The rapid urbanization in China since the 1970s has led to an exponential growth of metal stocks (MS) in use in cities. A retrospect on the quantity, quality, and patterns of these MS is a prerequisite for projecting future metal demand, identifying urban mining potentials of metals, and informing sustainable urbanization strategies. Here, we deployed a bottom-up stock accounting method to estimate stocks of iron, copper, and aluminum embodied in 51 categories of products and infrastructure across 10 Chinese megacities from 1980 to 2016. We found that the MS in Chinese megacities had reached a level of 2.6–6.3 t/cap (on average 3.7 t/cap for iron, 58 kg/cap for copper, and 151 kg/cap for aluminum) in 2016, which still remained behind the level of western cities or potential saturation level on the country level (e.g., approximately 13 t/cap for iron). Economic development was identified as the most powerful driver for MS growth based on an IPAT decomposition analysis, indicating further increase in MS as China’s urbanization and economic growth continues in the next decades. The latecomer cities should therefore explore a wide range of strategies, from urban planning to economy structure to regulations, for a transition toward more “metal-efficient” urbanization pathways.

In 2021, shared on-demand mobility services accounted for ***** percent of trips in megacities worldwide. On-demand mobility, including robo-taxis, is expected to play an increasing role in urban mobility in the future. By 2035, forecasts predict that this type of mobility service will account for ** percent of all urban trips in mega-cities.

In early 2018, Cape Town, South Africa nearly ran of water. It was one of the first major cities, but will not be the last, to confront this crisis. By the year 2050, more than two-thirds of the world’s population will live in urban settings. Sustaining the demand for clean water, healthy air, and flourishing natural ecosystems to support these communities will be one of the great challenges of this generation. Compounding this challenge is the existential threats brought by historically unprecedented changes in climate. Notably, some parts of cities are, have been, and will continue to be more stressed than others. These inequalities in environmental condition lead directly to disparities in health. They are created by a variety of historical and contemporary actions that affect where people live, work, and play; who participates in decision-making processes; and where environmental risks are created.

SummaryThis metadata record provides details of the data supporting the claims of the related manuscript: “Projecting future populations of urban agglomerations: around the world and through the 21st century ”.The data consist of HTML files with interactive maps for future populations projections of urban agglomerations, and HTML file displaying figures for postdictions of urban agglomerations, as well as 5 .csv files containing the raw data.The related study estimated population trends throughout the 21st century for approximately 20,000 urban agglomerations in 151 countries by working within the Shared Socioeconomic Pathways (SSPs) and using a simple urban growth model.Data accessThe following resources, which were among the sources of the data analyzed in the related study, are available from the links below.- Postdiction results for 1794 urban agglomerations http://stwww.eng.kagawa-u.ac.jp/~kii/Research/UPP_2020/UPP_2020.html#postdiction-for-1794-agglomerations-link- Temporal evolution from 2010 to 2100 of the geographical distribution of urban agglomerations, arranged by population scale, as predicted within the various SSP scenarios http://stwww.eng.kagawa-u.ac.jp/~kii/Research/UPP_2020/UPP_2020.htmlThese data are also available in raw .csv form via the 'Raw data' link on the same page, and also in the 5 files included as part of this data record.- Available urban-population data include the UN’s World Urbanization Prospects 2018 (https://population.un.org/wup/) and Gridded Population of the World, v4 (https://doi.org/10.7927/H4BC3WMT). Available settlement-point data include, in addition to the above urban population sources, World Gazetteer (https://www.arcgis.com/home/item.html?id=346ce13fa2d4468a9049f71bcc250f37) and GeoNames (https://www.geonames.org/). GDP per capita data is available from OECD.stat (https://stats.oecd.org/), Global Metro Monitor (https://www.brookings.edu/research/global-metro-monitor/), and World Development Indicators (http://datatopics.worldbank.org/world-development-indicators/). OpenStreetMap is available at https://www.openstreetmap.org/. Scenario data for SSPs are available at the IIASA-SSP database (https://doi.org/10.1016/j.gloenvcha.2016.05.009). CodeCode used for the analysis can be downloaded from the author's lab's website: http://stwww.eng.kagawa-u.ac.jp/~kii/Research/UPP_2020/UPP_2020.html#codes. These are written in R. They are provided only for the purpose of tracing the analytical procedure. They are not executable without appropriate datasets.

