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OZOEKWE, CHARLES IFEANYI
PG / M.Sc. / 12 / 63531
APPLICATION OF REMOTE SENSING AND GIS IN THE POPULATION STUDY OF ACHARA LAYOUT ENUGU, ENUGU
STATE, NIGERIA
FACULTY OF ENVIRONMENTAL STUDIES
DEPARTMENT OF GEOINFORMATICS AND SURVEYING
Azuka Ijomah
Digitally Signed by: Content manager’s Name
DN : CN = Webmaster’s name
O= University of Nigeria, Nsukka
OU = Innovation Centre
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APPLICATION OF REMOTE SENSING AND GIS IN THE POPULATION STUDY OF ACHARA
LAYOUT ENUGU, ENUGU STATE, NIGERIA
BY
OZOEKWE, CHARLES IFEANYI
PG / M.Sc. / 12 / 63531
DEPARTMENT OF GEOINFORMATICS AND SURVEYING
FACULTY OF ENVIRONMENTAL STUDIES
UNIVERSITY OF NIGERIA
ENUGU CAMPUS
DECEMBER, 2015
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APPLICATION OF REMOTE SENSING AND GIS IN THE POPULATION STUDY OF ACHARA
LAYOUT ENUGU, ENUGU STATE, NIGERIA
Submitted By
OZOEKWE, CHARLES IFEANYI
PG / M.Sc. / 12 / 63531
BEING A PROJECT REPORT PRESENTED TO THE DEPARTMENT OF GEOINFORMATICS AND SURVEYING, FACULTY OF ENVIRONMENTAL STUDIES, UNIVERSITY OF NIGERIA, ENUGU
CAMPUS, IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE AWARD MASTER OF
SCIENCE (M.Sc.) IN GEOINFORMATICS AND SURVEYING
SUPERVISOR
PROF. F. I. OKEKE
DECEMBER 2015
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CERTIFICATION
This is to certify that I, OZOEKWE CHARLES IFEANYI with registration
number PG/MSc/12/63531, a postgraduate student of the department of
Geoinformatics and Surveying, have satisfactorily completed the requirement
for this Project for the award of the M.Sc in Geoinformatics and Surveying.
The work embodied in this Project is original, and has not to my knowledge
been submitted in part or full for any other degree of this or other University
……….………………………… …............................... OZOEKWE, CHARLES IFEANYI DATE
STUDENT
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APPROVAL
This is to certify that I, OZOEKWE CHARLES IFEANYI with registration
number PG/MSc/12/63531, a postgraduate student of the department of
Geoinformatics and Surveying, have satisfactorily completed the requirement
for this Project for the award of the M.Sc in Geoinformatics and Surveying.
The work embodied in this Project is original, and has not to my knowledge
been submitted in part or full for any other degree of this or other University
………………………………. …………….............. PROF. F. I. OKEKE DATE PROJECT SUPERVISOR ……………………………… ……………………….. DR. E.C. MOKA DATE HEAD OF DEPARTMENT ……………………………… ..………………….…… EXTERNAL EXAMINER DATE ……………………………….. …………..………….. PROF. F. I. OKEKE DATE DEAN, FACULTY OF ENVIRONMENTAL STUDIES ………………………………………… ..………………… DEAN, SCHOOL OF POSTGRADUATE STUDIES DATE
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DEDICATION
This Research Work is dedicated to God Almighty who through His Grace,
Ace, and Pace, i was able to brace up and face this race to the end.
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ACKNOWLEDGEMENT
I am very grateful to my parents Sir (Dr) & Lady M. A. Ozoekwe for their
unlimited moral and parental support given to me during the course of my
study in school, post school and up till this moment. Also to a big thanks goes
to my siblings for their love and words of encouragement,
I also appreciate my supervisor Prof F.I Okeke for offering his time, wisdom
and expertise, for that I say more grease to his elbows. To my fellow students
and friends, I appreciate your contributions toward this project.
My thanks also goes to Dr. E.C Moka (Head of Department) and the entire
staff and Lecturers of the department of Geoinformatics and Surveying,
University of Nigeria Enugu Campus.
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ABSTRACT
This project is an attempt to study population distribution in Achara Layout Enugu, in Enugu State through integration of Remote sensing and Geographic Information System (GIS). The Remote sensing data used in this study included high spatial resolution Quick Bird imagery. Questionnaires were given out for use in generating the desired product in an interactive GIS environment. Field site capturing was based on basic survey principle, technique and instruction. Field work was done to determine the type of building appearing in the satellite imagery and the no of rooms estimated, thus a standard family size gotten. After estimating the population size of Achara Layout, the result was compared to the population data gotten from National Population Commission for Achara Layout Enugu state which was divided into two as it comprised of data for Achara Layout and Idaw River Layout Enugu as directed by National Population Commission Enugu. The methodology involved carrying out a user needs assessment, collection of map from Enugu State Town Planning Department of Ministry of Land, collection of Population census data from National Population Commission, rigorous field work, data processing, comparison of results and presentation of results. The average difference between the population recorded in the 2006 then projected to 2015, and that estimated from Quick Bird images for Achara Layout Enugu, is found to be equal to 1.61%. Results from Arc GIS were presented in form of map while results from Microsoft Excel environment were presented in form of table.
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TABLE OF CONTENT
Cover page..…………………………………………………………………….i
Title page ….…………………………………………………………………..ii
Certification……….…………….………………………………...….……....iii
Approval…..………………….…………………………………………….…iv
Dedication …….………………………………………………………………v
Acknowledgements ..………………………………………………………....vi
Abstract ….…………………………………………………………………..vii
Table of Contents …………………………………………………….……..viii
List of Tables ………………………………………………………………..xi
List of Figures …………………………………………………….….……...xii
CHAPTER ONE:
INTRODUCTION………………………………………………….……......1
1.0 Introduction………………………...…………………….………….…….1
1.1 Background of study…………………………….………….….…….……1
1.2 Statement of problem……………..………………….……….……….…..2
1.3 Aim………………….. …..…………………………..………..……….….3
1.4 Objectives…………………………………………………….…….……..3
1.5 Scope of work ……………………………………………..……….……...3
1.6 Study Area…………………………...…………………...……….……….4
1.7 Benefits And Contribution…………………………………..…..………...5
CHAPTER TWO: LITERATURE REVIEW……………..……………….6
2.1 Description of Geographic information system……………………...….………...7
2.2 Population studies using Remote sensing and GIS...…………..……...……..8
2.2.1 Background……………………………..……...….…………..……......8
2.2.2 Population density model from urban Geography…..……….…….........9
2.3 Population Estimation methods…………………………………………...9
2.3.1 Area interpolation methods…………………...………………….….....10
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2.3.2 Point Based Methods……………………………………...…………...11
2.3.3 Area based method………………………………...………………......13
2.3.4 Statisitcal Modelling method…………………………………………..19
2.4 Summary…….………………….……………...………………...............26
CHAPTER THREE: METHODOLOGY
3.1 Flow Chart………………………………….…………….……….….....29
3.2Project Planning.........................................................................................30
3.3Data Acquisition………………..................................................................30
3.4 Tools and components used…………………………………..…...........32
3.5Data Sources………………………………………......………….……..33
3.6Data Processing…………….....………………………..…………..……34
3.6.1 Scanning ……………………………………..…………..………....34
3.6.2 Data Georefrencing………………………………………………….....34
3.6.3Data Vectorization………………………………………………………38
3.6.4. Shape Files in Arc GIS………………..…………………………….....39
3.6.4.1Features in Shape files ……………………………...….…………......39
3.6.5 Digitizing a map………………………….………………..……....40
3.6.6Data base structure for Population census…………………...….……....41
3.6.6.1Conceptual Design ………………….....………………………..........42
3.6.6.2Entity Relationship Model……………………………….…………...43
3.6.6.3Logical Design……………………………………………..……..…..44
3.6.6.4Physical Database Design…………………….…………………..…..45
3.7 User Requirement……………………………………….…...............47
3.7.1 Database Design for Achara Layout…………………….....………….47
CHAPTER FOUR: RESULTS AND ANALYSIS
4.1 Results……………………….……….…………………………..…......49
4.1.1 Analyses…………………….………….…………..…………….……49
4.1.2 Estimation of population data per household through satellite image....49
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4.1.3 Estimation of population study by Block Parcel……………….……...51
4.1.4 Population Density………………………………………………..........54
4.2 Comparative analysis of population data from National Population
Commission and that gotten from Remote sensing and GIS application….....57
CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS
5.1 Conclusion……………………………………..……………………........59
5.2 limitations of using remote sensing method for population studies….......59
5.2.1 Image Classification……………………………..………………..……60
5.2.2 3D nature of urban areas…………………………………………….…60
5.3 Recommendations………………………………….…………………….61
REFERENCES………………………………………...................................68
APPENDICES…………………………………...………….………………69
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LIST OF FIGURES PAGE
Figure 1 Overview Map Showing Achara Layout ……………..………...…...5
Figure 3.1 Flow Chart of Project Plan……………………………...………...29
Figure 3.2 Map of Achara Layout as Designed by Town Planning unit……..31
Figure 3.3 QuickBird Image of Enugu Metropolis……………......................32
Figure 3.4 Selecting Georefrencing in ArcMap……………………...…........35
Figure 3.5 Selecting Georefrencing in ArcMap .……………………..……..35
Figure 3.6 Positioning of The Cross Hair…………………….…….………..36
Figure 3.7 Georefrencing The Scanned Map……………………….….…….37
Figure 3.8 Digitized Block Parcel....................................................................38
Figure 3.9 Digitizing Of Building Through Satellite Imagery Of Study Area.39
Figure 4.0 Joining Table in ArcGIS Environment……………….……..........42
Figure 4.1 Building ER Diagram……………………………….……….…...43
Figure 4.2 Block of ER Diagram…………………………………….….……43
Figure 4.3 Population of ER Diagram…………….………………………….43
Figure 4.4 Relationship Between Entities……………………………...…….44
Figure 4.5 Population Concentration Map By Block Parcel………….….…..51
Figure 4.6 Population Density Map By Block Parcel .....................................55
Figure 4.7 Enugu state 2006 Population Census Result………….…….…….56
Figure 4.8 Enugu South L.G.A. 2006 Population Census Result...…...……..57
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LIST OF TABLES PAGE
Table 1.0 Geodatabase of Extracted Houses from Imagery………...………..45
Table 2.0 Field Survey data of Achara Layout using MS Excel………….….46
Table 3.0 Field Survey data of Achara Layout using ArcGIS…………...…..47
Table 4.1 Sample Census Per Building………………………………………50
Table 4.2 Detail of Population Study Area Per Block Parcel…………..……53
Table 4.4 Comparative Analysis of Population Data From NPC And Data Got
From Remote Sensing for Achara Layout Enugu State ………………….....59
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CHAPTER ONE
INTRODUCTION
1.1 BACK GROUND OF STUDY
Although numerous estimates of the Nigerian population were made
during the colonial period, the first attempt at a nationwide census was during
1952-53. This attempt yielded a total population figure of 31.6 million within
the current boundaries of the country. This census has usually been considered
an undercount for a number of reasons: apprehension that the census was
related to tax collection; political tension at the time in eastern Nigeria;
logistical difficulties in reaching many remote areas; and inadequate training
of enumerators in some areas. The extent of undercounting has been estimated
at 10 percent or less, although accuracy probably varied among the regions.
