Please, don't forget to cite the original research article that result in this application: Galvão, M. L., Fogliaroni, P., Giannopoulos, I., Navratil, G., Kattenbeck, M., & Alinaghi, N. (2024). GeoAR: a calibration method for Geographic-Aware Augmented Reality. International Journal of Geographical Information Science, 1–27. https://doi.org/10.1080/13658816.2024.2355326 GeoAR getting started application This getting started tutorial provides the basic information so you can implement your own geographic-aware AR application. The project we provide here is described in the IJGIS article GeoAR: A calibration method for Geographic-aware augmented reality, and it provides the means for all four calibration approaches described in the article. The set-up we provide here is for the device Microsoft Hololens 2, but feel free to adpat the code to use in different devices. Basic requirements In order to run and develop your GeoAR application using this project it is required the following: AR device (Microsoft Hololens 2) Unity Hub with Unity 2021.3.2f1 installed (adaptations for a later version of Unity might be necessary) Microsoft Visual Studio (Version 16.11.15 or later) Mixed Reality Toolkit (MRTK) foundation package for Unity (2.8.0.0) If you do not have experience in developing with Unity or MRTK, we highly recommend you go through the following Microsoft training modules: Introduction to the Mixed Reality Toolkit – Set Up Your Project and Use Hand Interaction Introduction to mixed reality Download and open the project in Unity Download the project folder and unpack it in your local machine Use Unity Hub to open the project folder GeoARUnityProject (make sure you have the right version installed) If everything is correct, you will be able to play the application in the game mode. Further instructions with video tutorials can be found here : https://geoinfo.geo.tuwien.ac.at/geoar-getting-started/ License All data is published under the CC-BY 4.0 license. The code is under the GNU General public license

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

The idea of public geographies, inspired by Burawoy's call for public sociologies, places an emphasis on the co-construction of research data, accessible writing and the use of non-traditional media. As yet, however, the methodological implications of public geographies have not been explored. Hence this project seeks to develop the idea of rescue geography and explore the role of the walking interview as a research tool. Rescue geography takes its inspiration from rescue archaeology, which attempts to recover archaeological data from a site before new development takes place. Contemporary regeneration projects efface existing landscapes, deeming them as 'failed', making no effort to record that which is being destroyed. The physical traces of these urban spaces can be recorded through photography and mapping, but what of the embodied understandings possessed by the community that animated those spaces? Walked interviews may help enhance the quality of data produced by participants through placing them in the spaces being discussed, but this assumption has never been rigorously analysed. A combination of walked and in situ interviews will be employed alongside the use of a Global Positioning System (GPS) to spatially contextualise audio recordings in order to examine the effectiveness of this research technique.

This dataset was created for the following publication: Cheruvelil, K.S., S. Yuan, K.E. Webster, P.-N. Tan, J.-F. Lapierre, S.M. Collins, C.E. Fergus, C.E. Scott, E.N. Henry, P.A. Soranno, C.T. Filstrup, T. Wagner. Under review. Creating multi-themed ecological regions for macrosystems ecology: Testing a flexible, repeatable, and accessible clustering method. Submitted to Ecology and Evolution July 2016. This dataset includes lake total phosphorus (TP) and Secchi data from summer, epilimnetic water samples, as well as 52 geographic variables at the HU-12 scale; it is a subset of the larger LAGOS-NE database (Lake multi-scaled geospatial and temporal database, described in Soranno et al. 2015). LAGOS-NE compiles multiple, individual lake water chemistry datasets into an integrated database. We accessed LAGOSLIMNO version 1.054.1 for lake water chemistry data and LAGOSGEO version 1.03 for geographic data. In the LAGOSLIMNO database, lake water chemistry data were collected from individual state agency sampling and volunteer programs designed to monitor lake water quality. Water chemistry analyses follow standard lab methods. In the LAGOSGEO database geographic data were collected from national scale geographic information systems (GIS) data layers. The dataset is a subset of the following integrated databases: LAGOSLIMNO v.1.054.1 and LAGOSGEO v.1.03. For full documentation of these databases, please see the publication below: Soranno, P.A., E.G. Bissell, K.S. Cheruvelil, S.T. Christel, S.M. Collins, C.E. Fergus, C.T. Filstrup, J.F. Lapierre, N.R. Lottig, S.K. Oliver, C.E. Scott, N.J. Smith, S. Stopyak, S. Yuan, M.T. Bremigan, J.A. Downing, C. Gries, E.N. Henry, N.K. Skaff, E.H. Stanley, C.A. Stow, P.-N. Tan, T. Wagner, K.E. Webster. 2015. Building a multi-scaled geospatial temporal ecology database from disparate data sources: Fostering open science and data reuse. GigaScience 4:28 doi:10.1186/s13742-015-0067-4 .

