In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

The poly shapefile is based on Census data that compares Census 2000 population levels to estimates in July, 2006 for all the counties that are designated by NOAA (see the URL below) as "Coastal Region Counties". There are 645 jurisdictions, whose combined population in was over 146.6 million (Year 2000) and estimated to be 154.3 million in 2006. In both years, the largest and the smallest counties were Los Angeles, CA and Kenedy, TX. Flagler, FL registered the largest percent change (66.7%) and St. Bernard, LA registered the highest percent decrease (-76.9%). The later is direct result of the 2005 Katrina disaster. http://www.census.gov/geo/landview/lv6help/coastal_cty.pdf

This dataset includes shorelines from 161 years ranging from 1847 to 2008 within the North Shore B coastal region from the Annisquam River in Gloucester to the west side of Deer Island in Boston Harbor. Shorelines were compiled from T-sheets and air-photos obtained from the National Oceanic and Atmospheric Administration (NOAA) and the Massachusetts Office of Coastal Zone Management (MA CZM), and lidar obtained from the US Geological Survey (USGS). Historical shoreline positions serve as easily understood features that can be used to describe the movement of beaches through time. These data are used to calculate rates of shoreline change for the MA CZM Shoreline Change Project. Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3. DSAS uses a measurement baseline method to calculate rate-of-change statistics. Transects are cast from the reference baseline to intersect each shoreline, establishing measurement points used to calculate shoreline change rates. For publication purposes, the shoreline data for Massachusetts were organized by region in order match the extent of previously published uncertainty files used in shoreline change calculations. 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. The Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) in cooperation with the Massachusetts Office of Coastal Zone Management, has compiled reliable historical shoreline data along open-facing sections of the Massachusetts coast under 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 new shorelines were integrated with existing Massachusetts Office of Coastal Zone Management (MA CZM) 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)..

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of March 2025. The schema changed in February 2025 - please see below. We will post a roadmap of upcoming changes, but service URLs and schema are now stable. For deployment status of new services in February 2025, see https://gis.data.ca.gov/pages/city-and-county-boundary-data-status. Additional roadmap and status links at the bottom of this metadata.This dataset is continuously updated as the source data from CDTFA is updated, as often as many times a month. If you require unchanging point-in-time data, export a copy for your own use rather than using the service directly in your applications.PurposeCity boundaries along with third party identifiers used to join in external data. Boundaries are from the California Department of Tax and Fee Administration (CDTFA). These boundaries are the best available statewide data source in that CDTFA receives changes in incorporation and boundary lines from the Board of Equalization, who receives them from local jurisdictions for tax purposes. Boundary accuracy is not guaranteed, and though CDTFA works to align boundaries based on historical records and local changes, errors will exist. If you require a legal assessment of boundary location, contact a licensed surveyor.This dataset joins in multiple attributes and identifiers from the US Census Bureau and Board on Geographic Names to facilitate adding additional third party data sources. In addition, we attach attributes of our own to ease and reduce common processing needs and questions. Finally, coastal buffers are separated into separate polygons, leaving the land-based portions of jurisdictions and coastal buffers in adjacent polygons. This feature layer is for public use.Related LayersThis dataset is part of a grouping of many datasets:Cities: Only the city boundaries and attributes, without any unincorporated areasWith Coastal BuffersWithout Coastal Buffers (this dataset)Counties: Full county boundaries and attributes, including all cities within as a single polygonWith Coastal BuffersWithout Coastal BuffersCities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.With Coastal BuffersWithout Coastal BuffersCity and County AbbreviationsUnincorporated Areas (Coming Soon)Census Designated PlacesCartographic CoastlinePolygonLine source (Coming Soon)Working with Coastal BuffersThe dataset you are currently viewing includes the coastal buffers for cities and counties that have them in the source data from CDTFA. In the versions where they are included, they remain as a second polygon on cities or counties that have them, with all the same identifiers, and a value in the COASTAL field indicating if it"s an ocean or a bay buffer. If you wish to have a single polygon per jurisdiction that includes the coastal buffers, you can run a Dissolve on the version that has the coastal buffers on all the fields except OFFSHORE and AREA_SQMI to get a version with the correct identifiers.Point of ContactCalifornia Department of Technology, Office of Digital Services, odsdataservices@state.ca.govField and Abbreviation DefinitionsCDTFA_CITY: CDTFA incorporated city nameCDTFA_COUNTY: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.CDTFA_COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system. The boundary data originate with CDTFA's teams managing tax rate information, so this field is preserved and flows into this dataset.CENSUS_GEOID: numeric geographic identifiers from the US Census BureauCENSUS_PLACE_TYPE: City, County, or Town, stripped off the census name for identification purpose.GNIS_PLACE_NAME: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information SystemGNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.CDT_CITY_ABBR: Abbreviations of incorporated area names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 4 characters. Not present in the county-specific layers.CDT_COUNTY_ABBR: Abbreviations of county names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 3 characters.CDT_NAME_SHORT: The name of the jurisdiction (city or county) with the word "City" or "County" stripped off the end. Some changes may come to how we process this value to make it more consistent.AREA_SQMI: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.OFFSHORE: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".PRIMARY_DOMAIN: Currently empty/null for all records. Placeholder field for official URL of the city or countyCENSUS_POPULATION: Currently null for all records. In the future, it will include the most recent US Census population estimate for the jurisdiction.GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.Boundary AccuracyCounty boundaries were originally derived from a 1:24,000 accuracy dataset, with improvements made in some places to boundary alignments based on research into historical records and boundary changes as CDTFA learns of them. City boundary data are derived from pre-GIS tax maps, digitized at BOE and CDTFA, with adjustments made directly in GIS for new annexations, detachments, and corrections. Boundary accuracy within the dataset varies. While CDTFA strives to correctly include or exclude parcels from jurisdictions for accurate tax assessment, this dataset does not guarantee that a parcel is placed in the correct jurisdiction. When a parcel is in the correct jurisdiction, this dataset cannot guarantee accurate placement of boundary lines within or between parcels or rights of way. This dataset also provides no information on parcel boundaries. For exact jurisdictional or parcel boundary locations, please consult the county assessor's office and a licensed surveyor.CDTFA's data is used as the best available source because BOE and CDTFA receive information about changes in jurisdictions which otherwise need to be collected independently by an agency or company to compile into usable map boundaries. CDTFA maintains the best available statewide boundary information.CDTFA's source data notes the following about accuracy:City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. Boundary ProcessingThese data make a structural change from the source data. While the full boundaries provided by CDTFA include coastal buffers of varying sizes, many users need boundaries to end at the shoreline of the ocean or a bay. As a result, after examining existing city and county boundary layers, these datasets provide a coastline cut generally along the ocean facing coastline. For county boundaries in northern California, the cut runs near the Golden Gate Bridge, while for cities, we cut along the bay shoreline and into the edge of the Delta at the boundaries of Solano, Contra Costa, and Sacramento counties.In the services linked above, the versions that include the coastal buffers contain them as a second (or third) polygon for the city or county, with the value in the COASTAL field set to whether it"s a bay or ocean polygon. These can be processed back into a single polygon by dissolving on all the fields you wish to keep, since the attributes, other than the COASTAL field and geometry attributes (like areas) remain the same between the polygons for this purpose.SliversIn cases where a city or county"s boundary ends near a coastline, our coastline data may cross back and forth many times while roughly paralleling the jurisdiction"s boundary, resulting in many polygon slivers. We post-process the data to remove these slivers using a city/county boundary priority algorithm. That is, when the data run parallel to each other, we discard the coastline cut and keep the CDTFA-provided boundary, even if it extends into the ocean a small amount. This processing supports consistent boundaries for Fort Bragg, Point Arena, San Francisco, Pacifica, Half Moon Bay, and Capitola, in addition to others. More information on this algorithm will

