This dataset is about cities in South Africa. It has 198 rows. It features 7 columns including country, population, latitude, and longitude.

The “richness index” represents the level of economical wellbeing a country certain area in 2010. Regions with higher income per capita and low poverty rate and more access to market are wealthier and are therefore better able to prepare for and respond to adversity. The index results from the second cluster of the Principal Component Analysis preformed among 9 potential variables. The analysis identifies four dominant variables, namely “GDPppp per capita”, “agriculture share GDP per agriculture sector worker”, “poverty rate” and “market accessibility”, assigning weights of 0.33, 0.26, 0.25 and 0.16, respectively. Before to perform the analysis all variables were log transformed (except the “agriculture share GDP per agriculture sector worker”) to shorten the extreme variation and then were score-standardized (converted to distribution with average of 0 and standard deviation of 1; inverse method was applied for the “poverty rate” and “market accessibility”) in order to be comparable. The 0.5 arc-minute grid total GDPppp is based on the night time light satellite imagery of NOAA (see Ghosh, T., Powell, R., Elvidge, C. D., Baugh, K. E., Sutton, P. C., & Anderson, S. (2010).Shedding light on the global distribution of economic activity. The Open Geography Journal (3), 148-161) and adjusted to national total as recorded by International Monetary Fund for 2010. The “GDPppp per capita” was calculated dividing the total GDPppp by the population in each pixel. Further, a focal statistic ran to determine mean values within 10 km. This had a smoothing effect and represents some of the extended influence of intense economic activity for the local people. Country based data for “agriculture share GDP per agriculture sector worker” were calculated from GDPppp (data from International Monetary Fund) fraction from agriculture activity (measured by World Bank) divided by the number of worker in the agriculture sector (data from World Bank). The tabular data represents the average of the period 2008-2012 and were linked by country unit to the national boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). The first administrative level data for the “poverty rate” were estimated by NOAA for 2003 using nighttime lights satellite imagery. Tabular data were linked by first administrative unit to the first administrative boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). The 0.5 arc-minute grid “market accessibility” measures the travel distance in minutes to large cities (with population greater than 50,000 people). This dataset was developed by the European Commission and the World Bank to represent access to markets, schools, hospitals, etc.. The dataset capture the connectivity and the concentration of economic activity (in 2000). Markets may be important for a variety of reasons, including their abilities to spread risk and increase incomes. Markets are a means of linking people both spatially and over time. That is, they allow shocks (and risks) to be spread over wider areas. In particular, markets should make households less vulnerable to (localized) covariate shocks. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Data publication: 2014-05-15

Supplemental Information:

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Selvaraju Ramasamy

Resource constraints:

copyright

Online resources:

Richness index (2010)

Project deliverable D4.1 - Scenarios of major production systems in Africa

Climafrica Website - Climate Change Predictions In Sub-Saharan Africa: Impacts And Adaptations

This geodatabase reflects the U.S. Geological Survey’s (USGS) ongoing commitment to its mission of understanding the nature and distribution of global mineral commodity supply chains by updating and publishing the georeferenced locations of mineral commodity production and processing facilities, mineral exploration and development sites, and mineral commodity exporting ports in Africa. The geodatabase and geospatial data layers serve to create a new geographic information product in the form of a geospatial portable document format (PDF) map. The geodatabase contains data layers from USGS, foreign governmental, and open-source sources as follows: (1) mineral production and processing facilities, (2) mineral exploration and development sites, (3) mineral occurrence sites and deposits, (4) undiscovered mineral resource tracts for Gabon and Mauritania, (5) undiscovered mineral resource tracts for potash, platinum-group elements, and copper, (6) coal occurrence areas, (7) electric power generating facilities, (8) electric power transmission lines, (9) liquefied natural gas terminals, (10) oil and gas pipelines, (11) undiscovered, technically recoverable conventional and continuous hydrocarbon resources (by USGS geologic/petroleum province), (12) cumulative production, and recoverable conventional resources (by oil- and gas-producing nation), (13) major mineral exporting maritime ports, (14) railroads, (15) major roads, (16) major cities, (17) major lakes, (18) major river systems, (19) first-level administrative division (ADM1) boundaries for all countries in Africa, and (20) international boundaries for all countries in Africa.

Abstract

The Human Sciences Research Council (HSRC) carried out the Migration and Remittances Survey in South Africa for the World Bank in collaboration with the African Development Bank. The primary mandate of the HSRC in this project was to come up with a migration database that includes both immigrants and emigrants. The specific activities included: · A household survey with a view of producing a detailed demographic/economic database of immigrants, emigrants and non migrants · The collation and preparation of a data set based on the survey · The production of basic primary statistics for the analysis of migration and remittance behaviour in South Africa.

Like many other African countries, South Africa lacks reliable census or other data on migrants (immigrants and emigrants), and on flows of resources that accompanies movement of people. This is so because a large proportion of African immigrants are in the country undocumented. A special effort was therefore made to design a household survey that would cover sufficient numbers and proportions of immigrants, and still conform to the principles of probability sampling. The approach that was followed gives a representative picture of migration in 2 provinces, Limpopo and Gauteng, which should be reflective of migration behaviour and its impacts in South Africa.

Geographic coverage

Two provinces: Gauteng and Limpopo

Limpopo is the main corridor for migration from African countries to the north of South Africa while Gauteng is the main port of entry as it has the largest airport in Africa. Gauteng is a destination for internal and international migrants because it has three large metropolitan cities with a great economic potential and reputation for offering employment, accommodations and access to many different opportunities within a distance of 56 km. These two provinces therefore were expected to accommodate most African migrants in South Africa, co-existing with a large host population.

Analysis unit

• Household
• Individual

Universe

The target group consists of households in all communities. The survey will be conducted among metro and non-metro households. Non-metro households include those in: - small towns, - secondary cities, - peri-urban settlements and - deep rural areas. From each selected household, one adult respondent will be selected to participate in the study.

