A spatial population downscaling model for integrated human-environment analysis in the United States

NPCC4: Concepts and tools for envisioning New York City's futures

This chapter of the New York City Panel on Climate Change 4 (NPCC4) report discusses the many intersecting social, ecological, and technological-infrastructure dimensions of New York City (NYC) and their interactions that are critical to address in order to transition to and secure a climate-adapted future for all New Yorkers. The authors provide an assessment of current approaches to "future visioning and scenarios" across community and city-level initiatives and examine diverse dimensions of the NYC urban system to reduce risk and vulnerability and enable a future-adapted NYC. Methods for the integration of community and stakeholder ideas about what would make NYC thrive with scientific and technical information on the possibilities presented by different policies and actions are discussed. This chapter synthesizes the state of knowledge on how different communities of scholarship or practice envision futures and provides brief descriptions of the social-demographic and housing, transportation, energy, nature-based, and health futures and many other subsystems of the complex system of NYC that will all interact to determine NYC futures.

Spatially explicit downscaling and projection of population in mainland China

Spatially explicit population data is critical to investigating human-nature interactions, identifying at-risk populations, and informing sustainable management and policy decisions. Most long-term global population data have three main limitations: 1) they were estimated with simple scaling or trend extrapolation methods which are not able to capture detailed population variation spatially and temporally; 2) the rate of urbanization and the spatial patterns of settlement changes were not fully considered; and 3) the spatial resolution is generally coarse. To address these limitations, we proposed a framework for large-scale spatially explicit downscaling of populations from census data and projecting future population distributions under different Shared Socio-economic Pathways (SSP) scenarios with the consideration of distinctive changes in urban extent. We downscaled urban and rural population separately and considered urban spatial sprawl in downscaling and projection. Treating urban and rural populations as distinct but interconnected entities, we constructed a random forest model to downscale historical populations and designed a gravity-based population potential model to project future population changes at the grid level. This work built a new capacity for understanding spatially explicit demographic change with a combination of temporal, spatial, and SSP scenario dimensions, paving the way for cross-disciplinary studies on long-term socio-environmental interactions.

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

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.