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Using remotely sensed data for census surveys andpopulation estimation in developing countries:Examples from NigeriaPeter O. Adeniyi aa Laboratory for Cartography and Remote Sensing, Department of Geography,University of Lagos, NigeriaPublished online: 17 Sep 2008.

To cite this article: Peter O. Adeniyi (1987) Using remotely sensed data for census surveys and population estimationin developing countries: Examples from Nigeria, Geocarto International, 2:4, 11-31, DOI: 10.1080/10106048709354117

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Using Remotely Sensed Data for Census Surveys andPopulation Estimation in Developing Countries: Examplesfrom Nigeria

Peter O. AdeniyiLaboratory for Cartography and Remote SensingDepartment of GeographyUniversity of LagosNigeria

Abstract

The conduct of conventional census surveys in most developing countries and the rational use of the resultsobtained have been impaired largely by the lack of basic infrastructures. These infrastructures include up-to-dateadministrative and topographic maps, settlement and land use information and geographically referenced enumera-tion areas (EAs).

Using examples from Nigeria, this paper demonstrates how remotely sensed data can be used to acquire someof the basic data requirements for census surveys and to estimate population. The result obtained shows that visualidentification of settlements on Landsat MSS and TM is more accurate and economical than equivalent digital class-ification techniques. Black and white aerial photographs were used to estimate the population of a model town andto establish EAs. The population estimation method employed can be used to obtain intercensal population estimatesfor the rapidly growing central places, while the established EAs for the study area have created a permanent base forfuture census surveys and census cross-validation, population estimation and other social surveys.

Introduction

One of the fundamental variables in socio-economicplanning and development is population data. Populationdata is usually obtained either through national censussurveys, which are usually conducted every ten years, orthrough sample surveys. Census surveys and populationestimation are carried out primarily to obtain:

(i) the absolute number of people within anadministrative unit (e.g. a nation, state or localgovernment area) at a given point in time,

(ii) information about the characteristics of thepeople (e.g. age, sex, education, occupation,income, etc.).

In several cases, major controversies often arise on theissue of absolute number of people within administrativeunits.

In many developing countries, especially those inAfrica, and Nigeria in particular, properly conducted andnationally acceptable census surveys have not beenconducted during the last two decades. The reasons forthis state of affairs are complex but they include:

(i) political instability;(ii) poor economic situation;

Geocarto International (4) 1987

(iii) inadequate base data for census surveys;(iv) poor assimilation and utilization of contempo-

rary technology.Remotely sensed data, especially aerial photography, hasbeen used to estimate population during the last threedecades (Green, 1956; Hsu, 1971; Anderson and Ander-son, 1973; Henderson, 1979; Lo and Chan, 1980;Adeniyi, 1983; Olorunfemi, 1984; Polle, 1984; Watkinsand Morrow-Jones, 1985). Most of these works werecarried out in the United States of America. Although theirfindings have implications for universal consideration;most of the issues addressed were generally based on the.American situation. The use of remote sensing for censussurveys has recently attracted attention (Brugioni, 1983;Morrow-Jones and Watkins, 1984; Sinclair, 1984; Morgan,1984; and Paul, 1984). As in the case of population esti-mation, the discussions were largely on United StatesCensus. With the exception of the studies by Eyre, et al(1970) and Dayal and Khairzada, (1976) and thecomments made by Paul (1984) and Morgan (1984), theinadequacy of census infrastructure in developing coun-tries and the significant role which remote sensing can playin providing such infrastructure have not been properlyappreciated.

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The objectives of this paper, therefore, are twofold:(1) to examine the status of the basic data require-

ments for national census surveys and populationestimation in developing countries, especially inNigeria with a view to discussing how remotesensing can be used to improve the situation;

(2) to demonstrate, through examples from Nigeria,how remotely sensed data can be used to: (a) esti-mate population and (b) establish the necessaryspatial units (e.g. enumeration areas or censustracts) for census surveys.