Despite its difficulties, the 1952-53 census has generally been seen as less
problematic than any of its successors. Subsequent attempts to conduct a
reliable post-independence census have been mired in controversy, and only
one was officially accepted. A good census estimate of a country population
can help a country plan for present and future development and so much more.
The data generated from population census are analyzed and used to calculate
for various needs like gender ratio, indigenes and non-indigenes, migrants and
immigrants e.t.c However, remotely sensed data offers the means to measure
spatial attributes of the urban landscape. In the past, researchers remain
depended on “Aerial Photographs” because of their fine spatial resolution to
get accurate data about size of houses and their volumes and consequently
estimates of population (Adeniyi, 1983; Lo and Chan, 1980).In addition to
that, remotely sensed data provide a wider spectral coverage (number of
bands). Due to these advantages, there is a general trend towards using
remotely sensed data for census studies.
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The rapid growth of computer technology, such as Geographic Information
System (GIS), has been widely used in various fields since it was being born
in year 1989. A GIS is a powerful configuration of computer hardware and
software used for compiling, storing, managing, manipulating, analyzing, and
mapping (displaying) spatially-referenced information. (Haestad Methods,
2003).
The use of GIS for population studies ranges from creation of maps to
modelling the relationship between population variables (Baudot, 2001;
Forbes, 1984; Langford and Unwin, 1994; Martin, 1989; Rhind, 1991.
This project presents the integration of Geographic Information System
(GIS) and remote sensing to carry out some modeling related to population
density. The integration normally takes the form of using remote sensing data
as a source of data or using GIS as in-situ tool. The results of analysis are
integrated for further spatial analysis.
1.2 STATEMENT OF THE PROBLEM
There is need to represent properly the number of populace living in
every location. The inability to properly represent the figures or get a fairly
accurate census figure has contributed to poor planning, poor decision making
and implementation. The need to adequately represent fair estimate of the
population size is paramount and can never be over emphasized. Population
census process has been marred logistical difficulties in reaching many remote
areas, inadequate training of enumerators, inconsistent data management
process e.t.c Inability to manage acquired data can lead to poor data analysis
and usage. Previous studies have shown the importance of integrating remote
sensing with GIS for population studies (Donnay, 1992; Harris and Ventura,
1995; Wilkinson, 1996). The integration normally takes the form of using
remote sensing data as a source of data or using GIS as in-situ tool (Sadler and
Barnsley, 1990).
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1.3 AIM
The aim of this project is to estimate the population of Achara Layout in Enugu
State using remote Sensing and GIS Technology.
1.4 OBJECTIVES
The objectives of this project are to:
i. To estimate the population of Achara Layout using Remote Sensing
and GIS to population studies in Enugu State of Nigeria.
ii. Compare the population data gotten from National population
commission for Achara Layout, Enugu state and that estimated
using GIS and Remote sensing methods for Achara Layout, Enugu
State.
iii. Show the population density in Achara Layout Enugu State Nigeria.
1.5 SCOPE OF WORK
A good census estimate of a country population can help a country
plan for present and future development and so much more. The data
generated from population census are analyzed and used to calculate for
various needs like gender ratio, indigenes and non-indigenes, migrants and
immigrants e.t.c. Attempts to conduct a reliable post-independence census
have been mired in controversy especially using the traditional methods of
head counting. Modern GIS & Remote Sensing is capable of fulfilling many
of these requirements for an automated Population Census estimation within a
short period of time. The Data so obtained shall be used for future projections
using certain parameter obtained on field visitation..
The scope of work revolves around Achara Layout Enugu, Enugu State as this
is a good site to show case some of the capabilities of GIS because this area is
fully built up and its right about the oldest layout settlement in Enugu
Metropolis.
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1.6 STUDY AREA
Enugu is a large city in the Eastern region of the Nigeria with
geographic coordinates 6°30′N 7°30′E. Enugu State is one of the states in the
eastern part of Nigeria and occupies a total area of 7,161 km2 (2,765 Sq.
mi)The state shares borders with Abia State and Imo State to the south, Ebonyi
State to the east, Benue State to the northeast, Kogi State to the northwest and
Anambra State to the west. Enugu State has a population of over 3.3 million
people and counting. It is home of the Igbo of south-eastern Nigeria.
Achara Layout is located in Enugu South of Enugu State of Nigeria.
Enugu South is a local Government Area of Enugu State. Its headquarters are
in the city of Enugu. It has an area of about 106 km² and a population of about
244,852 at the 2006 census.
Achara Layout is one of the areas of Enugu that was mapped out in the
1960s and has grown from a residential suburb to a major commercial area
especially along Agbani Road, the main high street; the streets are heavily
built up. It can be assessed from all parts of Enugu. The figure 1 is the Google
Image of Achara Layout.
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Figure 1 overview map showing achara layout and environs
1.7 BENEFITS AND CONTRIBUTIONS
The use of Geographic information system shall benefit National
Population Commission and contribute to the development of Nigeria in the
following ways:
1. Site location
2. Provision and Access to social amenities information where detailed
attributes about individual age distribution and human concentration
are obtained, etc.
3. Availability of population data which is accessible via GIS and is
easily updated
4. Availability of digital maps that can be used as guides.
5. Creation of as-built drawings that have been geo-referenced and
digitized.
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6. Field access to flexible data, based on street- aids in finding street and
information about areas, and other information can still be inputted.
7. Used existing building, to build an integrated building / block system
data source.
8. It can be used to define long and short term action for map updates and
data cleanup that will move the City forward for the long term data set
and data management.
9. Creation of Zone maps, boundary maps were very important; this will
assist in planning and organizing excavation, rehabilitation and
election voting systematically.
10. It can be used to coordinate and manage Service calls since location of
streets and building information can be viewable in GIS.
11. Ability to view other City facilities and data in conjunction with street
data.
12. Draw and design plan and profile of streets.
13. It can improve better head counting of populates.
14. Provision of improved operational efficiencies.
15. Ability to query to extract information
16. Time and money savings
17. Better and timely decisions
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CHAPTER TWO
LITERATURE REVIEW 2.1 DESCRIPTION OF GEOGRAPHIC INFORMATION SYSTEM
A GIS is defined as “an organized collection of computer hardware,
software, geographic data, and personnel designed to efficiently capture, store,
update, manipulate, analyze, and display all forms of geographically
referenced information”. (ESRI, 1992) GIS technology has been widely used
in various fields, such as agriculture, business geographic, ecology, electricity
and gas, emergency management and public safety, environmental
management, forestry, health care, education, mining and geosciences, real
estate, remote sensing, telecommunication, transportation and water
distribution and resources.
More commonly, people use GIS to make maps; a GIS can also be
used as a powerful analysis tool. It can be used to create and link spatial and
descriptive data for problem solving, spatial modeling and to present the
results in tables, graphics or maps. The most powerful feature of a GIS, from a
planner’s perspective, is probably the ability of GIS to integrate databases,
through their spatial relationships, that would be difficult or impossible to do
outside a GIS environment, (Methods, 2003). Remotely sensed data offers the
means to measure spatial attributes of the urban landscape.
In the past, researchers remain dependent on “Aerial Photographs”
because of their fine spatial resolution to get accurate data about size of houses
and their volumes and consequently estimates of population (Adeniyi, 1983;
Lo and Chan, 1980). Aerial photography is not allowed to be exported from
some countries, but satellite images are occasionally available and can be
collected from anywhere. One meter resolution satellite sensor usually covers
a wider field of view compare to aerial photographs, meaning that satellite
data are usually less expensive than aerial photographs per km2. In addition to
that, remotely sensed data provide a wider spectral coverage (number of
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bands). Due to these advantages, there is a general trend towards using
remotely sensed data for census studies.
(Brugioni, 1983; Iisaka and Hegedua, 1982; Lo, 1995; Lo and Chan,
1980; Lo and Faber, 1997). While these efforts have shown promise, they
have all lacked the use of high-spatial resolution imagery. This work extends
previous studies that have tested the accuracy of the resultant population
estimates using remote sensing. Progressively switching over of remotely
sensed data obtained from government sponsored satellite programs, such as
LANDSAT, SPOT, RADARSAT, IRS to commercial satellites such as
IKONOS, QuickBird, and OrbView after 1999 has improved the state-of-art.
