GIS data: Community Districts (Water areas included)

Community Districts are mandated by the city charter to review and monitor quality of life issues for New York City (NYC) neighborhoods. NYC is currently comprised of 59 community districts. The first byte is a borough code and the second and third bytes are the community district number. There are also 12 Joint Interest Areas (JIAs). The JIAs are major parks and airports and are not contained within any community district. This dataset is being provided by the Department of City Planning (DCP) for informational purposes only. DCP does not warranty the completeness, accuracy, content, or fitness for any particular purpose or use of the dataset, nor are any such warranties to be implied or inferred with respect to the dataset as furnished on the website. DCP and the City are not liable for any deficiencies in the completeness, accuracy, content, or fitness for any particular purpose or use the dataset, or applications utilizing the dataset, provided by any third party.

All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

State Legislative Districts - Lower HousesThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau (USCB), displays State Legislative Districts (SLDs) in the lower houses of state legislatures in the United States. According to the USCB, "SLDs are the areas from which members are elected to state legislatures. They embody the upper (senate) and lower (house) chambers of a state legislature. Nebraska has a unicameral legislature and the District of Columbia has a single council, both of which the Census Bureau treats as upper-chamber legislative areas for data presentation; there are no data by lower houses for either Nebraska or the District of Columbia".Data currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (2018 State Legislative Districts - Lower) and will support mapping, analysis, data exports and OGC API – Feature access.Data.gov: TIGER/Line Shapefile, 2019, Series Information for the State Legislative District (SLD) Lower Chamber State-based ShapefileGeoplatform: TIGER/Line Shapefile, 2019, Series Information for the State Legislative District (SLD) Lower Chamber State-based ShapefileFor more information, please visit: 2018 State Legislative District Reference MapsFor feedback please contact: Esri_US_Federal_Data@esri.comThumbnail image courtesy of: Mark GoebelNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

The USDA Forest Service Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program produces geospatial and related data representing post-fire vegetation condition by means of standardized change detection methods based on Landsat or similar multispectral satellite imagery. RAVG data products characterize the impact of disturbance (fire) on vegetation within a fire perimeter, and include estimates of percent change in live basal area (BA), percent change in canopy cover (CC), and the standardized composite burn index (CBI). Standard thematic products include 7-class percent change in basal area (BA-7), 5-class percent change in canopy cover (CC-5), and 4-class CBI (CBI-4). Contingent upon the availability of suitable imagery, RAVG products are prepared for all wildland fires reported within the conterminous United States (CONUS) that include at least 1000 acres of forested National Forest System (NFS) land (500 acres for Regions 8 and 9 as of 2016). Data for individual fires are typically made available within 45 days after fire containment ("initial assessments"). Late-season fires, however, may be deferred until the following spring or summer ("extended assessments"). Annual national mosaics of each thematic product are prepared at the end of the fire season and updated, as needed, when additional fires from the given year are processed. The annual mosaics are available via the Raster Data Warehouse (RDW, see https://apps.fs.usda.gov/arcx/rest/services/RDW_Wildfire). A combined perimeter dataset, including the burn boundaries for all published Forest Service RAVG fires from 2012 to the present, is likewise updated as needed (at least annually). This current dataset is derived from the combined perimeter dataset and adds spatial information about land ownership (National Forest) and wilderness status, as well as the areal extent of forested land (pre-fire) that experience a modeled BA loss above 50 and 75 percent.

The election districts dataset is a combination of data from the NYC and County boards of elections.

Information and formatting varied with the source; some variation is still present in this data service. Spatially, the districts may not align with districts from neighboring counties or with other reference datasets such as civil boundaries.

The 2023 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. Congressional districts are the 435 areas from which people are elected to the U.S. House of Representatives. After the apportionment of congressional seats among the states based on census population counts, each state is responsible for establishing congressional districts for the purpose of electing representatives. Each congressional district is to be as equal in population to all other congressional districts in a state as practicable. The 118th Congress is seated from January 2023 through December 2024. In Connecticut, Illinois, and New Hampshire, the Redistricting Data Program (RDP) participant did not define the CDs to cover all of the state or state equivalent area. In these areas with no CDs defined, the code "ZZ" has been assigned, which is treated as a single CD for purposes of data presentation. The cartographic boundary files for the District of Columbia, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands) each contain a single record for the non-voting delegate district in these areas. The generalzied boundaries of all other congressional districts are based on information provided to the Census Bureau by the states by August 31, 2022.

County Board of Education districts for the purpose of establishing election divisions within a district. Created April 16, 2021. The districts contained within this dataset only represent districts that conduct an election. This dataset may not contain all districts within San Bernardino County. This dataset should only be used for the purpose of establishing election divisions within a district. It will be removed once the redistricting process has concluded.

Since 1978, voters have elected council members from among candidates living within their district, plus the mayor who is elected at large citywide. With the subsequent release of decennial census data by the US Census Bureau in the years 1980, 1990, 2000, and 2010, City Council District boundaries have been adjusted to meet legal requirements and San Jose's own redistricting criteria. The City Council District boundaries are updated every ten years.

This data set consists of 6 classes of zoning features: zoning districts, special purpose districts, special purpose district subdistricts, limited height districts, commercial overlay districts, and zoning map amendments.

All previously released versions of this data are available at BYTES of the BIG APPLE- Archive.

