copyright 2010, the world bank group. all rights reserved. part 1 insight into the application and...

Copyright 2010, The World Bank Group. All Rights Reserved.

Part 1

Insight into the application and use of Geotechnology for National Statistics Organizations in Africa

Produced in Collaboration between World Bank Institute and the Development Data Group (DECDG)


Spatial statistics in AfricaThe usefulness of spatial and statistical data when not being linked to each other is severely limited

Census data and statistics linked to small geographic areas forms the backbone of many development initiatives

The creation of a geo-statistical database which drives development planning, implementation and monitoring is imperative.

The information collected by NSOs is important for:Land information managementPoverty reduction and economic empowermentThe development of basic transport and tele-communications infrastructureThe provision of electricity, water and sanitationThe effective and accurate provision of primary health care to especially rural communitiesFighting the HIV/Aids pandemic, malaria and tuberculoses (TB)Implementing sustainable agricultural methods in order to curb food insecurityGranting disadvantaged individuals access to land, markets, welfare and social servicesMaintenance of democracy through better planned and more transparent elections


Challenges faced by NSOs regarding Geotechnology

The increased demand for developmental statistics has exposed current data collection, integration, analysis and dissemination techniques

Geotechnology in Africa still a novel concept – full potential still not reached

Typical challenges faced by African NSOs regarding Geotechnology:Inadequate awareness within the organization as a whole regarding the potential and use of GeotechnologyLack of institutional support regarding the on going maintenance and sustainability of GeotechnologyInadequately integrated and properly designed data warehouse and databasesInadequate spatial base, primary and secondary dataAvailable data lacking in accuracy and currencyInadequate equipmentLack of skilled staffLack of capacity buildingInadequate funding and operational assistance


How GIS can answer these challengesIncrease in the accuracy of spatial base data leads to increased accuracy in data collection, analysis and statistics results

Which will lead to an increased demand for and profile of statistics in the user community

Added analysis and dissemination possibilities enhances decision-making

Institutional capacity are increased

Master sampling frames and updated survey methodology are created and implemented increasing the accuracy of data collection and analysis

Time savings on planning, sample drawing, fieldwork activities, logistics management are realised

More cost effective field data collection needing fewer resources

Improved IT infrastructure and knowledge base


The use of GIS for NSOsThe use and impact of GIS on NSOs is aptly described by the graphic below.

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Strategy

Peoplem anagem ent

Inform ationm anagem ent &

environm ent

F inance andResource

m anagem ent

Custom er focus

Lead

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ip

Surveyplanning

Collect P rocess Analyse PublicationD issem i-

nateTrade & Industry

Labour statistics

Financialstatistics

Social statistics

Populationstatistics

Vital statistics

Nat

ion

al A

cco

un

ts

Statistical processOutput Outcome

Povertyalleviation

Access to basicservices

Em erging and re-em erging diseases

Ruraldevelopment

Economic grow th

Job creation

Tourism

Governance

Environment

Input

D ata item s

Q ues tionna iredes ign

P os ta l su rvey Q ua lity check

C ap tu re

E d it

H ouseho ldsu rvey

A na lyseda ta qua lity

E s tim a tion

C om p iles ta tis tica l

re lease /repo rt

E d iting andlayou t

P ub lica tionon the w ebD is tribu tion

v ia pos t

C lass ifica tion

Tabu la tion Q ua lity aud it -ce rtifica tion D is tribu tion

o f m e tada ta -C D

S pec ia lreques ts

Impact on:

A dm in is tra tivereco rds /

G ove rnm entM IS

SADC/NEPAD

Other official statistics (NSS)

Business andO perational

p lanning

Standards and definitions

Classification systems

Inte

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eco

no

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,s

oc

ial

an

d d

em

og

rap

hic

info

rma

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Price statistics

Povertystatistics

S am p ledes ign

Geographic frame/Business Frame

P rog ram m ing

Data management information delivery

Q ua lity p lanand c rite ria

Source: Statistics South Africa, 2007


The use of GIS for Census and Survey OperationsUsing Remote Sensing (RS) and Digital Aerial Photography (DAP) for census mapping purposes:

NSOs are always asking the “Where” question – with imagery that becomes very easy to answer

Nationwide coverage (Lesotho)From sketch mapping………… to imagery based mapping


Integration of imagery, GPS captured data into GIS leads to accurate imagery based map creation.

