Population data for a selection of countries, allocated to 1 arcsecond blocks and provided in a combination of CSV and Cloud-optimized GeoTIFF files. This refines CIESIN’s Gridded Population of the World using machine learning models on high-resolution worldwide Maxar satellite imagery. CIESIN population counts aggregated from worldwide census data are allocated to blocks where imagery appears to contain buildings.

Documentation

Project overview and instructions for use with AWS Athena

Update Frequency

Quarterly

Citation

Meta and Center for International Earth Science Information Network - CIESIN - Columbia University. 2022. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 Maxar. Accessed DAY MONTH YEAR.

License

https://creativecommons.org/licenses/by/4.0/

This layer shares SEDAC's population projections for U.S. counties for 2020-2100 in increments of 5 years, for each of five population projection scenarios known as Shared Socioeconomic Pathways (SSPs). This layer supports mapping, data visualizations, analysis and data exports.Before using this layer, read:The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview by Keywan Riahi, Detlef P. van Vuuren, Elmar Kriegler, Jae Edmonds, Brian C. O’Neill, Shinichiro Fujimori, Nico Bauer, Katherine Calvin, Rob Dellink, Oliver Fricko, Wolfgang Lutz, Alexander Popp, Jesus Crespo Cuaresma, Samir KC, Marian Leimbach, Leiwen Jiang, Tom Kram, Shilpa Rao, Johannes Emmerling, Kristie Ebi, Tomoko Hasegawa, Petr Havlik, Florian Humpenöder, Lara Aleluia Da Silva, Steve Smith, Elke Stehfest, Valentina Bosetti, Jiyong Eom, David Gernaat, Toshihiko Masui, Joeri Rogelj, Jessica Strefler, Laurent Drouet, Volker Krey, Gunnar Luderer, Mathijs Harmsen, Kiyoshi Takahashi, Lavinia Baumstark, Jonathan C. Doelman, Mikiko Kainuma, Zbigniew Klimont, Giacomo Marangoni, Hermann Lotze-Campen, Michael Obersteiner, Andrzej Tabeau, Massimo Tavoni. Global Environmental Change, Volume 42, 2017, Pages 153-168, ISSN 0959-3780, https://doi.org/10.1016/j.gloenvcha.2016.05.009.From the 2017 paper: "The SSPs are part of a new scenario framework, established by the climate change research community in order to facilitate the integrated analysis of future climate impacts, vulnerabilities, adaptation, and mitigation. The pathways were developed over the last years as a joint community effort and describe plausible major global developments that together would lead in the future to different challenges for mitigation and adaptation to climate change. The SSPs are based on five narratives describing alternative socio-economic developments, including sustainable development, regional rivalry, inequality, fossil-fueled development, and middle-of-the-road development. The long-term demographic and economic projections of the SSPs depict a wide uncertainty range consistent with the scenario literature."According to SEDAC, the purpose of this data is:"To provide subnational (county) population projection scenarios for the United States essential for understanding long-term demographic changes, planning for the future, and decision-making in a variety of applications."According to Francesco Bassetti of Foresight, "The SSP’s baseline worlds are useful because they allow us to see how different socioeconomic factors impact climate change. They include: a world of sustainability-focused growth and equality (SSP1); a “middle of the road” world where trends broadly follow their historical patterns (SSP2); a fragmented world of “resurgent nationalism” (SSP3); a world of ever-increasing inequality (SSP4);a world of rapid and unconstrained growth in economic output and energy use (SSP5).There are seven sublayers, each with county boundaries and an identical set of attribute fields containing projections for these seven groupings across the five SSPs and nine decades.Total PopulationBlack Non-Hispanic PopulationWhite Non-Hispanic PopulationOther Non-Hispanic PopulationHispanic PopulationMale PopulationFemale PopulationMethodology: Documentation for the Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, v1 (2020 – 2100)Data currency: This layer was created from a shapefile downloaded April 18, 2023 from SEDAC's Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, v1 (2020 – 2100)Enhancements found in this layer: Every field was given a field alias and field description created from SEDAC's Data Dictionary downloaded April 18, 2023. Citation: Hauer, M., and Center for International Earth Science Information Network - CIESIN - Columbia University. 2021. Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, 2020-2100. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/dv72-s254. Accessed 18 April 2023.Hauer, M. E. 2019. Population Projections for U.S. Counties by Age, Sex, and Race Controlled to Shared Socioeconomic Pathway. Scientific Data 6: 190005. https://doi.org/10.1038/sdata.2019.5.Distribution Liability: CIESIN follows procedures designed to ensure that data disseminated by CIESIN are of reasonable quality. If, despite these procedures, users encounter apparent errors or misstatements in the data, they should contact SEDAC User Services at +1 845-465-8920 or via email at ciesin.info@ciesin.columbia.edu. Neither CIESIN nor NASA verifies or guarantees the accuracy, reliability, or completeness of any data provided. CIESIN provides this data without warranty of any kind whatsoever, either expressed or implied. CIESIN shall not be liable for incidental, consequential, or special damages arising out of the use of any data provided by CIESIN.