The average level of the ocean has been rising since we started measuring and recording this data. According to the National Aeronautics and Space Agency (NASA), since 1900 the global mean sea level has risen more than 200 millimeters (nearly 8 inches) and nearly half of that increase has occurred since 1993 in a concerning change in rate of rise.Sea level rise is one of the many effects of global warming. Scientists attribute sea level rise to two things, melting ice and increased ocean water temperatures. Increasing air temperatures, particularly in the polar regions, has encouraged the melting of land-based ice reserves such as glaciers, ice sheets, and permafrost. Historically, warm season ice melt was balanced by replenishment during the cold season but warming temperatures have created conditions where melting exceeds the buildup of ice. This water flows through rivers and streams to the ocean in quantities sufficient to contribute to sea level rise.Oceans are also massive heat sinks. They pull large quantities of atmospheric heat and greenhouse gases such as carbon dioxide and store it in the ocean. The sea changes temperature much more slowly than the air and over time ocean temperatures have continued to build. As the ocean water warms it expands causing the sea levels to rise.Sea levels are not rising equally across Earth. Some areas are already experiencing significant impacts due to the rising water levels while others have seen minimal changes. This is due to a variety of reasons. First, despite how it is typically illustrated Earth is not perfectly round so the height of the ocean at any given point varies. This can be due to the Earth’s rotation, ocean currents, or prevailing wind speed and direction.Experts consider sea level rise and urgent climatic threat. Many low-lying places such as islands and coastal areas are already experiencing high waters. Higher waters also make storms such as hurricanes more dangerous due to higher storm surges and flooding. As coastlines could lose key infrastructure, land will become uninhabitable, and many people could lose their livelihoods. It is estimated 10 percent of the world’s population could be impacted as the waters rise. Many of the approximately 770 million people could be forced to migrate to higher ground, or in the case of island countries, such as Kiribati, to new countries once theirs sinks below the sea.This map was created with data from the National Oceanic and Atmospheric Administration (NOAA), NASA, and the United States Geological Survey. Experts used an elevation data and the NOAA model Scenarios of Future Mean Seal Level to illustrate the scale of potential coastal flooding. The mapmaker chose to remove levees from the data, so the areas flooded include places, particularly in the states of Texas and Louisiana, that are presently protected by this infrastructure. It is important to note that these are possible outcomes. This model does not include possible erosion, subsidence, or construction that may occur between 2022 when this data was created and 2030, 2050, or 2090 respectively. While models are powerful tools it is difficult to calculate every aspect that shapes our environment.Learn more about how coastal communities are impacted by sea level rise with this StoryMap by NOAA’s Office for Coastal Management, The King Tides Project: Snap the shore, See the Future.