Japan’s largest city, greater Tokyo, had a staggering ***** million inhabitants in 2023, making it the most populous city across the Asia-Pacific region. India had the second largest city after Japan with a population consisting of approximately ** million inhabitants. Contrastingly, approximately *** thousand inhabitants populated Papua New Guinea's largest city in 2023. A megacity regionNot only did Japan and India have the largest cities throughout the Asia-Pacific region but they were among the three most populated cities worldwide in 2023. Interestingly, over half on the world’s megacities were situated in the Asia-Pacific region. However, being home to more than half of the world’s population, it does not seem surprising that by 2025 it is expected that more than two thirds of the megacities across the globe will be located in the Asia Pacific region. Other megacities are also expected to emerge within the Asia-Pacific region throughout the next decade. There have even been suggestions that Indonesia’s Jakarta and its conurbation will overtake Greater Tokyo in terms of population size by 2030. Increasing populationsIncreased populations in megacities can be down to increased economic activity. As more countries across the Asia-Pacific region have made the transition from agriculture to industry, the population has adjusted accordingly. Thus, more regions have experienced higher shares of urban populations. However, as many cities such as Beijing, Shanghai, and Seoul have an aging population, this may have an impact on their future population sizes, with these Asian regions estimated to have significant shares of the population being over 65 years old by 2035.

Trace metals, as constituents of ambient air, can have impacts on human and environmental health. The Global Atmospheric Passive Sampling (GAPS) and GAPS Megacities (GAPS-MC) networks investigated trace metals in the air at 51 global locations by deploying polyurethane foam disk passive air samplers (PUF–PAS) for periods of 3–12 months. Aluminum and iron exhibited the highest concentrations in air (x̅ = 3400 and 4630 ng/m3, respectively), with notably elevated values at a rural site in Argentina thought to be impacted by resuspended soil. Urban sites had the highest levels of toxic Pb and Cd, with enrichment factors suggesting primarily anthropogenic influences. High levels of As at rural sites were also observed. Elevated trace metal concentrations in cities are associated with local emissions and higher PM2.5 and PM10 concentrations. Brake and tire wear-associated metals Sb, Cu, and Zn are significantly correlated and elevated at urban locations relative to those at background sites. These data demonstrate the versatility of PUF–PAS for measuring trace metals and other particle-associated pollutants in ambient air in a cost-effective and simple manner. The data presented here will serve as a global baseline for assessing future changes in ambient air associated with industrialization, urbanization, and population growth.

This database represents the historic, current and future estimates and projections with number of inhabitants for the world's largest urban areas from 1950-2050. The data covers cities and other urban areas with more than 750,000 people.

Despite ubiquitous urbanization and worldwide standardization, there is a lack of better differentiation between cities toward more customized environments. Although current advancements in computational design and digital fabrication technologies have been successfully applied in various architectural scales, they have rarely, if ever, been implemented in a larger urban context that can lead to broader benefit and responses for citizens. This research aims to describe the potential of digital fabrication methods for large-scale urban applications that can subsequently lead to more diverse and unique urban environments. This paper summarizes state-of-the-art principles for large-scale building construction that have been implemented in the past, along with current research and practice, and outlines a conceptual framework for possible future directions for large quantities of automatic and bespoke construction deliveries for future customized urban scenarios. This article also outlines the effects of end-users' participation on urban developments using online users' interface to inform building processes. To address aspects of end-users' engagement in customization of cities, the article elaborates the question of responsiveness, where the citizen actively interacts with the environment and building technology and vice versa in order to customize the urban space. This is theoretically and conceptually explained and illustrated in a case study related to the formerly industrial harbor area of Tanjong Pagar in the city of Singapore, which is a test-bed for new urban developments on 325 ha of waterfront land in the downtown port area within the context of a tropical city.

