Dataset Summary About this data: This layer presents the USA 2020 Census tracts within the City of Rochester boundary. The geography is sourced from US Census Bureau 2020 TIGER FGDB (National Sub-State) and cut by the City of Rochester boundary. Data Dictionary: STATE_ABBR: The two-letter abbreviation for a state (such as NY). STATE_FIPS: The two-digit Federal Information Processing Standards (FIPS) code assigned to each US state. New York State is 36. COUNTY_FIP: The three-digit Federal Information Processing Standards (FIPS) code assigned to each US county. Monroe County is 055. STCO_FIPS: The five-digit Federal Information Processing Standards (FIPS) code assigned to iedntify a unique county, typically as a concatenation of the State FIPS code and the County FIPS code. TRACT_FIPS: The six-digit number assigned to each census tract in a US county. FIPS: A unique geographic identifier, typically as a concatenation of State FIPS code, County FIPS code, and Census tract code. POPULATION: The population of a census tract. POP_SQMI: The population per square mile of a census tract. SQMI: The size of a census tract in square miles. Division: The name of the City of Rochester data division that the census tract falls in to. Source: This data comes from the Census Bureau.

Note: The schema changed in February 2025 - please see below. We will post a roadmap of upcoming changes, but service URLs and schema are now stable. For deployment status of new services beginning in February 2025, see https://gis.data.ca.gov/pages/city-and-county-boundary-data-status. Additional roadmap and status links at the bottom of this metadata.

Purpose
City boundaries along with third party identifiers used to join in external data. Boundaries are from the California Department of Tax and Fee Administration (CDTFA). These boundaries are the best available statewide data source in that CDTFA receives changes in incorporation and boundary lines from the Board of Equalization, who receives them from local jurisdictions for tax purposes. Boundary accuracy is not guaranteed, and though CDTFA works to align boundaries based on historical records and local changes, errors will exist. If you require a legal assessment of boundary location, contact a licensed surveyor.

This dataset joins in multiple attributes and identifiers from the US Census Bureau and Board on Geographic Names to facilitate adding additional third party data sources. In addition, we attach attributes of our own to ease and reduce common processing needs and questions. Finally, coastal buffers are separated into separate polygons, leaving the land-based portions of jurisdictions and coastal buffers in adjacent polygons. This feature layer is for public use.

Related Layers

This dataset is part of a grouping of many datasets:

1. Cities: Only the city boundaries and attributes, without any unincorporated areas
   • With Coastal Buffers
   • Without Coastal Buffers (this dataset)
2. Counties: Full county boundaries and attributes, including all cities within as a single polygon
   • With Coastal Buffers
   • Without Coastal Buffers
3. Cities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.
   • With Coastal Buffers
   • Without Coastal Buffers
4. City and County Abbreviations
5. Unincorporated Areas (Coming Soon)
6. Census Designated Places
7. Cartographic Coastline
   • Polygon
   • Line source (Coming Soon)

Point of Contact

California Department of Technology, Office of Digital Services, odsdataservices@state.ca.gov

Field and Abbreviation Definitions

• CDTFA_CITY: CDTFA incorporated city name
• CDTFA_COUNTY: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.
• CDTFA_COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system. The boundary data originate with CDTFA's teams managing tax rate information, so this field is preserved and flows into this dataset.
• CENSUS_GEOID: numeric geographic identifiers from the US Census Bureau
• CENSUS_PLACE_TYPE: City, County, or Town, stripped off the census name for identification purpose.
• GNIS_PLACE_NAME: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information System
• GNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.
• CDT_CITY_ABBR: Abbreviations of incorporated area names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 4 characters. Not present in the county-specific layers.
• CDT_COUNTY_ABBR: Abbreviations of county names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 3 characters.
• CDT_NAME_SHORT: The name of the jurisdiction (city or county) with the word "City" or "County" stripped off the end. Some changes may come to how we process this value to make it more consistent.
• AREA_SQMI: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.
• OFFSHORE: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".
• PRIMARY_DOMAIN: Currently empty/null for all records. Placeholder field for official URL of the city or county
• CENSUS_POPULATION: Currently null for all records. In the future, it will include the most recent US Census population estimate for the jurisdiction.
• GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.

