Catholic Carbon Footprint Story Map Map:DataBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.Map Development: Molly BurhansMethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

Catholic_CO2_Footprint_Beta_FullSees_MinusTop10Burhans, Molly A., Cheney, David M., Gerlt, R.. . “Catholic_CO2_Footprint_Beta_FullSees_MinusTop10”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.DEVELOPED AS A POPUP LAYERMethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

PerCapita_CO2_Footprint_InDioceses_FULLBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.MethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

PerCapita_CO2_Footprint_InDioceses_FULLBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.MethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

Integrated Geodatabase: The Global Catholic Foortprint of Healthcare and WelfareBurhans, Molly A., Mrowczynski, Jon M., Schweigel, Tayler C., and Burhans, Debra T., Wacta, Christine. The Catholic Foortprint of Care Around the World (1). GoodLands and GHR Foundation, 2019.Catholic Statistics Numbers:Annuarium Statisticum Ecclesiae – Statistical Yearbook of the Church: 1980 – 2018. LIBRERIA EDITRICE VATICAN.Historical Country Boundary Geodatabase:Weidmann, Nils B., Doreen Kuse, and Kristian Skrede Gleditsch. The Geography of the International System: The CShapes Dataset. International Interactions 36 (1). 2010.https://www.tandfonline.com/doi/full/10.1080/03050620903554614GoodLands created a significant new data set for GHR and the UISG of important Church information regarding orphanages and sisters around the world as well as healthcare, welfare, and other child care institutions. The data were extracted from the gold standard of Church data, the Annuarium Statisticum Ecclesiae, published yearly by the Vatican. It is inevitable that raw data sources will contain errors. GoodLands and its partners are not responsible for misinformation within Vatican documents. We encourage error reporting to us at data@good-lands.org or directly to the Vatican.GoodLands worked with the GHR Foundation to map Catholic Healthcare and Welfare around the world using data mined from the Annuarium Statisticum Eccleasiea. GHR supported the data development and GoodLands independently invested in the mapping of information.The workflows and data models developed for this project can be used to map any global, historical country-scale data in a time-series map while accounting for country boundary changes. GoodLands created proprietary software that enables mining the Annuarium Statisticum Eccleasiea (see Software and Program Library at our home page for details).The GHR Foundation supported data extraction and cleaning of this information.GoodLands’ supported the development of maps, infographics, and applications for all healthcare data.

PerCapita_CO2_Footprint_InDioceses_FULLBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.MethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

Integrated Geodatabase: The Global Catholic Foortprint of Care for the Vulnerable and ChildrenBurhans, Molly A., Mrowczynski, Jon M., Schweigel, Tayler C., and Burhans, Debra T., Wacta, Christine. The Catholic Foortprint of Care Around the World (1). GoodLands and GHR Foundation, 2019.Catholic Statistics Numbers:Annuarium Statisticum Ecclesiae – Statistical Yearbook of the Church: 1980 – 2018. LIBRERIA EDITRICE VATICAN.Historical Country Boundary Geodatabase:Weidmann, Nils B., Doreen Kuse, and Kristian Skrede Gleditsch. The Geography of the International System: The CShapes Dataset. International Interactions 36 (1). 2010.GoodLands created a significant new data set of important Church information regarding orphanages and sisters around the world as well as healthcare, welfare, and other child care institutions. The data was extracted from the gold standard of Church data, the Annuarium Statisticum Ecclesiae, published yearly by the Vatican. It is inevitable that raw data sources will contain errors. GoodLands and its partners are not responsible for misinformation within Vatican documents. We encourage error reporting to us at data@good-lands.org or directly to the Vatican.GoodLands worked with the GHR Foundation to map Catholic Healthcare around the world using data mined from the Annuarium Statisticum Eccleasiea.The workflows and data models developed for this project can be used to map any global, historical country-scale data in a time-series map while accounting for country boundary changes. GoodLands created proprietary software that enables mining the Annuarium Statisticum Eccleasiea (see Software and Program Library at the bottom of this page for details).