Status of Basic Data Requirements for CensusSurveys

It is logical to assume that most people typically live inone form of dwelling unit (or building) which is situated ina part of a settlement. A number of such settlements arespatially grouped together to form a local government area(LGA) (county or district); while the LGAs are also groupedtogether to form states (or provinces). The states/provincesadd-up to form a country (see Figure 1). In order toconduct a proper national population census, therefore, it

Fig.T. SPATIAL FRAMEWORK ANO BASIC DATA REQUIREMENTS FORCENSUS SURVEYS AND POPULATION ESTIMATION

r

COUNTRY

STATES

LOCAL GOVERNMENT AREAS (LGAs)

4

V

SETTLEMENTS IN LGA

1

1

1

1ENUMERATION AREAS (EAs)

POPULATIONCHARACTERISTICS

BUILDING TYPES AND NUMBERIN EACH EA

DWELLING UNIT (DU)/HOUSE HOLD

1NUMBER OF PEOPLE/DU

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is important and necessary to have:(i) up-to-date administrative maps to show, unam-

biguously, the international, state and localgovernment area boundaries.

(ii) up-to-date general (topographic) maps showingthe location, areal extent and distribution of allexisting settlements and the transportation routesleading to them;

(iii) sets of mapped spatial units with identifiableboundaries (enumeration areas (EAs) or censustracts).

With respect to administrative boundaries, severalAfrican countries are yet to have properly demarcatedboundaries. In Nigeria, for instance, not only are thereinternational border conflicts, there are also several inter-state and inter-local government area border conflicts.While this situation results from poor spatial definition ofboundaries, it has far-reaching consequences on theconduct of census surveys. First, the situation encouragesdouble enumeration of border towns. Second,enumerators who are aware of the conflict in such borderregions will attempt to avoid visiting the areas; and this, inturn, will lead to "under-enumeration" or the false manipu-lation of the result by the border communities.

Administrative maps are usually compiled from topog-raphic maps. Unfortunately, many developing countries,especially those in Africa are inadequately covered bytopographic maps. The status of world cartography in1980, as shown in Table 1, reveals that the availability ofvarious topographic maps in Africa is far less than theworld average. Compounding the situation is the fact thatthe few available topographic maps are not only consider-ably dated, many of them are also out of print. In effect,these countries do not have current information on thenumber, location, areal extent and distribution of allexisting settlements. It is therefore inconceivable toimagine how an accurate census can be conductedwithout accurate settlement information. The discovery ofa new settlement (COMA) in Gongola State Nigeria in1986, the findings of Eyre, et al (1970) in Jamaica whereseveral settlements not on the ground were counted andsome settlements on the ground were omitted, attest to thesignificance of topographic map coverage with up-to-dateinformation on existing settlements.

Similarly, enumeration area (EA) maps are poorlydefined in many developing countries while they aretotally lacking in others. Compilation of EA maps dependson the availability of spatial data on existing settlementsand detailed land use information for the large settlements.Urban land use maps are a rarity in most developing coun-tries. The report on the 1973 nullified census of Nigeria isused here to illustrate the nature of the situation. It states:

"Accurate and up-to-date maps are absolutely essen-tial for the prosecution of a successful census. Suchmaps are non-existent in this country. Most of the

maps that the survey departments of the Federationcan boast of are very old and out of date. It is indeeda sad revelation that even for the capital of the countryan up-to-date map is not available" (Nigeria, NationalCensus Board, 1975).

Given the scenario discussed above, it is clear thataccurate census in many developing countries is almost animpossible task. Equally, lack of information on the spatialorganization of settlements and their division (or grouping)into small geographical units (EAs) as bases for the collec-tion of socio-economic data, makes the application ofconventional demographic methods of population estima-tion ineffective. The acquisition of these basic data throughconventional methods is costly. In effect, and given theprevailing economic situations in most developing coun-tries, the conduct of a fairly accurate census is likely to beout of their reach. But, because of the important role ofpopulation data in socio-economic planning, it is neces-sary for these countries to explore other ways by which thenecessary data base and estimate of population could beobtained. From this perspective, the following sectionsattempt to demonstrate, using examples from Nigeria, howremotely sensed data can be used to speed up the provi-sion of the data bases as well as for the estimation of popu-lation.

Using Remotely Sensed Data to Generate DataBase for Census Surveys and Population Esti-mation

Remotely sensed data provides information aboutphysical and cultural environments with unique and valu-able characteristics. The information can be generated inunbiased form, acquired at a known point in time;displayed accurately; geographically referenced; preparedin real time (or nearly so) and assembled in useful, storableformat (Hardy, 1982). With these characteristics, severalscholars have used remotely sensed data to identify set-tlements (Reining, 1974; Tobler, 1968; Conant, 1982 andStem, 1982) and to revise topographic maps (Moore,1982; Gregory and Moore, 1986). Table 2 summarizessome of the specific roles which remote sensing can play incensus surveys and population estimation in developingcountries. Some of the remote sensing activities identifiedin Table 2 are dealt with below.