The Ancillary Data portion of the Compendium of Environmental Sustainability Indicator Collections contains 38 variables (time series data on population and gross domestic product as well as region codes, land area, and waterbody area) for 238 countries. The data are taken from the UN Population Division, the World Bank, the CIA Factbook, and CIESIN's Gridded Population of the World, and are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide population estimates, gross domestic product, and surface area data to be used in conjunction with the sustainability indicators included in the compendium.)

Air pollution is a leading global disease risk factor. Tracking progress (e.g., for Sustainable Development Goals) requires accurate, spatially resolved, routinely updated exposure estimates. A Bayesian hierarchical model was developed to estimate annual average fine particle (PM2.5) concentrations at 0.1° × 0.1° spatial resolution globally for 2010–2016. The model incorporated spatially varying relationships between 6003 ground measurements from 117 countries, satellite-based estimates, and other predictors. Model coefficients indicated larger contributions from satellite-based estimates in countries with low monitor density. Within and out-of-sample cross-validation indicated improved predictions of ground measurements compared to previous (Global Burden of Disease 2013) estimates (increased within-sample R2 from 0.64 to 0.91, reduced out-of-sample, global population-weighted root mean squared error from 23 μg/m3 to 12 μg/m3). In 2016, 95% of the world’s population lived in areas where ambient PM2.5 levels exceeded the World Health Organization 10 μg/m3 (annual average) guideline; 58% resided in areas above the 35 μg/m3 Interim Target-1. Global population-weighted PM2.5 concentrations were 18% higher in 2016 (51.1 μg/m3) than in 2010 (43.2 μg/m3), reflecting in particular increases in populous South Asian countries and from Saharan dust transported to West Africa. Concentrations in China were high (2016 population-weighted mean: 56.4 μg/m3) but stable during this period.

Based on a business as usual scenario, the prevalence of undernourishment was estimated to fall somewhat in the first years after 2012, before increasing again from 2030 as a growing global population and increasing food prices have an impact. The sustainability scenario was projected to have the most significant impact on reducing undernourishment, with more economically equal societies and sustainable ways of production leading to improved food security.

The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) consists of estimates of human population for the years 1990, 1995, and 2000 by 30 arc-second (1km) grid. The urban extent grids distinguish urban and rural areas based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).

The Low Elevation Coastal Zone (LECZ) Urban-Rural Population Estimates consists of country-level estimates of urban, rural and total population and land area country-wide and in the LECZ, if applicable. Additionally, the data set provides the number of urban extents, their population and land area that intersect the LECZ, by city-size population classifications of less than 100,000, 100,000 to 500,000, 500,000 to 1,000,000, 1,000,000 to 5,000,000, and more than 5,000,000. All estimates are based on GRUMP Alpha data products. The LECZ was generated using SRTM Digital Elevation Model data and includes all land area that is contiguous with the coast and 10 meters or less in elevation. All grids used for population, land area, urban mask, and LECZ were of 30 arc-second (~1 km ) resolution. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Institute for Environment and Development (IIED).

The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Population Count Grid estimates human population for the years 1990, 1995, and 2000 by 30 arc-second (1 km) grid cells and associated data sets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 1,000,000 national and sub-national geographic Units, is used to assign population values (counts, in persons) to grid cells. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).

In all scenarios, the number of undernourished worldwide declined until 2020. However, while it was projected to slowly increase in a business as usual-scenario and to increase more sharply in a stratified societies-scenario, it is estimated to continue decreasing in a towards sustainability-scenario until 2030. The growing global population and rising food prices were then estimated to have an impact even in the latter scenario.

