The data belong to a paper that empirically examines the correlation between population growth and real interest rates. Although this correlation is well founded in macroeconomic theory, the corresponding empirical results have been rather tenuous. Demographic interest rate theories are typically based on long-term relationships across generations. Accordingly, key population trends appear often only across decades, if not centuries, worth of data. To capture these trends, a distinction is made between population growth resulting from a birth surplus and net migration. Within a panel covering 12 countries and the years since 1820, the paper find robust evidence that the birth surplus is significantly correlated with the real interest rate.

This new product will present data for specific census topics and population groups according to selected demographic, cultural, and socio-economic characteristics. These detailed 'profile-type' tables expand the analytical depth of basic census information. Special interest profiles include: ethnic groups, Aboriginal peoples, occupation, industry, and place of work.

Sourcing accurate and up-to-date map data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

GapMaps Map Data uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent demographics data across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

GapMaps Map Data also includes the latest Point-of-Interest (POI) Data for leading retail brands across a range of categories including Fast Food/ QSR, Health & Fitness, Supermarket/Grocery and Cafe sectors which is updated monthly.

With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

• Better understand your customers
• Identify optimal locations to expand your retail footprint
• Define sales territories for franchisees
• Run targeted marketing campaigns.

GapMaps Map Data for Asia and MENA can be utilized in any GIS platform and includes the latest estimates (updated annually) on:

1. Population (how many people live in your local catchment)
2. Demographics (who lives within your local catchment)
3. Worker population (how many people work within your local catchment)
4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

Primary Use Cases for GapMaps Map Data:

1. Retail Site Selection - identify optimal locations for future expansion and benchmark performance across existing locations.
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular 150m x 150m grid levels using all the key metrics
4. Target Marketing: Develop effective marketing strategies to acquire more customers.
5. Integrate GapMaps demographic data with your existing GIS or BI platform to generate powerful visualizations.
6. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)
7. Customer Profiling
8. Target Marketing
9. Market Share Analysis

This is a statistic on the interest of urban French for collaborative consumption in 2015, by service. According to this survey, about ** percent of urban dwellers were interested in car pooling, and over ** percent in apartments or houses.

This statistic displays the share of increase in UFC total population interest in the biggest growth markets between 2014 and 2016, by country. In 2016, it was found that the total population's interest in UFC in Russia increased with *** percent, followed by Spain with a population interest increase of * percent and Poland with an increase of *** percent. More information about the UFC can be found in the report Ultimate Fighting Championship.

This dataset provides estimates of the number of groups registered to lobby in all 50 states based on 26 economic sectors. See Garlick and Cluverius (nd) for details on the coding procedure and for a codebook.

Effective conservation and management of animal populations requires knowledge of abundance and trends. For many species, these quantities are estimated using systematic visual surveys. Additional individual-level data are available for some species. Integrated population modelling (IPM) offers a mechanism for leveraging these datasets into a single estimation framework. IPMs that incorporate both population- and individual-level data have previously been developed for birds, but have rarely been applied to cetaceans. Here, we explore how IPMs can be used to improve the assessment of cetacean populations. We combined three types of data that are typically available for cetaceans of conservation concern: population-level visual survey data, individual-level capture-recapture data, and data on anthropogenic mortality. We used this IPM to estimate the population dynamics of the Cook Inlet population of beluga whales (CIBW; Delphinapterus leucas) as a case study. Our state-space IPM included a population process model and three observational submodels: 1) a group detection model to describe group size estimates from aerial survey data; 2) a capture-recapture model to describe individual photographic capture-recapture data; and 3) a Poisson regression model to describe historical hunting data. The IPM produces biologically plausible estimates of population trajectories consistent with all three datasets. The estimated population growth rate since 2000 is less than expected for a recovering population. The estimated juvenile/adult survival rate is also low compared to other cetacean populations, indicating that low survival may be impeding recovery. This work demonstrates the value of integrating various data sources to assess cetacean populations and serves as an example of how multiple, imperfect datasets can be combined to improve our understanding of a population of interest. The model framework is applicable to other cetacean populations and to other taxa for which similar data types are available.

Methods /Data/CIBW_RSideCapHist_McGuire&Stephens.csv contains a matrix of right side capture histories (1 = captured, 0 = not captured) for each individual (rows) and year (columns). Photographic capture-recapture data were collected by Tamara McGuire. These data are made available here, without restriction, but anyone wishing to use these data is requested to contact tamaracookinletbeluga@gmail.com, who can provide further information on how raw data were processed to provide capture histories.

/Data/CIBW_HuntData_Mahoney&Shelden2000.xlsx contains the minimum documented number of animals killed (MinKilled) for years between 1950 and 1998 as published in Mahoney and Shelden 2000. Entries which are NA indicate that no data were available for that year.

/Data/CIBW_Abundance_HobbsEtAl2015.xlsx contains the total group size estimates from Hobbs et al. 2015.

