This is a historical measure for Strategic Direction 2023. For more data on Austin demographics please visit austintexas.gov/demographics. This measure answers the question of what is the rate of change for the share of the total city population that is African-American. Calculated the difference of percentage of share over reporting period. Data collected from the U.S. Census Bureau, American Communities Survey (ACS) (1-yr), Race (table B02001). American Communities Survey is a survey with sampled statistics on the citywide level and is subject to a margin of error. ACS sample size and data quality measures can be found on the U.S. Census website in the Methodology section.

This is a historical measure for Strategic Direction 2023. For more data on Austin demographics please visit austintexas.gov/demographics.

This measure answers the question of what is the rate of change for the share of the total city population that is African-American. Calculated the difference of percentage of share over reporting period. Data collected from the U.S. Census Bureau, American Communities Survey (ACS) (1-yr), Race (table B02001), except for 2020 data, which are from the 2020 Decennial Census Count. American Communities Survey is a survey with sampled statistics on the citywide level and is subject to a margin of error. ACS sample size and data quality measures can be found on the U.S. Census website in the Methodology section.

View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/6p8t-s826

This is a repository of global and regional human population data collected from: the databases of scenarios assessed by the Intergovernmental Panel on Climate Change (Sixth Assessment Report, Special Report on 1.5 C; Fifth Assessment Report), multi-national databases of population projections (World Bank, International Database, United Nation population projections), and other very long-term population projections (Resources for the Future).

More specifically, it contains:

- in `other_pop_data` folder files from World Bank, the International Database from the US Census, and from IHME

- in the `SSP` folder, the Shared Socioeconomic Pathways, as in the version 2.0 downloaded from IIASA and as in the version 3.0 downloaded from IIASA workspace

- in the `UN` folder, the demographic projections from UN

- `IAMstat.xlsx`, an overview file of the metadata accompanying the scenarios present in the IPCC databases

- `RFF.csv`, an overview file containing the population projections obtained by Resources For the Future

'- the remaining `.csv` files with names `AR6#`, `AR5#`, `IAMC15#` contain the IPCC scenarios assessed by the IPCC for preparing the IPCC assessment reports. They can be downloaded from AR5, SR 1.5, and AR6

This data in intended to be downloaded for use together with the package downloadable here.

The dataset was used as a supporting material for the paper "Underestimating demographic uncertainties in the synthesis process of the IPCC" accepted on npj Climate Action (DOI : 10.1038/s44168-024-00152-y).

Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.

In this dataset:

We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

Please cite this dataset as:

Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4

Organization of data

The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.

HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.

HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.

target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

Column names

YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.

H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

License Creative Commons Attribution 4.0 International.

Related datasets

Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612

Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

The Public Use Microdata Samples (PUMS) contain person- and household-level information from the "long-form" questionnaires distributed to a sample of the population enumerated in the 1980 Census. The C Sample, containing 1 percent data, identifies census regions, divisions, 27 individual states, and the District of Columbia. Four types of areas are shown: inside central cities, urban fringe, other urban, and rural. The C Sample separately identifies every urbanized area with a total population over 800,000, and roughly half of the urbanized areas between 200,000 and 800,000. Household-level variables include housing tenure, year structure was built, number and types of rooms in dwelling, plumbing facilities, heating equipment, taxes and mortgage costs, number of children, and household and family income. Person-level variables include sex, age, marital status, race, Spanish origin, income, occupation, transportation to work, and education. (Source: downloaded from ICPSR 7/13/10)

This is a hybrid gridded dataset of demographic data for the world, given as 5-year population bands at a 0.5 degree grid resolution.

This dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4) with the ISIMIP Histsoc gridded population data and the United Nations World Population Program (WPP) demographic modelling data.

Demographic fractions are given for the time period covered by the UN WPP model (1950-2050) while demographic totals are given for the time period covered by the combination of GPWv4 and Histsoc (1950-2020)

Method - demographic fractions

Demographic breakdown of country population by grid cell is calculated by combining the GPWv4 demographic data given for 2010 with the yearly country breakdowns from the UN WPP. This combines the spatial distribution of demographics from GPWv4 with the temporal trends from the UN WPP. This makes it possible to calculate exposure trends from 1980 to the present day.

