Description: The purpose of this dataset is to determine a count of job listings through MWE per census tract, establishing a comprehensive base for probing geographical inconsistencies in employment characteristics in these areas. It is developed to aid public health research and urban planning initiatives by enhancing our understanding of, and ability to address, local health and socioeconomic concerns. The dataset is curated from MWE Job Order data, which lists employer address, type of position, and number of positions available.
Copyright Text: Baltimore County Department of Workforce and Economic Development, BCSTAT
Description: This feature class represents census tracts, enriched with data on traffic accidents and population demographics. Each polygon corresponds to a census tract and contains the total number of traffic accidents [TRAFFIC_ACCIDENT_COUNT], total population from the 2021 ACS 5-year estimates [POPULATION_TOTAL], and a calculated traffic accident rate per 1000 people [TRAFFIC_ACCIDENT_RATE]. The traffic accident rate is calculated using the provided expression, "(TRAFFIC_ACCIDENT_COUNT/(TOTAL_POPULATION/1000))", which offers a per capita perspective of traffic accidents. The dataset is generated in FME, where traffic accident count data from an internal database is spatially intersected and summarized within the 2020 Census Tracts polygons, ensuring a comprehensive and geographically accurate representation of traffic accident occurrence in relation to population density.
Copyright Text: Maryland State Police, Baltimore County Police Department, BCSTAT, United States Census Bureau
Description: This data model identifies pedestrian curb ramps geocoded as points, these points have a attribute which describes whether they are or are not in compliance with ADA standards. Ramps out of compliance are separated such that a rate can be generated which identifies the percent of ramps in each tract that are out of compliance with ADA Standards.
Copyright Text: United States Census, Baltimore County Department of Public Works, BCSTAT
Description: The feature class represents the 2020 census tracts specific to Baltimore County, featuring three critical columns: [TOTAL_POPULATION], which lists the population per census tract; [P1_CRIME_COUNT], illustrating the count of P1 criminal crime incidents for each tract; and [P1_CRIME_RATE], indicating the per capita rate of these incidents per 1000 residents. The data for the criminal incidents is sourced from the Baltimore County police database, PPPd. After extraction, it is transformed using FME to align and integrate with the geographic representation of the census tracts. This dataset is subsequently loaded into the GRSI GIS database for accessibility and analysis. Importantly, this feature class represents a dynamic snapshot, as it's based on a rolling year, ensuring the data remains relevant and reflective of recent crime trends in the context of population demographics.
Copyright Text: Baltimore County, BCSTAT, Baltimore County Police Department
Description: The purpose of this dataset is to enumerate the presence of fast food, liquor, and tobacco stores per census tract, establishing a comprehensive base for probing geographical inconsistencies in access to these types of retail outlets. It is developed to aid public health research and urban planning initiatives by enhancing our understanding of, and ability to address, local health and socioeconomic concerns. The dataset is curated from the FACILITIES.INFOGROUP_2020, selectively incorporating NAICS Codes (445320,722410, 722513, 459991). Utilizing FME, a spatial intersection and summary within census tracts in Baltimore County was performed.
Copyright Text: Baltimore County, United States Census Bureau, InforGroups, NAICS
Description: The feature class provides an in-depth representation of census tracts within Baltimore County, originating from the esteemed Centers for Disease Control. It is a crucial segment of the CDC's 2020 Social Vulnerability Index (SVI), a tool designed to assess and determine the resilience of communities when confronted by external stresses on human health, such as natural disasters or disease outbreaks. The SVI is underpinned by factors like poverty, lack of access to transportation, and crowded housing, among other variables. Each census tract polygon in this dataset encompasses comprehensive data on these factors, providing researchers, policymakers, and public health officials an essential tool to identify areas in Baltimore County that may need support in times of adversity. The dataset aids in the spatial interpretation of social vulnerability, enabling informed decisions on resource allocation, emergency preparedness, and community engagement initiatives.
