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Stipek C, Hauser T, Adams D, Epting J, Brelsford C, Moehl J, Dias P, Piburn J, Stewart R. Inferring building height from footprint morphology data. Sci Rep 2024; 14:18651. [PMID: 39134571 PMCID: PMC11319631 DOI: 10.1038/s41598-024-66467-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 07/01/2024] [Indexed: 08/15/2024] Open
Abstract
As cities continue to grow globally, characterizing the built environment is essential to understanding human populations, projecting energy usage, monitoring urban heat island impacts, preventing environmental degradation, and planning for urban development. Buildings are a key component of the built environment and there is currently a lack of data on building height at the global level. Current methodologies for developing building height models that utilize remote sensing are limited in scale due to the high cost of data acquisition. Other approaches that leverage 2D features are restricted based on the volume of ancillary data necessary to infer height. Here, we find, through a series of experiments covering 74.55 million buildings from the United States, France, and Germany, it is possible, with 95% accuracy, to infer building height within 3 m of the true height using footprint morphology data. Our results show that leveraging individual building footprints can lead to accurate building height predictions while not requiring ancillary data, thus making this method applicable wherever building footprints are available. The finding that it is possible to infer building height from footprint data alone provides researchers a new method to leverage in relation to various applications.
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Affiliation(s)
- Clinton Stipek
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
| | - Taylor Hauser
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Daniel Adams
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Justin Epting
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | | | - Jessica Moehl
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Philipe Dias
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Jesse Piburn
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Robert Stewart
- Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
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The Missing Millions in Maps: Exploring Causes of Uncertainties in Global Gridded Population Datasets. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2022. [DOI: 10.3390/ijgi11070403] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Gridded population datasets model the population at a relatively high spatial and temporal granularity by reallocating official population data from irregular administrative units to regular grids (e.g., 1 km grid cells). Such population data are vital for understanding human–environmental relationships and responding to many socioeconomic and environmental problems. We analyzed one very broadly used gridded population layer (GHS-POP) to assess its capacity to capture the distribution of population counts in several urban areas, spread across the major world regions. This analysis was performed to assess its suitability for global population modelling. We acquired the most detailed local population data available for several cities and compared this with the GHS-POP layer. Results showed diverse error rates and degrees depending on the geographic context. In general, cities in High-Income (HIC) and Upper-Middle-Income Countries (UMIC) had fewer model errors as compared to cities in Low- and Middle-Income Countries (LMIC). On a global average, 75% of all urban spaces were wrongly estimated. Generally, in central mixed or non-residential areas, the population was overestimated, while in high-density residential areas (e.g., informal areas and high-rise areas), the population was underestimated. Moreover, high model uncertainties were found in low-density or sparsely populated outskirts of cities. These geographic patterns of errors should be well understood when using population models as an input for urban growth models, as they introduce geographic biases.
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Schiavina M, Melchiorri M, Freire S, Florio P, Ehrlich D, Tommasi P, Pesaresi M, Kemper T. Land use efficiency of functional urban areas: Global pattern and evolution of development trajectories. HABITAT INTERNATIONAL 2022; 123:None. [PMID: 35685950 PMCID: PMC9097785 DOI: 10.1016/j.habitatint.2022.102543] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 01/21/2022] [Accepted: 03/03/2022] [Indexed: 06/15/2023]
Abstract
The application of last-generation spatial data modelling, integrating Earth Observation, population, economic and other spatially explicit data, enables insights into the sustainability of the global urbanisation processes with unprecedented detail, consistency, and international comparability. In this study, the land use efficiency indicator, as developed in the Sustainable Development Goals, is assessed globally for the first time at the level of Functional Urban Areas (FUAs). Each FUA includes the city and its commuting zone as inferred from statistical modelling of available spatial data. FUAs represent the economic area of influence of each urban centre. Hence, the analysis of land consumption within their boundary has significance in the fields of spatial planning and policy analyses as well as many other research areas. We utilize the boundaries of more than 9,000 FUAs to estimate the land use efficiency between 1990 and 2015, by using population and built-up area data extracted from the Global Human Settlement Layer. This analysis shows how, in the observed period, FUAs in low-income countries of the Global South evolved with rates of population growth surpassing the ones of land consumption. However, in almost all regions of the globe, more than half of the FUAs improved their land use efficiency in recent years (2000-2015) with respect to the previous decade (1990-2000). Our study concludes that the spatial expansion of urban areas within FUA boundaries is reducing compactness of settlements, and that settlements located within FUAs do not display higher land use efficiency than those outside FUAs.
