home.social

#demographics — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #demographics, aggregated by home.social.

  1. 💧🌏 Greg Cocks @[email protected] ·

    Annual High-Resolution Global Ambient Population Estimates From 1975 To 2024
    --
    doi.org/10.31223/X5MV3R <-- shared paper
    --
    landscan.ornl.gov/ <-- shared webmap and data link (via download -> LandScan Mosaic Time Series)
    --
    gee-community-catalog.org/proj <-- shared link to GEE community catalog collection ‘LandScan Mosaic Annual Global Ambient Population Time Series’
    --
    H/T @andrew Zimmer || Geographer | Research Scientist @ Oak Ridge National Laboratory
    “LSM-TS provides annual global estimates of ambient population from 1975–2024 at ~90 m resolution, creating a spatially and temporally consistent reconstruction of population change designed for longitudinal analysis. Reconstruction of historical populations are driven by current LandScan Mosaic building-level population distributions, scaled by built-surface change from #GHSL … and calibrated to annual national-level estimates from U.S. Census Bureau…”
    --
    “LandScan is a globally recognized, R&D100-winning population data platform developed at Oak Ridge National Laboratory (ORNL). Its datasets provide human population distribution estimates down to 100 meter grid resolution. LandScan supports disaster response, humanitarian aid, environmental analysis, and urban planning by providing insights into where people live and how they move…”
    --
    “Gridded population data support assessments of human exposure, settlement change, infrastructure demand, and access to services, yet global datasets combining annual coverage over multiple decades with fine spatial resolution remain limited. LandScan Mosaic Time Series provides 50 annual estimates of global ambient population distribution at 3 arc-second resolution from 1975 through 2024. The series is anchored to the 2024 LandScan Mosaic surface, produced through a building-level population modeling workflow. Historical surfaces are reconstructed using annualized changes in built surface and derived first-level administrative population trajectories. Every layer, including 2024, is normalized to administrative targets scaled to annual country totals from the U.S. Census Bureau International Database. The dataset is distributed as 50 single-band Cloud Optimized GeoTIFFs on an identical WGS84 grid, with values representing estimated persons per cell. Its common grid and methodology support fine-scale longitudinal analysis of ambient population distribution while documented validation and limitations guide appropriate reuse…”
    #demographics #change #spatiotemporal #1975 #2024 #population #change #grid #global #LandScan #POPGRID #LSMTS #ambientpopulation #GHSL #OakRidge #ORNL #usecase #opendata #disasterresponse #humanitarianaid #environmentalanalysis #urbanplanning #humanexposure #settlementchange #city #rural #infrastructure #demand #accesstoservices #GIS #spatial #mapping #building #outline #model #modeling
    @OAK Ridge National Laboratory | National Security Sciences at ORNL | @POPGRID Data Collaborative

    #ghsl #demographics #change #spatiotemporal #population #grid
  2. 💧🌏 Greg Cocks @[email protected] ·

    Annual High-Resolution Global Ambient Population Estimates From 1975 To 2024
    --
    doi.org/10.31223/X5MV3R <-- shared paper
    --
    landscan.ornl.gov/ <-- shared webmap and data link (via download -> LandScan Mosaic Time Series)
    --
    gee-community-catalog.org/proj <-- shared link to GEE community catalog collection ‘LandScan Mosaic Annual Global Ambient Population Time Series’
    --
    H/T @andrew Zimmer || Geographer | Research Scientist @ Oak Ridge National Laboratory
    “LSM-TS provides annual global estimates of ambient population from 1975–2024 at ~90 m resolution, creating a spatially and temporally consistent reconstruction of population change designed for longitudinal analysis. Reconstruction of historical populations are driven by current LandScan Mosaic building-level population distributions, scaled by built-surface change from #GHSL … and calibrated to annual national-level estimates from U.S. Census Bureau…”
    --
    “LandScan is a globally recognized, R&D100-winning population data platform developed at Oak Ridge National Laboratory (ORNL). Its datasets provide human population distribution estimates down to 100 meter grid resolution. LandScan supports disaster response, humanitarian aid, environmental analysis, and urban planning by providing insights into where people live and how they move…”
    --
    “Gridded population data support assessments of human exposure, settlement change, infrastructure demand, and access to services, yet global datasets combining annual coverage over multiple decades with fine spatial resolution remain limited. LandScan Mosaic Time Series provides 50 annual estimates of global ambient population distribution at 3 arc-second resolution from 1975 through 2024. The series is anchored to the 2024 LandScan Mosaic surface, produced through a building-level population modeling workflow. Historical surfaces are reconstructed using annualized changes in built surface and derived first-level administrative population trajectories. Every layer, including 2024, is normalized to administrative targets scaled to annual country totals from the U.S. Census Bureau International Database. The dataset is distributed as 50 single-band Cloud Optimized GeoTIFFs on an identical WGS84 grid, with values representing estimated persons per cell. Its common grid and methodology support fine-scale longitudinal analysis of ambient population distribution while documented validation and limitations guide appropriate reuse…”
    #demographics #change #spatiotemporal #1975 #2024 #population #change #grid #global #LandScan #POPGRID #LSMTS #ambientpopulation #GHSL #OakRidge #ORNL #usecase #opendata #disasterresponse #humanitarianaid #environmentalanalysis #urbanplanning #humanexposure #settlementchange #city #rural #infrastructure #demand #accesstoservices #GIS #spatial #mapping #building #outline #model #modeling
    @OAK Ridge National Laboratory | National Security Sciences at ORNL | @POPGRID Data Collaborative

