This statistic represents the percent increase of the 15 fastest-growing large cities in the U.S. between July 1, 2020 and July 1, 2021. Georgetown city in Texas is at the top of the fastest-growing large cities, with a growth rate of 10.5 percent over this period.

This graph shows the 15 fastest growing cities in the United States, by percentage increase in population, from the period April 1, 2010 to July 1, 2011. Over this time New Orleans was the fastest growing city at a rate of 4.9 percent.

This statistics shows the top 20 fastest growing large-metropolitan areas in the United States between July 1st, 2022 and July 1st, 2023. The total population in the Wilmington, North Carolina, metropolitan area increased by 0.05 percent from 2022 to 2023.

This statistic shows the population growth rate of the top twenty largest urban agglomerations in the United States from 2000 to 2030. Between 2025 and 2030, the average annual population growth rate of the New York-Newark agglomeration is projected to be roughly **** percent.

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.

VITAL SIGNS INDICATOR Population (LU1)

FULL MEASURE NAME Population estimates

LAST UPDATED October 2019

DESCRIPTION Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

DATA SOURCES U.S Census Bureau: Decennial Census No link available (1960-1990) http://factfinder.census.gov (2000-2010)

California Department of Finance: Population and Housing Estimates Table E-6: County Population Estimates (1961-1969) Table E-4: Population Estimates for Counties and State (1971-1989) Table E-8: Historical Population and Housing Estimates (2001-2018) Table E-5: Population and Housing Estimates (2011-2019) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census - via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University Population Estimates (1970 - 2010) http://www.s4.brown.edu/us2010/index.htm

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2011-2017) http://factfinder.census.gov

U.S. Census Bureau: Intercensal Estimates Estimates of the Intercensal Population of Counties (1970-1979) Intercensal Estimates of the Resident Population (1980-1989) Population Estimates (1990-1999) Annual Estimates of the Population (2000-2009) Annual Estimates of the Population (2010-2017) No link available (1970-1989) http://www.census.gov/popest/data/metro/totals/1990s/tables/MA-99-03b.txt http://www.census.gov/popest/data/historical/2000s/vintage_2009/metro.html https://www.census.gov/data/datasets/time-series/demo/popest/2010s-total-metro-and-micro-statistical-areas.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) All legal boundaries and names for Census geography (metropolitan statistical area, county, city, and tract) are as of January 1, 2010, released beginning November 30, 2010, by the U.S. Census Bureau. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of August 2019. For more information on PDA designation see http://gis.abag.ca.gov/website/PDAShowcase/.

Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970 -2010) and the American Community Survey (2008-2012 5-year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

Population estimates for Bay Area PDAs are from the decennial Census (1970 - 2010) and the American Community Survey (2006-2010 5 year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Population estimates for PDAs are derived from Census population counts at the tract level for 1970-1990 and at the block group level for 2000-2017. Population from either tracts or block groups are allocated to a PDA using an area ratio. For example, if a quarter of a Census block group lies with in a PDA, a quarter of its population will be allocated to that PDA. Tract-to-PDA and block group-to-PDA area ratios are calculated using gross acres. Estimates of population density for PDAs use gross acres as the denominator.

Annual population estimates for metropolitan areas outside the Bay Area are from the Census and are benchmarked to each decennial Census. The annual estimates in the 1990s were not updated to match the 2000 benchmark.

The following is a list of cities and towns by geographical area: Big Three: San Jose, San Francisco, Oakland Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville Unincorporated: all unincorporated towns

This dataset contains information about the 1000 largest US cities by population: population, population growth, geographic coordinates, population rank.

About this dataset

Context

This list ranks the 1208 cities in the Texas by Multi-Racial 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 Multi-Racial Black Population: This column displays the rank of cities in the Texas by their Multi-Racial 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.
• Multi-Racial Black Population: The Multi-Racial 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 Multi-Racial Black. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Texas Multi-Racial Black Population: This tells us how much of the entire Texas Multi-Racial 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/.

