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 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.

Land Area by Town reports the total area of land per town in square miles. Dimensions Measure Type,Variable Full Description Land Area by Town reports the total area of land per town in square miles. These values originate from the 2000 and 2010 Decennial Census and in both cases are taken from Summary File 1, table G001.

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.

Not many studies have documented climate and air quality changes of settlements at early stages of development. This is because high quality climate and air quality records are deficient for the periods of the early 18th century to mid 20th century when many U.S. cities were formed and grew. Dramatic landscape change induces substantial local climate change during the incipient stage of development. Rapid growth along the urban fringe in Phoenix, coupled with a fine-grained climate monitoring system, provide a unique opportunity to study the climate impacts of urban development as it unfolds. Generally, heat islands form, particularly at night, in proportion to city population size and morphological characteristics. Drier air is produced by replacement of the countryside's moist landscapes with dry, hot urbanized surfaces. Wind is increased due to turbulence induced by the built-up urban fabric and its morphology; although, depending on spatial densities of buildings on the land, wind may also decrease. Air quality conditions are worsened due to increased city emissions and surface disturbances. Depending on the diversity of microclimates in pre-existing rural landscapes and the land-use mosaic in cities, the introduction of settlements over time and space can increase or decrease the variety of microclimates within and near urban regions. These differences in microclimatic conditions can influence variations in health, ecological, architectural, economic, energy and water resources, and quality-of-life conditions in the city. Therefore, studying microclimatic conditions which change in the urban fringe over time and space is at the core of urban ecological goals as part of LTER aims. In analyzing Phoenix and Baltimore long-term rural/urban weather and climate stations, Brazel et al. (In progress) have discovered that long-term (i.e., 100 years) temperature changes do not correlate with populations changes in a linear manner, but rather in a third-order nonlinear response fashion. This nonlinear temporal change is consistent with the theories in boundary layer climatology that describe and explain the leading edge transition and energy balance theory. This pattern of urban vs. rural temperature response has been demonstrated in relation to spatial range of city sizes (using population data) for 305 rural vs. urban climate stations in the U.S. Our recent work on the two urban LTER sites has shown that a similar climate response pattern also occurs over time for climate stations that were initially located in rural locations have been overrun bu the urban fringe and subsequent urbanization (e.g., stations in Baltimore, Mesa, Phoenix, and Tempe). Lack of substantial numbers of weather and climate stations in cities has previously precluded small-scale analyses of geographic variations of urban climate, and the links to land-use change processes. With the advent of automated weather and climate station networks, remote-sensing technology, land-use history, and the focus on urban ecology, researchers can now analyze local climate responses as a function of the details of land-use change. Therefore, the basic research question of this study is: How does urban climate change over time and space at the place of maximum disturbance on the urban fringe? Hypotheses 1. Based on the leading edge theory of boundary layer climate change, largest changes should occur during the period of peak development of the land when land is being rapidly transformed from open desert and agriculture to residential, commercial, and industrial uses. 2. One would expect to observe, on average and on a temporal basis (several years), nonlinear temperature and humidity alterations across the station network at varying levels of urban development. 3. Based on past research on urban climate, one would expect to see in areas of the urban fringe, rapid changes in temperature (increases at night particularly), humidity (decreases in areas from agriculture to urban; increases from desert to urban), and wind speed (increases due to urban heating). 4. Changes of the surface climate on the urban fringe are expected to be altered as a function of various energy, moisture, and momentum control parameters, such as albedo, surface moisture, aerodynamic surface roughness, and thermal admittance. These parameters relate directly to population and land-use change (Lougeay et al. 1996).

The largest city in Poland in terms of area was Gdańsk in 2024, with *** square kilometers. Followed by the capital, Warsaw, and Gdynia.

The Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 2 data set consists of country-level estimates of urban population, rural population, total population and land area country-wide and in LECZs for years 1990, 2000, 2010, and 2100. The LECZs were derived from Shuttle Radar Topography Mission (SRTM), 3 arc-second (~90m) data which were post processed by ISciences LLC to include only elevations less than 20m contiguous to coastlines; and to supplement SRTM data in northern and southern latitudes. The population and land area statistics presented herein are summarized at the low coastal elevations of less than or equal to 1m, 3m, 5m, 7m, 9m, 10m, 12m, and 20m. Additionally, estimates are provided for elevations greater than 20m, and nationally. The spatial coverage of this data set includes 202 of the 232 countries and statistical areas delineated in the Gridded Rural-Urban Mapping Project version 1 (GRUMPv1) data set. The 30 omitted areas were not included because they were landlocked, or otherwise lacked coastal features. This data set makes use of the population inputs of GRUMPv1 allocated at 3 arc-seconds to match the SRTM elevations, and at 30 arc-seconds resolution in order to reflect uncertainty levels in the product resulting from the interplay of input population data resolutions (based on census units) and the elevation data. Urban and rural areas are differentiated by the GRUMPv1 Urban Extents. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN). To provide estimates of urban and rural populations and land areas for the years 1990, 2000, and 2010; and projections to the year 2100 for 202 countries with contiguous coastal elevations in the following categories: less than or equal to 1m, 3m, 5m, 7m, 9m, 10m, 12m, or 20m; as well as national totals.

3-for-3 activities designed to support Advanced Placement Human Geography.

Here we produced the first 10 m resolution urban green space (UGS) map for the main urban clusters across 371 major Latin American cities as of 2017. Our approach applied a supervised classification of Sentinel-2 satellite imagery and UGS samples derived from OpenStreetMap (OSM). The overall accuracy of this UGS map in 11 randomly selected cities was 0.87, evaluated by independently collected validation samples (‘ground truth’). We further improved mapping quality through a visual inspection and additional sample collection. The resulting UGS map enables studies to measure area, spatial configuration, and human exposures to UGS, facilitating studies about the relationship between UGS and human exposures to environmental hazards, public health outcomes, and environmental justice issues in Latin American cities.UGS in this map series includes grass, shrub, forest, and farmland, and non-UGS included buildings, pavement, roads, barren land, and dry vegetation.The UGS map series includes three sets of files:(1) binary UGS maps at 10 m spatial resolution in GEOTIFF format (UGS.zip), with each of the 371 cities being an individual map. Mapped value of 1 indicates UGS, 0 indicates non-UGS, and no data (with value of -32768) indicates areas outside the mapped boundary or water bodies;(2) a shapefile of mapped boundaries (Boundaries.zip). The boundary file contains city name, country name and its ISO-2 country code, and an ID field linking each city's boundary to the corresponding UGS map.(3) .prj files containing projection information for the binary UGS maps and boundary shapefile. The binary UGS maps are projected with World Geodetic System (WGS) 84 / Pseudo-Mercator projected coordinate system (EPSG: 3857), and the boundary shapefile is projected with WGS 1984 geographic coordinate system (EPSG: 4326)Reference: A 10 m resolution urban green space map for major Latin American cities from Sentinel-2 remote sensing images and OpenStreetMap, published by Scientific Data [link].Citation: Ju, Y., Dronova, I., & Delclòs-Alió, X. (2022). A 10 m resolution urban green space map for major Latin American cities from Sentinel-2 remote sensing images and OpenStreetMap. Scientific Data, 9, Article 1. https://doi.org/10.1038/s41597-022-01701-y

https://data.heatonresearch.com/images/wustl/kaggle/city-size/orbit-107.jpg" alt="Virtual City"> There are many different ways that engineers measure cities. What is the population? What is the total land area occupied by the city. In this exercise, we will see if you can create a model to predict the square feet contained in various virtual cities.

You are provided with images looking towards the city center. Each image is 512x512, compressed with JPEG. The following sample image shows a typical view of a virtual city. You can see towers of a variety of sizes and colors.

https://data.heatonresearch.com/images/wustl/kaggle/city-size/2.jpg" alt="Typical City View">

Not all views of the city will be the same. The following view looks at a city much closer up; however, results in some cropping of important detail.

https://data.heatonresearch.com/images/wustl/kaggle/city-size/10.jpg" alt="Up Close">

Some of the images will show you the city from somewhat above. Here you can see the city roads more clearly. https://data.heatonresearch.com/images/wustl/kaggle/city-size/321.jpg" alt="Above the City">

Some cities are very small.

https://data.heatonresearch.com/images/wustl/kaggle/city-size/350.jpg" alt="Small City">

Other cities are larger.

https://data.heatonresearch.com/images/wustl/kaggle/city-size/947.jpg" alt="Large City">