The present dataset provides necessary indicators of the climate change vulnerability of Bangladesh in raster form. Geospatial databases have been created in Geographic Information System (GIS) environment mainly from two types of raw data; socioeconomic data from the Bangladesh Bureau of Statistics (BBS) and biophysical maps from various government and non-government agencies. Socioeconomic data have been transformed into a raster database through the Inverse Distance Weighted (IDW) interpolation method in GIS. On the other hand, biophysical maps have been directly recreated as GIS feature classes and eventually, the biophysical raster database has been produced. 30 socioeconomic indicators have been considered, which has been obtained from the Bangladesh Bureau of Statistics. All socioeconomic data were incorporated into the GIS database to generate maps. However, the units of some variables have been adopted directly from BBS, some have been normalized based on population, and some have been adopted as percentages. 12 biophysical system indicators have also been classified based on the collected information from different sources and literature. Biophysical maps are mainly classified in relative scales according to the intensity. These geospatial datasets have been analyzed to assess the spatial vulnerability of Bangladesh to climate change and extremes. The analysis has resulted in a climate change vulnerability map of Bangladesh with recognized hotspots, significant vulnerability factors, and adaptation measures to reduce the level of vulnerability.

This dataset is called the Gridded SSURGO (gSSURGO) Database and is derived from the Soil Survey Geographic (SSURGO) Database. SSURGO is generally the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes, and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging traditional SSURGO digital vector map and tabular data into a Conterminous US-wide extent, and adding a Conterminous US-wide gridded map layer derived from the vector, plus a new value added look up (valu) table containing "ready to map" attributes. The gridded map layer is offered in an ArcGIS file geodatabase raster format. The raster and vector map data have a Conterminous US-wide extent. The raster map data have a 30 meter cell size. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link to raster cells and polygons to attribute tables, including the new value added look up (valu) table that contains additional derived data. The value added look up (valu) table contains attribute data summarized to the map unit level using best practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria. The Gridded SSURGO dataset was created for use in national, regional, and state-wide resource planning and analysis of soils data. The raster map layer data can be readily combined with other national, regional, and local raster layers, e.g., National Land Cover Database (NLCD), the National Agricultural Statistics Service (NASS) Crop Data Layer, or the National Elevation Dataset (NED).

This dataset contains identifiers, metadata, and a map of the locations where field measurements have been conducted at the East River Community Observatory located in the Upper Colorado River Basin, United States. This is version 3.0 of the dataset and replaces the prior version 2.0, which should no longer be used (see below for details on changes between the versions). Dataset description: The East River is the primary field site of the Watershed Function Scientific Focus Area (WFSFA) and the Rocky Mountain Biological Laboratory. Researchers from several institutions generate highly diverse hydrological, biogeochemical, climate, vegetation, geological, remote sensing, and model data at the East River in collaboration with the WFSFA. Thus, the purpose of this dataset is to maintain an inventory of the field locations and instrumentation to provide information on the field activities in the East River and coordinate data collected across different locations, researchers, and institutions. The dataset contains (1) a README file with information on the various files, (2) three csv files describing the metadata collected for each surface point location, plot and region registered with the WFSFA, (3) csv files with metadata and contact information for each surface point location registered with the WFSFA, (4) a csv file with with metadata and contact information for plots, (5) a csv file with metadata for geographic regions and sub-regions within the watershed, (6) a compiled xlsx file with all the data and metadata which can be opened in Microsoft Excel, (7) a kml map of the locations plotted in the watershed which can be opened in Google Earth, (8) a jpeg image of the kml map which can be viewed in any photo viewer, and (9) a zipped file with the registration templates used by the SFA team to collect location metadata. The zipped template file contains two csv files with the blank templates (point and plot), two csv files with instructions for filling out the location templates, and one compiled xlsx file with the instructions and blank templates together. Additionally, the templates in the xlsx include drop down validation for any controlled metadata fields. Persistent location identifiers (Location_ID) are determined by the WFSFA data management team and are used to track data and samples across locations. Dataset uses: This location metadata is used to update the Watershed SFA’s publicly accessible Field Information Portal (an interactive field sampling metadata exploration tool; https://wfsfa-data.lbl.gov/watershed/), the kml map file included in this dataset, and other data management tools internal to the Watershed SFA team. Version Information: The latest version of this dataset publication is version 3.0. The latest version contains a breaking change to the Location Map (EastRiverCommunityObservatory_Map_v3_0_20220613.kml), If you had previously downloaded the map file prior to version 3.0, it will no longer work. Use the updated Location Map (EastRiverCommunityObservatory_Map_v3_0_20220613.kml) in this version of the dataset. This version also contains a total of 51 new point locations, 8 new plot locations, and 1 new geographic region. Additionally, it corrects inconsistencies in existing metadata. Refer to methods for further details on the version history. This dataset will be updated on a periodic basis with new measurement location information. Researchers interested in having their East River measurement locations added in this list should reach out to the WFSFA data management team at wfsfa-data@googlegroups.com. Acknowledgements: Please cite this dataset if using any of the location metadata in other publications or derived products. If using the location metadata for the NEON hyperspectral campaign, additionally cite Chadwick et al. (2020). doi:10.15485/1618130.

ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)

**When using the GIS data included in these map packages, please cite all of the following:

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. http://doi.org/10.1371/journal.pone.0220457

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. https://doi.org/10.5281/zenodo.2572018

OVERVIEW OF CONTENTS

This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:

• Raw DEM and Soils data
  • Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the National Elevation Dataset)
    • DEM_Individual_Tiles: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.
    • DEMs_Merged: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM.
  • Soils Data (Map services and data available from Natural Resources Conservation Service Web Soil Survey, U.S. Department of Agriculture)
    • Animas-Dolores_Area_Soils: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).
    • Cortez_Area_Soils: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).
• ArcGIS Map Packages
  • Goodman_Watershed_Full_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).
  • Goodman_Watershed_Mesa-Only_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.
  • Crow_Canyon_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).
  • Coffey_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).

For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."

LICENSES

Code: MIT year: 2019
Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton

CONTACT

Andrew Gillreath-Brown, PhD Candidate, RPA
Department of Anthropology, Washington State University
andrew.brown1234@gmail.com – Email
andrewgillreathbrown.wordpress.com – Web

Data relevant to the Pan-Third Pole region are huge but fragmented due to these data are scattered in individuals or small groups, which hinders from better understanding the unprecedented environmental changes and the intense multi-sphere interactions over the Pan-Third Pole region. In this study, we adopt the latest data from multiple data platforms combined with the integrated method of data fusion to produce higher quality and detailed Pan-third Pole dataset. According to the scientific content of the data, the quality of these data is controlled, and then the data are integrated. For some datasets, using data fusion techniques, data from multiple sources will be combined to produce detailed data. The fusion of data from different sources will produce innovative data products with higher data quality and more recent times that will better serve the research on land surface process models The latest data from multiple data platforms, and data integration methods such as data fusion, were used to produce the Pan-Third Pole background Datasets with higher quality and more recent times. The Pan-Third Pole natural datasets adopt basin boundary and the Pan-Third Pole human geographic datasets adopt country boundary. Additionally, Robinson projection is uniformly adopted for the datasets. Six categories of datasets, including basic geographic datasets, cryospheric datasets, hydro-atmospheric datasets, ecological datasets, disaster datasets and human geographic datasets from multiple sources, were produced to promote the Earth system science of the Pan-Third Pole. (1) The basic geographic datasets include 30 meter land cover data, vegetation function data, 30 meter SRTM digital elevation data and HWSD soil texture data. For details, please refer to the documents in metadata page attachment information or datasets: "Pan-Third Pole Basic Geographic Data Document.docx". (2) The cryospheric datasets include permafrost datasets, glacier distribution data, ice lake distribution data and snow depth data. The permafrost datasets also include permafrost distribution data, permafrost hydrothermal zonation data, ruggedness index data and permafrost zonation index data. For details, please refer to the documents: "Pan-Third Pole Cryospheric Data Document.docx". (3) The hydro-atmospheric datasets include rivers and lakes datasets, evapotranspiration datasets and atmospheric datasets. The rivers and lakes datasets include rivers data and lakes data. And the evapotranspiration datasets include MODIS evapotranspiration data, soil evaporation data, ET_water data and vaporization of intercepted rainfall data. The atmospheric datasets include surface thermal radiation data, surface solar radiation data, total precipitation data, surface pressure data, 2m temperature data, 2m dewpoint temperature and wind field data. For details, please refer to the documents: "Pan-Third Pole Hydro-atmospheric Data Document.docx". (4) The ecological datasets include gross primary product data and vegetation transpiration data. For details, please refer to the documents: "Pan-Third Pole Ecological Data Document.docx". (5) The disaster datasets include landslide susceptibility data and seismic zonation data. For details, please refer to the documents: "Pan-Third Pole Disaster Data Document.docx". (6) The human geographic datasets include traffic road data, railway airport data, population density data, per capita GDP data, income level data and world heritage distribution data. For details, please refer to the documents: "Pan-Third Pole Human Geographic Data Document.docx". The Pan-Third Pole Integrated Datasets will provide convenience for relevant researchers, avoid repetitive work in the process of data acquisition and preprocessing, save precious time for researchers, and play an important role in the scientific research of land surface process model, hydrological model and ecological model, so as to promote the development of scientific research in Pan-Third Pole region.

