This data is part of the series of maps that covers the whole of Australia at a scale of 1:250 000 (1cm on a map represents 2.5km on the ground) and comprises 513 maps. This is the largest scale at which published topographic maps cover the entire continent.
Data is downloadable in various distribution formats.

This data is part of the series of maps that covers the whole of Australia at a scale of 1:250 000 (1cm on a map represents 2.5km on the ground) and comprises 513 maps. This is the largest scale at which published topographic maps cover the entire continent. Data is downloadable in various distribution formats.

Summary statistics and autocorrelation coefficient for the number of cases of severe acute malnutrition as assessed in 153 Jamaican communities.

Segmentation models perform a pixel-wise classification by classifying the pixels into different classes. The classified pixels correspond to different objects or regions in the image. These models have a wide variety of use cases across multiple domains. When used with satellite and aerial imagery, these models can help to identify features such as building footprints, roads, water bodies, crop fields, etc.Generally, every segmentation model needs to be trained from scratch using a dataset labeled with the objects of interest. This can be an arduous and time-consuming task. Meta's Segment Anything Model (SAM) is aimed at creating a foundational model that can be used to segment (as the name suggests) anything using zero-shot learning and generalize across domains without additional training. SAM is trained on the Segment Anything 1-Billion mask dataset (SA-1B) which comprises a diverse set of 11 million images and over 1 billion masks. This makes the model highly robust in identifying object boundaries and differentiating between various objects across domains, even though it might have never seen them before. Use this model to extract masks of various objects in any image.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS. Fine-tuning the modelThis model can be fine-tuned using SamLoRA architecture in ArcGIS. Follow the guide and refer to this sample notebook to fine-tune this model.Input8-bit, 3-band imagery.OutputFeature class containing masks of various objects in the image.Applicable geographiesThe model is expected to work globally.Model architectureThis model is based on the open-source Segment Anything Model (SAM) by Meta.Training dataThis model has been trained on the Segment Anything 1-Billion mask dataset (SA-1B) which comprises a diverse set of 11 million images and over 1 billion masks.Sample resultsHere are a few results from the model.

This dataset is from the City of Boston's Street Address Management (SAM) system, containing Boston addresses. Updated nightly and shared publicly.

ObjectivesSevere acute malnutrition (SAM) is an important risk factor for illness and death globally, contributing to more than half of deaths in children worldwide. We hypothesized that SAM is positively correlated to poverty, low educational attainment, major crime and higher mean soil concentrations of lead, cadmium and arsenic.MethodsWe reviewed admission records of infants admitted with a diagnosis of SAM over 14 years (2000–2013) in Jamaica. Poverty index, educational attainment, major crime and environmental heavy metal exposure were represented in a Geographic Information System (GIS). Cases of SAM were grouped by community and the number of cases per community/year correlated to socioeconomic variables and geochemistry data for the relevant year.Results375 cases of SAM were mapped across 204 urban and rural communities in Jamaica. The mean age at admission was 9 months (range 1–45 months) and 57% were male. SAM had a positive correlation with major crime (r = 0.53; P < 0.001), but not with educational attainment or the poverty index. For every one unit increase in the number of crimes reported, the rate of occurrence of SAM cases increased by 1.01% [Incidence rate ratio (IRR) = 1.01 (95% CI = 1.006–1.014); P P

Unadjusted incidence rate ratios with 95% confidence intervals, from Poisson regression models with variance correct for intragroup correlation, for the association of explanatory variables with rate of admission to the TMRU ward from 153 communities across Jamaica (2000–2013).

Ground survey route covered by the NTP team for the June 21, 2025, Samuel de Champlain Provincial Park, ON, downburst. Ground survey conducted June 23, 2025. Survey route tracked by iPads while surveying in car and on foot.View survey summary map.

