Using ARC/INFO to Model Urban Development for Six African Cities Mourad Mjahed, Gary L. Christopherson

Abstract

This paper examines the use of Arc/INFO to model urban development, integrating socioeconomic and spatial data collected from six cities in Africa (Marrakech, Dakar, , Bamako, Dodoma, and Gaborone). Applied social science research in contemporary urban contexts is necessarily multi-disciplinary, requiring the collection and integration of data from many sources that vary in completeness as well as resolution. While data collection can be straightforward, coordinating these disparate data sets in detailed analyses presents difficulties that can be addressed through the use of a geographic information systems (GIS) technology

Introduction

This paper reports on the use of GIS in an NSF funded research to integrate a variety of socioeconomic and spatial data collected from six cities in Africa: Marrakech, Dakar,

Niamey, Bamako, Dodoma, and Gaborone (Figure 1). The authors of this paper worked with Thomas Park, Mamadou Baro, and Stuart Marsh, and students from the University, along with colleagues and students from cooperating institutions in Morocco, Senegal,

Mali, , Tanzania and Botswana to collect a wide variety of both spatial and non- spatial data.

The intent of the project was to put a behavioral approach to social science research in a spatial context at the level of individual households, neighborhoods, and cities, by using remote sensing and GIS to document and assess the linkages between land cover change, urban growth, and livelihood strategies of the urban population of the six African cities. It was also hoped that the project’s use of remote sensing data, GIS techniques, and field surveys would offer significant benefits in the data-poor context of most

African countries. Namely, to determine if it was possible to use remotely sensed data to say something about the socioeconomic realities of neighborhoods in these six cities. In the following sections, discussion will include an introduction to the concept of sustainability in urban settings with a particular focus on Africa, reports on the various data sources, and a discussion of the use of GIS to integrate different sources of data.

Sustainability in an Urban World

Urban sustainability is a concept that has not received the attention it deserves. In this section, sustainability in general and specifically in an African urban context will be discussed.

Sustainability in Urban Settings

The World Commission on Environment and Development (1987: 43) defines sustainable development as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs.” In Africa, such balance, if it has ever existed, is constantly challenged by the weakening status of most

African economies. Barbier (1987:103) observes that: “poor people often have no choice but to opt for immediate economic benefits at the expense of the long run sustainability of their livelihoods.”

In general, the concept of sustainability launched a new discussion with a new agenda on economic and environmental development. It also helped to challenge many fundamental goals and assumptions of the conventional neoclassical economics of growth and development (Sen 1999). By drawing attention to the earth’s limited carrying capacity and humanity’s dependence on resources exploited without concern for future generations, the concept of sustainability redirected international debates into stepping beyond measures of growth to focus on the environment and humanity’s common future. However, sustainable development has long been primarily concerned with rural areas. With a focus on preserving natural resources, urban areas came to be perceived as somewhat independent of their immediate environment. With this rural bias, proponents of sustainable development have overlooked the fact that nearly half of humanity lives in cities where issues of sustainability take a very different shape. In Africa, for instance, the locus of poverty is shifting to urban areas, partly because of the large scale rural- urban migration, and partly because of the effects of recession and structural adjustment

(Watt 2000).

People moving to African cities experience rapid changes in location, composition, and function due to urban policies, changing environmental conditions, and a myriad of schemes stemming from international development and finance programs and projects.

This particular project examines household socio-economic livelihoods in six African cities with the general aim of providing tools to explore the conditions of modern African city life and the means to understand it. Specifically these tools are remote sensing and

GIS. The specific goal of the project is to develop an explicitly city-centered model of urban household dynamics that integrates issues of critical social, economic, and environmental importance. This research also aims at contributing to the efforts of others

(e.g. Rakodi 2002, Jamal and Weeks 1993) to recast African urban studies into a complex systems framework that integrates natural resource changes, institutional and informal conventions, as well as key elements of social organization.