The cities expected by industry experts to have the highest investor demands in the United States in 2023 were chosen due to their sustained population and job growth, attraction to educated millennials, high levels of economic diversity, and white-collar employment among others. Austin, Nashville, and Dallas Fortworth ranked highest among the top 15 cities with the highest projected investor demand in real estate in the United States for 2023.

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.

About this dataset

Context

The dataset tabulates the Jersey City population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Jersey City across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Jersey City was 291,657, a 0.65% increase year-by-year from 2022. Previously, in 2022, Jersey City population was 289,772, an increase of 1.64% compared to a population of 285,105 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Jersey City increased by 51,565. In this period, the peak population was 291,949 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Jersey City is shown in this column.
• Year on Year Change: This column displays the change in Jersey City population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Jersey City Population by Year. You can refer the same here

In 2020, about 82.66 percent of the total population in the United States lived in cities and urban areas. As the United States was one of the earliest nations to industrialize, it has had a comparatively high rate of urbanization over the past two centuries. The urban population became larger than the rural population during the 1910s, and by the middle of the century it is expected that almost 90 percent of the population will live in an urban setting. Regional development of urbanization in the U.S. The United States began to urbanize on a larger scale in the 1830s, as technological advancements reduced the labor demand in agriculture, and as European migration began to rise. One major difference between early urbanization in the U.S. and other industrializing economies, such as the UK or Germany, was population distribution. Throughout the 1800s, the Northeastern U.S. became the most industrious and urban region of the country, as this was the main point of arrival for migrants. Disparities in industrialization and urbanization was a key contributor to the Union's victory in the Civil War, not only due to population sizes, but also through production capabilities and transport infrastructure. The Northeast's population reached an urban majority in the 1870s, whereas this did not occur in the South until the 1950s. As more people moved westward in the late 1800s, not only did their population growth increase, but the share of the urban population also rose, with an urban majority established in both the West and Midwest regions in the 1910s. The West would eventually become the most urbanized region in the 1960s, and over 90 percent of the West's population is urbanized today. Urbanization today New York City is the most populous city in the United States, with a population of 8.3 million, while California has the largest urban population of any state. California also has the highest urbanization rate, although the District of Columbia is considered 100 percent urban. Only four U.S. states still have a rural majority, these are Maine, Mississippi, Montana, and West Virginia.

With social media ubiquitous in our daily communication, local governments have growingly relied on this new media platform for communicating and interacting with their citizens. However, it is still unclear how to assess the effectiveness of social media communication efforts by the governments. Based on the Lasswell communication framework, this study proposes a social media communication index that can be used to compare and evaluate the degree of social media communication effectiveness among different cities. The index was then applied to the social media platforms used by the top growing U.S. cities. The results show that City of Orlando and New York City exhibit top communication effectiveness in social media. This dataset was developed along with the research.

This statistic shows the change in Millennial incoming population in selected cities in the United States between 2010 and 2015. Richmond, Virginia saw the second highest change in Millennial residents in the measured period, with a 14.9 percent increase.

Smart City & Connected City Solutions Market Outlook

The global market size for Smart City & Connected City Solutions is poised to grow from $520 billion in 2023 to an impressive $1.2 trillion by 2032, exhibiting a robust CAGR of 9.5% over the forecast period. This substantial growth is driven by advancements in IoT technology, increased urbanization, and the rising demand for energy-efficient systems and infrastructure.

One of the primary growth factors for this market is the rapid urbanization across the globe. More than half of the world’s population now resides in urban areas, and this figure is expected to rise exponentially over the coming decades. As cities grow, the strain on infrastructure, energy resources, and governance systems increases, creating a necessity for smarter and more efficient solutions. The integration of IoT and AI technologies into urban planning and management is enabling cities to meet these demands by optimizing resource use, reducing waste, and improving the quality of life for residents.

