The size of the Asia Pacific GIS market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 9.08% during the forecast period.Geographic Information Systems are very powerful tools for capturing, storing, analyzing, and visualizing geographic data. The technology integrates maps with databases that assist organizations in understanding spatial relationships, patterns, and trends. Applications can be found across a broad spectrum of industries, such as urban planning, environmental management, agriculture, and public health.Asia Pacific is growing most rapidly in the regions relevant to the global market for Asia Pacific GIS. Growth is encouraged by factors like increasing levels of urbanization, increased infrastructures investments, and growth levels of awareness about GIS and what benefits it can offer to any organization. Lately, with the advancement of GIS technology like GIS solutions offered both on cloud and mobile environment has made access and usabilities much easier to the organizations.The applications of GIS in solving problems such as disaster management and climate change in the Asia Pacific region have become incredibly extensive. Examples of using GIS include mapping flood-prone areas, monitoring deforestation, and improving transportation networks. The greater the environmental and social challenge that faces this developing region, the more GIS is going to play a significant role in the discovery of meaningful insights for the guidance of informed decisions. Recent developments include: February 2024 - John Deere announced a strategic partnership with Hexagon’s Leica Geosystems to accelerate the digital transformation of the heavy construction industry. John Deere and Hexagon joined forces to bring cutting-edge technologies and solutions to construction professionals worldwide., January 2024 - BlackSky Technology Inc. won a first-in-class contract to support the Indonesian Ministry of Defence (MoD), supplying Gen-3 earth observation satellites, ground station capabilities, and flight operations support. BlackSky also won a multi-year contract to support the MoD in the supply of assured subscription-based real-time imagery (RTI) and analytics services. The multi-year contract was won by BlackSky Technology Inc. in partnership with Alenia Space, a subsidiary of Thales Group, to supply Assured subscription-based RTI and analytics services to the Indonesian Ministry of Defense. The total value of the two contracts is approximately USD 50 million.. Key drivers for this market are: Ease of Convenience of Shoppers Elevated Through No Traveling and Simpler Access Across Global Borders, Higher Return on Investment. Potential restraints include: Incidents of Fraudulent Transactions and Cyber Crime, Opening of Physical Spaces, Galleries, and Auctions Impacting Online Sales. Notable trends are: Cloud Deployment Segment to Hold Significant Market Share.

This data layer includes key performance metrics collected by the City and partners tracking the progress towards the goals of the Internet for All Seattle Initiative. Internet for All Seattle Dashboards. The data points reflect activities in five categories: 1) Affordable Connectivity Program, 2) Internet Connectivity, 3) Devices, 4) Digital Skills & Technical Support, and 5) Outreach & Assistance. The majority of the Internet for All Seattle Action Plan items and data fall under these five areas. Source data for Internet for All maps and dashboards.Updated quarterly. Last update: March 4, 2024. ATTRIBUTE NAME DEFINITION ADDITIONAL INFORMATION

Resource Organization or program providing metrics for this dashboard. Access for All Program - City of Seattle program to connect eligible organizations and locations in Seattle with free high speed internet service in partnership with Comcast, Astound Broadband, and Lumen. City of Seattle Facilities - City owned buildings, including Community Centers, City Hall, Seattle Center and others. Internet Essentials Program - Low-cost internet program provided by Comcast offering $9.95/month + tax for eligible households. Internet First Program - Low-cost internet program provided by Astound offering $50 Mbps Internet* to qualifying low-income households. Other Partners - Other organizations partnering with the City of Seattle. Seattle Housing Authority - An independent public corporation in the city of Seattle responsible for public housing for low-income, elderly, and disabled residents. Seattle IT Digital Equity - City of Seattle, Seattle Information Technology Department Digital Equity Program. Seattle IT Digital Navigator - Seattle IT grant program providing funding to community-based organizations to provide digital navigation services. Seattle IT Technology Matching Fund - City of Seattle grant program providing funding to community-based organizations to increase internet access and adoption. Seattle Public Library - The public library system serving the city of Seattle Seattle Public Schools - The public school district serving the city of Seattle. Simply Internet Program - Low-cost internet program provided by Astound offering for $9.95/month + tax for eligible households.

Location_Name Additional info about physical location.

Organization Nonprofit or community group funded by the City.

Project_Title Title of a project funded by the City.

Budget Budget value associated with a resource.

Date Date metrics were reported.

Award_Year Year a grant was awarded to a grantee.

Street_Address Address of physical location.

City City of physical location.

State State of physical location.

ZIP ZIP of physical location.

Council_District Council District resource is located in.

Longitude Longitude of physical location.

Latitude Latitude of physical location.

ISP An organization that provides services for accessing, using, or participating in the Internet.

Citywide_Y_N Is resource provided throughout City.

Devices_Distributed The number of devices that were provided to residents.

Devices_Distributed_Y_N Is there a value in Devices_Distributed field (used to create dashboards).

Devices_Loaned The number of devices that were loaned to residents for temporary use.

Devices_Loaned_Y_N Is there a value in Devices_Loaned field (used to create dashboards).

DSTS_TotalServed The number of residents served by digital skills training and technical support programs. DSTS refers to Digital Skills and Training Support

DSTS_TotalServed_Y_N Is there a value in DSTS_TotalServed field (used to create dashboards).

DSTS_Hours The number of hours of digital skills training and technical support provided.

DSTS_Hours_Y_N Is there a value in DSTS_Hours field (used to create dashboards).

IC_Hotspots_Sponsored Number of residents provided with hotspots or sponsored internet service. IC refers to Internet Connectivity

IC_Hotspots_Sponsored_Y_N Is there a value in IC_Hotspots_Sponsored field (used to create dashboards).

IC_PubWiFiConnections Number of Wi-Fi connections provided at public Wi-Fi sites.

IC_PubWiFiConnections_Y_N Is there a value in IC_PubWiFiConnections field (used to create dashboards).

IC_PubWiFiSites Number of sites providing public Wi-Fi.

IC_PubWiFiSites_Y_N Is there a value in IC_PubWiFiSites field (used to create dashboards).

IC_LowCostServices The number of residents enrolled in Low-cost internet programs offered by Comcast and Astound.

IC_LowCostServices_Y_N Is there a value in IC_LowCostServices field (used to create dashboards).

IC_Organizations Sites providing internet connectivity through their organization. Federal Subsidy Program Emergency Broadband Program (EBB) was a federal program to help low-income households afford broadband services and internet-connected devices during the pandemic. The program officially ended in early 2022 and was replaced by the Affordable Connectivity Program. The Affordable Connectivity Program (ACP) is a federal program to help low-income households afford broadband services and internet-connected devices during the pandemic. The Program provides a discount of up to $30 per month for broadband services for eligible consumers.

IC_Organizations_Y_N Is there a value in IC_Organizations field (used to create dashboards).

IC_FedSubsEBBACP Number of total households that participated in the EBB or ACP programs.

IC_FedSubsEBBACP_Y_N Is there a value in IC_FedSubsEBBACP field (used to create dashboards).

OA_InternetServReqs The number of requests from the public for information about internet service. These requests come to the City and are fulfilled by Seattle IT Digital Equity staff. OA refers to Outreach and Assistance

OA_InternetServReqs_Y_N Is there a value in OA_InternetServReqs field (used to create dashboards).

OA_LowInternetInfo The number of requests from the public for information about low-income internet service. These requests come to the City and are fulfilled by Seattle IT Digital Equity staff.

OA_LowInternetInfo_Y_N Is there a value in OA_LowInternetInfo field (used to create dashboards).

OA_LowInternetevent Number of residents provided with information about free or low-cost internet at outreach events. This outreach is conducted by Seattle IT Digital Equity staff.

