In the near future, urban population growth in developing countries will be 16 times that of developed countries [1
], especially in Africa and Asia [3
]. By 2030, the population of cities in developing countries (i.e., >100,000 inhabitants) may double, while their built-up areas will triple due to a decrease in population density [1
]. In addition, such demographic pressure may promote rapid urban expansion and cause irreversible implications for the soil and land use, compromising areas for recreation, food production, renewable energy, and resource extraction [4
]. In many cases, urban expansion has been unsustainable (e.g., landscape fragmentation, deforestation), especially when it is dispersed [5
], causing high soil consumption, soil sealing, increases in the cost and need for infrastructure [6
], and impoverishment of the urban fringe [7
]. The situation worsens when planning, control, and fiscal resources are limited [8
In small island developing states (SIDS), about 59% of the population lives in urban settlements [9
]; in some countries (e.g., Singapore, Nauru), the rate is 100%. Their small size creates intense competition between land use options [10
]. As a result, their urban land cover as a percentage of total arable land is generally higher than the world average for developing countries [11
], showing limited land and arable soil. Cape Verde has a high urban growth rate (2.1%) when compared to the world average (1.7%) [12
] and limited arable land (10%). As a response, the Praia Master Plan (PDM) [13
] and National Institute of Territory Management of Cape Verde (INGT) [14
] have shown great concern over excessive land consumption and changes in urban pattern by defining as priorities the containment of the urban perimeter and its extension into potential agricultural land, the reduction of travel costs, increased accessibility to public services, and the promotion of urban infill growth. Furthermore, the PDM of Praia has prescribed the minimum number of floors permitted for residential construction as being two, in order to minimize such urban expansion. This requirement will be controlled using remote sensing techniques in semi-automatic form. In 2010, the Praia Municipality Council (CMP) created the municipal guard service in order to reinforce the supervision and monitoring of illegal construction in the capital city. The CMP aims to implement the municipal housing policies in order to reduce the habitational deficit based on vertical construction, reducing the demand and the pressure on the land [13
Urban expansion as a dynamic process requires deep understanding of its historical evolution and driving forces. Such analysis is important for decision makers in order to predict the amount of land to allocate to accommodate the fast increase in population [5
] and minimize its adverse environmental and socioeconomic effects [15
]. Recently, different approaches have been used in order to examine the effects of driving forces on urban expansion. Bivariate [16
], logistic [15
], and multivariate regression [18
] are most widely used in such studies, especially in large cities. Usually, such studies are conducted at static points of view or short time periods, instead of examining its multi-temporal changes.
The common driving forces in urban expansion studies are grouped as proximity, site-specificity and neighborhood characteristics [17
]. Nevertheless, this list of factors can be updated as new driving forces are revealed. Most such driving forces are related to actual lifestyles and dwellers’ preferences [22
On the other hand, for small scales of analysis, quantifying multi-temporal urban expansion require detailed multi-temporal datasets to delineate urban areas, which are sometimes limited in some areas [24
]. This limitation persists even if we consider the satellite imagery data, which is often used for this purpose [25
], (e.g., non-coverage for early times and a lack of consistent spatial resolution). Beyond this, satellite data from earlier generations is not ideal for the delineation of urban areas on such a scale of analysis [27
] unless we use posterior commercial high-resolution satellite images [28
]. Consequently, finding solutions to coordinate and calibrate different data types at different scales into a consistent dataset is a challenge.
Alternatively, aggregation of vector data is used to delineate built-up areas [29
] by analyzing the density and layout of road networks [31
] and aggregation of building footprints [6
] using a GIS algorithm [32
]. Such techniques were often applied at regional scales [33
], primarily to identify the urban boundary, without the discrimination of the elements of urban areas (e.g., roads and small vacant land) [31
]. Hence, at large scales, the challenge is to make a clear delineation of built-up areas by including all human structures, with as little generalization as possible.
Urban areas include building footprints (i.e., residential, commercial, and industrial areas) and road networks [34
]. Research at small scales of analysis that uses both building footprints and roads in the delineation of urban areas is sparse. However, roads within the cluster of urban settlements should be considered part of urban areas [27
]. The methodology proposed by Ferreira [27
] is still generic for small scales of analysis and does not include some basic ideas. For example, even if they are between urban settlements, roads that interconnect zones passing through vacant land should not be considered part of urban areas if they do not have any buildings on either side. If a road has some buildings close to it, this entire road should not be a part of the urban area, but only of the roads closest to it. For larger scales, building footprints and roads are well detailed. Therefore, in order to capture more detail, less generalization and more adjustments are necessary for urban area delineation.
The main objective of this paper is to delineate, map, and analyze the dynamics of urban expansion in the city of Praia between 1969 and 2015 using vector data, and then explore which factors may have contributed to such expansion in each period (1969–1993, 1993–2003, 2003–2010, and 2010–2015) using Ordinary Least Squares (OLS) regression. We also introduce some improvements in the delineation of urban areas at large scales, using vector data. The main approach in this delineation is to include roads with certain conditions as a part of built-up areas, using GIS techniques.
The rest of the paper is organized as follows, Section 2
describes the city of Praia (Section 2.1
), cartography and alphanumeric data preparation (Section 2.2
), delineation of urban areas and mapping urban expansion (Section 2.3
), selection of driving forces of urban expansion (Section 2.4
), and OLS regression requirements (Section 2.5
). Section 3
presents the results of urban expansion and historic driving forces of urban expansion in Praia city. Finally, Section 4
discusses the results obtained, and Section 5
gives the conclusion of this research.
Based on the integration of multi-temporal data into the consistent vector dataset, we observed a rapid urban expansion in Praia during the period 1969–2015. The urban land increased by 960%, from 97 ha to 1028 ha. The majority of Praia’s built-up areas (62.8%) have emerged in the last 22 years, showing the fast urbanization process in recent decades. However, the period 1993–2003 was more dynamic (36.8 ha/year). The UD had decreased (238 people/ha to 140 people/ha), with a consequent increase in LCR (42.1 m2/person to 71.6 m2/person).
Different factors have driven the urban expansion in Praia in different periods. The population has influenced the urban expansion from 1993 to 2003. From this time until 2015, significant urban expansion did not occur in zones where we verified population growth. Therefore, population and density of roads showed a positive relationship with urban expansion and distance to the coast and slope, but a negative relationship in the period 1969–1993. Population, distance from industrial zones, density of roads, socioeconomic indicators, and age of zones positively influenced urban expansion during 1993–2003, while slope negatively influenced it. The distance to arterial roads, density of roads, neighborhood land available, and socioeconomic indicators positively influenced the urban expansion during the period 2003–2010. The infrastructure and number of floors have a negative relationship. After this period until 2015, the density of roads and distance to center had a positive effect on urban expansion. The distance to the coast, infrastructure, industrial zone, and distance from urban perimeter have also influenced urban expansion, but with a negative relationship.
Planners and policy-makers should consider the increase in land consumption in their urban plans, by qualifying the spaces for future urban expansion, promoting vertical construction, coverage for infrastructure, and improvement of accessibility in the periphery while improving the quality of life of the inhabitants. This study can provide useful data that will contribute to decision makers’ understanding of urban expansion and predictions of land area for future planning.