Another significant driver is the rising governmental and private sector investment in smart city initiatives. Governments worldwide are recognizing the benefits of smart city solutions in terms of energy conservation, better traffic management, enhanced security, and improved public services. For example, the European Union has committed substantial funding for smart city projects under its Horizon 2020 initiative, focusing on sustainability and technological innovation. Similarly, various countries in Asia-Pacific, North America, and the Middle East are launching extensive smart city programs, backed by both public and private investments.

The proliferation of advanced technologies such as 5G, blockchain, and AI is also playing a crucial role in the market's growth. 5G technology, in particular, is set to revolutionize smart city infrastructure by providing faster, more reliable connectivity. This will enable the high-speed data transfer required for real-time applications in smart governance, smart healthcare, and smart mobility. Additionally, blockchain technology offers enhanced security and transparency for various smart city applications, including energy grids, public services, and transportation systems.

The regional outlook for the Smart City & Connected City Solutions market is highly promising, with Asia-Pacific and North America leading the charge. Asia-Pacific is expected to witness the highest growth rate due to the rapid urbanization in countries like China and India and substantial government initiatives focused on building smart cities. North America, with its advanced technological infrastructure and significant investments in smart city projects, is also poised for considerable growth.

Component Analysis

The Smart City & Connected City Solutions market can be segmented by components into hardware, software, and services. Each of these components plays a critical role in the development and implementation of smart city solutions. The hardware segment includes sensors, cameras, smart meters, and other connected devices that form the backbone of smart city infrastructure. These devices collect vast amounts of data, which is crucial for monitoring and managing various urban functions. The increasing adoption of IoT devices is driving the growth of this segment, as cities aim to become more efficient and responsive.

Software solutions are essential for analyzing the data collected by hardware components and transforming it into actionable insights. This segment covers a wide range of applications, including data analytics platforms, urban planning software, and smart governance solutions. The demand for such software is growing as cities seek to harness the power of big data and AI to improve decision-making processes. Cloud-based software solutions have become particularly popular due to their scalability, flexibility, and cost-effectiveness, contributing to the overall growth of the software segment.

Services are another vital component of the Smart City & Connected City Solutions market. These services include consulting, system integration, and maintenance services, which are crucial for the successful implementation and ongoing operation of smart city projects. The complexity of integrating various hardware and software components into a cohesive system necessitates specialized expertise. As a result, there is a growing market for service providers who can offer end-to-end solutions, from initial planning and design to implementation and continuous support.<

Commercial Pest Control Market Outlook

The global commercial pest control market size was valued at approximately USD 20 billion in 2023 and is projected to reach around USD 32 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 5.5% during the forecast period. This steady growth is driven by increasing urbanization, stringent regulations concerning health and hygiene, and the rising prevalence of pest-related issues globally.

One of the primary growth factors for the commercial pest control market is the expanding urban population. With more people moving into cities, there is a greater concentration of potential food sources and habitats for pests. This urban growth necessitates more rigorous pest control measures to ensure public health and sanitation. Additionally, the growing awareness among business owners regarding the importance of maintaining a pest-free environment to protect their brand reputation and ensure customer satisfaction further fuels market expansion.

The increasing stringency of government regulations across various regions aimed at maintaining public health and safety standards is another significant driver. Many countries have established strict guidelines and frequent inspections to ensure businesses, particularly those in the food and healthcare sectors, adhere to pest control standards. Non-compliance can lead to severe penalties, including business closures, thus compelling enterprises to invest in reliable pest control solutions. This regulatory landscape continues to boost demand for professional pest control services.

Technological advancements and innovations in pest control methods also act as growth catalysts for this market. Modern pest control solutions involve the use of advanced technologies such as Integrated Pest Management (IPM), which combines multiple control methods and minimizes the use of harmful chemicals. Furthermore, the development of eco-friendly and sustainable pest control products addresses the rising environmental concerns and consumer demand for green solutions. These innovations not only enhance the efficiency and effectiveness of pest control measures but also expand the scope of services offered by pest control companies.

