Predicting urban growth of a developing country city using a statistical modeling approach

INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES

Volume 5, No 4, 2015

© Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0

Research article ISSN 0976 – 4380

Submitted on January 2015 published on May2015 603

Predicting urban growth of a developing country city using a statistical

modeling approach Andrew Manu1, Yaw A. Twumasi2, Kang Shou Lu3, Tommy L. Coleman4

1- Department of Agronomy, Iowa State University, IA 50011, U.S.A.

2- Department of Urban & Regional Planning, Jackson State University,

MS 39211, U.S.A.

3- Department of Geography & Environmental Planning, Towson University,

MD 21252, U.S.A.

4- Department of Biological & Environmental Sciences, Alabama A&M University,

AL 35762, U.S.A.

[email protected]

ABSTRACT

Rapid population growth is pushing African cities to unprecedented sizes. The negative social

and environmental consequences of the uncontrolled urban expansion have been documented.

The purpose of this project was to develop appropriate databases to study the temporal land

use changes in urban expansion and predict future urban growth for the city of Niamey, Niger.

Land use transition was obtained from satellite imagery of 1973, 1988, and 2001. Spatial

layers of driving forces of urban growth were derived from a digital orthophoto and extensive

GPS mapping. A multinomial binary logistic regression was used to derive urban transition

probabilities and simulated maps of future urban growth were subsequently generated. This

study suggests that urbanized areas of the city will increase by 65.64 km2 between 1988 and

2010 and the population will increase from 397,437 to 2,063,894. The implications will

include overcrowding, destruction of scarce farmlands and deforestation of the surrounding

plateaus. Deforestation of the fragile uplands results in the generation of uncontrolled runoff

that provokes serious erosion. Sediment carried in the runoff creates unproductive alluvial

fans at the footslopes of the plateau.

Keywords: Urban growth, remote sensing, urban growth model, land use

1. Introduction

Africa is experiencing tremendous urban growth with an annual urbanization rate estimated at

5% (Denis and Moriconi-Ebrard, 2009). Nairobi, Dar es Salaam, Lagos and Kinshasa grew

sevenfold from 1950-1980 (Olivera et al., 2003). The rapid population growth and

subsequent expansion of African cities are the result of high birth rates and rural–to–urban

migration, the latter of which accounts for between 40 to 60% of the annual urban population

growth. The rural–to–urban migration is often fueled by prospects of jobs, higher incomes

and a higher standard of living. This is the result of many African cities remaining

administrative centers and simultaneously serving as national and regional engines of

economic growth. On the other hand, people are pushed out of rural areas by poverty, lack of

opportunities, declining agriculturally related work, war and famine (Lipton, 1977). The rapid

growth rates of many cities in Africa are pushing cities to unprecedented sizes. The

development of these mega cities has associated social and environmental implications. Local,

regional and national legislature has been enacted to assist with improving urban planning

and environmental management in many African cities. Sustained investments in urban

Predicting urban growth of a developing country city using a statistical modeling approach

Andrew Manu et al.,

International Journal of Geomatics and Geosciences

Volume 5 Issue 4, 2015 604

upgrading and development of infrastructure have become priorities for most of these

countries. Prior to the inaction of these legislatures and action plans, however, planners and

policy makers should be knowledgeable about historical and chronological trends in urban

expansion, identify and map spatial distribution of driving forces of urbanization such as

socioeconomic amenities and infrastructure networks, and use appropriate simulation models

to compare potential future scenarios to make informed decisions. (Klostermann, 1999).

There has been a long research tradition in urban geography and urban modeling (Batty,

1989; Batty and Xie, 1994; Knox, 1994; Verburget al. 1999; Wang and Zhang, 2001; Manu

et al. 2004). Research on urban growth and land use change may have one of two objectives:

(1) to develop an understanding of urban dynamics, both spatial and temporal (Allen, 1997);

or (2) to provide better decision support for urban planning (Brail and Klosterman, 2001). A

model that can make appropriate projections about future urban growth and expansion will

serve both educational and scientific purposes. The model will help scientists understand how

growth occurs. The model can also serve as an educational tool for the general public,

politicians and city planners who can take advantage of the visualization of different growth

scenarios (Wu 1988; Strange et al., 1999).