Aim: Protected areas are frequently defined on the basis of biological importance. Ecosystem services are expected to be under protection when biodiversity is preserved; however, new approaches are needed to confirm this statement. We evaluated how spatial associations between ecosystem services and plant biodiversity on a large spatial scale influence their representativeness in current protected areas. Location: Brazilian seasonally tropical dry forest (Caatinga). Methods: We produced woody plant biodiversity maps (species richness, narrow-range species richness and beta diversity) using species distribution modelling. We estimated regulating services (water purification, carbon storage and erosion control), provisioning services (water supply, fodder and agriculture) and supporting services (water balance, net primary productivity and soil fertility) using primary data and a proxy-based approach. We performed spatial correlation analyses between biodiversity and ecosystem services using Pearson's correlation test. After estimating the percentage of hotspot areas of biodiversity and ecosystem services presented in two types of protected areas (strict protection and sustainable use), we compared it to expected distribution by null model. Results: Mostly weak and intermediary positive correlations arose among biodiversity and ecosystem services (beta diversity with water balance and species richness with water purification and carbon storage). Negative correlations occurred among water balance with both species richness and narrow-range species richness. Strict protection areas were well represented in terms of carbon storage and underrepresented for fodder and agriculture. Sustainable use protected areas were important for water balance. Plant biodiversity variables were not represented in current protected areas. Main conclusions: Positive correlations between biodiversity and ecosystem services do not assure the protection of these targets in protected areas. Surrogates choice based only on spatial correlations might not effectively protect biodiversity and ecosystem services. Selection of priority areas must include biodiversity and ecosystem services as distinct conservation targets.

This layer shares SEDAC's population projections for U.S. counties for 2020-2100 in increments of 5 years, for each of five population projection scenarios known as Shared Socioeconomic Pathways (SSPs). This layer supports mapping, data visualizations, analysis and data exports.Before using this layer, read:The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview by Keywan Riahi, Detlef P. van Vuuren, Elmar Kriegler, Jae Edmonds, Brian C. O’Neill, Shinichiro Fujimori, Nico Bauer, Katherine Calvin, Rob Dellink, Oliver Fricko, Wolfgang Lutz, Alexander Popp, Jesus Crespo Cuaresma, Samir KC, Marian Leimbach, Leiwen Jiang, Tom Kram, Shilpa Rao, Johannes Emmerling, Kristie Ebi, Tomoko Hasegawa, Petr Havlik, Florian Humpenöder, Lara Aleluia Da Silva, Steve Smith, Elke Stehfest, Valentina Bosetti, Jiyong Eom, David Gernaat, Toshihiko Masui, Joeri Rogelj, Jessica Strefler, Laurent Drouet, Volker Krey, Gunnar Luderer, Mathijs Harmsen, Kiyoshi Takahashi, Lavinia Baumstark, Jonathan C. Doelman, Mikiko Kainuma, Zbigniew Klimont, Giacomo Marangoni, Hermann Lotze-Campen, Michael Obersteiner, Andrzej Tabeau, Massimo Tavoni. Global Environmental Change, Volume 42, 2017, Pages 153-168, ISSN 0959-3780, https://doi.org/10.1016/j.gloenvcha.2016.05.009.From the 2017 paper: "The SSPs are part of a new scenario framework, established by the climate change research community in order to facilitate the integrated analysis of future climate impacts, vulnerabilities, adaptation, and mitigation. The pathways were developed over the last years as a joint community effort and describe plausible major global developments that together would lead in the future to different challenges for mitigation and adaptation to climate change. The SSPs are based on five narratives describing alternative socio-economic developments, including sustainable development, regional rivalry, inequality, fossil-fueled development, and middle-of-the-road development. The long-term demographic and economic projections of the SSPs depict a wide uncertainty range consistent with the scenario literature."According to SEDAC, the purpose of this data is:"To provide subnational (county) population projection scenarios for the United States essential for understanding long-term demographic changes, planning for the future, and decision-making in a variety of applications."According to Francesco Bassetti of Foresight, "The SSP’s baseline worlds are useful because they allow us to see how different socioeconomic factors impact climate change. They include: a world of sustainability-focused growth and equality (SSP1); a “middle of the road” world where trends broadly follow their historical patterns (SSP2); a fragmented world of “resurgent nationalism” (SSP3); a world of ever-increasing inequality (SSP4);a world of rapid and unconstrained growth in economic output and energy use (SSP5).There are seven sublayers, each with county boundaries and an identical set of attribute fields containing projections for these seven groupings across the five SSPs and nine decades.Total PopulationBlack Non-Hispanic PopulationWhite Non-Hispanic PopulationOther Non-Hispanic PopulationHispanic PopulationMale PopulationFemale PopulationMethodology: Documentation for the Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, v1 (2020 – 2100)Data currency: This layer was created from a shapefile downloaded April 18, 2023 from SEDAC's Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, v1 (2020 – 2100)Enhancements found in this layer: Every field was given a field alias and field description created from SEDAC's Data Dictionary downloaded April 18, 2023. Citation: Hauer, M., and Center for International Earth Science Information Network - CIESIN - Columbia University. 2021. Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, 2020-2100. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/dv72-s254. Accessed 18 April 2023.Hauer, M. E. 2019. Population Projections for U.S. Counties by Age, Sex, and Race Controlled to Shared Socioeconomic Pathway. Scientific Data 6: 190005. https://doi.org/10.1038/sdata.2019.5.Distribution Liability: CIESIN follows procedures designed to ensure that data disseminated by CIESIN are of reasonable quality. If, despite these procedures, users encounter apparent errors or misstatements in the data, they should contact SEDAC User Services at +1 845-465-8920 or via email at ciesin.info@ciesin.columbia.edu. Neither CIESIN nor NASA verifies or guarantees the accuracy, reliability, or completeness of any data provided. CIESIN provides this data without warranty of any kind whatsoever, either expressed or implied. CIESIN shall not be liable for incidental, consequential, or special damages arising out of the use of any data provided by CIESIN.