    #ghsl #demographics #change #spatiotemporal #population #grid
  3. 💧🌏 Greg Cocks @[email protected] ·

    Annual High-Resolution Global Ambient Population Estimates From 1975 To 2024
    --
    doi.org/10.31223/X5MV3R <-- shared paper
    --
    landscan.ornl.gov/ <-- shared webmap and data link (via download -> LandScan Mosaic Time Series)
    --
    gee-community-catalog.org/proj <-- shared link to GEE community catalog collection ‘LandScan Mosaic Annual Global Ambient Population Time Series’
    --
    H/T @andrew Zimmer || Geographer | Research Scientist @ Oak Ridge National Laboratory
    “LSM-TS provides annual global estimates of ambient population from 1975–2024 at ~90 m resolution, creating a spatially and temporally consistent reconstruction of population change designed for longitudinal analysis. Reconstruction of historical populations are driven by current LandScan Mosaic building-level population distributions, scaled by built-surface change from #GHSL … and calibrated to annual national-level estimates from U.S. Census Bureau…”
    --
    “LandScan is a globally recognized, R&D100-winning population data platform developed at Oak Ridge National Laboratory (ORNL). Its datasets provide human population distribution estimates down to 100 meter grid resolution. LandScan supports disaster response, humanitarian aid, environmental analysis, and urban planning by providing insights into where people live and how they move…”
    --
    “Gridded population data support assessments of human exposure, settlement change, infrastructure demand, and access to services, yet global datasets combining annual coverage over multiple decades with fine spatial resolution remain limited. LandScan Mosaic Time Series provides 50 annual estimates of global ambient population distribution at 3 arc-second resolution from 1975 through 2024. The series is anchored to the 2024 LandScan Mosaic surface, produced through a building-level population modeling workflow. Historical surfaces are reconstructed using annualized changes in built surface and derived first-level administrative population trajectories. Every layer, including 2024, is normalized to administrative targets scaled to annual country totals from the U.S. Census Bureau International Database. The dataset is distributed as 50 single-band Cloud Optimized GeoTIFFs on an identical WGS84 grid, with values representing estimated persons per cell. Its common grid and methodology support fine-scale longitudinal analysis of ambient population distribution while documented validation and limitations guide appropriate reuse…”
    #demographics #change #spatiotemporal #1975 #2024 #population #change #grid #global #LandScan #POPGRID #LSMTS #ambientpopulation #GHSL #OakRidge #ORNL #usecase #opendata #disasterresponse #humanitarianaid #environmentalanalysis #urbanplanning #humanexposure #settlementchange #city #rural #infrastructure #demand #accesstoservices #GIS #spatial #mapping #building #outline #model #modeling
    @OAK Ridge National Laboratory | National Security Sciences at ORNL | @POPGRID Data Collaborative

    #ghsl #demographics #change #spatiotemporal #population #grid
  4. 💧🌏 Greg Cocks @[email protected] ·

    Annual High-Resolution Global Ambient Population Estimates From 1975 To 2024
    --
    doi.org/10.31223/X5MV3R <-- shared paper
    --
    landscan.ornl.gov/ <-- shared webmap and data link (via download -> LandScan Mosaic Time Series)
    --
    gee-community-catalog.org/proj <-- shared link to GEE community catalog collection ‘LandScan Mosaic Annual Global Ambient Population Time Series’
    --
    H/T @andrew Zimmer || Geographer | Research Scientist @ Oak Ridge National Laboratory
    “LSM-TS provides annual global estimates of ambient population from 1975–2024 at ~90 m resolution, creating a spatially and temporally consistent reconstruction of population change designed for longitudinal analysis. Reconstruction of historical populations are driven by current LandScan Mosaic building-level population distributions, scaled by built-surface change from #GHSL … and calibrated to annual national-level estimates from U.S. Census Bureau…”
    --
    “LandScan is a globally recognized, R&D100-winning population data platform developed at Oak Ridge National Laboratory (ORNL). Its datasets provide human population distribution estimates down to 100 meter grid resolution. LandScan supports disaster response, humanitarian aid, environmental analysis, and urban planning by providing insights into where people live and how they move…”
    --
    “Gridded population data support assessments of human exposure, settlement change, infrastructure demand, and access to services, yet global datasets combining annual coverage over multiple decades with fine spatial resolution remain limited. LandScan Mosaic Time Series provides 50 annual estimates of global ambient population distribution at 3 arc-second resolution from 1975 through 2024. The series is anchored to the 2024 LandScan Mosaic surface, produced through a building-level population modeling workflow. Historical surfaces are reconstructed using annualized changes in built surface and derived first-level administrative population trajectories. Every layer, including 2024, is normalized to administrative targets scaled to annual country totals from the U.S. Census Bureau International Database. The dataset is distributed as 50 single-band Cloud Optimized GeoTIFFs on an identical WGS84 grid, with values representing estimated persons per cell. Its common grid and methodology support fine-scale longitudinal analysis of ambient population distribution while documented validation and limitations guide appropriate reuse…”
    #demographics #change #spatiotemporal #1975 #2024 #population #change #grid #global #LandScan #POPGRID #LSMTS #ambientpopulation #GHSL #OakRidge #ORNL #usecase #opendata #disasterresponse #humanitarianaid #environmentalanalysis #urbanplanning #humanexposure #settlementchange #city #rural #infrastructure #demand #accesstoservices #GIS #spatial #mapping #building #outline #model #modeling
    @OAK Ridge National Laboratory | National Security Sciences at ORNL | @POPGRID Data Collaborative