Copyright Text: Center for Disease Control (CDC), Baltimore County, BCSTAT
E_HBURD
(
type: esriFieldTypeInteger, alias: Housing cost-burdened occupied housing units with annual income less than $75,000 (30%+ of income spent on housing costs) estimate, 2016-2020 ACS
)
M_HBURD
(
type: esriFieldTypeInteger, alias: Housing cost-burdened occupied housing units with annual income less than $75,000 (30%+ of income spent on housing costs) estimate MOE, 2016-2020 ACS
)
E_NOHSDP
(
type: esriFieldTypeInteger, alias: Persons (age 25+) with no high school diploma estimate, 2016-2020 ACS
)
M_NOHSDP
(
type: esriFieldTypeInteger, alias: Persons (age 25+) with no high school diploma estimate MOE, 2016-2020 ACS
)
E_UNINSUR
(
type: esriFieldTypeInteger, alias: Uninsured in the total civilian noninstitutionalized population estimate, 2016-2020 ACS
)
M_UNINSUR
(
type: esriFieldTypeInteger, alias: Uninsured in the total civilian noninstitutionalized population estimate MOE, 2016-2020 ACS
)
E_DISABL
(
type: esriFieldTypeInteger, alias: Civilian noninstitutionalized population with a disability estimate, 2016-2020 ACS
)
M_DISABL
(
type: esriFieldTypeInteger, alias: Civilian noninstitutionalized population with a disability estimate MOE, 2016-2020 ACS
)
E_SNGPNT
(
type: esriFieldTypeInteger, alias: Single-parent household with children under 18 estimate, 2016-2020 ACS
)
M_SNGPNT
(
type: esriFieldTypeInteger, alias: Single-parent household with children under 18 estimate MOE, 2016-2020 ACS
)
E_LIMENG
(
type: esriFieldTypeInteger, alias: Persons (age 5+) who speak English [double_quote]less than well[double_quote] estimate, 2016-2020 ACS
)
M_LIMENG
(
type: esriFieldTypeInteger, alias: Persons (age 5+) who speak English [double_quote]less than well[double_quote] estimate MOE, 2016-2020 ACS
)
E_MINRTY
(
type: esriFieldTypeInteger, alias: Minority (Hispanic or Latino (of any race); Black and African American, Not Hispanic or Latino; American Indian and Alaska Native, Not Hispanic or Latino; Asian, Not Hispanic or Latino; Native Hawaiian and Other Pacific Islander, Not Hispanic or Latino; T
)
M_MINRTY
(
type: esriFieldTypeInteger, alias: Minority (Hispanic or Latino (of any race); Black and African American, Not Hispanic or Latino; American Indian and Alaska Native, Not Hispanic or Latino; Asian, Not Hispanic or Latino; Native Hawaiian and Other Pacific Islander, Not Hispanic or Latino; T
)
E_MUNIT
(
type: esriFieldTypeInteger, alias: Housing in structures with 10 or more units estimate, 2016-2020 ACS
)
M_MUNIT
(
type: esriFieldTypeInteger, alias: Housing in structures with 10 or more units estimate MOE, 2016-2020 ACS
)
E_MOBILE
(
type: esriFieldTypeInteger, alias: Mobile homes estimate, 2016-2020 ACS
)
M_MOBILE
(
type: esriFieldTypeInteger, alias: Mobile homes estimate MOE, 2016-2020 ACS
)
E_CROWD
(
type: esriFieldTypeInteger, alias: At household level (occupied housing units), more people than rooms estimate, 2016-2020 ACS
)
M_CROWD
(
type: esriFieldTypeInteger, alias: At household level (occupied housing units), more people than rooms estimate MOE, 2016-2020 ACS
)
E_NOVEH
(
type: esriFieldTypeInteger, alias: Households with no vehicle available estimate, 2016-2020 ACS
)
M_NOVEH
(
type: esriFieldTypeInteger, alias: Households with no vehicle available estimate MOE, 2016-2020 ACS
)
E_GROUPQ
(
type: esriFieldTypeInteger, alias: Persons in group quarters estimate, 2016-2020 ACS
)
M_GROUPQ
(
type: esriFieldTypeInteger, alias: Persons in group quarters estimate MOE, 2016-2020 ACS
)
EP_HBURD
(