Note: The schema changed in February 2025 - please see below. We will post a roadmap of upcoming changes, but service URLs and schema are now stable. For deployment status of new services beginning in February 2025, see https://gis.data.ca.gov/pages/city-and-county-boundary-data-status. Additional roadmap and status links at the bottom of this metadata.This dataset is regularly updated as the source data from CDTFA is updated, as often as many times a month. If you require unchanging point-in-time data, export a copy for your own use rather than using the service directly in your applications. PurposeCounty boundaries along with third party identifiers used to join in external data. Boundaries are from the California Department of Tax and Fee Administration (CDTFA). These boundaries are the best available statewide data source in that CDTFA receives changes in incorporation and boundary lines from the Board of Equalization, who receives them from local jurisdictions for tax purposes. Boundary accuracy is not guaranteed, and though CDTFA works to align boundaries based on historical records and local changes, errors will exist. If you require a legal assessment of boundary location, contact a licensed surveyor.This dataset joins in multiple attributes and identifiers from the US Census Bureau and Board on Geographic Names to facilitate adding additional third party data sources. In addition, we attach attributes of our own to ease and reduce common processing needs and questions. Finally, coastal buffers are separated into separate polygons, leaving the land-based portions of jurisdictions and coastal buffers in adjacent polygons. This feature layer is for public use. Related LayersThis dataset is part of a grouping of many datasets:Cities: Only the city boundaries and attributes, without any unincorporated areasWith Coastal BuffersWithout Coastal BuffersCounties: Full county boundaries and attributes, including all cities within as a single polygonWith Coastal Buffers (this dataset)Without Coastal BuffersCities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.With Coastal BuffersWithout Coastal BuffersCity and County AbbreviationsUnincorporated Areas (Coming Soon)Census Designated PlacesCartographic CoastlinePolygonLine source (Coming Soon) Working with Coastal Buffers The dataset you are currently viewing includes the coastal buffers for cities and counties that have them in the source data from CDTFA. In the versions where they are included, they remain as a second polygon on cities or counties that have them, with all the same identifiers, and a value in the COASTAL field indicating if it"s an ocean or a bay buffer. If you wish to have a single polygon per jurisdiction that includes the coastal buffers, you can run a Dissolve on the version that has the coastal buffers on all the fields except OFFSHORE and AREA_SQMI to get a version with the correct identifiers. Point of ContactCalifornia Department of Technology, Office of Digital Services, gis@state.ca.gov Field and Abbreviation DefinitionsCDTFA_COUNTY: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.CDTFA_COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system. The boundary data originate with CDTFA's teams managing tax rate information, so this field is preserved and flows into this dataset.CENSUS_GEOID: numeric geographic identifiers from the US Census BureauCENSUS_PLACE_TYPE: City, County, or Town, stripped off the census name for identification purpose.GNIS_PLACE_NAME: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information SystemGNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.CDT_COUNTY_ABBR: Abbreviations of county names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 3 characters.CDT_NAME_SHORT: The name of the jurisdiction (city or county) with the word "City" or "County" stripped off the end. Some changes may come to how we process this value to make it more consistent.AREA_SQMI: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.OFFSHORE: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".PRIMARY_DOMAIN: Currently empty/null for all records. Placeholder field for official URL of the city or countyCENSUS_POPULATION: Currently null for all records. In the future, it will include the most recent US Census population estimate for the jurisdiction.GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead. Boundary AccuracyCounty boundaries were originally derived from a 1:24,000 accuracy dataset, with improvements made in some places to boundary alignments based on research into historical records and boundary changes as CDTFA learns of them. City boundary data are derived from pre-GIS tax maps, digitized at BOE and CDTFA, with adjustments made directly in GIS for new annexations, detachments, and corrections.Boundary accuracy within the dataset varies. While CDTFA strives to correctly include or exclude parcels from jurisdictions for accurate tax assessment, this dataset does not guarantee that a parcel is placed in the correct jurisdiction. When a parcel is in the correct jurisdiction, this dataset cannot guarantee accurate placement of boundary lines within or between parcels or rights of way. This dataset also provides no information on parcel boundaries. For exact jurisdictional or parcel boundary locations, please consult the county assessor's office and a licensed surveyor. CDTFA's data is used as the best available source because BOE and CDTFA receive information about changes in jurisdictions which otherwise need to be collected independently by an agency or company to compile into usable map boundaries. CDTFA maintains the best available statewide boundary information. CDTFA's source data notes the following about accuracy: City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. Boundary ProcessingThese data make a structural change from the source data. While the full boundaries provided by CDTFA include coastal buffers of varying sizes, many users need boundaries to end at the shoreline of the ocean or a bay. As a result, after examining existing city and county boundary layers, these datasets provide a coastline cut generally along the ocean facing coastline. For county boundaries in northern California, the cut runs near the Golden Gate Bridge, while for cities, we cut along the bay shoreline and into the edge of the Delta at the boundaries of Solano, Contra Costa, and Sacramento counties. In the services linked above, the versions that include the coastal buffers contain them as a second (or third) polygon for the city or county, with the value in the COASTAL field set to whether it"s a bay or ocean polygon. These can be processed back into a single polygon by dissolving on all the fields you wish to keep, since the attributes, other than the COASTAL field and geometry attributes (like areas) remain the same between the polygons for this purpose. SliversIn cases where a city or county"s boundary ends near a coastline, our coastline data may cross back and forth many times while roughly paralleling the jurisdiction"s boundary, resulting in many polygon slivers. We post-process the data to remove these slivers using a city/county boundary priority algorithm. That is, when the data run parallel to each other, we discard the coastline cut and keep the CDTFA-provided boundary, even if it extends into the ocean a small amount. This processing supports consistent boundaries for Fort Bragg, Point Arena, San Francisco, Pacifica, Half Moon Bay, and Capitola, in addition to others. More information on this algorithm will be provided soon. Coastline CaveatsSome cities have buffers extending into water bodies that we do not cut at the shoreline. These include South Lake Tahoe and Folsom, which extend into neighboring lakes, and San Diego and surrounding cities that extend into San Diego Bay, which our shoreline encloses. If you have feedback on the exclusion of these items, or others, from the shoreline cuts,

The average level of the ocean has been rising since we started measuring and recording this data. According to the National Aeronautics and Space Agency (NASA), since 1900 the global mean sea level has risen more than 200 millimeters (nearly 8 inches) and nearly half of that increase has occurred since 1993 in a concerning change in rate of rise.Sea level rise is one of the many effects of global warming. Scientists attribute sea level rise to two things, melting ice and increased ocean water temperatures. Increasing air temperatures, particularly in the polar regions, has encouraged the melting of land-based ice reserves such as glaciers, ice sheets, and permafrost. Historically, warm season ice melt was balanced by replenishment during the cold season but warming temperatures have created conditions where melting exceeds the buildup of ice. This water flows through rivers and streams to the ocean in quantities sufficient to contribute to sea level rise.Oceans are also massive heat sinks. They pull large quantities of atmospheric heat and greenhouse gases such as carbon dioxide and store it in the ocean. The sea changes temperature much more slowly than the air and over time ocean temperatures have continued to build. As the ocean water warms it expands causing the sea levels to rise.Sea levels are not rising equally across Earth. Some areas are already experiencing significant impacts due to the rising water levels while others have seen minimal changes. This is due to a variety of reasons. First, despite how it is typically illustrated Earth is not perfectly round so the height of the ocean at any given point varies. This can be due to the Earth’s rotation, ocean currents, or prevailing wind speed and direction.Experts consider sea level rise and urgent climatic threat. Many low-lying places such as islands and coastal areas are already experiencing high waters. Higher waters also make storms such as hurricanes more dangerous due to higher storm surges and flooding. As coastlines could lose key infrastructure, land will become uninhabitable, and many people could lose their livelihoods. It is estimated 10 percent of the world’s population could be impacted as the waters rise. Many of the approximately 770 million people could be forced to migrate to higher ground, or in the case of island countries, such as Kiribati, to new countries once theirs sinks below the sea.This map was created with data from the National Oceanic and Atmospheric Administration (NOAA), NASA, and the United States Geological Survey. Experts used an elevation data and the NOAA model Scenarios of Future Mean Seal Level to illustrate the scale of potential coastal flooding. The mapmaker chose to remove levees from the data, so the areas flooded include places, particularly in the states of Texas and Louisiana, that are presently protected by this infrastructure. It is important to note that these are possible outcomes. This model does not include possible erosion, subsidence, or construction that may occur between 2022 when this data was created and 2030, 2050, or 2090 respectively. While models are powerful tools it is difficult to calculate every aspect that shapes our environment.Learn more about how coastal communities are impacted by sea level rise with this StoryMap by NOAA’s Office for Coastal Management, The King Tides Project: Snap the shore, See the Future.