Pest Control Solutions have evolved significantly over the years, driven by the need for more effective and environmentally friendly approaches. These solutions encompass a range of methods, from traditional chemical treatments to modern integrated pest management strategies. The focus is increasingly on minimizing environmental impact while maximizing efficacy. Businesses are now more inclined to adopt comprehensive pest control solutions that not only address immediate pest issues but also prevent future infestations. This shift is largely due to the growing awareness of the long-term benefits of sustainable pest management practices, which align with global trends towards environmental responsibility.

Regionally, North America holds a significant share of the commercial pest control market, driven by the high awareness and stringent regulatory framework in the United States and Canada. The Asia Pacific region is expected to witness the highest growth rate, attributed to rapid urbanization, increasing disposable incomes, and a burgeoning middle class that prioritizes hygiene and cleanliness. Europe also represents a substantial market share, with a focus on sustainable and eco-friendly pest control solutions. Latin America and the Middle East & Africa are emerging markets, where improving economic conditions and growing awareness are likely to drive demand for commercial pest control services.

Service Type Analysis

The commercial pest control market can be segmented by service type into Chemical Control, Mechanical Control, Biological Control, and Others. Chemical control is currently the most widely used method, given its effectiveness in quickly eliminating pests. This segment includes the use of pesticides and insecticides, which are crucial in areas with severe pest infestations. Despite concerns regarding the environmental impact of chemical control, it remains a preferred choice due to its immediate results and ability to handle large-scale infestations.

Mechanical control methods are gaining traction, especially in settings where the use of chemicals is restricted or undesirable. Mechanical control involves the use of physical devices and barriers, such as traps and machines, to capture or ki

In the first quarter of 2025, San Francisco, Chicago, New York, and Honolulu were some of the U.S. cities with the highest housing construction costs. Meanwhile, Phoenix had one of the lowest construction costs for high-end multifamily homes at 190 U.S. dollars per square foot and Las Vegas for single-family homes between 240 and 480 U.S. dollars per square foot. Construction cost disparities As seen here, the construction cost for a high-end multi-family home in San Francisco in the first quarter of 2024 was over twice more expensive than in Phoenix. Meanwhile, there were also great differences in the cost of building a single-family house in New York and in Portland or Seattle. Some factors that may cause these disparities are the construction materials, installation, and composite costs, differing land values, wages, etc. For example, although the price of construction materials in the U.S. was rising at a slower level than in 2022 and 2023, several materials that are essential in most construction projects had growth rates of over five percent in 2024. Growing industry revenue Despite the economic uncertainty and other challenges, the size of the private construction market in the U.S. rose during the past years. It is important to consider that supply and demand for housing influences the revenue of this segment of the construction market. On the supply side, single-family home construction fell in 2023, but it is expected to rise in 2024 and 2025. On the demand side, some of the U.S. metropolitan areas with the highest sale prices of single-family homes were located in California, with San Jose-Sunnyvale-Santa Clara at the top of the ranking.

50 year Projected Urban Growth scenarios. Base year is 2000. Projected year in this dataset is 2050.

By 2020, most forecasters agree, California will be home to between 43 and 46 million residents-up from 35 million today. Beyond 2020 the size of California's population is less certain. Depending on the composition of the population, and future fertility and migration rates, California's 2050 population could be as little as 50 million or as much as 70 million. One hundred years from now, if present trends continue, California could conceivably have as many as 90 million residents. Where these future residents will live and work is unclear. For most of the 20th Century, two-thirds of Californians have lived south of the Tehachapi Mountains and west of the San Jacinto Mountains-in that part of the state commonly referred to as Southern California. Yet most of coastal Southern California is already highly urbanized, and there is relatively little vacant land available for new development. More recently, slow-growth policies in Northern California and declining developable land supplies in Southern California are squeezing ever more of the state's population growth into the San Joaquin Valley. How future Californians will occupy the landscape is also unclear. Over the last fifty years, the state's population has grown increasingly urban. Today, nearly 95 percent of Californians live in metropolitan areas, mostly at densities less than ten persons per acre. Recent growth patterns have strongly favored locations near freeways, most of which where built in the 1950s and 1960s. With few new freeways on the planning horizon, how will California's future growth organize itself in space? By national standards, California's large urban areas are already reasonably dense, and economic theory suggests that densities should increase further as California's urban regions continue to grow. In practice, densities have been rising in some urban counties, but falling in others.