Settlement Location on Landsat MSS and TMData

Settlement identification forms part of a joint researchproject being carried out by the departments of Geog-raphy, Universities of Lagos (Nigeria) and Waterloo

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(Ontario, Canada). This project is on resource mapping ofSokoto-Rima Basin in the north-western part of Nigeria. Inthis semi-arid environment, accessibility to rural settle-ments is very poor. Most of the settlements are only linkedup with a maze of narrow foot paths which often changefrom season to season. The seasonal changes of the footpaths make the (old) available maps less useful.

Given this situation, an attempt was made to identifysettlements on Landsat MSS and TM data. Digital andvisual analysis techniques were employed with a view toassessing their relative accuracy and cost effectiveness. The

digital analysis was carried out on the Dipix (ARIES-IIImage Analysis System) at the University of Waterloo,Waterloo, Ontario, Canada and on a micro-computer(IBM/PC/XT) at the University of Lagos, Lagos, Nigeria.

The ARIES system was used for unsupervised andsupervised (maximum-likelihood) classifications for an areaof 30km by 30km on the 1984 MSS Data (see Figure 2).The results of these techniques (see Figures 3 & 4) showthat all the settlements were divided into different landcover types. The reason for the failure of the twotechniques to properly discriminate settlements is partly

CONTINENT

TABLE 1

STATE OF UORLD CARTOGRAPHY(Un i ted Nat ions 1980)

SCALES

1:25,000 1:50,000 1:100,000 1s250,000

Africa

Asia (without USSR)

Europe (without USSR)

North & CentralAmerica

Oceania & Australia

South America

USSR

2%

115É

91%

34%

13^5

10%

536

24%

51%

91%

61%

42%

27%

61%

17%

62%

77%

7%

42%

42%

100%

7B%

80%

9 5%

88%

80%

50%

100%

Uorld 13% 42% 42% 80%

Annual Progress

Annual Updating

Expend i tu res :

0.28%310 YEARS

3 o 2%30 YEARS

1.20%40 YEARS

2.7%40 YEARS

0.28%170 YEARS

2o7%40 YEARS

0%/°

0o3% of Public Expenditures0.03% of Gross National Product

Source: Konecny, 1986.

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TABLE 2

SOTIE APPLICATIONS OF REPTÓTE SENSING THAT ARE RELEVANTTO CENSUS SURVEYS AND POPULATION ESTIMATION

Type of RemoteSensing Data

Landsat MSS

Landsat TM andSpot (MultispectralMode)

Spot (panchromaticmode) and HighAltitude photographs

Medium-large scaleaerial photographs

Relevant applications

Regional inventory of Land use and settle-ment systems. Revision of 1:100,000 (orless) topographic maps. Data generated canbe used uith other data to demarcate poorlydefined administrative boundaries.

More detailed inventory of Land use/Cover(Settlement identification; Revision oftopographic maps (1:50,000 or less).

Urban land use mapping; Grouping of(smaller) rural settlements into appropriateenumeration areas and identification ofroutes leading to the settlements.Regional population estimation. '

Residential building classification,Housing count,Duelling unit estimation >Establishment of enumeration area andPopulation estimation.

caused by their total reliance on the spectral reflectanceproperties of the objects and partly by the spatialcomplexity of the objects. Most of the building roofs in thevillages were composed of mixture of mud, guinea corn/millet stocks and grasses which make them assume similarspectral characteristics like those of the guinea com/millet,and sometimes, rice farmlands which surround them. Inmany of the settlements, shelter trees are usually grown.Depending on the density of these trees, the spectralcharacteristics of the areas covered by the trees are oftensimilar to those of shrubland and vegetated wetlands (seeFigure 5).

A similar attempt was made on a micro-computer,using MICROPIPS software. A small area of 100 by 160pixels was selected on the 1984 Landsat MSS data aroundDankaiwa and Lake Malamawa (see Figure 2). The landuse/cover of the area was classified using the Parallelpipedclassification approach. Individual pixels were segimentedas training areas.

The signature statistics of the five land use/coverclasses for the area are shown in Table 3. Some measuresof class separability are shown in Table 4 (A & B). Twosmall settlements are within the test area. In spite of this,the separability of the settlements from rice, water and

mixed cropland is rather poor.Unlike the digital classification procedure, the visual

identification of the settlements was based on colourcomposite (positive film) of Landsat MSS and TM data.The interpretation was carried out on PROCOM-2 equip-ment. The identification of the settlements was not basedmainly on the colour rendition of the objects but moreimportantly on their shape, size and site association.