Integrated Geodatabase: The Global Catholic Foortprint of Healthcare and WelfareBurhans, Molly A., Mrowczynski, Jon M., Schweigel, Tayler C., and Burhans, Debra T., Wacta, Christine. The Catholic Foortprint of Care Around the World (1). GoodLands and GHR Foundation, 2019.Catholic Statistics Numbers:Annuarium Statisticum Ecclesiae – Statistical Yearbook of the Church: 1980 – 2018. LIBRERIA EDITRICE VATICAN.Historical Country Boundary Geodatabase:Weidmann, Nils B., Doreen Kuse, and Kristian Skrede Gleditsch. The Geography of the International System: The CShapes Dataset. International Interactions 36 (1). 2010.https://www.tandfonline.com/doi/full/10.1080/03050620903554614GoodLands created a significant new data set for GHR and the UISG of important Church information regarding orphanages and sisters around the world as well as healthcare, welfare, and other child care institutions. The data were extracted from the gold standard of Church data, the Annuarium Statisticum Ecclesiae, published yearly by the Vatican. It is inevitable that raw data sources will contain errors. GoodLands and its partners are not responsible for misinformation within Vatican documents. We encourage error reporting to us at data@good-lands.org or directly to the Vatican.GoodLands worked with the GHR Foundation to map Catholic Healthcare and Welfare around the world using data mined from the Annuarium Statisticum Eccleasiea. GHR supported the data development and GoodLands independently invested in the mapping of information.The workflows and data models developed for this project can be used to map any global, historical country-scale data in a time-series map while accounting for country boundary changes. GoodLands created proprietary software that enables mining the Annuarium Statisticum Eccleasiea (see Software and Program Library at our home page for details).The GHR Foundation supported data extraction and cleaning of this information.GoodLands’ supported the development of maps, infographics, and applications for all healthcare data.

Integrated Geodatabase: The Global Catholic Foortprint of Healthcare and WelfareBurhans, Molly A., Mrowczynski, Jon M., Schweigel, Tayler C., and Burhans, Debra T., Wacta, Christine. The Catholic Foortprint of Care Around the World (1). GoodLands and GHR Foundation, 2019.Catholic Statistics Numbers:Annuarium Statisticum Ecclesiae – Statistical Yearbook of the Church: 1980 – 2018. LIBRERIA EDITRICE VATICAN.Historical Country Boundary Geodatabase:Weidmann, Nils B., Doreen Kuse, and Kristian Skrede Gleditsch. The Geography of the International System: The CShapes Dataset. International Interactions 36 (1). 2010.https://www.tandfonline.com/doi/full/10.1080/03050620903554614GoodLands created a significant new data set for GHR and the UISG of important Church information regarding orphanages and sisters around the world as well as healthcare, welfare, and other child care institutions. The data were extracted from the gold standard of Church data, the Annuarium Statisticum Ecclesiae, published yearly by the Vatican. It is inevitable that raw data sources will contain errors. GoodLands and its partners are not responsible for misinformation within Vatican documents. We encourage error reporting to us at data@good-lands.org or directly to the Vatican.GoodLands worked with the GHR Foundation to map Catholic Healthcare and Welfare around the world using data mined from the Annuarium Statisticum Eccleasiea. GHR supported the data development and GoodLands independently invested in the mapping of information.The workflows and data models developed for this project can be used to map any global, historical country-scale data in a time-series map while accounting for country boundary changes. GoodLands created proprietary software that enables mining the Annuarium Statisticum Eccleasiea (see Software and Program Library at our home page for details).The GHR Foundation supported data extraction and cleaning of this information.GoodLands’ supported the development of maps, infographics, and applications for all healthcare data.

