Hamidreza Zoraghein

Climate change is a potentially important driver of migration within the US but its impact on future population distribution remains under-explored. We project the impact of climate change over the 21st century on state-level population by integrating a detailed demographic model with a model of migration responses to changes in temperature and their consequences for regional economic conditions. We find that climate change is unlikely to fundamentally alter the large-scale distribution of people across the US, but it could have important consequences for some states. End-of-century population in states projected to experience either large, direct climate impacts or large inflows of climate migrants is altered by 10-30% relative to comparable projections where this influence is omitted. The dominant pattern of climate-induced migration is from the South to the Northeast and West with the pattern driven by the relative change in climate experienced across locations, distance, and the changes in regional economic conditions wrought by migration itself. The follow-on effects of migration on natural growth also influence state population change. Climate scenarios which produce the greatest degree of warming do not unambiguously produce the largest changes in population, because widespread warming results in relatively few safe havens, rendering migration an ineffective form of adaptation to climate change.

India is one of the world’s most flood-prone countries, with present-day risks likely to be exacerbated by climate change in the coming decades. The type of risk varies by location, with the lives, homes, and livelihoods of residents of India’s coastal megacities threatened by coastal floods and storm surges while village-dwellers residing in rural flood plains may additionally lose both crops and livestock. Schools and health facilities throughout the country are also at risk. This multi-scale study employs several datasets, from multiple domains, to generate high-resolution estimates of potential exposure to fluvial and coastal floods for (1) urban and rural populations, (2) health facilities, and (3) educational facilities. Our results, presented at the state level, suggest high exposure to fluvial flooding with about 184 million or more than 1:7 of India’s population at risk. This proportion is somewhat higher for rural dwellers (15.8%) compared with urban residents (14.2%). Urban residents, however, are much more likely to be affected by coastal floods, likely due to the high population densities of India’s coastal megacities. In total, around 19,218 (15%) of health and 34,519 (18%) of educational facilities are exposed to either coastal or fluvial flood risks. A spatially detailed, locally refined, comprehensive flood risk assessment such as this is critical to inform and target public policy and guide disaster risk reduction plans. By improving infrastructure, increasing awareness, and developing proactive, targeted, and inclusive flood plans, communities can build resilience.

Measurements of human interaction through proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19, which is a challenging task, especially at high spatial resolutions. In this study, we develop a Spatiotemporal autoregressive model to predict county-level new cases of COVID-19 in the coterminous US using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features. We capture human interactions through 1) Facebook- and 2) cell phone-derived measures of connectivity and human mobility, and use them in two separate models for predicting county-level new cases of COVID-19. We evaluate the model on 14 forecast dates between 2020/10/25 and 2021/01/24 over one- to four-week prediction horizons. Comparing our predictions with a Baseline model developed by the COVID-19 Forecast Hub indicates an average 6.46% improvement in prediction Mean Absolute Errors (MAE) over the two-week prediction horizon up to 20.22% improvement in the four-week prediction horizon, pointing to the strong predictive power of our model in the longer prediction horizons.

There is a growing demand for subnational population projections for informing potential demographic influences on many aspects of society and the environment at the scale at which interactions occur and actions are taken. Existing US subnational population projections have not fully accounted for regional variations of demographic rates and therefore under-estimate the uncertainties in and heterogeneity of population trends. We present a first set of population projections for US states that span a wide but plausible range of population outcomes driven by changing state-level demographic rates consistent with the widely used SSP scenario framework. The projections are carried out for all 50 states integrated through bilateral gross migration flows. They update the original national-level SSP population projections based on recently available data and introduce more plausible assumptions on long-term international migration. We project a national population ranging from about 250–650 million by 2100, somewhat lower than the SSP projections due mainly to updated base year data. Utah and other states in the Rocky Mountain region see the largest increases in population in proportional terms, while the Northeast and Great Lakes regions see the slowest growth or most decline, along with individual states like Alaska, California, Louisiana, and Mississippi. Aging occurs in all states and scenarios, but is most prominent in the Northeast, Florida, and in some cases states in the West and the Great Lakes region. The relative contributions of fertility, mortality, and migration to population change varies substantially across states.

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.

There is an increasing availability of geospatial data describing patterns of human settlement and population such as various global remote-sensing based built-up land layers, fine-grained census-based population estimates, and publicly available cadastral and building footprint data. This development constitutes new integrative modeling opportunities to characterize the continuum of urban, peri-urban, and rural settlements and populations. However, little research has been done regarding the agreement between such data products in measuring human presence which is measured by different proxy variables (i.e. presence of built-up structures derived from different remote sensors, census-derived population counts, or cadastral land parcels). In this work, we quantitatively evaluate and cross-compare the ability of such data to model the urban continuum, using a unique, integrated validation database of cadastral and building footprint data, U.S. census data, and three different versions of the Global Human Settlement Layer (GHSL) derived from remotely sensed data. We identify advantages and shortcomings of these data types across different geographic settings in the U.S., which will inform future data users on implications of data accuracy and suitability for a given application, even in data-poor regions of the world.

This research evaluates the performance of areal interpolation coupled with dasymetric refinement to estimate different demographic attributes, namely population sub-groups based on race, age structure and urban residence, within consistent census tract boundaries from 1990 to 2010 in Massachusetts. The creation of such consistent estimates facilitates the study of the nuanced micro-scale evolution of different aspects of population, which is impossible using temporally incompatible small-area census geographies from different points in time. Various unexplored ancillary variables, including the Global Human Settlement Layer (GHSL), the National Land-Cover Database (NLCD), parcels, building footprints and the proprietary ZTRAX® dataset are utilized for dasymetric refinement prior to areal interpolation to examine their effectiveness in improving the accuracy of multi-temporal population estimates. Different areal interpolation methods including Areal Weighting (AW), Target Density Weighting (TDW), Expectation Maximization (EM) and its data-extended approach are coupled with different dasymetric refinement scenarios based on these ancillary variables. The resulting consistent small area estimates of white and black subpopulations, people of age 18–65 and urban population show that dasymetrically refined areal interpolation is particularly effective when the analysis spans a longer time period (1990–2010 instead of 2000–2010) and the enumerated population is sufficiently large (e.g., counts of white vs. black). The results also demonstrate that current census-defined urban areas overestimate the spatial distribution of urban population and dasymetrically refined areal interpolation improves estimates of urban population. Refined TDW using building footprints or the ZTRAX® dataset outperforms all other methods. The implementation of areal interpolation enriched by dasymetric refinement represents a promising strategy to create more reliable multi-temporal and consistent estimates of different population subgroups and thus demographic compositions. This methodological foundation has the potential to advance micro-scale modeling of various subpopulations, particularly urban population to inform studies of urbanization and population change over time as well as future population projections.