Atelier regional sur lorganisation et la cartographie des recensements, Rabat, Maroc, 12-16 novembre...

Atelier regional sur l’organisation et la cartographie des recensements, Rabat, Maroc,12-16 novembre 2007

GIS Fundamentals/Geographic Database Design

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

Information Cycle (Contexte d’émergence des SIG)

Territory

Information DecisionDSS

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.

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.

Information System (IS)

• An Information System is a set organized globally and

comprising elements (data, logiciel, equipment, procedures,

users) that coordinate for working towards doing a result

(information).

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.

SIG: Système d’Information «Géographique»:

• Définition: Un SIG peut être défini comme un ensemble organisé intégrant le matériel, le logiciel, le personnel, les méthodes et les données nécessaires pour permettre la saisie, le stockage, l’analyse et l’affichage de l’information géoréférencée en vue de résoudre des problèmes complexes de gestion et de planification.

Components of a GIS

GIS Components

Input Output

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

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

Data Capture

Storage

ManipulationAnalysis

Display

ModelsOther GISUser Interface

Geographic Data Geographic Information

GIS: Main Characteristics

• Integration of Multiple data:

- Sources- Scales- Formats

• Geographic Database

• Spatial Analysis

GPS/ air photos/ satellite images

Census/ Tabular data

Picture & Multimedia

Data from multiple sources-at multiple scales-in multiple formats

• To integrate geographic data from many different sources, we need to use a consistent spatial referencing system for all data sets

Referencing map features: Coordinate systems & map projections

The Latitude/Longitude reference system

• latitude φ : angle from the equator to the parallel

• longitude λ : angle from Greenwich meridian

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, distance, shape and angles,

• Some distortion is inevitable:• Less distortion if maps show only small areas, but large if

the entire earth is shown

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 of longitude around the world

Space as an indexing system

The concept of cartographic 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

Multi-scale

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

Large scale

(1:25.000)

Small scale

1:500.000

Multi-formats

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

Geographic Database

• Geographic Data

• Characteristics/Examples

• Definitions:

• Entity/Attribute/Dataset/Database

• Data Modeling

• Spatial representation

• Vector/Raster

• Topology

Descriptive Data vs Geographic Data

• Descriptive Data:• Descriptive attributes

• Geographic Data:• Descriptive attributes

• Spatial attributes• Location• Form

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

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)

Example 2: District

• Attribute (Descriptive) data:• District-Code• District-Name• Population 1990• Population 2000• Population 2010

• Spatial data:• Geographical Position • Polygon

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

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.

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.

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

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

Modeling Process

Reality

Geographic Database

Modeling(data & processing.)

Abstracting the Real World

Conceptual Model

Logical Model

“Real World”

Physical Model

External Model 1

Different users have different views of the world

ANSI/SPARC: Study Group on Data Base Management Systems (1975)

External Model 2 External Model 3

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.

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

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.

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

• Object-Oriented model- Focus on Objects

Entity-relationship Formalism

ENTITY_NAME1

-attribute 1

-attribute 2

ENTITY_NAME2

-attribute 1

-attribute 2

0-N 0-1

Minimum cardinality

Maximum cardinality

(indeterminable/any number)

Attributes

Association (relationship)

Entity Entity name

Identifier (key-attribute)

(0,N) refers to the cardinality of the relationship

An example of land parcels

The E/R diagram for land parcels

STREET

-namePARCEL

-number

2-N 3-N

SEGMENT

-number

LANDOWNER

-date-of-birth

0-1 1-2

A: Streets have edges (segments)

B: parcels have boundaries (segments)

C: line have two endpoints

D: parcels have owners, and people own land.

Data Tables

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.

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

Sample components of a digital EA map

891011

272829

454647

5051 52

636465

777879

848586

1 2 3 4

25 26 27

585960

41424344

Enumeration Area Map Symbols

National Statistical Office -July 1998

Census2000

Locality

District EA-Code

Hospital

Church

School

Buildingnumber

Province:District:Locality:EA-Code:

CartaniaChartesMaptown

022100361

Approximate scale

Street Network Buildings

Boundaries Annotation and symbols

Building numbers Neatlines and legend

349350

b ert A

rcator Avenue

Cassini Drive

Imhof Drive

Eckert Drive

Miller

eB on n e S tree t

M o llw e id e S tre et

Grinte

n Stree

B es se l S tre et

S tree t

R o bins on S tre et

Tisso t S tre et

G a ll S tre e t

lius Stre

Clarke S

To b le r Stre etSnyd

er Stre

Krasso

wsk ij S

349350

Enum eration Area Map Sym bols

Nationa l S tatistica l O ffice - Ju ly 1998Census 2000

Locality

D istrict EA-Code

Hospital

Church

School

Buildingnumber

Province:D istrict:Locality:EA-Code:

