Spatiotemporal Characteristics of Urban Land Expansion and Population Growth in Africa from 2001 to 2019: Evidence from Population Density Data

International Journal of Geo-Information

Article Spatiotemporal Characteristics of Urban Land Expansion and Population Growth in Africa from 2001 to 2019: Evidence from Population Density Data

Shengnan Jiang 1,2 , Zhenke Zhang 1,2,3,*, Hang Ren 4 , Guoen Wei 1,2 , Minghui Xu 1,2 and Binglin Liu 1,2,3

1 School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China; [email protected] (S.J.); [email protected] (G.W.); [email protected] (M.X.); [email protected] (B.L.) 2 Institute of African Studies, Nanjing University, Nanjing 210023, China 3 Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing 210093, China 4 Institute of Population Studies, Nanjing University of Posts and Telecommunications, Nanjing 210042, China; [email protected] * Correspondence: [email protected]; Tel.: +86-25-8968-6694

Abstract: Africa has been undergoing a rapid urbanization process, which is critical to the achievement of the 11th Sustainable Development Goal (SDG11). Using population density data from LandScan, we proposed a population density-based thresholding method to generate urban land and urban population data in Africa from 2001 to 2019, which were further applied to detect the

spatiotemporal characteristics of Africa’s urbanization. The results showed that urban land and urban population have both grown rapidly in Africa, which increased by about 5.92% and 4.91%,

Citation: Jiang, S.; Zhang, Z.; Ren, respectively. The top three countries with the most intense urbanization process in Africa are Nigeria, H.; Wei, G.; Xu, M.; Liu, B. the Democratic Republic of the Congo, and Ethiopia. The coupling relationship index of urban Spatiotemporal Characteristics of land expansion and population growth was 0.76 in Africa during 2001–2019. Meanwhile, the total Urban Land Expansion and proportion of uncoordinated development types at the provincial level was getting higher, which Population Growth in Africa from indicated an uncoordinated relationship between urban land expansion and population growth in 2001 to 2019: Evidence from Africa. Cropland, grassland, rural land, and forests were the most land-use types occupied by urban Population Density Data. ISPRS Int. J. expansion. The proportion of cropland, grassland, and forests occupied was getting higher and Geo-Inf. 2021, 10, 584. https:// higher from 2001 to 2019. The extensive urban land use may have an impact on the environmental doi.org/10.3390/ijgi10090584 and economic beneﬁts brought by urbanization, which needs further research.

Academic Editor: Wolfgang Kainz Keywords: Africa; urbanization; sustainable development; urban expansion; coordinated relationship Received: 5 July 2021 Accepted: 26 August 2021 Published: 29 August 2021

Publisher’s Note: MDPI stays neutral 1. Introduction with regard to jurisdictional claims in In 2018, 55% of the world’s population lived in urban areas. By 2050, it is estimated published maps and institutional afﬁl- that 68% of the world’s population will be urban. Africa is the least urbanized region iations. in the world with only about 43% of the population being urban [1], compared with North America (82% of the population in 2018 lives in urban areas), Latin America and the Caribbean (81%), Europe (74%), and Oceania (68%). However, Africa has the highest rate of urban population growth, exceeding 4% per year during 1950–1990, and is expected Copyright: © 2021 by the authors. to remain at 3% or more per year during 2035–2040. Between 1950 and 2018, the urban Licensee MDPI, Basel, Switzerland. population of Africa increased more than 16-fold, from 33 million to 548 million. Due to the This article is an open access article relatively high growth rate of the urban population in Africa in the future, by 2050, Africa distributed under the terms and will have 1.5 billion urban residents, second only to Asia’s 3.5 billion, although it will still conditions of the Creative Commons be the geographic region with the lowest degree of urbanization in the world [1]. Whether Attribution (CC BY) license (https:// in the past, present, or future, Africa is undergoing a very rapid process of urbanization. creativecommons.org/licenses/by/ Urbanization is one of the key factors affecting the development of African cities [2,3]. 4.0/).

ISPRS Int. J. Geo-Inf. 2021, 10, 584. https://doi.org/10.3390/ijgi10090584 https://www.mdpi.com/journal/ijgi ISPRS Int. J. Geo-Inf. 2021, 10, 584 2 of 18

In 2015, 17 Sustainable Development Goals (SDGs) anchoring the 2030 Agenda were proposed by United Nations (UN) and adopted by more than 150 world leaders [4,5]. The target of SDG11 is to make the city inclusive, safe, resilient, and sustainable. With the continuous urbanization of the world, sustainable development increasingly depends on the successful management of urban growth, especially in low-income and low-middle-income countries, which are expected to have the fastest urbanization rate [1]. As the African continent has the largest number of low-income and low-middle-income countries and has been undergoing rapid urbanization, special attention should be paid to the growth of urban population and urban land expansion, which is a critical part of the world’s sustainable development. Recent studies have shown that many urban areas in Africa are finding it increasingly difficult to cope with intensive, unplanned, and unsustainable urbanization [3,6]. For example, with rapid urbanization, the increasingly crowded informal residential areas in Africa are eroding the socio-economic and environmental benefits associated with urbanization and sustainable development [7,8]. Meanwhile, cropland loss from urban expansion is very serious in Africa [9]. Before studying the characteristics of urbanization in Africa, we need to determine the urban land and corresponding urban population in Africa. The definition of urban is complex, which implies the combination of population and built-up land [10]. In many studies, researchers often choose one of the people-based or building-based criteria to define the city [11]. The database https://www.citypopulation.de (accessed on 15 June 2021) [12] regularly updates the national census statistics of different countries: the listed cities start at 10,081 inhabitants in Egypt, 40,048 in Nigeria, 20,079 in the Democratic Republic of the Congo, 13,700 in Ethiopia, and 13,108 in South Africa in the latest updated data. As for the database of World Urbanization Prospects proposed by the UN, a global threshold of 300,000 inhabitants was imposed [1]. Different countries and different datasets have different statistical standards, and the statistical time may be discontinuous. At the same time, it is common to regard the built-up land as the urban area in many studies. For example, the Global Urban Footprint (GUF) [13] is an available urban extent data product derived from the impervious surface of radar satellites TerraSAR X and TanDEM X. There are also many studies to obtain urban areas through the impervious surface of remote sensing data such as Landsat [14], MODIS [15], and nighttime light images [16]. The most common problem of building-based criteria is that it is inconvenient to distinguish between urban and rural areas. Studies have shown that in the past 40 years, in many parts of the world, the number of cities and the speed of urban expansion defined by buildings are faster than those defined by population [11,17]. As a result, we hope to propose a method to identify urban land and urban population at the same time. Population density is one of the most important characteristics of a city [18]. As suggested by the UN, population density can be an additional criterion to classify urban- rural [19]. Areas with at least 1500 people per km2 and a minimum population of 50,000 were defined as urban centers in the Global Human Settlement (GHS) database [20–22]. Levin and Zhang (2017) regarded adjacent grid cells of LandScan with >1500 people/km2 as cities, and thus identified 4153 global cities [23]. The same as African countries, China is also a developing country and has experienced a rapid urban transformation since the early 1980s. An average population density of more than 1500 people/km2 has been one of the criteria of the 2000 Census urban definitions in China [24]. As a result, we tried to divide urban and rural areas using the population density dataset of LandScan with 1500 people/km2 as the threshold. Meanwhile, the population located in urban areas was regarded as urban population. We assessed the accuracy of this thresholding method with both quantitative and qualitative techniques of evaluation. After defining the urban land and urban population in Africa, we raised and ad- dressed the following questions to understand the characteristics of urban land expansion and population growth in Africa: (1) temporal and spatial changes of urban land and urban population during the different periods; (2) whether the change rate of urban land ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 3 of 21