The Big Data in Smarter Cities market report offers a thorough competitive analysis, mapping key players’ strategies, market share, and business models. It provides insights into competitor dynamics, helping companies align their strategies with the current market landscape and future trends.

The Urban Air Mobility (UAM) market is poised for significant growth, projected to reach a market size of $7.8 billion in 2025 and exhibiting a robust Compound Annual Growth Rate (CAGR) of 8.8% from 2025 to 2033. This expansion is driven by several key factors. Increasing urbanization and traffic congestion in major cities globally are creating a compelling demand for faster, more efficient transportation solutions. Technological advancements in electric vertical takeoff and landing (eVTOL) aircraft, coupled with improving battery technology and autonomous flight capabilities, are making UAM a more viable and attractive alternative to traditional ground transportation. Furthermore, growing investments from both private and public sectors are fueling innovation and infrastructure development, paving the way for wider adoption. The regulatory landscape, while still evolving, is showing signs of progress with several countries actively working on establishing frameworks to support the safe integration of UAM into existing airspace. However, the market also faces challenges. High initial investment costs associated with aircraft development and infrastructure deployment pose a significant barrier to entry for many companies. Public acceptance and addressing concerns related to safety and noise pollution are crucial for long-term market success. Establishing robust air traffic management systems and ensuring the seamless integration of UAM into existing airspace are also essential considerations. Despite these hurdles, the long-term potential of UAM is undeniable, with its ability to revolutionize urban commuting and potentially alleviate some of the pressing challenges faced by rapidly growing megacities. The competitive landscape is dynamic, with established aerospace giants like Airbus and Honeywell alongside innovative startups such as Kitty Hawk, Lilium, and Volocopter vying for market share. This competition is likely to accelerate innovation and drive down costs, making UAM more accessible to a wider range of consumers in the coming years.

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.

In 2023, approximately ** percent of the population in Papua New Guinea were living in rural areas. In comparison, approximately ***** percent of the population in Japan were living in rural areas that year. Urbanization and development Despite the desirable outcomes that urbanization entails, these rapid demographic shifts have also brought about unintended changes. For instance, in countries like India, rapid urbanization has led to unsustainable and crowded cities, with **** of the urban population in India estimated to live in slums. In China, population shifts from rural to urban areas have aggravated regional economic disparities. For example, the migration of workers into coastal cities has made possible the creation of urban clusters of immense economic magnitude, with the Yangtze River Delta city cluster accounting for about a ******of the country’s gross domestic product. Megacities and their future Home to roughly 60 percent of the world’s population, the Asia-Pacific region also shelters most of the globe’s largest urban agglomerations. Megacities, a term used for cities or urban areas with a population of over ten million people, are characterized by high cultural diversity and advanced infrastructure. As a result, they create better economic opportunities, and they are often hubs of innovation. For instance, many megacities in the Asia-Pacific region offer high local purchasing power to their residents. Despite challenges like pollution, income inequality, or the rising cost of living, megacities in the Asia-Pacific region have relatively high population growth rates and are expected to expand.

The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.

Future City Solution Market Outlook

The global market size of Future City Solutions was valued at approximately $78.5 billion in 2023 and is projected to reach around $220.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.1% during the forecast period. The market growth is driven by the increasing adoption of smart city initiatives by governments worldwide, aimed at enhancing urban living through the integration of advanced technologies and sustainable practices.

One of the primary growth factors of the Future City Solution market is the rapid urbanization that necessitates the development of smart infrastructure to manage the growing population efficiently. Urban areas are expanding at an unprecedented rate, and traditional infrastructure systems are no longer adequate to meet the needs of modern cities. Smart city solutions offer innovative ways to manage resources, reduce energy consumption, and improve the quality of life for residents. Additionally, governments around the world are investing heavily in smart city projects to address urban challenges such as traffic congestion, pollution, and inadequate public services.