Kind of data

Sample survey data [ssd]

Sampling procedure

Migration data for South Africa are available for 2007 only at the level of local governments or municipalities from the 2007 Census; for smaller areas called "sub places" (SPs) only as recently as the 2001 census, and for the desired EAs only back so far as the Census of 1996. In sum, there was no single source that provided recent data on the five types of migrants of principal interest at the level of the Enumeration Area, which was the area for which data were needed to draw the sample since it was going to be necessary to identify migrant and non-migrant households in the sample areas in order to oversample those with migrants for interview.

In an attempt to overcome the data limitations referred to above, it was necessary to adopt a novel approach to the design of the sample for the World Bank's household migration survey in South Africa, to identify EAs with a high probability of finding immigrants and those with a low probability. This required the combined use of the three sources of data described above. The starting point was the CS 2007 survey, which provided data on migration at a local government level, classifying each local government cluster in terms of migration level, taking into account the types of migrants identified. The researchers then spatially zoomed in from these clusters to the so-called sub-places (SPs) from the 2001 Census to classifying SP clusters by migration level. Finally, the 1996 Census data were used to zoom in even further down to the EA level, using the 1996 census data on migration levels of various typed, to identify the final level of clusters for the survey, namely the spatially small EAs (each typically containing about 200 households, and hence amenable to the listing operation in the field).

A higher score or weight was attached to the 2007 Community Survey municipality-level (MN) data than to the Census 2001 sub-place (SP) data, which in turn was given a greater weight than the 1996 enumerator area (EA) data. The latter was derived exclusively from the Census 1996 EA data, but has then been reallocated to the 2001 EAs proportional to geographical size. Although these weights are purely arbitrary since it was composed from different sources, they give an indication of the relevant importance attached to the different migrant categories. These weighted migrant proportions (secondary strata), therefore constituted the second level of clusters for sampling purposes.

In addition, a system of weighting or scoring the different persons by migrant type was applied to ensure that the likelihood of finding migrants would be optimised. As part of this procedure, recent migrants (who had migrated in the preceding five years) received a higher score than lifetime migrants (who had not migrated during the preceding five years). Similarly, a higher score was attached to international immigrants (both recent and lifetime, who had come to SA from abroad) than to internal migrants (who had only moved within SA's borders). A greater weight also applied to inter-provincial (internal) than to intra-provincial migrants (who only moved within the same South African province).

How the three data sources were combined to provide overall scores for EA can be briefly described. First, in each of the two provinces, all local government units were given migration scores according to the numbers or relative proportions of the population classified in the various categories of migrants (with non-migrants given a score of 1.0. Migrants were assigned higher scores according to their priority, with international migrants given higher scores than internal migrants and recent migrants higher scores than lifetime migrants. Then within the local governments, sub-places were assigned scores assigned on the basis of inter vs. intra-provincial migrants using the 2001 census data. Each SP area in a local government was thus assigned a value which was the product of its local government score (the same for all SPs in the local government) and its own SP score. The third and final stage was to develop relative migration scores for all the EAs from the 1996 census by similarly weighting the proportions of migrants (and non-migrants, assigned always 1.0) of each type. The the final migration score for an EA is the product of its own EA score from 1996, the SP score of which it is a part (assigned to all the EAs within the SP), and the local government score from the 2007 survey.

Based on all the above principles the set of weights or scores was developed.

In sum, we multiplied the proportion of populations of each migrant type, or their incidence, by the appropriate final corresponding EA scores for persons of each type in the EA (based on multiplying the three weights together), to obtain the overall score for each EA. This takes into account the distribution of persons in the EA according to migration status in 1996, the SP score of the EA in 2001, and the local government score (in which the EA is located) from 2007. Finally, all EAs in each province were then classified into quartiles, prior to sampling from the quartiles.

From the EAs so classified, the sampling took the form of selecting EAs, i.e., primary sampling units (PSUs, which in this case are also Ultimate Sampling Units, since this is a single stage sample), according to their classification into quartiles. The proportions selected from each quartile are based on the range of EA-level scores which are assumed to reflect weighted probabilities of finding desired migrants in each EA. To enhance the likelihood of finding migrants, much higher proportions of EAs were selected into the sample from the quartiles with the higher scores compared to the lower scores (disproportionate sampling). The decision on the most appropriate categorisations was informed by the observed migration levels in the two provinces of the study area during 2007, 2001 and 1996, analysed at the lowest spatial level for which migration data was available in each case.

Because of the differences in their characteristics it was decided that the provinces of Gauteng and Limpopo should each be regarded as an explicit stratum for sampling purposes. These two provinces therefore represented the primary explicit strata. It was decided to select an equal number of EAs from these two primary strata.

The migration-level categories referred to above were treated as secondary explicit strata to ensure optimal coverage of each in the sample. The distribution of migration levels was then used to draw EAs in such a way that greater preference could be given to areas with higher proportions of migrants in general, but especially immigrants (note the relative scores assigned to each type of person above). The proportion of EAs selected into the sample from the quartiles draws upon the relative mean weighted migrant scores (referred to as proportions) found below the table, but this is a coincidence and not necessary, as any disproportionate sampling of EAs from the quartiles could be done, since it would be rectified in the weighting at the end for the analysis.