and population growth in Africa: (1) temporal and spatial changes of urban land and urban population during the different periods; (2) whether the change rate of urban land expansion and population growth in Africa coordinated; (3) situation and sustainability of other land types occupied by urban land in Africa during expansion. To increase the comparability and consistency of urbanization study in Africa across ISPRS Int. J. Geo-Inf. 2021, 10, 584 time and space, this study proposes a population density-based 3thresholding of 18 method to generate urban land and urban population. The rest of this study is organized as follows. Section 2 introduces the study area and data source; Section 3 describes details of meth- expansion and population growth in Africa coordinated; (3) situation and sustainability of odologiesother land types adopted occupied in bythis urban study; land inSection Africa during 4 presents expansion. and analyzes the results, followed by the discussionTo increase the and comparability conclusion and in consistency Sections of 5 urbanization and 6, respectively. study in Africa across time and space, this study proposes a population density-based thresholding method to generate urban land and urban population. The rest of this study is organized as 2.follows. Study Section Area2 introducesand Data the study area and data source; Section3 describes details of 2.1.methodologies Study Area adopted in this study; Section4 presents and analyzes the results, followed by the discussion and conclusion in Sections5 and6, respectively. The total area of Africa is about 32 million km², which includes 55 countries or re- 2. Study Area and Data gions, with a total of 751 provinces. In 2018, the urbanization level of Africa was 43%, with 2.1. Study Area 547.6The million total area people of Africa living is about in 32 cities million [1]. km2 ,Twen which includesty African 55 countries cities or were regions, selected to verify the feasibilitywith a total ofof 751the provinces. methods In used 2018, thein urbanizationour study, level and of their Africa geographical was 43%, with locations are pre- sented547.6 million in Figure people 1. living According in cities [1 to]. Twenty the corresponding African cities were urban selected population to verify the in 2018 and the clas- feasibility of the methods used in our study, and their geographical locations are presented sificationin Figure1. Accordingstandards to the of corresponding[25] and [1], urban these population 20 cities in 2018 can and be the divided classiﬁcation into six categories, and thestandards detailed of [25 information] and [1], these 20is citiesshown can bein dividedTable into1. The six categories,selection and of thecities detailed for evaluation is in line withinformation randomness. is shown in Table1. The selection of cities for evaluation is in line with randomness.

Figure 1. Location of 20 evaluated cities and population density from LandScan in 2018. Figure 1. Location of 20 evaluated cities and population density from LandScan in 2018. Table 1. Classiﬁcation standard of city-scale and cities included.

Table 1.City Classification Scale standard Population of city-scale and cities Citiesincluded. Megacity behemoth ≥10 million Cairo, Kinshasa, Lagos CityMegacity Scale 5~10 millionPopulation Alexandria, Johannesburg, Khartoum, LuandaCities Addis Ababa, Algiers, Bamako, Ibadan, Kampala, MegacityLarge behemoth city 1~5 million≥10 million Cairo, Kinshasa, Lagos Kigali, Lubumbashi MediumMegacity city 0.5~1 5~10 million million Alexandria, Ndola Johannesburg, Khartoum, Luanda Small city 0.3~0.5 millionAddis Gombe, Ababa, Nakuru, OyoAlgiers, Bamako, Ibadan, Kam- LargeTown city <0.3 million 1~5 million Kairouan, Tebessa pala, Kigali, Lubumbashi Medium city 0.5~1 million Ndola Small city 0.3~0.5 million Gombe, Nakuru, Oyo Town <0.3 million Kairouan, Tebessa

2.2. Data Source

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2.2. Data Source LandScan is a time-series community standard for spatially disaggregated global population data produced by the Oak Ridge National Laboratory (ORNL) [26]. The spatial resolution of LandScan is approximately 1 km. LandScan is the most commonly used global population distribution data, as it has the longest time series and the ﬁnest resolution. In this study, LandScan data spanning from 2001 to 2019 were generated from the website https://landscan.ornl.gov/ (accessed on 15 May 2021). The global public land cover dataset MCD12Q1 with a spatial resolution of 500 m from the Moderate Resolution Imaging Spectroradiometer (MODIS) products was used to study the urban land-use change [27]. The time series of this dataset spans from 2001 to 2019, which can be obtained from the EARTHDATA website https://search.earthdata.nasa.gov/ (accessed on 20 May 2021). The ground truth land-use data used in this study were derived from The Atlas of Urban Expansion (2016 Edition) (http://atlasofurbanexpansion.org/, accessed on 20 May 2021). This dataset selected 200 cities around the world and produced land-use maps circa the years 1990, 2000, and 2014. Landsat imagery, with a spatial resolution of 30 m, was classified into seven classes using the unsupervised classification method, which includes urban built-up [28]. Moreover, the administrative boundary data was collected from the Database of Global Administrative Areas (GADM) (https://gadm.org/, accessed on 8 June 2021). Additionally, the statistical urban population data in Africa were obtained from the UN (https://population.un.org/wup/, accessed on 10 June 2021). In addition, the nighttime light data used in this study was from harmonized global nighttime light dataset produced by Li Xuecao (2020) [29].