Integrated Geodatabase: The Global Catholic Foortprint of Healthcare and WelfareBurhans, Molly A., Mrowczynski, Jon M., Schweigel, Tayler C., and Burhans, Debra T., Wacta, Christine. The Catholic Foortprint of Care Around the World (1). GoodLands and GHR Foundation, 2019.Catholic Statistics Numbers:Annuarium Statisticum Ecclesiae – Statistical Yearbook of the Church: 1980 – 2018. LIBRERIA EDITRICE VATICAN.Historical Country Boundary Geodatabase:Weidmann, Nils B., Doreen Kuse, and Kristian Skrede Gleditsch. The Geography of the International System: The CShapes Dataset. International Interactions 36 (1). 2010.https://www.tandfonline.com/doi/full/10.1080/03050620903554614GoodLands created a significant new data set for GHR and the UISG of important Church information regarding orphanages and sisters around the world as well as healthcare, welfare, and other child care institutions. The data were extracted from the gold standard of Church data, the Annuarium Statisticum Ecclesiae, published yearly by the Vatican. It is inevitable that raw data sources will contain errors. GoodLands and its partners are not responsible for misinformation within Vatican documents. We encourage error reporting to us at data@good-lands.org or directly to the Vatican.GoodLands worked with the GHR Foundation to map Catholic Healthcare and Welfare around the world using data mined from the Annuarium Statisticum Eccleasiea. GHR supported the data development and GoodLands independently invested in the mapping of information.The workflows and data models developed for this project can be used to map any global, historical country-scale data in a time-series map while accounting for country boundary changes. GoodLands created proprietary software that enables mining the Annuarium Statisticum Eccleasiea (see Software and Program Library at our home page for details).The GHR Foundation supported data extraction and cleaning of this information.GoodLands’ supported the development of maps, infographics, and applications for all healthcare data.

Top 10 Dioceses with Highest Carbon FootprintBurhans, Molly A., Cheney, David M., Gerlt, Robert, Thompson, Helen. “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.MethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

Integrated Geodatabase: The Global Catholic Foortprint of Healthcare and WelfareBurhans, Molly A., Mrowczynski, Jon M., Schweigel, Tayler C., and Burhans, Debra T., Wacta, Christine. The Catholic Foortprint of Care Around the World (1). GoodLands and GHR Foundation, 2019.Catholic Statistics Numbers:Annuarium Statisticum Ecclesiae – Statistical Yearbook of the Church: 1980 – 2018. LIBRERIA EDITRICE VATICAN.Historical Country Boundary Geodatabase:Weidmann, Nils B., Doreen Kuse, and Kristian Skrede Gleditsch. The Geography of the International System: The CShapes Dataset. International Interactions 36 (1). 2010.https://www.tandfonline.com/doi/full/10.1080/03050620903554614GoodLands created a significant new data set for GHR and the UISG of important Church information regarding orphanages and sisters around the world as well as healthcare, welfare, and other child care institutions. The data were extracted from the gold standard of Church data, the Annuarium Statisticum Ecclesiae, published yearly by the Vatican. It is inevitable that raw data sources will contain errors. GoodLands and its partners are not responsible for misinformation within Vatican documents. We encourage error reporting to us at data@good-lands.org or directly to the Vatican.GoodLands worked with the GHR Foundation to map Catholic Healthcare and Welfare around the world using data mined from the Annuarium Statisticum Eccleasiea. GHR supported the data development and GoodLands independently invested in the mapping of information.The workflows and data models developed for this project can be used to map any global, historical country-scale data in a time-series map while accounting for country boundary changes. GoodLands created proprietary software that enables mining the Annuarium Statisticum Eccleasiea (see Software and Program Library at our home page for details).The GHR Foundation supported data extraction and cleaning of this information.GoodLands’ supported the development of maps, infographics, and applications for all healthcare data.