/Data/CIBW_Abundance_BoydEtAl2019.txt contains an array with dimensions [1:1000, 1:8, 1:11] containing 1000 posterior samples of total group size for up to 8 survey days over 11 years, as described in Boyd et al. 2019.

According to a 2024 survey, ** percent of electric vehicle owners lived in city centers. Furthermore, ** percent of EV prospects who declare an intention to purchase an EV within five years reside in city centers, too. On the other hand, the share of EV sceptics, who have no interest in EVs, living in city centers was the lowest.

There is a persistent shortage of underrepresented minority (URM) faculty who are involved in basic biomedical research at medical schools. We examined the entire training pathway of potential candidates to identify the points of greatest loss. Using a range of recent national data sources, including the National Science Foundation’s Survey of Earned Doctorates and Survey of Doctoral Recipients, we analyzed the demographics of the population of interest, specifically those from URM backgrounds with an interest in biomedical sciences. We examined the URM population from high school graduates through undergraduate, graduate, and postdoctoral training as well as the URM population in basic science tenure track faculty positions at medical schools. We find that URM and non-URM trainees are equally likely to transition into doctoral programs, to receive their doctoral degree, and to secure a postdoctoral position. However, the analysis reveals that the diversions from developing a faculty career are found primarily at two clearly identifiable places, specifically during undergraduate education and in transition from postdoctoral fellowship to tenure track faculty in the basic sciences at medical schools. We suggest focusing additional interventions on these two stages along the educational pathway.

European Health Survey: Level of other people's interest in what is happening by gender and autonomous community. Population of 15 and older. National.

Our consumer data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences.

1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc.
2. Demographics - Gender, Age Group, Marital Status, Language etc.
3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc
4. Persona - Consumer type, Communication preferences, Family type, etc
5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc.
6. Household - Number of Children, Number of Adults, IP Address, etc.
7. Behaviours - Brand Affinity, App Usage, Web Browsing etc.
8. Firmographics - Industry, Company, Occupation, Revenue, etc
9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc.
10. Auto - Car Make, Model, Type, Year, etc.
11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases:

360-Degree Customer View:Get a comprehensive image of customers by the means of internal and external data aggregation.

Data Enrichment:Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment

Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity.

Advertising & Marketing:Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

Using Factori Consumer Data graph you can solve use cases like:

Acquisition Marketing Expand your reach to new users and customers using lookalike modeling with your first party audiences to extend to other potential consumers with similar traits and attributes.

Lookalike Modeling

Build lookalike audience segments using your first party audiences as a seed to extend your reach for running marketing campaigns to acquire new users or customers

And also, CRM Data Enrichment, Consumer Data Enrichment B2B Data Enrichment B2C Data Enrichment Customer Acquisition Audience Segmentation 360-Degree Customer View Consumer Profiling Consumer Behaviour Data

Here's the schema of Consumer Data: person_id first_name last_name age gender linkedin_url twitter_url facebook_url city state address zip zip4 country delivery_point_bar_code carrier_route walk_seuqence_code fips_state_code fips_country_code country_name latitude longtiude address_type metropolitan_statistical_area core_based+statistical_area census_tract census_block_group census_block primary_address pre_address streer post_address address_suffix address_secondline address_abrev census_median_home_value home_market_value property_build+year property_with_ac property_with_pool property_with_water property_with_sewer general_home_value property_fuel_type year month household_id Census_median_household_income household_size marital_status length+of_residence number_of_kids pre_school_kids single_parents working_women_in_house_hold homeowner children adults generations net_worth education_level occupation education_history credit_lines credit_card_user newly_issued_credit_card_user credit_range_new
credit_cards loan_to_value mortgage_loan2_amount mortgage_loan_type
mortgage_loan2_type mortgage_lender_code
mortgage_loan2_render_code
mortgage_lender mortgage_loan2_lender
mortgage_loan2_ratetype mortgage_rate
mortgage_loan2_rate donor investor interest buyer hobby personal_email work_email devices phone employee_title employee_department employee_job_function skills recent_job_change company_id company_name company_description technologies_used office_address office_city office_country office_state office_zip5 office_zip4 office_carrier_route office_latitude office_longitude office_cbsa_code
office_census_block_group
office_census_tract office_county_code
company_phone
company_credit_score
company_csa_code
company_dpbc
company_franchiseflag
company_facebookurl company_linkedinurl company_twitterurl
company_website company_fortune_rank
company_government_type company_headquarters_branch company_home_business
company_industry
company_num_pcs_used
company_num_employees
company_firm_individual company_msa company_msa_name
company_naics_code
company_naics_description
company_naics_code2 company_naics_description2
company_sic_code2
company_sic_code2_desc...

Population aged 18 and over according to degree of interest in participating in a public project and phases of a public project by regions of the Canary Islands. 2018.