Several urban growth models have been tested in several areas of the developed world and

models have been evaluated and compared on the basis of their data demands. In general the

data inputs are spatial, temporal and thematic (Herold et al., 2003). The application of urban

growth models is in its infancy in Africa. One of the significant bottlenecks in addressing

urbanization issues in the continent using these models is the dearth of information. This

could be the result of unavailability of appropriate databases, unreliability of existing

information, and the lack of economic resources for research and data collection (Stren,

1994).

Multivariate logistic regression models are used to represent the non-linear nature of urban

growth problems (Landis and Zhang, 1997; Hua and Lo, 2007;Al-sharif and Pradhan, 2014).

This is a special case of the multinomial logit model developed by MacFadden (1973) and it

is conceptually based on the random utility theory and discrete choice theory in urban

economics and behavior science. Whether the utility of land is measured in terms of

consumer benefit, bid-rent, land price, or developer’s profit, it is a function of attributes of

land use choices and characteristics of land decision-makers. The utility function can be

defined as a linear combination of attributes of land use choices as per Landis and Zhang

(1997):

[1]

with constant α, coefficients β j ( j = 1, 2, · · · , k), and predictors X j for k predictor variables

( j = 1, 2, · · · , k).

Once the landowners or developers make their land use decisions independently with the

objective to maximize the utility of their lands, the probability of land transition can be

calculated using the following equation

[2]

where P i is the estimated probability that the ith land cell unit ( i = 1,· · · , n) is urban and u is

the utility function usually defined as a linear regression equation.

This linear regression equation creates the logit or log of the odds:


Andrew Manu et al.,



[3]

Thus, the linear regression equation is the natural log of the probability of being urban

divided by the probability of being non-urban. The procedure employed for estimating

coefficients was maximum likelihood, with a goal to find the best linear combination of

predictors to maximize the likelihood of obtaining the observed outcome of land use

frequencies. Compared with the multinomial logit model, this binary logistic model is

extremely flexible and easily applicable (Hosmer and Lemeshow 1989).

The goal of this paper is to evaluate the operational implementation of a representative

statistical urban growth model in investigating the long-term growth of a developing country

city. The objectives are to; (1) develop spatial and aspatial database for the city; (2)

parameterize and calibrate a composite urban growth model developed by Allen and Lu

(2003); and (3) to forecast the growth of the city through to the year 2030.

2. Materials and Method

2.1 The Study Area

Niamey is the capital city as well as the administrative and economic center of Niger, a land

locked country in West Africa. Niger is bounded on the north by Algeria and Libya, on the

east by Chad, on the south by Nigeria and Benin, and on the west by Burkina Faso and Mali

(Figure 1). It has a total area of 1,267,000 sq km. Niamey is a river port and road and trade

center and it is located at latitude 130520N2012’E. The city had a population of 242,973

inhabitants in 1974. The population grew at an annual rate of almost 10% to 674,950 in 2001.

There has been a significant areal expansion of the city during this same time period but the

development has been restricted to the northern part of the Niger River that runs through the

city.

Figure 1: Location of the City of Niamey, Niger

2.2 Data acquisition and preparation


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Urban Growth Evaluation of the growth and land use transition of the city was carried out

from satellite imagery. Landsat Multispectral Scanner (MSS), Thematic Mapper TM) and

Enhanced Thematic Mapper (ETM+) images were acquired for 1973, 1986, and 2001,

respectively. The images were geometrically corrected and ground control points obtained

through intensive ground surveys permitted the co-registration of all images to a Universal

Transverse Mercator (UTM). A supervised classification was performed on false color

composites (bands 2, 3, and 4) into different land use classes. Information collected during

field surveys was combined with the digital orthophoto base which was developed for the city

to assess the accuracy of the classification. A binary land use classification system (Anderson

et al., 1976) was used in which “urban” was categorized as areas built up by humans,

including residential, industrial, transportation industrial complexes and mixed urban. Other

land uses, including protected areas, were put into the non-urban category. This included

areas occupied by the Niger River, the green belt, rice paddies along the river, areas within

the airport, gully systems, (a 600 m buffer was put around all gullies), areas in and around the

airport and high elevation plateau surfaces.