Recent advances in quantitative tools for examining urban morphology enable the development of morphometrics that can characterize the size, shape, and placement of buildings; the relationships between them; and their association with broader patterns of development. Although these methods have the potential to provide substantial insight into the ways in which neighborhood morphology shapes the socioeconomic and demographic characteristics of neighborhoods and communities, this question is largely unexplored. Using building footprints in five of the ten largest U.S. metropolitan areas (Atlanta, Boston, Chicago, Houston, and Los Angeles) and the open-source R package, foot, we examine how neighborhood morphology differs across U.S. metropolitan areas and across the urban-exurban landscape. Principal components analysis, unsupervised classification (K-means), and Ordinary Least Squares regression analysis are used to develop a morphological typology of neighborhoods and to examine its association with the spatial, socioeconomic, and demographic characteristics of census tracts. Our findings illustrate substantial variation in the morphology of neighborhoods, both across the five metropolitan areas as well as between central cities, suburbs, and the urban fringe within each metropolitan area. We identify five different types of neighborhoods indicative of different stages of development and distributed unevenly across the urban landscape: these include low-density neighborhoods on the urban fringe; mixed use and high-density residential areas in central cities; and uniform residential neighborhoods in suburban cities. Results from regression analysis illustrate that the prevalence of each of these forms is closely associated with variation in socioeconomic and demographic characteristics such as population density, the prevalence of multifamily housing, and income, race/ethnicity, homeownership, and commuting by car. We conclude by discussing the implications of our findings and suggesting avenues for future research on neighborhood morphology, including ways that it might provide insight into issues such as zoning and land use, housing policy, and residential segregation.

This statistic provides the land area that is built-up and non-built up in the ** largest cities worldwide in 2015. As of this year, about ******** square meters of Los Angeles is still considered non-built-up land mass.

About this dataset

Context

This list ranks the 2 cities in the Southeast Fairbanks Census Area, AK by Hispanic White 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 White Population: This column displays the rank of cities in the Southeast Fairbanks Census Area, AK by their Hispanic White population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Hispanic White Population: The Hispanic White 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 White. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Southeast Fairbanks Census Area Hispanic White Population: This tells us how much of the entire Southeast Fairbanks Census Area, AK Hispanic White 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/.

Urban heat island is among the most evident aspects of human impacts on the earth system. Here we assess the diurnal and seasonal variation of surface urban heat island intensity (SUHII) defined as the surface temperature difference between urban area and suburban area measured from the MODIS. Differences in SUHII are analyzed across 419 global big cities, and we assess several potential biophysical and socio-economic driving factors. Across the big cities, we show that the average annual daytime SUHII (1.5 ± 1.2 °C) is higher than the annual nighttime SUHII (1.1 ± 0.5 °C) (P < 0.001). But no correlation is found between daytime and nighttime SUHII across big cities (P = 0.84), suggesting different driving mechanisms between day and night. The distribution of nighttime SUHII correlates positively with the difference in albedo and nighttime light between urban area and suburban area, while the distribution of daytime SUHII correlates negatively across cities with the difference of vegetation cover and activity between urban and suburban areas. Our results emphasize the key role of vegetation feedbacks in attenuating SUHII of big cities during the day, in particular during the growing season, further highlighting that increasing urban vegetation cover could be one effective way to mitigate the urban heat island effect.

Bell Ringer activities designed to support Advanced Placement Human Geography.

Continuous Household Survey: Number of households by Autonomous cities and communities by size of the household and usable floor area. Autonomous cities and communities.

A geographic depiction of city boundaries in Broward County, Florida.