The resultant proportions of migrants then led to the following proportional allocation of sampled EAs (Quartile 1: 5 per cent (instead of 25% as in an equal distribution), Quartile 2: 15 per cent (instead

Abstract (en): The Research on Early Life and Aging Trends and Effects (RELATE) study compiles cross-national data that contain information that can be used to examine the effects of early life conditions on older adult health conditions, including heart disease, diabetes, obesity, functionality, mortality, and self-reported health. The complete cross sectional/longitudinal dataset (n=147,278) was compiled from major studies of older adults or households across the world that in most instances are representative of the older adult population either nationally, in major urban centers, or in provinces. It includes over 180 variables with information on demographic and geographic variables along with information about early life conditions and life course events for older adults in low, middle and high income countries. Selected variables were harmonized to facilitate cross national comparisons. In this first public release of the RELATE data, a subset of the data (n=88,273) is being released. The subset includes harmonized data of older adults from the following regions of the world: Africa (Ghana and South Africa), Asia (China, India), Latin America (Costa Rica, major cities in Latin America), and the United States (Puerto Rico, Wisconsin). This first release of the data collection is composed of 19 downloadable parts: Part 1 includes the harmonized cross-national RELATE dataset, which harmonizes data from parts 2 through 19. Specifically, parts 2 through 19 include data from Costa Rica (Part 2), Puerto Rico (Part 3), the United States (Wisconsin) (Part 4), Argentina (Part 5), Barbados (Part 6), Brazil (Part 7), Chile (Part 8), Cuba (Part 9), Mexico (Parts 10 and 15), Uruguay (Part 11), China (Parts 12, 18, and 19), Ghana (Part 13), India (Part 14), Russia (Part 16), and South Africa (Part 17). The Health and Retirement Study (HRS) was also used in the compilation of the larger RELATE data set (HRS) (N=12,527), and these data are now available for public release on the HRS data products page. To access the HRS data that are part of the RELATE data set, please see the collection notes below. The purpose of this study was to compile and harmonize cross-national data from both the developing and developed world to allow for the examination of how early life conditions are related to older adult health and well being. The selection of countries for this study was based on their diversity but also on the availability of comprehensive cross sectional/panel survey data for older adults born in the early to mid 20th century in low, middle and high income countries. These data were then utilized to create the harmonized cross-national RELATE data (Part 1). Specifically, data that are being released in this version of the RELATE study come from the following studies: CHNS (China Health and Nutrition Study) CLHLS (Chinese Longitudinal Healthy Longevity Survey) CRELES (Costa Rican Study of Longevity and Healthy Aging) PREHCO (Puerto Rican Elderly: Health Conditions) SABE (Study of Aging Survey on Health and Well Being of Elders) SAGE (WHO Study on Global Ageing and Adult Health) WLS (Wisconsin Longitudinal Study) Note that the countries selected represent a diverse range in national income levels: Barbados and the United States (including Puerto Rico) represent high income countries; Argentina, Cuba, Uruguay, Chile, Costa Rica, Brazil, Mexico, and Russia represent upper middle income countries; China and India represent lower middle income countries; and Ghana represents a low income country. Users should refer to the technical report that accompanies the RELATE data for more detailed information regarding the study design of the surveys used in the construction of the cross-national data. The Research on Early Life and Aging Trends and Effects (RELATE) data includes an array of variables, including basic demographic variables (age, gender, education), variables relating to early life conditions (height, knee height, rural/urban birthplace, childhood health, childhood socioeconomic status), adult socioeconomic status (income, wealth), adult lifestyle (smoking, drinking, exercising, diet), and health outcomes (self-reported health, chronic conditions, difficulty with functionality, obesity, mortality). Not all countries have the same variables. Please refer to the technical report that is part of the documentation for more detail regarding the variables available across countries. Sample weights are applicable to all countries exc...

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in South Gorin. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2011 and 2021, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In South Gorin, the median household income for the households where the householder is White decreased by $3,706(9.07%), between 2011 and 2021. The median household income, in 2022 inflation-adjusted dollars, was $40,862 in 2011 and $37,156 in 2021.
• Black or African American: As per the U.S. Census Bureau population data, in South Gorin, there are no households where the householder is Black or African American; hence, the median household income for the Black or African American population is not applicable.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

https://i.neilsberg.com/ch/south-gorin-mo-median-household-income-by-race-trends.jpeg" alt="South Gorin, MO median household income trends across races (2011-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in South Gorin.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• Please note: 2020 1-Year ACS estimates data was not reported by Census Bureau due to impact on survey collection and analysis during COVID-19, thus for large cities (population 65,000 and above) median household income data is not available.
• Please note: All incomes have been adjusted for inflation and are presented in 2022-inflation-adjusted dollars.

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 South Gorin median household income by race. You can refer the same here

The principal data collection units were sites where policy was discussed and acted on. These comprised 2 national Departments of Education (in Kenya and South Africa), 2 provincial departments, 2 schools, 2 NGOs located in large cities, and 2 located in rural areas. Data collected included interviews, focus groups, observations, analysis of school records and records of report back meetings. In addition 12 interviews with staff in global organisations dealing with this policy area were interviewed. Comparative case study was used in Kenya and South Africa to investigate similar kinds of relationship – negotiations with global policy agendas on gender, education and poverty reduction – in somewhat different sites. A selected range of units of analysis were examined for hierarchies in which policy and practice are related from global levels, ranked ‘above’ the national and local level (vertically) and forms of connection, exclusion or boundary setting between different kinds of organisation (horizontally). Both countries have in place policies on poverty, education and gender equality, and are active global policy players. However, they differ in their engagements with global policy transfer, histories of attention to gender. There was thus potential to look at how the cases did and did not vary, and the explanatory weight that could be accorded to local conditions. Five case studies were conducted in each country: the National Department of Education, South Africa, Ministry of Education in Kenya, a provincial department in each country, a matched school attended by children from a peri-urban community with high levels of poverty, a rural NGO working on education and poverty, and a global NGO engaged with the global policy agenda and local implementation. The project aims to examine initiatives which engage with global aspirations to advance gender equality in and through schooling in contexts of poverty. It looks at how these are understood, who participates in implementation, what meanings of gender, schooling and global relations are negotiated, what constraints are experienced, in what ways these are overcome, and what concerns about global obligations emerge. A key focus is what conditions how global policy goals are interpreted and acted on in different sites. Case study research will be conducted in Kenya and South Africa, two countries where reforming governments have sought to address questions of poverty and gender in the expansion of education provision. In each country data will be collected in five sites: the national Department of Education, a provincial education department, a rural primary school, the offices of a Non Governmental Organisation (NGO) engaging with global education and poverty policy, and an education NGO operating at a local level. The main methods of data collection will be documentary analysis, individual and group interviews, focus group discussions, and observations. Advisory committees in Kenya and South Africa will guide the process of data collection, comment critically on emerging analysis, and give support with dissemination. Research methods comprised documentary analysis, interviews, observations, field notes, and focus group discussions. Documents written over the last ten years including websites, policies, and publications of all the organisations were analysed. One hundred and thirty three hours of interviews and group discussions were recorded and transcribed. Observation and analysis of site dynamics were made using ethnographic methods. Report back meetings on preliminary findings in all the ten case study sites took place after the first round of data collection and were recorded and transcribed. In a second round of data collection up to a year later participants were interviewed regarding changes that had taken place. A small number of interviews were conducted with children at the peri-urban schools and rural NGO projects.