OA_LowInternetevent_Y_N Is there a value in OA_LowInternetevent field (used to create dashboards)

The Wildland Fire Interagency Geospatial Services (WFIGS) Group provides authoritative geospatial data products under the interagency Wildland Fire Data Program. Hosted in the National Interagency Fire Center ArcGIS Online Organization (The NIFC Org), WFIGS provides both internal and public facing data, accessible in a variety of formats.This service includes perimeters for wildland fire incidents that meet the following criteria:Categorized in the IRWIN (Integrated Reporting of Wildland Fire Information) integration service as a Wildfire (WF) or Prescribed Fire (RX) recordFire Discovery Date is in the year 2025Is Valid and not "quarantined" in IRWIN due to potential conflicts with other recordsAttribution of the source polygon is set to a Feature Access of Public, a Feature Status of Approved, and an Is Visible setting of YesPerimeters are not available for every incident. For a complete set of features that meet the same IRWIN criteria, see the 2025 Wildland Fire Incident Locations to Date service.No "fall-off" rules are applied to this service. Criteria were determined by an NWCG Geospatial Subcommittee task group. Data are refreshed every 5 minutes. Changes in the perimeter source may take up to 15 minutes to display.Perimeters are pulled from multiple sources with rules in place to ensure the most current or most authoritative shape is used.Attributes and their definitions can be found below. More detail about the NWCG Wildland Fire Event Polygon standard can be found here.Attributes:poly_SourceOIDThe OBJECTID value of the source record in the source dataset providing the polygon.poly_IncidentNameThe incident name as stored in the polygon source record.poly_MapMethodThe mapping method with which the polygon was derived.poly_GISAcresThe acreage of the polygon as stored in the polygon source record.poly_CreateDateSystem generated date for the date time the source polygon record was created (stored in UTC).poly_DateCurrentSystem generated date for the date time the source polygon record was last edited (stored in UTC).poly_PolygonDateTimeRepresents the date time that the polygon data was captured.poly_IRWINIDIRWIN ID stored in the polygon record.poly_FORIDFORID stored in the polygon record.poly_Acres_AutoCalcSystem calculated acreage of the polygon (geodesic WGS84 acres).poly_SourceGlobalIDThe GlobalID value of the source record in the source dataset providing the polygon.poly_SourceThe source dataset providing the polygon.attr_SourceOIDThe OBJECTID value of the source record in the source dataset providing the attribution.attr_ABCDMiscA FireCode used by USDA FS to track and compile cost information for emergency initial attack fire suppression expenditures. for A, B, C & D size class fires on FS lands.attr_ADSPermissionStateIndicates the permission hierarchy that is currently being applied when a system utilizes the UpdateIncident operation.attr_ContainmentDateTimeThe date and time a wildfire was declared contained. attr_ControlDateTimeThe date and time a wildfire was declared under control.attr_CreatedBySystemArcGIS Server Username of system that created the IRWIN Incident record.attr_IncidentSizeReported for a fire. The minimum size is 0.1.attr_DiscoveryAcresAn estimate of acres burning upon the discovery of the fire. More specifically when the fire is first reported by the first person that calls in the fire. The estimate should include number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.attr_DispatchCenterIDA unique identifier for a dispatch center responsible for supporting the incident.attr_EstimatedCostToDateThe total estimated cost of the incident to date.attr_FinalAcresReported final acreage of incident.attr_FFReportApprovedByTitleThe title of the person that approved the final fire report for the incident.attr_FFReportApprovedByUnitNWCG Unit ID associated with the individual who approved the final report for the incident.attr_FFReportApprovedDateThe date that the final fire report was approved for the incident.attr_FireBehaviorGeneralA general category describing the manner in which the fire is currently reacting to the influences of fuel, weather, and topography. attr_FireBehaviorGeneral1A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireBehaviorGeneral2A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireBehaviorGeneral3A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireCauseBroad classification of the reason the fire occurred identified as human, natural or unknown. attr_FireCauseGeneralAgency or circumstance which started a fire or set the stage for its occurrence; source of a fire's ignition. For statistical purposes, fire causes are further broken into specific causes. attr_FireCauseSpecificA further categorization of each General Fire Cause to indicate more specifically the agency or circumstance which started a fire or set the stage for its occurrence; source of a fire's ignition. attr_FireCodeA code used within the interagency wildland fire community to track and compile cost information for emergency fire suppression expenditures for the incident. attr_FireDepartmentIDThe U.S. Fire Administration (USFA) has created a national database of Fire Departments. Most Fire Departments do not have an NWCG Unit ID and so it is the intent of the IRWIN team to create a new field that includes this data element to assist the National Association of State Foresters (NASF) with data collection.attr_FireDiscoveryDateTimeThe date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.attr_FireMgmtComplexityThe highest management level utilized to manage a wildland fire event. attr_FireOutDateTimeThe date and time when a fire is declared out. attr_FireStrategyConfinePercentIndicates the percentage of the incident area where the fire suppression strategy of "Confine" is being implemented.attr_FireStrategyFullSuppPrcntIndicates the percentage of the incident area where the fire suppression strategy of "Full Suppression" is being implemented.attr_FireStrategyMonitorPercentIndicates the percentage of the incident area where the fire suppression strategy of "Monitor" is being implemented.attr_FireStrategyPointZonePrcntIndicates the percentage of the incident area where the fire suppression strategy of "Point Zone Protection" is being implemented.attr_FSJobCodeA code use to indicate the Forest Service job accounting code for the incident. This is specific to the Forest Service. Usually displayed as 2 char prefix on FireCode.attr_FSOverrideCodeA code used to indicate the Forest Service override code for the incident. This is specific to the Forest Service. Usually displayed as a 4 char suffix on FireCode. For example, if the FS is assisting DOI, an override of 1502 will be used.attr_GACCA code that identifies one of the wildland fire geographic area coordination center at the point of origin for the incident.A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.attr_ICS209ReportDateTimeThe date and time of the latest approved ICS-209 report.attr_ICS209RptForTimePeriodFromThe date and time of the beginning of the time period for the current ICS-209 submission.attr_ICS209RptForTimePeriodToThe date and time of the end of the time period for the current ICS-209 submission. attr_ICS209ReportStatusThe version of the ICS-209 report (initial, update, or final). There should never be more than one initial report, but there can be numerous updates, and even multiple finals (as determined by business rules).attr_IncidentManagementOrgThe incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.attr_IncidentNameThe name assigned to an incident.attr_IncidentShortDescriptionGeneral descriptive location of the incident such as the number of miles from an identifiable town. attr_IncidentTypeCategoryThe Event Category is a sub-group of the Event Kind code and description. The Event Category further breaks down the Event Kind into more specific event categories.attr_IncidentTypeKindA general, high-level code and description of the types of incidents and planned events to which the interagency wildland fire community responds.attr_InitialLatitudeThe latitude location of the initial reported point of origin specified in decimal degrees.attr_InitialLongitudeThe longitude location of the initial reported point of origin specified in decimal degrees.attr_InitialResponseAcresAn estimate of acres burning at the time of initial response. More specifically when the IC arrives and performs initial size up. The minimum size must be 0.1. The estimate should include number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.attr_InitialResponseDateTimeThe date/time of the initial response to the incident. More specifically when the IC arrives and performs initial size up. attr_IrwinIDUnique identifier assigned to each incident record in IRWIN.attr_IsFireCauseInvestigatedIndicates if an investigation is underway or was completed to determine the cause of a fire.attr_IsFSAssistedIndicates if the Forest Service provided assistance on an incident outside their jurisdiction.attr_IsMultiJurisdictionalIndicates if the

This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into country boundaries, administrative 1 boundaries, and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The layer shows the annual average PM 2.5 from 1998 to 2016, highlighting if the overall mean for an area meets the World Health Organization guideline of 10 micrograms per cubic meter annually. Areas that don't meet the guideline and are above the threshold are shown in red, and areas that are lower than the guideline are in grey.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality globally. Some of the things we can learn from this layer:What is the average annual PM 2.5 value over 19 years? (1998-2016)What is the annual average PM 2.5 value for each year from 1998 to 2016?What is the statistical trend for PM 2.5 over the 19 years? (downward or upward)Are there hot spots (or cold spots) of PM 2.5 over the 19 years?How many people are impacted by the air quality in an area?What is the death rate caused by the joint effects of air pollution?Choose a different attribute to symbolize in order to reveal any of the patterns above.A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis, trends, and a 19-year average. The country and administrative 1 layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries and population figures:Antarctica is excluded from all maps because it was not included in the original NASA grids.50km hex bins generated using the Generate Tessellation tool - projected to Behrmann Equal Area projection for analysesPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Administrative boundaries from World Administrative Divisions layer from ArcGIS Living Atlas - projected to Behrmann Equal Area projection for analyses and hosted in Web MercatorSources: Garmin, CIA World FactbookPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Country boundaries from Esri 2019 10.8 Data and Maps - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator. Sources: Garmin, Factbook, CIAPopulation figures attached to the country boundaries come from the World Population Estimate 2016 Sources Living Atlas layer Data processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The layers are hosted in Web Mercator Auxillary Sphere projection, but were processed using an equal area projection: Behrmann. If using this layer for analysis, it is recommended to start by projecting the data back to Behrmann.The country and administrative layer were dissolved and joined with population figures in order to visualize human impact.The dissolve tool ensures that each geographic area is only symbolized once within the map.Country boundaries were generalized post-analysis for visualization purposes. The tolerance used was 700m. If performing analysis with this layer, find detailed country boundaries in ArcGIS Living Atlas. To create the population-weighted attributes on the country and Admin 1 layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and the PM2.5 and population figures were summarized within the country and Admin 1 boundaries.The summation of the PM 2.5 values were then divided by the total population of each geography. This population value was determined by summarizing the population values from the hex bins within each geography.Some artifacts in the hex bin layer as a result of the input NASA rasters. Because the gridded surface is created from multiple satellites, there are strips within some areas that are a result of satellite paths. Some areas also have more of a continuous pattern between hex bins as a result of the input rasters.Within the country layer, an air pollution attributable death rate is included. 2016 figures are offered by the World Health Organization (WHO). Values are offered as a mean, upper value, lower value, and also offered as age standardized. Values are for deaths caused by all possible air pollution related diseases, for both sexes, and all age groups. For more information visit this page, and here for methodology. According to WHO, the world average was 95 deaths per 100,000 people.To learn the techniques used in this analysis, visit the Learn ArcGIS lesson Investigate Pollution Patterns with Space-Time Analysis by Esri's Kevin Bulter and Lynne Buie.