Human forensic STRs used for individual identification have been reported to have little power for inter-population analyses. Several methods have been developed which incorporate information on the spatial distribution of individuals to arrive at a description of the arrangement of diversity. We genotyped at 16 forensic STRs a large population sample obtained from many locations in Italy, Greece and Turkey, i.e. three countries crucial to the understanding of discontinuities at the European/Asian junction and the genetic legacy of ancient migrations, but seldom represented together in previous studies. Using spatial PCA on the full dataset, we detected patterns of population affinities in the area. Additionally, we devised objective criteria to reduce the overall complexity into reduced datasets. Independent spatially explicit methods applied to these latter datasets converged in showing that the extraction of information on long- to medium-range geographical trends and structuring from the overall diversity is possible. All analyses returned the picture of a background clinal variation, with regional discontinuities captured by each of the reduced datasets. Several aspects of our results are confirmed on external STR datasets and replicate those of genome-wide SNP typings. High levels of gene flow were inferred within the main continental areas by coalescent simulations. These results are promising from a microevolutionary perspective, in view of the fast pace at which forensic data are being accumulated for many locales. It is foreseeable that this will allow the exploitation of an invaluable genotypic resource, assembled for other (forensic) purposes, to clarify important aspects in the formation of local gene pools.

This dataset is a series of digital map-posters accompanying the AdaptNRM Guide: Helping Biodiversity Adapt: supporting climate adaptation planning using a community-level modelling approach.

These represent supporting materials and information about the community-level biodiversity models applied to climate change. Map posters are organised by four biological groups (vascular plants, mammals, reptiles and amphibians), two climate change scenario (1990-2050 MIROC5 and CanESM2 for RCP8.5), and five measures of change in biodiversity.

The map-posters present the nationally consistent data at locally relevant resolutions in eight parts – representing broad groupings of NRM regions based on the cluster boundaries used for climate adaptation planning (http://www.environment.gov.au/climate-change/adaptation) and also Nationally.

Map-posters are provided in PNG image format at moderate resolution (300dpi) to suit A0 printing. The posters were designed to meet A0 print size and digital viewing resolution of map detail. An additional set in PDF image format has been created for ease of download for initial exploration and printing on A3 paper. Some text elements and map features may be fuzzy at this resolution.

Each map-poster contains four dataset images coloured using standard legends encompassing the potential range of the measure, even if that range is not represented in the dataset itself or across the map extent.

Most map series are provided in two parts: part 1 shows the two climate scenarios for vascular plants and mammals and part 2 shows reptiles and amphibians. Eight cluster maps for each series have a different colour theme and map extent. A national series is also provided. Annotation briefly outlines the topics presented in the Guide so that each poster stands alone for quick reference.

An additional 77 National maps presenting the probability distributions of each of 77 vegetation types – NVIS 4.1 major vegetation subgroups (NVIS subgroups) - are currently in preparation.

