What drives land take and urban land expansion? A systematic review

Colsaet, Alice; Laurans, Yann; Levrel, Harold

doi:10.1016/j.landusepol.2018.08.017

Cited by 174 publications

(116 citation statements)

References 15 publications

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“…This resulted in the intensified trend of dispersed urban land expansion in these towns. The socioeconomic, cultural, political, and biophysical reflections are the main drivers of land-use trends in several countries and could not be explicitly analyzed [5,42].…”

Section: Urban Landscape Dynamics and Associated Triggersmentioning

confidence: 99%

“…Rapid population growth and the related need for housing and other amenities have resulted in an increasing urbanized land cover, mostly along the major roads and rural countryside, which is typically scattered and inefficient in resource utilization [1,2]. The scattered urban growth and disaggregated landscape structures as a result of human activities also influence the function and structure of the ecosystem [3][4][5]. Dispersed urban development can pose a challenge for planning The Oromia Special Zone surrounding Finfinnee/Addis Ababa is one of the most rapidly growing towns in Ethiopia.…”

Section: Introductionmentioning

confidence: 99%

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Urbanization in Small Cities and Their Significant Implications on Landscape Structures: The Case in Ethiopia

et al. 2020

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Fundamental ideas concerning urbanization are primarily based on studies performed in large cities. It is of interest to study whether or not similar phenomena take place in smaller cities. Small cities are an inherent component of urbanization, and in the future, the majority of globalization is expected to occur in small and mid-sized cities. Understanding the effects of small cities on landscape structures is, therefore, an essential component in planning city land expansion. Accordingly, this study focused on six towns of the Oromia Special Zone Surrounding Finfinnee, Ethiopia, which is broadly known to be experiencing dramatic growth. Time-series Landsat images from 1987 to 2019 with an integrated method, landscape metrics, and built-up density analysis were employed to characterize and compare the dynamics of landscape structures, urban expansion patterns, process, and overall growth status in the towns. The results highlight that all the towns experienced accelerated growth in the built-up areas and highly scattered nature in spatial growth. Landscape ecology analysis confirmed a highly fragmented urban landscape, a significant loss of natural land covers, and disconnected and complicated agro-vegetation patches in all towns, suggesting a lack of rigorous implementation of the master plan. Results also indicated that the Oromia Special Zone surrounding Finfinnee has failed to control urban sprawl to surrounding ecological sensitive areas. The study results, more broadly, highlight that the small cities would have a limited physical and demographic footprint and relatively less contribution to the national economic agglomeration; nonetheless, they can have a notable and important impact in terms of their ecological and environmental influence. Hence, the study suggests policies for monitoring such dynamics and protecting agro-environmental connectivity with particular focus on the small cities.Sustainability 2020, 12, 1235 2 of 19 infrastructure and services. Whereas a compact spatial form can assist in implementing an enhanced transportation system as well as minimize the consumption of traffic energy-efficiency and resource utilization such as water pipe networks and drainage system [6,7]. However, some studies argue that the potential for damage to urban green spaces [8], increased traffic congestion, pollution, and crowded services [9] are notable shortcomings of compacting urban development. Even though there is some controversy regarding spatial growth types, the general agreement is that the urban spatial morphology can determine the social, environmental, and ecological situations of a city. Consequently, exploration of the urban spatial dynamics and landscape structures analysis continues to of interest due to its high importance in creating sustainable cities.Several studies have investigated the effects of urban expansion on the landscape composition and configuration [10][11][12], investigating the existing and projected urban land dynamics [13], and exploring the patterns and possible drivers [...

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Section: Urban Landscape Dynamics and Associated Triggersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Urbanization in Small Cities and Their Significant Implications on Landscape Structures: The Case in Ethiopia

et al. 2020

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“…Urban expansion has been the focus of attention in the process of urbanization and industrialization in countries throughout the world [1][2][3][4]. From the perspective of regional planning, there are two modes of city expansion, one is "exogenous" and the other is "endogenous".…”