3. Methods Four main procedures were undertaken to identify spatiotemporal characteristics of urban land expansion and population growth in Africa. Firstly, urban land and urban population were generated using LandScan data with thresholding method and overlay analysis. Secondly, we assessed the accuracy of urban land and urban population obtained from the first step. Thirdly, elasticity coefficient was adopted to detect the coupling development between urban land expansion and population growth. At last, the reclassified ISPRS Int. J. Geo-Inf. 2021, 10, x FOR MCD12Q1PEER REVIEW land-use data were integrated with the acquired urban land to study5 of 21 the urban

land-use change (Figure2).

LandScan MCD12Q1 (2001~2019) (2001~2019) Data layer

Thresholding & Covert Reclassify to binary image Overlay analysis

Post-processing Integrate

Yearly urban Yearly urban Yearly land- population land use map

Land-use maps from The Atlas of Urban Expansion Statistical urban (2016 Edition) Assessment & population data Comparison from UN Nighttime light data

Coupling development Land-use change

Step2: Step3: Step1: Step4: Spatiotemporal changes of Coupling results of urban Assessment results of urban Other types of land occupied urban population and urban land expansion and population and urban land by urban land expansion land population growth Results layer Results FigureFigure 2. 2. TheThe flowchart ﬂowchart and and technical technical framework framework of the of study. the study.

3.1. Identification of Urban Land and Urban Population Population and population density are common indicators for dividing urban and rural areas [30,31]. As stated above, 1500 people/km² was selected as the threshold to generate spatial layers of urban land and urban population. The steps were as follows: (1) we considered grid cells of LandScan with >1500 people/km² as urban land and the images were converted to binary images; (2) the binary images were post-processed using the post-classification sieve function within Envi 5.3.1 (© 2015 Exelis) to eliminate the isolated classified pixels; (3) the population of the corresponding urban area was regarded as urban population. As a result, the spatial layers of urban land and urban population were obtained.

3.2. Accuracy Assessment of Urban Population and Urban Land Generated from LandScan We verified the obtained data to confirm the feasibility of collecting urban population and urban land from LandScan. A linear regression model was applied to the generated urban population and statistical urban population from the UN. As for the generated urban land, we verified it qualitatively and quantitatively. From a qualitative point of view, we visually compared the generated five African cities’ urban scope and shape with the existing land-use map and gradient nighttime light level map, in which the calculation of gradient nighttime light types can be referred to Jiang (2021) [32]. From the perspective of quantitative, we selected the generated urban land of 20 African cities to calculate its user’s accuracy, producer’s accuracy, and kappa coefficient with the ground truth data from The Atlas of Urban Expansion (2016 Edition).

3.3. Coupling Development of Urban Land Expansion and Population Growth To depict the human–land relationship in the process of urbanization, the elasticity coefficient was adopted to partition the types of coupling development between urban land expansion and population growth [33]. The elasticity coefficient can be calculated as follows: EC = PR/LR (1)

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3.1. Identification of Urban Land and Urban Population Population and population density are common indicators for dividing urban and rural areas [30,31]. As stated above, 1500 people/km2 was selected as the threshold to generate spatial layers of urban land and urban population. The steps were as follows: (1) we considered grid cells of LandScan with >1500 people/km2 as urban land and the images were converted to binary images; (2) the binary images were post-processed using the post-classification sieve function within Envi 5.3.1 (© 2015 Exelis) to eliminate the isolated classified pixels; (3) the population of the corresponding urban area was regarded as urban population. As a result, the spatial layers of urban land and urban population were obtained.

3.2. Accuracy Assessment of Urban Population and Urban Land Generated from LandScan We verified the obtained data to confirm the feasibility of collecting urban population and urban land from LandScan. A linear regression model was applied to the generated urban population and statistical urban population from the UN. As for the generated urban land, we verified it qualitatively and quantitatively. From a qualitative point of view, we visually compared the generated five African cities’ urban scope and shape with the existing land-use map and gradient nighttime light level map, in which the calculation of gradient nighttime light types can be referred to Jiang (2021) [32]. From the perspective of quantitative, we selected the generated urban land of 20 African cities to calculate its user’s accuracy, producer’s accuracy, and kappa coefficient with the ground truth data from The Atlas of Urban Expansion (2016 Edition).

3.3. Coupling Development of Urban Land Expansion and Population Growth To depict the human–land relationship in the process of urbanization, the elasticity coefﬁcient was adopted to partition the types of coupling development between urban land expansion and population growth [33]. The elasticity coefﬁcient can be calculated as follows: ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW EC = PR/LR6 of 21 (1)

where EC denotes the elasticity coefﬁcient between urban land expansion and population growth, PR is the average annual change rate of the urban population, whereas LR is the where EC denotes the elasticity coefficient between urban land expansion and population average annual change rate of urban land. growth, PR is the average annual change rate of the urban population, whereas LR is the averageAccording annual change to the rate increase of urban andland. decrease changes of PR and LR and the numerical comparison,According the to couplingthe increase relationship and decrease betweenchanges of urban PR and population LR and the and numerical urban land can be dividedcomparison, into the six coupling types. Therelationship illustration between can urban be seen population in Figure and3 andurban Table land2 can. Taking be type I as andivided example, into six PR types. and The LR areillustration positive can at be the seen same in Figure time, 3 andand theTable former 2. Taking is greater,type I indicating thatas an urban example, population PR and LR andare positive urban at land the same increase time, at and the the same former time is greater, and the indicat- growth of urban populationing that urban is fasterpopulation than and that urban of urban land increase land. Based at the onsame the time numerical and the growth comparison of between urban population is faster than that of urban land. Based on the numerical comparison PRbetweenand LRPR, and the LR six, the coupling six coupling types types can can be dividedbe divided into into twotwo categories: coordi- coordinated and uncoordinatednated and uncoordinated development. development. Detailed Detail informationed information is is shown shownin in TableTable 22..

FigureFigure 3. Schematic diagram diagram of ofthe the partitioning partitioning criter criterionion of different of different coordination coordination relationship relationship types. types.

Table 2. Types of coupling relationship between urban population and land.

Coordination Types Criteria Explanation Relationship PR > 0, LR > 0, The growth rate of population is Ⅰ PR/LR > 1 greater than that of land, which Coordinated de- Ⅱ PR > 0, LR < 0 means the intensive degree of land velopment use is higher, and the relationship be- PR < 0, LR < 0, Ⅲ tween population and land tends to PR/LR < 1 be coordinated. PR < 0, LR < 0, The population growth rate is lower Ⅳ PR/LR > 1 than that of land, which may lead to Uncoordinated Ⅴ PR < 0, LR > 0 extensive use of land resources and development PR > 0, LR > 0, an imbalance between population Ⅵ PR/LR < 1 and land.

