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#geostatistics — Public Fediverse posts

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

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

    Researchers Have Analyzed The Roman Empire’s 190,000-Mile Network Of Roads. Turns Out, Not All Of Them Led To Rome
    (for a new study, archaeologists attempted to answer age-old questions about Rome’s roads, including how connected the capital was and whether current motorways trace Roman routes)
    --
    smithsonianmag.com/smart-news/ <-- shared technical article
    --
    doi.org/10.1038/s41467-026-754 <-- shared paper
    --
    newscientist.com/article/25893 <-- shared technical article
    --
    thetimes.com/uk/science/articl <-- shared media article
    --
    doi.org/10.5281/zenodo.17122148 <-- shared (Zendo) open data access, ‘A high-resolution dataset of roads of the Roman Empire: Itiner-e’ static version 2024 (#GPKG, #GeoJSON and SHP)
    --
    itiner-e.org/ <-- shared itiner-e.org Roman Roads web map
    --
    itiner-e.org/about <-- shared itiner-e.org overview and description of data downloads, etc
    --
    youtu.be/OTSe7MsJXbo?si=T7shjF <-- shared video overview, Travel the Roads of the Roman Empire on itiner-e.org
    --
    “… “All roads lead to Rome.” Everyone knows the adage. But according to new research [link above]…, the ancient Roman Empire’s sprawling highways weren’t as connected to the Eternal City, or quite as sophisticated, as that saying and other commonly held notions about them suggest.
    “All roads lead to Rome? They are all straight? We still walk Roman roads in the region today? These are widely held assumptions, even among academics,”[said one of the authors.]
    Such claims have prevented historians from interrogating the Roman road system [they also said, 3rd link above] “We just assume that Rome was at the center of a spider’s web of roads, and that’s simply not true.”
    Last year, [they] created a digital, publicly accessible map of the Roman Empire’s nearly 190,000 miles of roads in 150 C.E. For their recent study, the researchers analyzed topographical data and historical context to make judgments about that network…”
    #history #ancienthistory #Rome #roads #romanroads #mapping #spatialanalysis #webmap #opendata #Itiner-e #RomanEmpire #infrastructure #archaeology #connections #network #routing #highways #topography #elevation #grade #settlements #community #culture #cities #towns #commerce #trade #spatial #GIS #mapping #mobility #empire #military #modernday #roads #equivalency #slope #sinuosity #density #kernaldensity #statistics #geostatistics #regression #centrality #ports #riverine #maritime #trade #shipping

    #geojson #history #ancienthistory #rome #roads #romanroads
  2. 💧🌏 Greg Cocks @[email protected] ·

    Researchers Have Analyzed The Roman Empire’s 190,000-Mile Network Of Roads. Turns Out, Not All Of Them Led To Rome
    (for a new study, archaeologists attempted to answer age-old questions about Rome’s roads, including how connected the capital was and whether current motorways trace Roman routes)
    --
    smithsonianmag.com/smart-news/ <-- shared technical article
    --
    doi.org/10.1038/s41467-026-754 <-- shared paper
    --
    newscientist.com/article/25893 <-- shared technical article
    --
    thetimes.com/uk/science/articl <-- shared media article
    --
    doi.org/10.5281/zenodo.17122148 <-- shared (Zendo) open data access, ‘A high-resolution dataset of roads of the Roman Empire: Itiner-e’ static version 2024 (#GPKG, #GeoJSON and SHP)
    --
    itiner-e.org/ <-- shared itiner-e.org Roman Roads web map
    --
    itiner-e.org/about <-- shared itiner-e.org overview and description of data downloads, etc
    --
    youtu.be/OTSe7MsJXbo?si=T7shjF <-- shared video overview, Travel the Roads of the Roman Empire on itiner-e.org
    --
    “… “All roads lead to Rome.” Everyone knows the adage. But according to new research [link above]…, the ancient Roman Empire’s sprawling highways weren’t as connected to the Eternal City, or quite as sophisticated, as that saying and other commonly held notions about them suggest.
    “All roads lead to Rome? They are all straight? We still walk Roman roads in the region today? These are widely held assumptions, even among academics,”[said one of the authors.]
    Such claims have prevented historians from interrogating the Roman road system [they also said, 3rd link above] “We just assume that Rome was at the center of a spider’s web of roads, and that’s simply not true.”
    Last year, [they] created a digital, publicly accessible map of the Roman Empire’s nearly 190,000 miles of roads in 150 C.E. For their recent study, the researchers analyzed topographical data and historical context to make judgments about that network…”
    #history #ancienthistory #Rome #roads #romanroads #mapping #spatialanalysis #webmap #opendata #Itiner-e #RomanEmpire #infrastructure #archaeology #connections #network #routing #highways #topography #elevation #grade #settlements #community #culture #cities #towns #commerce #trade #spatial #GIS #mapping #mobility #empire #military #modernday #roads #equivalency #slope #sinuosity #density #kernaldensity #statistics #geostatistics #regression #centrality #ports #riverine #maritime #trade #shipping