Integrated Geodatabase: The Global Catholic Foortprint of Care for the Vulnerable and ChildrenBurhans, Molly A., Mrowczynski, Jon M., Schweigel, Tayler C., and Burhans, Debra T., Wacta, Christine. The Catholic Foortprint of Care Around the World (1). GoodLands and GHR Foundation, 2019.Catholic Statistics Numbers:Annuarium Statisticum Ecclesiae – Statistical Yearbook of the Church: 1980 – 2018. LIBRERIA EDITRICE VATICAN.Historical Country Boundary Geodatabase:Weidmann, Nils B., Doreen Kuse, and Kristian Skrede Gleditsch. The Geography of the International System: The CShapes Dataset. International Interactions 36 (1). 2010.GoodLands created a significant new data set of important Church information regarding orphanages and sisters around the world as well as healthcare, welfare, and other child care institutions. The data was extracted from the gold standard of Church data, the Annuarium Statisticum Ecclesiae, published yearly by the Vatican. It is inevitable that raw data sources will contain errors. GoodLands and its partners are not responsible for misinformation within Vatican documents. We encourage error reporting to us at data@good-lands.org or directly to the Vatican.GoodLands worked with the GHR Foundation to map Catholic Healthcare around the world using data mined from the Annuarium Statisticum Eccleasiea.The workflows and data models developed for this project can be used to map any global, historical country-scale data in a time-series map while accounting for country boundary changes. GoodLands created proprietary software that enables mining the Annuarium Statisticum Eccleasiea (see Software and Program Library at the bottom of this page for details).

Integrated Geodatabase: The Global Catholic Foortprint of Healthcare and WelfareBurhans, Molly A., Mrowczynski, Jon M., Schweigel, Tayler C., and Burhans, Debra T., Wacta, Christine. The Catholic Foortprint of Care Around the World (1). GoodLands and GHR Foundation, 2019.Catholic Statistics Numbers:Annuarium Statisticum Ecclesiae – Statistical Yearbook of the Church: 1980 – 2018. LIBRERIA EDITRICE VATICAN.Historical Country Boundary Geodatabase:Weidmann, Nils B., Doreen Kuse, and Kristian Skrede Gleditsch. The Geography of the International System: The CShapes Dataset. International Interactions 36 (1). 2010.https://www.tandfonline.com/doi/full/10.1080/03050620903554614GoodLands created a significant new data set for GHR and the UISG of important Church information regarding orphanages and sisters around the world as well as healthcare, welfare, and other child care institutions. The data were extracted from the gold standard of Church data, the Annuarium Statisticum Ecclesiae, published yearly by the Vatican. It is inevitable that raw data sources will contain errors. GoodLands and its partners are not responsible for misinformation within Vatican documents. We encourage error reporting to us at data@good-lands.org or directly to the Vatican.GoodLands worked with the GHR Foundation to map Catholic Healthcare and Welfare around the world using data mined from the Annuarium Statisticum Eccleasiea. GHR supported the data development and GoodLands independently invested in the mapping of information.The workflows and data models developed for this project can be used to map any global, historical country-scale data in a time-series map while accounting for country boundary changes. GoodLands created proprietary software that enables mining the Annuarium Statisticum Eccleasiea (see Software and Program Library at our home page for details).The GHR Foundation supported data extraction and cleaning of this information.GoodLands’ supported the development of maps, infographics, and applications for all healthcare data.

App Development: Burhans, Molly. October 2019.Integrated Geodatabase: The Global Catholic Foortprint of Care for the Vulnerable and ChildrenBurhans, Molly A., Mrowczynski, Jon M., Schweigel, Tayler C., and Burhans, Debra T., Wacta, Christine. The Catholic Foortprint of Care Around the World (1). GoodLands and GHR Foundation, 2019.Catholic Statistics Numbers:Annuarium Statisticum Ecclesiae – Statistical Yearbook of the Church: 1980 – 2018. LIBRERIA EDITRICE VATICAN.Historical Country Boundary Geodatabase:Weidmann, Nils B., Doreen Kuse, and Kristian Skrede Gleditsch. The Geography of the International System: The CShapes Dataset. International Interactions 36 (1). 2010.GoodLands created a significant new data set of important Church information regarding orphanages and sisters around the world as well as healthcare, welfare, and other child care institutions. The data was extracted from the gold standard of Church data, the Annuarium Statisticum Ecclesiae, published yearly by the Vatican. It is inevitable that raw data sources will contain errors. GoodLands and its partners are not responsible for misinformation within Vatican documents. We encourage error reporting to us at data@good-lands.org or directly to the Vatican.GoodLands worked with the GHR Foundation to map Catholic Healthcare around the world using data mined from the Annuarium Statisticum Eccleasiea.The workflows and data models developed for this project can be used to map any global, historical country-scale data in a time-series map while accounting for country boundary changes. GoodLands created proprietary software that enables mining the Annuarium Statisticum Eccleasiea (see Software and Program Library at the bottom of this page for details).