    #modeling #model #outline #building #mapping #spatial
  5. 💧🌏 Greg Cocks @GregCocks ·

    Annual High-Resolution Global Ambient Population Estimates From 1975 To 2024
    --
    doi.org/10.31223/X5MV3R <-- shared paper
    --
    landscan.ornl.gov/ <-- shared webmap and data link (via download -> LandScan Mosaic Time Series)
    --
    gee-community-catalog.org/proj <-- shared link to GEE community catalog collection ‘LandScan Mosaic Annual Global Ambient Population Time Series’
    --
    H/T @andrew Zimmer || Geographer | Research Scientist @ Oak Ridge National Laboratory
    “LSM-TS provides annual global estimates of ambient population from 1975–2024 at ~90 m resolution, creating a spatially and temporally consistent reconstruction of population change designed for longitudinal analysis. Reconstruction of historical populations are driven by current LandScan Mosaic building-level population distributions, scaled by built-surface change from … and calibrated to annual national-level estimates from U.S. Census Bureau…”
    --
    “LandScan is a globally recognized, R&D100-winning population data platform developed at Oak Ridge National Laboratory (ORNL). Its datasets provide human population distribution estimates down to 100 meter grid resolution. LandScan supports disaster response, humanitarian aid, environmental analysis, and urban planning by providing insights into where people live and how they move…”
    --
    “Gridded population data support assessments of human exposure, settlement change, infrastructure demand, and access to services, yet global datasets combining annual coverage over multiple decades with fine spatial resolution remain limited. LandScan Mosaic Time Series provides 50 annual estimates of global ambient population distribution at 3 arc-second resolution from 1975 through 2024. The series is anchored to the 2024 LandScan Mosaic surface, produced through a building-level population modeling workflow. Historical surfaces are reconstructed using annualized changes in built surface and derived first-level administrative population trajectories. Every layer, including 2024, is normalized to administrative targets scaled to annual country totals from the U.S. Census Bureau International Database. The dataset is distributed as 50 single-band Cloud Optimized GeoTIFFs on an identical WGS84 grid, with values representing estimated persons per cell. Its common grid and methodology support fine-scale longitudinal analysis of ambient population distribution while documented validation and limitations guide appropriate reuse…”
    #1975 #2024
    @OAK Ridge National Laboratory | National Security Sciences at ORNL | @POPGRID Data Collaborative

    #ghsl #demographics #change #spatiotemporal #population #grid
  6. Psychology News Robot @[email protected] ·

    DATE: August 25, 2026 at 06:00AM
    SOURCE: PSYPOST.ORG

    ** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
    -------------------------------------------------

    TITLE: Individualism and income appear to shape relationship status across 59 countries

    URL: psypost.org/personal-demograph

    Whether a person remains single is linked to a combination of personal circumstances and the cultural values of the country they live in. A large study published in PLOS One found that personal traits like age and income relate to relationship status differently depending on how much a society values individualism and adaptability. These findings suggest that singlehood is shaped by broad cultural environments rather than just individual choices.

    Over the past few decades, the traditional family structure has shifted in many parts of the world. In the United States, Canada, and various European and Asian nations, the population of single adults has grown steadily. Researchers want to understand the broad societal forces driving this trend. Past sociological research has mostly focused on personal characteristics, such as education levels or economic stability.

    Psychologists Marta Kowal and Katarzyna Adamczyk, based at the University of Wrocław and Adam Mickiewicz University respectively, wanted to expand on this past research. They suspected that personal demographic characteristics might operate differently depending on the surrounding culture. The researchers decided to examine how national culture interacts with personal circumstances to predict relationship status.

    To measure culture, the researchers relied on a framework known as the Minkov-Hofstede model. This model divides national cultures into two main spectrums. The first spectrum is individualism versus collectivism. Individualistic cultures value personal freedom, independence, and self-reliance. Collectivistic cultures prioritize tradition, strict social rules, and group loyalty.

    The second cultural spectrum is flexibility versus monumentalism. Flexible cultures, often found in East Asia, value adaptability, humility, self-control, and long-term goals like acquiring a good education. Monumentalist cultures, which the model associates with regions like Latin America and the Arab world, value an unchanging and proud sense of self. Monumentalist societies encourage people to follow their impulses, seek immediate gratification, and remain invariant in their values.

    To conduct the study, Kowal and Adamczyk analyzed an existing dataset from the World Values Survey. The researchers utilized a large sample of 71,169 individuals across 59 countries. The data, collected between 2017 and 2021, included detailed interviews conducted at the participants’ homes.

    The researchers categorized participants based on their relationship status. People who were married or living together as married were classified as partnered. Those who were single and never married were classified as single. For the main analysis, the researchers excluded people who were divorced, widowed, or separated.

    The researchers then gathered personal details for each participant, including their age, sex, hometown size, education level, income, and employment status. Next, they assigned each participant a cultural score based on their country of residence. They used established national scores for both the individualism and flexibility spectrums.

    Finally, the researchers used a statistical technique that allowed them to look at data on multiple levels at once. This method let them evaluate how national cultural trends might alter the effects of personal demographics on a person’s relationship status. A secondary analysis that included divorced and widowed individuals as single yielded the exact same statistical patterns.