    #geojson #history #ancienthistory #rome #roads #romanroads
  3. 💧🌏 Greg Cocks @[email protected] ·

    Researchers Have Analyzed The Roman Empire’s 190,000-Mile Network Of Roads. Turns Out, Not All Of Them Led To Rome
    (for a new study, archaeologists attempted to answer age-old questions about Rome’s roads, including how connected the capital was and whether current motorways trace Roman routes)
    --
    smithsonianmag.com/smart-news/ <-- shared technical article
    --
    doi.org/10.1038/s41467-026-754 <-- shared paper
    --
    newscientist.com/article/25893 <-- shared technical article
    --
    thetimes.com/uk/science/articl <-- shared media article
    --
    doi.org/10.5281/zenodo.17122148 <-- shared (Zendo) open data access, ‘A high-resolution dataset of roads of the Roman Empire: Itiner-e’ static version 2024 (#GPKG, #GeoJSON and SHP)
    --
    itiner-e.org/ <-- shared itiner-e.org Roman Roads web map
    --
    itiner-e.org/about <-- shared itiner-e.org overview and description of data downloads, etc
    --
    youtu.be/OTSe7MsJXbo?si=T7shjF <-- shared video overview, Travel the Roads of the Roman Empire on itiner-e.org
    --
    “… “All roads lead to Rome.” Everyone knows the adage. But according to new research [link above]…, the ancient Roman Empire’s sprawling highways weren’t as connected to the Eternal City, or quite as sophisticated, as that saying and other commonly held notions about them suggest.
    “All roads lead to Rome? They are all straight? We still walk Roman roads in the region today? These are widely held assumptions, even among academics,”[said one of the authors.]
    Such claims have prevented historians from interrogating the Roman road system [they also said, 3rd link above] “We just assume that Rome was at the center of a spider’s web of roads, and that’s simply not true.”
    Last year, [they] created a digital, publicly accessible map of the Roman Empire’s nearly 190,000 miles of roads in 150 C.E. For their recent study, the researchers analyzed topographical data and historical context to make judgments about that network…”
    #history #ancienthistory #Rome #roads #romanroads #mapping #spatialanalysis #webmap #opendata #Itiner-e #RomanEmpire #infrastructure #archaeology #connections #network #routing #highways #topography #elevation #grade #settlements #community #culture #cities #towns #commerce #trade #spatial #GIS #mapping #mobility #empire #military #modernday #roads #equivalency #slope #sinuosity #density #kernaldensity #statistics #geostatistics #regression #centrality #ports #riverine #maritime #trade #shipping

    #shipping #maritime #riverine #ports #centrality #regression
  4. 💧🌏 Greg Cocks @GregCocks ·