2.3 Selection and preparation of driving forces of urban growth

Driving forces are most often considered exogenous to the land use system to facilitate

modeling. These include socio-economic and biophysical drivers as well as proximate causes.

There are a lack of accurate databases on driving forces of urbanization for socioeconomic

amenities and infrastructure networks for the city of Niamey. It was, therefore, essential to

identify, document and prepare predictor variables that influence the growth and development

of the city. Table 1 gives the list of predictor variables used for the urban modeling. Contour

lines were digitized from a topographic map to create a (Triangular Integrated Network) TIN

coverage in order to generate digital elevation model (DEM) and slope grids.

Table 1: Predictor variables used for urban growth modeling of Niamey, Niger

Variable Years Source

Digital Elevation

Model 1989

1989 topographic map

(1:50,0000)

Road network 1973, 1988, 2001 Satellite imagery, topographic

map, Digital orthophoto

Water bodies 1973, 1988, 2001 Satellite imagery, Digital

orthophoto

Green belt 1973, 1988, 2001 Satellite imagery, topographic


Gullies 1973, 1988, 2001 Satellite imagery, topographic


Open markets 1973, 1999, 2000 Field surveys, Digital orthophoto

Water towers 1973, 1999, 2000 Field surveys, Digital orthophoto

A geometrically corrected digital orthophoto base of Niamey was developed from black and

white aerial photos and the DEM of the city and its surroundings. The orthophoto was used to

derive the road network, water bodies, the green belt, gullies, and plateau surfaces. Point

locations of marketplaces, water towers and water bodies and wells were obtained using

Trimble handheld global positioning systems. These points were converted into shape files


Andrew Manu et al.,



from which proximity and cost distance variable grids were derived to measure the effects of

infrastructure, facilities, and services on urban expansions.

Two sets of predictor variables were generated for 1973 and 1988. The spatial distribution of

these variables for 1988 is presented in Figure 2. The model used in this study is grid based

and all the data were converted into 30 x 30 m grids to match the satellite imagery used to

assess land use transition of the city. The urban growth model adopted was a holistic

modeling approach developed by Allen and Lu (2003). In this model, multivariate logistics

regression was used to predict urban transition probabilities because of the non-linear nature

of urban growth (Landis and Zhang, 1977). During the preliminary study, we used 14

predictor variables for the logistic regression analysis. They included gullies, slopes, DEM,

distance to the Niger River (D2NRIVER), distance to the greenbelt protection, distance to

major roads (D2MROADS), distance to local streets (D2LCLRDS), distance to airport

(D2AIRPRT), distance to water wells (D2WTRWELL), distance to water towers

(D2WTOWER), distance to urbanized area (D2HURBAN), coast distance to urban center

(CD2URBAN), cost distance to marketplace (CD2MARKT), and distance to marketplace

(D2MARKET). For the final analysis, two of the 14 predictor variables were dropped

because they were not statistically significant.

The model was calibrated using data from 1973 and 1988 and verified using the 2001 data.

Maps of future urban growth (Figure 3) were generated by setting some urban growth

scenarios as given in Table 3.

Figure 2: Spatial distribution of predictor variables for Niamey, Niger, 1998

3. Results and discussion

3.1 Performance of the model

The ability of the logistic regression to predict urban growth was evaluated through the

goodness-of-fit test and the Wald Statistic. The goodness-of-fit and prediction success rates

of the logistics regression are presented in Table 2. At the cut-off value of 0.5 for the

classification, the success rate for prediction of non-urban areas was 97.5% while the urban

areas were predicted at a respectable success rate of 77.3%. The overall success rate of

prediction was 93.7%. These results corroborates those of previous findings by Landis and

Zhang (1997) and Lu (2001) in which, as shown in Table 3, all the independent variables


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were found to be significant predictors of urban land use and growth at the probability of α =

0.05.

Table 2: Performance of Logistic Regression

Statistic Value

-2 log Likelihood 30873.3

Cox and Snell R2 0.48

Nagelkerke R2 0.77

Prediction Success Rate (%)

Urban 77.3

Non-urban 97.5

Overall 93.7

The parameter estimates for the logistic regression model are shown in Table 3. According to

Wald coefficients and probabilities, distance to previous urban, distance to local roads,

distances to petite marketplace, distance to gully, distance to airport are among the most

significant predictor variables for the city of Niamey. However, standard error for variable

distance to petite marché was market was too large.