To combine the UN WPP demographics with the GPWv4 demographics, we calculate for each country the proportional change in fraction of demographic in each age band relative to 2010 as:

\(\delta_{year,\ country,age}^{\text{wpp}} = f_{year,\ country,age}^{\text{wpp}}/f_{2010,country,age}^{\text{wpp}}\)

Where:

- \(\delta_{year,\ country,age}^{\text{wpp}}\) is the ratio of change in demographic for a given age and and country from the UN WPP dataset.

- \(f_{year,\ country,age}^{\text{wpp}}\) is the fraction of population in the UN WPP dataset for a given age band, country, and year.

- \(f_{2010,country,age}^{\text{wpp}}\) is the fraction of population in the UN WPP dataset for a given age band, country for the year 2020.

The gridded demographic fraction is then calculated relative to the 2010 demographic data given by GPWv4.

For each subset of cells corresponding to a given country c, the fraction of population in a given age band is calculated as:

\(f_{year,c,age}^{\text{gpw}} = \delta_{year,\ country,age}^{\text{wpp}}*f_{2010,c,\text{age}}^{\text{gpw}}\)

Where:

- \(f_{year,c,age}^{\text{gpw}}\) is the fraction of the population in a given age band for given year, for the grid cell c.

- \(f_{2010,c,age}^{\text{gpw}}\) is the fraction of the population in a given age band for 2010, for the grid cell c.

The matching between grid cells and country codes is performed using the GPWv4 gridded country code lookup data and country name lookup table. The final dataset is assembled by combining the cells from all countries into a single gridded time series. This time series covers the whole period from 1950-2050, corresponding to the data available in the UN WPP model.

Method - demographic totals

Total population data from 1950 to 1999 is drawn from ISIMIP Histsoc, while data from 2000-2020 is drawn from GPWv4. These two gridded time series are simply joined at the cut-over date to give a single dataset covering 1950-2020.

The total population per age band per cell is calculated by multiplying the population fractions by the population totals per grid cell.

Note that as the total population data only covers until 2020, the time span covered by the demographic population totals data is 1950-2020 (not 1950-2050).

Disclaimer

This dataset is a hybrid of different datasets with independent methodologies. No guarantees are made about the spatial or temporal consistency across dataset boundaries. The dataset may contain outlier points (e.g single cells with demographic fractions >1). This dataset is produced on a 'best effort' basis and has been found to be broadly consistent with other approaches, but may contain inconsistencies which not been identified.

Datasets, conda environments and Softwares for the course "Population Genomics" of Prof Kasper Munch. This course material is maintained by the health data science sandbox. This webpage shows the latest version of the course material.

1. Data.tar.gz Contains the datasets and executable files for some of the softwares
   You can unpack by simply doing
   tar -zxf Data.tar.gz -C ./
   This will create a folder called Data with the uncompressed material inside
2. Course_Env.packed.tar.gz Contains the conda environment used for the course. This needs to be unpacked to adjust all the prefixes (Note this environment is created on Ubuntu 22.10). You do this in the command line by
   1. creating the folder Course_Env: mkdir Course_Env
   2. untar the file: tar -zxf Course_Env.packed.tar.gz -C Course_Env
   3. Activate the environment: conda activate ./Course_Env
   4. Run the unpacking script (it can take quite some time to get it done): conda-unpack
3. Course_Env.unpacked.tar.gz The same environment as above, but will work only if untarred into the folder /usr/Material - so use the version above if you are using it in another folder. This file is mostly to execute the course in our own cloud environment.
4. environment_with_args.yml The file needed to generate the conda environment. Create and activate the environment with the following commands:
   1. conda env create -f environment_with_args.yml -p ./Course_Env
   2. conda activate ./Course_Env

The data is connected to the following repository: https://github.com/hds-sandbox/Popgen_course_aarhus. The original course material from Prof Kasper Munch is at https://github.com/kaspermunch/PopulationGenomicsCourse.

Description

The participants will after the course have detailed knowledge of the methods and applications required to perform a typical population genomic study.