Los Angeles County includes 88 incorporated cities and over 2,600 square miles of unincorporated area. The majority of the County’s 10 million residents live inincorporated cities, and about 1 million residents live in unincorporated areas. To ensure that communities across the County received equal representation in the Parks Needs Assessment, the County was divided into individual Study Areas. These geographic boundaries were developed using a GIS-based process that considered existing jurisdictional boundaries such as supervisorial districts, city borders, and County planning areas alongside information about population.The initial Study Area boundaries were reviewed by the Steering Committee at their first meeting. Revised Study Area boundaries incorporated Steering Committeecomments and resulted in a total of 189 Study Areas. However, due to its annexation into the City of Santa Clarita, one unincorporated community was later eliminated, bringing the final total number of Study Areasto 188. The process of establishing Study Area boundaries is illustrated in Figure 5. Each incorporated city was initially assigned a single Study Area. Cities with population over 150,000 were split into two or more Study Areas, to create a more even distribution of population among Study Areas. Each of these larger cities was allocated a number of Study Areas based on their total population:»» City of Los Angeles: 43 Study Areas»» City of Long Beach: 5 Study Areas»» City of Glendale: 2 Study Areas»» City of Santa Clarita: 2 Study Areas»» City of Lancaster: 2 Study Areas»» City of Palmdale: 2 Study Areas»» City of Pomona: 2 Study Areas»» City of Torrance: 2 Study Areas»» City of Pasadena: 2 Study AreasFor each of these cities, project consultants suggested internal Study Area boundaries based on input from city staff, geographic barriers such as major roadways, Citydeveloped boundaries such as council districts or planning areas, and population distribution. Final determination of the internal boundaries of the Study Areas was at the discretion of city staff.Unincorporated communities in the County were evaluated based on population size and geographic location. Each of the 187 incorporated communities was addressed as follows:»» Geographically isolated communities with small populations were added to the Study Area of the adjacent, like-named city. A total of 18 cities agreed toinclude an adjacent unincorporated community within their Study Area boundaries.»» Distinct and/or geographically isolated communities with larger populations each became an individual Study Area. Any of these communities with more than150,000 people was split into two Study Areas, similar to what was done for large cities.»» Geographically adjacent communities with small populations were grouped according to community name and geography, population distribution, andstatistical areas.»» Each Study Area was assigned a unique identification number, illustrated in Figure 6, Figure 7, and Table 1.