Hydrogel Market size was valued at USD 25.68 Billion in 2024 and is projected to reach USD 41.24 Billion by 2031 growing at a CAGR of 7.1% from 2024 to 2031.

Global Hydrogel Market Drivers

The market drivers for the Hydrogel Market can be influenced by various factors. These may include:

Applications in Medicine and Healthcare: Hydrogels are widely employed in medicine and healthcare for tasks like tissue engineering, medication delivery, contact lens wearers, and wound treatment. The hydrogel market may be significantly impacted by the rising demand for these medicinal applications.

Wound Care Management: Because hydrogels may generate a moist environment, encourage wound healing, and act as a barrier against infection, they are frequently employed in wound dressings. The need for enhanced wound care solutions is fueled by the aging of the global population and the rising occurrence of chronic wounds.

Drug Delivery Systems: Hydrogels are essential components of systems that distribute drugs under control. They are appealing for pharmaceutical applications because of their regulated drug release and biocompatibility. The market for hydrogels may be driven by the growing requirement for individualized treatment plans and focused drug administration.

Personal Care items: A variety of personal care items, including sanitary napkins, skincare products, and diapers, use hydrogels. The demand for these goods is influenced by the growing population and growing awareness of cleanliness, which has an effect on the hydrogel market.

Applications in Agriculture: Hydrogels are used in agriculture to increase irrigation efficiency and retain more water in the soil. The need for hydrogels in this industry may be fueled by the growing emphasis on sustainable agriculture techniques brought about by the world’s population growth.

Cosmetics and Aesthetics: Hydrogels are used in a range of cosmetic goods, such as skincare formulas and face masks. The hydrogel market in this industry may be propelled by consumer demand for skincare and cosmetics, particularly those that use cutting-edge technologies.

Technological Advancements: Continuous material science research and development leads to the creation of novel hydrogel formulations with improved qualities, such as heightened strength, durability, and stimulus reactivity.

Environmental Concerns: Demand for bio-based or biodegradable hydrogels may be fueled by the growing emphasis on sustainable practices and environmentally friendly materials, particularly in applications where disposability is a problem.

