This dataset contains geographic information concerning cities and towns in the United States, Puerto Rico, and the U.S. Virgin Islands. A city or town is a place with a recorded population, usually with at least one central area that provides commercial activities. Cities are generally larger than towns; no distinction is made between cities and towns in this map layer.

This dataset contains a listing of incorporated places (cities and towns) and counties within the United States including the GNIS code, FIPS code, name, entity type and primary point (location) for the entity. The types of entities listed in this dataset are based on codes provided by the U.S. Census Bureau, and include the following: C1 - An active incorporated place that does not serve as a county subdivision equivalent; C2 - An active incorporated place legally coextensive with a county subdivision but treated as independent of any county subdivision; C3 - A consolidated city; C4 - An active incorporated place with an alternate official common name; C5 - An active incorporated place that is independent of any county subdivision and serves as a county subdivision equivalent; C6 - An active incorporated place that partially is independent of any county subdivision and serves as a county subdivision equivalent or partially coextensive with a county subdivision but treated as independent of any county subdivision; C7 - An incorporated place that is independent of any county; C8 - The balance of a consolidated city excluding the separately incorporated place(s) within that consolidated government; C9 - An inactive or nonfunctioning incorporated place; H1 - An active county or statistically equivalent entity; H4 - A legally defined inactive or nonfunctioning county or statistically equivalent entity; H5 - A census areas in Alaska, a statistical county equivalent entity; and H6 - A county or statistically equivalent entity that is areally coextensive or governmentally consolidated with an incorporated place, part of an incorporated place, or a consolidated city.

This dataset is part of the Geographical repository maintained by Opendatasoft. This dataset contains data for places and equivalent entities in United States of America.This layer both incorporated places (legal entities) and census designated places or CDPs (statistical entities). An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division (MCD), which generally is created to provide services or administer an area without regard, necessarily, to population. Places always nest within a state, but may extend across county and county subdivision boundaries. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide data for settled concentrations of population that are identifiable by name, but are not legally incorporated under the laws of the state in which they are located. The boundaries for CDPs often are defined in partnership with state, local, and/or tribal officials and usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP boundaries often change from one decennial census to the next with changes in the settlement pattern and development; a CDP with the same name as in an earlier census does not necessarily have the same boundary. The only population/housing size requirement for CDPs is that they must contain some housing and population. Processors and tools are using this data. Enhancements Add ISO 3166-3 codes. Simplify geometries to provide better performance across the services. Add administrative hierarchy.

This map layer includes cities and towns in Oregon. These cities were clipped from a larger dataset of cities collected from the 1970 National Atlas of the United States. Where applicable, U.S. Census Bureau codes for named populated places were associated with each name to allow additional information to be attached. The Geographic Names Information System (GNIS) was also used as a source for additional information. This is a revised version of the December 2003 map layer.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national filewith no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independentdata set, or they can be combined to cover the entire nation. Combined New England City and Town Areas (CNECTA) are defined by the Office of Management and Budget (OMB) and consist of two or more adjacent New England City and Town Areas (NECTA) that have significant employment interchanges. The NECTAs that combine to create a CNECTA retain separate identities within the larger combined statistical area. Because CNECTAs represent groupings of NECTAs, they should not be ranked or compared with individual NECTAs. The CNECTA boundaries are those defined by OMB based on the 2010 Census, published in 2013, and updated in 2017.

This dataset contains information about the demographics of all US cities and census-designated places with a population greater or equal to 65,000. This data comes from the US Census Bureau's 2015 American Community Survey. This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.

This data set includes cities in the United States, Puerto Rico and the U.S. Virgin Islands. These cities were collected from the 1970 National Atlas of the United States. Where applicable, U.S. Census Bureau codes for named populated places were associated with each name to allow additional information to be attached. The Geographic Names Information System (GNIS) was also used as a source for additional information. This is a revised version of the December, 2003, data set.

This layer is sourced from maps.bts.dot.gov.

