Knowing where people live is a fundamental component of many decision making processes such as urban development, infectious disease containment, evacuation planning, risk management, conservation planning, and more. While bottom-up, survey driven censuses can provide a comprehensive view into the population landscape of a country, they are expensive to realize, are infrequently performed, and only provide population counts over broad areas. Population disaggregation techniques and population projection methods individually address these shortcomings, but also have shortcomings of their own. To jointly answer the questions of "where do people live" and "how many people live there," we propose a deep learning model for creating high-resolution population estimations from satellite imagery. Specifically, we train convolutional neural networks to predict population in the USA at a @math resolution grid from 1-year composite Landsat imagery. We validate these models in two ways: quantitatively, by comparing our model's grid cell estimates aggregated at a county-level to several US Census county-level population projections, and qualitatively, by directly interpreting the model's predictions in terms of the satellite image inputs. We find that aggregating our model's estimates gives comparable results to the Census county-level population projections and that the predictions made by our model can be directly interpreted, which give it advantages over traditional population disaggregation methods. In general, our model is an example of how machine learning techniques can be an effective tool for extracting information from inherently unstructured, remotely sensed data to provide effective solutions to social problems.
On average, county population can be reliably extrapolated over short time horizons with simple linear models, however if some counties experience disproportionally higher or lower growth rates, more complicated models are needed @cite_2 . The US Census has led research into population and demographic projections, and uses a variety of different population and demographic projection methods to create sub-national projections broken down by age, sex, and race @cite_22 @cite_5 . Census postcensal projections, projections done in between census years, are created with a method known as the ratio-correlation method @cite_21 @cite_23 @cite_5 . This method uses the current year's estimated population, number of live births, registered vehicles, public school enrollment, registered voters, deaths, and other information to determine the estimated population change at the next census date. More recently, the American Community Survey has been used as annual supplemental surveys to update the demographics profiles of a variety of sub-national areas in between census years @cite_25 @cite_35 .
@cite_35 @cite_22 @cite_21 @cite_23 @cite_2 @cite_5 @cite_25
1989753770 776178606 997247147 159124996
Abstract This article deals with the forecast accuracy and bias of population projections for 2,971 counties in the United States. It uses three different projection techniques and data from 1950, 1960, 1970, and 1980 to make two sets of 10-year projections and one set of 20-year projections. These projections are compared with census counts to determine forecast errors. The size, direction, and distribution of forecast errors are analyzed by size of place, rate of growth, and length of projection horizon. A number of consistent patterns are noted, and an extension of the empirical results to the production of confidence intervals for population projections is considered. THIS study describes and tests a proposed method of estimating the postcensal population of counties and similar geographic areas. The suggested technique (the "ratiocorrelation method") makes use of a multiple regression equation relating changes in population distribution during the most recent intercensal period (currently 1940-1950) to accompanying changes in the geographic distribution of live births, registered vehicles, and public school enrollment. Postcensal data are then substituted in the equation to obtain estimated population. Both this method and three alternate techniques were tested by preparing 1950 population estimates for the thirty-nine counties of Washington State, then comparing the estimates to 1950 U. S. Census totals. This paper proposes a new method for short-term county population projections. It is based on a modification of the ratio-correlation method of population estimation. The modified ratio-correlation method can produce projections with a high potential for accuracy without requiring substantial data and intensive intellectual labor inputs. Tests of accuracy are examined for the modified ratio-correlation method and two currently available alternatives using data from Washington state. The tests suggest that the new method performs well. (EXCERPT) The U. S. Bureau of the Census produces population estimates for the nation, states, counties, and places (cities, towns, and townships) as part of its program to quantify changes in population size and distribution since the last census. These estimates provide updates to the population counts by demographic and geographic characteristics from the last census. They also indicate the pace of population change since the last census and the relative influence of the components of population change. While the national estimates can be produced by a careful accounting system that adds annual births, deaths, and international migration to the previous year’s population, subnational estimates require development of methods for dealing with the largely unmeasured component effects of internal migration. Many of these methods represent the type of small domain estimates that constitute the subject of this report.