1. At present, there is limited knowledge of how best to reconcile urban development with biodiversity conservation, and in particular whether populations of wild species would be greater under low-density housing (with larger gardens), or high-density housing (allowing more area to be left as undeveloped green spaces). The land sharing/sparing framework – originally developed in the context of farming – can be applied to address this question.
2. We sampled the abundance of trees in the city of Cambridge, UK, along a gradient of human density. We designed different scenarios of urban growth to accommodate the human population predicted in 2031. For each scenario, we projected the future city-wide tree population size and quantified its carbon sequestration potential. We also considered, for the first time in an urban sharing-sparing context, the implications of habitat restoration on degraded urban green space.
3. We found that the density of most native and non-native tree species is presently highest in areas of low human density, compared to both higher-density areas and green space (which is largely maintained with few trees). However, restoring woodland in green spaces would lead to far greater densities of native trees than on any existing land use. Hence provided >2% of green space is restored, native tree population sizes would be larger if urban growth followed a land-sparing approach. Likewise, carbon sequestration would be maximised under land sparing coupled with restoration, but even so only a maximum of 2.5% of the city's annual greenhouse gas emissions could be offset.
4. Whilst both tree populations and carbon storage thus appear to benefit from land-sparing development, the risk that this might widen the existing disconnect between people and nature must also be addressed – perhaps through a combination of adding housing in low density areas while ensuring these are in close proximity to high-quality green space.
5. Synthesis and applications. In regions which have already been cleared of intact habitat, a combination of land-sparing urban development with the restoration of green space could accommodate urban population growth whilst dramatically improving the existing status of local tree populations. Where cities are expanding into intact habitat, the merits of urban development by land sparing may be even more pronounced. Studies in such regions are urgently needed.

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Liberia in 2009 and 2017, as part of the Enterprise Survey initiative of the World Bank. An Indicator Survey is similar to an Enterprise Survey; it is implemented for smaller economies where the sampling strategies inherent in an Enterprise Survey are often not applicable due to the limited universe of firms.

The objective of the 2009-2017 Enterprise Survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to build a panel of enterprise data that will make it possible to track changes in the business environment over time and allow, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the Indicator Survey data provides information on the constraints to private sector growth and is used to create statistically significant business environment indicators that are comparable across countries.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2009-2017 Liberia Enterprise Survey (ES) was selected using stratified random sampling, following the methodology explained in the Sampling Note. Stratified random was preferred over simple random sampling for several reasons: - To obtain unbiased estimates for different subdivisions of the population with some known level of precision. - To obtain unbiased estimates for the whole population. The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except subsector 72, IT, which was added to the population under study), and all public or utilities sectors.

• To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions.
• To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.)
• Stratification may produce a smaller bound on the error of estimation than would be produced by a simple random sample of the same size. This result is particularly true if measurements within strata are homogeneous.

• The cost per observation in the survey may be reduced by stratification of the population elements into convenient groupings.

  Three levels of stratification were used in this country: industry, establishment size, and region. Industry stratification was designed as follows: the universe was stratified as into manufacturing and services industries. Manufacturing (ISIC Rev. 3.1 codes 15 - 37), and Services (ISIC codes 45, 50-52, 55, 60-64, and 72). For the Liberia ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).
  Regional stratification for the Liberia ES was done across three regions: Montserrado, Margibi, and Nimba.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Services and Manufacturing Questionnaire - Screener Questionnaire.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country's business environment. The remaining questions assess the survey respondents' opinions on what are the obstacles to firm growth and performance.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

There was a high response rate especially as a result of positive attitude towards the international community in collaboration with the government in their reconstruction efforts after a period of civil strife.There was also very positive attitude towards World Bank initiatives.

The Global Drought Mortality Risks and Distribution is a 2.5 minute grid of global drought mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provide a baseline estimation of population per grid cell from which to estimate potential mortality risks due to drought hazard. Mortality loss estimates per hazard event are calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of drought hazard are obtained from the Global Drought Hazard Frequency and Distribution data set. In order to more accurately reflect the confidence associated with the data and procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing risk with an approximately equal number of grid cells per class, producing a relative estimate of drought-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

Urban Planning and Design Software Market Outlook

As of 2023, the global urban planning and design software market size is estimated at approximately USD 6.5 billion and is projected to grow at a compound annual growth rate (CAGR) of 9.8% from 2024 to 2032, reaching a forecasted size of USD 14.1 billion by 2032. This impressive growth is driven by the increasing demand for smart city initiatives and sustainable urban development, which are crucial in addressing the rapid urbanization challenges worldwide. The integration of advanced technologies such as artificial intelligence (AI), machine learning (ML), and geographic information systems (GIS) into urban planning processes is significantly enhancing the efficiency and effectiveness of designing urban spaces, further propelling market growth.

The primary growth factor for the urban planning and design software market is the global trend of urbanization, with more than 68% of the world’s population expected to live in urban areas by 2050. This surge in urban populations demands efficient infrastructure planning and development to ensure sustainable living conditions. Urban planners and local governments are increasingly relying on advanced software solutions to analyze and manage data, optimize resource allocation, and design urban spaces that can accommodate this significant influx of residents. Furthermore, these software solutions are instrumental in creating smart cities that leverage technology to enhance urban living, thereby driving their adoption across the globe.