These satellites promised to provide unprecedented access to accurate and
timely information (Baker et al., 2001).
2.2 POPULATION CENSUS STUDIES USING REMOTE SENSING
AND GIS APPLICATION
2.2.1 Background
Many methods for population estimation have been reported in the GIS
and remote sensing literatures. Depending on the intended goal and the
required information, these methods can be grouped into two categories: areal
interpolation and statistical modeling. Areal interpolation methods are
primarily designed for the zone transformation problem that involves
transforming data from one set of spatial units to another. This approach uses
census population data as the input and applies interpolation or disaggregation
techniques to obtain a refined population surface. In contrast, the statistical
modeling approach is more interested in inferring the relationship between
population and other variables for the purpose of estimating the total
population for an area. The statistical modeling approach does not directly use
census data as the input. Rather, it makes use of socioeconomic variables and
applies theories in urban geography for population estimation; census
population data only participate in the model training process. This approach
22
is originally designed to estimate the intercensal population or population of
an area difficult to enumerate, though it can also be incorporated into the
process of interpolating census population. Before reviewing these two
approaches of population estimation, we would like to review the early
population density models from urban geography.
2.2.2 Population Density Models From Urban Geography
The simple gravitational population density model from urban
geography is the heart of what has been called social physics (Stewart and
Warntz, 1958). Although many people have noticed the decrease of population
density from inner city to outer.
2.3 POPULATION ESTIMATION METHODS
it was Clark (1951) who first associated this observation with specific
mathematical functions (Liu, 2003) in the following negative exponential
function: d (r) = K * e–λ r , (1) where d(r) is the population density at distance
r from the center of the city (r = 0); K is a constant that equals the central
density d(0); M describes the rate of decline of density. This relationship has
been demonstrated to exist for many cities of the United States (Weiss, 1961),
as well as for many cities outside the U.S. (Newling, 1965). Although the
goodness of fit varies, the model always holds statistically significantly in
every place studied. Kramer (1958) also incorporated a sectoral model of a
city and showed how various models of urban forms interact with each other.
Some studies have explored other mathematical forms to describe the
relationship between population density and location. For example, Sutton et
al. (1997) examined the Gaussian and the parabolic forms and found that both
are statistically significant. Some studies have criticized the use of the
negative exponential function. For example, Batty and Longley (1994) stated
that the exponential population density function has been used solely for its
23
convenience and elegance, rather than its appropriateness to empirical data.
Parr (1985) suggested that an inverse power function is more appropriate to
the urban fringe and hinterland, and the negative exponential function is more
appropriate for describing density in the urban area. A similar suggestion to
make a modification at the fringe of the urban area is also found in Tobler’s
(1999) comment on Martin’s (1996) population interpolation algorithm.
Tobler pointed out that “the exponential distance decay function is a relevant
approximation for the whole of an urban area, yet its repeated use farther out
from the urban center hardly seems reasonable. In the periphery, far from the
center, the density gradient is much more nearly linear” (1999, p. 85). To
correct this problem, Tobler proposed a “tent function” by first decomposing
each census unit into triangles, with one vertex being the geometric centroid of
the unit; populations inside triangles are then assigned based on the
coordinates and population of the vertices.
2.3.1 Areal Interpolation Methods
The negative exponential function can only be regarded as the empirical
results showing how population is distributed in urban areas. It has not been
used, in existing literatures, for practical population estimation that concerns
accuracy. Instead, most studies used areal interpolation or statistical modeling
methods for population estimation. Areal interpolation, as mentioned above, is
primarily designed for zone transformation that involves transforming data
from one set of spatial units to another. The two sets of spatial units could be
referred as the source zone and the target zone (Lam, 1983). The general
strategy for zone transformation is to apply certain areal interpolation
operations to transform source zone data to finer-scale raster data and then
aggregate them for target zones. In the context of population interpolation,
census data are the vector-based source zone data and are interpolated to finer-
scale raster data by a certain interpolation method. Areal interpolation is
24
subject to errors from original areal aggregation. The quality of the
interpolation estimates depends largely on how source zones and target zones
are defined, the degree of generalization in interpolation process, and the
characteristics of the partitioned surface (Lam, 1983). Areal interpolation
methods can be further separated into two categories depending on whether
ancillary information is used. Areal Interpolation without Ancillary
Information For areal interpolation methods without ancillary information,
there are point based methods and areal-based methods (Lam, 1983). In point-
based interpolation, a control point is assigned to represent each source zone
and a grid map is generated with grid point values estimated from control
points. In contrast, area-based interpolation uses the source zone itself as the
unit of operation instead of arbitrarily assigned control points. Also, area-
based interpolation is more concerned with volume preservation; i.e., the
summation of population data to the original set of areal units is preserved in
the transformation to a new set of areal units. Based on theoretical and limited
empirical evidence, volume preservation is an essential requirement for
accurate interpolation estimates (Lam, 1983).
2.3.2 Point-Based Methods.
There have been many point-based interpolation methods developed in
the past. Some researchers put such methods into two groups, global and local,
depending on whether they consider all of the data values at once or the values
within a pre-defined neighborhood of each point. Here we adopt Lam’s (1983)
approach to group point-based methods into exact methods and approximate
methods, depending on whether they are concerned with preserving the
original sample point values or with determining an overall surface function
f(x, y). The reason for this categorization is that whether interpolation methods
preserve original data values on the inferred surface is fundamental in
analyzing their accuracy (Lam, 1983). The exact methods include
interpolating polynomials, most distance-weighting methods, kriging, spline
25
functions, and finite difference methods, while the approximate methods
include power-series trend models, Fourier series models, distance weighted
least squares, and least-squares fitting with splines. Each of these methods has
its own advantages and disadvantages, and none of them is superior to all
others for all applications (Lam, 1983).
Furthermore, the results from all the methods are seriously affected by
the quality of the original data, especially the density and the spatial
arrangement of data points, and the complexity of the surface. The choice of
an appropriate interpolation method depends largely on the type of data, the
degree of accuracy desired, and the amount of computational effort afforded.
In general, exact methods are more reliable than approximate methods because
of the former’s simplicity, flexibility, and reliability (Lam, 1983). One of the
point-based methods widely used in the UK census is a kernel-based
interpolation proposed by Martin (1989) (Bracken, 1991; Martin and Bracken,
1991; Bracken and Martin, 1989). This method uses a source zone centroid as
the control point. A window is positioned over each control point in turn and
the source zone population is allocated to grid cells falling inside the window
using a unique weighting based on the distance decay function between the
source zone centroid and the grid cell. Point-based areal interpolation methods
experience a few problems (Lam, 1983; Liu, 2003). First, the use of a control
point, usually the centroid or the center of an area, to represent the source zone
often introduces errors. The calculation of the centroid or center of an area
depends on the coordinates of the points defining the boundary of a source
zone. If the source zone is symmetrical and relatively simple, the center or
centroid would be a convenient control point, and the estimated value for each
grid cell would be reliable. However, if the boundaries are not symmetrical or
well generalized, the location of the centroid can be significantly effected, and
the interpolation results may be biased. In reality, census units are rarely
symmetrical, and the non-uniform distribution of population within a census
unit further complicates this issue. Another problem associated with point-
26
based interpolation methods is that all have some kind of a priori assumption
about the surface involved. This rather arbitrary assumption rarely fits the
complex geographical phenomena in the real world. Nevertheless, it is worth
noting that the task of areal interpolation is to search for the best method,
whose output is as close to the ground truth as possible. Violation of the
assumption of a method only implies that the results obtained may not be
optimal, but does not mean that this method is necessarily inferior to others.
For example, in the context of kriging interpolation, if the source zones can be
reduced to control points and the population distribution can be described by
the semi-variogram, kriging is the best linear unbiased estimation. If the
assumption is not satisfied, results from kriging interpolation are not
necessarily inferior to those from others. Comparative studies using empirical
datasets are needed to further research this issue. The most important problem
of point-based methods is that they mostly do not conserve the total value
within each source zone. Volume preservation is important in that it gives
reliability to the approximation of grid values for source zone, and thus the
subsequent estimation for target zone is less subject to error. Besides, in the
context of population interpolation, people should not be “destroyed” or
“manufactured” during the redistribution process (Langford and Unwin,
1994). To correct this problem, Martin (1996) modified the original kernel-
based interpolation algorithm (which is a point-based method) to ensure that
the populations reported for target zones are constrained to match the overall
sum of the source units.
2.3.3 Area-Based Methods.
In contrast to point-based interpolation, area-based methods are
volume-preserving methods. The simplest method in this category is the
overlay operation based on the geometric properties of the source and target
zones. It superimposes the target zone on the source zone to obtain the
proportion of each source zone in each target zone. The proportion then serves
27
as a weight and the values of target zones become a weighted linear function
of source zones. The major problem with the overlay method is that it assumes
homogeneity within each source zone. Source zones having homogeneous
distributions, unfortunately, seldom occur in the real world. This may well be
true of some phenomena such as rainfall or agricultural productivity, but is
harder to justify for human phenomena such as population. In addition, very
often the source zones were originally delineated for other purposes and may
not show the important distribution information for the target zones. For these
reasons, the reliability of target zone estimates is controlled mainly by the
nature and degree of the homogeneity of the source zone and by the size of the
target zone in relation to the source zone (Lam, 1983). Tobler’s (1979)
pycnophylactic interpolation is probably the most widely quoted area-based
interpolation method. This method assumes a smooth density function that
takes into account the effect of adjacent source zones while preserving its
volume.
