Learn how defense and intelligence users can leverage ArcGIS GeoEvent Server and ArcGIS GeoAnalytics Server to connect to real-time data feeds and run analytics on the stored data. From tracking units in the field to analyzing intelligence feeds and weather, ArcGIS GeoEvent Server enables users to stay current on what is happening. When you want to analyze massive amounts of stored track and report data, ArcGIS GeoAnalytics Server uses distributed computing to return spatiotemporal insight helping you make better planning decisions.

The National Population Database (NPD) is a point-based Geographical Information System (GIS) dataset that combines locational information from providers like the Ordnance Survey with population information about those locations, mainly sourced from Government statistics. The points (and sometimes polygons) represent individual buildings, so the NPD allows detailed local analysis for anywhere in Great Britain.

The Health & Safety Laboratory (HSL) working with Staffordshire University originally created the NPD in 2004 to help its parent organisation, the Health and Safety Executive (HSE), assess the risks to society of major hazard sites e.g. oil refineries, chemical works and gas holders. Of particular interest to HSE were 'sensitive' populations e.g. schools and hospitals where the people at those locations may be more vulnerable to harm and potentially harder to evacuate in an emergency. The data is split into 5 themes: residential, sensitive populations, transport, workplaces and leisure.

More information about the NPD can be found here:

https://www.hsl.gov.uk/what-we-do/better-decisions/geoanalytics/national-population-database

The NPD was created using various datasets available within Government as part of the Public Sector Mapping Agreement (PSMA) and contains other intellectual property so is only available under license and for a fee. Please contact the HSL GIS Team if you would like to discuss gaining access to the sample or full dataset.

CyberGIS-Jupyter for Water Quarterly Release Announcement (2020 Q2)

Dear HydroShare Users,

We are pleased to announce a new quarterly release of CyberGIS-Jupyter for Water (CJW) platform at https://go.illinois.edu/cybergis-jupyter-water. This release includes new capabilities to support the geoanalytics suite of GRASS for model pre/post-processing, PostGIS database, and Landlab Earth surface modelling toolkit along with several enhancements to job submission middleware, system security as well as service infrastructure. Please refer to the following list for details and examples.

Please let us know if you have any questions or run into any problems (help@cybergis.org). Any feedback would be greatly appreciated.

Best regards, CyberGIS-Hydro team

GRASS GIS for model pre/post-processing: Learn how to consolidate the features of the GRASS geoanalytics suite to support pre/post-processing for SUMMA and RHESSYs models in CJW. Example notebooks: https://www.hydroshare.org/resource/4cbcfdd6e7f943e2969dd52e780bc52d/

Manage geospatial data with PostGIS: PostGIS is an extension to the PostgreSQL object-relational database system which allows geospatial data to be efficiently stored while providing various advanced functions for in-situ data analysis and processing. Example notebooks: https://www.hydroshare.org/resource/bb779d4cce564dd6afcf463c8910786f/

Security and service infrastructure enhancements Trusted group: Starting from this release, all users are required to join the “CyberGIS-Jupyter for Water” trusted group at https://www.hydroshare.org/group/157 in order to access the CJW platform, which is a preventive measure to protect the shared computing resources from being abused by malicious users. A complete user profile page is highly recommended to expedite the approval process. User metric submission to XSEDE: CJW, as a science gateway, is now sending unique user usage metrics to XSEDE to comply with its requirements.

Landlab for enabling collaborative numerical modeling in Earth sciences using knowledge infrastructure Example notebooks: https://www.hydroshare.org/resource/370c288b61b84794b847ef85c4dd4ffb/ https://www.hydroshare.org/resource/6add6bee06bb4050bfe23e1081627614/

Job submission enhancements Refactored the structure of the cyberGIS job submission system Data-driven implementation for avoiding excessive data transmission between HydroShare and CJW Add the specification of input parameters into a JSON file to improve the flexibility and generality of model management Enable HPC-SUMMA object that can directly call SUMMA Example notebooks: https://www.hydroshare.org/resource/4a4a22a69f92497ead81cc48700ba8f8/

This feature layer consists of paired GLOBE Observer Mosquito Habitat Mapper (MHM) and GLOBE Observer Land Cover (LC) observation data resulting from the following processing steps:MHM
GEOJSON Data was pulled from this GLOBE API URL: https://api.globe.gov/search/v1/measurement/protocol/measureddate/?protocols=mosquito_habitat_mapper&startdate=2017-05-01&enddate=2022-12-31&geojson=TRUE&sample=FALSE Only device-reported measurements are kept- "DataSource" = "GLOBE Observer App"
As we are only interested in device measurements, latitude and longitude are determined from "MeasurementLatitude" and "MeasurementLongitude". All instances of duplicate photos have been removed from the dataset.LC
GEOJSON Data was pulled from this GLOBE API URL:https://api.globe.gov/search/v1/measurement/protocol/measureddate/?protocols=land_covers&startdate=2018-09-01&enddate=2022-12-31&geojson=TRUE&sample=FALSE Only device-reported measurements are kept- "DataSource" = "GLOBE Observer App"
As we are only interested in device measurements, latitude and longitude are determined from "MeasurementLatitude" and "MeasurementLongitude".ConcurrenceThese two layers were then combined using a spatiotemporal join with the following conditions: Tool: Geoanalytics Desktop Tools -> Join Features Target Layer: LC Join Type: one to many Join Layer: MHM Coordinate fields used: MeasurementLatitude, MeasurementLongitude Time fields used: MeasuredAt (UTC time) Spatial Proximity: 100 meters (NEAR_GEODESIC) Temporal Proximity: 60 minutes (NEAR) Attribute match: UserIDThe result is a dataset consisting of all paired instances where the same observer (Userid) collected a Mosquito Habitat Mapper observation within 100 meters and 1 hour of collecting a Land Cover observation.Additional fields include:lc_mhm_obsID_pair': A string representing the two paired observations- {lc_LandCoverId}_{mhm_MosquitoHabitatMapperId}'lc_latlon': A string representing the coordinates of the LC observation - "({lc_MeasurementLatitude}, {lc_MeasurementLongitude})"'mhm_latlon': A string representing the coordinates of the MHM observation - "({mhm_MeasurementLatitude}, {mhm_MeasurementLongitude})"'spatialDistanceMeters': Numeric value representing the distance between the two paired observations in meters'temporalDistanceMinutes': Numeric value representing the time delta between the two paired observations in minutes'squareBuffer': A polygon string representing a 100m square centered on the LC observation coordinates. This may be used in conjunction with additional map layers to evaluate the land cover types near the observation coordinates. (n.b. This is not the buffer used in calculating spatiotemporal concurrence)For the purposes of this visualization, geometry is a 100m x 100m square centered on the Land Cover observation coordinates.

The GeoAnalytics software development team has created a new tool to trace proximity events– a tool we’re calling Proximity Tracing. This tool searches for when and where individual entities (for example, animals, people, vehicles, devices) are within a given proximity to other individuals in space and time – what we’re calling proximity events. Tracing potential proximity events can be applied to contact tracing – to help find potential contact events._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...