A Feature web service of the Address Point file of buildings and properties in New York State. Please note that, due to the large size, the NYS Address Point statewide layer cannot be downloaded in shapefile format. A map service of the Street and Address Maintenance (SAM) Program Address Point file is available here: https://gisservices.its.ny.gov/arcgis/rest/services.SAM Address Points Data Dictionary: https://gis.ny.gov/system/files/documents/2024/02/address-points-data-dictionary.pdf. If the purpose of accessing the address points service is for geocoding, NYS ITS has a publicly available geocoding service which includes the address points along with other layers. For more information about the geocoding service, please visit: https://gis.ny.gov/address-geocoder. For more information about the SAM Program, please visit: https://gis.ny.gov/streets-addresses.Please contact NYS ITS Geospatial Services at nysgis@its.ny.gov if you have any questions. Publication Date: See Update Frequency. Current as of Date: 2 business days prior to Publication Date. Update frequency: Second and fourth Friday of each month. Spatial Reference of Source Data: NAD_1983_UTM_Zone_18N. Spatial Reference of Map Service: WGS 1984 Web Mercator Auxiliary.This feature service is available to the public.

Original Data: These files contain rasterized topographic lidar elevations generated from data collected using a Teledyne ALTM Galaxy PRIME sensor. Native lidar data is not generally in a format accessible to most Geographic Information Systems (GIS). Specialized in-house and commercial software packages are used to process the native lidar data into 3-dimensional positions that can be imported into GIS software for visualization and further analysis. Horizontal positions are referenced to the North American Datum of 1983 Universal Transverse Mercator Zone 16 North (NAD83 UTM Zone 16N). Vertical positions are referenced to the NAD83 (2011) ellipsoid and provided in meters. The National Geodetic Survey's (NGS) GEOID18 model is used to transform the vertical positions from ellipsoid to orthometric heights referenced to the North American Vertical Datum of 1988 (NAVD88). The 3-D position data are sub-divided into a series of LAS files, which are tiled into 1-km by 1-km boxes defined by the Military Grid Reference System. The data were provided to the NOAA Office for Coastal Management (OCM) by the USACE Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX) to make the data publicly available for bulk and custom downloads from the NOAA Digital Data Access Viewer (DAV).

Summary statistics and Spearman rank correlation coefficient for the associations between socioeconomic and geochemical variables and number of cases of severe acute malnutrition as assessed in up to 204 Jamaican communities.

Autocorrelation coefficient (lag 1) for the poverty and crime indices and Pearson correlation coefficient for correlation of these variables with the number of cases of severe acute malnutrition as assessed in up to 153 Jamaican communities.

This project explores the feasibility of integrating solar-powered infrastructure into bike pathways as a sustainable energy and transportation solution for California. Using advanced tools like ArcGIS (for analysis), PVWatts, SAM, and JEDI, this study evaluates the economic, environmental, and technical implications through a conceptual case study based in Riverside. Insights drawn from global case studies and stakeholder feedback highlight challenges such as financial constraints, regulatory complexities, and technical design considerations, while also identifying opportunities for renewable energy generation, greenhouse gas emission reductions, and enhanced urban mobility. The conceptual case study serves as a framework for assessing potential benefits and informing actionable strategies. Recommendations address barriers and align implementation with California’s climate action and sustainability goals, offering a roadmap for integrating renewable energy with active transportation sy..., The data collection and processing methods for this project utilized a combination of publicly available tools and resources to ensure accuracy and usability. Key geospatial, energy modeling, and economic analysis data were gathered using reliable tools such as ArcGIS, SAM, JEDI, and PVWatts, with outputs systematically processed into accessible formats. This approach enabled comprehensive analysis of bike path integration, energy performance, and economic impacts.

Data Collection:

BikePaths_Riverside.qgz: Geospatial data detailing bike paths in Riverside was gathered from publicly available sources and initially analyzed using ArcGIS Pro. To ensure open access and reusability, the data has been converted to a .qgz project file compatible with QGIS (version 3.42), a free and open-source GIS platform.

SAM_Input_Variable_Values.csv: Input parameters were collected based on standard system specifications, financial assumptions, and default or adjusted inputs available in the System Ad..., , # Data for: Solar bike path feasibility study in California

https://doi.org/10.5061/dryad.4tmpg4fn1

Description of the data and file structure

The data was collected to evaluate the feasibility, technical requirements, and potential impacts of integrating solar-powered infrastructure into bike pathways. The study utilized geospatial data from ArcGIS for spatial analysis and site evaluation, combined with energy modeling tools such as PVWatts and SAM to estimate energy production, greenhouse gas reductions, and financial metrics. The JEDI model was employed to assess economic and job creation impacts. These efforts were guided by a conceptual case study in Riverside, California, to simulate real-world scenarios and inform actionable strategies for renewable energy integration. Feedback from stakeholders further shaped the analysis, addressing technical, economic, and regulatory challenges while aligning with California's sustainability goa...,

City of Boston street segments data from the Street Address Management (SAM) system. Updated nightly.