Cloud GIS Market size was valued at USD 890.81 Million in 2023 and is projected to reach USD 2298.38 Million by 2031, growing at a CAGR of 14.5% from 2024 to 2031.

Key Market Drivers
• Increased Adoption of Cloud Computing: Cloud computing provides scalable resources that can be adjusted based on demand, making it easier for organizations to manage and process large GIS datasets. The pay-as-you-go pricing models of cloud services reduce the need for significant upfront investments in hardware and software, making GIS more accessible to small and medium-sized enterprises.
• Growing Need for Spatial Data Integration: The ability to integrate and analyze large volumes of spatial and non-spatial data helps organizations make more informed decisions. The proliferation of Internet of Things (IoT) devices generates massive amounts of spatial data that can be processed and analyzed using Cloud GIS.
• Advancements in GIS Technology: User-friendly interfaces and visualization tools make it easier for non-experts to use GIS applications. Advanced analytical tools and machine learning algorithms available in cloud platforms enhance the capabilities of traditional GIS.
• Increased Demand for Real-Time Data: Industries like disaster management, transportation, and logistics require real-time data processing and analysis, which is facilitated by Cloud GIS. The need for up-to-date maps and spatial data drives the adoption of cloud-based GIS solutions.
• Collaboration and Sharing Needs: The ability to access GIS data and collaborate from anywhere enhances productivity and supports remote work environments. Cloud GIS supports simultaneous access by multiple users, facilitating better teamwork and data sharing.
• Urbanization and Smart Cities Initiatives: Cloud GIS is crucial for smart city initiatives, urban planning, and infrastructure development, providing the tools needed for efficient resource management. Supports planning and monitoring of sustainable development projects by providing comprehensive spatial analysis capabilities.
• Government and Policy Support: Increased government investment in geospatial technologies and smart infrastructure projects drives the adoption of Cloud GIS. Compliance with regulatory requirements for environmental monitoring and land use planning necessitates the use of advanced GIS tools.
• Industry-Specific Applications: Precision farming and land management benefit from the advanced analytics and data integration capabilities of Cloud GIS. Epidemiology and public health monitoring rely on spatial data analysis for tracking disease outbreaks and resource allocation.

Natural Disaster Detection IoT Market size was valued at USD 1.2 Billion in 2023 and is projected to reach USD 4.06 Billion by 2030, growing at a CAGR of 37.2 % during the forecast period 2024-2030.

Global Natural Disaster Detection IoT Market Drivers

Improved Early Warning Systems: The Internet of Things (IoT) makes it possible to implement sophisticated early warning systems for natural disasters such hurricanes, floods, tsunamis, earthquakes, and wildfires. Sensors placed in disaster-prone locations are able to identify environmental anomalies and precursor signals, sending real-time data to central monitoring systems. This makes it easier to notify authorities and locals in a timely manner, lessening the effects of calamities and maybe saving lives.

Enhanced Surveillance and Forecasting: Internet of Things-capable sensors and surveillance apparatuses furnish constant data gathering and examination capacities, imparting discernment into environmental factors like temperature, humidity, pressure, seismic activity, and meteorological trends. This data is processed using sophisticated analytics and machine learning algorithms to find patterns, trends, and early warning signs of impending disasters. This allows for more accurate forecasting and preparedness planning.

Remote sensing and surveillance of disaster-prone locations are made possible by Internet of Things (IoT) devices outfitted with cameras, drones, and satellite imaging technology. Emergency responders and decision-makers can benefit greatly from the situational awareness that these sensors can provide by monitoring changes in the topography, vegetation, water levels, and integrity of infrastructure. Efforts to assess damage, prepare for emergencies, and conduct catastrophe assessments are improved by real-time imagery and video feeds.

Integration with Geographic Information Systems (GIS): Spatial analysis, mapping, and visualization of disaster-related data are made easier by the integration of IoT data with GIS platforms. Decision-making processes are improved by geographic data overlays, risk maps, and geospatial modeling tools, which help authorities identify high-risk areas, allocate resources wisely, and schedule evacuation routes and shelter places.

Developments in Sensor Technology: The spread of IoT devices for natural disaster detection is driven by ongoing developments in sensor technology, such as downsizing, enhanced sensitivity, and low power consumption. Highly weatherproof and resilient sensors can survive extreme weather conditions, which makes them appropriate for use in dangerous and remote areas that are vulnerable to natural disasters.

Government Initiatives and Regulations: Across the globe, governments and regulatory agencies are investing more money and requiring the use of Internet of Things (IoT)-based technologies for resilience and disaster management. Adoption of IoT technologies to improve catastrophe warning, response, and recovery capacities is encouraged by national disaster preparedness programs, financing initiatives, and regulatory frameworks.

Collaborations between the Public and Private Sectors: In the development of Internet of Things (IoT)-based solutions for natural disaster detection, cooperation between public agencies, private businesses, academic institutions, and non-governmental organizations (NGOs) promotes innovation and knowledge exchange. In order to improve community safety and catastrophe resilience, technological development, pilot projects, and field testing are driven by public-private partnerships (PPPs) and collaborative research activities.

Growing Concern and Awareness of Climate Change: The need for Internet of Things (IoT) solutions for disaster detection and mitigation has increased as a result of growing global awareness of climate change and its effects on the frequency and intensity of natural catastrophes. The necessity for preventive actions to mitigate climate-related hazards is acknowledged by stakeholders from all industries, which motivates investments in IoT infrastructure, research, and innovation.

This feature layer provides digital tax parcels for the Organized Towns of the State of Maine. Within Maine, real property data is maintained by the government organization responsible for assessing and collecting property tax for a given location. Organized towns and townships maintain authoritative data for their communities and may voluntarily submit these data to the Maine GeoLibrary Parcel Project. "Maine Parcels Organized Towns Feature" and "Maine Parcels Organized Towns ADB" are the product of these voluntary submissions. Communities provide updates to the Maine GeoLibrary on a non-regular basis, which affects the currency of Maine GeoLibrary parcels data. Another resource for real property transaction data is the County Registry of Deeds, although organized town data should very closely match registry information, except in the case of in-process property conveyance transactions. In Unorganized Territories (defined as those regions of the state without a local government that assesses real property and collects property tax), the Maine Revenue Service is the authoritative source for parcel data. "Maine Parcels Unorganized Territory Feature" is the authoritative GIS data layer for the Unorganized Territories. However, it must always be used with auxiliary data obtained from the online resources of Maine Revenue Services (https://www.maine.gov/revenue/taxes/property-tax) to compile up-to-date parcel ownership information. Property maps are a fundamental base for many municipal activities. Although GIS parcel data cannot replace detailed ground surveys, the data can assist municipal officials with functions such as accurate property tax assessment, planning and zoning. Towns can link maps to an assessor's database and display local information, while town officials can show taxpayers how proposed development or changes in municipal services and regulations may affect the community. In many towns, parcel data also helps to provide public notices, plan bus routes, and carry out other municipal services.

This dataset contains municipality-submitted parcel data along with previously developed parcel data acquired through the Municipal Grants Project supported by the Maine Library of Geographic Information (Maine GeoLibrary). Grant recipient parcel data submissions were guided by standards presented to the Maine GeoLibrary Board on May 21, 2005, which are outlined in the "Standards for Digital Parcel Files" document available on the Maine GeoLibrary publications page (https://www.maine.gov/geolib/policies/standards.html). This dataset also contains municipal parcel data acquired through other sources; the data sources are identified (where available) by the field “FMSCORG”. Note: Join this feature layer with the "Maine Parcels Organized Towns ADB" table (https://maine.hub.arcgis.com/maps/maine::maine-parcels-organized-towns-feature/about?layer=1) for available ownership information. A date field, “FMUPDAT”, is attributed with the most recent update date for each individual parcel if available. The "FMUPDAT" field will not match the "Updated" value shown for the layer. "FMUPDAT" corresponds with the date of update for the individual data, while "Updated" corresponds with the date of update for the ArcGIS Online layer as a whole. Many parcels have not been updated in several years; use the "FMUPDAT" field to verify currency.

