Exploring prospective urban growth trends under different economic outlooks and land-use planning scenarios: The case of Athens

Gounaridis, Dimitrios; Chorianopoulos, Ioannis; Koukoulas, Sotirios

doi:10.1016/j.apgeog.2017.12.001

Cited by 67 publications

(64 citation statements)

References 48 publications

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“…The generation of the LULCC scenario shows two important aspects: expert knowledge and knowledge based on data. Expert knowledge is useful to establish methodological processes according to the needs of the user (Gounaridis et al, 2018). Knowledge based on data, helps to understand the general behavior between the factors of change of land use in a spatial way (Olmedo et al, 2018).…”

Section: Manuscript To Be Reviewedmentioning

confidence: 99%

Peer Review #2 of "Spatial near future modeling of land use and land cover changes in the temperate forests of Mexico (v0.2)"

Galford¹

2019

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The loss of temperate forests of Mexico has continued in recent decades despite wide recognition of their importance to maintaining biodiversity. This study analyzes land use/land cover change scenarios, using satellite images from the Landsat sensor. Images corresponded to the years 1990, 2005 and 2017. The scenarios were applied for the temperate forests with the aim of getting a better understanding of the patterns in land use/land cover changes. The Support Vector Machine (SVM) multispectral classification technique served to determine the land use/land cover types, which were validated through the Kappa Index. For the simulation of land use/land cover dynamics, a model developed in Dinamica-EGO was used, which uses stochastic models of Markov Chains, Cellular Automata and Weight of Evidences. For the study, a stationary, an optimistic and a pessimistic scenario were proposed. The projections based on the three scenarios were simulated for the year 2050. Five types of land use/land cover were identified and evaluated. They were primary forest, secondary forest, human settlements, areas without vegetation and water bodies. Results from the land use/land cover change analysis show a substantial gain for the secondary forest. The surface area of the primary forest was reduced from 55.8% in 1990 to 37.7% in 2017. Moreover, the three projected scenarios estimate further losses of the surface are for the primary forest, especially under the stationary and pessimistic scenarios. This highlights the importance and probably urgent implementation of conservation and protection measures to preserve these ecosystems and their services. Based on the accuracy obtained and, on the models generated, results from these methodologies can serve as a decision tool to contribute to the sustainable management of the natural resources of a region.

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Section: Manuscript To Be Reviewedmentioning

confidence: 99%

Peer Review #2 of "Spatial near future modeling of land use and land cover changes in the temperate forests of Mexico (v0.2)"

Galford¹

2019

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“…[6][7][8]. It is currently being very used, in particular, for simulations in geography (especially in urban development planning [9], future development of cities [10], and land use [11]) pedestrian or vehicular traffic [12,13], and bio-medicine (applied to physiological modeling; for example, for cancer [14], or epidemic modeling [15]).…”

Section: Introductionmentioning

confidence: 99%

Developing Efficient Discrete Simulations on Multicore and GPU Architectures

et al. 2020

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In this paper we show how to efficiently implement parallel discrete simulations on multicore and GPU architectures through a real example of an application: a cellular automata model of laser dynamics. We describe the techniques employed to build and optimize the implementations using OpenMP and CUDA frameworks. We have evaluated the performance on two different hardware platforms that represent different target market segments: high-end platforms for scientific computing, using an Intel Xeon Platinum 8259CL server with 48 cores, and also an NVIDIA Tesla V100 GPU, both running on Amazon Web Server (AWS) Cloud; and on a consumer-oriented platform, using an Intel Core i9 9900k CPU and an NVIDIA GeForce GTX 1050 TI GPU. Performance results were compared and analyzed in detail. We show that excellent performance and scalability can be obtained in both platforms, and we extract some important issues that imply a performance degradation for them. We also found that current multicore CPUs with large core numbers can bring a performance very near to that of GPUs, and even identical in some cases.

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“…or terrains prone to natural hazards, such as floods, landslides, and volcanic eruptions, among others [25][26][27]. Urban growth is also important for Economists as they use growth forecasts as input features in different econometric analyses [28][29][30].With the premise that the population drives the urbanization, it is possible to use population growth models to estimate binary urban growth using a classifier, by assigning urban or non-urban labels to each piece of land. This classifier can be as simple as stating that a piece of land gets urbanized when the number of inhabitants increases above a certain threshold or it can consider a spatiotemporal neighborhood in the decision.…”

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confidence: 99%

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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...

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Exploring prospective urban growth trends under different economic outlooks and land-use planning scenarios: The case of Athens

Cited by 67 publications

References 48 publications

Peer Review #2 of "Spatial near future modeling of land use and land cover changes in the temperate forests of Mexico (v0.2)"

Peer Review #2 of "Spatial near future modeling of land use and land cover changes in the temperate forests of Mexico (v0.2)"

Developing Efficient Discrete Simulations on Multicore and GPU Architectures

Spatiotemporal Modeling of Urban Growth Using Machine Learning

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