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of 2024. Expected changes:Metadata is missing or incomplete for some layers at this time and will be continuously improved.We expect to update this layer roughly in line with CDTFA at some point, but will increase the update cadence over time as we are able to automate the final pieces of the process.This dataset is continuously updated as the source data from CDTFA is updated, as often as many times a month. If you require unchanging point-in-time data, export a copy for your own use rather than using the service directly in your applications.PurposeCounty and incorporated place (city) boundaries along with third party identifiers used to join in external data. Boundaries are from the authoritative source the California Department of Tax and Fee Administration (CDTFA), altered to show the counties as one polygon. This layer displays the city polygons on top of the County polygons so the area isn"t interrupted. The GEOID attribute information is added from the US Census. GEOID is based on merged State and County FIPS codes for the Counties. Abbreviations for Counties and Cities were added from Caltrans Division of Local Assistance (DLA) data. Place Type was populated with information extracted from the Census. Names and IDs from the US Board on Geographic Names (BGN), the authoritative source of place names as published in the Geographic Name Information System (GNIS), are attached as well. Finally, the coastline is used to separate coastal buffers from the land-based portions of jurisdictions. This feature layer is for public use.Related LayersThis dataset is part of a grouping of many datasets:Cities: Only the city boundaries and attributes, without any unincorporated areasWith Coastal BuffersWithout Coastal BuffersCounties: Full county boundaries and attributes, including all cities within as a single polygonWith Coastal BuffersWithout Coastal BuffersCities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.With Coastal Buffers (this dataset)Without Coastal BuffersPlace AbbreviationsUnincorporated Areas (Coming Soon)Census Designated Places (Coming Soon)Cartographic CoastlinePolygonLine source (Coming Soon)Working with Coastal BuffersThe dataset you are currently viewing includes the coastal buffers for cities and counties that have them in the authoritative source data from CDTFA. In the versions where they are included, they remain as a second polygon on cities or counties that have them, with all the same identifiers, and a value in the COASTAL field indicating if it"s an ocean or a bay buffer. If you wish to have a single polygon per jurisdiction that includes the coastal buffers, you can run a Dissolve on the version that has the coastal buffers on all the fields except COASTAL, Area_SqMi, Shape_Area, and Shape_Length to get a version with the correct identifiers.Point of ContactCalifornia Department of Technology, Office of Digital Services, odsdataservices@state.ca.govField and Abbreviation DefinitionsCOPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering systemPlace Name: CDTFA incorporated (city) or county nameCounty: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.Legal Place Name: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information SystemGNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.GEOID: numeric geographic identifiers from the US Census Bureau Place Type: Board on Geographic Names authorized nomenclature for boundary type published in the Geographic Name Information SystemPlace Abbr: CalTrans Division of Local Assistance abbreviations of incorporated area namesCNTY Abbr: CalTrans Division of Local Assistance abbreviations of county namesArea_SqMi: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.COASTAL: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.AccuracyCDTFA"s source data notes the following about accuracy:City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. COUNTY = county name; CITY = city name or unincorporated territory; COPRI = county number followed by the 3-digit city primary number used in the California State Board of Equalization"s 6-digit tax rate area numbering system (for the purpose of this map, unincorporated areas are assigned 000 to indicate that the area is not within a city).Boundary ProcessingThese data make a structural change from the source data. While the full boundaries provided by CDTFA include coastal buffers of varying sizes, many users need boundaries to end at the shoreline of the ocean or a bay. As a result, after examining existing city and county boundary layers, these datasets provide a coastline cut generally along the ocean facing coastline. For county boundaries in northern California, the cut runs near the Golden Gate Bridge, while for cities, we cut along the bay shoreline and into the edge of the Delta at the boundaries of Solano, Contra Costa, and Sacramento counties.In the services linked above, the versions that include the coastal buffers contain them as a second (or third) polygon for the city or county, with the value in the COASTAL field set to whether it"s a bay or ocean polygon. These can be processed back into a single polygon by dissolving on all the fields you wish to keep, since the attributes, other than the COASTAL field and geometry attributes (like areas) remain the same between the polygons for this purpose.SliversIn cases where a city or county"s boundary ends near a coastline, our coastline data may cross back and forth many times while roughly paralleling the jurisdiction"s boundary, resulting in many polygon slivers. We post-process the data to remove these slivers using a city/county boundary priority algorithm. That is, when the data run parallel to each other, we discard the coastline cut and keep the CDTFA-provided boundary, even if it extends into the ocean a small amount. This processing supports consistent boundaries for Fort Bragg, Point Arena, San Francisco, Pacifica, Half Moon Bay, and Capitola, in addition to others. More information on this algorithm will be provided soon.Coastline CaveatsSome cities have buffers extending into water bodies that we do not cut at the shoreline. These include South Lake Tahoe and Folsom, which extend into neighboring lakes, and San Diego and surrounding cities that extend into San Diego Bay, which our shoreline encloses. If you have feedback on the exclusion of these items, or others, from the shoreline cuts, please reach out using the contact information above.Offline UseThis service is fully enabled for sync and export using Esri Field Maps or other similar tools. Importantly, the GlobalID field exists only to support that use case and should not be used for any other purpose (see note in field descriptions).Updates and Date of ProcessingConcurrent with CDTFA updates, approximately every two weeks, Last Processed: 12/17/2024 by Nick Santos using code path at https://github.com/CDT-ODS-DevSecOps/cdt-ods-gis-city-county/ at commit 0bf269d24464c14c9cf4f7dea876aa562984db63. It incorporates updates from CDTFA as of 12/12/2024. Future updates will include improvements to metadata and update frequency.

In 2024, the total land area of the Guangdong - Hong Kong - Macao Greater Bay Area cities amounted to around ****** square kilometers. The land area of Zhaoqing alone was nearly ****** square kilometers, making it the largest city by area in the region. In terms of population size, however, Zhaoqing is one of the smaller cities in the Greater Bay Area.