The participants must at the end of the course be able to:

• Identify an experimental platform relevant to a population genomic analysis.
• Apply commonly used population genomic methods.
• Explain the theory behind common population genomic methods.
• Reflect on strengths and limitations of population genomic methods.
• Interpret and analyze results of population genomic inference.
• Formulate population genetics hypotheses based on data

The course introduces key concepts in population genomics from generation of population genetic data sets to the most common population genetic analyses and association studies. The first part of the course focuses on generation of population genetic data sets. The second part introduces the most common population genetic analyses and their theoretical background. Here topics include analysis of demography, population structure, recombination and selection. The last part of the course focus on applications of population genetic data sets for association studies in relation to human health.

Curriculum

The curriculum for each week is listed below. "Coop" refers to a set of lecture notes by Graham Coop that we will use throughout the course.

Course plan

1. Course intro and overview:
   • Coop chapters 1, 2, 3, Paper: Genome Diversity Project
2. Drift and the coalescent:
   • Coop chapter 4; Paper: Platypus
   • Exercise: Read mapping and base calling
3. Recombination:
   • Lecture: Review: Recombination in eukaryotes, Review: Recombination rate estimation
   • Exercise: Phasing and recombination rate
4. Population strucure and incomplete lineage sorting:
   • Lecture: Coop chapter 6, Review: Incomplete lineage sorting
   • Exercise: Working with VCF files
5. Hidden Markov models:
   • Lecture: Durbin chapter 3, Paper: population structure
   • Exercise: Inference of population structure and admixture
6. Ancestral recombination graphs:
   • Lecture: Paper: Approximating the ARG, Paper: Tree inference
   • Exercise: ARG dashboard exercises + Inference of trees along sequence
7. Past population demography:
   • Lecture: Coop chapter 4, Paper: PSMC, revisit Paper: Tree inference
   • Exercise: Inferring historical populations
8. Direct and linked selection:
   • Lecture: Coop chapters 12, 13, revisit Paper: Tree inference
9. Admixture:
   • Lecture: Review: Admixture, Paper: Admixture inference
   • Exercise: Detecting archaic ancestry in modern humans
10. Genome-wide association study (GWAS):
    • Lecture: Coop lecture notes 99-120
    • Exercise: GWAS quality control
11. Heritability:
    • Lecture: Coop Lecture notes Sec. 2.2 (p23-36) + Chap. 7 (p119-142)
    • Exercise: Association testing
12. Evolution and disease:
    • Lecture: Coop Lecture notes Sec. 11.0.1 (p217-221)
    • Exercise: Estimating heritability

The predatory mirid bug, Cyrtorhinus lividipennis Reuter, feeds on brown planthopper (BPH) eggs that are deposited on rice and gramineous plants surrounding rice fields. The development and reproduction of C. lividipennis are inhibited by feeding on BPH eggs from gramineous species, and the underlining regulatory mechanism for this phenomenon is unclear. In the present study, HPLC-MS/MS analysis revealed that the concentrations of six amino acids (AAs:Ala, Arg, Ser, Lys, Thr, and Pro) were significantly higher in rice than in five gramineous species. When C. lividipennis fed on gramineous plants with BPH eggs, expression of several genes in the target of rapamycin (TOR) pathway (Rheb, TOR, and S6K) were significantly lower than that in the insects fed on rice plants with BPH eggs. Treatment of C. lividipennis females with rapamycin, dsRheb, dsTOR, or dsS6K caused a decrease in Rheb, TOR, and S6K expression, and these effects were partially rescued by the juvenile hormone (JH) analog, methoprene. Dietary dsTOR treatment significantly influenced a number of physiological parameters and resulted in impaired predatory capacity, fecundity, and population growth. This study indicates that these six AAs play an important role in the mediated-TOR pathway, which in turn regulates vitellogenin (Vg) synthesis, reproduction, and population growth in C. lividipennis.

Analysis of ‘Strategic Measure_CLL.C.3 Change in percentage of Austin population that is African American’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/edca35a8-0fc4-4352-a3d2-351b705af618 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

This measure answers the question of what is the rate of change for the share of the total city population that is African-American. Calculated the difference of percentage of share over reporting period. Data collected from the U.S. Census Bureau, American Communities Survey (ACS) (1-yr), Race (table B02001). American Communities Survey is a survey with sampled statistics on the citywide level and is subject to a margin of error. ACS sample size and data quality measures can be found on the U.S. Census website in the Methodology section.