CartaniaChartesMaptown

022100361

500 100

Approximate scale

1 2 1 3

891 011

1 51 6

2 12 2

2 42 5 2 6

2 72 82 9

3 13 2

3 5 3 6

4 04 1

4 54 64 7

4 8 4 9 5 0 5 1 5 2

5 35 4

5 75 8

6 06 1

6 36 46 5

7 27 3

7 4 7 5

7 77 87 9

8 08 1 8 2

8 48 58 6

8 78 8

1 2 3 4

891 011

2 5 2 6 2 7

3 33 4

2 22 3

2 82 9

3 13 2

5 25 4

4 44 5

4 34 0

4 14 2

6 15 7

5 85 96 0

6 5 6 2

6 36 4

2 02 1

2 2 3 1

3 33 4

4 14 24 34 4

1 2 1 3

1 9 2 02 1

2 72 8

EA database entities

EA-code

Street

Number

Buildings

Number

Crew leader area

CL-code

RO responsible

Admin. Unit

AU_Pop.

Landmark

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.

Crew leader area

CL-code

RO responsible

EA-code

Entity: Enumeration areas

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

Type (attributes)

Components of a digital EA database

Boundary database

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

Data Representation

Raster Raster

Vector Vector

Real WorldReal World

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

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

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

Vector vs Raster or Discrete vs Continuous

Vector Raster

Raster-Vector conversion (“vectorization”)

Vector data

image (raster)

Vector: Points, lines, polygons

• Set of geometric primitives:

points lines polygons

vertex

Vector Structure

• Spaghetti

• Topology

5 47 6

81 1 2

SpaghettiTopology

Spaghetti File

No Topology = raw file or ‘spagehetti file’

Lines not connected; have no ‘intelligence’

Example of “Spaghetti” data structure

1 2 3 4 5 6

6Poly 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)

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?

Topology: Spatial Relationships

Adjacency

Connectivity

Containment

Left Polygon = A

Right Polygon = B

Node 1 = Chains A,B,CChain A is connected to chains B & C

Polygon B Contained within polygon A

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 2 3 4 5 6

I II III

Poly Lines

A 1,4,5

B 2,4,6

C 3,5,6

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 BO = “outside” polygon

Encoding Topology (not): CAD

Encoding Topology: GIS

Comparison

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

Advantages:

…cont.

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

Disadvantages:

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

TIGER Data: Polygon

CountiesMCD’sCensus TractsBlock GroupsVoting DistrictsZip CodesCities

TIGER Data: Line

StreamsStreetsRailroads

TIGER Data: Point

Key LocationsLandmarksPlace NamesZip+4 Centroids

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

Spatial Analysis

• 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

Examples (query): Id 0012376027 Name Limop Population 31838 Popdens 37.5 Num_HH

8719 Clinics 8

Population density greater than 100 persons/sqkm?

What is at…?

Features that meet a set of criteria

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

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?

“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

“is nearest to”: Thiessen Polygons

“is near to”: Buffer Operations

• Point buffer

• Affected area around a polluting facility

• Catchment area of a water source

Buffer Operations

• Line buffer

• How many people live near the polluted river?

• What is the area impacted by highway noise

Buffet Operations

• Polygon buffer

• Area around a reservoir where development

should not be permitted

“ is within”: point in polygon

• Which of the cholera cases are within the

containment area

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.

Polygon Overlay

“overlaps”: Polygon overlay

Data Layers

B u ild in gs

E le va tion

A dm in is tra tiveu n its

H yd ro lo g y

R o a ds

Ve g e ta tion

Spatial aggregation

• Example of Spatial aggregation:• fusion of many provinces constituting an

economic region

Spatial data transformation: interpolation

12.715.9

Example 1: Based on a set of station precipitation surface estimates, we can create a raster surface that shows rainfall in the entire region

GIS capabilities:Visualization

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

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 datathat need to be processed, interpretedand stored

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

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.

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

Literature related to Census Mapping & GIS• US National Research Council:

• Tools and Methods for EstimatingPopulations 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

Contact Information:

Demographic Statistics SectionUN Statistics DivisionNew York globalcensus2010@un.org

Merci pour votre attention

Compromise projections

Vector to Raster Conversion: Polygons

Vector to Raster Conversion: Lines

Raster to Vector Conversion: Polygons

Spatial Operations

• “adjacent to”

• “connected to”

• “near to”

• “intersects with”

• “within”

• “overlaps”

• etc.

Spatial relationships

• Logical connections between spatial objects represented by points, lines and polygons

• e.g.,- point-in-polygon- line-line- polygon-polygon

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