This dataset shows the patient demographic environment for 2017

Preface:

This is a part of my contribution to the Kiva, to help them continue and expand their initiative to alleviate global poverty.

Context:

In order for Kiva to best set its investment priorities, help inform lenders, and understand their target communities, knowing the level of poverty for each borrower is crucial. However, attaining individual-level information is time-consuming, labor-intensive and expensive.

Therefore, I propose a method that combines machine learning and satellite imagery to predict poverty. This approach is easier, less expensive and scalable since satellite data is often inexpensive and open source. Before developing this model we have to first collect the data that would be relevant for prediction regionalized poverty, which is why I have created this dataset.

Content:

This data set is a collection of the following:

1. Environmental Data: Vegetation indices, soil characteristics, evaporation
2. Climate Data: Temperature, precipitation, elevation
3. Socioeconomic and Demographic Data: Population Density, Access to major cities, Nightlight, Land usage
4. Conflict Data: Conflicts, death tolls, civilian casualties
5. Natural Disaster Data (coming shortly)

I have provided the data in the following formats:

• Individual Level Data: For each region in the loans (kiva_loans) and MPI study set, I have extracted all of the information listed above. The data is provided in the csv files; MPIData_augmented.csv, kivaData_augmented.csv
• Stacked satellite images for each country with a loan. For each country, a satellite image is provided as a .grd file in the Satellite Imagery folder

Usage:

If you are unfamiliar with working with satellite images I suggest you utilize the csv files. I will put out a tutorial on working with the satellite images in the near future. If you have any questions, please post them an I will try to answer them as soon as I can. If you have any questions related to the data, please refer to the data dictionary.

Documentation:

Documentation for this dataset is an ongoing process given the complex and extensive process to preparing this dataset, amd the wide range of data sources. If you have a particular question please post it and I'll answer it as soon as possible.

Upcoming Work:

As I mentioned above, I am going to use this data to build a machine learning model that will be able to predict the poverty for any region in any impoverished country! Stay tuned!

LivWell is a global longitudinal database which provides a range of key indicators related to women’s socioeconomic status, health and well-being, access to basic services, and demographic outcomes. Data are available at the sub-national level for 52 countries and 447 regions. A total of 134 indicators are based on 199 Demographic and Health Surveys for the period 1990-2019, supplemented by extensive information on socioeconomic and climatic conditions in the respective regions for a total of 190 indicators. The resulting data offer various opportunities for policy-relevant research on gender inequality, inclusive development, and demographic trends at the sub-national level.

For a full description, please refer to the article describing the database here: (link to come)

The companion repository livwelldata allows to easily use the database in R. The R package can be downloaded following the instructions on the following git repository: https://gitlab.pik-potsdam.de/belmin/livwelldata. The version of the database in the package is the same as in this repository.

The dataset contains economic, social, demographic, and environmental metrics for all countries in 2023. The cleaned and organised data comes from the World Bank and its open database.

EJSCREEN is an environmental justice mapping and screening tool that provides EPA with a nationally consistent dataset and approach for combining environmental and demographic indicators. EJSCREEN users choose a geographic area; the tool then provides demographic and environmental information for that area. All of the EJSCREEN indicators are publicly-available data. EJSCREEN simply provides a way to display this information and includes a method for combining environmental and demographic indicators into EJ indexes.

EJSCREEN includes:

11 environmental indicators 6 demographic indicators 11 EJ indexes

Link Downloaded here

This EnviroAtlas dataset is a summary of key demographic groups for the EnviroAtlas community. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This county geography dataset includes selected indicators (2011-2015 5-Year Averages) pertaining to population, age, race/ethnicity, language, housing, poverty/income, education, disability, health insurance, employment, and age*race*gender groups. This dataset is assembled annually from the U.S. Census American Community Survey American Factfinder website and is maintained by the Colorado Department of Public Health and Environment.

Contains monthly averages of frost days (minimum temperatures below 0°C) across Europe starting from 1980. Each record includes the date, the average count of frost days for that month, and corresponding year and month values. This dataset supports climate analysis, seasonal assessments, and studies on environmental impacts linked to frost frequency.

This dataset provides a curated and comprehensive overview of global health, demographic, economic, and environmental metrics for 188 recognized countries over a period of 10 years (2012-2021). It was created by combining reliable data from the World Bank and the World Health Organization (WHO). Due to the absence of a single source containing all necessary indicators, over 60 datasets were analyzed, cleaned, and merged, prioritizing completeness and significance.