A black and white reproduction of a TM colourcomposite (generated on the ARIES-II Image AnalysisSystem) of a portion of the Sokoto-Rima Basin (aroundBimin Kebbi) is shown in Figure 6 to illustrate the distribu-tion of the settlements.

Most of the settlements are located within the farm-land areas, and are therefore easier to visually identifythem than the few ones located within the shrubland andflood plain areas. A careful utilization of the geometric andthe spatial arrangements of the image elements enhancedthe identification of those settlements located within theshrubland and flood plain areas. All settlements withapproximately twenty clustered buildings or compoundswere noted to be identifiable on the Landsat MSS & TMdata used. The settlements, as shown on the TM data(Figure 6) appear with varying dark tones and round

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Fig. 2 : STUDY AREAS

M Study Area t —Bakolori Irrigation project Arta

12 Study Area 2 — Area around Birnin Kebbi

Boundaries of Study Area forimage AnalysisIrrigation Water Supply CanalSecondary RoadPrimary Road

Settlements

Waterbodies

i] Designed Rice Plantation Areas

10 15 Km

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Figure 3: Unsupervised classification. Results for December1984 Landsat Data showing Nine Naturally OccurringSpectral Classes. The arroued locations aresettlements but they fall into different spectralclasses. (Neu and old Talata Mafara, Yarkofoji,Dankaiua, Rini and Gora, see figure 2 fororientation)e

Figure 4: Supervised classification of the area shoun inFigure 2, Again, the settlements as arrowed donot fall into the same class (compare this resultwith that shoun in Figure 3) 0

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POT *—™- - ^ Ä - J-^^sa-

sP-r-*«-*.

* :f:" " •

'", ¿rf

Figure 5: A typical village (Taloka) in the Sokoto-Rima Basin.Photograph taken by the author on August 26, 1986.

TADLE 3

SIGNATURE STATISTICS

1 IM I N1 AUG1 MAX

2 miN2 AUG2 MAX

3 HIN3 AUG3 MAX

4 HIN4 AUG4 MAX

1 Lakeuater

354652

405567

465559

263545

2 Settlement

404245

545963

596367

525458

3 Ri

363943

414755

566265

495558

394749

526971

667577

616667

464851

,677276

768085

686972

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TABLE 4

A. INTERSIGNATURE DISTANCES

SIG 1 2 3 4 5

12345

• 0

35426678

350173951

421705466

663954012

785166120

Bo MINinUri DISTANCES

FromSignature

4212123311

ToSignature

5324354545

Distance

12173539425154666678

shapes. An examination of the TM data (Figure 6) and thetopographical map (Figure 7) of the same area reveals thatthe TM data shows all the settlements on the map andseveral other settlements that were not shown on the map.While the detailed evaluation of the research (part of whichis reported here) is currently being undertaken, it sufficiesto conclude that digital identification of settlements in thestudy area is less accurate and more expensive than simplevisual interpretation.

Population Estimation and the Establishmentof Enumeration Areas Using Aerial Photo-graphs

This section deals with the practical procedure of howaerial photographs can be used to estimate population andto establish enumeration areas (EAs). The town selectedfor this demonstration study is a model sub-urban townlocated at the south-western part of Metropolitan Lagos(Figure 8). Although the town contains different residentialtypes, its spatial structure is not as complex as the typicaltraditional settlements. The town was selected to illustrate

the procedure for using aerial photographs to estimatepopulation and to establish appropriate EAs. The towncovers an area of 460 hectares with a designed capacity of11,000 dwelling units and 55,000 people. The building ofthe town started in 1977.

Procedure for Population Estimation

Given the fact that most people live in buildings withspecific physical structure and dwelling characteristics, it isassumed that the population of a settlement can be esti-mated by:

(i) identifying and classifying the residential build-ings into appropriate groups;

(ii) counting the number of buildings or dwellingunits in each residential class; and

(iii) determining the average number of people perbuilding or dwelling unit.

On the basis of these assumptions, the population of asettlement can then be estimated thus:

.. + (BP)Rn ... (1)(dp)Rn ... (2)

EP = (BP)R1 + (BP)R2 +or EP = (dp)Rl + (dp)R2 + .where:

EP = estimated populationB = number of buildingP = average population per buildingd = number of dwelling unitsp = average population per dwelling unit-Rl Rn = different residential types

The procedure of how the above steps were accomplishedis described below.

