Each of the State of Maryland’s 1,406 2010 census tracts was analyzed to determine whether it represented a typical census tract as defined by the U. S. Bureau of the Census. Nationally these are census tracts that optimally are 4,000 inhabitants but generally range from 1,200 to 8,000 persons. In Maryland the average census tract contains 4,106 persons. Nationally the housing unit threshold for each census tract generally ranges from 480 to 3,200 housing units, with an optimum size of 1,600 housing units. In Maryland the average census tract contains 1,692 housing units. The Emergency Management Planning Database and the Emergency Planning Vulnerable Population Index are intended to assist State agency emergency officials plan tactics, develop strategies, allocate resources and prioritize responses for emergencies and to identify potentially vulnerable population areas for special attention. Statewide, there are 222 census tracts containing persons at “Very High” socio – economic risk or vulnerability in the event of an emergency. “Very High” risk census tracts account for 16 – percent of the State’s 1,390 specified census tracts. These census tracts are located throughout the State in 20 of 24 jurisdictions. There are 773,808 persons living in these areas making up 13.4 percent of the State’s 2010 Census population of 5,773,552 persons.

This dataset captures data maintained by the Record Research Request Service (R3) at the University of Pittsburgh. R3 is a service of the Department of Biomedical Informatics (DBMI), provisioning UPMC clinical data and authorizing additional UPMC data sources for research. Data captures emergency department (ED) visits and primary care visits for the pediatric population (ages 0-17) served by UPMC in Allegheny County by Census Block Group geography (using 2010 U.S. Census geographies). ED Data captures total visits, along with visits related to asthma, injury, low-acuity visits, and respiratory tract infections. Low acuity visits are often defined as visits by patients that didn't arrive by ambulance and visits that were less-serious or less-urgent where the patient was discharged to their normal place of residence. The primary care visit data includes total visits, well child visits, and asthma diagnoses. Each dataset includes the estimated population in each blockgroup. This estimate was obtained from the U.S. Census Bureau's 2015-19 5-year American Community Survey. Pediatric populations are defined as those under age 18. UPMC data captures a large share of ED visits due to the health system's role with UPMC Children's Hospital of Pittsburgh. The Regional Data Center teamed with colleagues at the University of Pittsburgh Center for Social Research to geocode data to blockgroup, and the totals were computed by R3. Special thanks to partners at The Pittsburgh Study for helping us make this data available, and the Data Across Sectors for Health program for providing financial support for this work.

Contains the CSD identifier (CSDUID) and the spaces population weighted average travel time to closest 24-hr Emergency department

This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows demographic context for emergency response efforts. This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of households with no vehicle available. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B01001, B08201, B16003, B16004, B17020, B18101, B25040, B25117, B27010 (Not all lines of these tables are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 28, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. Each of the State of Maryland's 1 - 406 2010 census tracts was analyzed to determine whether it represented a typical census tract as defined by the U. S. Bureau of the Census. Nationally these are census tracts that optimally are 4 - 000 inhabitants but generally range from 1 - 200 to 8 - 000 persons. In Maryland the average census tract contains 4 - 106 persons. Nationally the housing unit threshold for each census tract generally ranges from 480 to 3 - 200 housing units - with an optimum size of 1 - 600 housing units. In Maryland the average census tract contains 1 - 692 housing units. The Emergency Management Planning Database and the Emergency Planning Vulnerable Population Index are intended to assist State agency emergency officials plan tactics - develop strategies - allocate resources and prioritize responses for emergencies and to identify potentially vulnerable population areas for special attention. Statewide - there are 222 census tracts containing persons at 'Very High' socio - economic risk or vulnerability in the event of an emergency. 'Very High'� risk census tracts account for 16 - percent of the State's 1 - 390 specified census tracts. These census tracts are located throughout the State in 20 of 24 jurisdictions. There are 773 - 808 persons living in these areas making up 13.4 percent of the State's 2010 Census population of 5 - 773 - 552 persons. Feature Service Layer Link: https://mdgeodata.md.gov/imap/rest/services/PublicSafety/MD_VeryHighRiskCensusTracts/FeatureServer 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.