Conflicts of interest for members of the Medical Research Council's (MRC) Population Health Sciences group.

BackgroundUnderstanding the social environmental around obesity has been limited by available data. One promising approach used to bridge similar gaps elsewhere is to use passively generated digital data.PurposeThis article explores the relationship between online social environment via web-based social networks and population obesity prevalence.MethodsWe performed a cross-sectional study using linear regression and cross validation to measure the relationship and predictive performance of user interests on the online social network Facebook to obesity prevalence in metros across the United States of America (USA) and neighborhoods within New York City (NYC). The outcomes, proportion of obese and/or overweight population in USA metros and NYC neighborhoods, were obtained via the Centers for Disease Control and Prevention Behavioral Risk Factor Surveillance and NYC EpiQuery systems. Predictors were geographically specific proportion of users with activity-related and sedentary-related interests on Facebook.ResultsHigher proportion of the population with activity-related interests on Facebook was associated with a significant 12.0% (95% Confidence Interval (CI) 11.9 to 12.1) lower predicted prevalence of obese and/or overweight people across USA metros and 7.2% (95% CI: 6.8 to 7.7) across NYC neighborhoods. Conversely, greater proportion of the population with interest in television was associated with higher prevalence of obese and/or overweight people of 3.9% (95% CI: 3.7 to 4.0) (USA) and 27.5% (95% CI: 27.1 to 27.9, significant) (NYC). For activity-interests and national obesity outcomes, the average root mean square prediction error from 10-fold cross validation was comparable to the average root mean square error of a model developed using the entire data set.ConclusionsActivity-related interests across the USA and sedentary-related interests across NYC were significantly associated with obesity prevalence. Further research is needed to understand how the online social environment relates to health outcomes and how it can be used to identify or target interventions.

European Health Survey: Level of other people's interest in what is happening by gender and age groups. Population of 15 and older. National.

Apex carnivores are wide-ranging, low-density, hard to detect, and declining throughout most of their range, making population monitoring both critical and challenging. Rapid and inexpensive index calibration survey (ICS) methods have been developed to monitor large African carnivores. ICS methods assume constant detection probability and a predictable relationship between the index and the actual population of interest. The precision and utility of the resulting estimates from ICS methods have been questioned. We assessed the performance of one ICS method for large carnivores - track counts - with data from two long-term studies of African lion populations. We conducted Monte Carlo simulation of intersections between transects (road segments) and lion movement paths (from GPS collar data) at varying survey intensities. Then, using the track count method we estimated population size and its confidence limits.

We found that estimates either overstate precision or are too imprecise to ...

Auxiliary Data.gdb: Land_use: original land use data POI_name: interests-point-data from the Amap platform (name indicates category)

New_gridded_population_dataset(.gdb): experimental result data, i.e., a gridded population map of mainland China with a resolution of 100 meters

New_minus_WorldPop_PopulationResidual(.gdb): pixel-level residuals of the new gridded population dataset with the Worldpop dataset

PopulationData_AdministrativeUnitLevel.gdb: Population_data_mainlandChina_level3: population data at the district and county level in mainland China Population_data_Name_level4_Table: township and street-level population data for provinces and municipalities

POI_Correlation_Coefficient: Python script: programming implementation for selecting the optimal bandwidth for POI Zonal statistical output of POI kernel density values: summary of various POI kernel densities in residential areas of administrative units Summary of POI Pearson correlation coefficients: sum of Pearson's correlation coefficients for 13 types of POIs at a certain bandwidth

Note: Due to the storage space limitation, 3D building, nighttime light, and WorldPop datasets have not been uploaded. To access these publicly available data, please visit the official website via the "Related links" at the bottom. In addition, we are not authorized to share data for the fourth level of administrative boundaries, so we only share the corresponding population data in tabular form.

Conflicts of interest for members of the Medical Research Council's (MRC) Population & Systems Medicine board.

This data collection contains de-identified clinical health service utilisation data from Bendigo Health and the General Practitioners Practices associated with the Loddon Mallee Murray Medicare Local. The collection also includes associated population health data from the ABS, AIHW and the Municipal Health Plans. Health researchers have a major interest in how clinical data can be used to monitor population health and health care in rural and regional Australia through analysing a broad range of factors shown to impact the health of different populations. The Population Health data collection provides students, managers, clinicians and researchers the opportunity to use clinical data in the study of population health, including the analysis of health risk factors, disease trends and health care utilisation and outcomes.Temporal range (data time period):2004 to 2014Spatial coverage:Bendigo Latitude -36.758711200000010000, Bendigo Longitude 144.283745899999990000

The dataset ‘Interest group preferences in deficit countries – Ireland, Spain and Greece’ provides a wide range of information on interest group positions on economic and social policy issues during the Eurozone crisis, which took place between 2010-15. The data was collected via population-surveys directed to interest group populations in Ireland, Spain, and Greece during the summer of 2017.