Data Source:

The primary data source for this study is a set of tenblack and white aerial photographs acquired at a scale of1:10,000 in November 1981. The photographs werepurchased from the Lagos State, Survey Department at acost of five Naira (approximately 1.67 U.S. dollars) perphotograph. Complementary data was collected in thefield.

Interpretation and Classification:

In order to establish appropriate classes, the geometricand the spatial arrangements of the buildings as theyappear on the photographs were employed. The interpre-tation was carried out by the aid of a mirror stereoscope(MS 27 with 3X magnification). The photographs were firstscanned to identify the image elements that could be usedin the identification and classification processes.

The following interpretation elements were used:(i) size, shape and height of structures;(ii) plot size and layout of structures;(iii) sidewalks and pathways;

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FIGURE 6: Landsat TH irrita of December 1984 of a portion ofthe Sokoto-Himn Basin illustrating thG potentialof visual identification of settlements. Theimagery has not been geometrically corrected.Its scale is slightly larger than 1:250,000,For comparative examination, the imagery should berelated to the (1970) 1:250,000 topographic mapshown in Figure 7. Notations i-r on the TMimagery represent some of the settlements that areshoun in Figure 7¿ uhile notations x-z illustratesome of the settlements on the TM imagery thatare not on the topographic map. For ease ofcomparison tha names of the settlements are

a - Birnin-Kebi ; b = Guadu Gaji; c => Zaurod = Ambursn; e = Kardi; f = Gulumbe; g = Danhadi;h = Uuribaji; i = Gotomo; j = Dali jam; k = Batan Uankn;1 = Holnnara; m = Gulma; n = Kola; o = Makera;p = Karyo; q = Kuareu and r = Bagga

Bll = Bango H i l l , and GH = Gulma H i l l .

Halt: thnt the road that runs from SU to HZ has beenre-aligned as at the time of the acquisit ion of theTM data.

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ND Figure 7: A portion of the 1:250,000 (1970) topographic mapcovering the area shown in Figure 6O

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fê ™ ^ ™ R o a d under construction

^ — ^ ™ Express Roads

• Major Roods and Streets

^~^^-^/* Coast Line

p f Study Area (FESTAC PHASED

0 2000 4000 «DOOM

I

I\ 1 ̂ ^ ^ _ ^. If

\ I J ^ — — - ^ ^ ^ ^ ^ ^ g r - FFSTAr TOWN J

/ ( «"^p, yr •— - *" ~ " ^ 1 /TtGOi-atfe*^*^

\

v V

I ÍMUSH INQ# 1

7 ^ v

y /* " ^ S U R U CE (

Y

& r APAPA IAJEGUNLE r"

OREMEJI I \

F"***—*̂ \ J'sOMOLU \ j

° IGBOBI ( \

\ \

A) //'w

=̂[^ ^ >OBALENDE

VICTORIA!IS. 1

BIGHT

J

COS

G 0 0 N

/ IKOYI V

/ l\MAR0K0

Of BEN IN

FIGURE 8: LOCATION MAP: FESTAC TOUN (used for the populationestimation and the establishment of enumeration

areas)

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(iv) type and location of parking facilities;(v) roof pattern.

These elements were used to create an interpretationkey for the classification. Nine residential classes identifiedare described in Table 5.

The most discriminant variable is the roof design (seeTable 6).

The areas covered by each residential type weredelineated on acetate overlays and were transferred to1:10,000 base map. The map was checked in the field andit was found to be 100 per cent accurate in terms of thegrouping of the residential buildings (see Figures 9 and 10).

Counting of Residential Buildings and Dwelling UnitEstimation:

To ease the counting process, each residential classwas divided into block zones. The counting was done

directly on the photographs with acetate overlays. Eachcompleted residential building was represented by a circleand an uncompleted building by a cross on the acetateoverlay. This was done to aid the checking in the field andto assist the process of establishing the enumeration areaslater. However, only the completed buildings were usedfor the 1981 population estimation. The counting wasdone independently by two interpreters with an average oftwo hours, six minutes. There was no disagreement in thetwo counts.