This dataset contains ground reference crop type labels and multispectral and synthetic aperture radar (SAR) imagery from multiple satellites in an area located in Western Cape, South Africa. There are five crop types from the year 2017: Wheat, Barely, Canola, Lucerne/Medics, Small grain grazing. The AOI is split to three tiles. Two tiles are provided as training labels, and one tile will be used for scoring in the competition.

Input imagery consist of time series of Sentinel-2, Sentinel-1 and Planet Fusion (daily and 5-day composite) data. You can access each source from a different collection.

The Planet fusion data are made available under a CC-BY-SA license. As an exception to the AI4EO Terms and Conditions published on the competition website, you confirm, by participating in it, that you agree that your results will be made public under the same, open-source license.

The Western Cape Department of Agriculture (WCDoA) vector data are supplied via Radiant Earth Foundation with limited distribution rights. Data supplied by the WCDoA may not be distributed further or used for commercial purposes. The vector data supplied are intended strictly for use within the scope of this remote sensing competition - for the purpose of academic research to our mutual benefit. The data is intended for research purposes only and the WCDoA cannot be held responsible for any errors or omissions which may occur in the data.

This is groundwater and stormwater quality and groundwater level data from a stormwater infiltration site in Mitchel's Plain, Cape Town, South Africa collected between June 2021 and August 2023. The study site, a stormwater detention pond owned by the City of Cape Town was retrofitted to enhance stormwater infiltration, as part of the project "Pathways to water resilient South African cities".Paper AbstractCape Town, South Africa, is a water scarce city which is diversifying its water supply in line with its commitment to becoming a water sensitive city (WSC). Blue-green infrastructure (BGI) comprise multifunctional spaces which incorporate surface water and vegetation: the key infrastructure of a WSC. Over 200 stormwater detention ponds overlie the Cape Flats Aquifer (CFA) and offer potential to be transformed into BGI. The study site (a detention pond retrofitted with an infiltration swale and with vegetation that is allowed to grow without complete mowing) provides BGI at the stormwater-groundwater interface. This study investigated the impact of stormwater infiltration on the groundwater chemistry. Mean contaminant removal during infiltration ranged from 83% to 92% on a concentration basis after accounting for dilution effects. Furthermore, a significant decrease in the groundwater nitrate concentration was found below the BGI compared to background groundwater suggesting a net benefit for water quality.Description of the data fileThe data consists of one file with three tabs. Water Chemistry Results: Column headers included the sample point, sample date and water quality parameters.Water Levels: Column headers include the data and the ID of each groundwater monitoring well.Sample Points: Column headers include a description of each of the sample points, coordinates of the monitoring wells, elevation of monitoring wells and depths of monitoring wells.

