Table contains total population and population density summarized at county, city, zip code, and census tract level. Population density is defined as number of people residing per square mile of area. Data are presented for zip codes (ZCTAs) fully within the county. Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-year estimates, Table B01001; data accessed on April 11, 2022 from https://api.census.gov. The 2020 Decennial geographies are used for data summarization.METADATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographyGEOID (String): Geography IDNAME (String): Name of geographyt_pop (Numeric): Total populationpop_density (Numeric): Area in square milesarea (Numeric): Population density

These datasets contain measures of socioeconomic and demographic characteristics by U.S. census tract for the years 1990-2022 and ZIP code tabulation area (ZCTA) for the years 2008-2022. Example measures include population density; population distribution by race, ethnicity, age, and income; income inequality by race and ethnicity; and proportion of population living below the poverty level, receiving public assistance, and female-headed or single parent families with kids. The datasets also contain a set of theoretically derived measures capturing neighborhood socioeconomic disadvantage and affluence, as well as a neighborhood index of Hispanic, foreign born, and limited English.

A shapefile for mapping data by Modified Zip Code Tabulation Areas (MODZCTA) in NYC, based on the 2010 Census ZCTA shapefile. MODZCTA are being used by the NYC Department of Health & Mental Hygiene (DOHMH) for mapping COVID-19 Data.

Frontier and Remote Area (FAR) codes provide a statistically-based, nationally-consistent, and adjustable definition of territory in the U.S. characterized by low population density and high geographic remoteness.

To assist in providing policy-relevant information about conditions in sparsely settled, remote areas of the U.S. to public officials, researchers, and the general public, ERS has developed ZIP-code-level frontier and remote (FAR) area codes. The aim is not to provide a single definition. Instead, it is to meet the demand for a delineation that is both geographically detailed and adjustable within reasonable ranges, in order to be usefully applied in diverse research and policy contexts. This initial set, based on urban-rural data from the 2000 decennial census, provides four separate FAR definition levels, ranging from one that is relatively inclusive (18 million FAR residents) to one that is more restrictive (4.8 million FAR residents).This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: State and ZIP code level tables For complete information, please visit https://data.gov.

A dataset listing Florida zip codes by population for 2024.

** A Newer Version of this data is available here: https://dallasgis.maps.arcgis.com/home/item.html?id=0a2fde8aa7404187917488bafcbc77e6The United States Postal Service (USPS) does not define ZIP codes as fixed geographic boundaries, such as polygons on a map. Instead, ZIP codes are structured as collections of carrier routes designed to optimize mail delivery. These routes are established based on logistical considerations, such as population density, delivery efficiency, and infrastructure changes, rather than adhering to precise geographic outlines.When ZIP codes are mapped, the resulting visualization is essentially an estimation of these delivery routes. However, these approximations are inherently subject to change, as the Postal Service frequently adjusts routes to accommodate new developments, address shifts in demand, or enhance operational efficiency. Consequently, any representation of ZIP codes on a map should be understood as a general reference and not as an exact or permanent delineation.National ZipCodes: https://dallasgis.maps.arcgis.com/home/item.html?id=0a2fde8aa7404187917488bafcbc77e6

Abstract

Dataset quality **: Medium/high quality dataset, not quality checked or modified by the EIDC team

Census data plays a pivotal role in academic data research, particularly when exploring relationships between different demographic characteristics. The significance of this particular dataset lies in its ability to facilitate the merging of various datasets with basic census information, thereby streamlining the research process and eliminating the need for separate API calls.

The American Community Survey is an ongoing survey conducted by the U.S. Census Bureau, which provides detailed social, economic, and demographic data about the United States population. The ACS collects data continuously throughout the decade, gathering information from a sample of households across the country, covering a wide range of topics

Methodology

The Census Data Application Programming Interface (API) is an API that gives the public access to raw statistical data from various Census Bureau data programs.

We used this API to collect various demographic and socioeconomic variables from both the ACS and the Deccenial survey on different geographical levels:

ZCTAs:

ZIP Code Tabulation Areas (ZCTAs) are generalized areal representations of United States Postal Service (USPS) ZIP Code service areas. The USPS ZIP Codes identify the individual post office or metropolitan area delivery station associated with mailing addresses. USPS ZIP Codes are not areal features but a collection of mail delivery routes.

Census Tract:

Census Tracts are small, relatively permanent statistical subdivisions of a county or statistically equivalent entity that can be updated by local participants prior to each decennial census as part of the Census Bureau’s Participant Statistical Areas Program (PSAP).

Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. A census tract usually covers a contiguous area; however, the spatial size of census tracts varies widely depending on the density of settlement. Census tract boundaries are delineated with the intention of being maintained over a long time so that statistical comparisons can be made from census to census.

Block Groups:

Block groups (BGs) are the next level above census blocks in the geographic hierarchy (see Figure 2-1 in Chapter 2). A BG is a combination of census blocks that is a subdivision of a census tract or block numbering area (BNA). (A county or its statistically equivalent entity contains either census tracts or BNAs; it can not contain both.) A BG consists of all census blocks whose numbers begin with the same digit in a given census tract or BNA; for example, BG 3 includes all census blocks numbered in the 300s. The BG is the smallest geographic entity for which the decennial census tabulates and publishes sample data.

Census Blocks:

Census blocks, the smallest geographic area for which the Bureau of the Census collects and tabulates decennial census data, are formed by streets, roads, railroads, streams and other bodies of water, other visible physical and cultural features, and the legal boundaries shown on Census Bureau maps.

Health regions are defined by provincial governments as the areas of responsibility for regional healthboards (i.e., legislated) or as regions of interest to health care authorities. In 1998, Statistics Canada, together with the Canadian Institute for Health Information and the Advisory Council on Health Info-Structure (Health Canada),consulted stakeholders across Canada to identify current and future needs for health information. These consultations identified a need for comprehensive and comparable sub-provincial data. In response to this need, health regions were investigated as an alternative geographic unit for disseminating health information. This report provides an overview of health regions in Canada, along with sourcesand methodologies for developing and understanding the health region data linkage and digital boundary files, geographic attributes, and population estimates. The same health region boundaries contained in Health Regions - 2000 have been used in the sample design for the Canadian Community Health Survey. Future boundary changes may cause adjustments to the survey collection and dissemination process, or sample revisions for future survey cycles. For current Health Regions data, refer to Statistics Canada.

A dataset listing Washington zip codes by population for 2024.

A dataset listing Indiana zip codes by population for 2024.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. For a deep dive into the data model including every specific metric, see the ACS 2017-2021 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e21Estimate from 2017-21 ACS_m21Margin of Error from 2017-21 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_21Change, 2010-21 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLine (buffer)BeltLine Study (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Planning Unit STV (3 NPUs merged to a single geographic unit within City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)City of Atlanta Neighborhood Statistical Areas E02E06 (2 NSAs merged to single geographic unit within City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)SPARCC = Strong, Prosperous And Resilient Communities ChallengeState of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)WFF = Westside Future Fund (subarea of City of Atlanta)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2017-2021). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2017-2021Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://garc.maps.arcgis.com/sharing/rest/content/items/34b9adfdcc294788ba9c70bf433bd4c1/data

A dataset listing Connecticut zip codes by population for 2024.

Dataset Description: Washington Grocery Store Locations

This dataset contains detailed information about the locations and operational status of grocery stores in Washington, spanning multiple years. It includes both spatial and temporal data, offering a comprehensive view of how grocery stores are distributed and have evolved over time. Below is a breakdown of the columns included in the dataset:

1. X, Y: Geographic coordinates (latitude and longitude) representing the store's location in the dataset.

2. STORENAME: The name of the grocery store.

3. ADDRESS: The physical address of the grocery store.

4. ZIPCODE: The ZIP code of the store’s location.

5. PHONE: The contact phone number for the store.

6. WARD: The local government ward in which the store is located.

7. SSL: A unique identifier or code related to the store, possibly referring to specific data collection attributes.

8. NOTES: Additional comments or information about the store.

9. PRESENT: Temporal indicators showing the presence (likely open or closed) of each store across various years. These columns provide insights into the longevity and temporal trends of grocery store operations.

10. GIS_ID: A unique identifier for geographic information system (GIS) data.

11. XCOORD, YCOORD: Coordinates (likely more specific) used for spatial data analysis, providing the exact location of the store.

12. MAR_ID: A unique identifier for marketing or regional analysis purposes.

13. GLOBALID: A global unique identifier for the store data.

14. CREATOR: The individual or system that created the data entry.

15. CREATED: Timestamp showing when the data entry was created.

16. EDITOR: The individual or system that edited the data entry.

17. EDITED: Timestamp showing when the data entry was last edited.

18. SE_ANNO_CAD_DATA: Specific annotation or data related to CAD (computer-aided design), possibly linked to store location details.