Integrated Geodatabase: The Global Catholic Foortprint of Healthcare and WelfareBurhans, Molly A., Mrowczynski, Jon M., Schweigel, Tayler C., and Burhans, Debra T., Wacta, Christine. The Catholic Foortprint of Care Around the World (1). GoodLands and GHR Foundation, 2019.Catholic Statistics Numbers:Annuarium Statisticum Ecclesiae – Statistical Yearbook of the Church: 1980 – 2018. LIBRERIA EDITRICE VATICAN.Historical Country Boundary Geodatabase:Weidmann, Nils B., Doreen Kuse, and Kristian Skrede Gleditsch. The Geography of the International System: The CShapes Dataset. International Interactions 36 (1). 2010.https://www.tandfonline.com/doi/full/10.1080/03050620903554614GoodLands created a significant new data set for GHR and the UISG of important Church information regarding orphanages and sisters around the world as well as healthcare, welfare, and other child care institutions. The data were extracted from the gold standard of Church data, the Annuarium Statisticum Ecclesiae, published yearly by the Vatican. It is inevitable that raw data sources will contain errors. GoodLands and its partners are not responsible for misinformation within Vatican documents. We encourage error reporting to us at data@good-lands.org or directly to the Vatican.GoodLands worked with the GHR Foundation to map Catholic Healthcare and Welfare around the world using data mined from the Annuarium Statisticum Eccleasiea. GHR supported the data development and GoodLands independently invested in the mapping of information.The workflows and data models developed for this project can be used to map any global, historical country-scale data in a time-series map while accounting for country boundary changes. GoodLands created proprietary software that enables mining the Annuarium Statisticum Eccleasiea (see Software and Program Library at our home page for details).The GHR Foundation supported data extraction and cleaning of this information.GoodLands’ supported the development of maps, infographics, and applications for all healthcare data.

Integrated Geodatabase: The Global Catholic Foortprint of Healthcare and WelfareBurhans, Molly A., Mrowczynski, Jon M., Schweigel, Tayler C., and Burhans, Debra T., Wacta, Christine. The Catholic Foortprint of Care Around the World (1). GoodLands and GHR Foundation, 2019.WHO Statistics Numbers:Clean Care is Safe Care, Registration Update. (2017). Retrieved n.d., from https://www.who.int/gpsc/5may/registration_update/en/.https://www.who.int/gpsc/5may/registration_update/en/Catholic Statistics Numbers:Annuarium Statisticum Ecclesiae – Statistical Yearbook of the Church: 1980 – 2018. LIBRERIA EDITRICE VATICAN.Historical Country Boundary Geodatabase:Weidmann, Nils B., Doreen Kuse, and Kristian Skrede Gleditsch. The Geography of the International System: The CShapes Dataset. International Interactions 36 (1). 2010.https://www.tandfonline.com/doi/full/10.1080/03050620903554614GoodLands created a significant new data set for GHR and the UISG of important Church information regarding orphanages and sisters around the world as well as healthcare, welfare, and other child care institutions. The data were extracted from the gold standard of Church data, the Annuarium Statisticum Ecclesiae, published yearly by the Vatican. It is inevitable that raw data sources will contain errors. GoodLands and its partners are not responsible for misinformation within Vatican documents. We encourage error reporting to us at data@good-lands.org or directly to the Vatican.GoodLands worked with the GHR Foundation to map Catholic Healthcare and Welfare around the world using data mined from the Annuarium Statisticum Eccleasiea. GHR supported the data development and GoodLands independently invested in the mapping of information.The workflows and data models developed for this project can be used to map any global, historical country-scale data in a time-series map while accounting for country boundary changes. GoodLands created proprietary software that enables mining the Annuarium Statisticum Eccleasiea (see Software and Program Library at our home page for details).The GHR Foundation supported data extraction and cleaning of this information.GoodLands’ supported the development of maps, infographics, and applications for all healthcare data.

