector vs Raster or Discrete vs Continuous
Raster
Vector
River
x1,y1
xn,yn
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Raster-Vector conversion (“vectorization”)
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Vector to Raster Conversion: Polygons
b
a
c
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Vector to Raster Conversion: Lines
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Raster to Vector Conversion: Polygons
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Raster to Vector Conversion: Polygons
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Vector data
+
image (raster)
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Vector: Points, lines, polygons
Set of geometric primitives:
•
points
lines
polygons
y
node
vertex
x
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Vector Structure
•
•
•
Spaghetti
Topology
I
II
Network
(graph)
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Spaghetti File
No Topology = raw file or ‘spagehetti file’
Lines not connected; have no ‘intelligence’
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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
2
1
B
1
2
C
3 4 5
6
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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?
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Topology: Spatial Relationships
Left Polygon = A
Right Polygon = B
Adjacency
Node 1 = Chains
A,B,C
Chain A is
connected to
chains B & C
Connectivity
Polygon B
Contained within
polygon A
Containment
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Example of Topological data structure
1
6
5
4
3
A
I
II
4
2
1
III
5
B
6
IV
2
3
1
2
Node
I
II
III
IV
C
4 5
3
6
O = “outside” polygon
X
1
4
6
4
Y Lines
4 1,2,4
4 4,5,6
4 1,3,5
1 2,3,6
From
Line
Node
1
I
2
I
3
III
4
I
5
II
6
II
Poly
A
B
C
To
Left
Node Poly
III
O
IV
B
IV
O
II
A
III
A
IV
C
Lines
1,4,5
2,4,6
3,5,6
Right
Poly
A
O
C
B
C
B
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Encoding Topology (not): CAD
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Encoding Topology: GIS
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Comparison
Advantages:
Spaghetti
-Set of independent
objects
- Representation of
heterogonous objects
within the same model
-Appropriate to CAD
Topology
-Pre-calculation of
topological relations
-Maintenance of
topological constraints
- correspondence with
exchange formats
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Comparison (cont.)
Disavantages
Spaghetti
-Spatial Relationships
calculated
- Risk of incoherence
(duplication of common
boundaries)
Topology
-High cost of up-to-date
-Many levels of
indirections for complex
objects
-Maintenance
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
TIGER Data: Polygon
Cities
Census
MCD’s
Zip
Codes
Counties
Block
Voting
Tracts
Groups
Districts
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
TIGER Data: Line
Railroads
Streets
Streams
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
TIGER Data: Point
Zip+4
Key
Place
Landmarks
Locations
Names
Centroids
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Examples:
What is
at…?
Features that
meet a set of
criteria
Id
0012376027
Name
Population
Popdens
Num_H
H
Clinics
Limop
31838
37.5
8719
8
Population density
greater than 100
persons/sqkm?
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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?
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Spatial relationships
•
•
Logical connections between spatial objects
represented by points, lines and polygons
e.g.,
- point-in-polygon
- line-line
- polygon-polygon
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Spatial Operations
•
“adjacent to”
•
“connected to”
•
“near to”
•
“intersects with”
•
“within”
•
“overlaps”
•
etc.
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
“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
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
“is nearest to”: Thiessen Polygons
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
“is near to”: Buffer Operations
•
Point buffer
•
Affected area around a polluting facility
•
Catchment area of a water source
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Buffer Operations
•
Line buffer
•
How many people live near the polluted river?
•
What is the area impacted by highway noise
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Buffet Operations
•
Polygon buffer
•
Area around a reservoir where development
should not be permitted
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
“ is within”: point in polygon
•
Which of the cholera cases are within the
containment area
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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.
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
“overlaps”: Polygon overlay
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Polygon Overlay
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Data Layers
A
d m
in is t r a t iv e
u n i t s
E
le v a t i o n
B
u i ld in g s
H
y d r o lo g y
R
o a d s
V e g e t a t io n
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Spatial aggregation
•
Example of Spatial aggregation:
• fusion of many provinces constituting an
economic region
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
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
12.7
20.1
26.0
27.2
15.9
24.5
26.1
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
GIS capabilities:
Visualization
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Implementing a GIS
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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
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
GIS:
Enables us to handle very large amounts of data
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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
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
GIS:
Helps to make data re-usable and useful to
many more users
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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
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
In Conclusion
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GIS for inventory/visualization
• GIS creates maps from data pulled from databases
anytime to any scale for anyone
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GIS for database management
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GIS for spatial analysis/modeling
• GIS a tool to query, analyze, and map data in support
of the decision making process.
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
What is Not GIS
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GPS – Global Positioning System
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…not just software!
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…not just for making maps!
• Maps are an input data to and a “product” of a GIS
• A way to visualize the analysis
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Literature related to Census Mapping & GIS
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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
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007
Contact Information:
Demographic Statistics Section
UN Statistics Division
New York
globalcensus2010@un.org
Workshop on Census Cartography and Management, Lusaka, Zambia, 08–12 October 2007