type: esriFieldTypeDouble, alias: Percentage of housing cost-burdened occupied housing units with annual income less than $75,000 (30%+ of income spent on housing costs) estimate, 2016-2020 ACS estimate, 2016-2020 ACS
)
MP_HBURD
(
type: esriFieldTypeDouble, alias: Percentage of housing cost-burdened occupied housing units with annual income less than $75,000 (30%+ of income spent on housing costs) estimate MOE, 2016-2020 ACS
)
EP_NOHSDP
(
type: esriFieldTypeDouble, alias: Percentage of persons with no high school diploma (age 25+) estimate
)
MP_NOHSDP
(
type: esriFieldTypeDouble, alias: Percentage of persons with no high school diploma (25+) estimate MOE
)
EP_UNINSUR
(
type: esriFieldTypeDouble, alias: Percentage uninsured in the total civilian noninstitutionalized population estimate, 2016-2020 ACS
)
MP_UNINSUR
(
type: esriFieldTypeDouble, alias: Percentage uninsured in the total civilian noninstitutionalized population estimate MOE, 2016-2020 ACS
)
EP_AGE65
(
type: esriFieldTypeDouble, alias: Percentage of persons aged 65 and older estimate, 2016-2020 ACS
)
MP_AGE65
(
type: esriFieldTypeDouble, alias: Percentage of persons aged 65 and older estimate MOE, 2016-2020 ACS
)
EP_AGE17
(
type: esriFieldTypeDouble, alias: Percentage of persons aged 17 and younger estimate, 2016-2020 ACS
)
MP_AGE17
(
type: esriFieldTypeDouble, alias: Percentage of persons aged 17 and younger estimate MOE, 2016-2020 ACS
)
EP_DISABL
(
type: esriFieldTypeDouble, alias: Percentage of civilian noninstitutionalized population with a disability estimate, 2016-2020 ACS
)
MP_DISABL
(
type: esriFieldTypeDouble, alias: Percentage of civilian noninstitutionalized population with a disability estimate MOE, 2016-2020 ACS
)
EP_SNGPNT
(
type: esriFieldTypeDouble, alias: Percentage of single-parent households with children under 18 estimate, 2016-2020 ACS
)
MP_SNGPNT
(
type: esriFieldTypeDouble, alias: Percentage of single-parent households with children under 18 estimate MOE, 2016-2020 ACS
)
EP_LIMENG
(
type: esriFieldTypeDouble, alias: Percentage of persons (age 5+) who speak English [double_quote]less than well[double_quote] estimate, 2016-2020 ACS
)
MP_LIMENG
(
type: esriFieldTypeDouble, alias: Percentage of persons (age 5+) who speak English [double_quote]less than well[double_quote] estimate MOE, 2016-2020 ACS
)
EP_MINRTY
(
type: esriFieldTypeDouble, alias: Percentage minority (Hispanic or Latino (of any race); Black and African American, Not Hispanic or Latino; American Indian and Alaska Native, Not Hispanic or Latino; Asian, Not Hispanic or Latino; Native Hawaiian and Other Pacific Islander, Not Hispanic o
)
MP_MINRTY
(
type: esriFieldTypeDouble, alias: Percentage minority (Hispanic or Latino (of any race); Black and African American, Not Hispanic or Latino; American Indian and Alaska Native, Not Hispanic or Latino; Asian, Not Hispanic or Latino; Native Hawaiian and Other Pacific Islander, Not Hispanic o
)
EP_MUNIT
(
type: esriFieldTypeDouble, alias: Percentage of housing in structures with 10 or more units estimate
)
MP_MUNIT
(
type: esriFieldTypeDouble, alias: Percentage of housing in structures with 10 or more units estimate MOE
)
EP_MOBILE
(
type: esriFieldTypeDouble, alias: Percentage of mobile homes estimate
)
MP_MOBILE
(
type: esriFieldTypeDouble, alias: Percentage of mobile homes estimate MOE
)
EP_CROWD
(
type: esriFieldTypeDouble, alias: Percentage of occupied housing units with more people than rooms estimate
)
MP_CROWD
(
type: esriFieldTypeDouble, alias: Percentage of occupied housing units with more people than rooms estimate MOE