NB This is the second of two interviews with Connie Eggink. Due to GDPR considerations the interview and accompanying visual materials are not open to public review. In 1970, Connie Eggink led a Schiedam exchange program starting in Esslingen, Germany. Esslingen played a significant role in organizing the exchange, which involved three other cities: Schiedam, Norrköping, and for the first time, Velenje from Yugoslavia. Otto Weinmann, a key figure in the Stadtjungendring, was instrumental in involving Velenje and promoting the European idea. The Velenje group stood out for being older and carefully selected to represent their country during the Tito era. Despite language barriers, the Yugoslavian group was intriguing for the other participants. The exchange garnered attention from South German Radio, which conducted interviews discussing perspectives on European unity. In 1971, Connie's group traveled to Norrkoping, Sweden. They were invited to visit the Swedish Air Force, which was proud of its military equipment, showcasing their newly acquired F-13 aircraft. Connie was impressed by the presence of many women in the military, reflecting a more advanced state of gender equality compared to the Netherlands at the time. Concerns about the Iron Curtain were prevalent, with a collective desire to prevent intrusion from communist nations. The group expressed relief that countries behind the Iron Curtain were capable of defending themselves, though there was a reluctance for NATO involvement. Annually, a meeting convened in Esslingen where administrators from various regions gathered to arrange exchange programs, ensuring simultaneous involvement of three groups from three countries. Photographs from these meetings captured the stark contrast between Dutch representatives, characterized by their casual appearance with beards and long hair, and officials from France, Germany, Italy, and Sweden, who appeared more formal and well-groomed. These meetings involved decision-makers such as administrators, councilors, and mayors, who were keen on their cities participating. Despite diverse backgrounds, they shared a commitment to promoting peace, driven by their experiences of war and a determination to prevent its recurrence, marking the ethos of their generation. Connie was 18 or 19 when she joined the community. “You have grand ideas at that age. I've always been interested in languages; I found foreign languages fascinating at school. I've always tried to learn as many different languages as possible because it helps you understand other cultures and people.” Later on, she traveled extensively as a backpacker through South America, North Africa, and Africa to meet people and understand why they do what they do. She also pursued studies in psychology to seek understanding for what motivates people, why they act the way they do, why they stand opposed to each other instead of alongside. She had the idea for many years that youth exchanges did have an effect lasting 10 or 20 years. However, Connie thinks society has changed and that not much remains of what was created back then. She thinks the generations who are in their thirties now are not very concerned with this kind of thing anymore. (Project Friends in a Cold Climate 2023) Friends in a Cold Climate: After the Second World War a number of friendship ties were established between towns in Europe. Citizens, council-officials and church representatives were looking for peace and prosperity in a still fragmented Europe. After a visit of the Royal Mens Choir Schiedam to Esslingen in 1963, representatives of Esslingen asked Schiedam to take part in friendly exchanges involving citizens and officials. The connections expanded and in 1970, in Esslingen, a circle of friends was established tying the towns Esslingen, Schiedam, Udine (IT) Velenje (SL) Vienne (F) and Neath together. Each town of this so called “Verbund der Ringpartnerstädte” had to keep in touch with at least 2 towns within the wider network. Friends in a Cold Climate looks primarily through the eyes the citizen-participant. Their motivation for taking part may vary. For example, is there a certain engagement with the European project? Did parents instil in their children a a message of fraternisation stemming from their experiences in WWII? Or did the participants only see youth exchange only as an opportunity for a trip to a foreign country? This latter motivation of taking part for other than Euro-idealistic reasons should however not be regarded as tourist or consumer-led behaviour. Following Michel de Certeau, Friends in a Cold Climate regards citizen-participants as a producers rather than as a consumers. A participant may "put to use" the Town Twinning facilities of travel and activities in his or her own way, regardless of the programme. Integration of West-Europe after the Second World War was driven by a broad movement aimed at peace, security and prosperity. Organised youth exchange between European cities formed an important part of that movement. This research focuses on young people who, from the 1960s onwards, participated in international exchanges organised by twinned towns, also called jumelage. Friends in a Cold Climate asks about the interactions between young people while taking into account the organisational structures on a municipal level, The project investigates the role of the ideology of a united West-Europe, individual desires for travel and freedom, the upcoming discourse about the Second World War and the influence of the prevalent “counterculture” of that period, thus shedding a light on the formative years of European integration.

In order to bring a thorough and comprehensive understanding of social, economic and environmental sustainability challenges faced by cities and local communities in the developing countries, the SHLC team conducted a major household survey followed by a neighbourhood focus group interview in seven Asian and African countries from late 2021 to early 2022. In each country the study includes two case study cities: one large city and one smaller regional cities. Within each case study cities, neighbourhoods were identified and categorised into five income and wealth bands: the rich, upper middle income, middle income, lower middle and low income neighbourhoods. A household survey was carried out face to face by trained interviewers with a random adult member of the household. The 20 page common questionnaire was designed and adopted by all teams, which cover topics of housing, residence, living conditions, migration, education, health, neighbourhood infrastructure, facilities, governance and relations, income and employments, gender equality and impacts from Covid-19. The sample was distributed in the city to representative the five neighbourhood types. The survey was completed in 13 of the 14 case study cities (fieldwork in Chongqing in China was delayed by the Covid-19 lockdowns and implemented in August 2023). The target sample for each city was 1000; the total sample in the database (SPSS and STATA) include 14245 households. The survey was followed by focus group interviews. A carefully designed and agreed common interview guide was used by all team. The target was to have one focus group for one neighbourhood in each income band in each city. A total of 74 focus group interviews were conducted (Fieldwork in Datong and Chongqing in China was delayed). The transcripts are the qualitative data shared here.The Centre for Sustainable, Healthy and Learning Cities and Neighbourhoods (SHLC) was funded by UKRI Global Challenge Research Fund (GCRF) from 2017 to 2023. Its main aim was to grow research capability to meet the challenges faced by developing countries (Grow). SHLC, led by University of Glasgow, was set up as an international collaborative research centre to address urban challenges across communities in Africa and Asia. Its work contributed to three UN 2030 Sustainable Development Goals: 11 - Make cities and human settlements sustainable; 3 - Ensure healthy lives for all; 4 - Ensure inclusive and equitable quality education for all. SHLC brought together the expertise of urban studies, education, health, geography, planning and data science from nine institutions in eight countries. Its international partners included: Ifakara Health Institute (Tanzania), Khulna University (Bangladesh), Nankai University (China), National Institute of Urban Affairs (India), The Human Sciences Research Council and University of Witwatersrand (South Africa), The University of the Philippines and The University of Rwanda. SHLC working programme had two streams of work and eight specific task packages. Stream one included four Capacity Strengthening Packages which involved the training of over 100 researchers and enhancing the associated academic networks. Steam two work consisted of four Research Task Packages. The co-designed research programme adopted a common research framework in all seven countries (14 case study cities), aiming to bring a thorough and comprehensive understanding of social, economic and environmental sustainability challenges faced by these cities and local communities. Apart from policy reviews, secondary data analysis, the project employed two major primary data collection methods – household questionnaire survey and neighbourhood focus groups. The team have overcome many challenges brought by the Covid-19 pandemics and completed the household survey in 13 cities with a total sample size of 14245, which covered five different types of neighbourhoods ranging from the rich to the poor. The team also completed 74 neighbourhood focus group interviews. Data collection was carried out from late 2021 to early 2022. Huge resources and researchers’ time were dedicated to coordinate, collect, translate, clean and merge these quantitative and qualitative data. In each country the study selected one large city and one smaller regional cities as case studied. Within each case study cities, neighbourhoods were categorised roughly into five income and wealth bands: the rich, upper middle income, middle income, lower middle and low income neighbourhoods. A household survey was carried out face to face by trained interviewers with a random adult member of the household. A common questionnaire was designed and adopted by all teams. The sample was distributed in the city to representative the five neighbourhood types. The survey was followed by focus group interviews. A carefully designed and agreed common interview guide was used by all team. The target was one focus group for a sample neighbourhood in each income band in each city. Focus groups were recorded, all transcripts were translated into English for analysis.