The smooth condition intends to minimize the curvature of estimated
surface by requiring the value of any grid point to approach the averages of its
four neighbors. Other smoothing conditions may be used depending on the
type of application. The interpolation process begins by assigning the mean
density to each grid cell superimposed on the source zones, and then modifies
this by a slight amount to bring the density closer to the value required by the
smoothing condition. The volume-preserving requirement is then enforced by
either incrementing or decrementing grid densities within each source zone
after each computation. The result is a smooth population density surface. The
original pycnophylactic interpolation uses regular lattice grids as its spatial
configuration. Rase (2001) extended it to a surface representation based on an
triangular irregular network (TIN). The basic step is to generate a TIN from
the boundary network first, and then to interpolate a smooth surface by an
iterative procedure, in which the two steps of smoothing and difference
distribution are repeated until the threshold for the overall smoothness
28
measured by the relative variance is reached or the maximum number of
iterations is exceeded. Compared to the original grid-based method, the TIN-
based pycnophylactic interpolation is argued to have several advantages (Rase,
2001), including that the error resulting from converting source zone polygons
to a regular grid is avoided, and that the TIN-based method is more suitable
for fast display in real-time applications. On the other hand, the TIN model is
more difficult to implement because it requires more effort and support for the
data and program structures. Lam (1983) stated that the overlay methods will
yield better estimates if the surface is discontinuous, whereas the
pycnophylactic method gives better results when smoothness is a real property
of the surface. In cases where the surface is intermediate between
discontinuous and maximally smooth, different target equations and side
conditions should be imposed for reliable results, but such methods are yet to
be developed.
Areal Interpolation with Ancillary Information Population is related to
other information, e.g., land use and transportation networks, that can be used
to assist population interpolation. This section will review the interpolation
methods with ancillary information, particularly those can be extracted from
remotely sensed data. The dasymetric method is the most well-known method
in this category. It was originally developed by Wright (1936) out of a concern
that choropleth maps do not give a valid representation of population
distribution within enumeration units. Wright’s idea was to use knowledge of
the locality to identify areas within zones that have different population
densities, thus allowing refinement of the assumption of an even distribution
(Fisher and Langford, 1995). In his population density mapping of Cape Cod,
Wright made binary partitions iteratively to disaggregate general zones to
detailed zones of population density while making certain that the original
zone population was preserved. In the past, Wright’s dasymetric mapping of
binary partition was difficult to implement. With the development of digital
data and GIS technology, the dasymetric method became easier through use of
29
the GIS overlay process, which also provides the convenience of integrating
various types of ancillary spatial data. For example, Monmonier and Schnell
(1984) demonstrated the integration of classified residential land use classes
from Landsat satellite imagery as the ancillary information in the dasymetric
method. Wright’s dasymetric method relies on knowledge of the local areas to
determine subzone population densities. Flowerdew and Green (1989)
proposed using statistical regression analysis to estimate subzone population
densities; yet Langford et al. (1991) first applied multivariable regression
techniques to estimate dasymetric subzone population densities. Their
approach is based on the following function:
Pi = Σ Pij = Σ Aij * Dj, …………………………………… (2)
where Pi is the total population of source zone i;
Pij is the total population of land use j within source zone i (subzone
ij);
Aij is the total area of land use j within source zone I;
and Dj is the average population density of land use j.
Aij can be obtained by a GIS overlay operation of a land use map and a
source zone map. Since there are multiple source zones, multivariable
regression can be applied to estimate Dj of multiple land use types. Volume
preservation is further maintained by scaling up or down derived density
measures to fit the original total population for each source unit. Despite the
ease of implementation, the dasymetric method is still subject to the problem
of an even distribution assumption within subzones. In other words, while the
difference between subzones is recognized, differences within subzones are
ignored. For example, for single-family land use, there is the difference
between low density, medium-density, and high-density zones. To incorporate
such a consideration, one may conduct a more detailed land use classification,
and associate each land use class with a certain population density. Although
30
this approach could improve population interpolation accuracy, it requires
effective ways to classify detailed land use types and to estimate their
population densities. The easiest dasymetric mapping approach with remote
sensing–derived land use data is a binary division approach in which land use
is classified to “populated” and “unpopulated” and census populations are
simply redistributed to those populated areas; some example studies included
Holt et al. (2004), Fisher and Langford (1996), and Langford and Unwin
(1994). Furthermore, a more specific dasymetric mapping approach would
classify a number of land use classes and redistributed census populations to
these classes; some example studies include Mennis (2003), Eicher and
Brewer (2001), Yuan et al. (1997), and Langford et al. (1991). For the latter
group of studies, some ways of determining the population density ratio
between land use classes must be applied. Some studies used an empirical
sampling approach (e.g, Mennis, 2003), some used pre-defined population
density statistics (e.g., Eicher and Brewer, 2001), whereas some used
regression analysis to derive population density estimates (e.g., Yuan et al.,
1997; Langford et al., 1991). The regression analysis seems to provide a
preferred approach because of its objectivity in testing model accuracies
through statistical significance tests. Harvey (2002b; 2000) adopted an
extreme approach to deal with the homogeneity assumption within subzones
by estimating population density in the spatial unit of pixels. His method first
assigned all residential pixels within a source zone with an equal share of the
total population in the following equation:
Pij = Pi /n, i = 1, 2, …, n
……………………………………………………….(3)
Where Pij is the population initially assigned to the jth pixel in source
zone i whose total population is Pi , and n is the number of pre-classified
residential pixels in source zone i. Since there were many source zones, each
31
of which had some residential pixels with different digital values, an ordinary
least-squares regression could be conducted between the population and the
digital value of the pixels. With the regression coefficients obtained, the
population of each pixel was adjusted by the following equation: , (4) where is
the regression estimate, and. (5) The result was that the adjusted reference
population lay closer to the regression line than the initially assigned
population. If the iteration was run again with the adjusted value, the R2
would be improved. The process was repeated iteratively, and R2 continued to
increase monotonically with decreasing increments, and stopped when a
predefined threshold was reached. Harvey proved that this iterated regression
procedure is a least-square approximation to the Expectation Maximization
(EM) algorithm that was originally presented by Dempster et al. (1977) and
applied by Flowerdew and Green (1989, 1991) for combining data from two
incompatible sets of spatial zones. Harvey (2000, 2002b) argued that the pixel-
level dasymetric method has several advantages over zone-level dasymetric
methods, including: human habitation by individual residences should be
better delineated by pixels; the mathematical form of pixel-based model is
simple and relatively robust; and implementation and refinement of routine
pixel-based classifiers are easier. We separated areal interpolation methods
into two categories depending on whether ancillary information is utilized.
The methods using ancillary information, particularly the dasymetric method,
usually yield more accurate results than those without ancillary information,
assuming the ancillary information reflects the spatial distribution of the
variables being mapped. It is worth noting that methods in these two
categories can be incorporated with each other for certain purposes. For
example, Langford and Unwin (1994) applied a kernel-based smoothing
function to the result of a dasymetric method in order to create a
cartographically pleasing and informative map, so that the readers won’t see
too many pixel-level details. Also, most methods in the first category (without
ancillary information) can still make use of ancillary information when it is
32
available. For example, in the case of Tobler’s pycnophylactic interpolation, if
information about residential areas is available, one can first allocate Pij( ) adj
P ˆ ij r = + ˆ P ˆ ij r ˆ Pij P ˆ – ij ( ) j = 1 n ∑ n = -- the population of a census
unit to residential polygons within it, assuming nonresidential polygons have
no population, and then perform the smoothing interpolation operation.
2.3.4 Statistical Modeling Methods
We will review the second category of population estimation methods,
the statistical modeling methods, in this section. As reviewed previously,
theories in urban geography have demonstrated that population distribution in
an urban area is affected by morphological factors such as distance to the
central business district (CBD), distance to roads, etc. Many of the
morphological factors can be extracted from remotely sensed data.
Consequently, remote sensing has been actively explored as a means to study
population distribution. Strictly speaking, models from this group are mainly
designed to estimate an overall population count rather than population density
that is relevant to population distribution. However, since population count
and population density can be derived from each other through the size of the
area of interest, the method designed to estimate population counts can also be
used to estimate population distribution. Statistical modeling approaches for
population estimation started in the 1950s. The initial motivation was to
remedy the shortcomings of the decennial population census, such as high
cost, low frequency, labor intensity, etc. (e.g., Kraus et al., 1974), but these
approaches also have been applied to check the reliability of the census
enumeration (e.g., Clayton and Estes, 1980), and the inference of
socioeconomic characteristics such as housing value and residential quality
(e.g., Forster, 1983). The use of remote sensing in statistical modeling
approaches started in the mid-1950s, particularly with the goal of searching for
an alternative to a population census. Researchers have conducted various
33
statistical modeling methods for population estimation on different scales with
different types of remotely sensed imagery. In general, there are five
categories of approaches, based on the relationship between population and (1)
urban areas, (2) land use, (3) dwelling units, (4) image pixel characteristics,
and (5) other physical or socioeconomic characteristics (Lo, 1986; Liu, 2003).
Correlation with Urban Areas This category of methods is a general approach
based on a functional relationship between urban areas and population size.
Inspired by the biological law of allometric growth (Huxley, 1932), Nordbeck
(1965) studied the relationship between urban areas and population size of
many U.S. cities and concluded that the built-up area (A) of a settlement is
proportional to its population (P) raised to some power: A = a * Pb. (6) Tobler
(1969) was the first to use satellite imagery to study the relationship between
population and urban areas. He used images from the Gemini manned space
flight program to study populations of many cities in the world. Assuming that
if cities can be considered circular in shape, and if shape varies insignificantly
with time, Tobler found the correlation coefficients between radii and
populations of 0.87 or higher in the following function: r = a * Pb. (7) The
results of his study also indicated that the coefficient (a) and exponent (b) for
cities in the United States was comparable to those for cities in Sweden and
Canada; cities in Japan and in the Nile Delta, however, had coefficients and
exponents reflecting the dense and compact structure of settlements in Asia
and the Middle East. The availability of Landsat satellite imagery and
advancement in image processing techniques allowed researchers to efficiently
study the relationship between population and urban areas, although one of the
major difficulties involved differentiating rural/urban boundaries (Lo and
Welch, 1977). Using 1972 to 1974 Landsat MSS imagery of 10 large cities in
China with 500,000 to 2,000,000 populations, Lo and Welch (1977) found
correlation coefficients of 0.82 or higher between populations and classified
urban areas in a modified function from (6): P = a * Ab. (8) This function can
be referred as the allometric growth model (Lee, 1989; Lo, 2003), which
34
describes that the relative growth rate of population is proportional to the
relative growth rate of the residential land area. Researchers also used urban
light as an indicator for population size. Prosperie and Eyton (2000) found a
quite high R2 of 0.974 between light volumes and populations of 254 Texas
counties using DMSP (Defense Meteorological Satellite Program) imagery.