)
EP_NOVEH
(
type: esriFieldTypeDouble, alias: Percentage of households with no vehicle available estimate
)
MP_NOVEH
(
type: esriFieldTypeDouble, alias: Percentage of households with no vehicle available estimate MOE
)
EP_GROUPQ
(
type: esriFieldTypeDouble, alias: Percentage of persons in group quarters estimate, 2016-2020 ACS
)
MP_GROUPQ
(
type: esriFieldTypeDouble, alias: Percentage of persons in group quarters estimate MOE, 2016-2020 ACS
)
SPL_THEME1
(
type: esriFieldTypeDouble, alias: Sum of series for Socioeconomic Status theme
)
RPL_THEME1
(
type: esriFieldTypeDouble, alias: Percentile ranking for Socioeconomic Status theme summary
)
EPL_AGE65
(
type: esriFieldTypeDouble, alias: Percentile percentage of persons aged 65 and older estimate
)
EPL_AGE17
(
type: esriFieldTypeDouble, alias: Percentile percentage of persons aged 17 and younger estimate
)
EPL_DISABL
(
type: esriFieldTypeDouble, alias: Percentile percentage of civilian noninstitutionalized population with a disability estimate
)
EPL_SNGPNT
(
type: esriFieldTypeDouble, alias: Percentile percentage of single-parent households with children under 18 estimate
)
EPL_LIMENG
(
type: esriFieldTypeDouble, alias: Percentile percentage of persons (age 5+) who speak English [double_quote]less than well[double_quote] estimate
)
SPL_THEME2
(
type: esriFieldTypeDouble, alias: Sum of series for Household Characteristics theme
)
EPL_MINRTY
(
type: esriFieldTypeDouble, alias: Percentile percentage minority (Hispanic or Latino (of any race); Black and African American, Not Hispanic or Latino; American Indian and Alaska Native, Not Hispanic or Latino; Asian, Not Hispanic or Latino; Native Hawaiian and Other Pacific Islander, Not
)
SPL_THEME3
(
type: esriFieldTypeDouble, alias: Sum of series for Racial and Ethnic Minority Status theme
)
RPL_THEME3
(
type: esriFieldTypeDouble, alias: Percentile ranking for Racial and Ethnic Minority Status theme
)
EPL_MUNIT
(
type: esriFieldTypeDouble, alias: Percentile percentage housing in structures with 10 or more units estimate
)
EPL_MOBILE
(
type: esriFieldTypeDouble, alias: Percentile percentage mobile homes estimate
)
EPL_CROWD
(
type: esriFieldTypeDouble, alias: Percentile percentage households with more people than rooms estimate
)
EPL_NOVEH
(
type: esriFieldTypeDouble, alias: Percentile percentage households with no vehicle available estimate
)
EPL_GROUPQ
(
type: esriFieldTypeDouble, alias: Percentile percentage of persons in group quarters estimate
)
SPL_THEME4
(
type: esriFieldTypeDouble, alias: Sum of series for Housing Type/ Transportation theme
)
F_POV150
(
type: esriFieldTypeInteger, alias: Flag - the percentage of persons below 150% poverty is in the 90th percentile (1 = yes, 0 = no)
)
F_UNEMP
(
type: esriFieldTypeInteger, alias: Flag - the percentage of civilian unemployed is in the 90th percentile (1 = yes, 0 = no)
)
F_HBURD
(
type: esriFieldTypeInteger, alias: Flag - the percentage of housing cost-burdened occupied housing units is in the 90th percentile (1 = yes, 0 = no)
)
F_NOHSDP
(
type: esriFieldTypeInteger, alias: Flag - the percentage of persons with no high school diploma is in the 90th percentile (1 = yes, 0 = no)
)
F_UNINSUR
(
type: esriFieldTypeInteger, alias: Flag - the percentage of uninsured is in the 90th percentile (1 = yes, 0 = no)
)
F_THEME1
(
type: esriFieldTypeInteger, alias: Sum of flags for Socioeconomic Status theme
)
F_AGE65
(
type: esriFieldTypeInteger, alias: Flag - the percentage of persons aged 65 and older is in the 90th percentile (1 = yes, 0 = no)