With respect to dwelling unit, an attempt was notmade, at the interpretation stage, to classify the residentialbuildings on the basis of dwelling units. This was done inorder to examine the extent to which the residential struc-tures reflect the cultural attributes of the people and/or theplanned objectives of the Federal Housing Authority. As itturned out in this test case, the residential classes estab-lished through photo-interpretation have specific dwelling

TABLE 5

DESCRIPTION OF RESIDENTIAL CLASSES

ResidentialCode

• R 1

R2

R3

R4

R5

R6

R7

RB

R9

Descripti on

Modern residential one storeyed detached buildings;each having 5 bedrooms, a locked-up garage and boys'quarters.

flodern residential one storeyed detached buildings,each having 3 bedrooms, a locked-up garage and boy.1;1

quarters.

Semi-detached one storeyed buildings with tuo separateapartments, each having tuo bedrooms, a locked-upgarage and boys1 quarters.

Three bedroom attached one storeyed houses with locked-up garages.

Tuo bedroom rou houses uith locked-up garages.

Tuo bedroom rou houses uithout garages.

Three storeyed buildings of eight (3 bedroom) apart-ments,,

Three storeyed buildings oF sixteen (2- and 3-bedroom) apartments.

Three storeyed buildings of thirty-tuo (one bedroom)apartme nts 0

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s

FIGURE 9: RESIDENTIAL LAND USE MAP OF FESTAC TOUN, COMPILEDFRON 1981 AERIAL PHOTOGRAPHS

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TADLE 6

BUILDING ROOF DESIGN AS SEEN ON AIR PHOTOS

Residential Code Images Description

R1 O Tilted flat white roof

R2 Tilted flat black roof

R3 H Flat unite roof uith blacksquare

R4 Jointed roofs

R5 Continuous roof uith uhitedots

R6 Continuous roof uith black dots

R7 t>—q Short pitchEjd roof

RQ Long pitched roof

R9 Rectangular flat roof uithtwo side projections

FIGURE 10: AERIAL PHOTOGRAPHY OF FESTAC TOUN, 1983*

*This is not the photograph used for the analysis in the text.This photograph became available after the analysis reportedherein had been completed scale (approx.) 1:30,000,

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characteristics. This outcome should, however, not betaken to be applicable in traditional African settlements(see Adeniyi, 1983).

In the test area, only residential classes R4, R5 and R6have varying dwelling units. Since the number ofcompleted buildings in these classes were relatively few,the number of dwelling units for these three residentialclasses were obtained directly from the field. The numberof completed residential buildings and the dwelling unitsfor each residential class are shown in Table 7. There weremany buildings under construction in the vicinity of resi-dential classes R2, R3 and R8.

Determination of Average Number of People PerDwelling Unit and Population Estimation for the TestArea:

There was no previous population data on which theaverage number of people per dwelling unit could bedetermined. Given the size of the dwelling units in the testarea, it was decided that the determination of the average

number of people per dwelling in each residential classwould be based on sample survey. A twenty per centsample from each residential class was originally taken (i.e.1,951 dwelling units). A simple questionnaire which askedfor the number of people in each dwelling unit in 1981and 1986 was developed. The questionnaires were distri-buted at the rate of twenty per cent for each residentialzone created for the counting of residential buildings.Within each stratum, the appropriate number of dwellingunits were selected randomly. Where the head of thefamily was met at home, the informtion was retrievedimmediately, otherwise, the questionnaires were left forlater collection.

Out of the 1,951 questionnaires distributed, 1,710 (i.e.87.6%) were returned. There was no residential class with-out, at least, a 15 per cent return. Thus, a common base of15 per cent sample (i.e. 1,463 dwelling units) was used.The average population per dwelling unit for each residen-tial type is calculated by dividing the population of thesampled dwelling units by the number of the sampleddwelling units. The population of each residential class is

TABLE 7

RESIDENTIAL BUILDINGS, DUELLING UNITS AND POPULATION ESTIMATESIN FESTAC, 1981

Res.Code

R1

R2

R3

R4

R5

R6

R7

R8

R9

TOTAL

26

CountingZones

4

6

10

2

4

2

3

8

7

46

No. ofCompl.Resid.

A71

333

650

37

35

25

56

347

36

1590

DuellingUnits/Bldg«

B1

1

2

1-13Varies

1-11Varies

1-11Varies

8

6

32

TotalNo. of

DuellingUnits(DU)

C71

333

1300

330

338

232

448

5552

1152

9756

No.ofDU

Samp.( 1556of

Total)

D

11

50

195

50

51

34

67

837

173

1463

Pop.inSamp.DU s

E63

295

665

210

199

126

288

3910

1003

6885

Ave0Pop.per DU

F=D/E

5.7

5O9

3O4

4,2

3.9

3.7

4,3

4,7

5O8

EstofPop.