Urbanization and Sustainability in the African Context

According to the United Nations’ statistics, the world’s urban population is set to rise by almost 1.5 billion in the next 20 years. The most explosive growth has been in countries that in the 1960s would have been considered “Third World” and that have now undergone perhaps one of the most rapid processes of industrialization and urbanization in recorded history. In developing countries, the number of large cities has increased dramatically and will continue to increase: in 1970 there were 163 cities with populations of 1 million or more; today there are about 350 (UNDP 2002). For instance, between 1970 and 2000, Mexico City grew from a population of 8.8 million to 18.2 million. Similarly, Sao Paulo went from 8.3 million to 18 million. Both have superseded

New York City. Bombay (or Monbay) grew in the same period from 6.2 million to 16.1 million.

As a region, Africa has, by world standards, one of the lowest levels of urbanization, typically ranging between 28% and 52%. The exception is South Africa where the levels reach 60%. These levels may be compared to ones that are above 70% in the Americas and Europe (Becker and Morrison 1994). African rates of urban growth range between

3.5% and 5.6% except for North and South Africa where they are around 3.2%.

Although these rates are comparable to Asia and seem generally low, they are still vastly higher than rates prevailing in Europe (0.4%), North America (0.8%), and even Latin

America (2.1%).

The evident quantitative growth of urban areas does reflect a more complex shift. The true global cities of the twenty-first century may well be those large metropolises that are simultaneously emerging as production motors not of national economies, but of the global economy. Industrialization and urbanization are more, not less, interwoven, and the cities of most intense population growth are also those of greatest industrial expansion. This correlation has held in Europe, Latin America, and more recently much of Asia. The East Asian experience with sustained economic growth and successful rural development suggests that the pattern might be replicable elsewhere. In Africa, however, links between economic growth and urbanization have not been evident. Only 9% of

Africa’s labor force is employed in industry compared with 18% in Asia (World Bank

2000).

Data Collection

A variety of data types were collected and utilized in this study. After a brief introduction to the six countries in the study, this section of the paper will introduce the data in the same order that they were collected during the project; 1) remotely sensed data, 2) survey data, and 3) GIS data.

An Introduction to Six Countries

The six countries in this study exhibit a range from low income to upper middle income and from moderate (for Africa) to extremely high rates of urban growth.

Although they span different regions of Africa (North, South, East, and West), they all share arid or semi-arid environments. These countries, with the exception of Morocco and Botswana, collectively have a negative or substantially negligible per-capita GNP growth rates. Although population figures for African cities are notoriously out of date or inaccurate, the subset of selected cities covers a significant and useful proportion of

Africa’s variability. Roughly, the following are population estimates for each city:

Marrakech (740,000), Dakar (1,700,000), Bamako (880,000), Niamey (800,000),

Dodoma (850,000), and Gabarone (320,000). Overall, Africa seems to be the only global region in which economic wellbeing has declined or at best stagnated for most citizens over the last few decades (World Bank 2000). The 1970s denouncement of urban biases in aid programs (Lipton 1977) switched more attention to the rural areas just when urbanization was rapidly increasing, mainly as a result of major rural-urban influx migrations principally motivated by long term droughts (Rakodi 2002).

Table 1: Economic and Demographic Indicators of the Six Countries

Urban Population Urban GDP per capita Country Income As % of Total Growth (PPP$), 2000 Level Population Rate 1975 2000 1975-2000 Morocco 37.8 55.5 3.5 3,546 Lower Middle Senegal 34.2 47.4 3.1 1,510 Low Mali 16.2 30.2 4.6 797 Low Niger 10.6 20.6 5.8 746 Low Tanzania 10.1 32.3 7.7 523 Low Botswana 12.8 49 8.7 7,184 Upper Middle Sources: UNDP 2002, World Bank 2002

Remotely Sensed Data

Remotely sensed data was classified and used to select a stratified random sample of points for ground-truthing of socioeconomic data. Reliable demographic data does not exist for many developing nations, especially for the rapidly growing urban centers. This makes any study of urban sustainability especially difficult. For this reason, the first step of the project was to collect and classify remotely sensed data for the six cities. These data were used to classify urban areas into neighborhoods thought to reflect particular socioeconomic conditions (Figure2) and were used as the basis of stratification for a random sample of points. Because a time series of images was used, two sets of random sample points were generated: (1) sample points falling only in areas of change from non- urban to urban; and (2) sample points falling in areas that had been urban for more than

20 years. Twelve points were selected from the former, and 40 from the latter, for a total of 40 sample points. These 40 points were used to locate areas for household level surveys. Teams of workers from each of the six cities were given a GPS and dispatched to the 40 sample points. At each location, they conducted interviews of the six nearest households, providing data on 240 households in each city (Figure 3).