)
F_AGE17
(
type: esriFieldTypeInteger, alias: Flag - the percentage of persons aged 17 and younger is in the 90th percentile (1 = yes, 0 = no)
)
F_DISABL
(
type: esriFieldTypeInteger, alias: Flag - the percentage of persons with a disability is in the 90th percentile (1 = yes, 0 = no)
)
F_SNGPNT
(
type: esriFieldTypeInteger, alias: Flag - the percentage of single-parent households is in the 90th percentile (1 = yes, 0 = no)
)
F_LIMENG
(
type: esriFieldTypeInteger, alias: Flag - the percentage those with limited English is in the 90th percentile (1 = yes, 0 = no)
)
F_THEME2
(
type: esriFieldTypeInteger, alias: Sum of flags for Household Characteristics theme
)
F_MINRTY
(
type: esriFieldTypeInteger, alias: Flag - the percentage of minority is in the 90th percentile (1 = yes, 0 = no)
)
F_THEME3
(
type: esriFieldTypeInteger, alias: Sum of flags for Racial and Ethnic Minority Status theme
)
F_MUNIT
(
type: esriFieldTypeInteger, alias: Flag - the percentage of households in multi-unit housing is in the 90th percentile (1 = yes, 0 = no)
)
F_MOBILE
(
type: esriFieldTypeInteger, alias: Flag - the percentage of mobile homes is in the 90th percentile (1 = yes, 0 = no)
)
F_CROWD
(
type: esriFieldTypeInteger, alias: Flag - the percentage of crowded households is in the 90th percentile (1 = yes, 0 = no)
)
F_NOVEH
(
type: esriFieldTypeInteger, alias: Flag - the percentage of households with no vehicles is in the 90th percentile (1 = yes, 0 = no)
)
F_GROUPQ
(
type: esriFieldTypeInteger, alias: Flag - the percentage of persons in group quarters is in the 90th percentile (1 = yes, 0 = no)
)
F_THEME4
(
type: esriFieldTypeInteger, alias: Sum of flags for Housing Type/ Transportation theme
)
F_TOTAL
(
type: esriFieldTypeInteger, alias: Sum of flags for the four themes
)
E_NOINT
(
type: esriFieldTypeInteger, alias: Adjunct variable -Households without a computer with a broadband Internet subscription estimate, 2016-2020 ACS
)
M_NOINT
(
type: esriFieldTypeInteger, alias: Adjunct variable -Households without a computer with a broadband Internet subscription estimate MOE, 2016-2020 ACS
)
E_AFAM
(
type: esriFieldTypeInteger, alias: Adjunct variable -Black/African American, not Hispanic or Latino persons estimate, 2016-2020 ACS
)
M_AFAM
(
type: esriFieldTypeInteger, alias: Adjunct variable -Black/African American, not Hispanic or Latino persons estimate MOE, 2016-2020 ACS
)
E_ASIAN
(
type: esriFieldTypeInteger, alias: Adjunct variable – Asian, not Hispanic or Latino persons estimate, 2016-2020 ACS
)
M_ASIAN
(
type: esriFieldTypeInteger, alias: Adjunct variable – Asian, not Hispanic or Latino persons estimate MOE, 2016-2020 ACS
)
E_AIAN
(
type: esriFieldTypeInteger, alias: Adjunct variable -American Indian or Alaska Native, not Hispanic or Latino persons estimate, 2016-2020 ACS
)
M_AIAN
(
type: esriFieldTypeInteger, alias: Adjunct variable -American Indian or Alaska Native, not Hispanic or Latino persons estimate MOE, 2016-2020 ACS
)
E_NHPI
(
type: esriFieldTypeInteger, alias: Adjunct variable -Native Hawaiian or Other Pacific Islander, not Hispanic or Latino persons estimate, 2016-2020 ACS
)
M_NHPI
(
type: esriFieldTypeInteger, alias: Adjunct variable -Native Hawaiian or Other Pacific Islander, not Hispanic or Latino persons estimate MOE, 2016-2020 ACS
)
E_TWOMORE
(
type: esriFieldTypeInteger, alias: Adjunct variable -Two or more races, not Hispanic or Latino persons estimate, 2016-2020 ACS
)
M_TWOMORE
(