This dataset contains ground reference crop type labels and multispectral and synthetic aperture radar (SAR) imagery from multiple satellites in an area located in Brandenburg, Germany. There are nine crop types in this dataset from years 2018 and 2019: Wheat, Rye, Barley, Oats, Corn, Oil Seeds, Root Crops, Meadows, Forage Crops. The 2018 labels from one of the tiles are provided for training, and the 2019 labels from a neighboring tile will be used for scoring in the competition.

Input imagery consist of time series of Sentinel-2, Sentinel-1 and Planet Fusion (daily and 5-day composite) data. You can access each source from a different collection.

The Planet fusion data are made available under a CC-BY-SA license. As an exception to the AI4EO Terms and Conditions published on the competition website, you confirm, by participating in it, that you agree that your results will be made public under the same, open-source license.

It is an important basis for the research on the prevention and early warning mechanism of alien invasive plants in China to figure out the types of alien plants in China, where they come from, how to enter China, what kind of groups of these alien plants are, as well as their biological and ecological characteristics. The information of alien plants recorded in Flora of China (Chinese edition), Flora of China (English edition) and their records in the Chinese province flora is very limited since various reason. At present, there is no complete database reflecting the information of alien plants in China. By integrating materials related to alien plants in recent years, and textual research on the origin and added habits of alien plants through literature, and then using computer network, databases and big data analysis technical means, after information treatment and taxonomic correction, with reconstruction of the classification, this paper finally determines the species directory data set of the book. There are 14710 data in this set, with 14710 groups of Chinese alien plants belonging to 3233 genera and 283 families (including 13401 original species, 332 hybrids, 2 chimeras, 458 subspecies, 503 varieties and 14 forms). Each taxa includes basic information such as categories of plants, Chinese family, family name, Chinese genus, genus, Chinese name, alias, scientific name, author, survival status, survival time, growth status, country or region of origin and province of Chinese distribution. The data set shows that alien plants have accounted for a considerable proportion in the composition of the Chinese plant species (at present, there are 37464 groups of native plants in China (including infraspecies), and with 14710 alien groups, the proportion of exotic plants is as high as 28.19%). In terms of survival status, cultivated plants account for 91% of all exotic plants, escape plants account for 7.36%, naturalized plant account for 6.69% and invasive plants account for 2.66%; The analysis of life forms shows that perennial groups account for the vast majority of alien plants (13625 species, about 92.6%), and the number of herbs (8937 species, about 60.8%) is more than that of trees (2752 species, about 18.7%), shrubs (4916 species, about 33.4%) as well as other life forms. Most of the alien plants in China were from North America (4242 species), Africa (3707 species), South America (3645 species), Asia (3102 species), Europe (1690 species) and Oceania (1305 species). The top 10 provinces and cities in China with more exotic plants are Taiwan (6122 species), Beijing (5244 species), Fujian (3667 species), Guangdong (3544 species), Yunnan (3404 species), Shanghai (2924 species), Jiangsu (2183 species), Jiangxi (1789 species), Zhejiang (1658 species) and Hubei (973 species). This data set is the first comprehensive and systematic collation of alien plants in China. It can be used as a reference for research related to alien plants, as well as basic data for plant diversity research. It can also be used as a reference book for people in agriculture, forestry, grassland, gardens, herbal medicine, nature protection and environmental protection, as well as teachers and students in colleges and universities.

Abstract

Polluted air is a major health hazard in developing countries. Improvements in pollution monitoring and statistical techniques during the last several decades have steadily enhanced the ability to measure the health effects of air pollution. Current methods can detect significant increases in the incidence of cardiopulmonary and respiratory diseases, coughing, bronchitis, and lung cancer, as well as premature deaths from these diseases resulting from elevated concentrations of ambient Particulate Matter (Holgate 1999).

Scarce public resources have limited the monitoring of atmospheric particulate matter (PM) concentrations in developing countries, despite their large potential health effects. As a result, policymakers in many developing countries remain uncertain about the exposure of their residents to PM air pollution. The Global Model of Ambient Particulates (GMAPS) is an attempt to bridge this information gap through an econometrically estimated model for predicting PM levels in world cities (Pandey et al. forthcoming).

The estimation model is based on the latest available monitored PM pollution data from the World Health Organization, supplemented by data from other reliable sources. The current model can be used to estimate PM levels in urban residential areas and non-residential pollution hotspots. The results of the model are used to project annual average ambient PM concentrations for residential and non-residential areas in 3,226 world cities with populations larger than 100,000, as well as national capitals.

The study finds wide, systematic variations in ambient PM concentrations, both across world cities and over time. PM concentrations have risen at a slower rate than total emissions. Overall emission levels have been rising, especially for poorer countries, at nearly 6 percent per year. PM concentrations have not increased by as much, due to improvements in technology and structural shifts in the world economy. Additionally, within-country variations in PM levels can diverge greatly (by a factor of 5 in some cases), because of the direct and indirect effects of geo-climatic factors.

The primary determinants of PM concentrations are the scale and composition of economic activity, population, the energy mix, the strength of local pollution regulation, and geographic and atmospheric conditions that affect pollutant dispersion in the atmosphere.