This dataset contains information on all United States of America counties.

I have scraped this data from the following Wikipedia website: https://en.wikipedia.org/wiki/List_of_United_States_counties_and_county_equivalents

Data scientists spend most of their time on data cleaning. Hence, this dataset can be ideal for sharpening your data-cleaning skills.

Columns specification: county: Name of each county. state: State name. founded: The year when it was founded. largest_city: Name of the largest city. pop_total: Population in total on that state. pop_den: Population density per square mile and km square. total_area: Total area(land + water) on mile square and km square. land_area: Total land area in mile square and km square. water_area: Total water area on mile square and km square.

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

This graph shows the population density of the United States of America from 1790 to 2019. In 2019, the population density was approximately 92.9 residents per square mile of land area. Population density in the United States Population density has been tracked for over two hundred years in the United States. Over the last two centuries, the number of people living in the United States per square mile has grown from 4.5 in 1790 to 87.4 in 2010. After examining the data in detail, it becomes clear that a major population increase started around 1870. Population density was roughly 11 at the time and has doubled in the last century. Since then, population density grew by about 16 percent each decade. Population density doubled in 1900, and grew in total by around 800 percent until 2010.

The population density of the United States varies from state to state. The most densely populated state is New Jersey, with 1,208 people per square mile living there. Rhode Island is the second most densely populated state, with slightly over 1,000 inhabitants per square mile. A number of New England states follow at the top of the ranking, making the northeastern region of the United States the most densely populated region of the country.

The least populated U.S. state is the vast territory of Alaska. Only 1.3 inhabitants per square mile reside in the largest state of the U.S.

Compared to other countries around the world, the United States does not rank within the top 50, in terms of population density. Most of the leading countries and territories are city states. However, the U.S. is one of the most populous countries in the world, with a total population of over 327 million inhabitants, as of 2018.

CONTENT

This is a list of cities worldwide by population density. The population, population density and land area for the cities listed are based on the entire city proper, the defined boundary or border of a city or the city limits of the city. The population density of the cities listed is based on the average number of people living per square kilometer or per square mile. This list does not refer to the population, population density or land area of the greater metropolitan area or urban area, nor particular districts in any of the cities listed.

This is a graphical polygon dataset depicting the polygon boundaries of the incorporated city limits of Baton Rouge, Baker, and Zachary within East Baton Rouge Parish, Louisiana. The incorporated city of Baton Rouge covers an area of approximately 74.4 square miles (2004) and has an approximate population of 230,000 (2000 Census). The incorporated city of Baker covers approximately 7.9 square miles (2004) with a population of 13,793 (2000 Census). The incorporated city of Zachary covers an area of approximately 23.7 square miles (2004) with a population of 11,275 (2000 Census). The availability of public services and the application of a wide array of taxes will depend on whether or not a property is located within city limits. This dataset conveys the best currently (2004) available representation of the city limits of the cities of Baton Rouge, Baker and Zachary, Louisiana.

This statistics shows a list of the top 20 largest-metropolitan areas in the United States in 2010, by land area. Riverside-San Bernardino-Ontario in California was ranked first enclosing an area of 70,612 square kilometers.

Hamamatsu was the largest major city in Japan based on city area in 2024, with a size of close to **** thousand square kilometers. It was followed by Shizuoka, with a size of more than **** square kilometers. Overconcentration in Tokyo Economic, political, and financial activity in Japan is heavily concentrated in Tokyo. With around **** million inhabitants, the metropolitan area of Tokyo is the largest urban conglomeration in the world. Most of Japan’s largest companies have their headquarters in Tokyo, and the region attracts many young people who move there to study or work. A breakdown of the net migration flow in Japan showed that the prefectures of Tokyo, Kanagawa, Saitama, and Chiba, all part of the Tokyo metropolitan area, attract the largest number of people. In contrast, the majority of prefectures, especially those located in rural parts of the country, lose a substantial part of their population every year. Demographic trend in rural regions The overconcentration of economic activity in Tokyo has an impact on the demographic situation in rural parts of the country. Japan’s population is shrinking and aging, and rural regions are particularly affected by this. Many young people leave their rural hometowns to seek better opportunities in urban parts of Japan, leaving behind an aging population. As a result, many rural communities in Japan struggle with depopulation and a notable share of municipalities are even threatened with disappearance in the coming decades.

