#demographics — Public Fediverse posts on home.social

PPC Land @[email protected] · 2026-09-14 · 07:19 UTC

ICYMI: Explaining millennials: Millennials are the demographic cohort born 1981 to 1996, aged 30 to 45 in 2026. How a label built to mean young consumers now describes middle-aged buyers. https://ppc.land/millennials/ #Millennials #Demographics #GenY #ConsumerBehavior #Marketing

#millennials #demographics #geny #consumerbehavior #marketing

PPC Land @[email protected] · 2026-09-14 · 07:19 UTC

ICYMI: Explaining millennials: Millennials are the demographic cohort born 1981 to 1996, aged 30 to 45 in 2026. How a label built to mean young consumers now describes middle-aged buyers. https://ppc.land/millennials/ #Millennials #Demographics #GenY #ConsumerBehavior #Marketing

#marketing #consumerbehavior #geny #demographics #millennials

PPC Land @[email protected] · 2026-09-14 · 07:19 UTC

ICYMI: Explaining millennials: Millennials are the demographic cohort born 1981 to 1996, aged 30 to 45 in 2026. How a label built to mean young consumers now describes middle-aged buyers. https://ppc.land/millennials/ #Millennials #Demographics #GenY #ConsumerBehavior #Marketing

#millennials #demographics #geny #consumerbehavior #marketing

💧🌏 Greg Cocks @[email protected] · 2026-09-01 · 02:30 UTC

Annual High-Resolution Global Ambient Population Estimates From 1975 To 2024
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https://doi.org/10.31223/X5MV3R <-- shared paper
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https://landscan.ornl.gov/ <-- shared webmap and data link (via download -> LandScan Mosaic Time Series)
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https://gee-community-catalog.org/projects/landcast/ <-- shared link to GEE community catalog collection ‘LandScan Mosaic Annual Global Ambient Population Time Series’
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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…”
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“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…”
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“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

novaTopFlex @[email protected] · 2026-06-05 · 15:41 UTC

Hype for the Future 214A: Chicago Communities

Introduction Chicago is a notable major city representative of the Midwestern region of the United States along the southwestern shore of Lake Michigan at the nearest point of the Great Lakes to the Mississippi River watershed in the northeastern portion of the State of Illinois. While the city is considered the third-largest in the United States (after New York and Los Angeles), Cook County, where the city is primarily located, is the second-largest in terms of population (after only Los […]

https://novatopflex.wordpress.com/2026/06/05/hype-for-the-future-214a-chicago-communities/

#activism #chicago #chicagoarea #chicagoland #chinatown #cityofchicago

💧🌏 Greg Cocks @[email protected] · 2026-05-12 · 04:28 UTC

Half Of European Municipalities Have Fewer Inhabitants Than 60 Years Ago
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https://correctiv.org/aktuelles/2026/04/21/die-haelfte-der-europaeischen-gemeinden-hat-weniger-einwohner-als-vor-60-jahren/ <-- shared technical / spatiotemporal article
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https://data.jrc.ec.europa.eu/dataset/37fcacbf-12e2-4b31-b1af-83117a74b2c7 <-- shared European Joint Research Centre (JRC) ARDECO Local Population Time-Series – 1961-2024
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#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