Household Survey Data

Once the stratified random sample points had been created, household surveys were conducted in each of the six cities. A questionnaire was developed and teams of interviewers from the local cooperating agencies and universities were dispatched to locate and interview the six households nearest to each sample point. The questionnaire was divided into three substantive areas: livelihood strategies, natural resource use, and credit and aid usage (including governmental and non-governmental programs as well as use of extended family or neighborhood networks). The survey included questions on housing, migration, livelihood strategies past and present, household structure, labor contributions to household tasks, credit, ties to the rural area, environmental concerns and financial and social ties outside the household. Teams also administered a neighborhood focus group survey to elicit general issues of significance in different parts of the city.

The aim of the survey was to illicit information on how city sectors change in location, composition, and size over time. Questions about residence history and livelihood strategies for all adults in the household combined with a focus on what household members do as well as what they know others to be doing. Inquiries about primary and secondary natural resource use were designed to allow reconstruction of past and present patterns of natural resource use. The third focus of the survey documented the history of credit and aid usage. This last focus area was motivated by the significant increase in regional and international donor agencies and programs to counter the exponential increase in Africa’s poverty. These sources of international aid have almost acquired the status of a second public sector, and poor people in the city, as well as other benefactors have been significantly impacted by these programs. The following are examples of how these data have been used in socioeconomic analyses at household, inter-city and intra-city levels.

Household size is an important indicator of socioeconomic status. The histograms of household size in Figure 4 suggest both that there was a significant qualitative difference between cities and that the range within some cities, e.g. Bamako, was much greater than in others, e.g. Marrakech. This rather simple observation illustrates more than simple differences in household size and probably reflects major differences in both household dynamics and the economic and cultural context of the cities themselves. A more fine grained analysis of the data focused on some characteristics of household heads in

Marrakech (Table 2) and another brings comparison at the level of intra-city comparisons based on household size in Niamey (Figure 5).

Table 2 – Some Characteristics of Household Heads in Marrakech (N=238) Male (87%) Female (12%) Mean Age 54% 52% Mean Household Size 5.7% 5.3% No Formal Schooling 37% 70% Completed Primary Level 15% 11% Completed Secondary Level 34% 11% Completed Superior Level 13% 0% Literacy Rate in Official 60% 26% Language Mean Annual Income $4,658 $3,222 Households in Marrakech (Table 3) were not only smaller in contrast to Bamako, they were overwhelmingly comprised of nuclear families (91.26 of members compared with

63.55 for Bamako) (Data for Tables 3-5 is adapted from Park, 2002). They included the parents of the household head half as often, more distant relatives one seventh as often

and were one twentieth as likely to have members not related by kinship or marriage to

the household head. The Bamako data did not report in-laws separately presumably

because they were both seen as relatives and because marriage was almost invariably

between relatives due to the very broad definition of kinship in Mali.

Table 3 - Comparison of Household Kin ties in Bamako and Marrakech Relationship to household head Marrakech Bamako % Cumulative % % Cumulative % Household head 17.6 17.6 9.48 9.48 Spouse of household head 15.75 33.35 11.46 20.94 Son / daughter 57.91 91.26 42.61 63.55 Mother / father 0.96 92.22 1.95 65.5 Sister / brother 1.85 94.07 7.86 73.36 Grandparent 0.3 94.37 0.22 73.58 In-laws 2.44 96.81 0 73.58 Other relatives 2.89 99.7 20.19 93.76 Not related 0.3 100 6.24 100

These inter-city based comparisons can be contrasted with intra-city comparisons. In

this example the city of Niamey was used because it is divided into three distinct

communes that differ significantly at many socio-economic levels. In Table 4, it can be

noted that while Commune III resembled the household kin structure of Marrakech with

nearly 90% nuclear families, Commune II had households that were comprised of only

76.28% nuclear family. in this sense clearly occupied an intermediate

position between the other two communes with 81.38% nuclear family members. In

counterpart, Commune III had only a fraction of the sisters or brothers of the household

head residing within the household as the other two communes, and approximately half as many “other” relatives in residence as well as two thirds and one half the non-kin in residence as Communes I and II respectively. Thus within a city there were major differences in household organization apparent even at the broad level of Niamey’s three communes.