The map is based on 10 year return coastal floods simulation run on HAZUS. The data includes number of residential, commercial, Goverment, schools and colleges by census block that may be inundated by the 10 year floods (measured in feet). http://www.fema.gov/plan/prevent/hazus/

Coastal Surge Evacuation Routing Market Outlook

According to our latest research, the global market size for the Coastal Surge Evacuation Routing Market reached USD 1.72 billion in 2024, driven by an increasing frequency of extreme weather events and rising coastal populations. The market is set to expand at a robust CAGR of 10.8% from 2025 to 2033, with the forecasted market size expected to reach USD 4.26 billion by 2033. This growth is primarily attributed to technological advancements in evacuation routing solutions, heightened government focus on disaster preparedness, and the integration of real-time data analytics for efficient emergency response.

One of the primary growth factors propelling the Coastal Surge Evacuation Routing Market is the intensification of climate change impacts, which has led to more frequent and severe coastal surge events worldwide. Coastal communities are increasingly vulnerable to hurricanes, typhoons, and storm surges, necessitating robust evacuation planning and execution. Governments and municipal agencies are investing heavily in advanced routing software and services to ensure the safety of residents and minimize loss of life and property. The integration of Geographic Information Systems (GIS), artificial intelligence, and big data analytics into evacuation planning has transformed traditional approaches, enabling real-time route optimization and dynamic decision-making under rapidly changing emergency conditions. These technological innovations are not only enhancing the efficiency of evacuation operations but are also fostering greater public trust in emergency management authorities, further fueling market growth.

Another significant driver for the Coastal Surge Evacuation Routing Market is the increasing adoption of cloud-based solutions and mobile technologies. Cloud-based deployment models offer scalability, flexibility, and rapid updates, which are crucial for managing large-scale evacuations across multiple jurisdictions. Mobile applications are empowering both authorities and the public with real-time alerts, route guidance, and situational updates, thereby improving communication and coordination during emergencies. The ability to integrate evacuation routing solutions with other smart city infrastructure, such as traffic management systems and public transportation networks, is also contributing to market expansion. Moreover, the rise in public-private partnerships is facilitating the deployment of cutting-edge evacuation technologies, particularly in regions with high coastal population densities.

Furthermore, stringent regulatory frameworks and increased funding for disaster risk reduction are playing a pivotal role in market growth. Governments across North America, Europe, and Asia Pacific are mandating the adoption of comprehensive evacuation planning systems as part of their national disaster management strategies. International bodies such as the United Nations and the World Bank are also supporting capacity-building initiatives, providing technical assistance and financial resources for the implementation of advanced evacuation routing solutions. The emphasis on community resilience, coupled with the need to comply with evolving safety standards, is prompting municipalities, state agencies, and private organizations to invest in state-of-the-art evacuation technologies. This trend is expected to continue, with ongoing research and development initiatives further enhancing the capabilities of coastal surge evacuation routing systems.

From a regional perspective, North America currently dominates the Coastal Surge Evacuation Routing Market, accounting for the largest share owing to its advanced infrastructure, high awareness levels, and proactive government initiatives. The United States, in particular, has made significant investments in hurricane preparedness and evacuation planning, setting benchmarks for other regions to follow. Europe and Asia Pacific are also witnessing substantial growth, driven by increasing urbanization, coastal development, and heightened vulnerability to climate-induced disasters. Latin America and the Middle East & Africa are emerging markets, with governments gradually recognizing the importance of robust evacuation systems to safeguard coastal communities. The regional outlook for the market remains positive, with all major regions expected to experience steady growth through 2033.

Solution Type Analysis

The Solution Type segment in th

Coastal Flood Early Action Platforms Market Outlook

According to our latest research, the global Coastal Flood Early Action Platforms market size reached USD 1.83 billion in 2024, reflecting a significant surge in demand for advanced flood risk mitigation technologies. The market is projected to expand at a robust CAGR of 12.6% from 2025 to 2033, reaching a forecasted market size of USD 5.36 billion by 2033. The primary growth factor fueling this expansion is the increasing frequency and severity of coastal flooding events, which has prompted governments, municipalities, and private organizations to prioritize proactive disaster response and risk reduction measures.

The growth of the Coastal Flood Early Action Platforms market is intrinsically tied to the escalating impacts of climate change, particularly the rise in sea levels and the intensification of extreme weather events. As coastal populations and critical infrastructure become more vulnerable, there is a pressing need for sophisticated solutions that can provide accurate, real-time data and actionable insights to minimize loss of life and property. The integration of advanced sensor technologies, artificial intelligence, and predictive analytics within these platforms has greatly enhanced their effectiveness, enabling stakeholders to make timely decisions and deploy resources efficiently. Additionally, global initiatives such as the Sendai Framework for Disaster Risk Reduction and increasing investments in disaster resilience are further catalyzing market growth.

Another significant growth driver is the rapid digital transformation occurring within the public safety and environmental monitoring sectors. Coastal Flood Early Action Platforms are increasingly being adopted by urban planners and emergency response teams to support data-driven decision-making. The proliferation of Internet of Things (IoT) devices and cloud-based infrastructure allows for seamless data collection, aggregation, and dissemination, ensuring that critical information reaches the right stakeholders at the right time. Moreover, the availability of scalable, customizable solutions has made these platforms accessible to a broader range of end-users, including small municipalities and non-governmental organizations (NGOs), expanding the market’s addressable base.

Regulatory frameworks and funding initiatives are also playing a pivotal role in shaping the market landscape. Governments across North America, Europe, and parts of Asia Pacific are launching dedicated programs to enhance coastal resilience, often mandating the implementation of early warning systems as part of broader climate adaptation strategies. These policy measures, combined with public-private partnerships and international aid, are creating a favorable environment for market growth. Furthermore, the increasing emphasis on community engagement and inclusivity in disaster risk management is driving demand for platforms that facilitate collaboration among diverse stakeholders, including local communities, research institutes, and emergency responders.

From a regional perspective, North America and Europe currently lead the market in terms of adoption and technological innovation, owing to their advanced infrastructure and strong regulatory support. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period, driven by rapid urbanization, high population density in coastal areas, and increasing vulnerability to natural disasters. Latin America and the Middle East & Africa are also emerging as important markets, supported by rising awareness and international cooperation on climate resilience. Overall, the global outlook for the Coastal Flood Early Action Platforms market remains highly positive, with ample opportunities for innovation and expansion across all regions.