School Districts is a polygon feature used to denote the district boundaries of Elementary, Middle and High Schools in the County of Roanoke.School Districts are maintained within the Administration Feature and is dissolved out weekly.Administration is a polygon feature consisting of the smallest statistical areas bounded by visible features such as roads, streams, railroad tracks, and mountain ridges, as well as by nonvisible boundaries such as jurisdictional limits, school district, public safety boundaries, voting precincts, and census blocks. This methodology allows for single stream editing to move coincidental boundaries across many aggregate datasets simultaneously. Administration is maintained though an ArcGIS topology class in conjunction with County Parcels and Zoning. The topology prevents self-intersection and gaps, while ensuring complete coverage amongst the participating features.

This dataset contains boundaries and associated attribute information for all designated historic districts or areas under consideration for historic district designation (i.e. calendared) by the New York City Landmarks Preservation Commission (LPC), including items that may have been denied designation or overturned. Please note that some areas may have multiple records in the database if different actions were taken over time. Please pay close attention to the "CURRENT" and "LAST_ACTION_ON_BOUNDARY" fields to determine the status of a particular area. The geographic locations of the polygons in this dataset are derived primarily from the Department of City Planning's PLUTO dataset, and therefore discrepancies may arise where the LPC dataset has not been updated with information from the most recent PLUTO releases. Please pay close attention to the field descriptions present in the file's metadata to understand how to use this dataset. And please contact LPC if there are questions or concerns

About this dataset

Context

The dataset presents median household incomes for various household sizes in District Heights, MD, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, District Heights did not include 5-person households. Across the different household sizes in District Heights the mean income is $115,068, and the standard deviation is $75,170. The coefficient of variation (CV) is 65.33%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $32,334. It then further increased to $172,645 for 7-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/district-heights-md-median-household-income-by-household-size.jpeg" alt="District Heights, MD median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for District Heights median household income. You can refer the same here

About this dataset

Context

This list ranks the 1 cities in the District of Columbia by Hispanic Asian population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Hispanic Asian Population: This column displays the rank of cities in the District of Columbia by their Hispanic Asian population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Hispanic Asian Population: The Hispanic Asian population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Hispanic Asian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total District of Columbia Hispanic Asian Population: This tells us how much of the entire District of Columbia Hispanic Asian population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

About this dataset

Context

The dataset tabulates the data for the District Heights, MD population pyramid, which represents the District Heights population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for District Heights, MD, is 28.6.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for District Heights, MD, is 25.0.
• Total dependency ratio for District Heights, MD is 53.6.
• Potential support ratio, which is the number of youth (working age population) per elderly, for District Heights, MD is 4.0.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the District Heights population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the District Heights for the selected age group is shown in the following column.
• Population (Female): The female population in the District Heights for the selected age group is shown in the following column.
• Total Population: The total population of the District Heights for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for District Heights Population by Age. You can refer the same here

Boundaries depicting federal congressional districts within Montgomery County. Districts may include areas outside of Montgomery County that are not included in this dataset.For more information, contact: GIS Manager Information Technology & Innovation (ITI) Montgomery County Planning Department, MNCPPC T: 301-650-5620

Community District boundaries for New York City including portions under water.

Community Districts (CD) are mandated by the city charter to review and monitor quality of life issues for New York City (NYC) neighborhoods. NYC is currently comprised of 59 community districts. There are also 12 Joint Interest Areas (JIAs). The JIAs are major parks and airports and are not contained within any community district. The BoroCD value is the unique ID for CDs and JIAs with the first byte representing the borough code and the second and third bytes are the CD or JIA number.

All previously released versions of this data are available at DCP Website: BYTES of the BIG APPLE.

District Government Land Line Dimensions. A layer showing District of Columbia government related properties (owned, operated, and or managed) to be used by many DC Government agencies, private companies and the public. It supports the daily business process of District agencies that originate and manage land records. Transfers of Jurisdiction (TOJ) are also in this layer. This map should not be considered comprehensive as District agencies continuously work to update properties as transactions occur.

Programmatically generated Data Dictionary document detailing the Texas US House Districts service.

    The PDF contains service metadata and a complete list of data fields.
    For any questions or issues related to the document, please contact the data owner of the service identified in the PDF and Credits of this portal item.


  Related Links
  Texas US House Districts Service URL
  Texas US House Districts Portal Item

This data set provides geographic boundaries and basic information for Philadelphia’s 15 Business Improvement Districts (BID) as well the University City District and Sports Complex District. More information available here This data set may be helpful to property owners, property purchasers or title companies seeking to know if a property exists within a BID. Note that this dataset may include errors or outdated information. Therefore, it is strongly recommended that interested parties contact BID organizations directly with inquiries.

The 2022 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. State Legislative Districts (SLDs) are the areas from which members are elected to state legislatures. The SLDs embody the upper (senate) and lower (house) chambers of the state legislature. Nebraska has a unicameral legislature and the District of Columbia has a single council, both of which the Census Bureau treats as upper-chamber legislative areas for the purpose of data presentation; there are no data by SLDL for either Nebraska or the District of Columbia. A unique three-character census code, identified by state participants, is assigned to each SLD within a state. In Connecticut, Illinois, Louisiana, New Hampshire, Wisconsin, and Puerto Rico, the Redistricting Data Program (RDP) participant did not define the SLDs to cover all of the state or state equivalent area. In these areas with no SLDs defined, the code "ZZZ" has been assigned, which is treated as a single SLD for purposes of data presentation. The generarlized boundaries in this file are based on the most recent state legislative district boundaries collected by the Census Bureau for the 2022 election year and provided by state-level participants through the RDP.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in District of Columbia, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 15,545, while the mean income for the highest quintile (20% of households with the highest income) is 424,296. This indicates that the top earners earn 27 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 749,564, which is 176.66% higher compared to the highest quintile, and 4821.90% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for District of Columbia median household income. You can refer the same here