type: esriFieldTypeInteger, alias: Adjunct variable -Two or more races, not Hispanic or Latino persons estimate MOE, 2016-2020 ACS
)
E_OTHERRACE
(
type: esriFieldTypeInteger, alias: Adjunct variable -Some other race, not Hispanic or Latino persons estimate, 2016-2020 ACS
)
M_OTHERRACE
(
type: esriFieldTypeInteger, alias: Adjunct variable -Some other race, not Hispanic or Latino persons estimate MOE, 2016-2020 ACS
)
EP_NOINT
(
type: esriFieldTypeDouble, alias: Adjunct variable -Percentage of households without a computer with a broadband Internet subscription estimate, 2016-2020 ACS
)
MP_NOINT
(
type: esriFieldTypeDouble, alias: Adjunct variable -Percentage of households without a computer with a broadband Internet subscription estimate MOE, 2016-2020 ACS
)
EP_AFAM
(
type: esriFieldTypeDouble, alias: Adjunct variable -Percentage of Black/African American, not Hispanic or Latino persons estimate, 2016-2020 ACS
)
MP_AFAM
(
type: esriFieldTypeDouble, alias: Adjunct variable -Percentage of Black/African American, not Hispanic or Latino persons estimate MOE, 2016-2020 ACS
)
EP_HISP
(
type: esriFieldTypeDouble, alias: Adjunct variable -Percentage of Hispanic or Latino persons estimate, 2016-2020 ACS
)
MP_HISP
(
type: esriFieldTypeDouble, alias: Adjunct variable -Percentage of Hispanic or Latino persons estimate MOE, 2016-2020 ACS
)
EP_ASIAN
(
type: esriFieldTypeDouble, alias: Adjunct variable -Percentage of Asian, not Hispanic or Latino persons estimate, 2016-2020 ACS
)
MP_ASIAN
(
type: esriFieldTypeDouble, alias: Adjunct variable -Percentage of Asian, not Hispanic or Latino persons estimate MOE, 2016-2020 ACS
)
EP_AIAN
(
type: esriFieldTypeDouble, alias: Adjunct variable -Percentage of American Indian or Alaska Native, not Hispanic or Latino persons estimate, 2016-2020 ACS
)
MP_AIAN
(
type: esriFieldTypeDouble, alias: Adjunct variable -Percentage of American Indian or Alaska Native, not Hispanic or Latino persons estimate MOE, 2016-2020 ACS
)
EP_NHPI
(
type: esriFieldTypeDouble, alias: Adjunct variable -Percentage of Native Hawaiian or Other Pacific Islander, not Hispanic or Latino persons estimate, 2016-2020 ACS
)
MP_NHPI
(
type: esriFieldTypeDouble, alias: Adjunct variable -Percentage of Native Hawaiian or Other Pacific Islander, not Hispanic or Latino persons estimate MOE, 2016-2020 ACS
)
EP_TWOMORE
(
type: esriFieldTypeDouble, alias: Adjunct variable -Percentage of two or more races, not Hispanic or Latino persons estimate, 2016-2020 ACS
)
MP_TWOMORE
(
type: esriFieldTypeDouble, alias: Adjunct variable -Percentage of two or more races, not Hispanic or Latino persons estimate MOE, 2016-2020 ACS
)
EP_OTHERRACE
(
type: esriFieldTypeDouble, alias: Adjunct variable -Percentage of some other race, not Hispanic or Latino persons estimate, 2016-2020 ACS
)
MP_OTHERRACE
(
type: esriFieldTypeDouble, alias: Adjunct variable -Percentage of some other race, not Hispanic or Latino persons estimate MOE, 2016-2020 ACS
)
Description: The purpose of this dataset is to determine a temporally aggregated air quality index per census tract, establishing a comprehensive base for probing geographical inconsistencies in the concentrations of pollutants in these areas. It is developed to aid public health research and urban planning initiatives by enhancing our understanding of, and ability to address, local health and socioeconomic concerns. The dataset is curated from the EPA's EJScreen API, which temporally and spatially summarizes air quality data. Utilizing FME, a spatial intersection and summary within census tracts in Baltimore County was performed.