Component Analysis

The Coastal Flood Early Action Platforms market is segmented by component into Software, Hardware, and Services, each playing a distinct role

Split tract data is the intersection of 2020 census tracts by 2023 incorporated city boundaries and unincorporated countywide statistical areas (CSA) boundaries. The census tract boundaries have been altered and aligned where necessary with legal city boundaries and unincorporated areas, including along shoreline/coastal areas. This data is also known as the Split Tract data. This data can be used to estimate population changes over time. Census Tract:Every 10 years the Census Bureau counts the population of the United States as mandated by Constitution. The Census Bureau released 2020 geographic boundaries data including census tracts for the analysis and mapping of demographic information across the United States. City Boundary:City Boundary data is the base map information for the County of Los Angeles. These City Boundaries are based on the Los Angeles County Seamless Cadastral Landbase. The Landbase is jointly maintained by the Los Angeles County Assessor and the Los Angeles County Department of Public Works (DPW). This layer represents current city boundaries within Los Angeles County. The DPW provides the most current shapefiles representing city boundaries and city annexations. True, legal boundaries are only determined on the ground by surveyors licensed in the State of California.Countywide Statistical Areas (CSA):The countywide Statistical Area (CSA) was defined to provide a common geographic boundary for reporting departmental statistics for unincorporated areas and incorporated Los Angeles city to the Board of Supervisors. The CSA boundary and CSA names are established by the CIO and the LA County Enterprise GIS group worked with the Los Angeles County Board of Supervisors Unincorporated Area and Field Deputies that reflect as best as possible the general name preferences of residents and historical names of areas. This data is primarily focused on broad statistics and reporting, not mapping of communities. This data is not designed to perfectly represent communities, nor jurisdictional boundaries such as Angeles National Forest. CSA represent board approved geographies comprised of Census block groups split by cities.Data Field:CT20FIP23CSA: ID field (combination of 2020 census tract number, 2023 city FIP code and CSA name)CT20: 2020 Census TractFIP23: FIP code for legal cityCITY: Legal City Name (as of July 1, 2023)CSA: Countywide Statistical Area (CSA) and Los Angeles City neighborhood namesHow this data created?This polygon data is created by intersecting 2020 census tract polygons, LA Country City neighborhood polygons and Countywide Statistical Areas (CSA) data polygon. The resulting polygon boundary aligned and matched with the legal city boundary whenever possible. Updates:2023 July: The major updates include 2022 November Santa Clarita City annexation and Kinneloa Mesa community (previously it was a part of Unincorporated East Pasadena). This data also aligns with current city boundary along LA County shoreline areas.

Contains physical information on commercial facilities at the principal U.S. Coastal, Great Lakes and Inland Ports. The data consists of listings of port area's waterfront facilities, including information on berthing, cranes, transit sheds, grain elevators, marine repair plants, fleeting areas, and docking and storage facilities. Collection of data is performed on a rotational basis to ensure on-site accuracy at each facility.

Undeveloped coastal areas in Texas in the Coastal Barrier Resources System, with other protected areas of barrier islands. Data digitized by Texas General Land Office from U.S. Fish and Wildlife Service hardcopy maps produced pursuant to the Coastal Barrier Improvement Act (P.L. 101-591).

This web map provides and in-depth look at school districts within the United States. Clicking on a school district in the map will reveal different statistics about each district in the pop-up. The statistics presented in this map are approximations based on summarizing American Community Survey(ACS) data using tract centroids. They may differ from published statistics by school districts found on data.census.gov. A few things you will learn from this map:How many public and private schools fall within a district?Socioeconomic factors about the Census Tracts which fall within the district:School enrollment for grades Kindergarten through 12thDisconnected children in the districtChildren living below the poverty level Children with no internet at home Children without a working parentRace/ethnicity breakdown of population under the age of 19 in the districtFor more information about the data sources:This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases estimates, so values in the map always reflect the newest data available.Current School Districts Layer:The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated school district boundary composite files that include public elementary, secondary, and unified school district boundaries clipped to the U.S. shoreline. School districts are single-purpose administrative units designed by state and local officials to organize and provide public education for local residents. District boundaries are collected for NCES by the U.S. Census Bureau to support educational research and program administration, and the boundaries are essential for constructing district-level estimates of the number of children in poverty.The Census Bureau’s School District Boundary Review program (SDRP) (https://www.census.gov/programs-surveys/sdrp.html) obtains the boundaries, names, and grade ranges from state officials, and integrates these updates into Census TIGER. Census TIGER boundaries include legal maritime buffers for coastal areas by default, but the NCES composite file removes these buffers to facilitate broader use and cleaner cartographic representation. The NCES EDGE program collaborates with the U.S. Census Bureau’s Education Demographic, Geographic, and Economic Statistics (EDGE) Branch to develop the composite school district files. The inputs for this data layer were developed from Census TIGER/Line and represent the most current boundaries available. For more information about NCES school district boundary data, see https://nces.ed.gov/programs/edge/Geographic/DistrictBoundaries.Public Schools Layer:This Public Schools feature dataset is composed of all Public elementary and secondary education facilities in the United States as defined by the Common Core of Data (CCD, https://nces.ed.gov/ccd/ ), National Center for Education Statistics (NCES, https://nces.ed.gov ), US Department of Education for the 2017-2018 school year. This includes all Kindergarten through 12th grade schools as tracked by the Common Core of Data. Included in this dataset are military schools in US territories and referenced in the city field with an APO or FPO address. DOD schools represented in the NCES data that are outside of the United States or US territories have been omitted. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 3065 new records, modifications to the spatial location and/or attribution of 99,287 records, and removal of 2996 records not present in the NCES CCD data.Private Schools Layer:This Private Schools feature dataset is composed of private elementary and secondary education facilities in the United States as defined by the Private School Survey (PSS, https://nces.ed.gov/surveys/pss/), National Center for Education Statistics (NCES, https://nces.ed.gov), US Department of Education for the 2017-2018 school year. This includes all prekindergarten through 12th grade schools as tracked by the PSS. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 2675 new records, modifications to the spatial location and/or attribution of 19836 records, the removal of 254 records no longer applicable. Additionally, 10,870 records were removed that previously had a STATUS value of 2 (Unknown; not represented in the most recent PSS data) and duplicate records identified by ORNL.Web Map originally owned by Summers Cleary