City boundary data was last updated April 13th, 2021 and previously on February 22, 2021. A small edit was made between Tamarac and Fort Lauderdale just SW of the Executive Airport. In February adjustments were made between Pembroke Pines, Southwest Ranches and Cooper City where their geographies are coincidence and are meant to follow the northern boundaries of STR geography. Prior to this edit, the City of Coral Springs had annexed four parcels of land from unincorporated Broward County; Ordinances 2018-014 (1 parcel) and 2018-036 (3 parcels), effective Sept 15, 2019. Previously in May 2019, a correction was made to the boundaries of Southwest Ranches and Pembroke Pines at Dykes Road and Sheraton, just north of Sheraton, on the west side of Dykes. Prior to this change, a correction was made to the Lauderhill boundary at the Florida Turnpike interchange located at the Sunrise Blvd entrance on the east side of the turnpike in April 2019; the 1959 Lauderhill incorporation legal description, (Laws of Florida 59-1478) left this thirteen acre area as unincorporated. A 1994 boundary change between Plantation and Lauderhill, (Laws of Florida 94-427) de-annexed five parcels from Plantation and annexed them to Lauderhill in this area. However in 1996, Broward County's Strategic Planning and Growth Management Department made available data sets provided by Broward County’s Planning and Information Technology Division via a CD. This data set depicted this unincorporated area as being part of Lauderhill. This depiction remained such until a boundary adjustment in 2006-2007 incorrectly depicted this as being part of Plantation. In 2009 Broward County was made aware of this error and adjusted it partially using the CD boundary as a template. This resulted in the area being incorrectly assigned to Lauderhill. In September of 2018, Lauderhill revisited this boundary depiction by the County and in 2019 it was concluded this area is unincorporated following the 1959 and the 1994 boundary adjustment legal descriptions.

Prior to April 2019 there were other edits. The previous update of the data was Nov 7th, 2018, adusting the boundaries between Weston and Town of Davie to agree with House Bill 0871 which redefined a small area of their adjoining boundaries in the area of Weston Road and I-75. In July 2018, adjustments were made to the City of Margate to align with a city boundary shape file and written legal description as provided by John Shelton, GIS, City of Margate. The previous update was January 17th, 2018, correcting an unincorporated boundary line of the Triple H Ranch plat area within Parkland. This also reflects an adjustment made to Pembroke Pines southwest boundary between the Turnpike and SR 27 and the Sept 15th 2016 annexations of County unincorporated lands by Parkland. (City Ord 2016-06) and Coconut Creek (City Ord. 2015-027).Also a correction to the Hollywood/Davie boundary in the vicinity of Davie Blvd Ext and N 66 Ave and Oak St, per the City of Hollywood. Recent past boundary changes include annexations of county land to Pembroke Pines and Cooper City in 2015. And a Weston-Davie boundary adjustment in 2015; HB 871. And a July 2015 official resurvey of the City of Fort Lauderdale's boundaries which thus included adjustments to Oakland Park and Pompano Beach boundaries, (F. Gulliano, BC Engineering, M. Donaldson PSM, Fort Lauderdale). Also in 2015, a boundary adjustment was made to the eastern most boundary of Pompano Beach to match it to a more accurate depiction of the coastal erosion line by Broward County; (requested by the city to match their legal description). Further back, the were annexations for Parkland (2013) and Sunrise (Nov 2012) and updates to Lauderdale Lakes (per J. Petrov - BC Engineering 2012) and Plantation (I Reyes, GIS - Plantation 2012).

Source: BCGIS

Effective Date:

Last Update: 04/15/2021

Update Cycle: As needed.

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.

These data were created to describe the causes of land cover change that occurred in the Lower Rio Grande (LRG) Valley and Alluvial Floodplain ecoregions of Texas for the time intervals of 2001 to 2006 and 2006 to 2011. The study area covers approximately 600,000 hectares at the southernmost tip of Texas and is one of the fastest growing regions in the United States. Some of the largest cities in the area include Brownsville and Harlingen, Texas. Two raster maps showing the causes of land change were created at a 30-meter resolution using automated and manual photo interpretation techniques. There were 26 categories of land change causes (for example, urban expansion or surficial mining) identified across the LRG region. These categories can be used by researchers to summarize the historical patterns of land change for the region and to understand the impacts these land change causes may have on the region's ecology, hydrology, wildlife, and climate.

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.

Purpose

County 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, coastal buffers are removed, leaving the land-based portions of jurisdictions. 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
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 (this dataset)
4. Place Abbreviations
5. Unincorporated Areas (Coming Soon)
6. Census Designated Places (Coming Soon)
   
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

• 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
• Place Name: CDTFA incorporated (city) or county name
• 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.
• Legal 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.
• 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 System
• Place Abbr: CalTrans Division of Local Assistance abbreviations of incorporated area names
• CNTY Abbr: CalTrans Division of Local Assistance abbreviations of county names
• Area_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.

Accuracy

CDTFA"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 =