Another critical driver for the market is the rising importance of sustainable development and environmental conservation. With climate change and environmental degradation posing significant threats, urban planning software is essential in designing eco-friendly and sustainable urban environments. These tools help in reducing carbon footprints by optimizing energy use, integrating green spaces, and planning for sustainable transportation systems. Additionally, governments and organizations are increasingly investing in urban development projects that prioritize sustainability, thereby fueling the demand for software solutions that can facilitate such initiatives.

The increasing adoption of digital solutions and cloud technologies in the construction industry also significantly contributes to the market's growth. With the construction and real estate sectors rapidly digitalizing their operations, urban planning software acts as a critical enabler of digital transformation. These solutions provide comprehensive tools for architects, engineers, and planners to collaborate effectively and execute projects with precision. Moreover, the ability to simulate and model various urban scenarios before implementation reduces risks and enhances decision-making capabilities, which is highly valued in the construction industry.

Regionally, North America holds a significant share of the urban planning and design software market due to its advanced technological infrastructure and high investment in urban development projects. Europe follows closely, driven by the EU's stringent regulations on sustainable city planning. Asia Pacific is anticipated to register the highest growth rate, propelled by rapid urbanization and the increasing adoption of smart city projects in countries like China and India. Middle East & Africa and Latin America are also witnessing growing interest in urban planning solutions as these regions strive to modernize their infrastructure and accommodate growing urban populations.

Component Analysis

The urban planning and design software market is broadly segmented into software and services components. The software segment dominates the market, driven by the increasing need for advanced tools that facilitate comprehensive urban planning processes. Software solutions in this market range from computer-aided design (CAD) and building information modeling (BIM) to GIS and simulation tools. These applications enable urban planners to visualize, simulate, and optimize urban spaces effectively. The demand for cloud-based solutions is also rising within this segment, as they offer scalability, real-time collaboration, and cost-effectiveness, which are crucial for large-scale urban planning projects.

Within the software segment, GIS software plays a pivotal role in urban planning by providing spatial data analysis and visualization capabilities. This software allows planners to assess environmental impacts, infrastructure needs, and demographic trends, aiding in informed decision-making. As cities continue to expand and become more c

Balancing human communities’ and ecosystems’ need for freshwater is one of the major challenges of the 21st century as population growth and improved living conditions put increasing pressure on freshwater resources. While frameworks to assess the environmental impacts of freshwater consumption have been proposed at the regional scale, an operational method to evaluate the consequences of consumption on different compartments of the water system and account for their interdependence is missing at the global scale. Here, we develop depletion factors that simultaneously quantify the effects of water consumption on streamflow, groundwater storage, soil moisture, and evapotranspiration globally. We estimate freshwater availability and water consumption using the output of a global-scale surface water–groundwater model for the period 1960–2000. The resulting depletion factors are provided for 8,664 river basins, representing 93% of the landmass with significant water consumption, i.e., excluding Greenland, Antarctica, deserts, and permanently frozen areas. Our findings show that water consumption leads to the largest water loss in rivers, followed by aquifers and soil, while simultaneously increasing evapotranspiration. Depletion factors vary regionally with ranges of up to four orders of magnitude depending on the annual consumption level, the type of water used, aridity, and water transfers between compartments. Our depletion factors provide valuable insights into the intertwined effects of surface and groundwater consumption on several hydrological variables over a specified period. The developed depletion factors can be integrated into sustainability assessment tools to quantify the ecological impacts of water consumption and help guide sustainable water management strategies, while accounting for the performance limitations of the underlying model.