Technological advancements play a crucial role in propelling the Future City Solution market. The integration of Internet of Things (IoT), artificial intelligence (AI), big data analytics, and blockchain technologies into urban infrastructure has revolutionized the way cities operate. These technologies enable real-time monitoring, data collection, and analysis, leading to more efficient and sustainable city management. For instance, smart transportation systems leverage AI and IoT to optimize traffic flow, reduce congestion, and enhance public transportation services. Similarly, smart energy management systems utilize big data analytics to optimize energy consumption and reduce wastage.

The increasing focus on sustainability and environmental conservation is another significant factor driving the growth of the Future City Solution market. With the rising awareness of climate change and its impact on urban areas, there is a growing emphasis on developing eco-friendly and energy-efficient solutions. Smart city technologies offer innovative ways to reduce carbon emissions, promote renewable energy sources, and enhance waste management practices. For example, smart water management systems use IoT sensors and data analytics to monitor water usage, detect leaks, and optimize water distribution, thus conserving this precious resource.

From a regional perspective, North America is poised to dominate the Future City Solution market due to its early adoption of advanced technologies and substantial investments in smart city projects. The United States, in particular, has been at the forefront of smart city initiatives, driven by strong government support and the presence of key technology players. Europe follows closely, with countries like Germany, the United Kingdom, and the Netherlands investing in sustainable urban development. The Asia Pacific region is expected to witness significant growth, driven by rapid urbanization, government initiatives, and rising technology adoption in countries like China, Japan, and India.

Component Analysis

The Future City Solution market by component is segmented into software, hardware, and services. The software segment is anticipated to witness robust growth, driven by the increasing need for advanced applications that enable real-time monitoring, data analytics, and intelligent decision-making. Smart city software solutions encompass a wide range of applications, from traffic management systems and energy management platforms to public safety and security solutions. The demand for these applications is expected to rise as cities strive to enhance operational efficiency and provide better services to their residents.

Hardware components play a crucial role in the implementation of smart city solutions. This segment includes various sensors, communication devices, gateways, and other physical components that form the backbone of smart city infrastructure. The adoption of IoT devices and smart sensors is particularly significant, as they enable the collection of real-time data from various urban systems. For instance, smart lighting systems equipped with sensors can adjust brightness based on ambient light and human presence, thereby reducing energy consumption and improving public safety.

The services segment encompasses a wide range of professional and managed services that support the deployment, integration, and maintenance of smart city solutions.

This dataset contains the climate data and IDF files used in the paper "Changes in Residential Heating and Cooling Energy Consumption in Nine Major Cities of Japan’s Warm Climate Regions Under Future Climate Conditions."

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 study delves into the long-term dynamics of inter-city travel networks in South Korea by scrutinizing forty years of origin-destination data from both railway and highway systems. We uncover a pattern of increasing disparity in travel demand, concentrating more within certain hub cities over time. This trend points to an escalation in the prominence of these hubs, which have strengthened their connections with each other, emerging as critical nodes in the travel network. Meanwhile, non-hub cities have witnessed a decline in both population size and travel demand. Interestingly, while both railways and highways share a common trajectory toward centralization around hubs, they serve distinct roles. Highways have primarily expanded to connect neighboring cities, enhancing regional accessibility. Conversely, railways have evolved to cater to the long-distance connectivity of widely dispersed hub cities, reinforcing the role of these cities as key inter-regional links. These findings carry important implications for urban development and spatial planning. The evidence suggests a shift toward the formation of mega-regions, defined by a network of interconnected hub cities. Understanding the growth patterns and evolving connections of these expansive urban areas provides critical insights for informed urban planning and policy-making, ensuring that future developments align with the shifting landscape of urban connectivity and growth.