Example citations:

Williams KJ, Raisbeck-Brown N, Prober S, Harwood T (2015) Generalised projected distribution of vegetation types – NVIS 4.1 major vegetation subgroups (1990 and 2050), A0 map-poster 8.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

Williams KJ, Raisbeck-Brown N, Harwood T, Prober S (2015) Revegetation benefit (cleared natural areas) for vascular plants and mammals (1990-2050), A0 map-poster 9.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

This dataset has been delivered incrementally. Please check that you are accessing the latest version of the dataset. Lineage: The map posters show case the scientific data. The data layers have been developed at approximately 250m resolution (9 second) across the Australian continent to incorporate the interaction between climate and topography, and are best viewed using a geographic information system (GIS). Each data layers is 1Gb, and inaccessible to non-GIS users. The map posters provide easy access to the scientific data, enabling the outputs to be viewed at high resolution with geographical context information provided.

Maps were generated using layout and drawing tools in ArcGIS 10.2.2

A check list of map posters and datasets is provided with the collection.

Map Series: 7.(1-77) National probability distribution of vegetation type – NVIS 4.1 major vegetation subgroup pre-1750 #0x

8.1 Generalised projected distribution of vegetation types (NVIS subgroups) (1990 and 2050)

9.1 Revegetation benefit (cleared natural areas) for plants and mammals (1990-2050)

9.2 Revegetation benefit (cleared natural areas) for reptiles and amphibians (1990-2050)

10.1 Need for assisted dispersal for vascular plants and mammals (1990-2050)

10.2 Need for assisted dispersal for reptiles and amphibians (1990-2050)

11.1 Refugial potential for vascular plants and mammals (1990-2050)

11.1 Refugial potential for reptiles and amphibians (1990-2050)

12.1 Climate-driven future revegetation benefit for vascular plants and mammals (1990-2050)

12.2 Climate-driven future revegetation benefit for vascular reptiles and amphibians (1990-2050)

The gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS (Natural Resources Conservation Service) Soil & Plant Science Division (SPSD) composite ESRI file geodatabase that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase. The gNATSGO database contains a 10-meter raster of the soil map units and 70 related tables of soil properties and interpretations. It is designed to work with the SPSD gSSURGO ArcTools. Users can create full coverage thematic maps and grids of soil properties and interpretations for large geographic areas, such as the extent of a State or the conterminous United States. SSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. The current completion status of SSURGO mapping is displayed (PDF). STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods. The first version of gNATSGO was created in 2019. It is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. Three RSSs have been published as of 2019. These were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years. Resources in this dataset:Resource Title: Website Pointer for Gridded National Soil Survey Geographic Database (gNATSGO). File Name: Web Page, url: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625 The gNATSGO website provides an Overview slide presentation, Download links for gNATSGO databases (CONUS or States), ArcTools, Metadata, Technical Information, and Recommended Data Citations.

This qualitative data-set is of young people's spatial imaginaries within the UK context. It contains interviews carried out with young people aged 16/17 years across different geographic contexts. In particular, interviews focussed around the importance and significance of place, including: i) the role place has on the choices of young people who are socially and educationally similar but located in geographically diverse areas; ii) ways in which economically, socially, culturally or politically distinct places act as pull or push factors for different social groups; iii) what social, cultural, or economic importance particular localities hold for different groups.The creation of a fairer society through social mobility is high on the political agenda in the UK. It is often assumed that widening participation in higher education (HE), through various policies and initiatives, will equate to a fairer and more socially mobile society. Yet, while more disadvantaged groups are now progressing to HE, social mobility remains weak, suggesting that this is an over-simplified picture of the ways in which social inequalities are (re)produced in countries like the UK. The geographical (im)mobility of young people at this key transition point is rarely alluded to here, in terms of its significance in shaping social (im)mobility. In spatially diverse countries like the UK, access to universities, key labour markets, social networks, and other valuable resources often necessitate some degree of geographical mobility. In addressing social inequalities in wider society, it is therefore crucial to understand the nature of student flows across diverse parts of the UK, including the rationales different young people have for their (im)mobility to and from different places. There is already some evidence to suggest that the costs of HE study can deter the most disadvantaged young people from moving away for their studies, but what other place-based factors, including the cultural, social, and economic characteristics of localities might be important in shaping student (im)mobility? This interdisciplinary project will undertake an innovative and far-reaching programme of policy relevant research addressing the mobility patterns of UK HE students. The value of this research has been endorsed by all four UK HE Funding Councils, the UK Government's Social Mobility and Child Poverty Commission (Chaired by Rt. Hon. Alan Milburn), The Sutton Trust, and Universities UK. These organisations are members of the project stakeholder group and will be closely involved in the research and dissemination programme, ensuring that the research addresses areas of policy relevance and reaches a wide audience. This novel research will uncover, for the first time, the nature of student flows within and across the four countries of the UK, together with rich and in-depth understandings about how they are shaped. Taking into account the socially, economically, politically and culturally diverse nature of UK society, the project will seek to understand the placed nature of educational decision making in particular. This unique work is interdisciplinary in nature, drawing on, and contributing to, the academic disciplines of geography, education, and sociology. The research is mixed methods and organised around two distinct but sequential phases, which include large scale quantitative analysis of UK-wide student records data (phase 1) that will frame the collection of new qualitative data (phase 2). Phase 1 will involve advanced spatial analysis to examine student flows at country, region, and locality levels, producing innovative graphics displaying these spatial movements in visual form. This analysis will explore patterns and relationships between student movements and social as well as spatial characteristics. In the second phase, qualitative research will take place in 10 purposefully selected case study schools across the UK, selected on the basis of criteria developed from the quantitative analysis. To explore the sorts of factors shaping young people's mobility patterns, data collection will involve interviews with young people, two members of their social network, as well as observation of their school contexts. These rich qualitative data will dig beneath the surface of the quantitative patterns, capturing how young people's subjective experiences of space and their own geographical imaginaries impact on their geographic (im)mobility. It will explore how these relationships to place and mobility intentions are constructed and influenced by their individual biographies, social network and school. The data collection method involved semi-structured individual interviews with young people (aged 16/17) living in the UK which lasted around 45minutes to 1hour. The sample of young people (n. 112) was purposefully selected on the basis of those in school-based sixth forms and who indicated they wished to progress to university. Interviews were loosely structured around key topics which also allowed participants to express themselves in their own terms and discuss topics from their own vantage points. Interviews were transcribed verbatim and pseudonyms applied to people and places.

