Research Paper
Conceptualizing and characterizing micro-urbanization: A new perspective applied to Africa

https://doi.org/10.1016/j.landurbplan.2019.103595Get rights and content

Highlights

  • Micro-urbanization is small scale urban growth occurring away from main urban areas.

  • Micro-urbanization is sparse and disconnected from existing urban areas.

  • Micro-urbanization mostly takes place in regions with low urban intensity.

  • In the Nigeria study area, most micro-urbanization occurs in agricultural areas.

  • Micro-urbanization in the DRC study area occurs primarily in or near mining sites.

Abstract

Sustainable Development Goals (SDG) require sustainable urban development and management for better human life quality. Small urban settlements—those with fewer than 500,000 people—are home to 26.5% of the world’s population. Yet, relatively little research attention has been paid to understanding the structure and dynamics of these smaller cities. In this paper, we propose a new concept, micro-urbanization, to fill this knowledge gap, and develop a methodology to characterize and map it using dense remote sensing time series data and landscape pattern metrics. We define micro-urbanization as a process of urban land change that has five primary characteristics: small, patchy, far from main urban areas, with limited geographic connection with existing urban areas and low urban intensity. We apply the method to two rapidly urbanizing countries in Africa, Nigeria and the Democratic Republic of the Congo. Results show that the methodology is capable of detecting micro-urbanization with relatively high spatial and temporal accuracy.

Introduction

Most future urban growth will occur in small and medium-sized cities and towns. In 2018, 26.5% of the world’s population—or about 48% of the urban population—lived in settlements with fewer than 500,000 inhabitants (United Nations, 2018a, United Nations, 2018b). Only 6.9% of the world’s population live in megacities with more than 10 million inhabitants. In fact, only one out of four people lives in a city of more than 1 million inhabitants (United Nations, 2018b). Although the majority of the world’s urban population live in smaller urban settlements, there is significantly less research on small towns and settlements (Fahmi, Hudalah, Rahayu, & Woltjer, 2014).

It is well documented that the socioeconomic and spatial characteristics of small and medium-sized cities and towns are different compared to larger cities (Bell and Jayne, 2009, Van Heur, 2012, Xu et al., 2019). Moreover, many smaller urban settlements lack urban planning institutions and basic infrastructure services (Cobbinah and Aboagye, 2017, Fahmi et al., 2014, Kleemann et al., 2017). The United Nations’ 2030 Agenda on Sustainable Development Goals (SDG) underscores that sustainable urban development and management are crucial to people’s life quality (Anderson, Ryan, Sonntag, Kavvada, & Friedl, 2017). It is, therefore, necessary to understand the dynamics of urban expansion of small cities with significant spatial and temporal details. However, household survey data have a number of limitations such as low accuracy, lack of standardized and digital data (Anderson et al., 2017) and are often not routinely collected. Moreover, census data may not capture information about this kind of small scale urban expansion because some of these places may be categorized as rural. It is also almost impossible for cartographic surveys to map it due to its small size and sparse nature (Romano, Zullo, Fiorini, Ciabò, & Marucci, 2017). As such, reliable observations are critical to identifying, measuring and understanding the process of small scale urban expansion. A recent paper identifies understanding urban heterogeneity with remote sensing as a key strategic direction for future research (Zhu et al., 2019).

Remote sensing has been widely used to monitor urban land expansion, with the Landsat data archive which spans three decades and is openly available. However, most current urban remote sensing studies also focus on mega-cities and large cities (Reba and Seto, in review), while small scale urban expansion is rarely investigated. It remains unclear whether this kind of urban expansion can be detected via remote sensing. We propose to address the following key research questions:

  • (1)

    What are the characteristics of small scale urban expansion?

  • (2)

    How can remote sensing identify, characterize and map these patterns in space and time?

The purpose of this paper is threefold. First, we aim to advance knowledge about urban heterogeneity by developing a conceptual framework to characterize the growth of smaller towns and cities. Second, we develop simple to use algorithms, using existing methods in remote sensing and spatial analysis, to identify and measure these changes. Third and finally, we test this method in two rapidly urbanizing countries in Africa, Nigeria and the Democratic Republic of the Congo, where small scale growth is a dominant type of urbanization. Our overall objective is to develop a methodology that is both useful and easy to apply in order to shed light on a significant knowledge gap in contemporary urbanization.

Section snippets

Conceptualizing micro-urbanization and its characteristics

Scholars have long used different concepts to describe urban development with varying spatial patterns. For example, the concept of urban sprawl was first coined in the U.S. context to describe single-family detached homes and other single-use developments around the periphery of a city (Osborn, 1965). Later, the concept was used to describe unplanned and fragmented urban expansion with low-density physical development and without basic municipal infrastructure usually beyond urban fringes (

Characterizing micro-urbanization with remote sensing and landscape pattern metrics

Our goal in this paper is to both conceptualize micro-urbanization and to assess the ability of remote sensing and landscape pattern metrics to detect and characterize it. Time series remote sensing coupled with landscape pattern analysis has been used extensively to characterize and describe the evolution of urban form (Nong et al., 2018, Reis et al., 2016, Seto and Fragkias, 2005, Xu et al., 2019). With few exceptions, most of these studies have used a handful of remotely sensed images as

Study areas and data

We tested and applied the micro-urbanization framework to two countries in Africa, Nigeria and the Democratic Republic of the Congo (DRC). We chose Africa because the urban population on the continent is expected to increase by 172% between 2018 and 2050 (United Nations, 2018a). Within Africa, Nigeria and the DRC are among the fastest urbanizing countries (United Nations, 2017). Moreover, urbanization in both countries is dominated by small settlements. In 2015, 56% and 41% of the urban

Discussion

A well-known adage is that what cannot be measured cannot be managed. Although UN data show that most future urban growth will occur in small and medium-sized towns, we have not developed the conceptualization or methods to map this important process with remote sensing. This paper fills this knowledge gap. Only after we can measure micro-urbanization using data and methods such as with remote sensing, can these urban processes be included in and informative for policy and urban management.

Our

Conclusions

In this study, we proposed the concept of micro-urbanization and tested the ability of Landsat time series to detect and characterize micro-urbanization in two rapidly urbanizing African countries. Results show that remote sensing can effectively extract micro-urbanization with acceptable accuracy. Micro-urbanization is small, patchy, far from main urban areas, disconnected and has low urban intensity. This study sheds light on not only urban expansion in small cities, but also

Acknowledgements

We thank the constructive comments and suggestions from the anonymous reviewers. We also thank the Yale Center for Research Computing for providing us with access to the high performance computing resource. We further thank Peijun Li, Meredith Reba, Kangning Huang and Bhartendu Pandey for their comments that helped to improve the manuscript. Support for this research was from China Scholarship Council (CSC) grant No. 201706010073, NASA Land Cover/Land Use Change Program Grant NNX15AD43G and

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