G = CxF

405

1969

4420

1386

1318

858

1926

26094

6682

45054

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AVERAGE

Code

R1R2R3R4R5R6R7R8R9

POPULATION

No. of OU5%

4176516171222

27858

TABLE

PER DUELLING UNLEVEL

Total Pop. inSampled DU

239922267674495

1309337

8

IT AT FIVE PERCE

Aue. Pop./DU

5O755O823O424o193.943.674.324o715.81

NT SAMPLING

Pop. Est/Res. Class*

4081938444 613831332851

1935261506693

TOTAL 489 47199 45136

* These figures uere obtained by multiplying the averagepopulation in the 4th column with the total number ofduelling units in each residential class (See Table 7)

calculated by using equation 2. The result is shown inTable 7.

The result in Table 7 shows that there were 1,590completed residential buildings, 9,756 dwelling units and45,054 people in the test area in 1981. This gives anoverall average of 4.7 people per dwelling unit. Unfortu-nately, there is no other data set with which this result canbe compared. However, the result obtained is still withinthe projections of the Federal Housing Authority (FHA)(i.e. 11,000 dwelling units and 55,000 people).

In order to provide an alternative measure of accu-racy, a different set of five per cent sample of dwelling unitswas taken from each residential class. Table 8 shows thatthere is no major "significant" difference between theaverage population per dwelling unit at both 15 per centand 5 per cent sampling levels. In fact, the estimate of thepopulation for the test area, based on the five per centsample of the number of people per dwelling unit, is notmuch different from that obtained at 15 per cent samplinglevel.

Delimitation of Enumeration Areas (EAs):

Given the information about the number of buildings,dwelling units and the average number of people perdwelling unit, the delimitation of EAs requires two addi-tional basic steps:

(i) determination of the minimum number of build-ings for each EA;

(ii) identification of landscape features to serve as

boundaries for the EAs (e.g. roads, alleys,canals, fence lines, etc.).

Each country has a range of population for urban andrural EAs. In Nigeria, an urban EA is stipulated to containbetween 400 and 650 people. Thus, the minimumnumber of buildings to contain this range of population foran EA can be determined by dividing the minimum popu-lation for an EA (in this case 400) by the average popula-tion per building. For instance, if the average populationper building within a particular residential class is 10, thenthe minimum residential building for an EA that falls withinthat residential class is 400/10, i.e. 40. However, someEAs may cut across several residential classes. In suchcases, this approach has to be followed progressively,starting from the residential class with the largest 'out-lying'buildings. The counting of the appropriate number ofbuildings for each EA was carried out simultaneously withthe identification of appropriate landscape features (e.g.roads, alleys, canals, buildings and fence lines) that wereused for EA boundary delineation.

In this test case, the acetate overlay used for thehousing count, where both completed and uncompletedbuildings in 1981 where symbolised, was used togetherwith the aerial photographs. Those buildings which wereunder construction in 1981 but now completed werenoted in the field. They were integrated into the 1981 datafor the purposes of determining the minimum number ofbuildings for the EAs.

Changes in the number of completed buildingsoccurred in residential classes R2, R3, R6, R8 and R9.

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Another important change is the increase in the averagepopulation per dwelling unit in residential class R3, from3.4 in 1981 to 5 in 1986. All the other residential classesmaintained the same average population per dwelling unit(see Table 9).

Following the above procedure, 108 EAs were estab-lished for the test area. Table 10 shows the statistics of tenof the EAs while Figure 11 shows the enumeration areas.

The information on Table 10 and Figure 11 havesome essential features:

(i) The population figures for each EA are withinthe stipulated range and they allow for futuregrowth.

(ii) The data on the number of buildings, dwellingunits and average population per dwelling unitfor each EA permits the monitoring of growth,adherence of planning control and cross valida-tion of census surveys.

(iii) The delineation of the EAs in a map formprovides a permanent spatial base for censussurveys and other social surveys,

(iv) The EAs and any data aggregated into them canbe geo-coded and stored in the computer foreasy manipulation and retrieval.

Summary and Conclusion

The conventional methods of census surveys andpopulation estimation in many developing countries have

failed largely, not only because of political factors, butmore so because of the lack of basic infrastructures, suchas up-to-date administrative and topographic maps, settle-ment and land use information and geographically refer-enced spatial units for data aggregation (e.g. EAs).Although these basic infrastructures are already taken forgranted in the industrialized countries, their non-availabilityin developing countries has not only exacerbated theproblem of cross-validation of the previous census surveysand the conduct of population estimation, it has been, andwill continue to be, the cause of high cost of censussurveys.