Trawl data from 14 R/V Pandalus cruises to the Coastal Gulf of Alaska from 1999-2004 as part of the U.S. GLOBEC program access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=Nordic 264 surface rope trawl (198-m long, 25-m wide, 35-m vertical height, equipped with a 1.2-cm mesh liner in cod end) towed at the surface. Start times for trawls are when all warp wire and doors have been let out, and end times are when warp wire retrival begins i. Numbers of fish caught from an event listed in this file may not match the count of number of fish measured in the 'Glength_September_2005' file because in some cases the number of fish measured was a subsample of total catch. For cruises G01-3 and G02-2 field identifications of juvenile salmon were inconsistent. Pink, chum, and sockeye, therefore, are all grouped under salmon, unidentified juvenile, as noted in the comment column. awards_0_award_nid=54767 awards_0_award_number=OCE-0109078 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0109078 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=Phillip R. Taylor awards_0_program_manager_nid=50451 awards_1_award_nid=55044 awards_1_award_number=unknown NEP NOAA awards_1_funder_name=National Oceanic and Atmospheric Administration awards_1_funding_acronym=NOAA awards_1_funding_source_nid=352 cdm_data_type=Other comment=trawl_lev1 datafile: TRAWL_PA9901.dat
trawl_lev2 datafile: TRAWL_PA9901_S1C1.dat Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.2472.1 Easternmost_Easting=-147.6399 geospatial_lat_max=60.2097 geospatial_lat_min=58.383 geospatial_lat_units=degrees_north geospatial_lon_max=-147.6399 geospatial_lon_min=-149.7733 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/2472 institution=BCO-DMO instruments_0_acronym=Nordic Rope Trawl instruments_0_dataset_instrument_description=Nordic 264 surface rope trawl (198-m long, 25-m wide, 35-m vertical height, equipped with a 1.2-cm mesh liner in cod end) towed at the surface instruments_0_dataset_instrument_nid=4204 instruments_0_description=A Nordic 264 surface rope trawl is a 198-m long, 25-m wide, 35-m vertical trawl net, equipped with a 1.2-cm mesh liner in the cod end and towed at the surface. instruments_0_instrument_external_identifier=https://vocab.nerc.ac.uk/collection/L05/current/23/ instruments_0_instrument_name=Nordic 264 Rope Trawl instruments_0_instrument_nid=466 instruments_0_supplied_name=Nordic 264 Rope Trawl metadata_source=https://www.bco-dmo.org/api/dataset/2472 Northernmost_Northing=60.2097 param_mapping={'2472': {'lon_begin': 'flag - longitude', 'lat_begin': 'flag - latitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/2472/parameters people_0_affiliation=University of Alaska Fairbanks people_0_affiliation_acronym=UAF people_0_person_name=Dr Jennifer L Boldt people_0_person_nid=50478 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=University of Alaska Fairbanks people_1_affiliation_acronym=UAF people_1_person_name=Dr Lewis J Haldorson people_1_person_nid=50477 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=University of Alaska Fairbanks people_2_affiliation_acronym=UAF people_2_person_name=Dr Jack Piccolo people_2_person_nid=50479 people_2_role=Co-Principal Investigator people_2_role_type=originator people_3_affiliation=Woods Hole Oceanographic Institution people_3_affiliation_acronym=WHOI BCO-DMO people_3_person_name=Ms Dicky Allison people_3_person_nid=50382 people_3_role=BCO-DMO Data Manager people_3_role_type=related project=NEP projects_0_acronym=NEP projects_0_description=Program in a Nutshell Goal: To understand the effects of climate variability and climate change on the distribution, abundance and production of marine animals (including commercially important living marine resources) in the eastern North Pacific. To embody this understanding in diagnostic and prognostic ecosystem models, capable of capturing the ecosystem response to major climatic fluctuations. Approach: To study the effects of past and present climate variability on the population ecology and population dynamics of marine biota and living marine resources, and to use this information as a proxy for how the ecosystems of the eastern North Pacific may respond to future global climate change. The strong temporal variability in the physical and biological signals of the NEP will be used to examine the biophysical mechanisms through which zooplankton and salmon populations respond to physical forcing and biological interactions in the coastal regions of the two gyres. Annual and interannual variability will be studied directly through long-term observations and detailed process studies; variability at longer time scales will be examined through retrospective analysis of directly measured and proxy data. Coupled biophysical models of the ecosystems of these regions will be developed and tested using the process studies and data collected from the long-term observation programs, then further tested and improved by hindcasting selected retrospective data series. projects_0_geolocation=Northeast Pacific Ocean, Gulf of Alaska projects_0_name=U.S. GLOBEC Northeast Pacific projects_0_project_nid=2038 projects_0_project_website=http://nepglobec.bco-dmo.org projects_0_start_date=1997-01 sourceUrl=(local files) Southernmost_Northing=58.383 standard_name_vocabulary=CF Standard Name Table v55 subsetVariables=ship version=1 Westernmost_Easting=-149.7733 xml_source=osprey2erddap.update_xml() v1.3

Ocean Alkalinity Enhancement Market Outlook

According to our latest research, the Global Ocean Alkalinity Enhancement market size was valued at $412 million in 2024 and is projected to reach $2.37 billion by 2033, expanding at a robust CAGR of 21.6% during 2024–2033. The primary growth driver for this market is the urgent global demand for scalable carbon dioxide removal solutions, as nations and organizations strive to meet ambitious net-zero targets and mitigate the escalating impacts of climate change. Ocean Alkalinity Enhancement (OAE) technologies are increasingly recognized as a promising pathway for both atmospheric CO₂ removal and ocean acidification mitigation, fueling significant investments, research initiatives, and pilot deployments across various regions. The convergence of technological innovation, supportive regulatory frameworks, and heightened environmental awareness is accelerating the commercialization and adoption of OAE solutions worldwide.

Regional Outlook

North America currently holds the largest share of the Ocean Alkalinity Enhancement market, accounting for approximately 38% of global revenue in 2024. This dominance is attributed to the region's mature environmental technology ecosystem, robust funding from both public and private sectors, and progressive climate policies spearheaded by the United States and Canada. Key coastal states, such as California and Massachusetts, have established dedicated research programs and pilot projects, supported by favorable regulatory environments and active collaboration between academia, government agencies, and private enterprises. The presence of leading research institutes and technology developers, combined with strong advocacy from environmental organizations, has positioned North America at the forefront of OAE innovation and deployment. Furthermore, the region benefits from well-developed marine infrastructure and advanced monitoring capabilities, enabling the scaling of demonstration projects and the translation of scientific breakthroughs into commercial applications.