19. OBJECTID: A unique identifier for the object or record within the dataset.

Insights We Can Extract:

• Geographic Distribution: By analyzing the X and Y coordinates along with ZIP codes and wards, we can identify where grocery stores are concentrated and map areas with high or low store density.
• Temporal Trends: The data in the "PRESENT" columns helps us track the opening and closure patterns of grocery stores over time, providing insights into market trends and store longevity.
• Service Gaps: We can identify areas with no grocery stores, possibly indicating food deserts or underserved communities, by mapping the stores and comparing coverage across ZIP codes and wards.
• Operational Trends: By analyzing the temporal data and comparing store turnover, we can uncover patterns in the longevity or turnover of grocery stores.
• Urban Planning and Accessibility: This dataset could help us assess whether the location of grocery stores aligns with urban infrastructure like transportation routes or population density, which could inform policy decisions to improve grocery access.

This dataset is invaluable for urban planners, policymakers, and business stakeholders looking to improve food access and urban infrastructure.

This issue describes in detail the health region limits as of October 2013 and their correspondence with the 2011 and 2006 Census geography. Health regions are defined by the provinces and represent administrative areas or regions of interest to health authorities. This product contains correspondence files (linking health regions to census geographic codes) and digital boundary files. User documentation provides an overview of health regions, sources, methods, limitations and product description (file format and layout). The 2013 Health Regions: Boundaries and Correspondence with Census Geography reflects the boundaries as of October 2013 and provides the geographic linkage to 2011 and 2006 Censuses. For current Health Regions data, refer to Statistics Canada.

This dataset contains the modeling results GIS data (maps) of the study “Sustainable Human Population Density in Western Europe between 560.000 and 360.000 years ago” by Rodríguez et al. (2022). The NPP data (npp.zip) was computed using an empirical formula (the Miami model) from palaeo temperature and palaeo precipitation data aggregated for each timeslice from the Oscillayers dataset (Gamisch, 2019), as defined in Rodríguez et al. (2022, in review). The Population densities file (pop_densities.zip) contains the computed minimum and maximum population densities rasters for each of the defined MIS timeslices. With the population density value Dc in logarithmic form log(Dc). The Species Distribution Model (sdm.7z) includes input data (folder /data), intermediate results (folder /work) and results and figures (folder /results). All modelling steps are included as an R project in the folder /scripts. The R project is subdivided into individual scripts for data preparation (1.x), sampling procedure (2.x), and model computation (3.x). The habitat range estimation (habitat_ranges.zip) includes the potential spatial boundaries of the hominin habitat as binary raster files with 1=presence and 0=absence. The ranges rely on a dichotomic classification of the habitat suitability with a threshold value inferred from the 5% quantile of the presence data. The habitat suitability (habitat_suitability.zip) is the result of the Species Distribution Modelling and describes the environmental suitability for hominin presence based on the sites considered in this study. The values range between 0=low and 1=high suitability. The dataset includes the mean (pred_mean) and standard deviation (pred_std) of multiple model runs.

2008 and 2012 CCES data along with population density and demographics at the zipcode level from the 2010 ACS (5-year)

Background:Aedes aegypti mosquitoes transmit dengue, yellow fever, Zika, and chikungunya viruses. Their range has recently been expanding throughout the world, including into desert regions such as Arizona in the southwestern United States. Little is understood about how these mosquitoes are surviving and behaving in arid environments, habitat that was previously considered inhospitable for the vector. The goal of this study is to create quarterly species distribution models based on satellite imagery and socioeconomic indicators for Ae. aegypti in Maricopa County, Arizona from 2014 to 2020.Methods: Trapping records for Ae. aegypti in Maricopa County, Arizona from 2014 to 2020 were split into 25 quarterly time periods. Quarterly species distribution models (Maxent) were created using satellite imagery-derived vegetation and moisture indices, elevation, and socioeconomic factors (population density, median income) as predictors. Maps of predicted habitat suitability were converted to binary presence/absence maps, and consensus maps were created that represent “core” habitat for the mosquito over 6 years of time. Results were summarized over census-defined zip code tabulation areas with the goal of producing more actionable maps for vector control.Results: Population density was generally the most important predictor in the models while median income and elevation were the least important. All of the 25 quarterly models had high test area under the curve values (>0.90) indicating good model performance. Multiple suburban areas surrounding the Phoenix metropolitan core area were identified as consistent highly suitable habitat.Conclusion: We identified long term “core” habitat for adult female Ae. aegypti over the course of 6 years, as well as “hotspot” locations with greater than average suitability. Binary maps of habitat suitability may be useful for vector control and public health purposes. Future studies should examine the movement of the mosquito in this region over time which would provide another clue as to how the mosquito is surviving and behaving in a desert region.

