![]() 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. It does not store any personal data.Assessment of socio-environmental problems and the search for solutions often require intersecting geospatial data on environmental factors and human population densities. The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. The cookie is used to store the user consent for the cookies in the category "Performance". This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary". ![]() The cookie is used to store the user consent for the cookies in the category "Other. The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional". The cookie is used to store the user consent for the cookies in the category "Analytics". These cookies ensure basic functionalities and security features of the website, anonymously. Necessary cookies are absolutely essential for the website to function properly. We hope you take a look and use the Explorer! This particular dataset, the High Resolution Population Density dataset is widely used in disaster response and humanitarian aid. It’s been a pleasure working with these teams on this project after our previous collaboration on COVID-19 Mobility Dashboard, using Data for Good’s country-level mobility data in conjunction with Direct Relief and the Harvard T.H. We see this as a critical tool for getting detailed population and demographic statistics in whatever region you need to identify. This provides a way to sift through this incredibly detailed dataset at a scale without having to download the data first. We also developed a way to draw custom boundaries from this dataset. This data at the country scale can be immensely powerful, but we wanted to provide smaller scale regions to make the data easier to work with. The preexisting data portals offer the data at the country scale via Humanitarian Data Exchange or AWS. This map explorer highlights the High Resolution Settlement Layer Dataset at a variety of scales, allowing you to explore the dataset like never before. The explorer is live at and we invite you to explore and play with this phenomenal dataset. We are thrilled to share a project we’ve been collaborating with the Social Impact Partnerships and Data for Good teams at Meta on to create a new interactive map tool for their High Resolution Settlement Layer (HRSL) Dataset.
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