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

Sustainable cities depend on urban forests. City trees -- a pillar of urban forests -- improve our health, clean the air, store CO2, and cool local temperatures. Comparatively less is known about urban forests as ecosystems, particularly their spatial composition, nativity statuses, biodiversity, and tree health. Here, we assembled and standardized a new dataset of N=5,660,237 trees from 63 of the largest US cities. The data comes from tree inventories conducted at the level of cities and/or neighborhoods. Each data sheet includes detailed information on tree location, species, nativity status (whether a tree species is naturally occurring or introduced), health, size, whether it is in a park or urban area, and more (comprising 28 standardized columns per datasheet). This dataset could be analyzed in combination with citizen-science datasets on bird, insect, or plant biodiversity; social and demographic data; or data on the physical environment. Urban forests offer a rare opportunity to intentionally design biodiverse, heterogenous, rich ecosystems. Methods See eLife manuscript for full details. Below, we provide a summary of how the dataset was collected and processed.

Data Acquisition We limited our search to the 150 largest cities in the USA (by census population). To acquire raw data on street tree communities, we used a search protocol on both Google and Google Datasets Search (https://datasetsearch.research.google.com/). We first searched the city name plus each of the following: street trees, city trees, tree inventory, urban forest, and urban canopy (all combinations totaled 20 searches per city, 10 each in Google and Google Datasets Search). We then read the first page of google results and the top 20 results from Google Datasets Search. If the same named city in the wrong state appeared in the results, we redid the 20 searches adding the state name. If no data were found, we contacted a relevant state official via email or phone with an inquiry about their street tree inventory. Datasheets were received and transformed to .csv format (if they were not already in that format). We received data on street trees from 64 cities. One city, El Paso, had data only in summary format and was therefore excluded from analyses.

Data Cleaning All code used is in the zipped folder Data S5 in the eLife publication. Before cleaning the data, we ensured that all reported trees for each city were located within the greater metropolitan area of the city (for certain inventories, many suburbs were reported - some within the greater metropolitan area, others not). First, we renamed all columns in the received .csv sheets, referring to the metadata and according to our standardized definitions (Table S4). To harmonize tree health and condition data across different cities, we inspected metadata from the tree inventories and converted all numeric scores to a descriptive scale including “excellent,” “good”, “fair”, “poor”, “dead”, and “dead/dying”. Some cities included only three points on this scale (e.g., “good”, “poor”, “dead/dying”) while others included five (e.g., “excellent,” “good”, “fair”, “poor”, “dead”). Second, we used pandas in Python (W. McKinney & Others, 2011) to correct typos, non-ASCII characters, variable spellings, date format, units used (we converted all units to metric), address issues, and common name format. In some cases, units were not specified for tree diameter at breast height (DBH) and tree height; we determined the units based on typical sizes for trees of a particular species. Wherever diameter was reported, we assumed it was DBH. We standardized health and condition data across cities, preserving the highest granularity available for each city. For our analysis, we converted this variable to a binary (see section Condition and Health). We created a column called “location_type” to label whether a given tree was growing in the built environment or in green space. All of the changes we made, and decision points, are preserved in Data S9. Third, we checked the scientific names reported using gnr_resolve in the R library taxize (Chamberlain & Szöcs, 2013), with the option Best_match_only set to TRUE (Data S9). Through an iterative process, we manually checked the results and corrected typos in the scientific names until all names were either a perfect match (n=1771 species) or partial match with threshold greater than 0.75 (n=453 species). BGS manually reviewed all partial matches to ensure that they were the correct species name, and then we programmatically corrected these partial matches (for example, Magnolia grandifolia-- which is not a species name of a known tree-- was corrected to Magnolia grandiflora, and Pheonix canariensus was corrected to its proper spelling of Phoenix canariensis). Because many of these tree inventories were crowd-sourced or generated in part through citizen science, such typos and misspellings are to be expected. Some tree inventories reported species by common names only. Therefore, our fourth step in data cleaning was to convert common names to scientific names. We generated a lookup table by summarizing all pairings of common and scientific names in the inventories for which both were reported. We manually reviewed the common to scientific name pairings, confirming that all were correct. Then we programmatically assigned scientific names to all common names (Data S9). Fifth, we assigned native status to each tree through reference to the Biota of North America Project (Kartesz, 2018), which has collected data on all native and non-native species occurrences throughout the US states. Specifically, we determined whether each tree species in a given city was native to that state, not native to that state, or that we did not have enough information to determine nativity (for cases where only the genus was known). Sixth, some cities reported only the street address but not latitude and longitude. For these cities, we used the OpenCageGeocoder (https://opencagedata.com/) to convert addresses to latitude and longitude coordinates (Data S9). OpenCageGeocoder leverages open data and is used by many academic institutions (see https://opencagedata.com/solutions/academia). Seventh, we trimmed each city dataset to include only the standardized columns we identified in Table S4. After each stage of data cleaning, we performed manual spot checking to identify any issues.