Date: 1991-07-01Date type: publicationDateURL: Not AvailableOriginator: Namibia Statistics AgencyResponsible party: Namibia Statistics AgencyResponsible party role: custodianAbstract:This layer displays enumeration areas for the year 1991. These are polygon features created from topographic maps.Purpose:To ensure complete and accurate geographical coverage of the country during enumeration. Mapping entails dividing the country into smaller unique geographic areas known as “Enumeration Area (EA)” to serve as small data collection units during enumeration.Status: completedMaintenance and update frequency: 5-10-yearsEntity/attribute description:GEO , POPSIZE_2 , EA_CODE_CS , EA_CODE_ME , EA_typeCompleteness:Enumeration Areas cover the whole country.Logical consistency:UnknownPositional accuracy:Digitized from topographic maps of 1: 50 000 scale.Temporal quality:Digitized from old topographic maps from the 1970s.Thematic accuracy:100% accuracyLineage:Demarcated based on the population and visible physical features on the ground.Process description:Digitized from topographic mapsSpatial reference information: EPSG:900999Metadata date: 2016-10-02Metadata date type: creationMetadata contact name: Namibia Statistics AgencyMetadata contact role code: pointOfContactMetadata contact person: Nevel Ngahahe-HangeroMetadata contact address postal code:P O Box 2133, WindhoekMetadata contact telephone: +264 61 431 3200Metadata standard name: ISO:19115 (NSA custom schema)Metadata standard version: 2014/NSA-1:2016 version

This table shows the 2021 population and dwelling counts for reported forward sortation areas.