The dataset includes 29 key indicators, ranging from life expectancy, population metrics, and economic factors to environmental conditions and health-related behaviors. Missing values were carefully handled, and only the most relevant data with substantial coverage were retained.

This dataset is ideal for researchers, analysts, and policymakers interested in exploring relationships between economic development, health outcomes, and environmental factors at a global scale.

This dataset integrates demographic indicators—such as infant mortality rates—with monthly climate variables for Portugal from 2000 to 2024. It is designed to enable exploration of climate-health relationships, including analyses of how temperature, humidity, precipitation, and other climate factors may correlate with public health outcomes over time. The panel format allows longitudinal studies across multiple years and regions within Portugal, supporting research in environmental epidemiology, socioeconomic disparities, and climate change impacts on population health.

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

Country: Name of the country.

Density (P/Km2): Population density measured in persons per square kilometer.

Abbreviation: Abbreviation or code representing the country.

Agricultural Land (%): Percentage of land area used for agricultural purposes.

Land Area (Km2): Total land area of the country in square kilometers.

Armed Forces Size: Size of the armed forces in the country.

Birth Rate: Number of births per 1,000 population per year.

Calling Code: International calling code for the country.

Capital/Major City: Name of the capital or major city.

CO2 Emissions: Carbon dioxide emissions in tons.

CPI: Consumer Price Index, a measure of inflation and purchasing power.

CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.

Currency_Code: Currency code used in the country.

Fertility Rate: Average number of children born to a woman during her lifetime.

Forested Area (%): Percentage of land area covered by forests.

Gasoline_Price: Price of gasoline per liter in local currency.

GDP: Gross Domestic Product, the total value of goods and services produced in the country.

Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.

Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.

Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.

Largest City: Name of the country's largest city.

Life Expectancy: Average number of years a newborn is expected to live.

Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.

Minimum Wage: Minimum wage level in local currency.

Official Language: Official language(s) spoken in the country.

Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.

Physicians per Thousand: Number of physicians per thousand people.

Population: Total population of the country.

Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.

Tax Revenue (%): Tax revenue as a percentage of GDP.

Total Tax Rate: Overall tax burden as a percentage of commercial profits.

Unemployment Rate: Percentage of the labor force that is unemployed.

Urban Population: Percentage of the population living in urban areas.

Latitude: Latitude coordinate of the country's location.

Longitude: Longitude coordinate of the country's location.

Potential Use Cases

Analyze population density and land area to study spatial distribution patterns.

Investigate the relationship between agricultural land and food security.

Examine carbon dioxide emissions and their impact on climate change.

Explore correlations between economic indicators such as GDP and various socio-economic factors.

Investigate educational enrollment rates and their implications for human capital development.

Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.

Study labor market dynamics through indicators such as labor force participation and unemployment rates.

Investigate the role of taxation and its impact on economic development.

Explore urbanization trends and their social and environmental consequences.

This data set is comprised of four files related to the counts of wood frog (Lithobates sylvaticus) egg masses in the Northeast United States and climatic information derived for the count locations. One file contains data for the counts at all locations, the other files contain derived temperature and precipitation data for models used in the published manuscript.

This dataset reports the monthly average number of frost days (days with minimum temperature below 0°C) recorded across European regions from 1980 onward. Columns include the date, the computed average number of frost days for each month, and the corresponding year and month. The data is suitable for assessing cold-season trends, climate variability, and potential effects on sectors sensitive to frost events.

This EnviroAtlas dataset is a summary of key demographic groups for the EnviroAtlas community. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The EcoPop_ER database aims to provide new informational tools to understand and mitigate the impact of rapid climate change on the population of Emilia-Romagna by developing and disseminating a database containing demographic, environmental, and climatic data for all municipalities in the region. The database implementation is promoted as part of the project 'Memorec - Resilience and Climate Memory in Emilia-Romagna', funded by the University of Bologna through AlmaCares funds.

Version: Version1.0. Preliminary version of the dataset.

License: CC-BY 4.0 International

DOI: 10.5281/zenodo.13885778

Data for: Environmental complexity mitigates the demographic impact of sexual selection

https://doi.org/10.5061/dryad.bzkh189g8

Description of the data and file structure

The data underlying all analyses is available in the Excel file containing 5 spreadsheets, or as separate txt files, with the following names;

"Trait-values of founding lines" (used to ordinate experimental evolution line founders and test for differences between regimes in evolution of mating traits - Fig 1B) Means for each of the six evolution lines (given by the "Evolution Regime" and "founder" columns) for the following traits: - Sperm competition success (given as the proportion of offspring sired in competition with a reference male) - Sperm production (estimated number of sperm, matured over 25h, transferred by a male to a female) - remating rate (a relative measure of the probability of remating in the first 24h following the first mating) - male-male fertility decline (the proportional decline i...