Integrated Geodatabase: The Global Catholic Foortprint of Care for the Vulnerable and ChildrenBurhans, Molly A., Mrowczynski, Jon M., Schweigel, Tayler C., and Burhans, Debra T., Wacta, Christine. The Catholic Foortprint of Care Around the World (1). GoodLands and GHR Foundation, 2019.Catholic Statistics Numbers:Annuarium Statisticum Ecclesiae – Statistical Yearbook of the Church: 1980 – 2018. LIBRERIA EDITRICE VATICAN.Historical Country Boundary Geodatabase:Weidmann, Nils B., Doreen Kuse, and Kristian Skrede Gleditsch. The Geography of the International System: The CShapes Dataset. International Interactions 36 (1). 2010.GoodLands created a significant new data set of important Church information regarding orphanages and sisters around the world as well as healthcare, welfare, and other child care institutions. The data was extracted from the gold standard of Church data, the Annuarium Statisticum Ecclesiae, published yearly by the Vatican. It is inevitable that raw data sources will contain errors. GoodLands and its partners are not responsible for misinformation within Vatican documents. We encourage error reporting to us at data@good-lands.org or directly to the Vatican.GoodLands worked with the GHR Foundation to map Catholic Healthcare around the world using data mined from the Annuarium Statisticum Eccleasiea.The workflows and data models developed for this project can be used to map any global, historical country-scale data in a time-series map while accounting for country boundary changes. GoodLands created proprietary software that enables mining the Annuarium Statisticum Eccleasiea (see Software and Program Library at the bottom of this page for details).

Integrated Geodatabase: The Global Catholic Foortprint of Care for the Vulnerable and ChildrenBurhans, Molly A., Mrowczynski, Jon M., Schweigel, Tayler C., and Burhans, Debra T., Wacta, Christine. The Catholic Foortprint of Care Around the World (1). GoodLands and GHR Foundation, 2019.Catholic Statistics Numbers:Annuarium Statisticum Ecclesiae – Statistical Yearbook of the Church: 1980 – 2018. LIBRERIA EDITRICE VATICAN.Historical Country Boundary Geodatabase:Weidmann, Nils B., Doreen Kuse, and Kristian Skrede Gleditsch. The Geography of the International System: The CShapes Dataset. International Interactions 36 (1). 2010.GoodLands created a significant new data set of important Church information regarding orphanages and sisters around the world as well as healthcare, welfare, and other child care institutions. The data was extracted from the gold standard of Church data, the Annuarium Statisticum Ecclesiae, published yearly by the Vatican. It is inevitable that raw data sources will contain errors. GoodLands and its partners are not responsible for misinformation within Vatican documents. We encourage error reporting to us at data@good-lands.org or directly to the Vatican.GoodLands worked with the GHR Foundation to map Catholic Healthcare around the world using data mined from the Annuarium Statisticum Eccleasiea.The workflows and data models developed for this project can be used to map any global, historical country-scale data in a time-series map while accounting for country boundary changes. GoodLands created proprietary software that enables mining the Annuarium Statisticum Eccleasiea (see Software and Program Library at the bottom of this page for details).