The Asia Pacific region is projected to be the fastest-growing market for Ocean Alkalinity Enhancement, with a forecasted CAGR exceeding 25% from 2024 to 2033. This rapid growth is driven by escalating investments in climate resilience, increasing governmental focus on carbon neutrality, and the vulnerability of coastal ecosystems to ocean acidification and sea-level rise. Countries such as China, Japan, and Australia are ramping up R&D funding, forming strategic partnerships with international organizations, and piloting OAE technologies in diverse marine environments. The region’s vast coastline, high population density in coastal areas, and economic reliance on fisheries and aquaculture provide strong incentives for both carbon removal and ocean health restoration. Additionally, the Asia Pacific market is witnessing growing participation from energy and industrial players seeking to offset emissions and comply with evolving environmental regulations, further accelerating technology adoption and market expansion.

Emerging economies in Latin America, the Middle East, and Africa are beginning to explore Ocean Alkalinity Enhancement as part of their broader climate adaptation and mitigation strategies, although adoption remains at a nascent stage. These regions face unique challenges, including limited access to advanced marine research infrastructure, constrained funding, and varying levels of regulatory maturity. However, localized demand is increasing, particularly in countries with extensive coastlines and economies dependent on marine resources. International collaborations, knowledge transfer initiatives, and the involvement of global environmental organizations are helping to bridge capability gaps and foster pilot projects. Policy reforms aimed at climate resilience and blue economy development are expected to gradually improve the market landscape, although sustained investment and capacity-building will be essential for widespread adoption in these regions.

Report Scope

This data contains the hydrologic units boundaries for the 18 southeasternmost independent cities/counties in Virginia. This area is part of the Albemarle-Pamlico Estuarine Study (APES) area. Hydrologic units depict watershed areas. This data only covers the Virginia APES area. The APES area covers a large portion of the North Carolina coast, central and north-central NC and southeastern VA. This data was collected as part of a study on the APE area. The APES area covers a large portion

The 2015 TIGER Geodatabases are extracts of selected nation based and state based geographic and cartographic information from the U.S. Census Bureau's Master Address File/Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) database. The geodatabases include feature class layers of information for the fifty states, the District of Columbia, Puerto Rico, and the Island areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the United States Virgin Islands). The geodatabases do not contain any sensitive data. The 2015 TIGER Geodatabases are designed for use with Esriâ s ArcGIS.

        The State Geodatabase for Indiana geodatabase contains multiple layers. These layers are the Block, Block Group, Census Designated Place, Census
        Tract, Consolidated City, County, County Subdivision and Incorporated Place layers.

        Block Groups (BGs) are clusters of blocks within the same census tract. Each census tract contains at least one BG, and BGs are uniquely numbered
        within census tracts. BGs have a valid code range of 0 through 9. BGs have the same first digit of their 4-digit census block number from the same
        decennial census. For example, tabulation blocks numbered 3001, 3002, 3003,.., 3999 within census tract 1210.02 are also within BG 3 within that
        census tract. BGs coded 0 are intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and
        Great Lakes water areas. Block groups generally contain between 600 and 3,000 people. A BG usually covers a contiguous area but never crosses
        county or census tract boundaries. They may, however, cross the boundaries of other geographic entities like county subdivisions, places, urban
        areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas. The BG boundaries in this release are
        those that were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2010 Census. 

        An incorporated place, or census designated place, is established to provide governmental functions for a concentration of people as opposed to a
        minor civil division (MCD), which generally is created to provide services or administer an area without regard, necessarily, to population. Places
        always nest within a state, but may extend across county and county subdivision boundaries. An incorporated place usually is a city, town, village,
        or borough, but can have other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated
        places. CDPs are delineated to provide data for settled concentrations of population that are identifiable by name, but are not legally
        incorporated under the laws of the state in which they are located. The boundaries for CDPs often are defined in partnership with state, local,
        and/or tribal officials and usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP
        boundaries often change from one decennial census to the next with changes in the settlement pattern and development; a CDP with the same name as in
        an earlier census does not necessarily have the same boundary. The only population/housing size requirement for CDPs is that they must contain some
        housing and population. The boundaries of most incorporated places in this shapefile are as of January 1, 2013, as reported through the Census
        Bureau's Boundary and Annexation Survey (BAS). Limited updates that occurred after January 1, 2013, such as newly incorporated places, are also
        included. The boundaries of all CDPs were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2010
        Census.

        The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to
        previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people.
        When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living
        conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by
        highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to
        population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable
        features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to
        allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and
        county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may
        consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities
        that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that
        include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American
        Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little
        or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial
        park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area. 

         A consolidated city is a unit of local government for which the functions of an incorporated place and its county or minor civil division (MCD) have
        merged. This action results in both the primary incorporated place and the county or MCD continuing to exist as legal entities, even though the
        county or MCD performs few or no governmental functions and has few or no elected officials. Where this occurs, and where one or more other
        incorporated places in the county or MCD continue to function as separate governments, even though they have been included in the consolidated
        government, the primary incorporated place is referred to as a consolidated city. The Census Bureau classifies the separately incorporated places
        within the consolidated city as place entities and creates a separate place (balance) record for the portion of the consolidated city not within any
        other place. The boundaries of the consolidated cities are those as of January 1, 2013, as reported through the Census Bureau's Boundary and
        Annexation Survey(BAS).

        The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no
        counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The
        latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri,
        Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary
        divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data
        presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data
        presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto
        Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin
        Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for
        counties and equivalent entities are mostly as of January 1, 2013, primarily as reported through the Census Bureau's Boundary and Annexation Survey
        (BAS). However, some changes made after January 2013, including the addition and deletion of counties, are included.