Approximately 25% of mammals are currently threatened with extinction, a risk that is amplified under climate change. Species persistence under climate change is determined by the combined effects of climatic factors on multiple demographic rates (survival, development, reproduction), and hence, population dynamics. Thus, to quantify which species and regions on Earth are most vulnerable to climate-driven extinction, a global understanding of how different demographic rates respond to climate is urgently needed. Here, we perform a systematic review of literature on demographic responses to climate, focusing on terrestrial mammals, for which extensive demographic data are available. To assess the full spectrum of responses, we synthesize information from studies that quantitatively link climate to multiple demographic rates. We find only 106 such studies, corresponding to 87 mammal species. These 87 species constitute < 1% of all terrestrial mammals. Our synthesis reveals a strong mismatch between the locations of demographic studies and the regions and taxa currently recognized as most vulnerable to climate change. Surprisingly, for most mammals and regions sensitive to climate change, holistic demographic responses to climate remain unknown. At the same time, we reveal that filling this knowledge gap is critical as the effects of climate change will operate via complex demographic mechanisms: a vast majority of mammal populations display projected increases in some demographic rates but declines in others, often depending on the specific environmental context, complicating simple projections of population fates. Assessments of population viability under climate change are in critical need to gather data that account for multiple demographic responses, and coordinated actions to assess demography holistically should be prioritized for mammals and other taxa.

Methods For each mammal species i with available life-history information, we searched SCOPUS for studies (published before 2018) where the title, abstract, or keywords contained the following search terms:

Scientific species namei AND (demograph* OR population OR life-history OR "life history" OR model) AND (climat* OR precipitation OR rain* OR temperature OR weather) AND (surv* OR reprod* OR recruit* OR brood OR breed* OR mass OR weight OR size OR grow* OR offspring OR litter OR lambda OR birth OR mortality OR body OR hatch* OR fledg* OR productiv* OR age OR inherit* OR sex OR nest* OR fecund* OR progression OR pregnan* OR newborn OR longevity).

We used the R package taxize (Chamberlain and Szöcs 2013) to resolve discrepancies in scientific names or taxonomic identifiers and, where applicable, searched SCOPUS using all scientific names associated with a species in the Integrated Taxonomic Information System (ITIS; http://www.itis.gov).

We did not extract information on demographic-rate-climate relationships if:

A study reported on single age or stage-specific demographic rates (e.g., Albon et al. 2002; Rézoiki et al. 2016)
A study used an experimental design to link demographic rates to climate variation (e.g., Cain et al. 2008)
A study considered the effects of climate only indirectly or qualitatively. In most cases, this occurred when demographic rates differed between seasons (e.g., dry vs. wet season) but were not linked explicitly to climatic factors (e.g., varying precipitation amount between seasons) driving these differences (e.g., de Silva et al. 2013; Gaillard et al. 2013).

We included several studies of the same population as different studies assessed different climatic variables or demographic rates or spanned different years (e.g., for Rangifer tarandus platyrhynchus, Albon et al. 2017; Douhard et al. 2016).

We note that we can miss a potentially relevant study if our search terms were not mentioned in the title, abstract, or keywords. To our knowledge, this occurred only once, for Mastomys natalensis (we included the relevant study [Leirs et al. 1997] into our review after we were made aware that it assesses climate-demography relationships in the main text).

Lastly, we checked for potential database bias by running the search terms for a subset of nine species in Web of Science. The subset included three species with > three climate-demography studies published and available in SCOPUS (Rangifer tarandus, Cervus elaphus, Myocastor coypus); three species with only one climate-demography study obtained from SCOPUS (Oryx gazella, Macropus rufus, Rhabdomys pumilio); and another three species where SCOPUS did not return any published study (Calcochloris obtusirostris, Cynomops greenhalli, Suncus remyi). Species in the three subcategories were randomly chosen. Web of Science did not return additional studies for the three species where SCOPUS also failed to return a potentially suitable study. For the remaining six species, the total number of studies returned by Web of Science differed, but the same studies used for this review were returned, and we could not find any additional studies that adhered to our extraction criteria.