    The analysis revealed several consistent patterns across all countries. People were more likely to be single if they were younger, male, and living in larger towns. Higher education levels, lower income, and unemployment were also associated with a greater likelihood of being single.

    These demographic associations align with expectations from past sociological research. Younger people are still in the phase of searching for a partner. Educational expansion delays entry into the workforce, which often pushes back the timeline for marriage. A lack of economic stability, characterized by unemployment or low income, can discourage people from committing to serious relationships.

    The sex and location differences also matched established patterns. Women often face more social pressure to marry, which may make them less likely to remain single compared to men. Living in larger urban areas often correlates with a lower adherence to traditional family values, creating environments where singlehood is more socially accepted.

    National culture also played a direct role in predicting relationship status. People living in highly individualistic countries were more likely to be single. Individualism grants people the freedom to behave according to their own beliefs, making a single lifestyle an easier path to choose.

    People in less flexible, or more monumentalist, countries were also more likely to be single. Because monumentalist cultures encourage people to follow their immediate impulses and desires rather than prioritizing long-term goals, they may promote short-term dating over settling down into a long-term partnership.

    The study uncovered notable interactions between personal traits and cultural values. In more individualistic countries, the link between being single and having a lower income or being unemployed was much stronger. Individualistic societies expect young people to become self-reliant and secure their own financial resources before starting a family. If a person lacks those resources in an individualistic culture, they are highly likely to remain single.

    The connection between being male and being single was also stronger in individualistic nations. The researchers proposed that individualistic cultures emphasize independence. This cultural emphasis aligns with the traditional social expectation that men should be self-reliant, potentially making singlehood an expected path for men in those societies.

    In monumentalist countries, older age was more strongly associated with singlehood than it was in flexible countries. Monumentalist cultures encourage people to live authentically according to their personal desires rather than conforming to societal expectations. Since older adults tend to conform less to social pressures as they age, they may be more inclined to live independently in societies that celebrate personal authenticity.

    The researchers noted a few limitations to their work. The data used in the analysis was observational, meaning it cannot prove cause and effect. While lower income and unemployment predict a higher likelihood of singlehood, it is also possible that living alone leads to different financial outcomes. The relationship between these factors could go in both directions.

    The analysis also grouped all single and never-married people into one unified category. Single individuals are a highly diverse group with different dating histories and personal desires. Some people actively choose to remain single, while others are involuntarily single.

    Future research could expand on these findings by tracking people over time to observe how their relationship status evolves. Researchers could also look at other cultural dimensions to see how the freedom to choose new social groups affects the likelihood of finding a partner.

    The study, “Sociodemographic and cultural factors are related to singlehood rates: A multilevel analysis across 59 countries from the World Values Survey,” was authored by Marta Kowal and Katarzyna Adamczyk.

    URL: psypost.org/personal-demograph

    -------------------------------------------------

    Private, vetted email list for mental health professionals: clinicians-exchange.org

    Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot

    -------------------------------------------------

    #psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #Singlehood #RelationshipStatus #Individualism #CulturalFactors #WorldValuesSurvey #Sociology #Demographics #Urbanization #MonumentalistCulture #FlexibilityVsMonumentalism

    #psychology #counseling #socialwork #psychotherapy #mentalhealth #psychiatry
  7. 💧🌏 Greg Cocks @[email protected] ·

    Socio-Hydrology Modeling Captures How Inequalities Impact Community Flood Resilience
    --
    doi.org/10.1029/2025WR041393 <-- shared paper
    --
    americanprogress.org/article/h <-- shared technical/opinion article
    --
    carbonbrief.org/us-flooding-in <-- shared technical/opinion article
    --
    headwaterseconomics.org/natura <-- shared technical/opinion article
    --
    youtu.be/8jVRsD8wgMM?si=qBHchW <-- shared opinion video
    --
    fedcommunities.org/lower-incom <-- shared technical/opinion article
    --
    H/T @matthew Preisser | PhD Civil Engineering, Natural Hazard Modeler and Socio-Hydrologists
    “How can we better understand the dynamic feedbacks between the environment and society?
    Socio-hydrology models are often built around the assumption that cities act as homogeneous entities, without capturing the variable capacity of communities with different underlying socioeconomic characteristics to respond to and recover from disasters.
    In this study, [the authors] developed a disaggregated approach to model community-specific adaptive capacity, allowing [them] to examine how inequalities influence flood recovery and resilience. This framework provide[d] a basis for exploring hypotheses about the relationships between growth, inequality, and community resilience in the face of flood hazards.
    [Their] results highlighted] the importance of considering community-level dynamics when developing flood mitigation and disaster response strategies that balance economic growth with equity. Many challenges remain in applying socio-hydrology models to real-world settings, but this work takes a step toward incorporating more realistic representations of socioeconomic inequality into human–water systems while preserving the generality and flexibility that make conceptual models useful…”
    #risk #hazard #water #hydrology #flood #society #flooding #USA #SocioHydrology #naturalhazard #socioeconomic #population #demographics #infrastructure #damage #disaster #disaggregated #model #modeling #community #adaptiveresponse #growth #inequality #communityresilience #floodhazard #floodmitigation #disasterresponse #equity #city #town #rural #urban #economy #cost

    #risk #hazard #water #hydrology #flood #society
  8. Hacker News @[email protected] ·

    Half of Europe's towns and villages have fewer residents than 60 years ago

    correctiv.org/en/europe/2026/0

    Comments: news.ycombinator.com/item?id=4

    #HackerNews #Europe #Population #Decline #Rural #Areas #Demographics #Urbanization #Community #Challenges

    #hackernews #europe #population #decline #rural #areas
  9. TKSST • seethis.tv 🌈🪐✨ @[email protected] ·

    💁🏻‍♀️ TIL: A new paper links the 2007 #iPhone launch to declining #US birth rates, finding 4.5-8% drops among 15-19 year olds in areas with early iPhone access.