This layer contains information for locating past and present legal city boundaries within Los Angeles County. The Los Angeles County Department of Public Works provides the most current shapefiles representing city annexations and city boundaries on the Los Angeles County GIS Data Portal. True, legal boundaries are only determined on the ground by surveyors licensed in the State of California. Numerous records are freely available at the Land Records Information website, hosted by the Department of Public Works.Principal Attributes:NO: The row number in the attribute table of the PDF Annexation Maps. (See Below)

ANNEX_No: These values are only used for the City of Los Angeles and Long Beach.

NAME: The official annexation name.

TYPE: Indicates the legal action.

A - represents an Annexation to that city. D - represents a Detachment from that city. V - is used to indicate the annexation was void or withdrawn before an effective date could be declared. 33 - Some older city annexation maps indicate a city boundary declared 'as of February 8, 1933'.

ANNEX_AREA: is the land area annexed or detached, in square miles, per the recorded legal description.

TOTAL_AREA: is the cumulative total land area for each city, arranged chronologically.

SHADE: is used by some of our cartographers to store the color used on printed maps.

INDEXNO: is a matching field used for retrieving documents from our department's document management system.

STATE (Secretary of State): Date filed with the Secretary of State. These are not available for earlier annexations and are Null.

COUNTY (County Recorder): Date filed with the County Recorder. These are not available for earlier annexations and are Null.

EFFECTIVE (Effective Date): The effective date of the annexation or detachment.

CITY: The city to which the annexation or detachment took place.

URL: This text field contains hyperlinks for viewing city annexation documents. See the ArcGIS Help for using the Hyperlink Tool.

FEAT_TYPE: contains the type of feature each polygon represents:

Land - Use this value for your definition query if you want to see only land features on your map. Pier - This value is used for polygons representing piers along the coastline. One example is the Santa Monica Pier. Breakwater - This value is used for polygons representing man-made barriers that protect the harbors. Water - This value is used for polygons representing navigable waters inside the harbors and marinas. 3NM Buffer - Per the Submerged Lands Act, the seaward boundaries of coastal cities and unincorporated county areas are three nautical miles from the coastline. (A nautical mile is 1,852 meters, or about 6,076 feet.) Annexation Maps by City (PDF)Large format, high quality wall maps are available for each of the 88 cities in Los Angeles County in PDF format.Agoura HillsHermosa BeachNorwalkAlhambraHidden HillsPalmdaleArcadiaHuntington ParkPalos Verdes EstatesArtesiaIndustryParamountAvalonInglewoodPasadenaAzusaIrwindalePico RiveraBaldwin ParkLa Canada FlintridgePomonaBellLa Habra HeightsRancho Palos VerdesBell GardensLa MiradaRedondo BeachBellflowerLa PuenteRolling HillsBeverly HillsLa VerneRolling Hills EstatesBradburyLakewoodRosemeadBurbankLancasterSan DimasCalabasasLawndaleSan FernandoCarsonLomitaSan GabrielCerritosLong BeachSan MarinoClaremontLos Angeles IndexSanta ClaritaCommerceLos Angeles Map 1Santa Fe SpringsComptonLos Angeles Map 2Santa MonicaCovinaLos Angeles Map 3Sierra MadreCudahyLos Angeles Map 4Signal HillCulver CityLos Angeles Map 5South El MonteDiamond BarLos Angeles Map 6South GateDowneyLos Angeles Map 7South PasadenaDuarteLos Angeles Map 8Temple CityEl MonteLynwoodTorranceEl SegundoMalibuVernonGardenaManhattan BeachWalnutGlendaleMaywoodWest CovinaGlendoraMonroviaWest HollywoodHawaiian GardensMontebelloWestlake VillageHawthorneMonterey ParkWhittier

Historical data on Lincoln's land area from 1959 to present. This document maintained by the Planning Department contains the number of square miles within the City limits, as well as the amount of land annexed each year. The current year is updated periodically as new annexations occur.