This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into country boundaries, administrative 1 boundaries, and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The layer shows the annual average PM 2.5 from 1998 to 2016, highlighting if the overall mean for an area meets the World Health Organization guideline of 10 micrograms per cubic meter annually. Areas that don't meet the guideline and are above the threshold are shown in red, and areas that are lower than the guideline are in grey.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality globally. Some of the things we can learn from this layer:What is the average annual PM 2.5 value over 19 years? (1998-2016)What is the annual average PM 2.5 value for each year from 1998 to 2016?What is the statistical trend for PM 2.5 over the 19 years? (downward or upward)Are there hot spots (or cold spots) of PM 2.5 over the 19 years?How many people are impacted by the air quality in an area?What is the death rate caused by the joint effects of air pollution?Choose a different attribute to symbolize in order to reveal any of the patterns above.A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis, trends, and a 19-year average. The country and administrative 1 layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries and population figures:Antarctica is excluded from all maps because it was not included in the original NASA grids.50km hex bins generated using the Generate Tessellation tool - projected to Behrmann Equal Area projection for analysesPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Administrative boundaries from World Administrative Divisions layer from ArcGIS Living Atlas - projected to Behrmann Equal Area projection for analyses and hosted in Web MercatorSources: Garmin, CIA World FactbookPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Country boundaries from Esri 2019 10.8 Data and Maps - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator. Sources: Garmin, Factbook, CIAPopulation figures attached to the country boundaries come from the World Population Estimate 2016 Sources Living Atlas layer Data processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The layers are hosted in Web Mercator Auxillary Sphere projection, but were processed using an equal area projection: Behrmann. If using this layer for analysis, it is recommended to start by projecting the data back to Behrmann.The country and administrative layer were dissolved and joined with population figures in order to visualize human impact.The dissolve tool ensures that each geographic area is only symbolized once within the map.Country boundaries were generalized post-analysis for visualization purposes. The tolerance used was 700m. If performing analysis with this layer, find detailed country boundaries in ArcGIS Living Atlas. To create the population-weighted attributes on the country and Admin 1 layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and the PM2.5 and population figures were summarized within the country and Admin 1 boundaries.The summation of the PM 2.5 values were then divided by the total population of each geography. This population value was determined by summarizing the population values from the hex bins within each geography.Some artifacts in the hex bin layer as a result of the input NASA rasters. Because the gridded surface is created from multiple satellites, there are strips within some areas that are a result of satellite paths. Some areas also have more of a continuous pattern between hex bins as a result of the input rasters.Within the country layer, an air pollution attributable death rate is included. 2016 figures are offered by the World Health Organization (WHO). Values are offered as a mean, upper value, lower value, and also offered as age standardized. Values are for deaths caused by all possible air pollution related diseases, for both sexes, and all age groups. For more information visit this page, and here for methodology. According to WHO, the world average was 95 deaths per 100,000 people.To learn the techniques used in this analysis, visit the Learn ArcGIS lesson Investigate Pollution Patterns with Space-Time Analysis by Esri's Kevin Bulter and Lynne Buie.

The Cocos (Keeling) Islands Geographic Information System (CocosGIS) is a collection of spatial data, viewing and analysis tools dealing with the Cocos (Keeling) Islands. The data include orthophotography, topographic, cultural and environmental features both of the islands and the ocean immediately surrounding them. Compilation of data and its organisation into a GIS together with documentation was undertaken by the Australian Geological Survey Organisation (AGSO) at the request of the Territories Office, Department of Transport and Regional Services (DOTRS). The data are presented in both ESRI ArcView and ArcExplorer projects. The ArcView projects require a licensed copy of ArcView. ArcExplorer is a free viewer and is distributed with the Cocos GIS CD-ROM. Data are stored as ESRI shapefiles and therefore readily useable with most modern GIS applications. Data were received from a variety of custodians and in many cases had no accompanying documentation. Lack of documentation made it increasingly difficult for AGSO with interpretation, translation and documentation of data. AGSO has attempted to include metadata for all datasets to ANZLIC core metadata standards, but the value of this is limited by the poor initial documentation. In addition to limited documentation, many datasets had inconsistent spatial accuracy. The CocosGIS comprises four main CD-ROMs with additional CD-ROMs containing full-colour orthophotography. A hard-copy user guide is distributed with the main CD-ROM set.

Interagency Wildland Fire PerimetersOverviewThis national fire history perimeter data layer of conglomerated agency perimeters was developed in support of the WFDSS application and wildfire decision support. The layer encompasses the fire perimeter datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, CalFire, and WFIGS History. Perimeters are included thru the 2023 fire season. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies. WFIGS, NPS and CALFIRE data now include Prescribed Burns. Data InputSeveral data sources were used in the development of this layer, links are provided where possible below. In addition, many agencies are now using WFIGS as their authoritative source, beginning in mid-2020.Alaska fire history USDA FS Regional Fire History Data BLM Fire Planning and Fuels National Park Service - Includes Prescribed Burns Fish and Wildlife ServiceBureau of Indian AffairsCalFire FRAS - Includes Prescribed BurnsWFIGS - BLM & BIA and other S&LData LimitationsFire perimeter data are often collected at the local level, and fire management agencies have differing guidelines for submitting fire perimeter data. Often data are collected by agencies only once annually. If you do not see your fire perimeters in this layer, they were not present in the sources used to create the layer at the time the data were submitted. A companion service for perimeters entered into the WFDSS application is also available, if a perimeter is found in the WFDSS service that is missing in this Agency Authoritative service or a perimeter is missing in both services, please contact the appropriate agency Fire GIS Contact listed in the table below.AttributesThis dataset implements the NWCG Wildland Fire Perimeters (polygon) data standard.https://www.nwcg.gov/sites/default/files/stds/WildlandFirePerimeters_definition.pdfIRWINID - Primary key for linking to the IRWIN Incident dataset. The origin of this GUID is the wildland fire locations point data layer. (This unique identifier may NOT replace the GeometryID core attribute)INCIDENT - The name assigned to an incident; assigned by responsible land management unit. (IRWIN required). Officially recorded name.FIRE_YEAR (Alias) - Calendar year in which the fire started. Example: 2013. Value is of type integer (FIRE_YEAR_INT).AGENCY - Agency assigned for this fire - should be based on jurisdiction at origin.SOURCE - System/agency source of record from which the perimeter came.DATE_CUR - The last edit, update, or other valid date of this GIS Record. Example: mm/dd/yyyy.MAP_METHOD - Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality.GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; Digitized-Topo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; OtherGIS_ACRES - GIS calculated acres within the fire perimeter. Not adjusted for unburned areas within the fire perimeter. Total should include 1 decimal place. (ArcGIS: Precision=10; Scale=1). Example: 23.9UNQE_FIRE_ - Unique fire identifier is the Year-Unit Identifier-Local Incident Identifier (yyyy-SSXXX-xxxxxx). SS = State Code or International Code, XXX or XXXX = A code assigned to an organizational unit, xxxxx = Alphanumeric with hyphens or periods. The unit identifier portion corresponds to the POINT OF ORIGIN RESPONSIBLE AGENCY UNIT IDENTIFIER (POOResonsibleUnit) from the responsible unit’s corresponding fire report. Example: 2013-CORMP-000001LOCAL_NUM - Local incident identifier (dispatch number). A number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year. Field is string to allow for leading zeros when the local incident identifier is less than 6 characters. (IRWIN required). Example: 123456.UNIT_ID - NWCG Unit Identifier of landowner/jurisdictional agency unit at the point of origin of a fire. (NFIRS ID should be used only when no NWCG Unit Identifier exists). Example: CORMPCOMMENTS - Additional information describing the feature. Free Text.FEATURE_CA - Type of wildland fire polygon: Wildfire (represents final fire perimeter or last daily fire perimeter available) or Prescribed Fire or UnknownGEO_ID - Primary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature. Globally Unique Identifier (GUID).Cross-Walk from sources (GeoID) and other processing notesAK: GEOID = OBJECT ID of provided file geodatabase (4580 Records thru 2021), other federal sources for AK data removed. CA: GEOID = OBJECT ID of downloaded file geodatabase (12776 Records, federal fires removed, includes RX)FWS: GEOID = OBJECTID of service download combined history 2005-2021 (2052 Records). Handful of WFIGS (11) fires added that were not in FWS record.BIA: GEOID = "FireID" 2017/2018 data (416 records) provided or WFDSS PID (415 records). An additional 917 fires from WFIGS were added, GEOID=GLOBALID in source.NPS: GEOID = EVENT ID (IRWINID or FRM_ID from FOD), 29,943 records includes RX.BLM: GEOID = GUID from BLM FPER and GLOBALID from WFIGS. Date Current = best available modify_date, create_date, fire_cntrl_dt or fire_dscvr_dt to reduce the number of 9999 entries in FireYear. Source FPER (25,389 features), WFIGS (5357 features)USFS: GEOID=GLOBALID in source, 46,574 features. Also fixed Date Current to best available date from perimeterdatetime, revdate, discoverydatetime, dbsourcedate to reduce number of 1899 entries in FireYear.Relevant Websites and ReferencesAlaska Fire Service: https://afs.ak.blm.gov/CALFIRE: https://frap.fire.ca.gov/mapping/gis-dataBIA - data prior to 2017 from WFDSS, 2017-2018 Agency Provided, 2019 and after WFIGSBLM: https://gis.blm.gov/arcgis/rest/services/fire/BLM_Natl_FirePerimeter/MapServerNPS: New data set provided from NPS Fire & Aviation GIS. cross checked against WFIGS for any missing perimetersFWS -https://services.arcgis.com/QVENGdaPbd4LUkLV/arcgis/rest/services/USFWS_Wildfire_History_gdb/FeatureServerUSFS - https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_FireOccurrenceAndPerimeter_01/MapServer

