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ggim.un.org Positioning geospatial information to address global challenges Positioning geospatial information to address global challenges 1 Constructing an EA-level Database for the Census Amor Laaribi UN-GGIM Secretariat UN Statistics Division New York

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Page 1: Constructing an EA-level Database for the Censusggim.un.org/ggim_20171012/docs/meetings/Regional... · Editing and Building topology Paper maps, existing printed air photos and satellite

ggim.un.org

Positioning geospatial information to address global challenges

Positioning geospatial information to address global challenges

1

Constructing an EA-level Database

for the Census

Amor Laaribi

UN-GGIM Secretariat

UN Statistics Division

New York

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ggim.un.org

Positioning geospatial information to address global challenges

Overview

Stages in the Geographic Database

Development Sources of geographic information

Data conversion

Data integration

Implementation of the Database Data Modelling

Relational Data Model

Example

Conclusion

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ggim.un.org

Positioning geospatial information to address global challenges

Stages in the geographic database development

Geographic data sources for EA delineation Inventory of existing data sources

Additional geographic data collection

Geographic data conversion Digitizing/Scanning + raster-to-vector conversion

Editing Geographic features

Constructing and maintaining topology for geographic features

Data integration Geo-referencing/Coding

Combining and integrating/Additional delineation of EA boundaries

Parallel activity Develop geographic attribute database

Metadata development

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ggim.un.org

Positioning geospatial information to address global challenges

Sources of geographic information

Identify existing

data sources

Additional geographic

data collection

Paper maps,

existing printed

air photos and

satellite imagery

Field mapping

products

such as

sketch maps

Digital air photos

and satellite

images

GPS coordinate

collection

Existing digital

maps

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Positioning geospatial information to address global challenges

Inventory of existing sources

National mapping agency (often the lead agency in the

country);

Military mapping services;

Province, district and municipal governments.

(transportation, social services, utility services and planning

relevant information);

Various government/private organizations dealing with

spatial data;

Geological or hydrological survey, Environmental protection authority,

Utility and communication sector companies;

Donor activities

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ggim.un.org

Positioning geospatial information to address global challenges UNSD-CELADE Regional

Workshop on Census

Cartography for the 2010 Latin

America’s census round

Why Data Inventory?

Geographic data: Labor intensive, tedious and

error-prone

Up to 70% of GIS projects

Identify existing data sources

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ggim.un.org

Positioning geospatial information to address global challenges

Geographic data conversion

Data conversion:

The process of converting features that are visible on a hardcopy map into digital point, line, polygon and attribute information is called data automation or data conversion.

The best strategy for data conversion depends on many factors including data availability, time and resource constraints

Trade-off between the cost of a project, the amount of time required to complete data conversion, and the quality of the final product.

Cost

Speed Quality

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ggim.un.org

Positioning geospatial information to address global challenges

Data Conversion

Paper maps, existing printed

air photos and

satellite imagery

Field mapping products

such as sketch maps

Digital air photos

and satellite images

Digitizing Scanning

Raster-to-vector

conversion

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ggim.un.org

Positioning geospatial information to address global challenges

Geographic data conversion

2 main approaches for converting information

on hardcopy maps to digital data:

Scanning

Digitizing

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ggim.un.org

Positioning geospatial information to address global challenges

Scanning

Scanning has arguably bypassed digitizing as the main

method of spatial data input, mainly because of the potential

to automate some tedious data-input steps using large-format

feed scanners and interactive “vectorization” software.

The result of the scanning process is a raster image of the

original map which can be stored in a standard image format

such as GIF or TIFF.

After geo-referencing it can be displayed in GIS packages as

a backdrop to existing vector data.

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Positioning geospatial information to address global challenges

Advantages and Disadvantages of Scanning

Disadvantages

Converting large maps with small format scanners requires tedious re-assembly of the individual parts;

Scanning large volumes of hard-copy maps will present challenges for file storage on many desktop computer systems;

Despite recent advances in “vectorization” software, considerable manual editing and attribute labeling may still be required.

Advantages

Scanned maps can be used as image backdrops for vector information;

Clear base maps or original color separations can be “vectorized” relatively easily using raster-to-vector conversion software; and

Small-format scanners are relatively inexpensive and provide quick data capture.

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Positioning geospatial information to address global challenges

Raster to Vector Conversion

Raster to Vector Conversion

Since the end result of the conversion process

is a digital geographic database of points and

lines, the scanned information contained on the

raster images needs to be converted into

coordinate information.

Scanning

Raster-to-vector

conversion

Digital air photos

and satellite images

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Positioning geospatial information to address global challenges

Digitizing

Manual Digitizing Digitizing is often tedious and tiring to the operators

Heads up Digitizing

In the heads-up digitizing, a scanned map image is used

digitally to trace the outlines into a GIS layer

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Positioning geospatial information to address global challenges

Operator uses a Raster-scanned

image on the computer screen (a

scanned map, air photo or satellite

image) as a backdrop.