In 2023, New York led the ranking of the largest built-up urban areas worldwide, with a land area of ****** square kilometers. Boston-Providence and Tokyo-Yokohama were the second and third largest megacities globally that year.

This datalayer displays the Urbanized Areas (UAs) for the state based on a January 1, 1990 ground condition. Note that the Census Bureau made significant changes in Urban/Rural designations for the Census 2000 data layers. Some of these delineations and definitions are explained below. 1990 Urban/Rural The U.S. Census Bureau defined urban for the 1990 census as consisting of all territory and population in urbanized areas (UAs) and in the urban portion of places with 2,500 or more people located outside of the UAs. The 1990 urban and rural classification applied to the 50 states, the District of Columbia, and Puerto Rico. 1990 Urbanized Areas A 1990 urbanized area (UA) consisted of at least one central place and the adjacent densely settled surrounding territory that together had a minimum population of 50,000 people. The densely settled surrounding territory generally consisted of an area with continuous residential development and a general overall population density of at least 1,000 people per square mile. 1990 Extended Cities For the 1990 census, the U.S. Census Bureau distinguished the urban and rural population within incorporated places whose boundaries contained large, sparsely populated, or even unpopulated area. Under the 1990 criteria, an extended city had to contain either 25 percent of the total land area or at least 25 square miles with an overall population density lower than 100 people per square mile. Such pieces of territory had to cover at least 5 square miles. This low-density area was classified as rural and the other, more densely settled portion of the incorporated place was classified as urban. Unlike previous censuses where the U.S. Census Bureau defined extended cities only within UAs, for the 1990 census the U.S. Census Bureau applied the extended city criteria to qualifying incorporated places located outside UAs. 1990 Urbanized Area Codes Each 1990 UA was assigned a 4-digit numeric census code in alphabetical sequence on a nationwide basis based on the metropolitan area codes. Note that in Record Type C, the 1990 UA 4-digit numeric censu s code and Census 2000 UA 5-digit numeric census code share a 5-character field. Because of this, the 1990 4-digit UA code, in Record Type C only, appears with a trailing blank. For Census 2000 the U.S. Census Bureau classifies as urban all territory, population, and housing units located within urbanized areas (UAs) and urban clusters (UCs). It delineates UA and UC boundaries to encompass densely settled territory, which generally consists of: - A cluster of one or more block groups or census blocks each of which has a population density of at least 1,000 people per square mile at the time - Surrounding block groups and census blocks each of which has a population density of at least 500 people per square mile at the time, and - Less densely settled blocks that form enclaves or indentations, or are used to connect discontiguous areas with qualifying densities. Rural consists of all territory, population, and housing units located outside of UAs and UCs. For Census 2000 this urban and rural classification applies to the 50 states, the District of Columbia, Puerto Rico, American Samoa, Guam, the Northern Mariana Islands, and the Virgin Islands of the United States. Urbanized Areas (UAs) An urbanized area consists of densely settled territory that contains 50,000 or more people. The U.S. Census Bureau delineates UAs to provide a better separation of urban and rural territory, population, and housing in the vicinity of large places. For Census 2000, the UA criteria were extensively revised and the delineations were performed using a zero-based approach. Because of more stringent density requirements, some territory that was classified as urbanized for the 1990 census has been reclassified as rural. (Area that was part of a 1990 UA has not been automatically grandfathered into the 2000 UA.) In addition, some areas that were identified as UAs for the 1990 census have been reclassified as urban clusters. Urban Clusters (UCs) An urban cluster consists of densely settled territory that has at least 2,500 people but fewer than 50,000 people. The U.S. Census Bureau introduced the UC for Census 2000 to provide a more consistent and accurate measure of the population concentration in and around places. UCs are defined using the same criteria that are used to define UAs. UCs replace the provision in the 1990 and previous censuses that defined as urban only those places with 2,500 or more people located outside of urbanized areas. Urban Area Title and Code The title of each UA and UC may contain up to three incorporated place names, and will include the two-letter U.S. Postal Service abbreviation for each state into which the UA or UC extends. However, if the UA or UC does not contain an incorporated place, the urban area title will include the single name of a census designated place (CDP), minor civil division, or populated place recognized by the U.S. Geological Survey's Geographic Names Information System. Each UC and UA is assigned a 5-digit numeric code, based on a national alphabetical sequence of all urban area names. For the 1990 census, the U.S. Census Bureau assigned as four-digit UA code based on the metropolitan area codes. Urban Area Central Places A central place functions as the dominant center of an urban area. The U.S. Census Bureau identifies one or more central places for each UA or UC that contains a place. Any incorporated place or census designated place (CDP) that is in the title of the urban area is a central place of that UA or UC. In addition, any other incorporated place or CDP that has an urban population of 50,000 or an urban population of at least 2,500 people and is at least 2/3 the size of the largest place within the urban area also is a central place. Extended Places As a result of the UA and UC delineations, an incorporated place or census designated place (CDP) may be partially within and partially outside of a UA or UC. Any place that is split by a UA or UC is referred to as an extended place.