The gSSURGO dataset provides detailed soil survey mapping in raster format with ready-to-map attributes organized in statewide tiles for desktop GIS. gSSURGO is derived from the official Soil Survey Geographic (SSURGO) Database. SSURGO generally has 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 the traditional vector-based SSURGO digital map data and tabular data into statewide extents, adding a statewide gridded map layer derived from the vector layer, and adding a new value-added look up table (valu) containing ready-to-map attributes. The gridded map layer is in an ArcGIS file geodatabase in raster format, thus it has the capacity to store significantly more data and greater spatial extents than the traditional SSURGO product. The raster map data have a 10-meter cell size that approximates the vector polygons in an Albers Equal Area projection. 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 the raster cells and polygons to attribute tables.

For more information, see the gSSURGO webpage: https://www.nrcs.usda.gov/resources/data-and-reports/description-of-gridded-soil-survey-geographic-gssurgo-database

HSIP Local Emergency Operations Centers in the United States "The physical location at which the coordination of information and resources to support domestic incident management activities normally takes place. An Emergency Operations Center may be a temporary facility or may be located in a more central or permanently established facility, perhaps at a higher level of organization within a jurisdiction. Emergency Operations Centers may be organized by major functional disciplines (e.g., fire, law enforcement, and medical services), by jurisdiction (e.g., Federal, State, regional, county, city, tribal), or some combination thereof." (Excerpted from the National Incident Management System) The GFI source for this layer contains State and Federal Emergency Operations Centers in addition to local Emergency Operations Centers. This dataset contains these features as well. In cases where an Emergency Operations Center has a mobile unit, TechniGraphics captured the location of the mobile unit as a separate record. This record represents where the mobile unit is stored. If this location could not be verified, a point was placed in the approximate center of the Emergency Operations Centers service area. Effort was made by TechniGraphics to verify whether or not each Emergency Operations Center has a generator on-site and whether or not the Emergency Operations Center is located in a basement. This information is indicated by the values in the [GENERATOR] and [BASEMENT] fields respectively. In cases where more than one record existed for a geographical area (e.g., county, city), TechniGraphics verified whether or not one of the records represented an alternate location. This was indicated by appending "-ALTERNATE" to the value in the [NAME] field. Some Emergency Operations Centers are located at private residences. The [TYPE] field was manually evaluated during the delivery process to compare the records in which the [NAME] field contained "-ALTERNATE". In cases where these values contradicted information that was verified by TechniGraphics (e.g. [NAME] contained "-ALTERNATE" and [TYPE] = "PRIMARY"), the value in the [TYPE] field was changed to match the type indicated by the [NAME] of the verified record. TechniGraphics did not change values in this field if the type was not verified. Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. "#" and "*" characters were automatically removed from standard HSIP fields that TechniGraphics populated. Double spaces were replaced by single spaces in these same fields. At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based upon this attribute, the oldest record dates from 08/28/2009 and the newest record dates from 11/18/2009.

This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into country boundaries, administrative 1 boundaries, and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The layer shows the annual average PM 2.5 from 1998 to 2016, highlighting if the overall mean for an area meets the World Health Organization guideline of 10 micrograms per cubic meter annually. Areas that don't meet the guideline and are above the threshold are shown in red, and areas that are lower than the guideline are in grey.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality globally. Some of the things we can learn from this layer:What is the average annual PM 2.5 value over 19 years? (1998-2016)What is the annual average PM 2.5 value for each year from 1998 to 2016?What is the statistical trend for PM 2.5 over the 19 years? (downward or upward)Are there hot spots (or cold spots) of PM 2.5 over the 19 years?How many people are impacted by the air quality in an area?What is the death rate caused by the joint effects of air pollution?Choose a different attribute to symbolize in order to reveal any of the patterns above.A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis, trends, and a 19-year average. The country and administrative 1 layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries and population figures:Antarctica is excluded from all maps because it was not included in the original NASA grids.50km hex bins generated using the Generate Tessellation tool - projected to Behrmann Equal Area projection for analysesPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Administrative boundaries from World Administrative Divisions layer from ArcGIS Living Atlas - projected to Behrmann Equal Area projection for analyses and hosted in Web MercatorSources: Garmin, CIA World FactbookPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Country boundaries from Esri 2019 10.8 Data and Maps - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator. Sources: Garmin, Factbook, CIAPopulation figures attached to the country boundaries come from the World Population Estimate 2016 Sources Living Atlas layer Data processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The layers are hosted in Web Mercator Auxillary Sphere projection, but were processed using an equal area projection: Behrmann. If using this layer for analysis, it is recommended to start by projecting the data back to Behrmann.The country and administrative layer were dissolved and joined with population figures in order to visualize human impact.The dissolve tool ensures that each geographic area is only symbolized once within the map.Country boundaries were generalized post-analysis for visualization purposes. The tolerance used was 700m. If performing analysis with this layer, find detailed country boundaries in ArcGIS Living Atlas. To create the population-weighted attributes on the country and Admin 1 layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and the PM2.5 and population figures were summarized within the country and Admin 1 boundaries.The summation of the PM 2.5 values were then divided by the total population of each geography. This population value was determined by summarizing the population values from the hex bins within each geography.Some artifacts in the hex bin layer as a result of the input NASA rasters. Because the gridded surface is created from multiple satellites, there are strips within some areas that are a result of satellite paths. Some areas also have more of a continuous pattern between hex bins as a result of the input rasters.Within the country layer, an air pollution attributable death rate is included. 2016 figures are offered by the World Health Organization (WHO). Values are offered as a mean, upper value, lower value, and also offered as age standardized. Values are for deaths caused by all possible air pollution related diseases, for both sexes, and all age groups. For more information visit this page, and here for methodology. According to WHO, the world average was 95 deaths per 100,000 people.To learn the techniques used in this analysis, visit the Learn ArcGIS lesson Investigate Pollution Patterns with Space-Time Analysis by Esri's Kevin Bulter and Lynne Buie.