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Table 2. Types of coupling relationship between urban population and land.

Coordination Types Criteria Explanation Relationship ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW PR > 0, LR > 0, 7 of 21 I The growth rate of population is greater Coordinated PR/LR > 1 than that of land, which means the development II PR > 0, LR < 0 intensive degree of land use is higher, PR < 0, LR < 0, III and the relationship between population PR/LR < 1 and land tends to be coordinated. 3.4. Land use Change PR < 0, LR < 0, IV The population growth rate is lower Uncoordinated PR/LR > 1 than that of land, which may lead to developmentThere are five classification schemes included in the land cover products MCD12Q1, V PR < 0, LR > 0 extensive use of land resources and an PR > 0, LR > 0, of which the InternationalVI Geosphere-Biosphimbalanceere between Programme population and (IGBP) land. is the most widely PR/LR < 1 used [34]. In this study, the IGBP classification scheme was adopted to extract land-use types,3.4. Land which use Change were reclassified into seven types, namely, forest, grassland, built-up land, cropland,There are barren five classification land, water, schemes and included other. in To the study land cover the products land-use MCD12Q1, change of urban land, we integratedof which the Internationalthe urban Geosphere-Biosphereland generated above Programme with (IGBP) these is theseven most types widely of land use. Taking used [34]. In this study, the IGBP classification scheme was adopted to extract land-use Lagostypes, which as an were example, reclassified urban into seven land types, generated namely, forest, from grassland, LandScan built-up mainly land, overlapped on the built-upcropland, barrenland land,from water, MCD12Q1 and other. (Figure To study 4). the Using land-use the change simple of urban dichotomous land, we division method [10],integrated the built-up the urban landland generated that did above not withcoincide these seven with types urban of land land use. was Taking regarded as rural land. Lagos as an example, urban land generated from LandScan mainly overlapped on the built- Asup landa result, from MCD12Q1 land-use (Figure maps4). comprised Using the simple eight dichotomous types, including division method forest, [ 10 grassland,], urban land, ruralthe built-up land, land cropland, that did not barre coinciden land, with water, urban land and was other. regarded At aslast, rural through land. As athe transfer matrix of urbanresult, land-use land-use maps change, comprised we eight can types, get the including situatio forest,n of grassland, other land-use urban land, ty ruralpes occupied by urban land, cropland, barren land, water, and other. At last, through the transfer matrix of urban land.land-use change, we can get the situation of other land-use types occupied by urban land.

FigureFigure 4. 4.Overlapping Overlapping diagram diagram of urban of land urban generated land from generate LandScand andfrom built-up LandScan land from and built-up land from MCD12Q1: taking Lagos in 2019 as an example. MCD12Q1: taking Lagos in 2019 as an example. 4. Results 4.1. Verifying the Urban Population and Urban Land Generated from LandScan

To verify the accuracy of the urban population generated from LandScan in this study, statistical urban population from the UN was used for comparison. Linear regression analysis was applied to the two datasets and the comparison result is shown in Figure5. The results show that the urban population obtained from LandScan has a linear correlation with the statistical urban population of UN, with the R2 reaching 0.9871. At the same time, the linear correlation coefﬁcient is 0.8903, which is close to 1. As a result, the urban population generated from LandScan has a strong relationship with that from the UN.

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4. Results 4.1. Verifying the Urban Population and Urban Land Generated from LandScan To verify the accuracy of the urban population generated from LandScan in this study, statistical urban population from the UN was used for comparison. Linear regression analysis was applied to the two datasets and the comparison result is shown in Figure 5. The results show that the urban population obtained from LandScan has a linear correlation with the statistical urban population of UN, with the R2 reaching 0.9871. At the same time, the linear correlation coefficient is 0.8903, which is close to 1. As a result, the urban

ISPRS Int. J. Geo-Inf. 2021, 10, 584population generated from LandScan has a strong relationship with7 of 18 that from the UN.

Figure 5. Comparison between urban population obtained from UN and LandScan. Figure 5. Comparison between urban population obtained from UN and LandScan. For urban land generated from LandScan, we verified it from both qualitative and quantitative perspectives. Figure6 depicts the qualitative comparison of urban land generatedFor urban from LandScanland generated (Figure6d) with from land LandScan, use maps from Thewe Atlas verified of Urban it Expansion from both qualitative and quantitative(2016 Edition) perspectives.(Figure6a) and MCD12Q1Figure 6 (Figure depicts6b), andthe spatialqualitative distribution comparison maps of of urban land gen- eratednighttime from lightLandScan types (Figure (Figure6c). It is 6d) obvious with that land the spatial use distributionmaps from of urban The land Atlas of Urban Expansion generated from LandScan is similar to the urban built-up from The Atlas of Urban Expansion (2016(2016 Edition) Edition) and(Figure built-up 6a) land and from MCD12Q1 MCD12Q1. As (Figure for the distribution 6b), and of spatial nighttime distribution maps of nighttimelight types, light the types urban land (Figure generated 6c). from It is LandScan obviou wass that not completelythe spatial covered distribution by of urban land the high nighttime light, except for Cairo. However, the shape of high nighttime light generateddistribution from is very LandScan similar to that is ofsim urbanilar land. to Thisthe alsourban supports built-up demographers’ from viewsThe Atlas of Urban Expan- sion that(2016 urban Edition) transformation and inbuilt-up Sub-Saharan land Africa from can be MCD12Q1. better interpreted As as for a demographic the distribution of nighttime phenomenon rather than a strictly economic one [35,36]. light types,To better the verify urban the urbanland landgenerated generated from LandScan, LandScan 20 cities was were not selected completely to covered by the highconduct nighttime the accuracy light, assessment. except Thefor accuracy Cairo. was However, evaluated by the the groundshape true of land-usehigh nighttime light distri- butionmap is collected very similar from The to Atlas that of Urban of urban Expansion land (2016. This Edition) also. The supports kappa coefficient, demographers’ views that producer’s accuracy, and user’s accuracy of 20 cities are shown in Figure7. The kappa urbancoefficient transformation ranges from 0.66 in toSub-Saharan 0.99. The user’s Africa accuracy can in all be cities better is higher interpreted than 60%, as a demographic phenomenonand some even rather reach 96%, than except a stri forctly Gombe. economic Meanwhile, one the producer’s[35,36]. accuracy of the 19 cities is higher than 50%, and the highest is 86%. The derived urban center in LandScan is smaller than the ground truth data, which includes most artificial impervious areas and associated infrastructures, parks, lakes, and so on within the urban boundary [37]. Additionally, the spatial resolution of LandScan is 1 km, which is much coarser than that of the ground truth data (30 m). As a result, we think the method used in this study is reasonable and can be effectively implemented.

