Long-term changes in 3D urban form in four Spanish cities

Domingo, Darío; Vliet, Jasper van; Hersperger, Anna M.

doi:10.1016/j.landurbplan.2022.104624

Cited by 27 publications

(12 citation statements)

References 58 publications

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“…HISDAC-ES provides a valuable data source for urban analysts, regional planners, and policy makers, enabling or upscaling the quantitative measurement and interpretation of long-term urbanization and land development processes (e.g., Arribas-Bel et al, 2011;Alvarez-Palau et al, 2019;Sapena and Ruiz, 2019;Zornoza-Gallego 2022b;Domingo et al, 2023). Together with the sister product HISDAC-US, it will enable the comparative study of urban size, shape, and morphology over long time periods, across different continents and across historical as well as cultural settings.…”

Section: Discussionmentioning

confidence: 99%

“…We argue that cadastral data sources (i.e., parcel and building data including construction dates and other thematic information on building size, material, or function) allow to mitigate these two shortcomings and complement the traditional data sources (e.g., remote sensing data). Cadastral data are increasingly available as open data (von Meyer and Jones, 2013) and have been used in a variety of geographic, demographic, and economic studies (e.g., Tapp 2010;Leyk et al, 2014;Zoraghein et al, 2016;Nolte 2020;Domingo et al, 2023). In previous work, for example, (Uhl and Leyk, 2022a) integrated cadastral parcel data and building footprint data to generate multi-temporal building footprint data for some regions within the conterminous United States (CONUS), which constitutes a valuable data source for accuracy assessments of remote sensing derived built-up surface data Uhl and Leyk, 2022b;Uhl and Leyk, 2022c), and remote sensing based construction year estimation (Uhl and Leyk, 2017;Uhl and Leyk, 2020).…”

Section: Introductionmentioning

confidence: 99%

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HISDAC-ES: Historical Settlement Data Compilation for Spain (1900–2020)

Uhl

Royé²,

Burghardt

et al. 2023

Preprint

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Abstract. Multi-temporal measurements quantifying the changes to the Earth’s surface are critical for understanding many natural, anthropogenic, and social processes. Researchers typically use remotely sensed earth observation data to quantify and characterize such changes in land use and land cover (LULC). However, such data sources are limited in their availability prior to the 1980s. While an observational window of 40 to 50 years is sufficient to study most recent LULC changes, processes such as urbanization, land development, and the evolution of urban, and coupled nature-human systems often operate over longer time periods covering several decades or even centuries. Thus, to quantify and better understand such processes, alternative historical-geospatial data sources are required that extend farther back in time. However, such data are rare and processing is labor-intensive, often involving manual work. To overcome the resulting lack in quantitative knowledge of urban systems and the built environment prior to the 1980s, we leverage cadastral data with rich thematic property attribution, such as building usage and construction year. We scraped, harmonized, and processed over 12,000,000 building footprints including construction years to create a multi-faceted series of gridded surfaces, describing the evolution of human settlements in Spain from 1900 to 2020, at 100 m spatial and 5 years temporal resolution. These surfaces include measures of building density, built-up intensity, and built-up land use. We evaluated our data against a variety of data sources including remotely sensed human settlement data and land cover data, model-based historical land use depictions, as well as historical maps and historical aerial imagery, and find high levels of agreement. This new data product, the Historical Settlement Data Compilation for Spain (HISDAC-ES), is publicly available (https://doi.org/10.6084/m9.figshare.22009643; Uhl et al., 2023a) and represents a rich source for quantitative, long-term analyses of the built environment and related processes over large spatial and temporal extents and at fine resolutions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HISDAC-ES: Historical Settlement Data Compilation for Spain (1900–2020)

Uhl

Royé²,

Burghardt

et al. 2023

Preprint

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“…Human capital investment and economic development opportunities are highly unequal, resulting in high levels of development in high-tech industries but low income and educational opportunities in labor-intensive sectors. (5) The SSP5 scenario adopts a resource-and energy-intensive lifestyle, and each country's technology, economy, and population develop rapidly.…”

Section: Scenario Settingmentioning

confidence: 99%

“…At the same time, the high degree of homogeneity and heterogeneity of the LULC landscape dominated by human activity produces an irreversible negative impact on regional ecosystem services, such as the water cycle, biodiversity, and climate regulation [4]. Once a highly urbanized area forms, its spatial form and development pattern are difficult to change for a long time [5]. Therefore, the double effect of change in LULC due to population growth and the negative impact of this process on the population has attracted significant research attention to the study of how changes in LULC can contribute to global environmental sustainable development [6].…”

Section: Introductionmentioning

confidence: 99%

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Coupling Random Forest, Allometric Scaling, and Cellular Automata to Predict the Evolution of LULC under Various Shared Socioeconomic Pathways

2023

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Accurately estimating land-use demand is essential for urban models to predict the evolution of urban spatial morphology. Due to the uncertainties inherent in socioeconomic development, the accurate forecasting of urban land-use demand remains a daunting challenge. The present study proposes a modeling framework to determine the scaling relationship between the population and urban area and simulates the spatiotemporal dynamics of land use and land cover (LULC). An allometric scaling (AS) law and a Markov (MK) chain are used to predict variations in LULC. Random forest (RF) and cellular automata (CA) serve to calibrate the transition rules of change in LULC and realize its micro-spatial allocation (MKCARF-AS). Furthermore, this research uses several shared socioeconomic pathways (SSPs) as scenario storylines. The MKCARF-AS model is used to predict changes in LULC under various SSP scenarios in Jinjiang City, China, from 2020 to 2065. The results show that the figure of merit (FoM) and the urban FoM of the MKCARF-AS model improve by 3.72% and 4.06%, respectively, compared with the MKCAANN model during the 2005–2010 simulation period. For a 6.28% discrepancy between the predicted urban land-use demand and the actual urban land-use demand over the period 2005–2010, the urban FoM degrades by 21.42%. The growth of the permanent urban population and urban area in Jinjiang City follows an allometric scaling law with an exponent of 0.933 for the period 2005–2020, and the relative residual and R2 are 0.0076 and 0.9994, respectively. From 2020 to 2065, the urban land demand estimated by the Markov model is 19.4% greater than the urban area predicted under scenario SSP5. At the township scale, the different SSP scenarios produce significantly different spatial distributions of urban expansion rates. By coupling random forest and allometric scaling, the MKCARF-AS model substantially improves the simulation of urban land use.

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Characterizing urban densification in the city of Wuhan using time-series building information

Cao

Wang

et al. 2023

Landsc Ecol

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Long-term changes in 3D urban form in four Spanish cities

Cited by 27 publications

References 58 publications

HISDAC-ES: Historical Settlement Data Compilation for Spain (1900–2020)

HISDAC-ES: Historical Settlement Data Compilation for Spain (1900–2020)

Coupling Random Forest, Allometric Scaling, and Cellular Automata to Predict the Evolution of LULC under Various Shared Socioeconomic Pathways

Characterizing urban densification in the city of Wuhan using time-series building information

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