    Researchers Have Analyzed The Roman Empire’s 190,000-Mile Network Of Roads. Turns Out, Not All Of Them Led To Rome
    (for a new study, archaeologists attempted to answer age-old questions about Rome’s roads, including how connected the capital was and whether current motorways trace Roman routes)
    --
    smithsonianmag.com/smart-news/ <-- shared technical article
    --
    doi.org/10.1038/s41467-026-754 <-- shared paper
    --
    newscientist.com/article/25893 <-- shared technical article
    --
    thetimes.com/uk/science/articl <-- shared media article
    --
    doi.org/10.5281/zenodo.17122148 <-- shared (Zendo) open data access, ‘A high-resolution dataset of roads of the Roman Empire: Itiner-e’ static version 2024 (#GPKG, and SHP)
    --
    itiner-e.org/ <-- shared itiner-e.org Roman Roads web map
    --
    itiner-e.org/about <-- shared itiner-e.org overview and description of data downloads, etc
    --
    youtu.be/OTSe7MsJXbo?si=T7shjF <-- shared video overview, Travel the Roads of the Roman Empire on itiner-e.org
    --
    “… “All roads lead to Rome.” Everyone knows the adage. But according to new research [link above]…, the ancient Roman Empire’s sprawling highways weren’t as connected to the Eternal City, or quite as sophisticated, as that saying and other commonly held notions about them suggest.
    “All roads lead to Rome? They are all straight? We still walk Roman roads in the region today? These are widely held assumptions, even among academics,”[said one of the authors.]
    Such claims have prevented historians from interrogating the Roman road system [they also said, 3rd link above] “We just assume that Rome was at the center of a spider’s web of roads, and that’s simply not true.”
    Last year, [they] created a digital, publicly accessible map of the Roman Empire’s nearly 190,000 miles of roads in 150 C.E. For their recent study, the researchers analyzed topographical data and historical context to make judgments about that network…”
    -e

    #geojson #history #ancienthistory #rome #roads #romanroads
  5. 💧🌏 Greg Cocks @[email protected] ·

    Mapping Flood Agents In The Northern Pantanal Wetland Using Multiple Spatio-Temporal GIS And Remote Sensing Techniques
    --
    doi.org/10.1007/s13157-026-020 <-- shared paper
    --
    H/T @renato Gatto de Morais | Geógrafo | Mestre em Recursos Hídricos | Doutorando em Geografia
    “Combining MODIS data, TRMM, river discharge, a digital terrain model, and multinomial logistic regression, [the authors] identified three predominant classes of flood origin: local rainfall, bank overflow, and mixed areas. The model achieved a Nagelkerke Pseudo-R² of 0.63 and a classification accuracy of up to 81.2% (10-fold cross-validation). To the best of [their] knowledge, this is the first explicit approach to mapping flood agents for this wetland, a component that has historically remained unmapped despite its recognized influence on habitats and biodiversity…”
    --
    “The mapping of functional hydrologic units is crucial for enhancing our understanding of flooding and hydroecological processes in large wetlands. These units are typically defined by flooding frequency, duration, and magnitude, but another important hydrologic characteristic is the origin of flooding, known as the flood agent. This study presents an empirical framework utilizing remote sensing and GIS procedures for modeling flood agents in the northern Pantanal wetland. Eleven spatial data layers were derived from multi-year MODIS flood maps, daily rainfall estimates from the TRMM 3B42 grid, in situ discharge data, the BEST Digital Terrain Model, and a hydrographic network layer. These layers were tested for their predictive power in a multinomial logistic regression mapping model. Model performance metrics, along with qualitative validation of mapping outcomes using in situ flooding measurements, and vegetation and soil data from field test sites, support the plausibility of the proposed mapping scheme. However, they also highlight the challenges of flood agent mapping in large tropical wetlands…”
    #GIS #spatial #mapping #MODIS #TRMM #riverdischarge #digitalterrainmodel #multinomiallogisticregression #geostatistics #Pantanal #Cuiaba #Brazil #water #hydrology #spatialanalysis #spatiotemporal #remotesensing #earthobservation #flood #flooding #source #type #floodagent #tropical #wetland #habitat #biodiversity #ecosystem #hydrologicunit #hydroecology #model #modeling #rainfall #precipitation #weather #climate #discharge #network #metrics

    #gis #spatial #mapping #modis #trmm #riverdischarge
  6. 💧🌏 Greg Cocks @[email protected] ·