Description of Data Population estimates for the 850 Super Data Zones in Northern Ireland were published on 25th July 2024.

Time Period Estimates are provided for mid-2021 and mid-2022.

Notes: 1. Estimated populations are given as of 30th June for the year noted, rounded to the nearest person. 2. Rounding for estimates at this geographic level is independent. As such, figures may not add to higher geography totals.

Methodology The population estimates for small geographical areas are created from an average of two statistical methods: the ratio change and cohort-component methods. The ratio change method applies the change in secondary (typically administrative) data sources to Census estimates. The 2022 small geographical area estimates use a single statistical dataset which has been created by amalgamating a series of different administrative data sources. This statistical dataset is a de-duplicated admin based estimate for the usually resident population of NI. The cohort-component method updates the Census estimates by ‘ageing on’ populations and applying information on births, deaths and migration. An average of both methods is taken and constrained to the published population figures. Further information is available at: NISRA 2022 Mid-year Population Estimates webpage

Geographic Referencing Population Estimates are based on a large number of secondary datasets. Where the full address was available, the Pointer Address database was used to allocate a unique property reference number (UPRN) and geo-spatial co-ordinates to each home address. These can then be used to map the address to particular geographies. Where it was not possible to assign a unique property reference number to an address using the Pointer database, or where the secondary dataset contained only postcode information, the Central Postcode Directory was used to map home address postcodes to higher geographies. A small proportion of records with unknown geography were apportioned based on the spatial characteristics of known records.

Further Information The next estimates of the population for Northern Ireland will be released later in 2024.

Contact: NISRA Customer Services 02890 255156 census@nisra.gov.uk Responsible Statistician: Jonathan Harvey

Download Model Uncertainty for Predicted Geographical Distribution of Marginariella Urvilliana (Macroalage) on Subtidal Rocky Reefs around New Zealand DataView on MapView OGC WMS Web ServiceView ArcGIS Web Service Predicted geographical distribution of Marginariella urvilliana (macroalgae) on subtidal rocky reefs in New Zealand using ensemble Species Distribution Modelling (Bootstrapped Boosted Rregression Tree and Random Forest models). Detailed methods are available in Lundquist et al., 2020. Spatial predictions generated for all reef habitat (defined by DOC national rocky reef layer) less than 40m depth. Number of taxa records: 74 Statistical model performance: Good (TSS = 0.85) Expert evaluation of predicted geographical distribution: 1, Very accurate Spatial resolution: 250m