Adopting a similar approach but at a smaller scale using cities, Lo (2002)
found a correlation coefficient of 0.91 between the light volumes of 35
Chinese cities and their non-agricultural populations. He further evaluated
derived population models using data from other 18 Chinese cities and
obtained acceptable accuracies. Correlation with Land Use The second
approach for population estimation is based on correlating population counts
with different types of land use areas, which should achieve higher precision
than the first approach. The total population for an area can be calculated
according to the following function: *, (9) where P is the total estimated
population;
Aj is the area of land use j;
and Dj is the population density for land use j,
Which is to be determined through regression analysis. This basic
function is similar to that used in the dasymetric method reviewed previously,
only that the former intends to disaggregate census population by maintaining
the original census unit population count, whereas the latter intends to estimate
the total (intercensal) population for an area. P Aj j = ∑ Dj Areas for different
types of land use could be extracted from remote sensing images.
The accuracy of population estimation would largely rely on how
accurate different types of land use are classified. In Weber’s (1994) study of
population estimation for the City of Strasbourg, France, he classified six
types of urban land use from SPOT HRV XS images with a Kappa coefficient
of 0.915. Then he ran a regression analysis between population counts and
land use areas of 2,812 census units based on function (9) and obtained an R2
35
of 0.91. After applying his regression model to estimate the total population
for the city, his model estimate was 7.91% below the census population of the
city. In Lo’s (2003) study of Project ATLANTA, he classified six types of
land use from Landsat TM images and obtained a Kappa coefficient of 0.878.
The area of low density urban use class was then regressed with population
counts of 418 census tracts using a logarithmic transformed allometric growth
model. The result had an R2 of 0.68. He then applied the regression model to
estimate populations of 373 census tracts and the results had a relative error of
14.80% and a overall underestimate of 8.07%. Population densities for
different types of land use could also be determined from sample surveys or
census statistics, in addition to the regression analysis. For example, in Kraus
et al.’s (1974) study of population estimation for four California cities, four
types of urban land use were first classified. Then the authors calculated the
characteristic population densities for each type of land use from sampled
census block-level population data. Finally they estimated city populations
based on function (9). The results ranged from an underestimate of 9.17% to
an overestimate of 7.00% when compared with census populations of the four
cities. Correlation with Dwelling Units The total population of an area can be
estimated by multiplying the total number of dwelling units with the number
of persons normally living in a dwelling unit. It is also possible to categorize
dwelling units and apply a different persons-per-dwelling unit ratio to each
category. This ratio can be derived from sample surveys or calculated from
census data with the assumption that a single household occupies one dwelling
unit. The total number of dwelling units in an area may be estimated from
remote sensing images. Green (1956) was probably the first researcher to
propose using individual dwelling unit counts observed from aerial
photographs for population estimation. Porter (1956), however, was the first to
actually apply this methodology (Kraus et al., 1974), with the persons-per-
dwelling unit ratio established from ground observation in their study of
Liberia. Hsu (1971) applied the same methodology for intercensal population
36
estimation of the Atlanta area, but he derived his persons-per-dwelling unit
ratio from U.S. census tract data. Collins and El-Beik (1971) and Dueker and
Horton (1971) further identified different types of residential buildings from
aerial photographs for population estimation, with their population density
statistics calculated from census data. To obtain a more accurate persons-per-
dwelling unit ratio, Lo and Chan (1980) used a field survey methodology to
calculate the average population density for various types of housing.
Furthermore, in an effort to automate the time-consuming procedure of
counting dwelling units, Lo (1989) used a raster approach to extract residential
building density, on a grid cell by grid cell basis, from high-altitude aerial and
space photographs. He first calculated the maximum possible occurrence of
dwelling units in each grid cell with reference to the dwelling unit size. Then
the percentage of occurrence of residential buildings in each grid cell was able
to be estimated. In the past, no effective ways of automatically extracting
residential buildings existed. Researchers relied on manually identifying and
counting dwelling units from high-spatial-resolution aerial photographs, even
though visual interpretation is laborious and time consuming. With the
advance of very high spatial resolution satellite images, such as IKONOS and
QuickBirds, and the improvement of feature extraction techniques
(Haverkamp, 2004), automatic extraction of dwelling units from satellite
images has become possible. Another prospect for automatic building
extraction is the advancement of 3D object extraction techniques from LIDAR
data (Rottensteiner, 2003). With these new remote sensing data and building
extraction techniques, population estimation by dwelling unit counts may
become a viable approach. Correlation with Image Pixel Characteristics Other
than the physical characteristics extractable from remotely sensed imagery,
population density can also be directly correlated to the spectral reflectance
value of image pixels. Hsu (1973) was probably the first to suggest the idea of
using imagery pixel values to develop a multiple regression model for
population estimation (Lo, 1986). His idea, however, was not implemented
37
until Iisaka and Hegedus’s (1982) pioneering work in estimating population
distribution of Tokyo, Japan. They reported that the mean spectral values of
Landzat MSS bands 4, 6, and 7 were strongly correlated to population density.
Lo (1995) adopted a similar approach while using higher resolution imagery
of SPOT, and reported a correlation coefficient of –0.91 between population
density and the mean spectral values of SPOT band 3 for the Hong Kong area.
Webster (1996) argued that the spectral values alone cannot discriminate areas
of different population densities effectively.
Alternatively, he combined numerous spectral and textural measures
from Landsat TM imagery in a regression model and found textural measures
have more significant predictive power for housing densities than spectral
measures. Harvey (2002a, 2002b) also incorporated a variety of spectral
transformation measures, such as the band-to-band ratio and difference-to-sum
ratio, in addition to textural measures, in a series of stepwise regression
models for population estimation. Further, Harvey (2000; 2002b) developed
an innovative iterated regression procedure (reviewed previously as a
dasymetric method) to improve the predictive power of a regression model
based on pixel spectral values. There are also studies using imagery texture
analysis to categorize pixels first and then correlate pixel counts in different
categories with population density, which is similar to the approach of
inferring population through land use. For example, Chen (2002) used a
homogeneity texture measure to categorize pixels of different levels of
homogeneity and correlate the number of pixels in each category to housing
densities. Correlation with Other Physical and Socioeconomic Characteristics
Other than the mentioned physical and pixel characteristics extractable from
remotely sensed imagery, numerous other physical and socioeconomic
variables can also be incorporated for population estimation. A notable
example is the LandScan Global Population Project (Dobson et al., 2000), in
which light volume from nighttime imagery, land cover derived from various
types of remotely sensed imagery, and other information about demography,
38
topography, and transportation networks have all been combined in a model to
estimate population at a 30 × 30 second (approximately 1 × 1 km) resolution.
Many of the variables can be extracted from remotely sensed imagery. Similar
approaches have also been applied in a smaller scale. For example, Liu and
Clarke (2002) found that the total population in urban areas is correlated with
distance to the CBD, accessibility to the transportation system, slope, and the
time when the residential community was first built.
Overall, the accuracy and robustness are improved with increasing
model complexity. It is worth noting that although multivariable approaches
for population estimation tend to improve the overall accuracy compared to
methods using a single variable, the selection of variables in the model
requires guidance from theories in urban geography. Many physical and
socioeconomic variables can assist the estimation of population, yet only those
attributes that can be directly or indirectly observed and extracted from
remotely sensed imagery are applicable in the remote sensing context.
Residential areas constitute a major component of such analysis. The data are
usually of two types: (1) the structural conditions of individual residential
units; and (2) attributes reflecting the residential or neighborhood
environment. Green (1957) and Green and Monier (1959) pioneered research
using aerial photograph to study socioeconomic and demographic variables.
They cited an extensive literature to demonstrate that social values are
attached to housing and residential communities and, by extension, that
observable physical data have meaningful sociological correlations.
Regardless of whether socioeconomic or pixel characteristics are used in
statistical modeling for population estimation, all studies inferring population
from remotely sensed data have reported a consistent finding, i.e., that small-
area population estimation is often not as accurate as large-area estimation. It
may be explained that overestimation and underestimation are canceled out for
large-area population estimation and thus the overall accuracy is high (Lo,
39
1995). Nonetheless, more studies are needed before remote sensing can be
applied to population estimation on an operational basis.
2.4 SUMMARY
Of all the population estimation methods, the dasymetric method is
commonly regarded as a more accurate approach, provided that the used
ancillary information gives a truthful description of where people actually live.