This layer shows demographic context for emergency response efforts. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.

Emergency department visits in U.S. hospitals continue to surge, with *********************************** in Dallas leading the pack in 2024. The facility recorded ******* ED visits, followed closely by ******************************** in Florida with ******* visits. This trend highlights the growing demand for emergency medical services across the country, particularly in large urban centers. Evolving healthcare landscape While emergency departments are busier than ever, the overall number of hospitals in the U.S. has been decreasing since the 1970s. Meanwhile, there is a rise of large health systems. The Hospital Corporation of America, based in Nashville, Tennessee, stands as the largest health system in the country, operating *** hospitals as of February 2025. This consolidation trend reflects the changing dynamics of healthcare delivery and management in the United States. Specialization and capacity challenges As hospitals face increasing pressure on their emergency departments, many are also focusing on specialized services to meet diverse patient needs. For instance, the ****************************************************** performed ****** organ transplants between January 1988 and March 2025, making it the nation's ******* transplant center. Meanwhile, ******************** in Florida holds the title of the largest U.S. hospital with ***** beds.

The Los Angeles County Climate Vulnerability Assessment identified and incorporated 29 social vulnerability indicators. These indicators are listed below alongside their description and data source. Full report: https://ceo.lacounty.gov/cva-report/Note: All indicators are at the census tract level. Census tracts with no population (data) are omitted from this layer. Indicator Description Source Countywide Average

Asian Percent identifying as non-Hispanic Asian US Census Bureau, American Community Survey 2018 5-Year Estimates 14.4%

Asthma Age-adjusted rate of emergency department visits for asthma California Environmental Health Tracking Program (CEHTP) and Office of Statewide Health Planning and Development (OSHPD) 52.2

Black Percent identifying as non-Hispanic black or African American US Census Bureau, American Community Survey 2018 5-Year Estimates 7.9%

Cardiovascular Age-adjusted rate of emergency department visits for heart attacks per 10,000 California Environmental Health Tracking Program (CEHTP) and Office of Statewide Health Planning and Development (OSHPD) 8.4

Children Percent of people 18 and under US Census Bureau, American Community Survey 2018 5-Year Estimates 24.9%

Disability Percent of persons with either mental or physical disability US Census Bureau, American Community Survey 2018 5-Year Estimates 9.9%

Female Percent female US Census Bureau, American Community Survey 2018 5-Year Estimates 50.7%

Female householder Percent of households that have a female householder with no spouse present US Census Bureau, American Community Survey 2018 5-Year Estimates 16.2%

Foreign born Percent of the total population who was not born in the United States or Puerto Rico US Census Bureau, American Community Survey 2018 5-Year Estimates 35.2%

Hispanic Latinx Percent identifying as Hispanic or Latino US Census Bureau, American Community Survey 2018 5-Year Estimates 48.5%

Households without vehicle access Percent of households without access to a personal vehicle US Census Bureau, American Community Survey 2018 5-Year Estimates 8.8%

Library access Each tract's average block distance to nearest library LA County Internal Services Department 1.14 miles

Limited English Percent limited English speaking households US Census Bureau, American Community Survey 2018 5-Year Estimates 13.6%

Living in group quarters Percent of persons living in (either institutionalized or uninstitiutionalized) group quarters US Census Bureau, American Community Survey 2018 5-Year Estimates 1.8%

Median income Median household income of census tract US Census Bureau, American Community Survey 2018 5-Year Estimates $69,623

Mobile homes Percent of occupied housing units that are mobile homes US Census Bureau, American Community Survey 2018 5-Year Estimates 1.8%

No health insurance Percent of persons without health insurance US Census Bureau, American Community Survey 2018 5-Year Estimates 0.2%

No high school diploma Percent of persons 25 and older without a high school diploma US Census Bureau, American Community Survey 2018 5-Year Estimates 10.8%

No internet subscription Percent of the population without an internet subscription US Census Bureau, American Community Survey 2018 5-Year Estimates 22.6%