View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/6p8t-s826

--- Original source retains full ownership of the source dataset ---

julien-c/Norway_Cities_Population dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset captures data describing the members of each household from the first interim assessment of Feed the Future’s population-based indicators for the ZOI in Cambodia. The ZOI is the Pursat, Battambang, Kampong Thom, and Siem Reap Provinces. The sampling design called for a two-stage cluster sample. In the first stage, 84 villages were selected; in the second stage, households were selected within each sampled village. The sampling of villages was stratified by province, with the number of villages in each stratum proportional to the population in the stratum and with villages selected with probability proportional to size, based on the 2013 Commune Database. The data is split into survey modules. Modules A through C includes location information, informed consent, and the household roster. Module D includes household characteristics. Module E is the expenditures module broken up into 8 different parts. Modules F and G include the hunger scale data and WEIA index data. Data in modules H and I include mother and child dietary diversity.

Three subspecies of Northern Bahamian Rock Iguanas, Cyclura cychlura, are currently recognized: C. c. cychlura,restricted to Andros Island, and C. c. figginsi and C. c. inornata, native to the Exuma Island chain. Populations on Andros are genetically distinct from Exuma Island populations, yet genetic divergence among populations in the Exumas is inconsistent with the two currently recognized subspecies from those islands. The potential consequences of this discrepancy might include the recognition of a single subspecies throughout the Exumas rather than two. That inference also ignores evidence that populations of C. cychlura are potentially adaptively divergent. We compared patterns of population relatedness in a three-tiered host-parasite system: C. cychlura iguanas, their ticks (genus Amblyomma, preferentially parasitizing these reptiles), and Rickettsia spp. endosymbionts (within tick ectoparasites). Our results indicate that while C. c. cychlura on Andros is consistently supported as a separate clade, patterns of relatedness among populations of C. c. figginsi and C. c. inornata within the Exuma Island chain are more complex. The distribution of the hosts, different tick species, and Rickettsia spp., supports the evolutionary independence of C. c. inornata. Further, these patterns are also consistent with two independent evolutionarily significant units within C. c. figginsi. Our findings suggest coevolutionary relationships between the reptile hosts, their ectoparasites, and rickettsial organisms, suggesting local adaptation. This work also speaks to the limitations of using neutral molecular markers from a single focal taxon as the sole currency for recognizing evolutionary novelty in populations of endangered species.

Methods Data in this set have been produced thorugh sanger sequencing and fragment analysis.

411 Cyclura cychlura iguana individuals have been scored for variability at 21 microsatellites. Since the dataset has been compiled from a variety of sources no all individuals are scored for the same loci. Individuals come from 31 different sites.

The data is organized in a spreasheet formatted for GenAlEx.

Proportion of population covered by a mobile network, broken down by technology, refers to the percentage of inhabitants living within range of a mobile-cellular signal, irrespective of whether or not they are mobile phone subscribers or users. This is calculated by dividing the number of inhabitants within range of a mobile-cellular signal by the total population and multiplying by 100.

Context

This is a dataset of the most highly populated city (if applicable) in a form easy to join with the COVID19 Global Forecasting (Week 1) dataset. You can see how to use it in this kernel

Content

There are four columns. The first two correspond to the columns from the original COVID19 Global Forecasting (Week 1) dataset. The other two is the highest population density, at city level, for the given country/state. Note that some countries are very small and in those cases the population density reflects the entire country. Since the original dataset has a few cruise ships as well, I've added them there.

Acknowledgements

Thanks a lot to Kaggle for this competition that gave me the opportunity to look closely at some data and understand this problem better.

Inspiration

Summary: I believe that the square root of the population density should relate to the logistic growth factor of the SIR model. I think the SEIR model isn't applicable due to any intervention being too late for a fast-spreading virus like this, especially in places with dense populations.

After playing with the data provided in COVID19 Global Forecasting (Week 1) (and everything else online or media) a bit, one thing becomes clear. They have nothing to do with epidemiology. They reflect sociopolitical characteristics of a country/state and, more specifically, the reactivity and attitude towards testing.

