This graph shows the population density in the federal state of Maryland from 1960 to 2018. In 2018, the population density of Maryland stood at 622.5 residents per square mile of land area.

Population profile - total, rate of change, age, and density.

Moldova MD: Population Density: People per Square Km data was reported at 123.655 Person/sq km in 2017. This records a decrease from the previous number of 123.731 Person/sq km for 2016. Moldova MD: Population Density: People per Square Km data is updated yearly, averaging 123.918 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 129.097 Person/sq km in 1992 and a record low of 90.744 Person/sq km in 1961. Moldova MD: Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Moldova – Table MD.World Bank.WDI: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.; ; Food and Agriculture Organization and World Bank population estimates.; Weighted average;

A dataset listing Maryland counties by population for 2024.

243,53 (persons per sq. km) in 2022.

This is a MD iMAP hosted service. Find more information at http://imap.maryland.gov. After each decennial census - the Census Bureau delineates urban areas that represent densely developed territory - encompassing residential - commercial - and other nonresidential urban land uses. In general - this territory consists of areas of high population density and urban land use resulting in a representation of the urban footprint."" There are two types of urban areas: urbanized areas (UAs) that contain 50 - 000 or more people and urban clusters (UCs) that contain at least 2 - 500 people - but fewer than 50 - 000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50 - 000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes.Feature Service Link:http://geodata.md.gov/imap/rest/services/Demographics/MD_CensusDesignatedAreas/FeatureServer/0 ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively the ""Data"") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

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.

After the 2020 Census, there are three key changes to the Census Bureau’s urban area concept and criteria:We increased the minimum population threshold to qualify as urban from 2,500 to 5,000, and we added an alternative: instead of qualifying based on population size, areas can now qualify based on a minimum housing unit threshold.We now use housing unit density instead of population density.We no longer distinguish between different types of urban areas.The first two changes reflect a general shift by the Census Bureau toward using housing units to measure urbanization and identify qualifying urban areas. Each of these criteria changes is described more below (see this link from the Census Bureau).This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Feature Service Link:https://geodata.md.gov/imap/rest/services/Demographics/MD_CensusDesignatedAreas/FeatureServer/0

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. 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.

Moldova MD: Population Density: Inhabitants per sq km data was reported at 92.430 Person in 2022. This records an increase from the previous number of 91.710 Person for 2021. Moldova MD: Population Density: Inhabitants per sq km data is updated yearly, averaging 117.100 Person from Dec 1992 (Median) to 2022, with 31 observations. The data reached an all-time high of 135.560 Person in 1992 and a record low of 91.710 Person in 2021. Moldova MD: Population Density: Inhabitants per sq km data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Moldova – Table MD.OECD.GGI: Social: Demography: Non OECD Member: Annual.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Baltimore Ecosystem Study LTER (BES) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

This is a MD iMAP hosted service. Find more information on http://imap.maryland.gov. Coastal storm and flood events may increase the likelihood of danger or damage to life and property as a consequence of inundation and shoreline erosion. These hazards are caused by storms and exacerbated by sea level rise. The Community Flood Risk Areas represent residential areas at risk to coastal flooding where populations may be less equipped to prepare for - respond to - or recover from a coastal hazard event. Risk Areas are ranked from 1 to 5 to indicate relative risk - with 1 indicating very low risk and 5 indicating very high risk. Risk rankings incorporate population density - social parameters (i.e. age - income - language proficiency) - and probability of exposure to a flood hazard event in any one given year. 2013 US Census Bureau American Community Survey - 2010 Maryland Department of Planning land use land cover - and effective FEMA floodplain data as of December 2015 were used to identify and rank risk areas. Last Updated: 3/31/2016Feature Service Link: http://geodata.md.gov/imap/rest/services/Environment/MD_CoastalResiliencyAssessment/FeatureServer/4 ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

The Johns Hopkins Center for a Livable Future (CLF) obtained the food permit list from the Baltimore City Health Department in August 2011, which includes all sites that sell food, such as stores, restaurants and temporary locations such as farmers' market stands and street carts. The restaurants were grouped into three categories, including full service restaurants, fast food chains and carryouts. Carryout and fast food chain restaurants were extracted from the restaurant layer and spatially joined with the 2010 Community Statistical Area (CSA) data layer, provided by BNIA-JFI. The prepared foods density, per 1,000 people, was calculated for each CSA using the CSA's population and the total number of carryout and fast food restaurants, including vendors selling prepared foods in public markets, in each CSA. Source: Johns Hopkins University, Center for a Livable FutureYears Available: 2011, 2013, 2018, 2019

The 2015 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. 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 as of January 1, 2010.