Older adults Percent of people 65 and older US Census Bureau, American Community Survey 2018 5-Year Estimates 18.4%

Older adults living alone Percent of households in which the householder is 65 and over who and living alone US Census Bureau, American Community Survey 2018 5-Year Estimates 12.9%

Outdoor workers Percentage of outdoor workers - agriculture, fishing, mining, extractive, construction occupations US Census Bureau, American Community Survey 2018 5-Year Estimates 8.0%

Poverty Percent of the population living in a family earning below 100% of the federal poverty threshold US Census Bureau, American Community Survey 2018 5-Year Estimates 5.4%

Rent burden Percent of renters paying more than 30 percent of their monthly income on rent and utilities US Census Bureau, American Community Survey 2018 5-Year Estimates 16.1%

Renters Percentage of renters per census tract US Census Bureau, American Community Survey 2018 5-Year Estimates 54.3%

Transit access Percent of population residing within a ½ mile of a major transit stop Healthy Places Index, SCAG 52.8%

Tribal and Indigenous Percent identifying as non-Hispanic American Indian and Alaska native US Census Bureau, American Community Survey 2018 5-Year Estimates 54.9%

Unemployed Percent of the population over the age of 16 that is unemployed and eligible for the labor force US Census Bureau, American Community Survey 2018 5-Year Estimates 6.9%

Voter turnout rate Percentage of registered voters voting in the 2016 general election CA Statewide General Elections Database 2016 63.8%

This dataset captures data maintained by the Record Research Request Service (R3) at the University of Pittsburgh. R3 is a service of the Department of Biomedical Informatics (DBMI), provisioning UPMC clinical data and authorizing additional UPMC data sources for research.

Data captures emergency department (ED) visits and primary care visits for the pediatric population (ages 0-17) served by UPMC in Allegheny County by Census Block Group geography (using 2010 U.S. Census geographies).

ED Data captures total visits, along with visits related to asthma, injury, low-acuity visits, and respiratory tract infections. Low acuity visits are often defined as visits by patients that didn't arrive by ambulance and visits that were less-serious or less-urgent where the patient was discharged to their normal place of residence.

The primary care visit data includes total visits, well child visits, and asthma diagnoses.

Each dataset includes the estimated population in each blockgroup. This estimate was obtained from the U.S. Census Bureau's 2015-19 5-year American Community Survey. Pediatric populations are defined as those under age 18.

UPMC data captures a large share of ED visits due to the health system's role with UPMC Children's Hospital of Pittsburgh.

The Regional Data Center teamed with colleagues at the University of Pittsburgh Center for Social Research to geocode data to blockgroup, and the totals were computed by R3.

Special thanks to partners at The Pittsburgh Study for helping us make this data available, and the Data Across Sectors for Health program for providing financial support for this work.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

Number of times graduating medical students reported completing an emergency management skill during training and their associated confidence level in that skill compared with one-way ANOVA.

Colombian regions with number and percent study participation.

This layer shows the number of Households without Internet Access by Census Tract boundaries. [Source metadata] This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of households with no internet connection. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2017-2021ACS Table(s): B28001, B28002 (Not all lines of ACS table B28002 are available in this feature layer)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 8, 2022National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2021 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This layer shows demographic context for emergency response efforts.This is shown by tract, county, and state boundaries.This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of households who do not have access to internet. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right.Current Vintage: 2015-2019ACS Table(s): B01001, B08201, B09021, B16003, B16004, B17020, B18101, B25040, B25117, B27010, B28001, B28002 Data downloaded from:Census Bureau's API for American Community SurveyDate of API call: December 12, 2020National Figures:data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, seeAccuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule.Click hereto learn more about ACS data releases.Boundaries come from theUS Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintageas specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500kTIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specificationsdefined by the American Community Survey.Field alias names were created based on the Table Shells file available from theAmerican Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero.These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This layer shows the number of Households without Internet Access by County boundaries. [Source metadata] This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of households with no internet connection. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2017-2021ACS Table(s): B28001, B28002 (Not all lines of ACS table B28002 are available in this feature layer)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 8, 2022National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2021 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