Dataset SummaryAbout this data:This feature layer symbolizes the relative population counts for the City's 12 Data Divisions, aggregating the tract-level estimates from the the Census Bureau's American Community Survey 2021 five-year samples.If you click on each Data Division, you can view other Census demographic information about that Data Division in addition to the population count.About the Census Data:The data comes from the U.S. Census Bureau's American Community Survey's 2017-2021 five-year samples. The American Community Survey (ACS) is an ongoing survey conducted by the federal government that provides vital information annually about America and its population. Information from the survey generates data that help determine how more than $675 billion in federal and state funds are distributed each year.For more information about the Census Bureau's ACS data and process of constructing the survey, visit the ACS's About page.About the City's Data Divisions:As a planning analytic tool, an interdepartmental working group divided Rochester into 12 “data divisions.” These divisions are well-defined and static so they are positioned to be used by the City of Rochester for statistical and planning purposes. Census data is tied to these divisions and serves as the basis for analyses over time. As such, the data divisions are designed to follow census boundaries, while also recognizing natural and human-made boundaries, such as the River, rail lines, and highways. Historical neighborhood boundaries, while informative in the division process, did not drive the boundaries. Data divisions are distinct from the numerous neighborhoods in Rochester. Neighborhood boundaries, like quadrant boundaries, police precincts, and legislative districts often change, which makes statistical analysis challenging when looking at data over time. The data division boundaries, however, are intended to remain unchanged. It is hoped that over time, all City data analysts will adopt the data divisions for the purpose of measuring change over time throughout the city.Dictionary: Division: The name of the data division. Total_Popu: The total population of the division. The population is calculated from the Census Bureau’s American Community Survey 2021 five-year samples. Percentage: Represents the percentage of City of Rochester residents which live in the division. Area_in_Sq: The total area in square miles of a given division. Source:City of Rochester Office of Innovation

The smallest country in the world is Vatican City, with a landmass of just **** square kilometers (0.19 square miles). Vatican City is an independent state surrounded by Rome. Vatican City is not the only small country located inside Italy. San Marino is another microstate, with a land area of ** square kilometers, making it the fifth-smallest country in the world. Many of these small nations have equally small populations, typically less than ************** inhabitants. However, the population of Singapore is almost *** million, and it is the twentieth smallest country in the world with a land area of *** square kilometers. In comparison, Jamaica is almost eight times larger than Singapore, but has half the population.

Purpose and Use: Polygon feature that represents the city limit shape and boundaries. Data Source: Updated and maintained by the City of San Marcos through ordinanceContact: Planning Division, planninginfo@sanmarcostx.govData Collection Methodology: Data updated through ordinanceTemporal Coverage: Data collection is still ongoing.Update Frequency: Data is still being submitted as collection is still ongoing. If any errors occur during initial submission, those are reported and updated as needed.Jurisdiction: City of San MarcosField Descriptions: Acreage: The acres included in the city limits SQ_MILES: The square miles included in the city limits

City limits of the City of Aurora, Colorado. The City of Aurora, Colorado (at 164.8 square miles) sits in three different counties: Adams County, Arapahoe County, and Douglas County and lies just east of the City and County of Denver. The city's population is estimated at over 400,000 and is currently the 50th largest city in the U.S.A. The city is annexing land in enclaves and to the east of the city, please check back frequently for the latest data.