Table 3: Parameter estimates of the logistic regression model for urban growth in Niamey

Variable B S.E. Wald df Sig. Exp(B)

CD2GMRCH -5.153 0.728 50.108 1 0.000 0.006

CD2MARKT -7.902 2.732 8.367 1 0.004 0.000

CD2WELLS 9.572 1.844 26.945 1 0.000 14356.184

D2AIRPOT -2.974 0.494 36.172 1 0.000 0.051

D2HURBAN -23.778 1.435 274.651 1 0.000 0.000

D2MARKET -3.443 1.221 7.953 1 0.005 0.032

D2RDLCL88 -15.682 1.592 97.006 1 0.000 0.000

D2RDLCLM 7.453 1.353 30.351 1 0.000 1724.403

D2RIVER -2.488 0.495 25.264 1 0.000 0.083

D2WTOWER 3.980 0.866 21.139 1 0.000 53.502

DEMCNTRI -11.613 6532.401 0.000 1 0.999 0.000

GBPROTECT 1.227 0.218 31.755 1 0.000 3.413

Constant 2.105 0.222 90.184 1 0.000 8.207


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3.2 Predicted urban growth of Niamey 1988 - 2030

A summary of the urban and population growth for Niamey during the period of 1988 to

2030 is presented in Table 4. The population of the city will increase from 397,435

inhabitants in 1988 to 2,063, 894 in 2030. This will represent an increase of 419.3% at an

annual growth rate of 9.98%. At this rate, the population of the city will increase by 39,677

persons per year for the 42 years. The built up areas in the city will increase from 50.50 km2

in 1988 to 116.14 km2 in 2030. The net increase in the urbanized areas will be 65.64 km2

during the period which represents an increase in urban size of 130% at an annual expansion

rate of 3.09%. There will be a 125% increase in population density during the 42 year period.

It will go from 7,870 persons/km2 in 1988 to 17,769 persons/km2 in 2030. The sprawl index

or growth ratio indicates that there will be three fold increase in human population compared

to areas that are being transitioned into urban use.

Table 4: Summary of predicted urban growth in the Niamey region from 1994 to 2030

Population Growth Value

Population 1988 (in person) 397,437

Population in 2030 (in person) 2,063,894

(Based on Historical Growth Rate)

Net Growth 1,666.367

Change 419.3%

Annual Growth Rate 9.98%

Urban growth rate

Urban Area 1988 (km2) 50.50

Urban Area 2030 (km2) 116.14

Net Growth 65.64

Change 130%

Annual Growth Rate 3.09%

Population vs. urban area

Population Density 1988 (persons/km2) 7,870

Population Density 2030 (persons/km2) 17,769

Change 125.8%

Sprawl Index (Growth Ratio) 1:3.23

The spatial distribution of the predicted urban areas of the city of Niamey is presented in

Figure 3. The physical constraints to expansion are the plateaus to the southwest part of the

city, the gullies on the northeast side, and the greenbelt protection areas. A major assumption

made in this study is that the integrity of the green belt will be maintained between 1988 and

2030. This assumption was validated through a focus group assessment of city planners and

the protection area is being extended to the northwest part of the city. The model suggests

that by 2030, urban sprawl will fill in all the areas between the Niger River and the high

altitude plateaus to the southeast. Most of the city’s growth, however, will be towards the

northwest and the southwest. These areas present favorable physical environment for

construction and establishment of urban infrastructure. The topography is generally flat and it

will not require much erosion control during and after construction of buildings and roads.


Andrew Manu et al.,



There are also no laterite outcrops that sometimes impede the digging of foundations and

eventual building of structures. It is evident from Figure 3 that most of the urban expansion is

along the major roads, a phenomenon that is common to most developing cities.