    Mapping Flood Agents In The Northern Pantanal Wetland Using Multiple Spatio-Temporal GIS And Remote Sensing Techniques
    --
    doi.org/10.1007/s13157-026-020 <-- shared paper
    --
    H/T @renato Gatto de Morais | Geógrafo | Mestre em Recursos Hídricos | Doutorando em Geografia
    “Combining MODIS data, TRMM, river discharge, a digital terrain model, and multinomial logistic regression, [the authors] identified three predominant classes of flood origin: local rainfall, bank overflow, and mixed areas. The model achieved a Nagelkerke Pseudo-R² of 0.63 and a classification accuracy of up to 81.2% (10-fold cross-validation). To the best of [their] knowledge, this is the first explicit approach to mapping flood agents for this wetland, a component that has historically remained unmapped despite its recognized influence on habitats and biodiversity…”
    --
    “The mapping of functional hydrologic units is crucial for enhancing our understanding of flooding and hydroecological processes in large wetlands. These units are typically defined by flooding frequency, duration, and magnitude, but another important hydrologic characteristic is the origin of flooding, known as the flood agent. This study presents an empirical framework utilizing remote sensing and GIS procedures for modeling flood agents in the northern Pantanal wetland. Eleven spatial data layers were derived from multi-year MODIS flood maps, daily rainfall estimates from the TRMM 3B42 grid, in situ discharge data, the BEST Digital Terrain Model, and a hydrographic network layer. These layers were tested for their predictive power in a multinomial logistic regression mapping model. Model performance metrics, along with qualitative validation of mapping outcomes using in situ flooding measurements, and vegetation and soil data from field test sites, support the plausibility of the proposed mapping scheme. However, they also highlight the challenges of flood agent mapping in large tropical wetlands…”
    #GIS #spatial #mapping #MODIS #TRMM #riverdischarge #digitalterrainmodel #multinomiallogisticregression #geostatistics #Pantanal #Cuiaba #Brazil #water #hydrology #spatialanalysis #spatiotemporal #remotesensing #earthobservation #flood #flooding #source #type #floodagent #tropical #wetland #habitat #biodiversity #ecosystem #hydrologicunit #hydroecology #model #modeling #rainfall #precipitation #weather #climate #discharge #network #metrics

    #gis #spatial #mapping #modis #trmm #riverdischarge
  7. 💧🌏 Greg Cocks @[email protected] ·

    Mapping Flood Agents In The Northern Pantanal Wetland Using Multiple Spatio-Temporal GIS And Remote Sensing Techniques
    --
    doi.org/10.1007/s13157-026-020 <-- shared paper
    --
    H/T @renato Gatto de Morais | Geógrafo | Mestre em Recursos Hídricos | Doutorando em Geografia
    “Combining MODIS data, TRMM, river discharge, a digital terrain model, and multinomial logistic regression, [the authors] identified three predominant classes of flood origin: local rainfall, bank overflow, and mixed areas. The model achieved a Nagelkerke Pseudo-R² of 0.63 and a classification accuracy of up to 81.2% (10-fold cross-validation). To the best of [their] knowledge, this is the first explicit approach to mapping flood agents for this wetland, a component that has historically remained unmapped despite its recognized influence on habitats and biodiversity…”
    --
    “The mapping of functional hydrologic units is crucial for enhancing our understanding of flooding and hydroecological processes in large wetlands. These units are typically defined by flooding frequency, duration, and magnitude, but another important hydrologic characteristic is the origin of flooding, known as the flood agent. This study presents an empirical framework utilizing remote sensing and GIS procedures for modeling flood agents in the northern Pantanal wetland. Eleven spatial data layers were derived from multi-year MODIS flood maps, daily rainfall estimates from the TRMM 3B42 grid, in situ discharge data, the BEST Digital Terrain Model, and a hydrographic network layer. These layers were tested for their predictive power in a multinomial logistic regression mapping model. Model performance metrics, along with qualitative validation of mapping outcomes using in situ flooding measurements, and vegetation and soil data from field test sites, support the plausibility of the proposed mapping scheme. However, they also highlight the challenges of flood agent mapping in large tropical wetlands…”
    #GIS #spatial #mapping #MODIS #TRMM #riverdischarge #digitalterrainmodel #multinomiallogisticregression #geostatistics #Pantanal #Cuiaba #Brazil #water #hydrology #spatialanalysis #spatiotemporal #remotesensing #earthobservation #flood #flooding #source #type #floodagent #tropical #wetland #habitat #biodiversity #ecosystem #hydrologicunit #hydroecology #model #modeling #rainfall #precipitation #weather #climate #discharge #network #metrics

    #gis #spatial #mapping #modis #trmm #riverdischarge
  8. 💧🌏 Greg Cocks @[email protected] ·

