skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Dasymetric population mapping based on US census data and 30-m gridded estimates of impervious surface
Abstract Assessment of socio-environmental problems and the search for solutions often require intersecting geospatial data on environmental factors and human population densities. In the United States, Census data is the most common source for information on population. However, timely acquisition of such data at sufficient spatial resolution can be problematic, especially in cases where the analysis area spans urban-rural gradients. With this data release, we provide a 30-m resolution population estimate for the contiguous United States. The workflow dasymetrically distributes Census block level population estimates across all non-transportation impervious surfaces within each Census block. The methodology is updatable using the most recent Census data and remote sensing-based observations of impervious surface area. 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.  more » « less
Award ID(s):
1724433 2054939
PAR ID:
10370189
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
Nature Publishing Group
Date Published:
Journal Name:
Scientific Data
Volume:
9
Issue:
1
ISSN:
2052-4463
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract Tile drainage is one of the dominant agricultural management practices in the United States and has greatly expanded since the late 1990s. It has proven effects on land surface water balance and quantity and quality of streamflow at the local scale. The effect of tile drainage on crop production, hydrology, and the environment on a regional scale is elusive due to lack of high-resolution, spatially-explicit tile drainage area information for the Contiguous United States (CONUS). We developed a 30-m resolution tile drainage map of the most-likely tile-drained area of the CONUS (AgTile-US) from county-level tile drainage census using a geospatial model that uses soil drainage information and topographic slope as inputs. Validation of AgTile-US with 16000 ground truth points indicated 86.03% accuracy at the CONUS-scale. Over the heavily tile-drained midwestern regions of the U.S., the accuracy ranges from 82.7% to 93.6%. These data can be used to study and model the hydrologic and water quality responses of tile drainage and to enhance streamflow forecasting in tile drainage dominant regions. 
    more » « less
  2. Abstract The City of Atlanta, Georgia, is a fast-growing urban area with substantial economic and racial inequalities, subject to the impacts of climate change and intensifying heat extremes. Here, we analyze the magnitude, distribution, and predictors of heat exposure across the City of Atlanta, within the boundaries of Fulton County. Additionally, we evaluate the extent to which identified heat exposure is addressed in Atlanta climate resilience governance. First, land surface temperature (LST) was mapped to identify the spatial patterns of heat exposure, and potential socioeconomic and biophysical predictors of heat exposure were assessed. Second, government and city planning documents and policies were analyzed to assess whether the identified heat exposure and risks are addressed in Atlanta climate resilience planning. The average LST of Atlanta’s 305 block groups ranges from 23.7 °C (low heat exposure) in vegetated areas to 31.5 °C (high heat exposure) in developed areas across 13 summer days used to evaluate the spatial patterns of heat exposure (June–August, 2013–2019). In contrast to nationwide patterns, census block groups with larger historically marginalized populations (predominantly Black, less education, lower income) outside of Atlanta’s urban core display weaker relationships with LST (slopes ≈ 0) and are among the cooler regions of the city. Climate governance analysis revealed that although there are few strategies for heat resilience in Atlanta (n= 12), the majority are focused on the city’s warmest region, the urban core, characterized by the city’s largest extent of impervious surface. These strategies prioritize protecting and expanding the city’s urban tree canopy, which has kept most of Atlanta’s marginalized communities under lower levels of outdoor heat exposure. Such a tree canopy can serve as an example of heat resilience for many cities across the United States and the globe. 
    more » « less
  3. Abstract Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the COVID-19 pandemic. In this data descriptor, we introduce a regularly-updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analysing millions of anonymous mobile phone users’ visits to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behaviour changes under the unprecedented public health crisis. This up-to-date O-D flow open data can support many other social sensing and transportation applications. 
    more » « less
  4. Abstract The quantification of urban impervious area has important implications for the design and management of urban water and environmental infrastructure systems. This study proposes a deep learning model to classify 15‐cm aerial imagery of urban landscapes, coupled with a vector‐oriented post‐classification processing algorithm for automatically retrieving canopy‐covered impervious surfaces. In a case study in Corpus Christi, TX, deep learning classification covered an area of approximately 312 km2(or 14.86 billion 0.15‐m pixels), and the post‐classification effort led to the retrieval of over 4 km2(or 0.18 billion pixels) of additional impervious area. The results also suggest the underestimation of urban impervious area by existing methods that cannot consider the canopy‐covered impervious surfaces. By improving the identification and quantification of various impervious surfaces at the city scale, this study could directly benefit a variety of environmental and infrastructure management practices and enhance the reliability and accuracy of processed‐based models for urban hydrology and water infrastructure. 
    more » « less
  5. Eighty residential lawns were recruited across the seven-county Minneapolis-St. Paul Metropolitan Area to take part in a survey of bumble bee species, vegetation and soil characteristics across lawns with differing management regimes. Lawns were selected to capture a range across the urban to rural gradient (based on percent of impervious surface) and household incomes and ethnicity at the Census block group level. Approximately half the lawns were characterized as "traditional" lawns, while half were considered "bee lawns" based on initial plant and bee community data. Three non-residential sites were included in this study: Katharine Ordway Natural History Study Area, The Minnesota Bell Museum of Natural History, the University of St. Thomas Stewardship Garden. The Bombus spp. composition was surveyed NON-LETHALLY at each site twice in the summer, with six endangered rusty-patched bumble bees (Bombus affinis) observed in 2023. Each property was also surveyed for plant species composition and soil moisture. Additional soil and site characteristics will accompany this dataset at a later date. Locations of the lawns are jittered randomly to protect the privacy of participating residents in the study. 
    more » « less