    The researchers suggest #smartphones may reduce in-person socializing while increasing access to #porn and birth control #information. Skeptics note that teen birth rates were already falling since the #1990s.

    👉 smithsonianmag.com/smart-news/

    #birthrate #demographics #teens #technology #research #economics #middlebury #fertility #screentime #science #nber #statistics

    #iphone #us #smartphones #porn #information #1990s
  10. 💧🌏 Greg Cocks @[email protected] ·

    Geospatial Analysis of Urban Population Model Discrepancies Through Land Use and the Built Environment - A Case Study of Croatia
    --
    doi.org/10.3390/geographies602 <-- shared paper
    --
    H/T Olga Bjelotomić Oršulić
    “Using official Croatian census data as a reference, [the researchers] analysed three global population datasets across seven Croatian cities to investigate how population allocation differs between built-up and non-built-up areas.
    Although the datasets often produced similar population totals, their spatial allocation differed substantially. GHS-POP concentrated over 1 million more inhabitants within built-up areas, while WorldPop allocated approximately 290,000 more inhabitants to non-built-up land-cover classes, demonstrating how similar population totals can mask substantial differences in spatial population patterns.
    The question is not only how many people are estimated, but also where the model places them…”
    #GIS #spatial #mapping #gridded #population #urban #dataanalysis #shrinkingcities #census #censusvalidation #WorldPop #GHSPOP #GPWv4 #builtupareas #sustainability #SDG #casestudy #Croatia #spatialanalysis #spatiotemporal #SustainableDevelopmentGoals #populationdecline #demographics #urbanplanning #planning #city #cities #PopulationData #RemoteSensing #UrbanAnalytics #OpenData #SDG #model #modeling #urbanisation #density #QGIS #landcover
    @MDPI

    #gis #spatial #mapping #gridded #population #urban
  11. 💧🌏 Greg Cocks @[email protected] ·

    Geospatial Analysis of Urban Population Model Discrepancies Through Land Use and the Built Environment - A Case Study of Croatia
    --
    doi.org/10.3390/geographies602 <-- shared paper
    --
    H/T Olga Bjelotomić Oršulić
    “Using official Croatian census data as a reference, [the researchers] analysed three global population datasets across seven Croatian cities to investigate how population allocation differs between built-up and non-built-up areas.
    Although the datasets often produced similar population totals, their spatial allocation differed substantially. GHS-POP concentrated over 1 million more inhabitants within built-up areas, while WorldPop allocated approximately 290,000 more inhabitants to non-built-up land-cover classes, demonstrating how similar population totals can mask substantial differences in spatial population patterns.
    The question is not only how many people are estimated, but also where the model places them…”
    #GIS #spatial #mapping #gridded #population #urban #dataanalysis #shrinkingcities #census #censusvalidation #WorldPop #GHSPOP #GPWv4 #builtupareas #sustainability #SDG #casestudy #Croatia #spatialanalysis #spatiotemporal #SustainableDevelopmentGoals #populationdecline #demographics #urbanplanning #planning #city #cities #PopulationData #RemoteSensing #UrbanAnalytics #OpenData #SDG #model #modeling #urbanisation #density #QGIS #landcover
    @MDPI

    #gis #spatial #mapping #gridded #population #urban
  12. 💧🌏 Greg Cocks @[email protected] ·

    Geospatial Analysis of Urban Population Model Discrepancies Through Land Use and the Built Environment - A Case Study of Croatia
    --
    doi.org/10.3390/geographies602 <-- shared paper
    --
    H/T Olga Bjelotomić Oršulić
    “Using official Croatian census data as a reference, [the researchers] analysed three global population datasets across seven Croatian cities to investigate how population allocation differs between built-up and non-built-up areas.
    Although the datasets often produced similar population totals, their spatial allocation differed substantially. GHS-POP concentrated over 1 million more inhabitants within built-up areas, while WorldPop allocated approximately 290,000 more inhabitants to non-built-up land-cover classes, demonstrating how similar population totals can mask substantial differences in spatial population patterns.
    The question is not only how many people are estimated, but also where the model places them…”
    #GIS #spatial #mapping #gridded #population #urban #dataanalysis #shrinkingcities #census #censusvalidation #WorldPop #GHSPOP #GPWv4 #builtupareas #sustainability #SDG #casestudy #Croatia #spatialanalysis #spatiotemporal #SustainableDevelopmentGoals #populationdecline #demographics #urbanplanning #planning #city #cities #PopulationData #RemoteSensing #UrbanAnalytics #OpenData #SDG #model #modeling #urbanisation #density #QGIS #landcover
    @MDPI

    #gis #spatial #mapping #gridded #population #urban
  13. 💧🌏 Greg Cocks @[email protected] ·