Geographic coverage

The database covers the following countries: Afghanistan Albania Algeria Andorra Angola
Antigua and Barbuda Argentina
Armenia Australia
Austria Azerbaijan
Bahamas, The
Bahrain Bangladesh
Barbados
Belarus Belgium Belize
Benin
Bhutan
Bolivia Bosnia and Herzegovina
Brazil
Brunei
Bulgaria
Burkina Faso
Burundi Cambodia
Cameroon
Canada
Cayman Islands
Central African Republic
Chad
Chile
China
Colombia
Comoros Congo, Dem. Rep.
Congo, Rep. Costa Rica
Cote d'Ivoire
Croatia Cuba
Cyprus
Czech Republic
Denmark Dominica
Dominican Republic
Ecuador Egypt, Arab Rep.
El Salvador Eritrea Estonia Ethiopia
Faeroe Islands
Fiji
Finland France
Gabon
Gambia, The Georgia Germany Ghana
Greece
Grenada Guatemala
Guinea
Guinea-Bissau
Guyana
Haiti
Honduras
Hong Kong, China
Hungary Iceland India
Indonesia
Iran, Islamic Rep.
Iraq
Ireland Israel
Italy
Jamaica Japan
Jordan
Kazakhstan
Kenya
Korea, Dem. Rep.
Korea, Rep. Kuwait
Kyrgyz Republic Lao PDR Latvia
Lebanon Lesotho Liberia Liechtenstein
Lithuania
Luxembourg
Macao, China
Macedonia, FYR
Madagascar
Malawi
Malaysia
Maldives
Mali
Mauritania
Mexico
Moldova Mongolia
Morocco Mozambique
Myanmar Namibia Nepal
Netherlands Netherlands Antilles
New Caledonia
New Zealand Nicaragua
Niger
Nigeria Norway
Oman
Pakistan
Panama
Papua New Guinea
Paraguay
Peru
Philippines Poland
Portugal
Puerto Rico Qatar
Romania Russian Federation
Rwanda
Sao Tome and Principe
Saudi Arabia
Senegal Sierra Leone
Singapore
Slovak Republic Slovenia
Solomon Islands Somalia South Africa
Spain
Sri Lanka
St. Kitts and Nevis St. Lucia
St. Vincent and the Grenadines
Sudan
Suriname
Swaziland
Sweden
Switzerland Syrian Arab Republic
Tajikistan
Tanzania
Thailand
Togo
Trinidad and Tobago Tunisia Turkey
Turkmenistan
Uganda
Ukraine United Arab Emirates
United Kingdom
United States
Uruguay Uzbekistan
Vanuatu Venezuela, RB
Vietnam Virgin Islands (U.S.)
Yemen, Rep. Yugoslavia, FR (Serbia/Montenegro)
Zambia
Zimbabwe

Kind of data

Observation data/ratings [obs]

Mode of data collection

Other [oth]

Youth engagement and skills acquisition within Africa’s transport sector was a collaborative research project between Durham University, UK, the University of Sokoto, Nigeria, the South African Labour and Development Research Unit [SALDRU] at the University of Cape Town, South Africa, and the UK-based NGO Transaid. The project’s core data set deposited with RESHARE comprises in-depth interviews focused on daily mobility and transport, conducted by project academic staff and young unemployed women we trained as peer researchers at the outset of the study; a small number of focus group discussions conducted by academic staff; and diaries focused on daily mobility, mostly written by peer researchers during the pandemic. Anonymised data sets are provided for each of the three study cities. Note: The research team had also anticipated collecting quantitative data concerning the pilot trainings for transport users and transport workers led by Transaid. These were to have comprised baseline assessments, followed by post-intervention surveys after one month and six months to assess skills uptake among participating women. Although Transaid staff succeeded in implementing pilot training interventions in each city, in the final months of the project, COVID constraints limited recruitment numbers and the collection of baseline data amenable to statistical analysis. Collection of post-intervention data has not been possible due to COVID constraints and the requirement to end the project on 31st March 2022. Transaid’s reports on the pilot interventions will be made available on the project website: https://transportandyouthemploymentinafrica.com

The WEA dataset is derived from the WeatherBench repository and designed for medium-range weather forecasting at five geographically diverse cities: London (UK), New York (US), Hong Kong (China), Cape Town (South Africa), and Singapore. It spans the period from 1979 to 2018, with a temporal resolution of 6 hours and a spatial resolution of 5.625° in both latitude and longitude. Each city is matched to its nearest grid point on the WeatherBench grid using minimal absolute distance in both axes.

These data are responses to surveys administer via structured interviews with households in three Cape Flats townships in 2019. The surveys focused on household experiences of and responses to episodes of flooding (Sweet Home Farm); water scarcity (Delft South); and runaway fires (Overcome Heights). Responses were recorded from approximately 200 adults representing their households in each site. Due to the Covid-19 restrictions in place at the time, survey data was collected via telephone/digital device interviews. All interviews were undertaken in the language of the respondent’s choice – generally isiXhosa, Afrikaans or English. Responses have been translated into English to produce this final data set. The survey was organised in two parts: Section One recorded mainly demographic and macro-level quantitative data. Section Two focused on respondent disaster and resilience experiences and included both quantitative and open-ended, qualitative responses.Water and Fire explored the experiences of and responses of residents in the Cape Flat region of South Africa to episodes of flooding, water scarcity and fire. South Africa's township residents are beset with legacy economic and social challenges of apartheid, poverty, and constrained development opportunities. The scale of this challenge is heightened by migration to cities, and rapid, extensive growth of formal and informal settlements, which are extremely susceptible to environmental risks. Climate change has exacerbated the risk of disasters as destructive events and episodes such as flood, drought and fire have increased in severity and frequency. The project worked with residents in three townships of varying levels of informality to find out about people’s experiences of each of these hazards: Sweet Home Farm (floods), Delft (water scarcity) and Overcome Heights (fires). The project team worked with locally-based co-researchers with the aim of developing a set of community-driven resilience actions – the ‘Best Bets’ of the project title. The first step in this process was for the co-researchers to work with members of the core team to carry out household surveys (conducted using structured interviews) with approximately 200 households in each of the three sites. These data are available via Reshare. Following this, a series of creative and visual methods were used to facilitate the identification and selection of resilience actions that were ultimately shared with City of Cape Town officials. The data were collected using structured interviews carried out by field-based co-researchers using digital devices. The survey itself was run via the CommCare platform. Respondents were adults representing approximately 200 individual households in each of the three research sites. Initial respondents were identified and recruited by local advisory groups. Additional respondents were recruited via a snowball sampling process.

