Empirically based spatial projections of US population age structure consistent with the shared socioeconomic pathways

Striessnig, Erich; Gao, Jing; O’Neill, Brian C.; Jiang, Leiwen

doi:10.1088/1748-9326/ab4a3a

Cited by 15 publications

(15 citation statements)

References 29 publications

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“…Our future work will complete the SSP projections for SSP1 and SSP4 to generate a series of state-level population distributions consistent with all five SSPs. We also plan to incorporate age structure into our spatial projections [13]. These developments, combined with integration with alternative climate projections, will enable more effective analysis of questions about the exposure and vulnerability of the U.S. population to environmental hazards such as sea level rise and heat waves in the future.…”

Section: Discussionmentioning

confidence: 99%

U.S. State-level Projections of the Spatial Distribution of Population Consistent with Shared Socioeconomic Pathways

Zoraghein

O’Neill

2020

Sustainability

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Spatial population distribution is an important determinant of both drivers of regional environmental change and exposure and vulnerability to it. Spatial projections of population must account for changes in aggregate population, urbanization, and spatial patterns of development, while accounting for uncertainty in each. While an increasing number of projections exist, those carried out at relatively high resolution that account for subnational heterogeneity and can be tailored to represent alternative scenarios of future development are rare. We draw on state-level population projections for the US and a gravity-style spatial downscaling model to design and produce new spatial projections for the U.S. at 1 km resolution consistent with a subset of the Shared Socioeconomic Pathways (SSPs), scenarios of societal change widely used in integrated analyses of global and regional change. We find that the projections successfully capture intended alternative development patterns described in the SSPs, from sprawl to concentrated development and mixed outcomes. Our projected spatial patterns differ more strongly across scenarios than in existing projections, capturing a wider range of the relevant uncertainty introduced by the distinct scenarios. These projections provide an improved basis for integrated environmental analysis that considers uncertainty in demographic outcomes.

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Section: Discussionmentioning

confidence: 99%

U.S. State-level Projections of the Spatial Distribution of Population Consistent with Shared Socioeconomic Pathways

Zoraghein

O’Neill

2020

Sustainability

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“…The methods used included two decision tree-based ensemble methods, XGBoost (Georganos et al, 2018 ) and random forest (Belgiu & Drăguţ, 2016 ), and a neural network algorithm as implemented in Hu et al ( 2019 ). Striessnig et al ( 2019 ) prepared county population forecasts by broad age group for the USA consistent with various Shared Socioeconomic Pathway scenarios for the period 2000–2100. Instead of relying on the cohort-component model, they used regression trees to forecast the share of a county’s population in each broad age group based on past demographic characteristics.…”

Section: Small Area Population Forecasting Methods 2001–2020mentioning

confidence: 99%

Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs

Wilson

Grossman

Alexander

et al. 2021

Popul Res Policy Rev

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Small area population forecasts are widely used by government and business for a variety of planning, research and policy purposes, and often influence major investment decisions. Yet, the toolbox of small area population forecasting methods and techniques is modest relative to that for national and large subnational regional forecasting. In this paper, we assess the current state of small area population forecasting, and suggest areas for further research. The paper provides a review of the literature on small area population forecasting methods published over the period 2001–2020. The key themes covered by the review are extrapolative and comparative methods, simplified cohort-component methods, model averaging and combining, incorporating socioeconomic variables and spatial relationships, ‘downscaling’ and disaggregation approaches, linking population with housing, estimating and projecting small area component input data, microsimulation, machine learning, and forecast uncertainty. Several avenues for further research are then suggested, including more work on model averaging and combining, developing new forecasting methods for situations which current models cannot handle, quantifying uncertainty, exploring methodologies such as machine learning and spatial statistics, creating user-friendly tools for practitioners, and understanding more about how forecasts are used. Supplementary Information The online version contains supplementary material available at 10.1007/s11113-021-09671-6.

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“…While most of the existing spatial population projections are the result of downscaling the projected total number of people (often by rural and urban residence) to grid cells (as described in the section above), a recent work by Striessnig et al (2019) uses complete age structure (somewhat like the Hamilton-Perry method) with a regression-tree model-that is, it uses a predictive algorithm in machine learning to explain how a target variable's value can be predicted based on other values-to generate county-level projections of population by age groups for all counties consistent with the SSPs. Using an empirically-based spatial model, it reveals wide variations in the spatial pattern of county-level age structures across SSPs.…”

Section: Projecting Population In the Context Of The Sspsmentioning

confidence: 99%

Frameworks to envision equitable urban futures in a changing climate: A multi-level, multidisciplinary case study of New York City

Balk

Daniela²,

Jiang³

et al. 2022

Front. Built Environ.

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Cities are at the forefront of climate change action and planning for futures that are concomitantly more resilient and equitable, making local goals imperative for global sustainability. Under the multiple challenges of changing climatic, ecological and socio-economic conditions, cities need the means to meet these goals. We know cities are and will continue to be points of concentrated and diverse populations, socioeconomic vulnerability, amplified exposure, transformed ecosystems and are responsible for the bulk of the world’s greenhouse gas emissions. Yet, much is also unknown and intrinsically uncertain about urban futures: there is a range of potential plausible futures which have differing implications for both potential mitigation and adaptation actions. To better assess these plausible futures, the “global change” research community developed a framework including scenarios that are applicable for global and regional policy, entitled the Shared Socio-economic Pathways (SSPs) and scenarios exploring future emissions that will drive climate change, entitled Representative Concentration Pathways (RCPs). Importantly, this global scale framework does not include specific city-level perspectives or data at the spatial scales necessary to address questions of local relevance. While the SSPs address many of the key population and socioeconomic drivers of climate change, they do not address important concerns that are particularly relevant to cities, such as racial justice, ecosystem change or migration. Nevertheless, city-level impacts will evolve, in part, as a function of the global scale change characterized by the SSPs, and in part based on demographic and social processes already underway. As such, applying a modification of this framework to cities has the potential to help limit local climate impacts, and create a more resilient, equitable city. To address these needs and respond to city and regional stakeholders, we propose a framework for science-based narratives and quantitative projections for cities and metropolitan areas, such as Greater New York City. In this paper, we review a wide-range of existing approaches to generate estimates of future populations and identify their vulnerabilities to climate-change hazards, ranging from subnational population projections or the spatially-explicit allocation of populations linked to SSPs for the US and selected cities, city-specific population forecasting without climate considerations, and participatory approaches to future scenario development and fine-scale, within-city land use change models. By showcasing the strengths and limitations of various approaches and modeling efforts, their spatial and temporal scales, and thematic breadth, we propose a novel framework that leverages state-of-the art quantitative approaches and couples it with stakeholder engagement that can help cities plan equitably under uncertainty.

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Empirically based spatial projections of US population age structure consistent with the shared socioeconomic pathways

Cited by 15 publications

References 29 publications

U.S. State-level Projections of the Spatial Distribution of Population Consistent with Shared Socioeconomic Pathways

U.S. State-level Projections of the Spatial Distribution of Population Consistent with Shared Socioeconomic Pathways

Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs

Frameworks to envision equitable urban futures in a changing climate: A multi-level, multidisciplinary case study of New York City

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