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Figure 6. ComparisonFigure 6. Comparison among land useamong maps land for use 2014 maps from forThe 2014 Atlas from of Urban The Atlas Expansion of Urban (2016 Expansion Edition) (2016(a) and Edition) MCD12Q1 (b); distribution of(a) nighttime and MCD12Q1 light types (b); distribution for 2014 (c); of urban nighttime land for light 2014 types generated for 2014 from (c); LandScanurban land (d for). 2014 generated from LandScan (d).

To better verify the urban land generated from LandScan, 20 cities were selected to conduct the accuracy assessment. The accuracy was evaluated by the ground true land- use map collected from The Atlas of Urban Expansion (2016 Edition). The kappa coefficient, producer’s accuracy, and user’s accuracy of 20 cities are shown in Figure 7. The kappa coefficient ranges from 0.66 to 0.99. The user’s accuracy in all cities is higher than 60%, and some even reach 96%, except for Gombe. Meanwhile, the producer’s accuracy of the 19 cities is higher than 50%, and the highest is 86%. The derived urban center in LandScan is smaller than the ground truth data, which includes most artificial impervious areas and associated infrastructures, parks, lakes, and so on within the urban boundary [37]. Addi- tionally, the spatial resolution of LandScan is 1 km, which is much coarser than that of the ground truth data (30 m). As a result, we think the method used in this study is reasonable and can be effectively implemented.

Figure 7.Figure The 7. producer’sThe producer’s and and user’s user’s accuracy accuracy of 20 cities of in20 ascending cities in order ascending of the kappa order coefﬁcient. of the kappa coefficient.

4.2. Spatiotemporal Changes of Urban Population and Urban Land 4.2.1. Time-Series Changes of Urban Population and Urban Land in Africa Urban population and urban land showed a synchronous growth trend in Africa from 2001 to 2019 (Figure 8). The urban land increased from 37,160 to 104,647 km², with an average annual growth rate of 5.92%. At the same time, the urban population increased from 233.73 million to 554.33 million, with an average annual growth rate of 4.91%. The growth rate of urban land was higher than that of the urban population on the whole. Taking 2001 as the benchmark, we estimated the year-on-year growth rate of the urban population and urban land in Africa, and world urban population, which can be seen in Figure 8. The growth rate of the urban population in Africa has increased steadily from 2001 to 2019, which was significantly higher than the average growth rate of the world's urban population. At the same time, the expansion of urban land fluctuated greatly. The growth rate of urban land area exceeded that of the urban population in 2003.

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4.2. Spatiotemporal Changes of Urban Population and Urban Land 4.2.1. Time-Series Changes of Urban Population and Urban Land in Africa Urban population and urban land showed a synchronous growth trend in Africa from 2001 to 2019 (Figure8). The urban land increased from 37,160 to 104,647 km 2, with an average annual growth rate of 5.92%. At the same time, the urban population increased from 233.73 million to 554.33 million, with an average annual growth rate of 4.91%. The growth rate of urban land was higher than that of the urban population on the whole. Taking 2001 as the benchmark, we estimated the year-on-year growth rate of the urban population and urban land in Africa, and world urban population, which can be seen in ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 11 of 21 Figure8. The growth rate of the urban population in Africa has increased steadily from 2001 to 2019, which was signiﬁcantly higher than the average growth rate of the world’s urban population. At the same time, the expansion of urban land ﬂuctuated greatly. The growth rate of urban land area exceeded that of the urban population in 2003.

FigureFigure 8. 8.Temporal Temporal change change of urban of urban population population and urbanand ur landban in land Africa in fromAfrica 2001 from to 2019.2001 Note:to 2019. Note: thethe growth growth rate rate was was calculated calculated based based on 2001. on 2001.

4.2.2.4.2.2. Spatial Spatial Distribution Distribution of Urbanof Urban Land Land Expansion Expansion and Population and Population Growth Growth in Africa in in Africa in Different Periods Different Periods In order to explore the spatial development dynamics and distribution pattern of urbanIn land order expansion to explore and the population spatial development growth in Africa dynamics from 2001 and to distribution 2019, the spatial pattern of ur- distributionban land expansion of urban landand andpopulation urban population growth in growth Africa infrom African 2001 cities to 2019, from the 2001 spatial to distri- 2007,bution 2007–2013, of urban 2013–2019, land and and urban 2001–2019 population are presented growth on in the African vector mapcities of from provincial 2001 to 2007, administrative2007–2013, 2013–2019, divisions (Figures and 2001–20199 and 10). are presented on the vector map of provincial ad- 2 ministrativeThe urban divisions land areas (Figures increased 9 byand 67,487 10). km from 2001 to 2019, of which from 2001 to 2007, 2007–2013, and 2013–2019, the area increased by 15,567, 18,472, and 33,448 km2,

respectively. The urban land experienced the most dramatic growth in 2013–2019, and the increased area is 2.15 times and 1.81 times that in 2001–2007 and 2007–2013, respectively. As for the spatial distribution of urban land growth in Africa during the different periods, there were similar spatial distribution characteristics, which are demonstrated in Figure9. The growth of urban land was mainly concentrated in countries such as Nigeria, Egypt, the Democratic Republic of the Congo, and Ethiopia. Urban land has been growing rapidly in all provinces of some countries like Nigeria, the Democratic Republic of the Congo, and Kenya. Meanwhile, urban land growth in some countries is concentrated in some areas. For example, urban land growth in South Africa is mainly concentrated in coastal areas, Gauteng, and North West provinces. Nigeria has always been the leading country of urban land change during the study period. In addition, the proportion of urban land change in Nigeria to the total urban land change in Africa was getting higher and higher, with 17.73%, 20.85%, and 26.43% from 2001 to 2007, 2007–2013, 2013–2019, which was much higher than that of other African countries (Table3). At the same time, the urban land expansion of Ethiopia and the Democratic Republic of the Congo was getting faster and

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faster. Together with Nigeria, they constituted the top three countries in Africa with the largest urban land expansion from 2001 to 2019. The top 10 countries with the largest urban land expansion account for more than 60% of the total urban land expansion in Africa during the study periods.