Furthermore, the dasymetric method is not only more accurate, but also
relatively stable. It is robust to the variation of population density associated
with a certain type of land use, as well as the anomaly of highly urbanized but
sparsely inhabited areas (Fisher and Langford, 1996). The reason is because
the volume-preserving property preserves the population of the source unit in
the transformation to raster representation, and thus all associated errors are
inherently limited to variation within each individual source unit. The
dasymetric method used with remote sensing is also robust to imagery
classification error. Fisher and Langford (1996) reported that errors of up to
40% in the classified TM image still yield better estimates of the interpolated
populations than other regression or surface methods they tested. The reason
for the relative robustness of the dasymetric method under classification error
is due to the aggregated error within zones. Specifically, even if the
classification error is high, the frequency of pixels in different land classes
may not vary significantly within a zone. As observed by Donnay and Unwin
(2001, p. 220), “even though individual pixels may have a weak probability of
being correctly assigned to a land use category, when aggregated into a target
zone for density estimation, the relative frequencies within these target zones
do not degrade substantially.” Relevant empirical studies by Lo (1995),
Webster (1996), and Harvey (2002b) also indicated that classification errors at
the pixel level can be high without impacting the accuracy of areal population
estimates. In this review, we separated population estimation methods into
areal interpolation and statistical modeling. It is worth noting that the
40
statistical modeling approach can also be incorporated into the dasymetric
method. For example, Langford et al. (1991) described a dasymetric procedure
based on five types of land use classified from TM multispectral imagery, with
their average population densities derived from regression analysis. Yuan et al.
(1997) also applied a multivariate regression model to correlate census block-
group populations with different land use areas classified from Landsat TM
images in their dasymetric study.
Studies on population issues generally use census data as the primary
data source. The census, however, may not be applicable to the intended
purpose of these studies. This is because in many counties, including the
United States, the census population figure is actually a de jure population, in
contrast to a de facto population. A de jure population reports all usual
residents of the given area, whether or not they are physically present there at
the reference date. A de facto population, in contrast, reports all persons
physically present in the area at the reference date. The U.S. census is a de jure
census because it is based on people’s home address, rather than where they
work or travel during the day, or if people are out of town. The U.S. census is
mainly concerned with residential populations and the daytime population
distribution can be very different from that described by the census. For
example, Las Vegas has a much higher daytime population than that reported
by the census because of its high proportion of tourists. Since some
applications (e.g., emergency response) require knowledge of daytime
population whereas others (e.g., urban growth) require residential population,
it is desirable that both types of population be estimated. Unfortunately, to
date little research has attempted to model daytime population. Theoretically,
urban land use information is more related to daytime than nighttime
population because land uses such as industrial, commercial, and recreational
provide information about where people are during the day.
41
With the availability of high-spatial-resolution commercial images,
such as QuickBird and IKONOS, as well as the advancement of image
processing techniques, improvement in population estimation accuracies is
expected.
42
CHAPTER THREE
METHODOLOGY
3.1 PROJECT PLAN
The figure below shows the flow chart of the project plan.
Figure 3.1Flowchart of Project plan
In this project termed Application of GIS and Remote sensing in the
Population Study of Achara Layout Enugu, Enugu State, a well analyzed
methodology, and appropriate data structured models were carried out, using
relevant data, software and hardware. Since, maps are produced by the
combined efforts of many professions using a variety of technologies (Lo and
Yeung, 2002). Typically the execution of this project span through project
planning , data acquisition, database creation and data processing .Therefore,
the input and output specifics used in the work are as explained below:
43
3.2 PROJECT PLANNING
The planning of a survey project has a number of unique requirements
depending on its area, purpose, and overall goal. GIS technology helps fulfill
these demands by facilitating traditional tasks more efficiently and easily
accomplishing new tasks that were previously impractical or impossible
(ESRI). In this project a trip was made to Achara layout Enugu for field
reconnaissance to determine the social-impact on the study area. The GPS
coordinate was taken in order to source the spatial data for the study area, the
road networks also was observed. In this population study, critical interview
was made with the population about their dialect, culture, and other
information. These enhanced capabilities eliminate redundant efforts and
promote coordination with the execution of the project. The benefit of the
project planning is:
i. Automate tasks to increase efficiency and save money.
ii. Plan effectively for site location.
iii. Access vast amounts of publicly available geospatial data.
iv. Provide Decision support.
v. View Historic data.
vi. Integrate Survey projects in a single Database
3.3.1 DATA ACQUISITION
The key requirements for population management have been
enumerated to include, maps,census data and GIS tools (Ayeni et al, 2001). In
this project the following data were collected and used in the development of
the GIS / Remote sensing application in population study of Achara Layout,
Enugu, Enugu state. A High-resolution satellite image data (2008 Quickbird
0.5 meter in multi-spectral band) covering the study area and layout map of
the study area at scale of 1: 2000 all collected from Enugu state ministry of
land, survey and Urban Development . The data gotten from questionnaires
44
given out, were used to generate the desired products (Tables and Maps) in an
interactive GIS environment. Field (site) capturing of data were based on basic
survey principles, techniques and instruction. A handheld Garmin GPSMAP
76c receiver was employed in the traverse for point positioning. Also, A420
Cannon power shot Digital Camera was used to picture the area, so as to aid
better visual appreciation and spatial analysis of the area.
Figure 3.2 Map Of Achara Layout As Designed And Subdivided By Town
Planning Unit Of Ministry Of Land, Enugu
45
Figure 3.3 Quickbird satellite image of Enugu metropolis covering the study area
3.4 TOOLS AND COMPONENTS USED
The following tools and components were used for the project;
Hardware used
S/N NAME DESCRIPTION
1. HP Laptop &
accessories
Windows 7, Intel Pentium
2. Hand held GPS Garmin 72s
3. Digital Camera Canon
4. Printer HP Deskjet K7100 series and T1110 Series
5. Scanner Smart LF Colortrac
46
Software used
S/N NAME DESCRIPTION
1. ArcGIS 9.3 Environment
2. Microsoft Excel 2007
3. Microsoft word 2007
4. Microsoft Access 2007
3.5 DATA SOURCES
The following are the main data sources used in this project;
1. Interview: Interviews were carried out on key staff of National
Population census to ascertain how long a population data can stay
before an update is needed.
2. Enugu State Ministry of Lands and Urban Planning: A map of Achara
Layout was used. The scale used to create the map is 1:2000. The map
shows the street network, subdivision of plots and blocks etc.
3. Field work: Spatial data was also collection from field work methods
using Hand held GPS.
The data types collected were primary and secondary data. Primary data
was collected by administration of structured questionnaires. For the
distribution of questionnaires, the study area was divided into 71 blocks based
on the parcellation of the layout by blocks by the Urban and town planning
design. Each block was allocated at least 20 questionnaires bringing the total
number of administered questionnaires to 1420. A total of 1420 questionnaires
were however responded to number of building counting through the high
resolution satellite image. A GPS was also used in collecting data on the
geographic position as well as various relevant information concerning the
47
population of the area; the aspect socio-economic, cultural ethnic was also
analyzed during the field inspection.
3.6 DATA PROCESSING
3.6.1 Scanning
The Achara layout map from Enugu State Ministry of Lands and
Urban Develpoment was scanned through a large scanner. In order to be
scannable, a map should be in a very good condition with minimum text on it.
Hardcopy parcel maps are converted to digital form using a scanner. A
scanner recognizes the black or white value of each pixel location on a map.
The scanned map was saved in a storage media and later saved in a computer.
3.6.2 Data Georeferencing
The secondary data used included, layout map of the study area to the
scale 1:2000, obtained from ministry of land survey and Urban development
and the Quickbird high resolution satellite imagery was used to in extraction
of block and building through the process of vectorization using GIS
technology. The block layer and building layer were created in Arc-Catalogue
and then imported to Arc Map environment, the two raster dataset (image and
map) was also imported to the same arc map environment. Firstly the arc map
environment was reference to projected coordinate, WGS 84, UTM Zone 32 in
hemisphere North. The georeferening toolbar was turned on in other the
georeference the layout map. The layer target was set to Achara layout map
and then the add control point tool was picked to assign the right coordinate to
the four corner of the map (fig 3.3). After this process the Updating
georeference was chosen to validate the georeferenced map. Achara Layout
map was georeferenced in ArcGIS 9.3. Using the control points field survey,
points from fiducial points were chosen for purpose of Georeferencing. Four
points were added at the extreme four corners of the map.
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To achieve this, the following were done;
1. Click the View menu, point to Toolbars, and click Georeferencing.
Figure 3.4 Selecting Georeferencing in ArcMap
2. Click the Layer dropdown arrow
3. Click the Georeferencing dropdown arrow and click Fit To Display
Figure 3.5 Georeferencing toolbar
ArcMap scales the image to fit in the current window.
Since the window is currently zoomed to the four street intersections that
match the registration marks on the image, the streets and the image are
displayed at approximately the same scale. You can see, though, that the
49
control points aren’t located exactly at the intersections. Add four links to
register the image. To make it easier, use a magnifier window. You can add
control points within the magnifier window
4. Click Window and click Magnifier
5. Click the Add Control Points button on the georeferencing
toolbar.
The cursor turns into a crosshair.
6. Drag and center the magnifier window over the registration
mark in the upper right, labeled 602, and release the mouse
button. If necessary, reposition the window so you can see both
the registration mark and the corresponding street intersection.
Note that the registration mark and intersection may be in
slightly different positions on your map.
7. Center the cursor over the registration mark and click. A green
control point is added to the image. Move the cursor away from
the control point but dont click again. A line stretches from the
control point as you move the cursor. This is the link that will
connect the other end of it to the corresponding street
intersection.
8. Center the cursor over the intersection of the street layer (you
can see the link stretch as you do this) and click.
Figure 3.6 3positioning the cross hair
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9. Click the View Link Table button on the Georeferencing
toolbar.
For each link, the table lists the x- and y-coordinates for the source (the
scanned image) and the corresponding coordinates for the map (the streets
layer). If you make a mistake and need to delete a link, select it and click the
Delete button, which looks like the letter x.
10. Click Cancel to close the Link Table. The other points can be
added to the other corners of the map.
11. After adding the four points, Click the Georeferencing
dropdown arrow and click Update georeferencing to save the
new registration.