BackgroundRefugees face barriers to accessing healthcare despite provision of short-term services after arrival. Limited access to sustained primary care may lead to increased emergency department (ED) utilization and little is known regarding how refugee children access emergency care.ObjectiveTo compare the proportion of ED claims and median level of service (LOS) between refugee children and general population controls in a statewide claims database.MethodsWe conducted a retrospective cohort study of medical claims for patients aged 0 to 18 years old in a statewide claims database from 2014–2019. Refugee claims were identified using deterministic linkage of children with known refugee status. Procedure and diagnosis ICD9/10, Healthcare Common Procedure Coding System, and Current Procedural Terminology codes were obtained from the statewide database to indicate type of claim (ED vs outpatient) and LOS. Demographics were extracted from a data warehouse. Primary outcome was the number of ED claims per 1000 combined ED and outpatient claims. LOS was measured as a proxy for ED visit complexity. We compared demographics, frequency of claims, and median LOS using chi-square.ResultsThere were 5,590,808 total claims with 1,235,476 ED claims. Median number of ED claims per individual patient was the same between groups, however the proportion of claims related to an ED visit was significantly higher in the refugee population than the general population controls (244 vs 221, p = 0.001). Median LOS for ED claims was Level 3 (99283) and there was no difference between groups.ConclusionProportion of ED claims was higher in a refugee population compared to controls with no differences in LOS, indicating higher ED utilization among refugees for all acuity levels. Further study is needed to determine if healthcare disparities account for this difference and if population specific services may support the care of the refugee children.

This data collection comprises a data library, sample outputs, batch files and accompanying documentation from the ESRC-funded project “Population247NRT: Near real-time spatiotemporal population estimates for health, emergency response and national security”. The data comprise a structured set of input data for use with the authors’ SurfaceBuilder247 software and sample outputs which estimate the population distribution of England at specific times on specific dates, referenced to 2011 census population totals. The sample output files (provided as GeoTIFFs) contain population estimates in 200m grid cells, based on the British National Grid, for 02:00 (2am) and 14:00 (2pm) on a typical weekday in University and school term-time and out of term-time. The estimates are broken down by seven age/economic activity sub-groups for term-time and six for out of term-time, and include estimates of population activity in residential, workplace, education, healthcare and road transportation domains. The data library, which has been constructed entirely using open data sources, comprises population estimates, by age/economic activity sub-groups, for point locations (typically population-weighted centroids of census output areas and workplace zones, or postcode centroids of sites such as schools or hospitals); time profiles representing usual patterns of population activity at these sites during a 24-hour period; and background grid layers representing the land surface area and major road network. SurfaceBuilder247 uses the data library to generate time-specific gridded population estimates by redistributing the population of each sub-group across the available locations and background grid in accordance with the reference time profiles. The sample output grids provided in this resource may be used directly in GIS software or, alternatively, the input data library may be reprocessed using SurfaceBuilder247 to generate estimates for specific dates and times of interest to the user. Sample batch and session parameter files are included in the resource.Decision-making and policy formulation in sectors such as health, emergency/crisis response and national security, ideally require accurate dynamic information on the number of people in specific places at specific times of the day, week, season or year. Traditional census data do not provide this level of detail but are often used for such policy and planning purposes. The ESRC-funded Population247 programme of research (Martin et al, 2015) developed a framework, methodology and software tool (SurfaceBuilder247) for integrating diverse contemporary data sources to produce enhanced time-specific population estimates for small geographical areas. Its usefulness has since been demonstrated for flooding and radiation emergency response/planning, through collaborations with HR Wallingford and Public Health England. These models have primarily involved the integration of open administrative data for activities such as place of residence, work, education and health. Now, new and emerging forms of data, such as sensor data, live and static data feeds provided via the internet, and various commercial datasets which were not previously available, provide exciting opportunities to enhance these population estimates. Such new and emerging datasets are useful because they provide near real-time information on population activity in sectors which are particularly dynamic and have previously been difficult to model, such as retail, leisure and transport. However, extracting useful intelligence from these sources, and integrating and calibrating them with existing data sources, poses significant challenges for researchers and practitioners seeking to employ them in the creation of time-specific population estimates. This project will combine new, emerging and existing datasets in order to produce enhanced time-specific population estimates for more informed decision-making and policy formulation in the health, emergency/crisis response and national security sectors. It is a collaborative project between University of Southampton, Public Health England (PHE), Health and Safety Executive (HSE) and Defence Science and Technology Laboratory (Dstl). The project will enhance existing methods and tools for harvesting, processing, integrating and calibrating new, emerging and existing data sources in order to produce time-specific population estimates. It will deliver two substantive policy demonstrator case studies with the project partners. The first case study will demonstrate the potential for using time-specific population estimates for near real-time response in emergencies; the second will explore their usefulness for modelling variation in 'normal' population distributions through space and time in order to inform longer-term planning and policy formulation. Importantly, the project will also encourage the sharing of knowledge and expertise between academia and the public sector through joint design and implementation of the case studies, internal seminars and a jointly organised stakeholder workshop. Invitees to the workshop will be key stakeholders in policy and practice from within and beyond the partners' sectors. The workshop will showcase the data, methods and tools developed by the project, discuss the opportunities and challenges involved in implementing these for decision-making and policy formulation, and identify how such methods might realistically be scaled up within these sectors. Ultimately, the aim of the project is to help partners such as PHE, HSE and Dstl carry out their remits more effectively and efficiently through the provision of better time-specific population estimates. The data library and sample output files provided in this data collection have been generated by processing a range of open data sources including residential and workplace populations from the 2011 Census, school and college pupil numbers from the school census and services such as the government’s ‘Get Information About Schools’, university student numbers from the Higher Education Statistics Agency, hospital patient numbers and attendance time profiles from NHS Digital, road traffic estimates from the Department for Transport National Transportation Model, and GIS road network, inland water and coastline layers from Ordnance Survey and the Office for National Statistics. Information from the 2015 Time Use Survey has been used in the estimation of typical time profiles for workplace activities. GIS processing has been undertaken to estimate typical catchment area sizes for locations such as schools and hospitals. The principal input data are population counts for 2011 census output areas in England, which determine the base populations of all the estimates produced. The project team have georeferenced, reformatted and integrated all the input sources to create an input data library for the SurfaceBuilder247 software. All the necessary input files are provided, together with sample outputs for selected times of interest.