Copyright Text: U.S. EPA EJScreen API, United States Census
Description: This feature class is designed to calculate the number of library cards per census tract and standarize the counts based on population. It is developed to show the differences in Public Health use/access throughout the county.The library records and addresses where obtained from BCPL's database. Data was then processed and geocoded through FME. The result is a polygon feature class that has the number of library cards per census tract, and the number of library cards per 1,000 residents per census tract.
Copyright Text: Baltimore County Public Library, BCSTAT, Census Bureau
Description: The purpose of this dataset is to determine normalize the accessibility of recreation space to youth per census tract, establishing a comprehensive base for probing geographical inconsistencies in the usability of recreational facilities. It is developed to aid public health research and urban planning initiatives by enhancing our understanding of, and ability to address, local health and socioeconomic concerns. The dataset is curated from REC.ParkBoundaries, filtered such that SITE_CLASS_CATEGORY is in the subset ('COUNTY PARK', 'SCHOOL RECREATION CENTER', 'STATE OR NATIONAL PARK', 'BALTIMORE CITY RESERVOIR', 'NEIGHBORSPACE'). Utilizing FME, a spatial intersection and summary within census tracts in Baltimore County was performed.
Copyright Text: Baltimore County Department of Recreation and Parks, BCSTAT
Description: This feature class is designed to quantify the number of housing units within floodplain levels within Baltimore County. There is a 1% and 0.2% level (the lower the level, the larger the storm). Unoccupied housing units are included in this analyis.
Copyright Text: Baltimore County, FEMA, Census Bureau, BCSTAT
Description: This data model is designed to quantify the number of households in each tract that applied for the Eviction Protection Program as a rate to the total number of households in each tract. This can lead to insights as to where evictions are most likely in Baltimore County, or insights into which tracts may be most susceptible to evictions in the future.
Copyright Text: United States Census, Yardi Database (for EPP recording)
Description: The purpose of this feature class is to quantify the code enforcement violation rate across census tracts, allowing for advanced analysis of geographical patterns in the rate of enforcement and violation in these areas. It is developed to aid public health research and urban planning initiatives by enhancing our understanding of, and ability to address, local health and socioeconomic concerns. The dataset is built from the Baltimore County Accela database, which contains data on code enforcement activity.
Copyright Text: Baltimore County, BCSTAT, Code Enforcement, Department of Permits, Approvals, and Inspections (PAI)
Description: This feature class enables users to visualize the percentage of food facilities in Baltimore County that have experienced at least one closure due to critical health code violations, delineated by census tract. It was developed to highlight variations in restaurant closure rates due to health code infringements, thereby enhancing our understanding of how these violations are distributed across the county. The data was sourced from the Environmental Health Services' Envision Database and subsequently processed and geocoded using FME. The resulting polygon feature class provides details on the percentage of restaurants closed within the past year, the total count of restaurant closures, and the number of restaurants within each census tract
Copyright Text: Baltimore County Department of Health, BCSTAT, United States Census Bureau
TOTAL_POPULATION
(
type: esriFieldTypeInteger, alias: Total Population
)
SUM_ANY_MENTAL_HEALTH
(
type: esriFieldTypeSmallInteger, alias: Inpatient Hospital Admissions due to any Mental Health Condition
)
RATE_ANY_MENTAL_HEALTH
(
type: esriFieldTypeDouble, alias: Inpatient Hospital Admissions due to any Mental Health Condition per 1000 residents
)
SUM_ANY_OVERDOSE
(
type: esriFieldTypeSmallInteger, alias: Inpatient Hospital Admissions due to any Overdose
)
RATE_ANY_OVERDOSE
(
type: esriFieldTypeDouble, alias: Inpatient Hospital Admissions due to any Overdose per 1000 residents
)
SUM_ASTHMA_UNDER_18
(
type: esriFieldTypeSmallInteger, alias: Asthma related emergency department vists for residents under 18
)
RATE_ASTHMA_UNDER_18
(
type: esriFieldTypeDouble, alias: Asthma related emergency department vists for residents under 18 per 1000 residents
)
SUM_SELF_HARM_SUICIDE
(
type: esriFieldTypeSmallInteger, alias: Inpatient Hospital admissions due to Suicide and Self Harm
)
RATE_SELF_HARM_SUICIDE
(
type: esriFieldTypeDouble, alias: Inpatient Hospital admissions due to Suicide and Self Harm per 1000 residents