        County subdivisions are the primary divisions of

Major ecological realignments are already occurring in response to climate change. To be successful, conservation strategies now need to account for geographical patterns in traits sensitive to climate change, as well as climate threats to species-level diversity. As part of an effort to provide such information, we conducted a climate vulnerability assessment that included all anadromous Pacific salmon and steelhead (Oncorhynchus spp.) population units listed under the U.S. Endangered Species Act. Using an expert-based scoring system, we ranked 20 attributes for the 28 listed units and 5 additional units. Attributes captured biological sensitivity, or the strength of linkages between each listing unit and the present climate; climate exposure, or the magnitude of projected change in local environmental conditions; and adaptive capacity, or the ability to modify phenotypes to cope with new climatic conditions. Each listing unit was then assigned one of four vulnerability categories. Units ranked most vulnerable overall were Chinook (O. tshawytscha) in the California Central Valley, coho (O. kisutch) in California and southern Oregon, sockeye (O. nerka) in the Snake River Basin, and spring-run Chinook in the interior Columbia and Willamette River Basins. We identified units with similar vulnerability profiles using a hierarchical cluster analysis. Life history characteristics, especially freshwater and estuary residence times, interplayed with gradations in exposure from south to north and from coastal to interior regions to generate landscape-level patterns within each species. Nearly all listing units faced high exposures to projected increases in stream temperature, sea surface temperature, and ocean acidification, but other aspects of exposure peaked in particular regions. Anthropogenic factors, especially migration barriers, habitat degradation, and hatchery influence, have reduced the adaptive capacity of most steelhead and salmon populations. Enhancing adaptive capacity is essential to mitigate for the increasing threat of climate change. Collectively, these results provide a framework to support recovery planning that considers climate impacts on the majority of West Coast anadromous salmonids.

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission to represent the United States Census Bureau's 2000 Decennial Census at the block group geography.Attributes:FIPSSTCO = The Federal Information Processing Series (FIPS) state and county codes. FIPS codes were formerly known as Federal Information Processing Standards codes, until the National Institute of Standards and Technology (NIST) announced its decision in 2005 to remove geographic entity codes from its oversight. The Census Bureau continues to maintain and issue codes for geographic entities covered under FIPS oversight, albeit with a revised meaning for the FIPS acronym. Geographic entities covered under FIPS include states, counties, congressional districts, core based statistical areas, places, county subdivisions, subminor civil divisions, consolidated cities, and all types of American Indian, Alaska Native, and Native Hawaiian areas. FIPS codes are assigned alphabetically according to the name of the geographic entity and may change to maintain alphabetic sort when new entities are created or names change. FIPS codes for specific geographic entity types are usually unique within the next highest level of geographic entity with which a nesting relationship exists. For example, FIPS state, congressional district, and core based statistical area codes are unique within nation; FIPS county, place, county subdivision, and subminor civil division codes are unique within state. The codes for American Indian, Alaska Native, and Native Hawaiian areas also are unique within state; those areas in multiple states will have different codes for each state.TRACT = Census Tract Codes and Numbers. Census tracts are identified by an up to four-digit integer number and may have an optional two-digit suffix; for example 1457.02 or 23. The census tract codes consist of six digits with an implied decimal between the fourth and fifth digit corresponding to the basic census tract number but with leading zeroes and trailing zeroes for census tracts without a suffix. The tract number examples above would have codes of 145702 and 002300, respectively.GROUP_ = Block Group Codes. Block Groups have a valid code range of 0 through 9. Block Groups beginning with a zero only contain water area and are generally in coastal and Great Lakes water and territorial seas, but also in larger inland water bodies. STFID = A concatenation of FIPSSTCO, TRACT, and GROUP_, which creates the entire FIPS code for this geography.POP100 = The population of the Block Group at the time of the census.HU100 = The number of housing units in the Block group at the time of the census.WFD = Workforce Development Area (WFD) is a seven-county area created by agreement of county chief-elected officials, administered by the Atlanta Regional Commission and funded for training and employment activities under the federal Workforce Investment Act (WIA). For more information on ARC’s Workforce Development programs and services please consult www.atlantaregional.com/workforce/workforce.html.RDC_AAA = ARC Area Agency on Aging is a 10-county area funded by the Department of Human Resources and designated by the Older Americans Act to plan for the needs of the rapidly expanding group of older citizens in the Atlanta region. It is part of a statewide network of 12 AAAs and a national network of more than 670 AAAs. For more information on aging services please consult www.agewiseconnection.com.MNGWPD = The Metro North Georgia Water Planning District provides water resource plans, policies and coordination for metropolitan Atlanta. The District has developed regional plans for stormwater management, wastewater treatment and water supply and water conservation. The 15-county Water Planning District includes the ten counties in the ARC plus five additional counties (Bartow, Coweta, Forsyth, Hall, & Paulding). For more information please consult www.northgeorgiawater.org. MPO = The Metropolitan Planning Organization (MPO) is a 19-county area federally-designated for regional transportation planning to meet air quality standards and for programming projects to implement the adopted Regional Transportation Plan (RTP). The MPO planning area boundary includes the 10-county state-designated Regional Commission and nine additional counties (all of Coweta, Forsyth, & Paulding and parts of Barrow, Dawson, Newton, Pike, Spalding and Walton). This boundary takes into consideration both the current urbanized area as well as areas forecast to become urbanized in the next 20 years.MSA = the 29-County “Atlanta-Sandy Springs-Roswell, GA” Metropolitan Statistical Area (MSA) and the 39-county “Atlanta--Athens-Clarke County--Sandy Springs, GA” Combined Statistical Area (CSA), which includes the 29 counties of the Atlanta MSA along with the Athens-Clarke County and Gainesville MSAs and the micropolitan statistical areas of Calhoun, Cedartown, Jefferson, LaGrange and Thomaston, GA. The U.S. Office of Management and Budget (OMB) defines CSAs, MSAs and the smaller micropolitan statistical areas nationwide according to published standards applied to U.S. Census Bureau data. These various statistical areas describe substantial core areas of population together with adjacent communities having a high degree of economic and social integration, often illustrated in high rates of commuting from the adjacent areas to job locations in the core. For more information, please consult http://www.census.gov/population/metro/data/metrodef.htmlF1HR_NA = The Federal 1-Hour Air Quality Non-Attainment Area is a fine particulate matter standard (PM2.5). The non-attainment area under this standard includes the 15-county eight-hour ozone nonattainment area plus Barrow, Carroll, Hall, Spalding, Walton, and small parts of Heard and Putnam counties.F8HR_NA: The Federal 8-Hour Air Quality Non-Attainment Area for the 2008 eight-hour ozone standard is 15 counties.ACRES = The number of acres contained within the Block Group.SQ_MILES = The number of square miles contained within the Block Group.Source: United States Census Bureau, Atlanta Regional CommissionDate: 2000For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com.