Figure 3: Predicted urban expansion for the city of Niamey for the period 1988 - 2030

3.3 Implications of Urban Expansion of Niamey, Niger

Dense agglomerations of people and economic activities associated with urban growth

impose pressures on the environment that can shape the physical landscape and the

functioning state of the ecosystem. There are social ills such as poverty, poor quality housing

and traffic congestion and overcrowding which will put pressure on the government to

provide the basic amenities and infrastructure for the growing population. The serious

consequence of urban transition on the natural resource base in the city of Niamey and its

environment cannot be ignored. In an area where suitable agricultural lands are limited, the

conversion of prime agricultural lands into urban dwellings will have serious repercussions

on food production. Another consequence of urbanization is the sand and gravel mining for

the construction of roads and houses. The sandy clay loam soils on the sideslopes of the steep

plateaus are the preferred building material. The unsystematic excavation on sideslopes create

holes and channels that initiate further removal of soil material by runoff during the high

intensity rainfall events that is characteristic of the region.

Livestock populations have increased in and around Niamey with urbanization. The

uncontrolled grazing of goats, sheep and cattle leads to the depletion of protective vegetation

cover on the fragile lands and exposes them to erosion. Most of the urban dwellers use wood

harvested from surrounding wooded plateaus as fuel. It is estimated that the annual wood

consumption of the city is 700,000 cubic meters. In addition to loss of biodiversity, this

extensive wood harvesting leaves the plateau surfaces denuded making them vulnerable to

erosion.


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Sediment eroded from the steep plateaus also causes environmental problems. Materials

carried in waters are eventually deposited on the adjacent flat valley floors between the Niger

River and the plateaus. These form several acres of sandy alluvial fans that consummate

productive farmlands. Large amounts of sediments carried in runoff water also empty into the

Niger River. This is leading to serious sediment loading problems of the river that is

receiving national and international attention.

4. Conclusions

Rapid population growth is pushing African cities to unprecedented sizes. The negative social

and environmental consequences of the uncontrolled urban expansion have been documented.

Efforts are being made at the local, regional and national levels to improve urban planning

and promote sound and sustainable urban environments. For these legislatures to be

successful, city planners and policy makers should be cognizant of historical trends of land

use and they should also have prior understanding of the driving forces of urbanization.

Appropriate urban growth simulation models will also be necessary to compare present land

use to future scenarios. Urban growth models have been tested for several developed country

cities in terms of data demands. In general, data inputs are spatial, temporal and thematic.

Modeling of urban expansion for African cities has often been constrained by the dearth of

such inputs. The purpose of this paper was to develop appropriate databases to study the

temporal land use changes, evaluate spatial dependence the relationships among driving

factors that influence urban expansion, and predict future urban growth for the city of

Niamey, Niger.

Data for land use transition of the city was obtained from satellite imagery of 1973, 1988, and

2001. Some spatial layers of driving forces of urban growth were derived from a previously

produced digital orthophoto. Others were obtained from extensive GPS mapping. A

multinomial binary logistic regression was used to derive urban transition probabilities using

the predictor variables. Simulated maps of future urban growth were subsequently generated.

This study suggests that urbanized areas of the city will increase by 65.64 km2 between 1988

and 2030 and the population will also increase from 397,437 in 1988 to 2,063,894 in 2030.

We obtained a 97.5% success rate for the prediction of non-urban areas was predicted at a

respectable success rate of 77.3%. The overall success rate of prediction was 93.7%.

Simulation of the areal expansion of the city suggests that most of the cities expansion will be

mostly towards the northwest and southeast where physical conditions are favorable for

infrastructure development.

Implications of the expansion of the city could be varied. They include the use of the scarce

favorable farm lands, de-vegetation and loss of biodiversity from the surrounding plateaus.

Deforestation of the fragile uplands in turn results in the generation uncontrolled runoff that

provokes serious erosion. Sediment carried in the runoff creates unproductive alluvial fans at

the footslopes and on the level arable lands. Eroded sediments are eventually silting the Niger

River which is leading to serious sediment loading problems of the river that is receiving

national and international attention.

Acknowledgment

This paper was supported in part by Department of Agronomy, Iowa State University, and

Alabama A&M University Center for Hydrology, Soil Climatology, and Remote Sensing

(HSCaRS) through the National Aeronautics and Space Administration (NASA) Grant No.


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NAG5-10721. The authors greatly acknowledge the support Ms. Kristen Klaphake of the GIS

Lab of the Agronomy Department of Iowa State University. Any use of trade, product, or

firm names is for descriptive purpose only and does not imply endorsement by the U.S.

Government.

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