IntroductionClimate Central’s Surging Seas: Risk Zone map shows areas vulnerable to near-term flooding from different combinations of sea level rise, storm surge, tides, and tsunamis, or to permanent submersion by long-term sea level rise. Within the U.S., it incorporates the latest, high-resolution, high-accuracy lidar elevation data supplied by NOAA (exceptions: see Sources), displays points of interest, and contains layers displaying social vulnerability, population density, and property value. Outside the U.S., it utilizes satellite-based elevation data from NASA in some locations, and Climate Central’s more accurate CoastalDEM in others (see Methods and Qualifiers). It provides the ability to search by location name or postal code.The accompanying Risk Finder is an interactive data toolkit available for some countries that provides local projections and assessments of exposure to sea level rise and coastal flooding tabulated for many sub-national districts, down to cities and postal codes in the U.S. Exposure assessments always include land and population, and in the U.S. extend to over 100 demographic, economic, infrastructure and environmental variables using data drawn mainly from federal sources, including NOAA, USGS, FEMA, DOT, DOE, DOI, EPA, FCC and the Census.This web tool was highlighted at the launch of The White House's Climate Data Initiative in March 2014. Climate Central's original Surging Seas was featured on NBC, CBS, and PBS U.S. national news, the cover of The New York Times, in hundreds of other stories, and in testimony for the U.S. Senate. The Atlantic Cities named it the most important map of 2012. Both the Risk Zone map and the Risk Finder are grounded in peer-reviewed science.Back to topMethods and QualifiersThis map is based on analysis of digital elevation models mosaicked together for near-total coverage of the global coast. Details and sources for U.S. and international data are below. Elevations are transformed so they are expressed relative to local high tide lines (Mean Higher High Water, or MHHW). A simple elevation threshold-based “bathtub method” is then applied to determine areas below different water levels, relative to MHHW. Within the U.S., areas below the selected water level but apparently not connected to the ocean at that level are shown in a stippled green (as opposed to solid blue) on the map. Outside the U.S., due to data quality issues and data limitations, all areas below the selected level are shown as solid blue, unless separated from the ocean by a ridge at least 20 meters (66 feet) above MHHW, in which case they are shown as not affected (no blue).Areas using lidar-based elevation data: U.S. coastal states except AlaskaElevation data used for parts of this map within the U.S. come almost entirely from ~5-meter horizontal resolution digital elevation models curated and distributed by NOAA in its Coastal Lidar collection, derived from high-accuracy laser-rangefinding measurements. The same data are used in NOAA’s Sea Level Rise Viewer. (High-resolution elevation data for Louisiana, southeast Virginia, and limited other areas comes from the U.S. Geological Survey (USGS)). Areas using CoastalDEM™ elevation data: Antigua and Barbuda, Barbados, Corn Island (Nicaragua), Dominica, Dominican Republic, Grenada, Guyana, Haiti, Jamaica, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, San Blas (Panama), Suriname, The Bahamas, Trinidad and Tobago. CoastalDEM™ is a proprietary high-accuracy bare earth elevation dataset developed especially for low-lying coastal areas by Climate Central. Use our contact form to request more information.Warning for areas using other elevation data (all other areas)Areas of this map not listed above use elevation data on a roughly 90-meter horizontal resolution grid derived from NASA’s Shuttle Radar Topography Mission (SRTM). SRTM provides surface elevations, not bare earth elevations, causing it to commonly overestimate elevations, especially in areas with dense and tall buildings or vegetation. Therefore, the map under-portrays areas that could be submerged at each water level, and exposure is greater than shown (Kulp and Strauss, 2016). However, SRTM includes error in both directions, so some areas showing exposure may not be at risk.SRTM data do not cover latitudes farther north than 60 degrees or farther south than 56 degrees, meaning that sparsely populated parts of Arctic Circle nations are not mapped here, and may show visual artifacts.Areas of this map in Alaska use elevation data on a roughly 60-meter horizontal resolution grid supplied by the U.S. Geological Survey (USGS). This data is referenced to a vertical reference frame from 1929, based on historic sea levels, and with no established conversion to modern reference frames. The data also do not take into account subsequent land uplift and subsidence, widespread in the state. As a consequence, low confidence should be placed in Alaska map portions.Flood control structures (U.S.)Levees, walls, dams or other features may protect some areas, especially at lower elevations. Levees and other flood control structures are included in this map within but not outside of the U.S., due to poor and missing data. Within the U.S., data limitations, such as an incomplete inventory of levees, and a lack of levee height data, still make assessing protection difficult. For this map, levees are assumed high and strong enough for flood protection. However, it is important to note that only 8% of monitored levees in the U.S. are rated in “Acceptable” condition (ASCE). Also note that the map implicitly includes unmapped levees and their heights, if broad enough to be effectively captured directly by the elevation data.For more information on how Surging Seas incorporates levees and elevation data in Louisiana, view our Louisiana levees and DEMs methods PDF. For more information on how Surging Seas incorporates dams in Massachusetts, view the Surging Seas column of the web tools comparison matrix for Massachusetts.ErrorErrors or omissions in elevation or levee data may lead to areas being misclassified. Furthermore, this analysis does not account for future erosion, marsh migration, or construction. As is general best practice, local detail should be verified with a site visit. Sites located in zones below a given water level may or may not be subject to flooding at that level, and sites shown as isolated may or may not be be so. Areas may be connected to water via porous bedrock geology, and also may also be connected via channels, holes, or passages for drainage that the elevation data fails to or cannot pick up. In addition, sea level rise may cause problems even in isolated low zones during rainstorms by inhibiting drainage.ConnectivityAt any water height, there will be isolated, low-lying areas whose elevation falls below the water level, but are protected from coastal flooding by either man-made flood control structures (such as levees), or the natural topography of the surrounding land. In areas using lidar-based elevation data or CoastalDEM (see above), elevation data is accurate enough that non-connected areas can be clearly identified and treated separately in analysis (these areas are colored green on the map). In the U.S., levee data are complete enough to factor levees into determining connectivity as well.However, in other areas, elevation data is much less accurate, and noisy error often produces “speckled” artifacts in the flood maps, commonly in areas that should show complete inundation. Removing non-connected areas in these places could greatly underestimate the potential for flood exposure. For this reason, in these regions, the only areas removed from the map and excluded from analysis are separated from the ocean by a ridge of at least 20 meters (66 feet) above the local high tide line, according to the data, so coastal flooding would almost certainly be impossible (e.g., the Caspian Sea region).Back to topData LayersWater Level | Projections | Legend | Social Vulnerability | Population | Ethnicity | Income | Property | LandmarksWater LevelWater level means feet or meters above the local high tide line (“Mean Higher High Water”) instead of standard elevation. Methods described above explain how each map is generated based on a selected water level. Water can reach different levels in different time frames through combinations of sea level rise, tide and storm surge. Tide gauges shown on the map show related projections (see just below).The highest water levels on this map (10, 20 and 30 meters) provide reference points for possible flood risk from tsunamis, in regions prone to them.