    Geospatial Analysis of Urban Population Model Discrepancies Through Land Use and the Built Environment - A Case Study of Croatia
    --
    doi.org/10.3390/geographies602 <-- shared paper
    --
    H/T Olga Bjelotomić Oršulić
    “Using official Croatian census data as a reference, [the researchers] analysed three global population datasets across seven Croatian cities to investigate how population allocation differs between built-up and non-built-up areas.
    Although the datasets often produced similar population totals, their spatial allocation differed substantially. GHS-POP concentrated over 1 million more inhabitants within built-up areas, while WorldPop allocated approximately 290,000 more inhabitants to non-built-up land-cover classes, demonstrating how similar population totals can mask substantial differences in spatial population patterns.
    The question is not only how many people are estimated, but also where the model places them…”
    #GIS #spatial #mapping #gridded #population #urban #dataanalysis #shrinkingcities #census #censusvalidation #WorldPop #GHSPOP #GPWv4 #builtupareas #sustainability #SDG #casestudy #Croatia #spatialanalysis #spatiotemporal #SustainableDevelopmentGoals #populationdecline #demographics #urbanplanning #planning #city #cities #PopulationData #RemoteSensing #UrbanAnalytics #OpenData #SDG #model #modeling #urbanisation #density #QGIS #landcover
    @MDPI

    #landcover #qgis #density #urbanisation #modeling #model
  14. 💧🌏 Greg Cocks @GregCocks ·

    Geospatial Analysis of Urban Population Model Discrepancies Through Land Use and the Built Environment - A Case Study of Croatia
    --
    doi.org/10.3390/geographies602 <-- shared paper
    --
    H/T Olga Bjelotomić Oršulić
    “Using official Croatian census data as a reference, [the researchers] analysed three global population datasets across seven Croatian cities to investigate how population allocation differs between built-up and non-built-up areas.
    Although the datasets often produced similar population totals, their spatial allocation differed substantially. GHS-POP concentrated over 1 million more inhabitants within built-up areas, while WorldPop allocated approximately 290,000 more inhabitants to non-built-up land-cover classes, demonstrating how similar population totals can mask substantial differences in spatial population patterns.
    The question is not only how many people are estimated, but also where the model places them…”

    @MDPI

    #gis #spatial #mapping #gridded #population #urban
  15. 💧🌏 Greg Cocks @[email protected] ·

    Half Of European Municipalities Have Fewer Inhabitants Than 60 Years Ago
    --
    correctiv.org/aktuelles/2026/0 <-- shared technical / spatiotemporal article
    --
    data.jrc.ec.europa.eu/dataset/ <-- shared European Joint Research Centre (JRC) ARDECO Local Population Time-Series – 1961-2024
    --
    #GIS #spatial #mapping #emptyspain #demographics #population #agingpopulation #declining #birthrate #immigration #emigration #shrinking #interactive #webmap #spatialanalysis #spatiotemporal #Europe #geostatistics #logistics #rural #urban #city #transportation #cost #economics #services #infrastructure #workers #qualityoflife #planning
    @JointResearchCentre #JRC @europeancommission @correctiv_org

    #gis #spatial #mapping #emptyspain #demographics #population
  16. 💧🌏 Greg Cocks @[email protected] ·

    Hanging Glaciers In Himalaya Reveal Rising Avalanche Risk
    --
    nature.com/articles/d44151-026 <-- shared technical article
    --
    doi.org/10.1038/s44304-026-002 <-- shared paper
    --
    #GIS #spatial #mapping #risk #hazard #monitoring #engineeringgeology #naturalhazard #Himalaya #Himalayas #Alaknanda #basin #Garhwal #India #ice #cryosphere #massmovement #avalanche #glacier #hangingglacier #cryosphere #remotesensing #spatialanalysis #spatiotemporal #sentinel2 #DEM #elevation #model #modeling #GlabTop2 #unstable #hanging #BadrinathMana #impact #infrastructure #damage #HEP #urbanisation #development #population #publicsafety #demographics #glacialretreat #melting #Chamoli #disaster #earlywarningsystems #Himalayan #glaciers #instability #warming #climatechange #riskassessment #riskclassification #framework #avaflow #runout #downstream #downslope #water #hydrology #planning #policy #mitigation #geomorphology #geomorphometry

    #gis #spatial #mapping #risk #hazard #monitoring
  17. 💧🌏 Greg Cocks @[email protected] ·

    Influence Of Modeling Assumptions On Pedestrian Evacuation Success For Non-Eruptive Lahar Hazards At Mount Rainier, Washington
    --
    doi.org/10.1016/j.ijdrr.2026.1 <-- shared paper
    --
    sciencebase.gov/catalog/item/6 <-- shared, related open data source
    --
    [I still remember working on and being fascinated by lahars being an engineering geologist in Washington State (and from my time studying in New Zealand), although (of course) not to this level of detail/focus]
    #volcano #lahar #evacuation #exposure #model #modeling #engineeringeology #risk #hazard #naturalhazard #MountRainer #Washington #USA #spatialanalysis #spatiotemporal #emergencymanagement #GIS #spatial #mapping #publicsafety #hazardzone #vulcanism #downstream #debrisflow #massmovement #monitoring #detection #geostatistics #demographics #atrisk #fedscience #publicgood #fedservice #opendata
    @USGS

    #volcano #lahar #evacuation #exposure #model #modeling
  18. 💧🌏 Greg Cocks @[email protected] ·