These are important issues as California plans its long-term future. Will California have enough land of the appropriate types and in the right locations to accommodate its projected population growth? Will future population growth consume ever-greater amounts of irreplaceable resource lands and habitat? Will jobs continue decentralizing, pushing out the boundaries of metropolitan areas? Will development densities be sufficient to support mass transit, or will future Californians be stuck in perpetual gridlock? Will urban and resort and recreational growth in the Sierra Nevada and Trinity Mountain regions lead to the over-fragmentation of precious natural habitat? How much water will be needed by California's future industries, farms, and residents, and where will that water be stored? Where should future highway, transit, and high-speed rail facilities and rights-of-way be located? Most of all, how much will all this growth cost, both economically, and in terms of changes in California's quality of life? Clearly, the more precise our current understanding of how and where California is likely to grow, the sooner and more inexpensively appropriate lands can be acquired for purposes of conservation, recreation, and future facility siting. Similarly, the more clearly future urbanization patterns can be anticipated, the greater our collective ability to undertake sound city, metropolitan, rural, and bioregional planning.

Consider two scenarios for the year 2100. In the first, California's population would grow to 80 million persons and would occupy the landscape at an average density of eight persons per acre, the current statewide urban average. Under this scenario, and assuming that 10% percent of California's future population growth would occur through infill-that is, on existing urban land-California's expanding urban population would consume an additional 5.06 million acres of currently undeveloped land. As an alternative, assume the share of infill development were increased to 30%, and that new population were accommodated at a density of about 12 persons per acre-which is the current average density of the City of Los Angeles. Under this second scenario, California's urban population would consume an additional 2.6 million acres of currently undeveloped land. While both scenarios accommodate the same amount of population growth and generate large increments of additional urban development-indeed, some might say even the second scenario allows far too much growth and development-the second scenario is far kinder to California's unique natural landscape.

This report presents the results of a series of baseline population and urban growth projections for California's 38 urban counties through the year 2100. Presented in map and table form, these projections are based on extrapolations of current population trends and recent urban development trends. The next section, titled Approach, outlines the methodology and data used to develop the various projections. The following section, Baseline Scenario, reviews the projections themselves. A final section, entitled Baseline Impacts, quantitatively assesses the impacts of the baseline projections on wetland, hillside, farmland and habitat loss.

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The smart city market outlook presents a positive picture for vendors in Europe. It will offer the highest growth opportunities for market vendors and encourage them to make significant investments to improve their presence in the region.

The smart city market report also provides several other key information including:

CAGR of the market during the forecast period 2020-2024
Detailed information on factors that will drive smart city market growth during the next five years
Precise estimation of the smart city market size and its contribution to the parent market
Accurate predictions on upcoming trends and changes in consumer behavior
The growth of the smart city market industry across APAC, Europe, MEA, North America, and South America
A thorough analysis of the market’s competitive landscape and detailed information on vendors
Comprehensive details of factors that will challenge the growth of smart city market vendors

Surface Urban Heat Island (SUHI) hotspot data are defined as areas of statistically high land surface temperature (LST). A pixel is determined as statistically high if it exceeds one standard deviation above the mean of all pixels with similar land cover type. Data are provided across 50 regions throughout the Continental U.S. using previously generated annual maximum land surface temperature (MeanLST) – derived from Collection 1 Landsat U.S. Analysis Ready Data (ARD) for Surface Temperature. The data ranges from 1985-2020, and covers data within 5 km of each city. The data is further separated into persistent urban and new urban outputs. Persistent Urban is defined as areas that are reported as urban in 1985 and remained urban in 2020. Areas that changed from non-urban in 1985 to urban in 2020 are defined as new urban. NOTE: While a previous version is available from the author, all the datasets for pilot cities can be found in version 5.0.