Section: Introductionmentioning

confidence: 99%

How to Measure Urban Land Use Intensity? A Perspective of Multi-Objective Decision in Wuhan Urban Agglomeration, China

et al. 2018

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Urban land intensive use is an important part of regional planning and plays an important role in the sustainable development of cities. However, the research on how to measure urban land use intensity is still controversial. This paper gives a perspective of multi-objective decision to explore the criterion of urban land use intensity in the process of urbanization. The theory of factor substitution was applied to analyze the mechanism of urban land intensive use in different urbanization stage. Additionally, the matching degree model (MDM), curve estimation model, and multi-objective programming model were employed in the case study of Wuhan urban agglomeration to give a multi-objective decision on urban land intensive use. The results show that the level of urban land intensive use lagging behind the level of urbanization development in more than 60% of the counties. When the level of urbanization is less than 30%, 30–70% and greater than 70%, the optimal intensity of urban land use characterized by fixed investments of unit land is 2.6252, 4.6132, and 6.7648 million RMB respectively. Based on optimal intensity, the counties in Wuhan urban agglomeration were divided into key development zone and optimized development zone. Finally, different management strategies were designed for different zones for urban land intensive use.

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“…When diverse and skilled human capital interacts, cities can boost ecosystems of research, development, innovation, and entrepreneurship [15][16][17]. It is this duality between the challenges and benefits of cities that makes the understanding of their growth so important for us.To predict how cities are going to look like in the future, urban planners rely on historical data and urban growth models [10,[18][19][20] . These models can have multiple outputs, but they commonly provide estimations of binary urban footprints (i.e., maps with urban or non-urban categories) or population distribution within a region of study over time [21].…”

mentioning

confidence: 99%

“…To predict how cities are going to look like in the future, urban planners rely on historical data and urban growth models [10,[18][19][20] . These models can have multiple outputs, but they commonly provide estimations of binary urban footprints (i.e., maps with urban or non-urban categories) or population distribution within a region of study over time [21].…”

mentioning

confidence: 99%

Spatiotemporal Modeling of Urban Growth Using Machine Learning

et al. 2019

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This paper presents a general framework for modeling the growth of three important variables for cities: population distribution, binary urban footprint, and urban footprint in color. The framework models the population distribution as a spatiotemporal regression problem using machine learning, and it obtains the binary urban footprint from the population distribution through a binary classifier plus a temporal correction for existing urban regions. The framework estimates the urban footprint in color from its previous value, as well as from past and current values of the binary urban footprint using a semantic inpainting algorithm. By combining this framework with free data from the Landsat archive and the Global Human Settlement Layer framework, interested users can get approximate growth predictions of any city in the world. These predictions can be improved with the inclusion in the framework of additional spatially distributed input variables over time subject to availability. Unlike widely used growth models based on cellular automata, there are two main advantages of using the proposed machine learning-based framework. Firstly, it does not require to define rules a priori because the model learns the dynamics of growth directly from the historical data. Secondly, it is very easy to train new machine learning models using different explanatory input variables to assess their impact. As a proof of concept, we tested the framework in Valledupar and Rionegro, two Latin American cities located in Colombia with different geomorphological characteristics, and found that the model predictions were in close agreement with the ground-truth based on performance metrics, such as the root-mean-square error, zero-mean normalized cross-correlation, Pearson's correlation coefficient for continuous variables, and a few others for discrete variables such as the intersection over union, accuracy, and the f 1 metric. In summary, our framework for modeling urban growth is flexible, allows sensitivity analyses, and can help policymakers worldwide to assess different what-if scenarios during the planning cycle of sustainable and resilient cities.cities are also beacons of hope as they provide opportunities to their inhabitants to access to basic utilities (e.g., water, electricity, gas, internet), health facilities, employment with competitive wages, financial services, entertainment, technical training, formal education, and much more [13,14]. When diverse and skilled human capital interacts, cities can boost ecosystems of research, development, innovation, and entrepreneurship [15][16][17]. It is this duality between the challenges and benefits of cities that makes the understanding of their growth so important for us.To predict how cities are going to look like in the future, urban planners rely on historical data and urban growth models [10,[18][19][20] . These models can have multiple outputs, but they commonly provide estimations of binary urban footprints (i.e., maps with urban or non-urban categories) or population distr...

show abstract

What drives land take and urban land expansion? A systematic review

Cited by 174 publications

References 15 publications

Urbanization in Small Cities and Their Significant Implications on Landscape Structures: The Case in Ethiopia

Urbanization in Small Cities and Their Significant Implications on Landscape Structures: The Case in Ethiopia

How to Measure Urban Land Use Intensity? A Perspective of Multi-Objective Decision in Wuhan Urban Agglomeration, China

Spatiotemporal Modeling of Urban Growth Using Machine Learning

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