    What Shapes Earthquake Risk In Bangladesh? Geospatial Insights From Physical And Social Factors Using A Spatial Meta-Regression Approach
    --
    doi.org/10.1007/s41748-026-010 <-- shared paper
    --
    #EarthquakeRisk #GIS #Bangladesh #DisasterManagement #SEAsia #GIS #spatial #mapping #risk #hazard #earthquake #naturalhazard #urban #rural #infrastructure #publicsafety #model #modeling #spatialanalysis #spatiotemporal #geomorphology #geology #tectonics #social #cultural #demographics #fault #faulting #factoranalysis #seismichazard #model #modeling #socioeconomics #geophysics #remotesensing #geostatistics #landuse #framework #magnitude #engineering #buildingcode #mitigation #zoning #development #growth #vulnerability

    #earthquakerisk #gis #bangladesh #disastermanagement #seasia #spatial
  19. 💧🌏 Greg Cocks @[email protected] ·

    Growing Meltwater Reservoirs – Glacial Lakes Are Both A Resource And A Habitat Worthy Of Protection
    --
    uni-potsdam.de/en/headlines-an <-- shared technical article
    --
    doi.org/10.1038/s44221-025-005 <-- shared paper
    --
    github.com/geveh/LakeVolumes <-- shared GitHub ‘code base’
    --
    #glaciallakes #GLOF #geomorphology #glaciers #hydrology #waterresources #glacier #melting #retreating #hydrogeomorphology #GIS #spatial #mapping #global #spatialanalysis #global #cryosphere #ice #water #hydrology #mountains #highaltitude #naturalresource #freshwater #reservoir #sediment #sedimentation #spatiotemporal #remotesensing #earthobservation #landcover #Arctic #coastal #meltwater #lake #longevity #watersecurity #risk #hazard #ecosystem #habitat #naturalhazard #planning #tourism #economy #usecase #watersupply #worldwide #population #demographics #glacial
    @University of Potsdam | @University Of Leeds

    #glaciallakes #glof #geomorphology #glaciers #hydrology #waterresources
  20. (Roughly) Daily @[email protected] ·

    “Whoever oppresses the poor shows contempt for their Maker, but whoever is kind to the needy honors God”*…

    From the piece featured below: “GDP per capita in Madagascar is about the same today as it was in 1950. As a consequence, the number of people in extreme poverty increased in line with the country’s population growth” (image source)

    It’s easy to feel hope in the advances that the world has made in eraditcating extreme poverty over the last several decades. But as Max Roser writes, unless the poorest economies start growing, this period of progress against the worst form of poverty is over…

    In the last decades, the world has made fantastic progress against extreme poverty. In 1990, 2.3 billion people lived in extreme poverty. Since then, the number of extremely poor people has declined by 1.5 billion people.

    This means on any average day in the last 35 years, about 115,000 people left extreme poverty behind.1 Leaving the very worst poverty behind doesn’t mean a life free of want, but it does mean a big change. Additional income matters most for those who have the least. It means having the chance to leave hunger behind, to gain access to clean water, to access better healthcare, and to have at least some electricity — for light at night and perhaps even to cook and heat.

    Can we expect this rapid progress to continue?

    Unfortunately, we cannot. Based on current trends, progress against extreme poverty will come to a halt. As we’ll see, the number of people in extreme poverty is projected to decline, from 831 million people in 2025 to 793 million people in 2030. After 2030, the number of extremely poor people is expected to increase.

    To understand why the rapid progress against deep poverty will not continue into the future, we need to know why the world made progress in the past.

    Extreme poverty declined in the last three decades because, back in the 1990s, the majority of the poorest people on the planet lived in countries that subsequently achieved very fast economic growth. In Indonesia and China, more than two-thirds of the population lived in extreme poverty. But these economies then grew rapidly, so that by today, the share has declined to less than 10%. Other large Asian countries — including India, Pakistan, Bangladesh, and the Philippines — also achieved strong growth, and as a consequence, the share living in extreme poverty declined rapidly. Much of the progress happened in Asia, but conditions in other regions improved too: the share living in extreme poverty also declined in Ghana, Cape Verde, Cameroon, Panama, Bolivia, Mexico, Brazil, and many other countries.

    This chart shows the economic change in these countries over the past decades. As incomes increased, the share of people in extreme poverty declined.

    Share of population living in extreme poverty vs. GDP per capita, 1990 to 2024 (World Bank, Eurostat, OECD, IMF)

    What is different today is that the majority of the world’s poorest people are stuck in economies that have been stagnating for a long time.Consider the case of Madagascar. In the long run, the country has not seen any growth at all: GDP per capita in Madagascar is about the same today as it was in 1950. As a consequence, the number of people in extreme poverty increased in line with the country’s population growth. In richer countries, it is possible to reduce poverty by reducing inequality through redistribution, but a country like Madagascar cannot reduce its share of people in extreme poverty through redistribution. This is because the mean income is lower than the poverty line; if everyone had the same income, everyone would be living in extreme poverty.

    The situation is similar in other countries, as the chart below shows: in the Democratic Republic of Congo, Mozambique, Malawi, Burundi, and the Central African Republic, more than half of the population lives in extreme poverty. As their economies have stagnated, the deep poverty that most people live in has remained largely unchanged for decades.

    This is why we have to expect the end of progress against extreme poverty based on current trends. If the poorest economies remain stagnant, hundreds of millions of people will continue to live in extreme poverty.