Table 4- Household kin ties in the three communes of Niamey Kin relation to household head Commune I Commune II Commune III % Cum % % Cum % % Cum % Household head 15.05 15.05 11.73 11.73 14.89 14.89 Spouse household head 13.65 28.7 13.31 25.03 16.67 31.56 Daughter / Son 52.68 81.38 51.25 76.28 57.09 88.65 Father / Mother 0.38 81.76 0.92 77.21 - Grandparents 0.13 81.89 0.13 77.34 - Sisters / Brothers 2.17 84.06 3.03 80.37 1.06 89.72 Other relatives 12.75 96.81 13.96 94.33 7.8 97.52 Not kin 3.19 100 5.66 100 2.48 100

Another major contrast between households in the three communes showed up when activity status in Niamey’s three communes were compared. In Table 5, the high proportion of nuclear family members in Commune III (88.65) found its correlate in the very low proportion (32.98%) of household members who contributed productively to the household. In contrast, Commune II, which had the lowest percentage of household members being nuclear family (76.28), had almost 50% of household members contributing productively to the household. Commune I remained consistently intermediate on both scales.

Table 5 - Activity status of Household members in Niamey’s three Communes Status Commune I Commune II Commune III % Cum % % Cum % % Cum % Produces, eats, sleeps 35.96 35.96 45.74 45.74 24.82 24.82 Produces, eats 2.54 38.5 3.93 49.67 8.16 32.98 Eats, sleeps 61.1 99.6 49.67 99.34 65.25 98.23 Sleeps 0.4 100 0.64 100 1.77 100

These descriptive data suggest that socioeconomic differences perceived in the classification of remotely sensed images do have some basis in fact. They suggest that urban households had both widely varied dynamics, but that these dynamics were far from arbitrary, probably reflecting particular social, cultural and economic environments.

GIS Data

At the same time that the remotely sensed and survey data was being gathered, a GIS database was also being constructed in Arc/INFO. Working in six different cities, in six different countries, yielded a wide variety of GIS data types, formats, scales, and levels of quality. Although data variability is not a unique situation in GIS based projects, the variability found in this particular project was pronounced. Much of the problem can be attributed to the profound lack of spatial data for Africa, and especially at a scale and resolution necessary for research of urban centers. Because data was so difficult to locate, any data found, including data that would have been rejected if it were for more data rich parts of the world, was happily accepted.

Available GIS Data

The lack of spatial data for Africa meant that there were only four data themes developed that were common to all six cities. These were:

• Neighborhoods (based on the analysis of the remotely sensed images) • 40 Random Sample Points (for collection of household survey data) • Roads • Hydrography

In addition, there were a number of other data sets that were available for one or more of the cities. These included:

• Hypsography • Landuse/Landcover • 240 Household Locations • Lakes and Reservoirs • Agriculture • Squatter Settlements • Railroads

Source Data Formats

These data came to the project in both analog and digital formats. All data from

Dakar was supplied as AutoCAD files supplied by the Centre Suivi Ecologique (CSE).

These data were supplied in excellent shape, very complete, and fairly well documented, but the topological and tabular problems common to ACAD files meant that they needed considerable massaging before they were usable. For instance, the landuse/landcover data came as a series of files, each one containing a single type of landuse or landcover.

These were converted to Arc/INFO coverages and then combined, into a single coverage.