The testing method used (PCR tests) means that what we measure could potentially be a proxy for the number of people infected during the last 3 weeks, i.e the growth (with lag). It's not how many people have been infected and recovered. Antibody or serology tests would measure that, and by using them, we could go back to normality faster... but those will arrive too late. Way earlier, China will have experimentally shown that it's safe to go back to normal as soon as your number of newly infected per day is close to zero.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F197482%2F429e0fdd7f1ce86eba882857ac7a735e%2Fcovid-summary.png?generation=1585072438685236&alt=media" alt="">

My view, as a person living in NYC, about this virus, is that by the time governments react to media pressure, to lockdown or even test, it's too late. In dense areas, everyone susceptible has already amble opportunities to be infected. Especially for a virus with 5-14 days lag between infections and symptoms, a period during which hosts spread it all over on subway, the conditions are hopeless. Active populations have already been exposed, mostly asymptomatic and recovered. Sensitive/older populations are more self-isolated/careful in affluent societies (maybe this isn't the case in North Italy). As the virus finishes exploring the active population, it starts penetrating the more isolated ones. At this point in time, the first fatalities happen. Then testing starts. Then the media and the lockdown. Lockdown seems overly effective because it coincides with the tail of the disease spread. It helps slow down the virus exploring the long-tail of sensitive population, and we should all contribute by doing it, but it doesn't cause the end of the disease. If it did, then as soon as people were back in the streets (see China), there would be repeated outbreaks.

Smart politicians will test a lot because it will make their condition look worse. It helps them demand more resources. At the same time, they will have a low rate of fatalities due to large denominator. They can take credit for managing well a disproportionally major crisis - in contrast to people who didn't test.

We were lucky this time. We, Westerners, have woken up to the potential of a pandemic. I'm sure we will give further resources for prevention. Additionally, we will be more open-minded, helping politicians to have more direct responses. We will also require them to be more responsible in their messages and reactions.

Clonorchis sinensis, an ancient parasite that infects a number of piscivorous mammals, attracts significant public health interest due to zoonotic exposure risks in Asia. The available studies are insufficient to reflect the prevalence, geographic distribution, and intraspecific genetic diversity of C. sinensis in endemic areas. Here, a multilocus analysis based on eight genes (ITS1, act, tub, ef-1a, cox1, cox3, nad4 and nad5 [4.986 kb]) was employed to explore the intra-species genetic construction of C. sinensis in China. Two hundred and fifty-six C. sinensis isolates were obtained from environmental reservoirs from 17 provinces of China. A total of 254 recognized Multilocus Types (MSTs) showed high diversity among these isolates using multilocus analysis. The comparison analysis of nuclear and mitochondrial phylogeny supports separate clusters in a nuclear dendrogram. Genetic differentiation analysis of three clusters (A, B, and C) showed low divergence within populations. Most isolates from clusters B and C are geographically limited to central China, while cluster A is extraordinarily genetically diverse. Further genetic analyses between different geographic distributions, water bodies and hosts support the low population divergence. The latter haplotype analyses were consistent with the phylogenetic and genetic differentiation results. A recombination network based on concatenated sequences showed a concentrated linkage recombination population in cox1, cox3, nad4 and nad5, with spatial structuring in ITS1. Coupled with the history record and archaeological evidence of C. sinensis infection in mummified desiccated feces, these data point to an ancient origin of C. sinensis in China. In conclusion, we present a likely phylogenetic structure of the C. sinensis population in mainland China, highlighting its possible tendency for biogeographic expansion. Meanwhile, ITS1 was found to be an effective marker for tracking C. sinensis infection worldwide. Thus, the present study improves our understanding of the global epidemiology and evolution of C. sinensis.

Premise of the study: Interspecific hybridization can cause genetic structure across species ranges if the mating system and degree of sympatry/parapatry with close relatives varies geographically. The coastal dune endemic Camissoniopsis cheiranthifolia (Onagraceae) exhibits genetic subdivisions across its range, some of which are associated with shifts in mating system from outcrossing to selfing, while others are not. For instance, strong differentiation between large-flowered, self-incompatible (LF-SI) and large-flowered, self-compatible (LF-SC) populations occurs without much reduction in outcrossing or obvious barriers to gene flow. We hypothesized that LF-SI diverged from LF-SC via hybridization with the predominantly inland SI sister species C. bistorta.

