Population downscaling using high-resolution, temporally-rich U.S. property data

Wan, Heng; Yoon, Jim; Srikrishnan, Vivek; Daniel, Brent; Judi, David R.

doi:10.1080/15230406.2021.1991479

Cited by 12 publications

(4 citation statements)

References 29 publications

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“…The historical land use data products are published as part of the Historical Settlement Data Compilation for the U.S. (HISDAC-US) 23 , 24 and will be accessible through Harvard Dataverse ( https://dataverse.harvard.edu/dataverse/hisdacus ). HISDAC-US has been used in several recent studies on urban development and change 25 – 30 , landscape change analysis and modelling 31 , transportation infrastructure analysis 32 , population modelling 33 , as well as natural hazard risk assessment 34 , 35 .…”

Section: Background and Summarymentioning

confidence: 99%

Gridded land use data for the conterminous United States 1940–2015

2022

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Multiple aspects of our society are reflected in how we have transformed land through time. However, limited availability of historical-spatial data at fine granularity have hindered our ability to advance our understanding of the ways in which land was developed over the long-term. Using a proprietary, national housing and property database, which is a result of large-scale, industry-fuelled data harmonization efforts, we created publicly available sequences of gridded surfaces that describe built land use progression in the conterminous United States at fine spatial (i.e., 250 m × 250 m) and temporal resolution (i.e., 1 year - 5 years) between the years 1940 and 2015. There are six land use classes represented in the data product: agricultural, commercial, industrial, residential-owned, residential-income, and recreational facilities, as well as complimentary uncertainty layers informing the users about quantifiable components of data uncertainty. The datasets are part of the Historical Settlement Data Compilation for the U.S. (HISDAC-US) and enable the creation of new knowledge of long-term land use dynamics, opening novel avenues of inquiry across multiple fields of study.

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Section: Background and Summarymentioning

confidence: 99%

Gridded land use data for the conterminous United States 1940–2015

2022

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“…HISDAC-US consists of gridded datasets that measure built-up intensity and settlement age , building density , and building function (McShane et al, 2022), at 250m spatial resolution from 1810 to 2015, and from 1940 to 2015, respectively. These datasets have widely been used by researchers for various scientific studies (e.g., Millhouser 2019;Balch et al, 2020;Mietkiewicz et al, 2020;McDonald et al, 2021;Ferrara et al, 2021;Boeing 2021;Li et al, 2021;Dornbierer et al, 2021;Millard-Ball 2022;Salazar-Miranda 2022;Wan et al, 2022).…”

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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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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“…Besides, Steinnocher et al [17] compared top-down and bottom-up methods for two cities with different data availability levels and one evaluation metric. On the other hand, Wan et al [46] examined the performance of alternative sources of ancillary data, including imperviousness, land cover, road networks and night-time lights, on the accuracy of the dasymetric method. Only two studies performed a multi-fold validation, evidencing a high influence in the measured accuracies [11,21].…”

Section: Introductionmentioning

confidence: 99%

Empiric recommendations for population disaggregation under different data scenarios

et al. 2022

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High-resolution population mapping is of high relevance for developing and implementing tailored actions in several fields: From decision making in crisis management to urban planning. Earth Observation has considerably contributed to the development of methods for disaggregating population figures with higher resolution data into fine-grained population maps. However, which method is most suitable on the basis of the available data, and how the spatial units and accuracy metrics affect the validation process is not fully known. We aim to provide recommendations to researches that attempt to produce high-resolution population maps using remote sensing and geospatial information in heterogeneous urban landscapes. For this purpose, we performed a comprehensive experimental research on population disaggregation methods with thirty-six different scenarios. We combined five different top-down methods (from basic to complex, i.e., binary and categorical dasymetric, statistical, and binary and categorical hybrid approaches) on different subsets of data with diverse resolutions and degrees of availability (poor, average and rich). Then, the resulting population maps were systematically validated with a two-fold approach using six accuracy metrics. We found that when only using remotely sensed data the combination of statistical and dasymetric methods provide better results, while highly-resolved data require simpler methods. Besides, the use of at least three relative accuracy metrics is highly encouraged since the validation depends on level and method. We also analysed the behaviour of relative errors and how they are affected by the heterogeneity of the urban landscape. We hope that our recommendations save additional efforts and time in future population mapping.

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Population downscaling using high-resolution, temporally-rich U.S. property data

Cited by 12 publications

References 29 publications

Gridded land use data for the conterminous United States 1940–2015

Gridded land use data for the conterminous United States 1940–2015

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

Empiric recommendations for population disaggregation under different data scenarios

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