Proportion of population living in 1, 5 and 10km buffer zones for Pacific Island Countries and Territories, determined using most recent Population and Housing Census. Number of people living in 1,5 and 10km buffer zones determined by apportioning population projections.

Find more Pacific data on PDH.stat.

Dwelling and population counts in elevation classes within 10Km, 5Km and 1Km of the coastline by ecozone, ecoprovince, ecoregion and ecodistrict for every fifth year starting with 2016.

Proportion of population in Pacific Island Countries and Territories (PICTs) living in Low Elevation Coastal Zones (LECZ) of 0-10 and 0-20 meters above sea level. LECZ were delineated using the bathub method overlaid on the Advanced Land Observing Satellite (ALOS) Global Digital Surface Model (AW3D30). Populations within the LECZs were estimated using the Pacific Community (SPC) Statistics for Development Division’s 100m2 population grids.

Find more Pacific data on PDH.stat.

The Low Elevation Coastal Zone (LECZ) Global Delta Urban-Rural Population and Land Area Estimates, Version 1 data set provides country-level estimates of urban, quasi-urban, rural, and total population (count), land area (square kilometers), and built-up areas in river delta- and non-delta contexts for 246 statistical areas (countries and other UN-recognized territories) for the years 1990, 2000, 2014 and 2015. The population estimates are disaggregated such that compounding risk factors including elevation, settlement patterns, and delta zones can be cross-examined. The Intergovernmental Panel on Climate Change (IPCC) recently concluded that without significant adaptation and mitigation action, risk to coastal commUnities will increase at least one order of magnitude by 2100, placing people, property, and environmental resources at greater risk. Greater-risk zones were then generated: 1) the global extent of two low-elevation zones contiguous to the coast, one bounded by an upper elevation of 10m (LECZ10), and one by an upper elevation of 5m (LECZ05); 2) the extent of the world's major deltas; 3) the distribution of people and built-up area around the world; 4) the extents of urban centers around the world. The data are layered spatially, along with political and land/water boundaries, allowing the densities and quantities of population and built-up area, as well as levels of urbanization (defined as the share of population living in "urban centers") to be estimated for any country or region, both inside and outside the LECZs and deltas, and at two points in time (1990 and 2015). In using such estimates of populations living in 5m and 10m LECZs and outside of LECZs, policymakers can make informed decisions based on perceived exposure and vulnerability to potential damages from sea level rise.

Population growth vs distance to coast, 1991-2014 and 2011-2014. Data provided by the Australian Bureau of Statistics.

Data used to produce Figure COA1; https://soe.environment.gov.au/theme/coasts/topic/2016/population-growth-and-urban-development-population-growth#coasts-figure-mean-annual-growth-population

Spatial datasets utilized to conduct the spatial analysis and additional information from the research article: Coastal proximity of populations in 22 Pacific Island Countries and Territories. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0223249 https://sdd.spc.int/mapping-coastal

Spatial datasets utilized to conduct the spatial analysis and additional information from the research article: Coastal proximity of populations in 22 Pacific Island Countries and Territories. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0223249 https://sdd.spc.int/mapping-coastal

Access this dataset from the Pacific Data Hub

Spatial datasets utilized to conduct the spatial analysis and additional information from the research article: Coastal proximity of populations in 22 Pacific Island Countries and Territories. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0223249 https://sdd.spc.int/mapping-coastal

Access this dataset from the Pacific Data Hub

Spatial datasets utilized to conduct the spatial analysis and additional information from the research article: Coastal proximity of populations in 22 Pacific Island Countries and Territories. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0223249 https://sdd.spc.int/mapping-coastal

Access this dataset from the Pacific Data Hub

Human population density in the coastal zone and potential impacts of climate change underscore a growing conflict between coastal development and an encroaching shoreline. Rising sea-levels and increased storminess threaten to accelerate coastal erosion, while growing demand for coastal real estate encourages more spending to hold back the sea in spite of the shrinking federal budget for beach nourishment. As climatic drivers and federal policies for beach nourishment change, the evolution of coastline mitigation and property values is uncertain. We develop an empirically grounded, stochastic dynamic model coupling coastal property markets and shoreline evolution, including beach nourishment, and show that a large share of coastal property value reflects capitalized erosion control. The model is parameterized for coastal properties and physical forcing in North Carolina, U.S.A. and we conduct sensitivity analyses using property values spanning a wide range of sandy coastlines along the U.S. East Coast. The model shows that a sudden removal of federal nourishment subsidies, as has been proposed, could trigger a dramatic downward adjustment in coastal real estate, analogous to the bursting of a bubble. We find that the policy-induced inflation of property value grows with increased erosion from sea level rise or increased storminess, but the effect of background erosion is larger due to human behavioral feedbacks. Our results suggest that if nourishment is not a long-run strategy to manage eroding coastlines, a gradual removal is more likely to smooth the transition to more climate-resilient coastal communities.