IntroductionIn December 2019, several cases of pneumonia of an unknown origin appeared in China. Previously, in that same year, the World Health Organization (WHO) had already published a list of the ten major global health issues which included the risk of a pandemic from respiratory diseases [1]. Later, in January 2020, the cause of the pneumonia cases detected in China was identified as being a new coronavirus of the Severe Acute Respiratory Syndrome (SARS-CoV-2) [2].The first records of SARS-CoV-2 infection, identified in Wuhan, China, spread quickly causing the territorial spread of contagions across dozens of countries. This lead the World Health Organization (WHO) to declare a pandemic, at the time more than 100 thousand cases of infection had already been detected in 114 countries and a total number of deaths higher than 4.000 [3].Geographical analysis of diseases are common in scientific literature [4, 5, 6] and Geographic Information Systems (GIS) and Spatial Analysis techniques have proven to be useful for studying how they spread across space and time [7, 8]. The spatial dimension plays a key role in epidemiological studies partly due to the growing development of technologies in terms of algorithms and processing capacity that allows the modeling of epidemiological phenomena [8]. COVID-19 studies GIS-based are just as important to understand unknown attributes of the disease in this time of great uncertainty, although, only a few studies have focused on geographic hotspots analysis and have tried to unveil the community drivers associated with the spatial patterns of local transmission [9].ObjectivesThis applied study is twofold. First seeks to highlight the importance of geographical factors in the current context; and second, uses geographic analysis methods and techniques, especially spatial statistic methods, to create evidence-based knowledge upon COVID-19 spatial spread, as well as its patterns and trends.In this way, ArcGIS Pro, Esri’s GIS software, is used in a space-time approach to synthetize the most relevant spatial dynamics. The specific objectives of the study are:1. Analyze the spatial patterns of the pandemic diffusion;2. Identify important transmission clusters;3. Identify spatial determinants of the disease spread.Study Area and DataThe study area is mainland Portugal at a municipal scale, due to being the finest scale of analysis with epidemiological information available in the official reports of the Direção-Geral da Saúde (DGS). Portugal has been severely affected by the pandemic and various spatial dynamics can be identified through time, since the patterns of incidence have changed in successive waves. In this way, the study is focused on various moments during the first year of incidence of the disease, capturing the most important patterns, tendencies and processes. Data used for this analysis is the epidemiological information of DGS [10], for the epidemiologic dimension, and Instituto Nacional de Estatística (INE) database [11] and Carta Social [12] for the variables that will be used as independent variables grouped in 3 dimensions: economic, sociodemographic and mobility (Figure 1).Figure 1 - Variables and respective dimensions of analysisMethodologyThe methodology is divided in 2 parts (Figure 2): the first is related to data acquisition, editing, management and integration in GIS, and the second is in relation to the modeling itself, in order to respond to the objectives which comprises of 3 phases: (i) space-time analysis of confirmed cases of infections to understand the diffusion processes; (ii) analysis of hot spots, clusters and outliers to identify the different patterns and tendencies over time and (iii) ordinary least squares regression (OLS) to identify the most important determinant spatial factors and drivers of the virus propagation.Figure 2 - Methodology flowResultsResults demonstrate an initial tendency of a hierarchical diffusion process, from centers of larger population densities to those of which are less dense (Figure 3), which is replaced and dominated in following periods by contagion expansion. Geographically, the first confirmed cases appeared in coastal cities and progressively penetrated into the interior of the country with a strong spatial association with the main roads and the population size of the territorial units.Figure 3 - Evolution of confirmed cases and hot spots, clusters and outliers of incidence rate by municipalityThe Norte region, namely the Porto metropolitan region, recorded a very high rate of incidence in all periods and broke records in the numbers of new cases, except in the third wave, after the Christmas and New Year festivities, in which the number of new cases was the highest ever in every region and specially in Centro region inland municipalities.The results of OLS (Figure 4) are in line with other studies [13, 14] and show that there is a significant relationship in regard to family size that is visible during almost every period, demonstrating that it is difficult to avoid contagion between cohabitants. Population density also appears as important in various moments, although with lower coefficients.Figure 4 – Ordinary Least Squares resultsEmployment concentrations also appear with a strong spatial relationship with the incidences, as well as the socioeconomic conditions that appear to be represented by different variables (beneficiaries of unemployment benefits, social reintegration allowance, declared income, proportion of house-ownership).The importance of mobility in the virus’s propagation is confirmed, both by type of usual mode of transport and commuting time. The interrelation between school students and incidence may also indicate that increased mobility associated with school attendance is relevant for propagation.ConclusionsArcGIS Pro proved to be crucial and an added value for geographical visualization and for the use of spatial statistics methods, essential in providing evidence-based knowledge about the spatial dynamics of COVID-19 in mainland Portugal. The COVID-19 waves demonstrated different spatial behaviours, with different patterns and thus different community drivers. Income, mobility, population density, family size and employment concentrations appear as the most important spatial determinants. Results are in line with scientific literature and prove the relevance of spatial approaches in epidemiology.References1 - WHO - World Health Organization. (2019). Ten threats to global health in 2019. https://www.who.int/news-room/spotlight/ten-threats-to-global-health-in-20192 - WHO - World Health Organization. (2020a). Coronavirus disease 2019 (COVID-19): situation report, 94. https://apps.who.int/iris/handle/10665/3318653 - WHO - World Health Organization. (2020e). WHO Director-General’s opening remarks at the media briefing on COVID-19 - 11 March 2020. https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-20204 - Gould, P. (1993). The slow plague : a geography of the AIDS pandemic. Blackwell Publishers. https://books.google.pt/books?id=u3Z9QgAACAAJ&dq5 - Cliff, A. D., Hagget, P., Ord, J. K., & Versey, G. R. (1981). Spatial diffusion : an historical geography of epidemics in an island community. Cambridge University Press Cambridge ; New York. https://books.google.pt/books?id=OIaqxwEACAAJ&dq6 - Arroz, M. E. (1979). Difusão espacial da hepatite infecciosa. Finisterra - Revista Portuguesa de Geografia, LV(14) DOI: https://doi.org/10.18055/Finis22377 - Lyseen, A.K.; Nøhr, C.; Sørensen, E.M.; Gudes, O.; Geraghty, E.M.; Shaw, N.T.; Bivona-Tellez, C. (2014). A review and framework for categorizing current research and development in health related geographical information systems (GIS) studies. Yearb Med. Inform. https://doi.org/10.15265%2FIY-2014-00088 - Pfeiffer, D.; Robinson, T; Stevenson, M.; Stevens, K.; Rogers, D.; Clements, A. (2008). Spatial Analysis in Epidemiology. Oxford University Press. https://books.google.pt/books/about/Spatial_Analysis_in_Epidemiology.html?id=niTDr3SIEhUC&redir_esc=y9 - Franch-Pardo, I.; Napoletano, B.M.; Rosete-Verges, F.; Billa, L. Spatial analysis and GIS in the study of COVID-19. A review. (2020). Sci.10 – DGS – Direção-Geral da Saúde (2020). Relatório de Situação. Lisboa: Ministério da Saúde – Direção-Geral da Saúde. https://covid19.min-saude.pt/relatorio-de-situacao/11 – INE – Instituto Nacional de Estatística (s.d.). Portal do INE. Base de dados. https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_base_dados&contexto=bd&selTab=tab212 – GEP – Gabinete de Estratégia e Planeamento (2018). Carta Social. Ministério do Trabalho, Solidariedade e Segurança Social. www.cartasocial.pt13 – Sousa, P., Costa, N. M., Costa, E. M., Rocha, J., Peixoto, V. R., Fernandes, A. C., Gaspar, R., Duarte-Ramos, F., Abrantes, P., & Leite, A. (2021). COMPRIME - Conhecer mais para intervir melhor: Preliminary mapping of municipal level determinants of covid-19 transmission in Portugal at different moments of the 1st epidemic wave. Portuguese Journal of Public Health. https://doi.org/10.1159/00051433414 – Andersen, L. M.; Harden, S. R.; Sugg, M. M.; Runkle, J. D.; Lundquist, T. E. (2021). Analyzing the spatial determinants of local Covid-19 transmission in the United States. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.142396