Title: Boston Archdioceses 5 ft Sea Level InundationThese data are for planning, educational, and awareness purposes only and should not be used for site-specific analysis, navigation, or permitting.App and Map Development: “Boston Archdioceses 5 ft Sea Level Inundation”. Scale not given. Version 1.0. CT, USA: GoodLands Inc. 2019.Catholic Data: “USCCB Institution Extraction from Parcel Properties”. Scale not given. Version 1.0. CT, USA: GoodLands Inc. 2017.Sea Level Rise Data:Title:NOAA Office for Coastal Management Sea Level Rise Data: 1-6 ft Sea Level Rise Inundation ExtentShort Name:NOAA_OCM_SLR_1to6ft_metadataStatus:CompletedPublication Date:2016Abstract:These data were created as part of the National Oceanic and Atmospheric Administration Office for Coastal Management's efforts to create an onlinemapping viewer depicting potential sea level rise and its associated impacts on the nation's coastal areas. The purpose of the mapping vieweris to provide coastal managers and scientists with a preliminary look at sea level rise (slr) and coastal flooding impacts. The viewer is ascreening-level tool that uses nationally consistent data sets and analyses.Data and maps provided can be used at several scales to helpgauge trends and prioritize actions for different scenarios. The Sea Level Rise and Coastal Flooding Impacts Viewer may be accessed at:https://www.coast.noaa.gov/slrThese data depict the potential inundation of coastal areas resulting from a projected 1 to 6 feet rise in sea level above currentMean Higher High Water (MHHW) conditions. The process used to produce the data can be described as a modified bathtub approach that attemptsto account for both local/regional tidal variability as well as hydrological connectivity. The process uses two source datasets to derive thefinal inundation rasters and polygons and accompanying low-lying polygons for each iteration of sea level rise: the Digital Elevation Model (DEM)of the area and a tidal surface model that represents spatial tidal variability. The tidal model is created using the NOAA National GeodeticSurvey's VDATUM datum transformation software (http://vdatum.noaa.gov) in conjunction with spatial interpolation/extrapolation methods andrepresents the MHHW tidal datum in orthometric values (North American Vertical Datum of 1988).The model used to produce these data does not account for erosion, subsidence, or any future changes in an area's hydrodynamics. It is simplya method to derive data in order to visualize the potential scale, not exact location, of inundation from sea level rise.Purpose:The purpose of these data is to show potential sea level rise inundation ranging from 1 to 6 feet above current Mean Higher High Water (MHHW)for the area. Although the water surface mapped represents a particular increase in sea level in feet above MHHW, the actual cell values inthe raster dataset represent depth in meters.Notes:10963Supplemental Information:A detailed methodology for producing these data can be found via the following url:https://coast.noaa.gov/data/digitalcoast/pdf/slr-inundation-methods.pdfSpatial_Reference_Information:Horizontal_Coordinate_System_Definition:Geographic:Latitude_Resolution: 0.0000001Longitude_Resolution: 0.0000001Geographic_Coordinate_Units: Decimal degreesGeodetic_Model:Horizontal_Datum_Name: North American Datum of 1983Ellipsoid_Name: Geodetic Reference System 80Semi-major_Axis: 6378137.000000Denominator_of_Flattening_Ratio: 298.257222101Vertical_Coordinate_System_Definition:Altitude_System_Definition:Altitude_Datum_Name: North American Vertical Datum of 1988Altitude_Resolution: 0.001Altitude_Distance_Units: metersAltitude_Encoding_Method: Explicit elevation coordinate included with horizontal coordinatesKeil Schmid, Brian Hadley, and Kirk Waters (2014) Mapping and Portraying Inundation Uncertainty of Bathtub-Type Models. Journal of Coastal Research: Volume 30, Issue 3: pp. 548 – 561.Documentationhttps://coast.noaa.gov/digitalcoast/tools/slr.htmlBoston Archdioceses High Resolution Boundary, derived from:MassGIS Data: County Boundaries: https://docs.digital.mass.gov/dataset/massgis-data-county-boundariesContributorMassGIS (Bureau of Geographic Information)Modified Date2018-12-05Release Date2018-12-05Identifier2f6f9906-5088-47d7-917a-fee2e4ab1db1Spatial / Geographical Coverage LocationMassachusettsLicenseCreative Commons AttributionAuthorMassGISContact NameMassGISContact Emailmassgismail@mass.govPublic Access LevelPublicData QualityFalseContent TypeDataLanguageEnglish (United States)