Operator follows lines on-screen in

vector mode

Heads-Up Digitizing II

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Positioning geospatial information to address global challenges

Advantages and Disadvantages of Digitizing

Disadvantages

Digitizing is tedious possibly leading to operator fatigue and resulting quality problems which may require considerable post-processing;

Manual digitizing is quite slow;

In contrast to primary data collection using GPS or aerial photography, the accuracy of digitized maps is limited by the quality of the source material.

Advantages

Digitizing is easy to learn and thus does not require expensive skilled labor;

Attribute information can be added during the digitizing process;

High accuracy can be achieved through manual digitizing; i.e., there is usually no loss of accuracy compared to the source map.

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ggim.un.org

Positioning geospatial information to address global challenges

Editing and Building topology

Paper maps, existing printed

air photos and satellite imagery

Field mapping products

such as sketch maps

Digital air photos

and satellite images GPS coordinate

collection

Existing digital

maps

Digitizing Scanning

Raster-to-vector

conversion

Editing

geographic features

Construct

Topology for

Geographic features

Generate lines

and polygones

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Positioning geospatial information to address global challenges

Editing

Manual digitizing is error prone

Objective is to produce an accurate representation of the

original map data

This means that all lines that connect on the map must

also connect in the digital database

There should be no missing features and no duplicate

lines

The most common types of errors

• Reconnect disconnected line segments, etc.

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Positioning geospatial information to address global challenges

Some common digitizing errors

overshoot

line digitizedtwice

missingline

spike

undershoot

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Positioning geospatial information to address global challenges

Fixing Errors

Some of the common digitizing errors shown in the figure

can be avoided by using the digitizing software’s snap

tolerances that are defined by the user.

For example, the user might specify that all endpoints of a

line that are closer than 1 mm from another line will

automatically be connected (snapped) to that line.

Small sliver polygons that are created when a line is

digitized twice can also be automatically removed.

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Positioning geospatial information to address global challenges

Topology

Data structure in which each point, line and 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?

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Positioning geospatial information to address global challenges

Example of “Spaghetti” data structure

1 2 3 4 5 6

1

2

3

4

5

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

B C

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Positioning geospatial information to address global challenges

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 B O = “outside” polygon

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Positioning geospatial information to address global challenges

1

1

5

4

3

2

6

2 3 4 5 6

A

B C

1

1

5

4

3

2

6

2 3 4 5 6

A

B C

1

2 3

4 5

6

PolyABC

Lines1,4,52,4,63,5,6

Line123456

FromNodeIIIIIIIIII

LeftPolyOBOAAC

RightPolyAOCBCB

ToNodeIIIIVIVIIIIIIV

O = “outside” polygon

NodeIIIIIIIV

X1464

Y4441

Lines1,2,44,5,61,3,52,3,6

PolyABC

Coordinates(1,4), (1,6), (6,6), (6,4), (4,4), (1,4)(1,4), (4,4), (4,1), (1,1), (1,4)(4,4), (6,4), (6,1), (4,1), (4,4)

“Spaghetti” data structure

Topological data structure

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Positioning geospatial information to address global challenges

Constructing and maintaining topology (cont.)

Storing the topological information facilitates analysis,

since many GIS operations do not actually require

coordinate information, but are based only on topology

The user typically does not have to worry about how the

GIS stores topological information. How this is actually

done is software-specific.

Building topology thus also acts as a test of database

integrity

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Positioning geospatial information to address global challenges

Digital data integration

Construct Topology for

Geographic features

Existing digital

maps

Geo-referencing

(coordinate transformation and

projection change)

Coding (labeling)

of digital

geographic features

Combine and

integrate attribute data

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Positioning geospatial information to address global challenges

Integrating data

Geo-referencing

Converting map coordinates to the real world

coordinates corresponding to the source map’s

cartographic projection (or at digitizing stage).

Attaching codes to the digitized features

Integrating attribute data

Spreadsheets

links to external database

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Positioning geospatial information to address global challenges

Integrating attribute data

After the completed digital database has been verified

to be error-free, the final step is to add additional

attributes

These can be linked to the database permanently, or

the additional information about each database feature

can be stored in separate files which are linked to the

geographic database as needed

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Positioning geospatial information to address global challenges UNSD-CELADE

Regional Workshop on

Census Cartography for

the 2010 Latin America’s

census round

Implementation of an EA database

Geographic databases (hereafter referred to as geodatabases)

are more than spreadsheets

Entity types can be defined as having specific properties that

govern behavior in the real world.

The EA as a geographic unit is a kind of object whose function is

to delineate territory for the census canvassing operation.

Morphologically, the EA is contiguous, it nests within

administrative units, and it is composed of population-based units.

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Positioning geospatial information to address global challenges

Implementation of an EA database (cont.)

All large operational GISs are built on geodatabases;

Arguably the most important part of the GIS

Geodatabases form the basis for all queries, analysis,

and decision-making.

A DBMS, or database management system, is where

databases are stored.