The Wildland Fire Interagency Geospatial Services (WFIGS) Group provides authoritative geospatial data products under the interagency Wildland Fire Data Program. Hosted in the National Interagency Fire Center ArcGIS Online Organization (The NIFC Org), WFIGS provides both internal and public facing data, accessible in a variety of formats.This service includes perimeters for wildland fire incidents that meet the following criteria:Categorized in the IRWIN (Integrated Reporting of Wildland Fire Information) integration service as a Wildfire (WF) or Prescribed Fire (RX) recordFire Discovery Date is in the year 2024Is Valid and not "quarantined" in IRWIN due to potential conflicts with other recordsAttribution of the source polygon is set to a Feature Access of Public, a Feature Status of Approved, and an Is Visible setting of YesPerimeters are not available for every incident. For a complete set of features that meet the same IRWIN criteria, see the 2024 Wildland Fire Incident Locations to Date service.No "fall-off" rules are applied to this service. Criteria were determined by an NWCG Geospatial Subcommittee task group. Data are refreshed every 5 minutes. Changes in the perimeter source may take up to 15 minutes to display.Perimeters are pulled from multiple sources with rules in place to ensure the most current or most authoritative shape is used.Attributes and their definitions can be found below. More detail about the NWCG Wildland Fire Event Polygon standard can be found here.Attributes:poly_SourceOIDThe OBJECTID value of the source record in the source dataset providing the polygon.poly_IncidentNameThe incident name as stored in the polygon source record.poly_MapMethodThe mapping method with which the polygon was derived.poly_GISAcresThe acreage of the polygon as stored in the polygon source record.poly_CreateDateSystem generated date for the date time the source polygon record was created (stored in UTC).poly_DateCurrentSystem generated date for the date time the source polygon record was last edited (stored in UTC).poly_PolygonDateTimeRepresents the date time that the polygon data was captured.poly_IRWINIDIRWIN ID stored in the polygon record.poly_FORIDFORID stored in the polygon record.poly_Acres_AutoCalcSystem calculated acreage of the polygon (geodesic WGS84 acres).poly_SourceGlobalIDThe GlobalID value of the source record in the source dataset providing the polygon.poly_SourceThe source dataset providing the polygon.attr_SourceOIDThe OBJECTID value of the source record in the source dataset providing the attribution.attr_ABCDMiscA FireCode used by USDA FS to track and compile cost information for emergency initial attack fire suppression expenditures. for A, B, C & D size class fires on FS lands.attr_ADSPermissionStateIndicates the permission hierarchy that is currently being applied when a system utilizes the UpdateIncident operation.attr_ContainmentDateTimeThe date and time a wildfire was declared contained. attr_ControlDateTimeThe date and time a wildfire was declared under control.attr_CreatedBySystemArcGIS Server Username of system that created the IRWIN Incident record.attr_IncidentSizeReported for a fire. The minimum size is 0.1.attr_DiscoveryAcresAn estimate of acres burning upon the discovery of the fire. More specifically when the fire is first reported by the first person that calls in the fire. The estimate should include number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.attr_DispatchCenterIDA unique identifier for a dispatch center responsible for supporting the incident.attr_EstimatedCostToDateThe total estimated cost of the incident to date.attr_FinalAcresReported final acreage of incident.attr_FFReportApprovedByTitleThe title of the person that approved the final fire report for the incident.attr_FFReportApprovedByUnitNWCG Unit ID associated with the individual who approved the final report for the incident.attr_FFReportApprovedDateThe date that the final fire report was approved for the incident.attr_FireBehaviorGeneralA general category describing the manner in which the fire is currently reacting to the influences of fuel, weather, and topography. attr_FireBehaviorGeneral1A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireBehaviorGeneral2A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireBehaviorGeneral3A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireCauseBroad classification of the reason the fire occurred identified as human, natural or unknown. attr_FireCauseGeneralAgency or circumstance which started a fire or set the stage for its occurrence; source of a fire's ignition. For statistical purposes, fire causes are further broken into specific causes. attr_FireCauseSpecificA further categorization of each General Fire Cause to indicate more specifically the agency or circumstance which started a fire or set the stage for its occurrence; source of a fire's ignition. attr_FireCodeA code used within the interagency wildland fire community to track and compile cost information for emergency fire suppression expenditures for the incident. attr_FireDepartmentIDThe U.S. Fire Administration (USFA) has created a national database of Fire Departments. Most Fire Departments do not have an NWCG Unit ID and so it is the intent of the IRWIN team to create a new field that includes this data element to assist the National Association of State Foresters (NASF) with data collection.attr_FireDiscoveryDateTimeThe date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.attr_FireMgmtComplexityThe highest management level utilized to manage a wildland fire event. attr_FireOutDateTimeThe date and time when a fire is declared out. attr_FireStrategyConfinePercentIndicates the percentage of the incident area where the fire suppression strategy of "Confine" is being implemented.attr_FireStrategyFullSuppPrcntIndicates the percentage of the incident area where the fire suppression strategy of "Full Suppression" is being implemented.attr_FireStrategyMonitorPercentIndicates the percentage of the incident area where the fire suppression strategy of "Monitor" is being implemented.attr_FireStrategyPointZonePrcntIndicates the percentage of the incident area where the fire suppression strategy of "Point Zone Protection" is being implemented.attr_FSJobCodeA code use to indicate the Forest Service job accounting code for the incident. This is specific to the Forest Service. Usually displayed as 2 char prefix on FireCode.attr_FSOverrideCodeA code used to indicate the Forest Service override code for the incident. This is specific to the Forest Service. Usually displayed as a 4 char suffix on FireCode. For example, if the FS is assisting DOI, an override of 1502 will be used.attr_GACCA code that identifies one of the wildland fire geographic area coordination center at the point of origin for the incident.A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.attr_ICS209ReportDateTimeThe date and time of the latest approved ICS-209 report.attr_ICS209RptForTimePeriodFromThe date and time of the beginning of the time period for the current ICS-209 submission.attr_ICS209RptForTimePeriodToThe date and time of the end of the time period for the current ICS-209 submission. attr_ICS209ReportStatusThe version of the ICS-209 report (initial, update, or final). There should never be more than one initial report, but there can be numerous updates, and even multiple finals (as determined by business rules).attr_IncidentManagementOrgThe incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.attr_IncidentNameThe name assigned to an incident.attr_IncidentShortDescriptionGeneral descriptive location of the incident such as the number of miles from an identifiable town. attr_IncidentTypeCategoryThe Event Category is a sub-group of the Event Kind code and description. The Event Category further breaks down the Event Kind into more specific event categories.attr_IncidentTypeKindA general, high-level code and description of the types of incidents and planned events to which the interagency wildland fire community responds.attr_InitialLatitudeThe latitude location of the initial reported point of origin specified in decimal degrees.attr_InitialLongitudeThe longitude location of the initial reported point of origin specified in decimal degrees.attr_InitialResponseAcresAn estimate of acres burning at the time of initial response. More specifically when the IC arrives and performs initial size up. The minimum size must be 0.1. The estimate should include number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.attr_InitialResponseDateTimeThe date/time of the initial response to the incident. More specifically when the IC arrives and performs initial size up. attr_IrwinIDUnique identifier assigned to each incident record in IRWIN.attr_IsFireCauseInvestigatedIndicates if an investigation is underway or was completed to determine the cause of a fire.attr_IsFSAssistedIndicates if the Forest Service provided assistance on an incident outside their jurisdiction.attr_IsMultiJurisdictionalIndicates if the

The NOAA ENC data are in International Hydrographic Organization (IHO) S-57 format, which is the data standard for the exchange of digital hydrographic data. Nautical chart features contained within a NOAA ENC provide a detailed representation of the U.S. coastal and marine environment. This data includes coastal topography, bathymetry, landmarks, geographic place names, and marine boundaries. Features in a NOAA ENC are limited in that they only represent the geographic region depicted in that particular ENC. Aggregating nautical features from all NOAA ENCs in the creation of GIS data results in a contiguous depiction of the U.S. coastal and marine environment.To learn more about S-57, visit the IHO website.For questions or comments, contact us.File naming conventions and scale bandsENC Direct to GIS data is organized by scale band, and there are six scale bands available: Overview, General, Coastal,Approach, Harbour, and Berthing.SCALE RATIOS:Overview scale band consists of ENC files with a scale band of smaller than 1:1,500,000. General scale band consists of ENC files with a scale band from 1:600,001 – 1:1,500,000. Coastal scale band consists of ENC files with a scale band from 1:150,001 – 1:600,000. Approach scale band consists of ENC files with a scale band from 1:50,001 – 1:150,000. Harbour scale band consists of ENC files with a scale band from 1:5,000 – 1:50,000. Berthing scale band consists of ENC files with a scale band of larger than 1:5,000. The ENC filename is stored in the attribute named DSNM from the “coverage_area” feature layer. The third character within the filename is a numeric value referencing to the following scale band category. For example, a filename of US2EC02M.000 indicates that it is within the General scale band category.1 = Overview 2 = General 3 = Coastal 4 = Approach 5 = Harbour 6 = BerthingTo learn more about the object attributes for each S-57 object, see the S-57 Object Catalogue.