IntroductionGeographic Information Systems (GIS) and spatial analysis are emerging tools for global health, but it is unclear to what extent they have been applied to HIV research in Africa. To help inform researchers and program implementers, this scoping review documents the range and depth of published HIV-related GIS and spatial analysis research studies conducted in Africa.MethodsA systematic literature search for articles related to GIS and spatial analysis was conducted through PubMed, EMBASE, and Web of Science databases. Using pre-specified inclusion criteria, articles were screened and key data were abstracted. Grounded, inductive analysis was conducted to organize studies into meaningful thematic areas.Results and discussionThe search returned 773 unique articles, of which 65 were included in the final review. 15 different countries were represented. Over half of the included studies were published after 2014. Articles were categorized into the following non-mutually exclusive themes: (a) HIV geography, (b) HIV risk factors, and (c) HIV service implementation. Studies demonstrated a broad range of GIS and spatial analysis applications including characterizing geographic distribution of HIV, evaluating risk factors for HIV, and assessing and improving access to HIV care services.ConclusionsGIS and spatial analysis have been widely applied to HIV-related research in Africa. The current literature reveals a diversity of themes and methodologies and a relatively young, but rapidly growing, evidence base.

Link to the Sam Transit website that provides public transit for Sioux Falls, South Dakota.

A dataset describing exposed bedrock and surficial geology of Antarctica constructed by the GeoMAP Action Group of SCAR (The Scientific Committee on Antarctic Research) and GNS Science, New Zealand. Legacy geological map data have been captured into a geographic information system (GIS), refining its spatial reliability, harmonising classification, then improving representation of glacial sequences and geomorphology. A total 99,080 polygons have been unified for depicting geology at 1:250,000 scale, but locally there are some areas with higher spatial precision. Geological definition in GeoMAP v.2022-08 is founded on a mixed chronostratigraphic- and lithostratigraphic-based classification. Description of rock and moraine polygons employs international GeoSciML data protocols to provide attribute-rich and queriable data; including bibliographic links to 589 source maps and scientific literature. Data are provided under CC-BY License as zipped ArcGIS geodatabase, QGIS geopackage or GoogleEarth kmz files. GeoMAP is the first detailed geological dataset covering all of Antarctica. GeoMAP depicts 'known geology' of rock exposures rather than 'interpreted' sub-ice features and is suitable for continent-wide perspectives and cross-discipline interrogation.

Read on page 9 of the Leaving School magazine how Sam Keast (Senior GIS Analyst at the Ministry of Social Development) benefited from studying Geography.

Shows the management zones used by Bay of Plenty Regional Council to manage and monitor water use and water quality in the region. Full Architecture for this project can be found here.Created as part of BOPRC Biosecurity GIS development, commenced in April 2020.Scott Sambell from Ethos Environmental is contracted by the Biosecurity Team to create integrated system on boprc.maps.arcgis.com for recording, analysing and reporting pest weed observations and actions. Sam Stephens and Juliet O'Connell are the BOPRC contacts.Contacts:Scott Sambell: scott@ethosgis.comSam Stephens: Sam.Stephens@boprc.govt.nzJuliet O'Connell: Juliet.O'Connell@boprc.govt.nz

An important historic database on sea level change and its accompanying map are presented in a new digital version. The original database was compiled in 1924 by Ellen Louise Mertz and synthesises field observations collected in the late 19th and early 20th centuries pertaining to late glacial and postglacial relative sea level indicators across the Danish region. The original tables have been transcribed and expanded into a new digital database consisting of 658 entries. The original map sheet has been georeferenced and 392 mapped data points assigned coordinates. These have been linked to the digital data table, allowing them to be processed in a Geographic Information System (GIS). When using the dataset, please cite the data-doi and the accompaning paper: Jackson, S. P., Svennevig, K., & Kjeldsen, K. K. (2024). A new digital database of Ellen Louise Mertz’s 1924 ‘Overview of late- and postglacial elevation changes in Denmark’. GEUS Bulletin, 57. https://doi.org/10.34194/geusb.v57.8339