Figure 3.7 georefrencing the scanned map
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3.6.3 Data Vectorization
In the vectorisation process the editor toolbar was turned on , the start editing
was chosen to start the digitizing process firstly the editor target was set to
Block and the sketch tool pencil was picked to digitize all the Block parcel
through the scanned layout map (fig 3.4) the same process of digitizing was
made to digitize all the building in the study area through satellite imagery.
The field information and the information figured on the scanned map was
used in building the attribute information for each of the extracted data. In
other side the field data was used in database design using Microsoft excel, the
Block_id was used to define the primary Key in which the database will based
when link to the spatial data in ArcGis Environment. Various maps were
produced base on the population study analysis. The query by attribute also
was used to determine some critical information based on the population study
data. The Microsoft excel was used in compilation of the statistic analysis,
multiple table, histogram and chart was therefore produced.
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Figure 3.8 Digitizing Of Block Parcel Through Layout Map Of The Study Area
Figure 3.9Digitizing Of Building Through Satellite Imagery Of The Study Area
3.6.4 Shapefiles in ArcGIS
Shapefiles are useful for mapmaking and some kinds of analysis. A
great deal of geographic data is available in shapefile format. Shapefiles are
simpler than coverages because they do not store full topological associations
among different features and feature classes. Each shapefile stores features
belonging to a single feature class.
3.6.4.1 Features in shapefiles
Shapefiles have two types of point features: points and multipoints. They have
line features that can be simple lines or multipart polylines. They also have
area features that are simple or multipart areas called polygons.
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Point shapes are simply single-point features such as wells or monuments.
Multipoint shapes are collections of points that all represent one feature. A
group of small islands could be represented as a single multipoint shape.
Line shapes can be simple continuous lines such as a fault line on a map. They
can also be polylines that branch such as a river. Line shapes can also have
discontinuous parts.
Polygon shapes can be simple areas such as a single island. They can also be
multipart areas such as several islands that constitute a single state. Polygon
shapes can overlap, but the shapefile does not store topological relationships
between them. The market areas of two stores could be represented as
overlapping polygon shapes.
3.6.5 Digitizing a map
1. Zoom in to the Block enclosed by street by using the Zoom in
tool on the Tools toolbar to draw a box around the Block
boundary.
Add a bookmark to use when you start digitizing the boundary.
2. Click the View menu, point to Bookmarks, and click Create.
Name the bookmark Block Building and click
OK.
3. Click the Editor Toolbar button.
4. Click Editor and click Start Editing.
5. Click project\Population Studies.mdb, and then click OK.
Now set the snapping environment so the new park boundary
will align exactly with the existing parcel boundaries.
6. Click Editor, and then click Snapping.
7. Check the box in the Vertex column for pipeline layer.
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This will snap the editing cursor to the vertices of the parcels.
8. Close the Snapping Environment dialog box.
The snapping tolerance defines how close the cursor must be to
an object before it snaps to that object. You can change the
snapping tolerance by choosing Options from the Editor menu.
3.6.6 Database Structure for Population Census
The effective implementation of any geospatial project lies on the
proper planning and designing of spatial database. The process of designing a
database is called data modeling. According to (Kufoniyi 1997), he defined
database as the process by which the real world and their inter-relationship are
analyzed and modelled in such a way that maximum benefit are derived while
utilizing a minimum amount of data. Improper design often leads to
implementation problems. The attribute database for the population study was
modeled and developed for the purpose of interactiveness based on relational
data model. A relational database is efficient and flexible for data search, data
retrieval and creation of tabular reports (Chang, 2007). Each table in the
database was prepared, maintained and edited separately from other tables.
The tables remained separated until linked up by query or analysis. The
respective questionnaire forms were used for the basis of table creation and
database modeling. In addition some forms were created digitally using
attribute information from the scanned map, the Database Management
System capability in which platform the databases were modeled internally
and linked using the in-built table capabilities in the ArcGIS environment.
Though, external database tables created with Microsoft Excel was joined with
the spatial table of the GIS software (SQL server) which are the default
DBMS in ArcGIS for data manipulation and analysis. In the process, internal
tables the entities and attributes) were created and linked to Arc-GIS shape file
using geo-relational technology. The modeled tables were created in
consideration to linking mechanisms since the tables were properly
55
normalized (Ndukwe, 2001). The building database was modeled as the
primary table; while others were modelled as secondary tables. Unique fields
that serve as keys were inserted accordingly. Although there are few entries in
which tables that make up the database ordinarily does not necessitate
indexing. The entries in the table were indexed. This is because the system if
fully deployed is supposed to handle population data, which has complex and
large information
Figure 4.0 Process of joining table to spatial data in GIS environment
3.6.6.1 Conceptual Design
Conceptual database design involves modeling the collected information at a
high-level of abstraction without using a particular data model. The conceptual
design allows the following;
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• Independent of DBMS.
• Allows for easy communication between end-users and
developers.
• Has a clear method to convert from high-level model to
relational model.
• Conceptual schema is a permanent description of the database
Requirements
3.6.6.2 Entity-Relationship model (ER)
An ER diagram is a diagram that helps to design databases in an efficient way.
This model is has the following importance;
i. Most popular conceptual model for database design
ii. Basis for many other models
iii. Describes the data in a system and how that data is related
iv. Describes data as entities, attributes and relationships
The following diagrams show the association between various entities;
Figure 4.1 Building ER diagram
Building
Type
Street Name
Purpose Building I.D
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Figure 4.2 Block ER diagram
Figure 4.3 Population ER diagram
3.6.6.3 Logical Design
In this design, a review of the materials from earlier phases is made and the
system design begins. This involves identifying any additional system objects,
determining operations and data structures for all objects, validating
relationships and interactions between objects etc. The diagram shows a
logical structure and relationship among entities;
BLOCK
No of Building
Street name
Area Block-ID
Population
Gender
Density Unit-ID
Gender Ratio
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Figure 4.4 Relationship between entities
3.6.6.4 Physical Database Design
The physical design of the database specifies the physical configuration of the database on the storage media. This includes detailed specification of data elements, data types, etc. Using ArcGIS 9.3 and Excel a database was created see figure 3.6.6.5 & figure 3.6.6.6
Building
-Building ID
-Purpose
-Street name
-Type
Block
-Block ID
-No of building
-Size
-Street Names
Fitting
-Name
-Diameter
-Type
Population
-Population ID
-Gender
-Gender ratio
-Density
Passes
through
Connects
Controls
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Table 1 Geodatabase of extracted house from QuickBird satellite imagery of achara
Layout.
Table 2 Field Survey data of achara layout using Microsoft Exce
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3.7 USER REQUIREMENT
There is a high level of frustration regarding access to information about
the Population system in Nigeria, since so little of the information is
automated and readily available. Paper records are cumbersome and data
maintenance is difficult. There is concern that National Population Census
office is not integrated with GIS and that it’s data may not fulfill the needs for
a complete and accurate source of Population data and information.
Essentially, the Census data records are a disparate set of records in various
methods of storage and various states of accuracy and completeness. This
creates a large gap between express user needs and the possibility of achieving
them.
Furthermore, there is a need to have information for all populates for
the purpose of planning, budgeting, and allocation of basic amenities. Not all
populates are counted in the most recently concluded Census of Nigeria.
3.8 DATABASE DESIGN FOR ACHARA LAYOUT
After the verification exercise, a database summarizing the whole exercise
was created. It was stored in a microsoft excel format.
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Table 3 Field Survey data of achara layout using Arc GIS
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CHAPTER FOUR
RESULTS, DISCUSSION OF RESULTS AND ANALYSIS
4.1.1 ANALYSIS
The census data and the spatial data must be integrated in order to give
an accurate perspective on the number of people living in any geographical
area so that government at various levels can successfully carry out adequate
planning and management techniques that will enhance the quality of lives of
the populace (Ayeni & al 2001). In this project All of the quantitative analysis
we performed on the dataset for the population study was done using standard
statistical procedures in ArcGIS and Microsoft excel, such as quantities
symbolization (Graduated color, Graduated symbol, Proportional Symbol and
Dot density) also the chart symbolization was used, the table and histogram
was generated. For subsequent analyses, we needed to aggregate variables or
create new ones. For instance, by issuing appropriate querying commands,
following tasks can be performed on data obtained: Size of the population,
Patterns and trends of demographic events, Planning and Location of
Infrastructures, Transportation planning and traffic routing, Map out and
control spread of certain diseases and epidemics, Manpower development
planning and Other supportive information or tasks that can be performed
through the Query processes.
4.1.2 Estimation of Population Data Per Household through satellite
image
Satellite imagery has been used with respect to population estimation.
For example, Google Earth satellite images have been used to estimate coarse
population densities at the city and village levels (Javed & al 2012). By
identifying features such as dwelling units and residential areas satellite
63
images have also been applied for the purpose of population estimation
(Alsalman & al. 2011). The “night satellite” imagery was used to estimate
population sizes according to the local densities of light sources (Cheng & al
2007).For the first time, the population of an entire city has been estimated
from space through a pioneering project to speed up medical and disaster relief
efforts. By analysing satellite images of disaster zones and famine, war or
disease-hit areas where census data is unreliable, or cities unreachable,
researchers hope to make rapid and more accurate estimates of how many
people might need help. Surveyors, still had to visit households to estimate
how many people typically lived in each kind of building, but they could then
make a city-wide estimate by counting the total number of each type of
dwelling in the satellite image, either through a computer automated analysis
or by manual counting(Andy 2013).
In this project, the field work was carried out in the study area in order the
determine the type of building appearing in the satellite imagery (story
building or bungalow) and the number of the room was estimated. The
standard family count (number of children and parent per room) was used to
estimate the population per household. The organized population census
database per household was then imported to Arc Gis 9.3 environment for
further analysis.