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Key Words
- AAPDEA, Abstract Achieved Density in Expansion Area
- AOI, Area of interest
- BpC, Built-up area per capita
- EC, European Commission
- FAO, Food and Agriculture Organization
- FUA, Functional Urban Area
- GDP, Gross Domestic Product
- GHS-BUILT, GHSL built-up area spatial grid
- GHS-FUA, GHSL FUA layer
- GHS-POP, GHSL population spatial grid
- GHS-SMOD, GHSL settlement classification spatial grid
- GHSL
- GIS, Geospatial Information System
- GSARS, Global Strategy on Agricultural and Rural Statistics
- HIC, High-Income Countries
- LCR, Land Consumption Rate
- LCRPGR, Land Use Efficiency indicator
- LIC, Low Income Countries
- LMC, Lower-Middle Income Countries
- LN, Natural logarithm
- LUE, Land Use Efficiency
- Land consumption
- Metropolitan areas
- OECD, Organisation for Economic Cooperation and Development
- PGR, Population Growth Rate
- SDG 11.3.1
- UMC, Upper-Middle Income Countries
- UN, United Nations
- UNDESA, United Nations, Department of Economic and Social Affairs
- Urbanisation
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Affiliation(s)
- Marcello Schiavina
- European Commission, Joint Research Centre (JRC), via E. Fermi 2749, I-21027, Ispra, (VA), Italy
| | - Michele Melchiorri
- European Commission, Joint Research Centre (JRC), via E. Fermi 2749, I-21027, Ispra, (VA), Italy
| | - Sergio Freire
- European Commission, Joint Research Centre (JRC), via E. Fermi 2749, I-21027, Ispra, (VA), Italy
| | - Pietro Florio
- European Commission, Joint Research Centre (JRC), via E. Fermi 2749, I-21027, Ispra, (VA), Italy
| | - Daniele Ehrlich
- European Commission, Joint Research Centre (JRC), via E. Fermi 2749, I-21027, Ispra, (VA), Italy
| | - Pierpaolo Tommasi
- Fincons Group, Via Torri Bianche, 10, I-20871, Vimercate, (MB), Italy
| | - Martino Pesaresi
- European Commission, Joint Research Centre (JRC), via E. Fermi 2749, I-21027, Ispra, (VA), Italy
| | - Thomas Kemper
- European Commission, Joint Research Centre (JRC), via E. Fermi 2749, I-21027, Ispra, (VA), Italy
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Improvement of a Dasymetric Method for Implementing Sustainable Development Goal 11 Indicators at an Intra-Urban Scale. REMOTE SENSING 2021. [DOI: 10.3390/rs13142835] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Local and Regional Authorities require indicators at the intra-urban scale to design adequate policies to foster the achievement of the objectives of Sustainable Development Goal (SDG) 11. Updated high-resolution population density and settlement maps are the basic input products for such indicators and their sub-indicators. When provided at the intra-urban scale, these essential variables can facilitate the extraction of population flows, including both local and regular migrant components. This paper discusses a modification of the dasymetric method implemented in our previous work, aimed at improving the population density estimation. The novelties of our paper include the introduction of building height information and site-specific weight values for population density correction. Based on the proposed improvements, selected indicators/sub-indicators of four SDG 11 targets were updated or newly implemented. The output density map error values are provided in terms of the mean absolute error, root mean square error and mean absolute percentage indicators. The values obtained (i.e., 2.3 and 4.1 people, and 8.6%, respectively) were lower than those of the previous dasymetric method. The findings suggest that the new methodology can provide updated information about population fluxes and processes occurring over the period 2011–2020 in the study site—Bari city in southern Italy.
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