Methods: We analyzed spatial proximity using 1460 herbarium records, and genetic variation at 12 microsatellites assayed for 805 and 404 individuals from 32 C. cheiranthifolia and 18 C. bistorta populations, respectively. We also assayed nine chloroplast microsatellites for 124 and 111 individuals from 27 and 19 populations, respectively.

Key results: Closer parapatry was associated with unexpectedly high genetic continuity between LF-SI C. cheiranthifolia and C. bistorta. LF-SI genotypes clustered with C. bistorta exclusive of other C. cheiranthifolia genotypes. Similarly, pairwise FST among SI C. cheiranthifolia and C. bistorta, adjusted for geographic proximity, was not higher between heterospecific than conspecific populations.

Conclusions: The lack of genetic differentiation between LF-SI C. cheiranthifolia and C. bistorta populations, even those located away from the zone of parapatry, suggests that LF-SI C. cheiranthifolia instead of hybridizing with C. bistorta is rather an ecotype of C. bistorta that has adapted to coastal dune habitat independent of other lineages in C. cheiranthifolia proper.

Methods The nuclear microsatellite (nSSR) dataset represents 129 genotypes at 12 loci from 50 populations across the range of Camissoniopsis cheiranthifolia (32 populations) and C. bistorta (18 populations) collected in 2009 and 2010. Fragment sizes were binned using the MsatAllele package (version 1.04, Alberto 2009) for the R statistical computing environmt.For details about PCR conditions and other laboratory methods please see López-Villalobos et al., 2014.

We provide individual and population codes, latitude, longitude, location (coastal vs. inland), species (C.ch = C. cheiranthifolia, C.bi = C. bistorta) and the categorical variable used to identify gentic clusters and mating system variation (please, refer to the paper). Loci names are as presented in the supplementary information of this study and in López-Villalobos et al., 2014 and López-Villalobos and Eckert 2018.

The chloroplast microsatellite data represents number of variants (base pair repeats) at nine polymorphic chloroplast DNA microsatellites (cpSSR; following Weising and Gardner, 1999; Chung and Staub, 2003) assyed for 124 individuals from 27 populations of C. cheiranthifolia and 111 C. bistorta individuals from 19 populations.

The third dataset are pairwise FST [Weir and Cockerham,1984] and other genetic differentiation statistics from nSSR data estimated using the R package DiveRsity.This data was obtained using the methods described in the isolation-by-distance analyses from this study. Metadata for each colum is as follows: population comparison (codes as in nSSR dataset), population 1, population 2, latitude and longitude of populations 1 and 2, grpspp (species grouping, wCc = within C. cheiranthifolia, wCbi = within C. bistorta, bSp = between species), group according to whether populations are coastal (Co) or inland (In), gouping according to their genetic cluster as in Figure 5 (P=pink, B=Blue), geographic distance estimated as the great circle surface distance calculated using the R package geosphere, version 1.3-11 and several genetic differentiation statistics (Fst used in this paper) estimated using the R package DiveRsity.

As of 2019, about ** percent of households across India were segmented as belonging to the NCCS C category of consumers. Contrariwise only **** percent of the country's population fell under the NCCS E category that year. Between 2014 and 2019, the share of population classified as category A, B, and C consumers has grown tremendously, reflecting the trajectory of the booming middle class within the Indian economy.

Components of Population Change since 1926 (Number) by Intercensal Period, Province, CensusYear and Components of Population Change