This dataset is about cities in the United States. It has 4,171 rows. It features 7 columns including country, population, latitude, and longitude.

This layer presents the locations of major cities within the United States with populations of approximately 10,000 or greater, state capitals, and the national capital. Major Cities are locations containing population totals from the 2020 Census.The points represent U.S. Census Places polygons sourced from U.S. Census Bureau 2020 TIGER FGDB (National Sub-State). Attribute fields include 2020 total population from the U.S. Census Public Law 94 data that symbolize the city points using these six classifications: Class Population Range 5 2,500 – 9,999 6 10,000 – 49,999 7 50,000 – 99,999 8 100,000 – 249,999 9 250,000 – 499,999 10 500,000 and overThis ready-to-use layer can be used in ArcGIS Pro and in ArcGIS Online and its configurable apps, dashboards, StoryMaps, custom apps, and mobile apps. The data can also be exported for offline workflows. Cite the 'U.S. Census Bureau' when using this data.

View the diversity of challenges and opportunities across America's counties within different types of rural regions and communities. Get statistics on people, jobs, and agriculture.

This data collection provides statistics gathered from a variety of federal agencies and national associations. Demographic, economic, and governmental data from both the federal government and private agencies are presented to enable multiarea comparisons as well as single-area profiles. Current estimates and benchmark census results are included. Data are available for five types of geographic coverage: (1) Metro Areas data cover 249 metropolitan statistical areas (MSAs), 17 consolidated metropolitan statistical areas (CMSAs), 54 primary metropolitan statistical areas (PSMAs), and 16 New England county metropolitan areas (NECMAs). Metro Areas data include the following general subjects: area and population, households, vital statistics, health, education, crime, housing, money income, personal income, civilian labor force, employment, construction, commercial office space, manufacturing, wholesale and retail trade, service industries, banking, federal funds and grants, and government employment. There are 14 parts for Metro Areas. (2) State Metro/Nonmetro data cover the United States, the 50 states, the District of Columbia, and the metropolitan and nonmetropolitan portions of these areas. State Metro/Nonmetro data include most of the subjects listed for Metro Areas. There are six parts for State Metro/Nonmetro. (3) Metro Counties data cover 336 metropolitan areas and their component counties and include topics identical to those presented in the State Metro/Nonmetro data. Six parts are supplied for Metro Counties. (4) Metro Central Cities data cover 336 metropolitan areas and their 522 central cities and 336 outside central cities portions. Metro Central Cities variables are limited to 13 items, which include area and population, money income, civilian labor force, and retail trade. There is one part for Metro Central Cities. (5) States data cover the United States, the 50 states, the District of Columbia, and census regions and divisions. States data include the same items as the Metro Areas data, plus information on social welfare programs, geography and environment, domestic travel and parks, gross state product, poverty, wealth holders, business, research and development, agriculture, forestry and fisheries, minerals and mining, transportation, communications, energy, state government, federal government, and elections. There are 101 parts for States.

About this dataset

Context

This list ranks the 2 cities in the Towns County, GA by Hispanic Some Other Race (SOR) 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 Other Race Population: This column displays the rank of cities in the Towns County, GA by their Hispanic Some Other Race (SOR) population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Hispanic Other Race Population: The Hispanic Other Race 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 Other Race. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Towns County Hispanic Other Race Population: This tells us how much of the entire Towns County, GA Hispanic Other Race 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/.