    Mapping Flood Agents In The Northern Pantanal Wetland Using Multiple Spatio-Temporal GIS And Remote Sensing Techniques
    --
    doi.org/10.1007/s13157-026-020 <-- shared paper
    --
    H/T @renato Gatto de Morais | Geógrafo | Mestre em Recursos Hídricos | Doutorando em Geografia
    “Combining MODIS data, TRMM, river discharge, a digital terrain model, and multinomial logistic regression, [the authors] identified three predominant classes of flood origin: local rainfall, bank overflow, and mixed areas. The model achieved a Nagelkerke Pseudo-R² of 0.63 and a classification accuracy of up to 81.2% (10-fold cross-validation). To the best of [their] knowledge, this is the first explicit approach to mapping flood agents for this wetland, a component that has historically remained unmapped despite its recognized influence on habitats and biodiversity…”
    --
    “The mapping of functional hydrologic units is crucial for enhancing our understanding of flooding and hydroecological processes in large wetlands. These units are typically defined by flooding frequency, duration, and magnitude, but another important hydrologic characteristic is the origin of flooding, known as the flood agent. This study presents an empirical framework utilizing remote sensing and GIS procedures for modeling flood agents in the northern Pantanal wetland. Eleven spatial data layers were derived from multi-year MODIS flood maps, daily rainfall estimates from the TRMM 3B42 grid, in situ discharge data, the BEST Digital Terrain Model, and a hydrographic network layer. These layers were tested for their predictive power in a multinomial logistic regression mapping model. Model performance metrics, along with qualitative validation of mapping outcomes using in situ flooding measurements, and vegetation and soil data from field test sites, support the plausibility of the proposed mapping scheme. However, they also highlight the challenges of flood agent mapping in large tropical wetlands…”
    #GIS #spatial #mapping #MODIS #TRMM #riverdischarge #digitalterrainmodel #multinomiallogisticregression #geostatistics #Pantanal #Cuiaba #Brazil #water #hydrology #spatialanalysis #spatiotemporal #remotesensing #earthobservation #flood #flooding #source #type #floodagent #tropical #wetland #habitat #biodiversity #ecosystem #hydrologicunit #hydroecology #model #modeling #rainfall #precipitation #weather #climate #discharge #network #metrics

    #metrics #network #discharge #climate #weather #precipitation
  9. 💧🌏 Greg Cocks @GregCocks ·

    Mapping Flood Agents In The Northern Pantanal Wetland Using Multiple Spatio-Temporal GIS And Remote Sensing Techniques
    --
    doi.org/10.1007/s13157-026-020 <-- shared paper
    --
    H/T @renato Gatto de Morais | Geógrafo | Mestre em Recursos Hídricos | Doutorando em Geografia
    “Combining MODIS data, TRMM, river discharge, a digital terrain model, and multinomial logistic regression, [the authors] identified three predominant classes of flood origin: local rainfall, bank overflow, and mixed areas. The model achieved a Nagelkerke Pseudo-R² of 0.63 and a classification accuracy of up to 81.2% (10-fold cross-validation). To the best of [their] knowledge, this is the first explicit approach to mapping flood agents for this wetland, a component that has historically remained unmapped despite its recognized influence on habitats and biodiversity…”
    --
    “The mapping of functional hydrologic units is crucial for enhancing our understanding of flooding and hydroecological processes in large wetlands. These units are typically defined by flooding frequency, duration, and magnitude, but another important hydrologic characteristic is the origin of flooding, known as the flood agent. This study presents an empirical framework utilizing remote sensing and GIS procedures for modeling flood agents in the northern Pantanal wetland. Eleven spatial data layers were derived from multi-year MODIS flood maps, daily rainfall estimates from the TRMM 3B42 grid, in situ discharge data, the BEST Digital Terrain Model, and a hydrographic network layer. These layers were tested for their predictive power in a multinomial logistic regression mapping model. Model performance metrics, along with qualitative validation of mapping outcomes using in situ flooding measurements, and vegetation and soil data from field test sites, support the plausibility of the proposed mapping scheme. However, they also highlight the challenges of flood agent mapping in large tropical wetlands…”

    #gis #spatial #mapping #modis #trmm #riverdischarge