The conservation of many wildlife species requires understanding the demographic effects of climate change, including interactions between climate change and harvest, which can provide cultural, nutritional or economic value to humans. We present a demographic model that is based on the polar bear Ursus maritimus life cycle and includes density-dependent relationships linking vital rates to environmental carrying capacity (K). Using this model, we develop a state-dependent management framework to calculate a harvest level that (i) maintains a population above its maximum net productivity level (MNPL; the population size that produces the greatest net increment in abundance) relative to a changing K, and (ii) has a limited negative effect on population persistence. Our density-dependent relationships suggest that MNPL for polar bears occurs at approximately 0·69 (95% CI = 0·63–0·74) of K. Population growth rate at MNPL was approximately 0·82 (95% CI = 0·79–0·84) of the maximum intrinsic growth rate, suggesting relatively strong compensation for human-caused mortality. Our findings indicate that it is possible to minimize the demographic risks of harvest under climate change, including the risk that harvest will accelerate population declines driven by loss of the polar bear's sea-ice habitat. This requires that (i) the harvest rate – which could be 0 in some situations – accounts for a population's intrinsic growth rate, (ii) the harvest rate accounts for the quality of population data (e.g. lower harvest when uncertainty is large), and (iii) the harvest level is obtained by multiplying the harvest rate by an updated estimate of population size. Environmental variability, the sex and age of removed animals and risk tolerance can also affect the harvest rate. Synthesis and applications. We present a coupled modelling and management approach for wildlife that accounts for climate change and can be used to balance trade-offs among multiple conservation goals. In our example application to polar bears experiencing sea-ice loss, the goals are to maintain population viability while providing continued opportunities for subsistence harvest. Our approach may be relevant to other species for which near-term management is focused on human factors that directly influence population dynamics within the broader context of climate-induced habitat degradation.

IntroductionDietary choices affect both human and planetary health; however, they are not always linked to public policies. For example, Food Based Dietary Guidelines (FBDGs) do not always consider sustainability in their recommendations. To date, no methods have been developed and agreed upon to assess the five sustainability dimensions proposed by the Food and Agriculture Organization (FAO) (nutritional, environmental, cultural, physical, and economical access) as a whole. The objective of this study was to compare the levels of sustainability of traditional Chilean culinary preparations using a newly proposed method that integrates five unique dimensions of sustainable diets with reference databases to generate recommendations about sustainable culinary preparations; in which the Chilean population serves as a test case.MethodsA database composed of 651 traditional Chilean culinary preparations was used. It was obtained through 10 focus groups from the Metropolitan Region. Culinary preparations were divided into eight different food groups based on their main ingredients. Sustainability estimations were conducted for physically accessible preparations. All the dimensions were estimated based on approaches and indicators previously used in scientific literature. Different weights were provided for all other dimensions: 30% for cultural and price, respectively, and 20% for nutritional and environmental dimensions. Culinary preparations we recommended as sustainable if they achieved a global sustainability score of 66% or above, provided each dimension individually scored 40% or above.ResultsAfter data management, 351 culinary preparations were analyzed. A total of 94 were selected as sustainable: 21/38 vegetables; 6/7 fruits; 28/105 proteins; 14/78 cereals; 6/41 soups; 0/32 dairy; 1/6 lipids; 18/443 beverages. The main reason a preparation was not classified as sustainable was failing to obtain 66% of global sustainability. No culinary preparations were excluded based on the economic dimension.DiscussionThis study shows that estimating sustainability of individual culinary preparations based on the five dimensions of sustainable diets of FAO is possible through the development of an innovative methodological approach that is useful for making dietary recommendations for a population, such as within FBDGs. Future research should continue developing this methodology as a tool for public health decision-making for healthier and sustainable diets. This would allow dietary patterns to develop into more sustainable ones, which is a useful strategy for public health and planetary health

Settlement extents are polygons representing areas where there is likely a human settlement based on the presence of buildings detected in satellite imagery. Settlement extents are not meant to represent the boundaries of an administrative unit or locality. A single settlement extent may be made up of multiple localities, especially in urban areas. Each settlement extent has an associated population estimate. Provided is information on the common operational boundary that the extent fully resides within along with their associated place codes (PCodes). The data are in geodatabase format and consist of a single-feature class. This data product contains all information contained in the previous “GRID3 South Africa Settlement Extents, Version 01” product, with updates. Updates in this version include: revised terms of use and license, Boundary and Place Codes for each settlement extent. This work has been undertaken as part of the Geo-Referenced Infrastructure and Demographic Data for Development (GRID3) programme. The programme is funded by the Bill & Melinda Gates Foundation and the United Kingdom's Foreign, Commonwealth & Development Office. It is implemented by the Flowminder Foundation, WorldPop at the University of Southampton, the United Nations Population Fund, and the Center for International Earth Science Information Network (CIESIN) at Columbia University. Keywords: Settlements, Hamlets, Built-up Areas (BUA), Small Settlement Areas (SSA)