Coastal storm and flood events may increase the likelihood of danger or damage to life and property as a consequence of inundation and shoreline erosion. These hazards are caused by storms and exacerbated by sea level rise. The Community Flood Risk Areas represent residential areas at risk to coastal flooding where populations may be less equipped to prepare for, respond to, or recover from a coastal hazard event. Risk Areas are ranked from 1 to 5 to indicate relative risk, with 1 indicating very low risk and 5 indicating very high risk. Risk rankings incorporate population density, social parameters (i.e. age, income, language proficiency), and probability of exposure to a flood hazard event in any one given year. 2013 US Census Bureau American Community Survey, 2010 Maryland Department of Planning land use land cover, and effective FEMA floodplain data as of December 2015 were used to identify and rank risk areas. This dataset was produced under award number NA13NOS4190136 from the Office of Ocean and Coastal Resource Management (OCRM), National Oceanic and Atmospheric Administration (NOAA) through the Maryland Department of Natural Resources Chesapeake and Coastal Services (CCS). The statements, finding and recommendations are those of the authors and do not necessarily reflect the views of NOAA or the U.S. Department of Commerce. CCS and The Nature Conservancy (TNC) all contributed to the production of this dataset.The Department of Natural Resources makes no warranty, expressed or implied, as to the use or appropriateness of Spatial Data, and there are no warranties of merchantability or fitness for a particular purpose or use. The information contained in Spatial Data is from publicly available sources, but no representation is made as to the accuracy or completeness of Spatial Data. The Department of Natural Resources shall not be subject to liability for human error, error due to software conversion, defect, or failure of machines, or any material used in the connection with the machines, including tapes, disks, CD-ROMs or DVD-ROMs and energy. The Department of Natural Resources shall not be liable for any lost profits, consequential damages, or claims against the Department of Natural Resources by third parties. The liability of the Department of Natural Resources for damage regardless of the form of the action shall not exceed any distribution fees that may have been paid in obtaining Spatial Data. This is a MD iMAP hosted service. Find more information on https://imap.maryland.gov.Feature Service Link: https://geodata.md.gov/imap/rest/services/Environment/MD_CoastalResiliencyAssessment/FeatureServer/4

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Baltimore Ecosystem Study LTER (BES) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

description: The recovery plan for Sika Deer discusses the current status of the species, habitat requirements and limiting factors, recovery objectives and criteria, actions required for species recovery, the cost of recovery, and the expected date for recovery objectives to be met. A six year population study on sika deer, Cervus nippon, introduced in 1916 on James Island in Chesapeake Bay, Maryland, provided unique results because of the unusual completeness of the data due to an islandic situation. A density of one deer per acre was reached in 1955. In 1958, 60 percent of the population, mainly young and females, died during January and February. Gross and microscopic studies were made on 18 deer, shot and autopsied in 1955, 1957-60, plus one recently dead at the time of the die-off. Adrenal weight increased, especially in the young, from 1955 to 1958 and then dropped 50 percent following the die-off. Inhibition of growth observed before and during the die off vanished afterwards. Changes in the adrenal zona glomerulosa and medulla suggested overstimulation and a severe imbalance of fluid-electrolyte metabolism as the cause of the die-off. These changes may have been secondary to prolonged hyper-stimulation of the cortex as a result of excessive population density and its resultant social pressures. An inclusion hepatitis and glomerulonephritis are described which involved all deer, especially alter 1958, but not in 1955. These diseases were ruled out as causal factors in the die-off, as were malnutrition and poisoning. The deer were apparently in good nutritive status throughout. It was concluded that physiological derangements resulting from high population density produced the observed effects.; abstract: The recovery plan for Sika Deer discusses the current status of the species, habitat requirements and limiting factors, recovery objectives and criteria, actions required for species recovery, the cost of recovery, and the expected date for recovery objectives to be met. A six year population study on sika deer, Cervus nippon, introduced in 1916 on James Island in Chesapeake Bay, Maryland, provided unique results because of the unusual completeness of the data due to an islandic situation. A density of one deer per acre was reached in 1955. In 1958, 60 percent of the population, mainly young and females, died during January and February. Gross and microscopic studies were made on 18 deer, shot and autopsied in 1955, 1957-60, plus one recently dead at the time of the die-off. Adrenal weight increased, especially in the young, from 1955 to 1958 and then dropped 50 percent following the die-off. Inhibition of growth observed before and during the die off vanished afterwards. Changes in the adrenal zona glomerulosa and medulla suggested overstimulation and a severe imbalance of fluid-electrolyte metabolism as the cause of the die-off. These changes may have been secondary to prolonged hyper-stimulation of the cortex as a result of excessive population density and its resultant social pressures. An inclusion hepatitis and glomerulonephritis are described which involved all deer, especially alter 1958, but not in 1955. These diseases were ruled out as causal factors in the die-off, as were malnutrition and poisoning. The deer were apparently in good nutritive status throughout. It was concluded that physiological derangements resulting from high population density produced the observed effects.

The 2019 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the ""urban footprint."" There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. 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 generalized boundaries for counties and equivalent entities are as of January 1, 2010.

The selected characteristics of the study population.