Description: Topics included in the questionnaire two are cell phone usage, Batho Pele, voting, demographics and other classificatory variables. The data set has 3307 cases and 122 variables. Abstract: The primary objective of the South African Social Attitudes Survey (SASAS) is to design, develop and implement a conceptually and methodologically robust study of changing social attitudes and values in South Africa. In meeting this objective, the HSRC is carefully and consistently monitoring and providing insight into changes in attitudes among various socio-demographic groupings. SASAS is intended to provide a unique long-term account of the social fabric of modern South Africa, and of how its changing political and institutional structures interact over time with changing social attitudes and values. The survey has been designed to yield a national representative sample of adults aged 16 and older, using the Human Sciences Research Council's (HSRC) second Master Sample, which was designed in 2007 and consists of 1000 primary sampling units (PSUs). These PSUs were drawn, with probability proportional to size from a pre-census 2001 list of 80780 enumerator areas (EAs). As the basis of the 2009 SASAS round of interviewing, a sub-sample of 500 EAs (PSUs) was drawn from the second master sample. Three explicit stratification variables were used, namely province, geographic type and majority population group. The survey is conducted annually and the 2009 survey is the seventh wave in the series. Face-to-face interview National population: Adults (aged 16 and older) The South African Social Attitudes Survey (SASAS) is a nationally representative survey series that has been conducted on an annual basis by the Human Sciences Research Council's (HSRC) since 2003. The survey has been designed to yield a representative sample of adults aged 16 years and older. The sampling frame for the survey is the HSRC's second Master Sample, which was designed in 2007 and consists of 1 000 primary sampling units (PSUs). The 2001 population census enumerator areas (EAs) were used as PSUs. These PSUs (EAs) were drawn, with probability proportional to size, from a sampling frame created by Professor David Stoker containing all 80,787 of the 2001 EAs. This sampling frame uses the estimated number of dwelling units (DUs) in an EA (PSU) as a measure of size. The sampling frame was annually updated to coincide with StatsSA's mid-year population estimates in respect of the following variables: province, gender, population group and age group. In updating the 2007 version of this sampling frame, additional use was made of (a) the GeoTerraImage (GTI) residential structure count in all metropolitan EAs in 2004/2006 and (b) the ESKOM counts of dwelling units in all cities, towns, townships and villages. The HSRC's second master sample excludes special institutions (such as hospitals, military camps, old age homes, school and university hostels), recreational areas, industrial areas, vacant EAs as well as the 1000 EAs included in the first HSRC's master sample (2003-2006). It therefore focuses on dwelling units or visiting points as secondary sampling units (SSUs), which have been defined as 'separate (non-vacant) residential stands, addresses, structures, flats, homesteads, etc.'. For the 2009 SASAS round of interviewing, a sub-sample of 500 PSUs was drawn from the HSRC's 2nd Master Sample. Three explicit stratification variables were used, namely province, geographic type and majority population group. Within each stratum, the allocated number of PSUs was drawn using proportional to size probability sampling with the estimated number of dwelling units in the PSU as measure of size. In each of these drawn PSUs, 14 dwelling units were selected and systematically grouped into two sub-samples of seven, each corresponding to the two SASAS questionnaire versions. Selection of individuals Interviewers called at each visiting point selected from the 2nd HSRC master sample and listed all those eligible for inclusion in the sample, that is, all persons currently aged 16 or over and resident at the selected visiting point. The interviewer then selected one respondent using a random selection procedure based on a Kish grid.

Efforts are needed to better identify and measure both communities’ exposure to climate hazards and the social vulnerabilities that interact with these hazards, but the science of validating climate risk indicators is still in its infancy. Progress is needed to improve: (1) the selection of variables that are used as proxies to represent hazard exposure and vulnerability; (2) the applicability and scale for which these indicators are intended, including their suitability for transnational comparisons. We draw on an international urban survey in Buenos Aires, Argentina; Johannesburg, South Africa; London, United Kingdom; New York City, United States; and Seoul, South Korea that collected data on: exposure to various types of extreme weather events, socioeconomic characteristics commonly used as proxies for vulnerability (i.e., income, education level, gender, and age), and additional characteristics not often included in existing composite indices (i.e., Queer identity, disability identity, non-dominant primary language, and self-perceptions of both discrimination and vulnerability to climate hazard risk). We use feature importance analysis with gradient-boosted decision trees to measure the importance that these variables have in predicting exposure to various types of extreme weather events. Our results show that non-traditional variables were more relevant to self-reported exposure to extreme weather events than traditionally employed variables such as income or age. Furthermore, differences in variable relevance across different types of hazards and across urban contexts suggest that vulnerability indicators need to be fit to context and should not be used in a one-size-fits-all fashion.

LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet Asia contains data across Asia, which accounts for ~31% of the global dataset. Each pixel is identified as one of the seven land cover classes based on its annual time series. These classes are water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice.
There are a total of 2753 image chips of 256 x 256 pixels in LandCoverNet South America V1.0 spanning 92 tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files):
* Sentinel-1 ground range distance (GRD) with radiometric calibration and orthorectification at 10m spatial resolution
* Sentinel-2 surface reflectance product (L2A) at 10m spatial resolution
* Landsat-8 surface reflectance product from Collection 2 Level-2

Radiant Earth Foundation designed and generated this dataset with a grant from Schmidt Futures with additional support from NASA ACCESS, Microsoft AI for Earth and in kind technology support from Sinergise.