The Big Data in Smart Cities market is experiencing robust growth, driven by the increasing adoption of smart city initiatives globally. The market's expansion is fueled by the need for efficient urban management, improved public services, and enhanced citizen engagement. Governments and municipalities are increasingly leveraging big data analytics to optimize resource allocation, improve infrastructure planning, enhance public safety, and address environmental challenges. The integration of IoT devices, advanced analytics, and cloud computing technologies further accelerates this market growth. Key players like Cisco, IBM, and Microsoft are actively contributing to this expansion through the development of innovative data management and analytics solutions tailored for smart city applications. The market is segmented by deployment models (cloud, on-premise), data types (structured, unstructured), and application areas (traffic management, public safety, environmental monitoring). While the initial investment in infrastructure and data security can be a restraint, the long-term benefits of improved efficiency and citizen well-being are driving rapid adoption. The forecast period (2025-2033) anticipates continued significant expansion, as smart city initiatives gain wider acceptance and technological advancements continue to improve data processing and analytics capabilities. The competitive landscape is characterized by a mix of established technology vendors and specialized smart city solution providers. Strategic partnerships and mergers & acquisitions are likely to play a significant role in shaping the market dynamics. Regional variations in adoption rates are expected, with developed economies in North America and Europe leading the charge, while emerging economies in Asia-Pacific and Latin America are showing promising growth potential. Technological advancements in areas such as AI, machine learning, and edge computing will be critical drivers of future market expansion. Challenges remain, including data privacy concerns, interoperability issues, and the need for robust cybersecurity measures to ensure the secure management and analysis of sensitive urban data. However, the overall outlook for the Big Data in Smart Cities market remains highly positive, promising significant growth and transformation in urban environments globally.

Not surprisingly, the capital of the Netherlands is also its largest city. At around *******, Amsterdam has over ******* inhabitants more than the second-largest city in the country, Rotterdam. The Hague and Utrecht, the third and fourth-largest cities in the Netherlands, together have approximately as many inhabitants as Amsterdam alone. Amsterdam and the pressure on the housing market A rapidly growing city, Amsterdam’s population increased from roughly ***** thousand to around ***** thousand in the last decade. This has created pressure on the real estate market, where average rent and housing prices have skyrocketed. In the first quarter of 2010, the average rent of residential property amounted to roughly ***** euros per square meter. In the first quarter of 2021, this had increased to over ***** euros per square meter.  2030 Outlook  In the nearby future, Amsterdam is set to remain the Netherlands’ largest city. According to a recent forecast, by 2030 Amsterdam will have broken the barrier of one million inhabitants. Rotterdam, Den Haag and Utrecht are forecast to grow too, albeit at a much lower pace. In 2030, Rotterdam is expected to reach just under ******* inhabitants.

Equitable and effective planning of urban park green spaces (UPGSs) is an important way to promote green and healthy urban development and improve citizens’ quality of life. However, under the background of rapid urbanization, linear large cities, with their unique spatial forms and high-density population agglomerations, have brought special challenges for the planning and management of urban public green spaces. This study takes Lanzhou, a typical representative of high-density linear large cities in China, as a case study. Based on the improvement of the traditional Gaussian Two-Step Floating Catchment Area method (G2SFCA), combined with the Gini coefficient and the Lorentz curve, the social equity and spatial equity of UPGS supply-demand in the central urban area of Lanzhou were evaluated at the city and district scales. Meanwhile, the areas with shortage of UPGS supply-demand were accurately identified as the key areas for future optimization. The results show that: (1) There are significant differences in the equity of UPGS supply-demand in the linear large Lanzhou at the social and spatial levels, and most UPGS resources are enjoyed by a few people; (2) The spatial accessibility of UPGSs has an obvious “string of beads” distribution Characteristics, and the areas with high accessibility are mainly concentrated along rivers; (3) The equity of UPGS supply-demand exhibits a spatial gradient effect, which is characterized by a circle distribution. From the inside to the outside, it is as follows: good supply—dense population, good supply—sparse population, supply shortage—dense population, supply shortage—sparse population. Finally, based on the concept of “progressive micro-regeneration” and the Location Allocation model (LA), the optimal sites for new UPGSs were determined, maximizing the equity of UPGS supply-demand. This provides a practical reference for relevant management departments to optimize park layouts in the future.