    Share of population living in extreme poverty, 1992-2022 (World Bank)

    I’m always skeptical when people say that we are at a juncture in history where the future looks much different than the past. But when it comes to the fight against extreme poverty, I fear it is true. Today, the majority of the world’s poorest people are living in economies that have not achieved economic growth in the recent past… Based on current trends, we have to expect the end of progress against extreme poverty…

    … It’s no news that we should expect an end to progress against extreme poverty. This article is an update of an article I published in 2019, in which I wrote the same: the fact that the poorest economies are not growing means that the rapid progress against extreme poverty seen in the last decades will end.

    Although this prospect has been known for years, it has hardly received the attention it deserves. Progress against extreme poverty was one of humanity’s most outstanding achievements of the past decades — the end of it would be one of the very worst realities of the coming ones.

    Importantly, however, these projections are not predictions; their purpose is not to describe what the world in 2030 or 2040 will certainly look like. These projections describe what we have to expect based on current trends; they tell us about our present world rather than the reality of tomorrow. Current trends don’t have to become future facts: many countries left extreme poverty behind in the past, because they had a moment at which they broke out of stagnation.

    What these projections tell us, however, is that if the poorest countries do not start to grow, a very bleak future is ahead of us: a future in which extreme poverty remains the reality for hundreds of millions for many years to come…

    Eminently worth reading in full– and acting on: “The end of progress against extreme poverty?” from @maxroser.bsky.social and @ourworldindata.org.

    * Proverbs 14:31, NIV

    ###

    As we put our shoulders to the wheel, we might spare a thought for a man who contributed mightily to our capacity to feed humanity, Kenneth V. Thimann; he died on this date in 1997. A microbiologist, he was a pioneer in plant physiology (especially the hormones that control the development of plants). Building on the thinking of Frits Went, he identified the first plant hormone to be discovered– the first auxin, a class of growth hormones, and revealed its chemical structure– which proved very important to agriculture and its yields.

    source

    #agriculture #auxin #culture #demographics #growthHormones #history #KennethThimann #KennethVThimann #microbiology #OurWorldInData #plantPhysiology #plants #politics #poverty
    #agriculture #auxin #culture #demographics #growthhormones #history
  21. The-14 Media @[email protected] ·

    Quebec since 1995: decline or progress?
    #Quebec #Canada #Economy #FiscalPolicy #Poverty #PublicFinance #Demographics #SocialEquality #Taxation #Healthcare #Progress #Inequality #Housing #Sustainability #QuebecReferendum #EconomicGrowth #PublicPolicy
    the-14.com/quebec-since-1995-d

    #quebec #canada #economy #fiscalpolicy #poverty #publicfinance
  22. Nando161 @[email protected] ·

    "people who have mever met a #hillbilly in their #life love to pin every bit of american #fascism and #imperialism on #hillbillies. who, it should be noted, are one of the #poorest #demographics in this #country. instead of the fucking #facsists. who are overwhelmingly #billionaires."

    tumblr.com/nando161mando/79128

    #hillbilly #life #fascism #imperialism #hillbillies #poorest
  23. 💧🌏 Greg Cocks @[email protected] ·

    Palaeodemographic Modelling Supports A Population Bottleneck During The Pleistocene-Holocene Transition In Iberia
    --
    doi.org/10.1038/s41467-019-098 <-- shared paper
    --
    #GIS #spatial #mapping #demographics #palaeodemographic #palaeodemography #demography #spain #iberia #prehistoric #huntergatherers #glacial #interglacial #model #modeling #spatialanalysis #spatiotemporal #population #growth #shrinkage #climatechange #holocene #genetics #statistics #geostatistics #pleistocene #archaeology #ancienthistory #humanoids

    #gis #spatial #mapping #demographics #palaeodemographic #palaeodemography
  24. [email protected]❌👑 @[email protected] ·

    Use Demographic Geotargeting to Find Voters
    thedemlabs.org/2024/01/17/demo
    #geotargeting #demographics #mapping

    #geotargeting #demographics #mapping
  25. 💧🌏 Greg Cocks @[email protected] ·

    Interactive Map - The World As 1,000 People
    --
    visualcapitalist.com/all-the-p <-- shared technical article
    --
    “The world’s population has doubled in size over the last 50 years.
    In 2022, we reached the mark of 8 billion living on Earth. According to UN estimates, by July 2023, all the people in the world numbered 8,045,311,447.
    In this interactive map, [the authors] use population estimates from the United Nations Population Division to illustrate the world’s population as if the Earth had only 1,000 people..."
    #GIS #spatial #mapping #cartogram #world #global #population #infographic #representation #decline #populationhealth #populationgrowth #demographics #countries #Africa #Europe #China #India #USA #Nigeria #northamerica #southamerica #southasia #Asia

    #gis #spatial #mapping #cartogram #world #global
  26. 💧🌏 Greg Cocks @[email protected] ·

    Global Study Warns Water Security Threatened By Droughts And Heat Waves Worldwide
    --
    phys.org/news/2023-10-global-t <-- shared technical article
    --
    dx.doi.org/10.1088/1748-9326/a <-- shared paper
    --
    #GIS #spatial #mapping #model #hydrology #waterresources #watersecurity #global #usecase #users #extraction #drought #heatwave #compoundevents #climatechange #extrememweather #population #demographics #wateruse #sector #model #modeling #agriculture #farming #domestic #urban #irrigation #industrial #electricty #thermoelectric #energy #manufacturing #livestock #history #change #extremeevents #region #regional #spatialdata #humanimpacts

    #gis #spatial #mapping #model #hydrology #waterresources