This dataset is about books. It has 8 rows and is filtered where the book subjects is Cities and towns-United States-Case studies. It features 9 columns including author, publication date, language, and book publisher.

About this dataset

Context

This list ranks the 2 cities in the Towns County, GA by Hispanic Black or African American 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 Black Population: This column displays the rank of cities in the Towns County, GA by their Hispanic Black or African American population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Hispanic Black Population: The Hispanic Black 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 Black. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Towns County Hispanic Black Population: This tells us how much of the entire Towns County, GA Hispanic Black 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

This list ranks the 16 cities in the Cape May County, NJ by American Indian and Alaska Native (AIAN) 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 Native American Population: This column displays the rank of cities in the Cape May County, NJ by their American Indian and Alaska Native (AIAN) population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Native American Population: The Native American population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Native American. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Cape May County Native American Population: This tells us how much of the entire Cape May County, NJ Native American 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/.

Northeastern United States Town Boundary data are intended for geographic display of state, county and town (municipal) boundaries at statewide and regional levels. Use it to map and label towns on a map. These data are derived from Northeastern United States Political Boundary Master layer. This information should be displayed and analyzed at scales appropriate for 1:24,000-scale data. The State of Connecticut, Department of Environmental Protection (CTDEP) assembled this regional data layer using data from other states in order to create a single, seamless representation of political boundaries within the vicinity of Connecticut that could be easily incorporated into mapping applications as background information. More accurate and up-to-date information may be available from individual State government Geographic Information System (GIS) offices. Not intended for maps printed at map scales greater or more detailed than 1:24,000 scale (1 inch = 2,000 feet.)

This data collection contains information about the population of each county, town, and city of the United States in 1850 and 1860. Specific variables include tabulations of white, black, and slave males and females, and aggregate population for each town. Foreign-born population, total population of each county, and centroid latitudes and longitudes of each county and state were also compiled. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR -- https://doi.org/10.3886/ICPSR09424.v2. We highly recommend using the ICPSR version as they made this dataset available in multiple data formats.

The Census data utilized for developing the Community Layer used 2010 TIGER/Line shapefile datasets (TIGER = Topologically Integrated Geographic Encoding and Referencing). TIGER/Line shapefiles are available for free download from the US Census Bureau and include various legal and statistical geographic areas for which the Census tabulates data. The shapefiles are designed to be used in a GIS environment, with the ability to directly link the geographic areas to Census data via a unique GEOID number.The following TIGER/Line datasets should be used: - Counties and Equivalent Entities –primary legal divisions within each state (counties, parishes, etc)- County Subdivisions –includes both legal areas (Minor Civil Divisions or MCDs) and various statistical areas- Places –includes both legal areas (Incorporated Places) and statistical areas (Census Designated Places or CDPs)- Blocks –the smallest geographical area for which Census population counts are recorded; blocks never cross boundaries of any entity for which the Census Bureau tabulates data, including counties, county subdivisions, places, and American Indian, Alaska Native, and Native Hawaiian (AIANNH) areas- American Indian, Alaska Native, and Native Hawaiian (AIANNH) AreasExtracting and Formatting CIS DataA key component of the community layer is the ability to link CIS information spatially. Data from CIS cannot directly be joined with Census data. The two datasets have community name discrepancies which impede an exact match. Therefore, CIS data needs to be formatted to match Census community names. A custom report can be obtained from CIS to include a CID number, Community Name, County, State, Community Status, and Tribal status for all CIS records. Make sure all CID numbers are six digits and you follow the CIS community naming convention outlined in Table 4.2.1.1 in the Community Layer Update Technical Guide 20131206. Converting the CIS name“ADDISON, VILLAGE OF” to “ADDISON TOWN”involves removing unneeded spaces, comma, and preposition to make the join successful to the Census data. Using a comprehensive report at a national level gains efficiencies as bulk edits can be made. Data for each state should be extracted as needed by separating the CIS data into each type of community corresponding to the Census geography layers used, and a new JoinID column (e.g. ADDISON TOWN) can be created for each dataset allowing the CIS data to be joined to the Census data.