View data using web pages

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Supporting Information. Figure S1, Payoff profiles. Payoffs for types A are represented by black squares, while red circles depict payoffs for types N. From left: Public goods game (PG, Example 1) for n = 20, C = 1 and B = 5. Iterated public goods game (IPG, Example 2) for n = 20, C = 1, B = 5, a = 4 and T = 2. Threshold model (THR, Example 3) for n = 20 C = 1, θ = 4 and A = A′ = 10. Figure S2, Perron-Frobenius eigenvalues ρ as a function of m for δ = 0.1, 0.2 and 0.4. From top to bottom: Public goods game (PG, Example 1) with n = 20, C = 1, B = 5. Iterated public goods (IPG, Example 2) with n = 20, C = 1, B = 5, a = 8 and T = 10. Threshold model (THR, Example 3) with n = 20, C = 1, θ = 4, A = A′ = 10. Critical migration values ms are obtained by solving ρ(ms) = 1. Figure S3, Public goods game (Example 1): Panel A represents critical values ms as a function of the strength of selection δ. Curves correspond to the case C = 1, B = 2 and n = 10 (top, black dotted line), n = 20 (middle, blue dashed line) and n = 50 (bottom, magenta full line). Short horizontal red lines indicate critical values at the weak selection limit obtained from (2) in the paper. The inset shows the same curves within the full range of possible values for ms, illustrating the well known fact that for this model, only under exceptional conditions can the allele A invade. Panel B depicts the same conditions except for B = 5. Figure S4, Public goods game (Example 1): Critical relatedness above which types A proliferate, as a function of the strength of selection δ. (R0(m) = (1– m)2/(n – (n –1)(1– m)2) ≈ 1/(1+2nm) is the relatedness obtained from neutral genetic markers; see Sections S5 and S7). Panels correspond to the same parameter values as in Figure S3: C = 1, B = 2 and n = 10 (bottom, black dotted line), n = 20 (middle, blue dashed line) and n = 50 (top, magenta full line). Panel B depicts the same conditions except for B = 5. Short horizontal red lines indicate critical values at the weak selection limit obtained from Hamilton’s rule , or, equivalently, from (2) in the paper. Note the appreciable effect of the strength of selection. Figure S5, Iterated public goods game (Example 2): Critical values ms as a function of the strength of selection δ. Panel A depicts the case n = 20, C = 1, B = 5, a = 4 with, respectively from bottom to top, T = 1 (dotted black line), T = 10 (dashed blue line), T = 100 (dot-dashed magenta) and T = 500 (green full line). Panel B depicts the same conditions except for a = 8. Short horizontal red lines indicate critical values at the weak selection limit obtained from (2) in the paper. Each curve has T fixed, but to compare different values of T, the product δT is a natural measure of strength of selection, and is used in the horizontal axis. Figure S6, Iterated public goods game (Example 2): Critical relatedness above which types A proliferate, as a function of the strength of selection δ. (R0(m) = (1– m)2/(n – (n –1)(1– m)2) is the relatedness obtained from neutral genetic markers; see Sections S5 and S7). Panels correspond to the same parameter values as in Figure S5: Panel A depicts the case n = 20, C = 1, B = 5, a = 4 with, respectively from top to bottom, T = 1 (dotted black line), T = 10 (dashed blue line), T = 100 (dot-dashed magenta) and T = 500 (green full line). Panel B depicts the same conditions except for a = 8. As in Figure S5, each curve has T fixed, but to compare different values of T, the product δT is a natural measure of strength of selection, and is used in the horizontal axis. Short horizontal red lines indicate critical values at the weak selection limit obtained from (2) in the paper. These values are: Panel A: 0.2000, 0.0865, 0.0402, 0.0243. Panel B: 0.2000, 0.1099, 0.0638, 0.0452. Note the very low values of critical relatedness in Panel A. Figure S7, Threshold model (Example 3): Critical values ms as a function of the strength of selection δ. Panel A depicts the case n = 20, C = 1, θ = 4, A′ = 2A, with, respectively from bottom to top, A = 5 (dotted black line), A = 10 (dashed blue line), A = 50 (dot-dashed magenta) and A = 100 (green full line). Panel B depicts the same conditions except for θ = 8. Short horizontal red lines indicate critical values at the weak selection limit obtained from (2) in the paper. Each curve has A fixed, but to compare different values of A, the product δA is a natural measure of strength of selection, and is used in the horizontal axis. Figure S8, Threshold model (Example 3): Critical relatedness above which types A proliferate, as a function of the strength of selection δ. (R0(m) = (1– m)2/(n – (n –1)(1– m)2) is the relatedness