The Utah Geospatial Resource Center (UGRC) services are the authoritative source for Utah cadastral data used by the BLM Utah State Office. This includes the statewide Public Land Survey System (PLSS) and the Geographic Coordinate System Database (GCDB).The GCDB dataset provides the BLM and the public with a set of geographic foundation data that accurately depicts the locations of PLSS corners. The GCDB is based on the best and most current survey records available, and uses known geographic positions of control stations within the PLSS network. The GCDB is the key component of all cadastral information.All users of PLSS datasets ought to be aware that UGRC is continually updating these data. Updates are expected annually as horizontal control positions from published sources and global positioning system (GPS) observations are added. The GCDB grid is adjusted using various methods to determine the best geographic positions of the survey points.Links to UGRC Datasets:Utah PLSS Townships GCDB - https://opendata.gis.utah.gov/datasets/utah::utah-plss-townships-gcdb/about ;Utah PLSS Sections GCDB - https://opendata.gis.utah.gov/datasets/utah::utah-plss-sections-gcdb/about ;Utah PLSS Quarter Sections GCDB - https://opendata.gis.utah.gov/datasets/utah::utah-plss-quarter-sections-gcdb/about ;Utah PLSS Quarter Quarter Sections GCDB - https://opendata.gis.utah.gov/datasets/utah::utah-plss-quarter-quarter-sections-gcdb/about ;Utah PLSS Point GCDB - https://opendata.gis.utah.gov/datasets/utah::utah-plss-point-gcdb/about ;BLM Point of Contact:Calvert Norton Land Surveyor/PLSS Dataset ManagerBureau of Land Management, Utah State Office 440 W. 200 S., Suite 500 Salt Lake City, UT 84101 Phone: 801-539-4140 Email: cnorton@blm.gov

Download Model Uncertainty for Predicted Geographical Distribution of Clymene Coleana (Macroalage) on Subtidal Rocky Reefs around New Zealand DataView on MapView OGC WMS Web ServiceView ArcGIS Web Service Predicted geographical distribution of Clymene coleana (macroalgae) on subtidal rocky reefs in New Zealand using ensemble Species Distribution Modelling (Bootstrapped Boosted Rregression Tree and Random Forest models). Detailed methods are available in Lundquist et al., 2020. Spatial predictions generated for all reef habitat (defined by DOC national rocky reef layer) less than 40m depth. Number of taxa records: 59 Statistical model performance: Good (TSS = 0.84) Expert evaluation of predicted geographical distribution: 2, Accurate Spatial resolution: 250m

Download Model Uncertainty for Predicted Geographical Distribution of Gymnogongrus Furcatus (Macroalage) on Subtidal Rocky Reefs around New Zealand DataView on MapView OGC WMS Web ServiceView ArcGIS Web Service Predicted geographical distribution of Gymnogongrus furcatus (macroalgae) on subtidal rocky reefs in New Zealand using ensemble Species Distribution Modelling (Bootstrapped Boosted Rregression Tree and Random Forest models). Detailed methods are available in Lundquist et al., 2020. Spatial predictions generated for all reef habitat (defined by DOC national rocky reef layer) less than 40m depth. Number of taxa records: 86 Statistical model performance: Good (TSS = 0.87) Expert evaluation of predicted geographical distribution: 2, Accurate Spatial resolution: 250m

Download Model Uncertainty for Predicted Geographical Distribution of Ulva Compressa (Macroalage) on Subtidal Rocky Reefs around New Zealand DataView on MapView OGC WMS Web ServiceView ArcGIS Web Service Predicted geographical distribution of Ulva compressa (macroalgae) on subtidal rocky reefs in New Zealand using ensemble Species Distribution Modelling (Bootstrapped Boosted Rregression Tree and Random Forest models). Detailed methods are available in Lundquist et al., 2020. Spatial predictions generated for all reef habitat (defined by DOC national rocky reef layer) less than 40m depth. Number of taxa records: 58 Statistical model performance: Good (TSS = 0.85) Expert evaluation of predicted geographical distribution: 3, Somewhat accurate Spatial resolution: 250m

Based on the collection by the Digital National Security Archive (DNSA) Colombia and the U.S.: Political Violence, Narcotics, and Human Rights, 1948-2010. Geographical names, dates and name of file were extracted and cleansed using a combination of computing method and manually. This dataset contain geographical information of the diplomatic cables issued by the U.S. Embassy in Colombia in 1999: name of place identified, name of the original file, date, and U.S Tags of the original documents.