Figure 9. Spatial changesFigure of urban 9. Spatial land changes in different of urban land periods. in different periods. From 2001 to 2019, the urban population increased by 320.60 million people, of The urban landwhich areas from increased 2001 to 2007, by 2007–2013,67,487 km² and from 2013–2019, 2001 the to urban 2019, population of which increased from 2001 by to 2007, 2007–2013,83.84 and million, 2013–2019, 76.97 million, the area and 159.80increased million by people, 15,567, respectively. 18,472, Similar and 33,448 to the urban km², land growth, the urban population grew the most rapidly from 2013 to 2019, which was respectively. The urbanalmost land the sum experienced of the urban population the most growth dramatic in the growth other two in periods 2013–2019, of 2001–2007 and and the increased area is 2.152007–2013. times and Nigeria 1.81 and times the Democratic that in 2001–2007 Republic of the and Congo 2007–2013, have been amongrespectively. the top As for the spatial distributionthree countries of with urban the most land urban growth population in Africa growth, during of which the Nigeria different has always periods, been the ﬁrst with absolute superiority (Table4). The urban population growth in Ethiopia has there were similar alsospatial been distribution relatively rapid, characteristics, except in 2007–2013. which Ethiopia’s are urban demonstrated population growth in Figure was 9. The growth of urbanmainly land concentrated was mainly in Addis concentrated Ababa, Amhara, in andcountries Southern such Nations, as NationalitiesNigeria, Egypt, and the Democratic RepublicPeople (Figure of the 10 Congo,). During and the study Ethiopia. period, Urban the top 10 land countries has withbeen the growing largest urban rap- population growth accounted for more than 60% of the total urban population growth idly in all provincesin of Africa. some countries like Nigeria, the Democratic Republic of the Congo, and Kenya. Meanwhile, urban land growth in some countries is concentrated in some areas. For example, urban land growth in South Africa is mainly concentrated in coastal areas, Gauteng, and North West provinces. Nigeria has always been the leading country of urban land change during the study period. In addition, the proportion of urban land change in Nigeria to the total urban land change in Africa was getting higher and higher, with 17.73%, 20.85%, and 26.43% from 2001 to 2007, 2007–2013, 2013–2019, which was much higher than that of other African countries (Table 3). At the same time, the urban land expansion of Ethiopia and the Democratic Republic of the Congo was getting faster and faster. Together with Nigeria, they constituted the top three countries in Africa with the largest urban land expansion from 2001 to 2019. The top 10 countries with the largest urban land expansion account for more than 60% of the total urban land expansion in Africa during the study periods.

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Figure 10. SpatialFigure changes 10. Spatial of the changes urban population of the urban in population different periods. in different periods.

Table 4. Ten majorTable African 3. Ten countries major African and their countries share and of urban their sharepopulation of urban growth land growthin different in different peri- periods. ods. Rank 2001–2007 2007–2013 2013–2019 2001–2019 Nigeria Nigeria Nigeria Nigeria Rank 2001–20071 2007–2013 2013–2019 2001–2019 (17.73%) (20.85%) (26.43%) (24.30%) Nigeria Nigeria Nigeria Nigeria 1 The Democratic Egypt Egypt Republic of the Ethiopia (15.28%)2 (31.50%) (27.71%) (25.35%) The(8.02%) Democratic Re- The(12.99%) Democratic Re- TheCongo Democratic Re- (8.48%) Ethiopia (8.28%) 2 public of the CongoThe public Democratic of the Congo public of the CongoThe Democratic (12.93%) South Africa Republic of the Ethiopia Republic of the 3 (12.94%) (10.85%) (10.44%) (6.94%) Congo (7.77%) Congo The Democratic Re- Egypt (10.23%)Ethiopia Ethiopia (8.32%) 3 public of the Congo The Democratic Republic(6.37%) of the Ethiopia(8.56%) Egypt(6.82%) Egypt 4 (7.37%) Congo (6.06%) (7.12%) (6.01%) Cameroon South(5.76%) Africa Egypt Egypt 4 Uganda Kenya Tanzania Ghana (4.88%)5 (4.80%) (7.98%) (6.57%) (5.12%) (5.62%) (4.41%) (4.10%) Zimbabwe KenyaMorocco SudanCôte d'Ivoire GhanaCôte d'Ivoire Sudan 5 6 (4.72%) (4.75%)(4.25%) (5.24%)(5.10%) (3.76%)(3.79%) (4.01%) Morocco South Africa Sudan South Africa Egypt7 Tanzania Sudan Ghana 6 (4.18%) (4.48%) (3.59%) (3.72%) Ethiopia Uganda Kenya Cameroon (4.07%)8 (4.17%) (3.87%) (3.47%) (4.07%) (3.50%) (2.58%) (3.08%) Ghana AlgeriaGhana TanzaniaMadagascar South AfricaSudan Tanzania 7 9 (3.96%) (4.04%)(4.09%) (3.19%)(3.04%) (2.57%)(3.46%) (2.61%) Côte d’Ivoire Cameroon Morocco Tunisia Tunisia10 Sudan Ghana Tanzania 8 (3.19%) (2.10%) (2.46%) (2.44%) (3.76%)Total 63.81%(3.34%) 74.26%(2.91%) 69.07%(3.03%) 67.08% Algeria Côte d'Ivoire Tanzania Tunisia 9 (3.59%) (3.29%) (2.36%) (2.80%) 10 Tanzania Tunisia Algeria South Africa

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Table 4. Ten major African countries and their share of urban population growth in different periods.

Rank 2001–2007 2007–2013 2013–2019 2001–2019 Nigeria Nigeria Nigeria Nigeria 1 (15.28%) (31.50%) (27.71%) (25.35%) The Democratic The Democratic The Democratic Ethiopia Republic of the Republic of the Republic of the 2 (12.93%) Congo Congo Congo (12.94%) (10.85%) (10.44%) The Democratic Republic of the Egypt Ethiopia Ethiopia 3 Congo (6.37%) (8.56%) (6.82%) (7.37%) Cameroon South Africa Egypt Egypt 4 (4.88%) (4.80%) (7.98%) (6.57%) Zimbabwe Morocco Côte d’Ivoire Côte d’Ivoire 5 (4.72%) (4.25%) (5.10%) (3.79%) Egypt Tanzania Sudan Ghana 6 (4.07%) (4.17%) (3.87%) (3.47%) Ghana Ghana Madagascar Sudan 7 (3.96%) (4.09%) (3.04%) (3.46%) Tunisia Sudan Ghana Tanzania 8 (3.76%) (3.34%) (2.91%) (3.03%) Algeria Côte d’Ivoire Tanzania Tunisia 9 (3.59%) (3.29%) (2.36%) (2.80%) Tanzania Tunisia Algeria South Africa 10 (3.25%) (2.74%) (2.35%) (2.74%) Total 63.80% 77.51% 74.73% 68.46%