The following is an excerpt from the U.S. Fish and Wildlife Service species status assessment report for the coastal marten (Martes caurina), Version 2.0 (July 2018); refer to this report for additional details: 4.2 Current Range and Distribution (1980–current) All current (since 1980) verifiable marten detections were used to delineate extant population areas (EPAs) within the historical home range. The number of detections available to guide the delineation of the boundaries of the EPAs varied across the analysis area (Figure 4.2). In addition, sampling techniques varied across the range. Marten detections were buffered by 2 km and connected using a minimum convex polygon tool. Similar to methods used in the Humboldt Marten Conservation Strategy and Assessment, a 2 km buffer distance was used because most coastal marten survey and monitoring grids use a 2–km grid spacing, thus to feel confident about where animals do not occur, one would need to survey the next grid point without detections. If the total number of detections in an area was less than 5 or they were separated by greater than 5 km from other verifiable detections, the combined detections were not designated as an EPA due to the insufficient level of information to suggest a likely self–sustaining population (Slauson et al., In review, Slauson et al., In press). Because some detections did not meet this definition of a population they appear on Figure 4.3 as points but are not included in the population areas. Based on the distributions of current verifiable marten detections and adjacent suitable habitat, we identified four EPAs within coastal Oregon and northern coastal California (Figures 4.3): 1) Central Coastal Oregon Extant Population Area (CCO_EPA) 2) Southern Coastal Oregon Extant Population Area (SCO_EPA) 3) Oregon–California Border Extant Population Area (CAOR_EPA) 4) Northern Coastal California Extant Population Area (NCC_EPA) This dataset contains the four EPAs described in the SSA excerpt above.

🇨🇦 Canada English Dwelling and population counts in elevation classes within 10Km, 5Km and 1Km of the coastline by ecozone, ecoprovince, ecoregion and ecodistrict for every fifth year starting with 2016.

Percent of coastline densely populated, by marine ecoregion.

The map shows the proportion of coastline (from the shore to within five kilometers of the coast) in each ecoregion where there are more than five hundred persons per square kilometer. By focusing attention on a narrow coastal strip, we believe that we are capturing areas with the highest likelihood of significant losses of intertidal and adjacent habitats as a result of building, dredging, land reclamation, and other forms of coastal engineering. It does not, of course, measure areas of coastal development per se and does not capture areas where aquaculture, agriculture, or low-density tourism have impacts.

These data were derived by The Nature Conservancy, and were displayed in a map published in The Atlas of Global Conservation (Hoekstra et al., University of California Press, 2010). More information at http://nature.org/atlas.

Data derived from:

Center for International Earth Science Information Network (CIESIN), Columbia University; and Centro Internacional de Agricultura Tropical (CIAT). 2005. Gridded Population of the World Version 3 (GPWv3), Socioeconomic Data and Applications Center (SEDAC), Columbia University Palisades, New York. Available at http://sedac.ciesin.columbia.edu/gpw. Digital media.

For more about The Atlas of Global Conservation check out the web map (which includes links to download spatial data and view metadata) at http://maps.tnc.org/globalmaps.html. You can also read more detail about the Atlas at http://www.nature.org/science-in-action/leading-with-science/conservation-atlas.xml, or buy the book at http://www.ucpress.edu/book.php?isbn=9780520262560

The West Africa Coastal Vulnerability Mapping: Population Projections, 2030 and 2050 data set is based on an unreleased working version of the Gridded Population of the World (GPW), Version 4, year 2010 population count raster but at a coarser 5 arc-minute resolution. Bryan Jones of Baruch College produced country-level projections based on the Shared Socioeconomic Pathway 4 (SSP4). SSP4 reflects a divided world where cities that have relatively high standards of living, are attractive to internal and international migrants. In low income countries, rapidly growing rural populations live on shrinking areas of arable land due to both high population pressure and expansion of large-scale mechanized farming by international agricultural firms. This pressure induces large migration flow to the cities, contributing to fast urbanization, although urban areas do not provide many opportUnities for the poor and there is a massive expansion of slums and squatter settlements. This scenario may not be the most likely for the West Africa region, but it has internal coherence and is at least plausible.