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Table 4.1 showing the sample census per building in the study area
4.1.3 Estimation of Population Study Data by Block Parcel
In the oxford dictionary the block parcel is defined as the length of one
side of a piece of land or group of buildings, from the place where one street
crosses it to the next. The block has been used many year ago to estimate the
population of a particular area. In 1970 it was used as a grouping of census
block within a census track or enumeration district designated for
measurement to determine if the density of population might be high enough
for the parcel to be included in urbanization area (Frederick,2013).in this study
the database generated from the field questionnaire was link to the building of
the study area using building_id as a key for joining table, the selection by
location was used in assigning the block code number for each building. The
summary of the building population for each block was then calculated the
block area was also populated in square meter using calculate geometric in
Arc Gis 9.3 to calculate the density (see table 4.1). in Arc Gis 9.3 the Block
dataset was enhanced using graduated colors to display the spatial
65
concentration of the population by block. The map derived presents the block
parcel in five classes with a graduated color from very low to very high (very
low, low, normal, high, very high). The shallow color represents the very low
and the thick color represents the very high population per block (see fig 4.2).
the value between 125 to 400 represent the very low population , the value
between 401 to 800 represent the low population , the value between 801 to
1200 represent the normal population, the value between 1201 to 1600
represent the high population and the population between 1601 to 2000 is the
very high population per block.
Figure 4.5 MAP OF THE POPULATION CONCENTRATION OF THE STUDY
AREA PER BLOCK PARCEL
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67
Table 4.2 showing detail of the population of the study area
per block parcel
4.1.4 Population Density
Population density according to the world bank is midyear population
divided by land area in square kilometers. Population is based on the de facto
definition of population, which counts all residents regardless of legal status or
citizenship--except for refugees not permanently settled in the country of
asylum, who are generally considered part of the population of their country of
origin. Land area is a country's total area, excluding area under inland water
bodies, national claims to continental shelf, and exclusive economic zones. In
68
most cases the definition of inland water bodies includes major rivers and
lakes. In this study the density was calculated in ArcGIS 9.3 using field
calculation. The result was enhanced using graduated symbol measured with
side. The output map displayed block with a five different graduated point size
in centre, the block with very small size represents the area with low density
while the area with very big size represents the area with very high density.
The value between 0.61 to 1.40 is the block with very low density, the value
between 1.33 to 1.40 represent the block with low density, the value between
1.40 to 1.66 represent the block with normal density, the value between 1.66
to 2.03 represent the block with high density and the value between 2.03 to
2.56 is the block with very high density (see fig 4.3 ) . in the histogram (fig
4.4) below the block CXIV and block XXXVII are the area with very high
density following by block XC and XXVIIA.
Figure 4.6 map showing detail of the population density of the study area per
block parcel
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Figure 4.7 2006 Population census Result For Enugu State
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Figure 4.8: 2006 Population census Result For Enugu South LGA State
4.2 COMPARATIVE ANALYSIS BETWEEN NATIONAL POPULATION CENSUS AND CENSUS FROM GIS TECHNOLOGY
The difference between observed houses from field surveys and that
counted from QuickBird image was found to be 1.62% with average
difference 772 for Achara Layout. Counting of houses was easy from the
Multi- spectral QuickBird image. Houses were easily counted from the image
because of their well-defined shape, distinguishable size, contrast with their
surroundings (concrete roofs had high brightness), widely spaced, and prior
knowledge with the area. Table 4.1 shows comparison between NPC census
2015 and Census counted from Quick Bird 2015 in Achara Layout. The table
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shows that the difference in population census is not much is about 1.61%. this
shows that Remote sensing and GIS is strongly recommended in population
census survey.
Table 4.4 Showing Comparative analysis between Nation Population 2015 data Census and Population extracted Using GIS technology
Population from 2015 Census
Computed population from Quick Bird
Difference Percentage of the Difference
Values 48394 47622 772 1.62
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CHAPTER FIVE
CONCLUSION AND RECOMMENDATIONS
5.1 CONCLUSION
The integration of remote sensing data with GIS technology for
population studies is vital because each has strength in certain aspects related
to population studies. Since this study utilized both remote sensing and GIS, it
can be said as integrated approach to study population in Enugu, Nigeria.
Remote sensing was used mainly to estimate the population and classify land
use types whereas GIS was used for modelling the relationship among
population variables and the whole study provides a typical approach to obtain
the geographical distribution of population in one city in the Nigeria. This
approach can be replicated for other urban areas in Nigeria. Remote sensing
and GIS technology is slowly becoming cost effective, easy to use, and a
viable technology that can produce fast, reliable, and lowcost alternative for
population estimation. Therefore, its use by census departments, especially in
highly developing countries is the need of the present time. The remote
sensing on its own provides no information relating to population metrics and
it needs to be combined with census data and ground survey in a GIS
environment.
5.2 LIMITATIONS OF USING REMOTE SENSING METHODS
FOR POPULATION STUDIES ENCOUNTERED:
Despite the positive developments in remotely sensed data (high
spatial, spectral, and temporal resolutions), there are still some limitations with
respect to the use of these data for monitoring urban areas in general (Donnay,
1999; Ehlers, 1995) and population studies in particular (Baudot, 2001). The
limitations include: Spatial resolution: The single most important technical
issue in urban remote sensing is the spatial resolution of the image data
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(Welch, 1982). The last thirty years have seen development in the spatial
resolution of satellite images form 80 m (Landsat MSS) to 10 m (SPOT HRV)
and to 1 m (IKONOS).
The improvement in spatial resolution allows greater discrimination of
the urban fabric. However, the use of very high spatial resolution images brings
with them some major problems such as availability in panchromatic
mode and large size (storage, processing time) (Donnay et al., 2001). Although
some solutions like image fusion and image compression had been made to
minimize and solve these problems. These solutions are not commonly
available for practitioners in the census field.
5.2.1 Image Classification
The heterogonous nature (mixed pixels) of urban environment and the
possibility that identical spectral reflectance values can correspond to very
different land uses and functions poses a classification problem (Baudot, 2001).
Efforts have been made to solve these problems, for example, by coupling
automatic and semi-automatic classification, improving the results achieved
using traditional per-pixel classification algorithms by using a priori
probabilities or aposteriori processing, using of new algorithms such as soft
classifier-Bayesian probabilities-fuzzy sets, combining spectral data with
measures of urban form and texture, and using of knowledge–based methods
and artificial neural networks (Zang and Foody, 1998). However, these
techniques are experimental and not yet included in standard image processing
software (Donnay et al., 2001).
5.2.2 3D Nature of Urban Areas
Imaging 3D buildings from satellites suffers from three problems. The
first one is related to displacement of buildings from their true location (relief
displacement), the second one is related to obscuring of lower buildings by
higher ones, and the third is related to shadow (Hartl and Cheng, 1995).
Although efforts have been made to minimize these problems, up-to-date
74
images such as those from IKONOS suffer from these problems. Due to the
above limitations, remote sensing has to be used in conjunction with other
sources of information (maps, census data, field survey) and GIS to provide
better results (Mesev, 1998). In addition to that, visual interpretation has to be
used to strengthen the digital image classification.
5.3 RECOMMENDATION
1. Sensitization of essential staff of National Population Census on GIS: The understanding of the role GIS will play is important at all levels in the organization. Everything is about people. People play an important part in a successful implementation of GIS. National Population Corporation needs GIS-leaders, so called champions on different levels. GIS-champions are charismatic people, people with an intuitive sense about what direction GIS could take, people who are able to communicate this through all levels. These people are rare. Find them, or keep them. You cannot create them at will. Needless to say, there should be informal communication between these champions. Our observation is that a census mapping process is akin to a typical geographical information process. There is, therefore, justification for the integration of GIS with census mapping operations at all the stages of the process. In doing so, we go beyond the technological approach to reflect the inherent nature of census mapping activities, which is mainly geographic. Obviously, the integration can be undertaken gradually. Therefore, we advocate a paradigm shift from a traditional mapping approach, which is repeated for every census, to a digital census mapping approach, which is an up-to-date and a continuous approach. We believe that an up-to-date approach will allow census planners to rethink, inter-alia, the periodicity of census taking, the frequency of inter-censal surveys, and eventually the way to conduct census itself (some countries are already reconsidering the way to conduct a whole census countrywide every 10 years).
2. Education and training: There is need for education and training as they are essential for the introduction of GIS. If users feel uncomfortable with training or use of GIS, it will not easily be adopted. A good training plan will facilitate GIS implementation. In order to achieve the full benefits of owning a GIS, it is necessary to have a substantive expertise, GIS skills, and an understanding of basic geographic and cartographic principles.
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3. Proper data management: A GIS without data or information is useless. Especially in a corporate approach, data management is important. There are many aspects of data management (in a GIS environment), important aspects are: co-ordination, quality, data storage, data archiving and access to data. GIS gives the possibility to analyze, integrate and summarize these data. With GIS new possibilities arise. Data can be collected, stored, and maintained once, to the benefit of all authorized users. The opportunities for information sharing are an enormous benefit and beside the quality of data, data agreements, methods and standards are important for successful implementation.
4. Provision of Hardware and software: It is anticipated a suite of software and hardware will be necessary to successfully deploy the enterprise GIS. While users may already have some desktop application of GIS, the existing inventory would need to be evaluated in terms of its ability to meet the requirements found during the needs assessment phase. Where there are gaps, new software will be required. In addition, it is anticipated that hardware purchases in the form of servers will be necessary to adequately store the centralized database.
5. GIS implementation by National Population Commission: Implementation of GIS in census data capture by National Population Census and various Government parastatals is very necessary. Although the implementation of GIS at the initial stage is expensive as implementation requires various hardware and software components as well as training of staff but since its database is flexible, it would save cost in future census mapping for the country.
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APPENDICES Appendix A: Map showing population concentration of the study area Appendix B: Map showing population density of the study area Appendix C: population data for Enugu State from National Population Commission