These maps act as a base for all field data collection and verification activities for both census and survey operations

Lesotho2006

Examples of A3 size EA field maps

The use of GIS for Census and Survey Operations


Examples of A3 size EA field

maps

Namibia2001



Tanzania2002

Examples of A3 size EA field

maps



Small area statistics creation through spatial analysis – can form a base layer for all developmental and intervention planning and implementation

Census data in South Africa

aggregated and linked to newly created place

name polygons

The use of GIS for Small Area Statistics creation


Dwelling frame creation – moving from EA level to dwelling level.

Having accurate base spatial and attribute information for every dwelling

Adapted from Statistics South Africa, 2007

The use of GIS for Dwelling Frame creation


A spatial point for every dwelling in the country


Note the spatial points captured next to each stand



Attribute information linked to every spatial dwelling point

Unique ID: 90600196_00077

Village name / Sub place: Masisi

Head of Household: Manoli, Amosi

Feature use: Du (Dwelling Unit)

City / Town / Traditional Authority: Mutale

Geo-referenced Coordinates

Dwelling type: Dwelling or Brick structure on a stand or yard or on a farm




Often, the potential use of GIS for spatial data analysis, census product creation and dissemination is ignored or implemented incorrectly

Reasons:Lack of sustained fundingLack of skillsLack of sustained trainingLack of institutional supportLack of data sharing among NSO departments

GIS should stand central in all analysis and dissemination of data with a spatial component

Internet based data dissemination the way of the future – users will demand it

Web GIS allows users to interact with the data – view, query, create maps and tables – while also keeping data secure and access regulated

The use of GIS for Analysis and Dissemination


Example of education statistics disseminated and accessed via a web based GIS

School specific data aggregated to district level

The use of GIS for Analysis and Dissemination


The use of Master Samples for survey implementation is beneficial because:Time is saved since a new sample need not be drawn for every surveyFieldworkers become familiar with the EAs selected for the master sample which makes fieldwork more accurateChanges in the selected EAs can be consistently updatedThe master sample frame provides a constant spatial reference for the data collected across various surveys

Master Sample of 1000 EAs created for the Human Sciences Research

Council (HSRC) in South Africa. This Master Sample has a five year life

span

The use of GIS for Master Sample implementation


Using GIS and imagery integration for Master Sample creation provides the following advantages:

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77601869-105

ZCC CHURCH

POWER SUB STATION

BP GARAGE

Individual dwelling units (DUs) can be identified and sampled

on screen in the office – minimises fieldwork for DU identification and listing .

Sampling is more accurate than traditional methods – fieldworker bias removed

Cost savings due to increased speed and effectiveness of

fieldwork

Quality control is improved due to the use of imagery and image based field maps

11 Dwelling Units have been selected in the office

for this survey

EA boundary in blue



Data collection errors due to fieldworker error

is minimised

Same imagery base used for subsequent

surveys – new cluster samples created for

each survey – minimises respondent

contamination and fatigue

Use of GPS and DU coordinates allows for accurate navigation

Updating of settlement and infrastructure

changes now possible

Selected dwelling units

Sample EA survey map – sourced from the Human Sciences Research Council

EA Boundary



The quest to eliminate poverty one of the great human endeavours of our time

Spatial information is key – lack of income, material goods and opportunity takes place in and is influenced by your spatial environment

Poverty maps can be used by international, national, and local decision-makers to direct investments in human development:

Pinpoints places where development lagsHighlights the location and condition of infrastructure and natural resource assets that are critical to poverty reduction

Policymakers can deploy highly targeted antipoverty expenditures and interventions

For statistical agencies, poverty mapping is an essential, long-term capacity development and institutional strengthening exercise

The use of GIS for Poverty Mapping


DefinitionThe spatial representation and analysis of indicators of human wellbeing and povertyPoverty maps are spatial representations of poverty assessments.

Poverty maps used when planning public investment, identify areas in need of development or intervention programmes

Assessment information comes from many sources and can be presented at various levels. Typical indicators are:

GDP per capitaDaily subsistence levelsLife expectancyChild mortality Literacy…….from census and household survey data

Indexes of poverty and human development – combine various indicators

What are poverty maps?


GIS references features in space as well as the attributes of those features. Poverty indicators are similarly linked and referenced to features/areas/elements in space and analysed within the spatial context

The need for GIS when mapping poverty


Examples of poverty mappingMapping poverty over time


Fewer roads means less access to markets, employment, etc. Note the correlation between higher

poverty and the lack of road infrastructure

Examples of poverty mappingMapping poverty and infrastructure


Mapping poverty and health

Examples of poverty mapping

copyright 2010, the world bank group. all rights reserved. part 1 insight into the application and...

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