The Low Elevation Coastal Zone (LECZ) Urban-Rural Population Estimates consists of country-level estimates of urban, rural and total population and land area country-wide and in the LECZ, if applicable. Additionally, the data set provides the number of urban extents, their population and land area that intersect the LECZ, by city-size population classifications of less than 100,000, 100,000 to 500,000, 500,000 to 1,000,000, 1,000,000 to 5,000,000, and more than 5,000,000. All estimates are based on GRUMP Alpha data products. The LECZ was generated using SRTM Digital Elevation Model data and includes all land area that is contiguous with the coast and 10 meters or less in elevation. All grids used for population, land area, urban mask, and LECZ were of 30 arc-second (~1 km ) resolution. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Institute for Environment and Development (IIED).

This data set contains sea turtle length and weight measurements, sex ratios, species composition, capture and release locations, tagging information, and information on biological samples collected for loggerhead, green, and Kemp's Ridley sea turtle populations in the coastal waters of North Carolina.

Sea turtles were double-tagged with Inconel Style 681 tags (National Band and Tag Company,...

Dataset replaced by: http://data.europa.eu/euodp/data/dataset/5k3SE4aWxZXI8c0GDUerg

The Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3 data set contains land areas with urban, quasi-urban, rural, and total populations (counts) within the LECZ for 234 countries and other recognized territories for the years 1990, 2000, and 2015. This data set updates initial estimates for the LECZ population by drawing on a newer collection of input data, and provides a range of estimates for at-risk population and land area. Constructing accurate estimates requires high-quality and methodologically consistent input data, and the LECZv3 evaluates multiple data sources for population totals, digital elevation model, and spatially-delimited urban classifications. Users can find the paper "Estimating Population and Urban Areas at Risk of Coastal Hazards, 1990-2015: How data choices matter" (MacManus, et al. 2021) in order to evaluate selected inputs for modeling Low Elevation Coastal Zones. According to the paper, the following are considered core data sets for the purposes of LECZv3 estimates: Multi-Error-Removed Improved-Terrain Digital Elevation Model (MERIT-DEM), Global Human Settlement (GHSL) Population Grid R2019 and Degree of Urbanization Settlement Model Grid R2019a v2, and the Gridded Population of the World, Version 4 (GPWv4), Revision 11. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) and the City University of New York (CUNY) Institute for Demographic Research (CIDR).

This dataset consists of long-term (100+ years) linear regression shoreline change rates for the North Shore region of Massachusetts. Rates of long-term shoreline change were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 5.0, an ArcGIS extension developed by the U.S. Geological Survey. The baseline is used as a reference line for the transects cast by the DSAS software. The transects intersect each shoreline at the measurement points, which are then used to calculate a linear regression rate for the Massachusetts Office of Coastal Zone Management Shoreline Change Project. Long-term linear regression statistics were calculated with all of the historical shorelines compiled for the Massachusetts Office of Coastal Zone Management Shoreline Change Project. Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In the Massachusetts Shoreline Change Mapping and Analysis Project 2013 Update, two oceanfront shorelines for Massachusetts (approximately 1,800 km) were (1) delineated using 2008/09 color aerial orthoimagery, and (2) extracted from topographic LIDAR datasets (2007) obtained from NOAA's Ocean Service, Coastal Services Center. The most recent 2018 data release includes rates that incorporate two new mean high water (MHW) shorelines for the Massachusetts coast extracted from lidar data collected between 2010 and 2014. The new shorelines were integrated with existing Massachusetts Office of Coastal Zone Management and USGS historical shoreline data in order to compute long- and short-term rates using the latest version of the Digital Shoreline Analysis System (DSAS)

Spatial datasets utilized to conduct the spatial analysis and additional information from the research article: Coastal proximity of populations in 22 Pacific Island Countries and Territories. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0223249 https://sdd.spc.int/mapping-coastal