US Emergency Lighting Market size was valued at USD 1.95 Billion in 2024 and is expected to reach USD 3.14 Billion by 2032, growing at a CAGR of 6.1% from 2025 to 2032.

Key Market Drivers

Increasing Frequency of Natural Disasters and Power Outages: The increasing frequency of natural disasters and power disruptions will propel the US emergency lighting market. According to the US Energy Information Administration (EIA), Americans will endure an average of 8 hours of power outages in 2023, a 60% increase from 2019. FEMA's study on 102 federal disaster declarations in 2022 emphasizes the growing importance of dependable emergency lighting infrastructure. This increase in disruptions emphasizes the necessity of dependable lighting in both the commercial and residential sectors.

Expanding Commercial Infrastructure Development: The expanding commercial infrastructure development will boost the US emergency lighting market. According to the United States Census Bureau, commercial construction spending will reach $1.31 trillion in 2023, representing a 9.2% increase over the previous year. This boom in building immediately affects demand for emergency lighting systems. Approximately 78% of new commercial buildings require sophisticated lighting to meet building requirements. As business buildings grow in size, so does the demand for reliable emergency lighting solutions.

Descriptive summary of dataset using median values.

The complete data set of annual utilization data reported by hospitals contains basic licensing information including bed classifications; patient demographics including occupancy rates, the number of discharges and patient days by bed classification, and the number of live births; as well as information on the type of services provided including the number of surgical operating rooms, number of surgeries performed (both inpatient and outpatient), the number of cardiovascular procedures performed, and licensed emergency medical services provided.

Model results for all CBGs, for trips up to 3km in length, in 2020.