This process created numerous sliver polygons and other problems common to ACAD conversion that had to be cleaned up. Once topologically correct, the features were attributed. In addition, the metadata for these files was minimal and did not include such things as identification of source data making it impossible to assess quality, adequate scale, or temporal setting for these data. Still, CSE did an excellent job developing the data and went out of their way to supply it to us. We were both fortunate and grateful to receive it. More problematic were the wide variety of analog data sources. Maps were collected

from the cooperating institutions in Africa, and from the map libraries at the University

of Arizona and Harvard University. Data contained in these maps were then digitized

using a large format tablet. The maps ranged from high quality cartographic products of

large scale and beautiful composition, to hand drawn, hand lettered, non-vertically

integrated blue-prints that had been left folded and placed in drawers for years.

Additionally, many of these maps had inadequate projection information. Normalization of these data involved many standard, and a few non-standard, processes to arrive at a common projection and coordinate systems for the digitized themes.

Variety of Scale

These maps also came at a variety of scales. The largest were the 1:5,000 scale maps

for Bamako. The smallest were the 1:50,000 scale maps of Niamey. These different map

scales meant that reasonable resolution for analyses and cartographic production differed

for each city.

Temporal Variety

Temporal variability was also an issue. Publication dates for the analog data ranged

from 1950 to 2002. Although many of the maps did not reveal the date that the data was

collected, it can be assumed it ranged from the 1940’s to the 1990’s. The digital data

from Dakar was created between 1998 and 2002, but as noted earlier there is no temporal

information for the source data. This means that data from the 1940’s was being used to

analyze conditions in 2002. Overall Quality

The overall quality and appropriateness of these data is obviously open to question since measures for controlling its quality were undermined by the limitations discussed above. While recognizing these limitations is important, it is equally important to note that the active participation of local researchers from the six cities as well as multiple site visits by the principal investigators have been instrumental in controlling data quality and insuring, as much as possible, its accuracy and precision.

Data Integration

The amount of data collected and the variety of formats in which it was supplied necessitated that there be a way to integrate the data sets for both management and analysis. Since data came in both spatial and tabular formats, the use of Arc/INFO was a natural choice for data organization and management, as well as an important tool for analysis. All data collected was normalized to meet standards set for the project and converted to formats compatible with Arc/INFO. Primary management responsibility lies with the University of Arizona, but during the next 12 months all these data and their metadata will be distributed to the cooperating institutions.

Data analysis is only beginning, but already there are many interesting avenues of exploration. The combination of the survey data with traditional kinds of spatial analysis is particularly exciting. For example, researchers unfamiliar with GIS are amazed that comparisons can be made between such things as the average expenses in a neighborhood and road density (Figure XXX). As data is distributed and questions informed by local residents are asked, more complex analyses are sure to follow. Finally, although more testing needs to be done, it is already clear to the researchers involved that integrating remotely sensed data with traditional survey data will be a powerful tool in social science research. In areas where social science data are rare, the use of satellites to characterize the socioeconomic status of neighborhoods is clearly an important technique for researchers. As satellite technology improves, so will the ability to characterize neighborhoods, and perhaps even households.

Conclusions

This attempt to use remote sensing and GIS to put social science research in a spatial context was successful on several levels. Data from the household surveys indicates that socioeconomic differences suggested by the classified satellite images existed on the ground as well as in the bits and bytes. The use of these technologies is especially important to data poor areas like Africa in that they allow for cost-effective data collection. These data can be updated on a regular basis and used a GIS to answer additional research questions, such as health, transportation, and environmental issues, among others.

This project’s research design provides an optimal framework for the study of urban development at both intra- and inter- city scales. In addition to translating survey questions from one cultural context to another, the comparability of the data sets is guaranteed by the compatibility of the sampling frames and procedures as well as the utilization of GIS technologies. The use of GIS allows disparate data to be integrated, and forces the normalization of data into a framework that encourages comparison between data sets. In the future, we would like to increase the number of household surveys to refine our abilities to use satellite imagery to predict socioeconomic conditions. We are also interested in increasing the number and variables examined, as well as improving the accuracy of the data collected. This is especially true for sensitive data about income, ethnicity, and credit.

We also believe that applying spatial technologies to the study of urban change and the analysis of demographic and socioeconomic data will open new avenues of data and research possibilities. Indeed, there is an urgent need for better research instruments and frameworks in the face of rapid change in urban Africa.