4.3. Spatiotemporal Characteristics of Coupling Development between Urban Land and Urban Population From 2001 to 2019, the coupling relationship index of urban population and urban land was 0.76. In general, the growth rate of urban land was greater than that of the urban population in Africa, and the type of coupling relationship was in type VI. Figure 11 shows the spatial change of the coordination relationship between urban land and urban population in different provinces of Africa in different periods. As shown in Figure 11, the urban development in African provinces was mainly in type I and type VI. Nigeria has always been dominated by type VI. The urban development of the Democratic Republic of the Congo was mainly in type VI except from 2013 to 2019. Meanwhile, the urban development of Ethiopia was more complex. As the most urbanized area in Ethiopia, Addis Ababa was mainly dominated by type VI, except 2013–2019. From 2001 to 2019, the EC values of Nigeria, the Democratic Republic of the Congo, and Ethiopia were 0.90, 0.49, and 0.82, respectively, which means that the growth rate of the urban population in these countries was lower than that of urban land, and urban land showed an extensive growth. We counted the proportion of urban development types in different periods, and the results are displayed in Figure 12. According to the change of the proportion of different types of cities over time, type VI was increasing, while type I was decreasing. The total proportion of uncoordinated development types (IV, V, and VI) was getting higher and higher. Therefore, the balance of urban land expansion and population growth in Africa is not optimistic. Among them, type VI represents the synchronous growth of urban population and urban land, and the growth of urban land is faster than that of population, which indicates that most provinces are still in the stage of rapid urbanization, and it is speculated that the imbalance of urban land expansion and population growth will continue or even intensify in the future. ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 15 of 21

(3.25%) (2.74%) (2.35%) (2.74%) Total 63.80% 77.51% 74.73% 68.46%

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proportion of uncoordinated development types (Ⅳ, Ⅴ, and Ⅵ) was getting higher and higher. Therefore, the balance of urban land expansion and population growth in Africa is not optimistic. Among them, type VI represents the synchronous growth of urban population and urban land, and the growth of urban land is faster than that of population, which indicates that most provinces are still in the stage of rapid urbanization, and it is speculated that the imbalance of urban land expansion and population growth will continue or even intensify in the future. Figure 11. Spatial changesFigure 11. ofSpatial coordination changes of re coordinationlationships relationships in different in differentperiods. periods.

We counted the proportion of urban development types in different periods, and the results are displayed in Figure 12. According to the change of the proportion of different types of cities over time, type Ⅵ was increasing, while type Ⅰ was decreasing. The total

Figure 12. The proportion of different couplingcoupling types in different periods.

4.4. Characteristics of Other Land Types Occupied by Urban Expansion According to the above analysis, the land expansion rate of African cities is higher than that of the urban population, which may lead to extensive use of land resources. Therefore, it is necessary to monitor the situation of other land types occupied by African cities. Figure 13 is a Pareto diagram of other land types occupied by urban expansion in different periods. Except for urban land, land use in Africa is divided into forest, grassland, rural land, cropland, barren land, water, and other. As can be seen from Figure 13, forest, grassland, rural land, and cropland are the four types that have been occupied mostly by urban expansion, accounting for more than 95% in different periods. From 2001 to 2019, among the types of land occupied by urban expansion, cropland was occupied up to 29%, followed by grassland, rural land, and forest (Figure 13d). During the study period, the proportion of cropland occupied by urban expansion has gradually increased and has become the most occupied land-use type (Figure 13a-c). At the same time, the proportion of grassland and forest was gradually increasing too, but the proportion of rural land occupied by urban land has dropped sharply. To sum up, with the rapid expansion of urban land in Africa, more and more cropland, grassland and forest have been invaded.

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4.4. Characteristics of Other Land Types Occupied by Urban Expansion According to the above analysis, the land expansion rate of African cities is higher than that of the urban population, which may lead to extensive use of land resources. Therefore, it is necessary to monitor the situation of other land types occupied by African cities. Figure 13 is a Pareto diagram of other land types occupied by urban expansion in different periods. Except for urban land, land use in Africa is divided into forest, grassland, rural land, cropland, barren land, water, and other. As can be seen from Figure 13, forest, grassland, rural land, and cropland are the four types that have been occupied mostly by urban expansion, accounting for more than 95% in different periods. From 2001 to 2019, among the types of land occupied by urban expansion, cropland was occupied up to 29%, followed by grassland, rural land, and forest (Figure 13d). During the study period, the proportion of cropland occupied by urban expansion has gradually increased and has become the most occupied land-use type (Figure 13a-c). At the same time, the proportion of grassland and forest was gradually increasing too, but the proportion of rural land ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 17 of 21 occupied by urban land has dropped sharply. To sum up, with the rapid expansion of urban land in Africa, more and more cropland, grassland and forest have been invaded.

FigureFigure 13. 13.Pareto Pareto chart chart of otherof other land land types types occupied occupied by urban by expansionurban expansion in different in different periods. periods. 5. Discussion 5. DiscussionIn this study, we propose a population density-based thresholding method to identify urbanIn land this and study, urban we population. propose Thisa population is a simple, de fast,nsity-based and effective thresholding method to method identify to iden- urbantify urban area, whichland and can provideurban population. continuous andThis comparable is a simple, urban fast, land and andeffective urban method popula- to iden- tiontify dataurban for differentarea, which regions. can Using provide this method,continuous we can and get comparable the data of urban urban population land and urban andpopulation urban land data in African for different countries regions. and conduct Using further this method, comparative we research.can get the data of urban Findings show that Africa has experienced considerable growth in urban land and population and urban land in African countries and conduct further comparative research. urban population from 2001 to 2019. Urban land averagely increased by 5.92% per annum, Findings show that Africa has experienced considerable growth in urban land and urban population from 2001 to 2019. Urban land averagely increased by 5.92% per annum, while urban population increased averagely by about 4.91% per annum during 2001–2019. The speed of urban land expansion is relatively faster than the growth of urban population, which is consistent with previous studies [17,38]. Nigeria, the Democratic Republic of the Congo, and Ethiopia are the three countries with the fastest urban land expansion and urban population growth in Africa. This is mainly due to the reason that Africa’s urbanization is commonly driven by demography [39–41]. In 2019, Nigeria was the most populous country in Africa with a population of 201 million, followed by Ethiopia (70 million), Egypt (68 million), and the Democratic Republic of the Congo (51 million) [42]. As for the average annual growth rate of population from 2001 to 2019, the Democratic Republic of the Congo was the highest with a growth rate of 3.8%, followed by Nigeria (3.7%), Ethiopia (3.3%), and Egypt (2.6%) [42,43]. Whether from the perspective of Africa as a whole or most provinces of African countries, the growth rate of urban land is greater than that of urban population, which is in line with Angel's prediction that urban land cover in sub-Saharan Africa will expand at the fastest speed during 2000–2050 [17]. From a global perspective, urban growth brought about by urbanization is reflected in the outward expansion, inward compression, or upward high-rise buildings [44,45]. However, the spatial expansion of African cities is characterized by large-scale and uncontrollable, which is technically known as urban sprawl.