Description of key collected data

From all studies quantitatively assessing climate-demography relationships, we extracted the following information:

Geographic location - The center of the study area was always used. If coordinates were not provided in a study, we assigned coordinates based on the study descriptions of field sites and data collection.
Terrestrial biome - The study population was assigned to one of 14 terrestrial biomes (Olson et al. 2001) corresponding to the center of the study area. As this review is focused on general climatic patterns affecting demographic rates, specific microhabitat conditions described for any study population were not considered.
Climatic driver - Drivers linked to demographic rates were grouped as either local/regional precipitation & temperature values or derived indices (e.g., ENSO, NAO). The temporal extent (e.g., monthly, seasonal, annual, etc.) and aggregation type (e.g., minimum, maximum, mean, etc.) of drivers was also noted.
Demographic rate modeled - To facilitate comparisons, we grouped the demographic rates into either survival, reproductive success (i.e., whether or not reproduction occurre, reproductive output (i.e., number or rate of offspring production), growth (including stage transitions), or condition that determines development (i.e., mass or size). 
Stage or sex modeled - We retrieved information on responses of demographic rates to climate for each age class, stage, or sex modeled in a given study.
Driver effect - We grouped effects of drivers as positive (i.e., increased demographic rates), negative (i.e., reduced demographic rate), no effect, or context-dependent (e.g., positive effects at low population densities and now effect at high densities). We initially also considered nonlinear effects (e.g., positive effects at intermediate values and negative at extremes of a driver), but only 4 studies explicitly tested for nonlinear effects, by modelling squared or cubic climatic drivers in combination with driver interactions. We therefore considered nonlinear demographic effects as context dependent.  
Driver interactions - We noted any density dependence modeled and any non-climatic covariates included (as additive or interactive effects) in the demographic-rate models assessing climatic effects.
Future projections of climatic driver - In studies that indicated projections of drivers under climate change, we noted whether drivers were projected to increase, decrease, or show context-dependent trends. For studies that provided no information on climatic projections, we quantified projections as described in Detailed description of climate-change projections below (see also climate_change_analyses_mammal_review.R).

The datasets presented here enable historical longitudinal studies of micro-level geographic factors in a rural setting. These types of datasets are new, as historical demography studies have generally failed to properly include the micro-level geographic factors. Our datasets describe the geography over five Swedish rural parishes and a geocoded population (at the property unit level) for this area for the time period 1813-1914. The population is a subset of the Scanian Economic Demographic Database (SEDD). The geographic information includes the following feature types: property units, wetlands, buildings, roads and railroads. The property units and wetlands are stored in object-lifeline time representations (information about creation, changes and ends of objects are recorded in time), whereas the other feature types are stored as snapshots in time. Thus, the datasets present one of the first opportunities to study historical spatio-temporal patterns at the micro-level.

GapMaps GIS data for USA and Canada sourced from Applied Geographic Solutions (AGS) includes an extensive range of the highest quality demographic and lifestyle segmentation products. All databases are derived from superior source data and the most sophisticated, refined, and proven methodologies.

GIS Data attributes include:

1. Latest Estimates and Projections The estimates and projections database includes a wide range of core demographic data variables for the current year and 5- year projections, covering five broad topic areas: population, households, income, labor force, and dwellings.

2. Crime Risk Crime Risk is the result of an extensive analysis of a rolling seven years of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, Crime Risk provides an accurate view of the relative risk of specific crime types (personal, property and total) at the block and block group level.

3. Panorama Segmentation AGS has created a segmentation system for the United States called Panorama. Panorama has been coded with the MRI Survey data to bring you Consumer Behavior profiles associated with this segmentation system.

4. Business Counts Business Counts is a geographic summary database of business establishments, employment, occupation and retail sales.

5. Non-Resident Population The AGS non-resident population estimates utilize a wide range of data sources to model the factors which drive tourists to particular locations, and to match that demand with the supply of available accommodations.

6. Consumer Expenditures AGS provides current year and 5-year projected expenditures for over 390 individual categories that collectively cover almost 95% of household spending.

7. Retail Potential This tabulation utilizes the Census of Retail Trade tables which cross-tabulate store type by merchandise line.

8. Environmental Risk The environmental suite of data consists of several separate database components including: -Weather Risks -Seismological Risks -Wildfire Risk -Climate -Air Quality -Elevation and terrain

Primary Use Cases for GapMaps GIS Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic & segmentation profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular census block level using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate AGS demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Finance / Insurance (eg. Hedge Funds, Investment Advisors, Investment Research, REITs, Private Equity, VC)

8. Network Planning

9. Customer (Risk) Profiling for insurance/loan approvals

10. Target Marketing

11. Competitive Analysis

12. Market Optimization

13. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

14. Tenant Recruitment

15. Target Marketing

16. Market Potential / Gap Analysis

17. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

18. Customer Profiling

19. Target Marketing

20. Market Share Analysis

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).