)
SUM_HOSP_UNDER_18
(
type: esriFieldTypeSmallInteger, alias: Any Inpatient Hospital Admission or Emergency Department visit for residents under 18
)
RATE_HOSP_UNDER_18
(
type: esriFieldTypeDouble, alias: Any Inpatient Hospital Admission or Emergency Department visit for residents under 18 per 1000 residents
)
Description: Derived from the Permits, Approvals, and Inspections (PAI) department's rental registration data, this feature class provides a spatial representation of both registered rental and exempt properties within each census tract in Baltimore County. Utilizing an automated FME ETL process, the dataset is updated daily by intersecting spatial data and counting registered rental properties, ensuring data relevance up to the previous day. In accordance with Baltimore County Code Section 35-5-201, all non-homeowner properties with up to six units—including short-term rentals—and those with seven or more units must possess a rental license. Exceptions include properties occupied exclusively by blood-related, marriage-related, or legally adopted kin of the property owner (limited to grandparents, parents, children, or grandchildren). An additional unrelated individual is also permissible. Properties listed on the National Register of Historic Places or the Baltimore County Landmarks List are also exempt. Detailed information on the rental registration program can be accessed here (https://www.baltimorecountymd.gov/departments/pai/rental-registration/index.html).
Copyright Text: Baltimore County, Maryland, Permits Approvals and Inspections (PAI) Department, BCSTAT, OIT, United Census Bureau
Description: This layer displays the number of Domestic/Family Disturbance calls made to 911 for each census tract in Baltimore County. For this metric, we define a disturbance as a situation where a problem or an argument arises between people and may involve verbal or physical abuse. A domestic relationship includes individuals who: 1) Are currently or formerly married to each other; 2) Have lived together as sexual partners in the same home; 3) Have a child together. A family relationship is defined as any individuals sharing a close family relationship (parent-child, stepparent-stepchild, siblings, etc.). The data and definitions for this layer were sourced from Baltimore County 911 and the BCPD. To standardize, a rate is calculated, defined as the number of domestic violence-related calls per 1,000 persons in a census tract. Data is obtained from Baltimore County's 911 calls database, covering the period from November 2022 to November 2023.
Copyright Text: Baltimore County, BCSTAT, Baltimore County Police Department, United States Census Bureau
Description: This layer is specifically designed to document and analyze Illegal Dumping incidents that occur on the roads within Baltimore County. Its primary purpose is to identify areas prone to illegal dumping, thereby helping to protect the integrity of our roadways. Incidents included in this layer are those recorded and addressed by the Baltimore County Bureau of Highways, highlighting the department's efforts in managing this issue.It's important to note that this layer does not capture all instances of Illegal Dumping within the county, as many occurrences are managed by other departments, such as Solid Waste and Code Enforcement. Therefore, the data provides a focused, but not exhaustive, view of illegal dumping activities affecting county roads.The dataset covers incidents from the most recent two-year period. However, since the data collection began on June 1, 2022, a comprehensive dataset spanning a full two years will only be complete by June 1, 2024. Incidents are aggregated by Census Tracts, with boundaries as determined by the Census Bureau, offering a detailed geographical analysis of dumping hotspots. The data is updated quarterly to ensure timely insights into trends and the effectiveness of mitigation efforts.
Copyright Text: Baltimore County, BCSTAT, Department of Public Works and Transportation, United States Census Bureau
Description: This data model is designed to calculate the number of animal services events (animal bites, animal adoptions, stray animals, surrendered animals, animal cruelty, and animal nuissance) per census tract and standarize the counts based on population. It is developed to show the differences in Public Health use/access throughout the county.Animal Services data was obtained directly from their database. Data was then processed and geocoded through FME. Most events had coordinate information, with the exception of adoption events. Any event without coordinate information had to be geocoded. The result is a polygon feature class that has the number of anmal services events per census tract, and the number of animal services events per 1,000 residents per census tract.