An attempt has been made in this paper todemonstrate how remote sensing can be applied to aid theprovision of the basic data requirements for census surveysand for the conduct of population estimation. The result ofidentifying settlements on Landsat MSS and TM datashows that the visual analysis of these low-medium resol-ution satellite data is more accurate and economical thandigital processing techniques used. Thus, with the avail-ability of 'cartographic' satellite data (i.e. SPOT), thecurrent lack of expensive hardware (e.g. Digital Imageanalysis equipment) in most developing countries, shouldnot preclude the use of the available satellite data for theacquisition or upgrading of settlement and other basic datarequirements for census surveys.

The paper further demonstrated the procedure of howblack and white aerial photographs can be used to esti-mate population and to establish the necessary spatial units(EAs) for census surveys in a model town in Nigeria. Whilethe population estimation method employed can be used

E5TI

Res.CodG

R1R2R3R4R5R6R7R8R9

KATES OF

No. oRes.

1901

7133365037352556

34736

RESIDENTIALFOR 1981

f CompletedBuilding

1966

71362

130037353656 .37142

TABLE

BUILDINGAND 1986

No. ofUnits

1981

71333130033033823244855521152

9

, DUELLINGIN FESTAC

Duelling

1986

71362170433033826044859361344

UNITT0UN

AND POPULATION

EstimatedPopul

1981

40 51965442013861318858

1926260946682

ation

1986

40521368520138613189621926

278927795

1590 1862 9756 10793 45054 52340

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TABLE 10

SAMPLES OF STATISTICS RELATING TO SOME OF THE ENUMERATION AREASESTABLISHED FOR FESTAC TOUN (1986)

EASeri al

1

2

3

4

5

6

7

8

9

10

ResidentialType(R)

231

3

7

7

3

3

3

89

8

8

No. ofBldgs.

541110

49

14

15

46

44

49

32

7

6

TotalDuellingUnits(DU)

542210

98

112

120

92

88

98

4864

112

96

AveragePop.per DU

5.95.05.7

5.0

4.3

4o3

5o0

5.0

5.0

4.75e8

4.7

4.7

TotalPop.per RClassin EA

31911057

490

482

516

4 60

440

490

266371

526

451

Sum ofPop.in EAs

486

4 90

482

516

460

440

490

597

526

451

Note that in EA number 1, three different residential classes areuithin the EA, two for EA number 8 and one each for all the otherEAs.

to obtain inter-censal population estimates for the rapidlygrowing central places, the established EAs for the studyarea have created a permanent base for future censussurveys and census cross-validation, population estimationand other social surveys.

In spite of the model town used for the populationestimation and the establishment of the EAs, the procedurefollowed can be adapted to more traditional settlements.What is probably required is the direct participation of thenationals of developing countries in the development andapplication of the contemporary technology of remotesensing. The current fashionable call by several developingcountries that international organizations or foreign expertsbe invited to carry out population census, does not onlyportray the inadequate appreciation of the ecological andsocio-cultural differences between them and the experts, itis also a proposition which only tends to avoid a bad situ-ation without necessarily providing a fundamental solutionto the problem. Research, being a necessary condition fordevelopment and a process, is not easily imported. Theresults of this study should encourage the governments,

universities, planners and demographers in developingcountries to invest in the development and application ofremote sensing for census surveys and other resourceinventories.

Acknowledgements

The author would like to acknowledge the support bythe International Development Research Centre (IDRC),Ottawa, Canada for providing the funds for the acquisitionof the Landsat MSS and TM data, micro-computer (IBM-XT) with MICROPIPS software and PROCOM-2 equip-ment for a joint research project between the University ofWaterloo, Waterloo, Ontario, Canada and the Universityof Lagos, Lagos, Nigeria on Resource Mapping of Sokoto-Rima Basin, Nigeria. The paper is part of an outgrowth ofthe project. Appreciation is also extended to Mrs. ChristieOgunsola for her assistance during the photo-interpreta-tion and data collection in the field and to Paul G. Pilon forreading the draft of this paper.

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8FigHFESTAC TOWN SHOWING ENUMERATION AREAS

en

m

* Bated on Air-photo Interprttatioiyfctldwork

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