![us population density map transparent 2019 us population density map transparent 2019](https://cdn.firespring.com/images/add8aba0-18e5-4f55-a118-cbb95f560b5f.png)
national census) to produce a finer resolution estimate of population distribution 5, 13. Once maps of these geographic features are in hand, dasymetric mapping is used to disaggregate coarse resolution population estimates (e.g. These approaches are based on the observation that geographic features indicating the built and natural environment are strong predictors of population distributions at fine spatial scales 12. Many studies have discussed approaches for generating and using gridded population data to overcome the pitfalls of aggregated national census data 5, 10, 11. Nevertheless, simple areal weighting within aggregation units is a practical approach to generating gridded population estimates, and it is commonly employed when coarse resolution population estimates over large spatial extents are the goal 5. It also suggests that locational accuracy of human populations is lower in rural communities than in more densely populated areas. This results in the modifiable areal unit problem, that makes census data inadequate to project population density and distribution particularly in rural communities 7, 9. For example, census units in urban areas are relatively small and populations are evenly distributed, whereas units in rural areas are larger and populations are irregularly distributed 8. Although this aggregation facilitates sampling design and ensures that responses remain anonymous, it can also lead to inferential problems when attempting to analyze SES at a fine spatial grain 6, 7. Typical census data is aggregated to ensure that each spatial unit has a minimum population size. Census Bureau aggregates population estimates to areal units such as counties or census blocks, which vary in size based on population density, but do not natively support high-resolution gridded population estimates. Census Bureau through the decennial census and American Community Survey, and many other countries have similar programs. In the United States (U.S.), population data is provided by the U.S. Population data only attains high accuracy when aggregated spatially. This data release answers the need for spatially resolved population distribution estimates that are integral to informing research, policy and management decisions across a range of SES challenges 5.
![us population density map transparent 2019 us population density map transparent 2019](https://vividmaps.com/wp-content/uploads/2020/08/us-median-population.jpg)
Whereas, environmental systems data is increasingly available at high levels of spatio-temporal resolution through advanced remote sensing technologies, the provision of population data at a similar resolution has been more challenging 4. Therefore, SES research requires a detailed understanding of where people live in relation to environmental factors. Socio-environmental systems (SES) are highly complex and key to the assessment of their dynamics, including the provisioning of ecosystem services and risks posed by environmental hazards and public health outcomes, is linking people to the environment with which they interact 3. There is growing awareness that solutions to pressing challenges in environmental science require characterizing interactions and feedbacks between social and natural systems 1, 2. The dataset, known as the U.G.L.I (updatable gridded lightweight impervious) population dataset, compares favorably against other population data sources, and provides a useful balance between resolution and complexity. The methodology is updatable using the most recent Census data and remote sensing-based observations of impervious surface area. The workflow dasymetrically distributes Census block level population estimates across all non-transportation impervious surfaces within each Census block. With this data release, we provide a 30-m resolution population estimate for the contiguous United States. However, timely acquisition of such data at sufficient spatial resolution can be problematic, especially in cases where the analysis area spans urban-rural gradients. In the United States, Census data is the most common source for information on population. Assessment of socio-environmental problems and the search for solutions often require intersecting geospatial data on environmental factors and human population densities.