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Levels of Data Abstraction

Increasing Abstraction

Reality

Conceptual Model (user's perception of the real world)

Logical Model (a formal description of the data model)

Physical Model (physical storage of the data

e.g., format, order, path)

Human-oriented

Computer-oriented

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Positioning geospatial information to address global challenges

Real World

Database Schema

Physical

Model

Levels of Data Abstraction Conceptual Model - software and hardware independent

- describes and defines included entities

- identifies how entities will be represented in

the database: i.e. selection of spatial

objects - points, lines, polygons, raster

cells

- requires decisions about how real-world

dimensionality and relationships

will be represented:

• based on the processing that will be done

on these objects

• e.g. should a building be represented as an

area or a point?

Logical Model

•software specific but hardware

independent

•sets out the logical structure of

the database elements,

determined by the database

management system used by

the software

- both hardware and

software specific

- how files will be

structured for access

from the disk -

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GIS Data Models

Vector data model

Raster data model

courtesy: Mary Ruvane, http://ils.unc.edu/

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Several types of data organization

Database management systems (DBMSs) can be

divided into various types, including:

Relational

Object

Object-relational

Relational (RDBMS)

RDMS is the most popular type of DBMS

• Over 95% of data in DBMS is in RDBMS

• DB2; SQL Server, Access; Oracle; Informix

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Example: the Relational Database Model

The relational database model is used to store, retrieve and

manipulate tables of data that refer to the geographic

features in the coordinate database.

It is based on the entity-relationship model

In a geographic context, an entity can be administrative or

census units, or any other spatial feature for which

characteristics will be compiled.

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Example of an urban EA map

358

Lambert A

venue

Mercator A

venue

Cassini Drive

Cassini Drive

Imhof Drive

Eckert Drive

Miller

Driv

eBonne Street

Mollweide Street

Grinten S

treet

Goode

Street

Bessel Street

Street

Robinson Street

Tissot Street

Gall Street

Ptolem

y Street

Ortelius S

treet

Clarke S

treet

Tobler StreetSnyder S

treet

Krassow

skij Street

361

378

374

349350

358

377

362

Enumeration Area Map Symbols

National Statistical Office - July 1998Census 2000

EA

Locality

District EA-Code

Hospital

Church

School

Buildingnumber

Province:District:Locality:EA-Code:

CartaniaChartesMaptown

14032

022100361

500 100

Approximate scale

200m

N

45

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

35 36

37

38

39

4041

42

43

44

454647

4849

50 51 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

2231

32

3334

35

41424344

1

2

34

5

6

10

11

12 13

18

1920

21

2728

29

17

Main components are:

Street network,

Buildings

EA boundaries layer

Annotation,

Symbols,

Labels

Building numbers

Neatlines

Legend

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Definition of EA database content

A spatial data model:

description of geographic features, such as administrative

units, streets, houses, buildings, hydrological

features/rivers, etc., and the relationships between those

entities.

A data model is independent from any specific

technology/software package

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Entity-Relationship Example:

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

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Positioning geospatial information to address global challenges

Implementation of an EA database

Example of an entity table

– enumeration area EA-Code

723101

723102

723103

723201

723202

723203

723204

Pop

763

593

838

832

632

839

388

CL-Code

88

88

88

88

89

89

89

Area

32.1

28.4

19.1

34.6

25.7

28.3

12.4

Entity: Enumeration areas

Type (attributes)

Instances

Primarykey

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An example of land parcels

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The E/R diagram for land parcels

STREET

-name

PARCEL

-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.

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Data Tables

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Object-oriented (OODBMS)

Based on OO concept to store state and behavior of GIS objects

in databases

Provide OO query tools

Commercially not successful

Object-Relational (ORDBMS)

Extend RDMS to handle GIS objects

Current Geographic Databases are ORDBMS

Object-Oriented and Object-Relational GIS DBMS

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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.

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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

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Positioning geospatial information to address global challenges

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

Advanced spatial analysis – Multi-criteria Analysis

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Positioning geospatial information to address global challenges

Summary

Data conversion

Digitizing/Scanning

Editing

Building Topology

Data integration

Geo-referencing; Projection change

Coding

Integration of attribute data

Building EA Database

Data Modelling: A spatial data model

Database Implementation

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Positioning geospatial information to address global challenges

Thank You!

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Positioning geospatial information to address global challenges

GPS coordinate

collection

Existing digital

maps

Scanning

Raster to vector

conversion

(automated or

semi-automated)

Editing

geographic

features

Dataconversion

Sources ofgeographicinformation

Digital mapdata integration

Digitizing

Combine and

integrate digital

map sheets

Coding (labelling)

of digital

geographic

features

Georeferencing(coordinate trans-

formation andprojection change)

Construct

topology for

geographicfeatures

Generate lines

and polygons

Additionalgeographic data

collection

Identify existing

data sources

Paper maps,

existing printed

air photos and

satellite images

Field mapping

productssuch as

sketch maps

Digital air photos

and satelliteimages

Additional

delineation ofEA boundaries

Parallel activity

Develop

geographic

attributes

database

Illustration