obtained from neutral genetic markers; see Sections S5 and S7). Panels correspond to the same parameter values as in Figure S7: Panel A depicts the case n = 20, C = 1, θ = 4, A′ = 2A, with, respectively from top to bottom, A = 5 (dotted black line), A = 10 (dashed blue line), A = 50 (dot-dashed magenta) and A = 100 (green full line). Panel B depicts the same conditions except for θ = 8. As in Figure S7, each curve has A fixed, but to compare different values of A, the product δA is a natural measure of strength of selection, and is used in the horizontal axis. Short horizontal red lines indicate critical values at the weak selection limit obtained from (2) in the paper. These values are: Panel A: 0.012, 0.017, 0.044, 0.071. Panel B: 0.061, 0.075, 0.137, 0.194. Note the extremely low values of critical relatedness in Panel A. The large values of A can result from contingent cooperation, based on feedback, as for the IPG. For instance, suppose that a certain activity repeats itself T times over a life-cycle. Suppose also that in each repetition the payoff is well described by the threshold model. If types A discontinue the participation when their payoff in the previous round was negative (as in the IPG discussed in Figure 2 in the paper), then the resulting payoff over the T iterations is also given by a threshold model, with the same value of C, but A replaced by (A – C)T+C, and A′ replaced with A′T. This gives plausibility to values of A and A′ as large as those in this figure, since T can be in the hundreds, or thousands (see discussion on the IPG in the paper). Figure S9, Public goods game (Example 1): Perron-Frobenius eigenvectors ν = (ν1, …, νn) represented in each box as a histogram, as a function of the strength of selection δ (rows) and of the migration rate parameter m (columns). Critical migration rates ms are annotated in each row. Perron-Frobenius eigenvalues ρ are also provided for each box. In this picture we have C = 1, B = 2 and n = 20. Figure S10, Self-organization of copies of A. In these pictures we have PG with n = 2, C = 1, B = 3, δ = 0.3, resulting in ms = 0.2889. Pictures show evolution of f(t) = (f1(t), f2(t)), started from several different initial distributions f(0). Circles over the lines mark f(t), with t = 0, 1, …, 500 obtained by iterations of the map f(t +1) = f(t)M(A+B). The direction spanned by the eigenvector ν is represented as a dotted green line. Left side (black): cases with ρ 1, the allele A spreads. In the top row, m is far from ms: (A1) m = 0.3389, ρ = 0.9340, ν = (0.8506, 0.1494); (B1) m = 0.2389, ρ = 1.078, ν = (0.7342, 0.2658). In the bottom row, m is close to ms: (A2) m = 0.2890, ρ = 0.999856, ν = (0.7342, 0.2658); (B2) m = 0.2888, ρ = 1.000014, ν = (0.7999, 0.2001). Note that in all cases f(t) reaches in a few generations a steady state, in which it shrinks (ρ 1), as a multiple of ν. When m approaches ms, the eigenvalue ρ becomes close to 1, the stationary movement along the direction given by ν slows down and the trajectories towards this direction straighten themselves, but are not slowed down. Figure S11, Self-organization of copies of A. In this picture we have IPG with n = 10, C = 1, B = 3, T = 100, a = 2, δ = 0.01, and m = 0.153, slightly smaller than ms = 0.163. Top part shows evolution of p(t), and bottom part shows corresponding evolution of f(t) = (f1(t), …, f10(t)), displayed as normalized histograms. Two initial conditions are compared: (Red) f(0) = 10–2(1, 0, …, 0), so that p(0) = 10–3. (Black) f(0) = 10–5(0, …, 0, 1), so that p(0) = 10–5. Note that from generation to generation the distribution of copies of A adjusts itself to the same stationary distribution, “losing memory of the initial distribution”. Figure S12, Self-organization of copies of A. This picture corresponds to the same model and situation described in Figure S11, but with a different time-frame, including later times. Note that eventually the two curves of p(t) become parallel straight lines, illustrating the exponential growth of p(t) at rate ρ independently of the initial condition. This picture also illustrates two other important points: 1) The possible non-monotonicity of p(t). 2) The fact that the asymptotic rate of growth may be smaller than the initial rate of growth. Indeed, computations of Δp only indicate the long term prospects for the allele A, when done under stationary conditions, as in (1). The initial distribution of copies of A in the red line produces neighbor modulated fitness for A below that of allele N, so that Δp(0) 0, but this growth happens at an unsustainably high rate. The distribution ν, towards which the copies of A self-organize is optimal for their stationary, stable, growth. This is so because (ρ, ν)