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

GIS

Data


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

GIS

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

Maps

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

SQL

• 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

POINT

-number

-x,y

2-N 3-N

2-N

SEGMENT

-number

LANDOWNER

-name

-date-of-birth

1-N

1-N

0-1 1-2

2-2

A B

C D

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

1

2

3

4 5

6

7

12 13

891011

14

1516

17

18

19

20

2122

23

2425

26

272829

30

3132

33

34

3536

37

38

39

4041

42

43

44

454647

4849

5051 52

5354

55

56

5758

59

6061

62

636465

66

67

68

69

70

71

7273

74 75

76

777879

80

81 82

83

848586

8788

1 2 3 4

9 10

1 2

7

89

1011

12

13

14

15

16

21

22

23

24

25 26 27

32

3334

19

20

21

2223

28

2930

3132

33

41

42

43

50

51

5254

58

59

27

28

31

37

38

42

43

4445

51

43

40

4142

6157

585960

65 62

6364

19

2021

22 31

32

3334

35

41424344

1

2

34

5

6

10

11

12 13

18

1920

21

2728

29

358

Enumeration Area Map Symbols

National Statistical Office -July 1998

Census2000

EA

Locality

District EA-Code

Hospital

Church

School

Buildingnumber

Province:District:Locality:EA-Code:

CartaniaChartesMaptown

14032

022100361

Approximate scale

N

45

17

Street Network Buildings

Boundaries Annotation and symbols

Building numbers Neatlines and legend

361

378

374

349350

358

377

362

358

Lam

b ert A

venu

e

Me

rcator Avenue

Cassini Drive

Cassini Drive

Imhof Drive

Eckert Drive

Miller

Driv

eB on n e S tree t

M o llw e id e S tre et

Grinte

n Stree

t

Go

ode

Stre

et

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

Ptole

my S

treet

Orte

lius Stre

et

Clarke S

tree

t

To b le r Stre etSnyd

er Stre

et

Krasso

wsk ij S

tree

t

361

378

374

349350

358

377

362

Enum eration Area Map Sym bols

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

EA

Locality

D istrict EA-Code

Hospital

Church

School

Buildingnumber

Province:D istrict:Locality:EA-Code:

CartaniaChartesMaptown

14032

022100361

500 100

Approximate scale

200m

N

4 5

1

2

3

4 56

7

1 2 1 3

891 011

1 4

1 51 6

1 7

1 8

1 9

2 0

2 12 2

2 3

2 42 5 2 6

2 72 82 9

3 0

3 13 2

3 3

3 4

3 5 3 6

3 7

3 8

3 9

4 04 1

4 2

4 3

4 4

4 54 64 7

4 8 4 9 5 0 5 1 5 2

5 35 4

5 5

5 6

5 75 8

5 9

6 06 1

6 2

6 36 46 5

6 6

6 7

6 8

6 9

7 0

7 1

7 27 3

7 4 7 5

7 6

7 77 87 9

8 08 1 8 2

8 3

8 48 58 6

8 78 8

1 2 3 4

9 1 0

1 2

7

891 011

1 2

1 3

1 4

1 5

1 6

2 1

2 2

2 3

2 4

2 5 2 6 2 7

3 2

3 33 4

1 9

2 0

2 1

2 22 3

2 82 9

3 0

3 13 2

3 3

4 1

4 2

4 3

5 0

5 1

5 25 4

5 8

5 9

2 7

2 8

3 1

3 7

3 8

4 2

4 3

4 44 5

5 1

4 34 0

4 14 2

6 15 7

5 85 96 0

6 5 6 2

6 36 4

1 9

2 02 1

2 2 3 1

3 2

3 33 4

3 5

4 14 24 34 4

1

2

34

5

6

1 0

11

1 2 1 3

1 8

1 9 2 02 1

2 72 8

2 9

1 7


EA database entities

EA

EA-code

Area

Pop.

Street

Number

Name

---

Buildings

Number

HHs

Etc.

Crew leader area

CL-code

Name

RO responsible

Admin. Unit

AU

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

Name

RO responsible

1-N

EA

EA-code

Area

Pop.

1-1

R


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

River

Vector Raster

x1,y1

xn,yn


Raster-Vector conversion (“vectorization”)


Vector data

+

image (raster)


Vector: Points, lines, polygons

• Set of geometric primitives:

points lines polygons

node

vertex

x

y


Vector Structure

• Spaghetti

• Topology

32

5 47 6

81 1 2

34

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

1

2

3

4

5

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)

A

B C


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

1

2

3

4

5

6

A

B C

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

IV

1

2 3

4 5

6

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

13.5

12.715.9

20.1

24.5

26.0

27.2

26.1

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


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 datathat need to be processed, interpretedand stored


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


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 [email protected]

Merci pour votre attention


Compromise projections


Vector to Raster Conversion: Polygons

c

ba


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

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

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