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while urban population increased averagely by about 4.91% per annum during 2001– 2019. The speed of urban land expansion is relatively faster than the growth of urban population, which is consistent with previous studies [17,38]. Nigeria, the Democratic Republic of the Congo, and Ethiopia are the three countries with the fastest urban land expansion and urban population growth in Africa. This is mainly due to the reason that Africa’s urbanization is commonly driven by demography [39–41]. In 2019, Nigeria was the most populous country in Africa with a population of 201 million, followed by Ethiopia (70 million), Egypt (68 million), and the Democratic Republic of the Congo (51 million) [42]. As for the average annual growth rate of population from 2001 to 2019, the Democratic Republic of the Congo was the highest with a growth rate of 3.8%, followed by Nigeria (3.7%), Ethiopia (3.3%), and Egypt (2.6%) [42,43]. Whether from the perspective of Africa as a whole or most provinces of African countries, the growth rate of urban land is greater than that of urban population, which is in line with Angel’s prediction that urban land cover in sub-Saharan Africa will expand at the fastest speed during 2000–2050 [17]. From a global perspective, urban growth brought about by urbanization is reﬂected in the outward expansion, inward compression, or upward high-rise buildings [44,45]. However, the spatial expansion of African cities is characterized by large-scale and uncontrollable, which is technically known as urban sprawl. Studies on African urbanization show that urban sprawl leads to unsustainable land development and often engulfs the surrounding urban areas, transforming non- urban land, especially agricultural land, into urban land [46,47]. It is consistent with the results of this study that cropland was the most land-use type occupied by urban land. Food insecurity has been a long-standing problem in Africa. About 257 million people in Africa are in malnutrition, accounting for nearly one-third of the world’s malnutrition population [48]. Additionally, a quarter of Africa’s population is still starving. Food insecurity in Africa has been a great challenge to the achievement of the Second Sustainable Development Goal (SDG2) that living in a world with no hunger proposed by the 2030 Agenda. There is no doubt that the disordered urban expansion in Africa will hinder the realization of SDG2 even more. The future development of cities and the subsequent occupation of land and natural resources will determine whether we can move towards an environmentally sustainable future. The unplanned or improperly managed urban expansion will lead to rapid sprawl [49], pollution [50], and environmental degradation [51], as well as unsustainable production and consumption patterns [52]. Unfortunately, many urban areas in Africa have been undergoing dense, unplanned, and unsustainable urbanization, which is considered to be eroding the socio-economic and environmental beneﬁts associated with urbanization and sustainable development [41]. Africa’s forests are mainly distributed in the equatorial region, stretching from Sierra Leone to Victoria Great Lakes region [53], including the Democratic Republic of the Congo, parts of Nigeria, and Ethiopia, which are undergoing rapid urbanization. As shown in the results of this article, encroachment of forests by the extensive urban land use in Africa is also obvious. Such urban expansion has the potential to destroy the habitats of key biodiversity hotspots and lead to carbon emissions associated with tropical deforestation and land use change. Sustainable development is still one of the most advocated development concepts in the world. However, there are still limited signs of progress in achieving sustainable development in Africa. Rapid and unplanned urbanization is a major threat to the sustainable development of Africa. This study provides a simple and easy way to monitor the urban population growth and urban land expansion in African cities. This will help to fully understand the complex urbanization process in Africa and formulate better urban development policies. With the rapid increase in the population of African cities, more and more related issues will emerge. Therefore, more research is needed to focus on the process of urbanization in Africa. ISPRS Int. J. Geo-Inf. 2021, 10, 584 16 of 18

6. Conclusions Urbanization is a complex geographical process with the simultaneous growth of population and land, closely related to social economy and environment, etc. Africa is experiencing rapid urbanization, and some problems related to rapid urbanization are emerging. In this study, we propose a population density-based thresholding method to generate urban land and urban population data in Africa, which can be applied to further study the characteristics of Africa’s urbanization process. Our method is proved to be feasible. The results show that from 2001 to 2019, the average annual growth rate of urban land in Africa was 5.92%, which was greater than that of the urban population (4.91%). Moreover, the growth rate of the urban population in African is greater than that of the world, which is consistent with previous research conclusions. The regional analysis of urban population and urban land change in Africa shows that the top three countries with the fastest urbanization process in Africa are Nigeria, the Democratic Republic of the Congo, and Ethiopia. As for the coordinated relationship between urban population growth and urban land expansion, results show that the total proportion of uncoordinated development types (IV, V, and VI) was getting higher, which means that the urban land use in Africa tends to be extensive, and land-use efﬁciency is reduced. With the rapid urbanization in Africa and extensive urban land use, more and more cropland, grassland, and forest are occupied. The encroachment of cropland may exacerbate food security in Africa. Additionally, with the expansion of cities, the deforestation of forests will cause more carbon emissions and will affect biodiversity, which will have an impact on the environment. According to the results of this paper, the extensive land use induced by rapid urbanization process in Africa is likely to damage the environmental benefits of Africa. As the second-largest continent in the world and home to a population of 1.29 billion, sustainable urbanization is very important for Africa and even the world. As a result, it is very important to monitor urban dynamics in Africa and formulate sustainable urban development policies. More efforts are needed to achieve the goal of sustainable urban environment in Africa.

Author Contributions: Conceptualization and methodology, Shengnan Jiang, Zhenke Zhang and Hang Ren; software and data curation, Shengnan Jiang and Guoen Wei; formal analysis, Shengnan Jiang and Guoen Wei; validation, Shengnan Jiang and Zhenke Zhang; writing—original draft, Shengnan Jiang; writing—review and editing, Minghui Xu and Binglin Liu; data collection and Processing Shengnan Jiang and Hang Ren; funding acquisition, Zhenke Zhang. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the National Key R&D Program of China (No. 2018YFE0105900). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data that we used in this study can be requested by contacting the corresponding author. Acknowledgments: The authors express thanks to anonymous reviewers for their constructive comments and advice. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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