The Wildland Fire Interagency Geospatial Services (WFIGS) Group provides authoritative geospatial data products under the interagency Wildland Fire Data Program. Hosted in the National Interagency Fire Center ArcGIS Online Organization (The NIFC Org), WFIGS provides both internal and public facing data, accessible in a variety of formats.This service includes perimeters for wildland fire incidents that meet the following criteria:Categorized in the IRWIN (Integrated Reporting of Wildland Fire Information) integration service as a Wildfire (WF) or Prescribed Fire (RX)Has not been declared contained, controlled, nor outHas not had fire report records completed (certified)Is Valid and not "quarantined" in IRWIN due to potential conflicts with other recordsAttribution of the source polygon is set to a Feature Access of Public, a Feature Status of Approved, and an Is Visible setting of YesPerimeters are not available for every incident. For a complete set of features that meet the same IRWIN criteria, see the Current Wildland Fire Locations service."Fall-off" rules are used to ensure that stale records are not retained. Records are removed from this service under the following conditions:If the fire size is less than 10 acres (Size Class A or B) and fire information has not been updated in more than 3 daysFire size is between 10 and 100 acres (Size Class C) and fire information hasn't been updated in more than 8 daysFire size is larger than 100 acres (Size Class D-L) but fire information hasn't been updated in more than 14 days.Fires which started earlier than December of the previous calendar years are excluded.Fire size used in the fall off rules is from the attr_IncidentSize field. Fire information last update is determined by the attr_ModifiedOnDateTime_dt field.Fires that are no longer in the Current Wildland Fire Perimeter service will be displayed in the Wildland Fire Perimeters Year to Date and/or the 'Full History' service. Criteria were determined by an NWCG Geospatial Subcommittee task group. Data are refreshed every 5 minutes. Changes in the perimeter source may take up to 15 minutes to display.Perimeters are pulled from multiple sources with rules in place to ensure the most current or most authoritative shape is used.Fall-off rules are enforced hourly.Attributes and their definitions can be found below. More detail about the NWCG Wildland Fire Event Polygon standard can be found here.Attributes:poly_SourceOIDThe OBJECTID value of the source record in the source dataset providing the polygon.poly_IncidentNameThe incident name as stored in the polygon source record.poly_MapMethodThe mapping method with which the polygon was derived.poly_GISAcresThe acreage of the polygon as stored in the polygon source record.poly_CreateDateSystem generated date for the date time the source polygon record was created (stored in UTC).poly_DateCurrentSystem generated date for the date time the source polygon record was last edited (stored in UTC).poly_PolygonDateTimeRepresents the date time that the polygon data was captured.poly_IRWINIDIRWIN ID stored in the polygon record.poly_FORIDFORID stored in the polygon record.poly_Acres_AutoCalcSystem calculated acreage of the polygon (geodesic WGS84 acres).poly_SourceGlobalIDThe GlobalID value of the source record in the source dataset providing the polygon.poly_SourceThe source dataset providing the polygon.attr_SourceOIDThe OBJECTID value of the source record in the source dataset providing the attribution.attr_ABCDMiscA FireCode used by USDA FS to track and compile cost information for emergency initial attack fire suppression expenditures. for A, B, C & D size class fires on FS lands.attr_ADSPermissionStateIndicates the permission hierarchy that is currently being applied when a system utilizes the UpdateIncident operation.attr_ContainmentDateTimeThe date and time a wildfire was declared contained. attr_ControlDateTimeThe date and time a wildfire was declared under control.attr_CreatedBySystemArcGIS Server Username of system that created the IRWIN Incident record.attr_IncidentSizeReported for a fire. The minimum size is 0.1.attr_DiscoveryAcresAn estimate of acres burning upon the discovery of the fire. More specifically when the fire is first reported by the first person that calls in the fire. The estimate should include number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.attr_DispatchCenterIDA unique identifier for a dispatch center responsible for supporting the incident.attr_EstimatedCostToDateThe total estimated cost of the incident to date.attr_FinalAcresReported final acreage of incident.attr_FFReportApprovedByTitleThe title of the person that approved the final fire report for the incident.attr_FFReportApprovedByUnitNWCG Unit ID associated with the individual who approved the final report for the incident.attr_FFReportApprovedDateThe date that the final fire report was approved for the incident.attr_FireBehaviorGeneralA general category describing the manner in which the fire is currently reacting to the influences of fuel, weather, and topography. attr_FireBehaviorGeneral1A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireBehaviorGeneral2A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireBehaviorGeneral3A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireCauseBroad classification of the reason the fire occurred identified as human, natural or unknown. attr_FireCauseGeneralAgency or circumstance which started a fire or set the stage for its occurrence; source of a fire's ignition. For statistical purposes, fire causes are further broken into specific causes. attr_FireCauseSpecificA further categorization of each General Fire Cause to indicate more specifically the agency or circumstance which started a fire or set the stage for its occurrence; source of a fire's ignition. attr_FireCodeA code used within the interagency wildland fire community to track and compile cost information for emergency fire suppression expenditures for the incident. attr_FireDepartmentIDThe U.S. Fire Administration (USFA) has created a national database of Fire Departments. Most Fire Departments do not have an NWCG Unit ID and so it is the intent of the IRWIN team to create a new field that includes this data element to assist the National Association of State Foresters (NASF) with data collection.attr_FireDiscoveryDateTimeThe date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.attr_FireMgmtComplexityThe highest management level utilized to manage a wildland fire event. attr_FireOutDateTimeThe date and time when a fire is declared out. attr_FireStrategyConfinePercentIndicates the percentage of the incident area where the fire suppression strategy of "Confine" is being implemented.attr_FireStrategyFullSuppPrcntIndicates the percentage of the incident area where the fire suppression strategy of "Full Suppression" is being implemented.attr_FireStrategyMonitorPercentIndicates the percentage of the incident area where the fire suppression strategy of "Monitor" is being implemented.attr_FireStrategyPointZonePrcntIndicates the percentage of the incident area where the fire suppression strategy of "Point Zone Protection" is being implemented.attr_FSJobCodeA code use to indicate the Forest Service job accounting code for the incident. This is specific to the Forest Service. Usually displayed as 2 char prefix on FireCode.attr_FSOverrideCodeA code used to indicate the Forest Service override code for the incident. This is specific to the Forest Service. Usually displayed as a 4 char suffix on FireCode. For example, if the FS is assisting DOI, an override of 1502 will be used.attr_GACCA code that identifies one of the wildland fire geographic area coordination center at the point of origin for the incident.A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.attr_ICS209ReportDateTimeThe date and time of the latest approved ICS-209 report.attr_ICS209RptForTimePeriodFromThe date and time of the beginning of the time period for the current ICS-209 submission.attr_ICS209RptForTimePeriodToThe date and time of the end of the time period for the current ICS-209 submission. attr_ICS209ReportStatusThe version of the ICS-209 report (initial, update, or final). There should never be more than one initial report, but there can be numerous updates, and even multiple finals (as determined by business rules).attr_IncidentManagementOrgThe incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.attr_IncidentNameThe name assigned to an incident.attr_IncidentShortDescriptionGeneral descriptive location of the incident such as the number of miles from an identifiable town. attr_IncidentTypeCategoryThe Event Category is a sub-group of the Event Kind code and description. The Event Category further breaks down the Event Kind into more specific event categories.attr_IncidentTypeKindA general, high-level code and description of the types of incidents and planned events to which the interagency wildland fire community responds.attr_InitialLatitudeThe latitude location of the initial reported point of origin specified in decimal

--------------------------------------------------------------------------------------------------------------------------This layer has been deprecated as of 1/15/2025. The newest available dataset can be found here: https://nifc.maps.arcgis.com/home/item.html?id=4107b5d1debf4305ba00e929b7e5971This dataset will remain available until 7/1/2025 to make the transition to the new data source as seamless as possible for the wildland fire community.--------------------------------------------------------------------------------------------------------------------------Jurisdictional Unit, 2023-07-19. For use with WFDSS, IFTDSS, IRWIN, and InFORM.This is a feature service which provides Identify and Copy Feature capabilities. If fast-drawing at coarse zoom levels is a requirement, consider using the tile (map) service layer located at https://nifc.maps.arcgis.com/home/item.html?id=3b2c5daad00742cd9f9b676c09d03d13.OverviewThe Jurisdictional Agencies dataset is developed as a national land management geospatial layer, focused on representing wildland fire jurisdictional responsibility, for interagency wildland fire applications, including WFDSS (Wildland Fire Decision Support System), IFTDSS (Interagency Fuels Treatment Decision Support System), IRWIN (Interagency Reporting of Wildland Fire Information), and InFORM (Interagency Fire Occurrence Reporting Modules). It is intended to provide federal wildland fire jurisdictional boundaries on a national scale. The agency and unit names are an indication of the primary manager name and unit name, respectively, recognizing that:There may be multiple owner names.Jurisdiction may be held jointly by agencies at different levels of government (ie State and Local), especially on private lands, Some owner names may be blocked for security reasons.Some jurisdictions may not allow the distribution of owner names. Private ownerships are shown in this layer with JurisdictionalUnitIdentifier=null,JurisdictionalUnitAgency=null, JurisdictionalUnitKind=null, and LandownerKind="Private", LandownerCategory="Private". All land inside the US country boundary is covered by a polygon.Jurisdiction for privately owned land varies widely depending on state, county, or local laws and ordinances, fire workload, and other factors, and is not available in a national dataset in most cases.For publicly held lands the agency name is the surface managing agency, such as Bureau of Land Management, United States Forest Service, etc. The unit name refers to the descriptive name of the polygon (i.e. Northern California District, Boise National Forest, etc.).These data are used to automatically populate fields on the WFDSS Incident Information page, in IRWIN, INFORM Wildfire, and INFORM Fuels.Relevant NWCG Definitions and StandardsUnit2. A generic term that represents an organizational entity that only has meaning when it is contextualized by a descriptor, e.g. jurisdictional.Definition Extension: When referring to an organizational entity, a unit refers to the smallest area or lowest level. Higher levels of an organization (region, agency, department, etc) can be derived from a unit based on organization hierarchy.Unit, JurisdictionalThe governmental entity having overall land and resource management responsibility for a specific geographical area as provided by law.Definition Extension: 1) Ultimately responsible for the fire report to account for statistical fire occurrence; 2) Responsible for setting fire management objectives; 3) Jurisdiction cannot be re-assigned by agreement; 4) The nature and extent of the incident determines jurisdiction (for example, Wildfire vs. All Hazard); 5) Responsible for signing a Delegation of Authority to the Incident Commander.See also: Unit, Protecting; LandownerUnit IdentifierThis data standard specifies the standard format and rules for Unit Identifier, a code used within the wildland fire community to uniquely identify a particular government organizational unit.Landowner Kind & CategoryThis data standard provides a two-tier classification (kind and category) of landownership. Attribute Fields JurisdictionalAgencyKind Describes the type of unit Jurisdiction using the NWCG Landowner Kind data standard. There are two valid values: Federal, and Other. A value may not be populated for all polygons.JurisdictionalAgencyCategoryDescribes the type of unit Jurisdiction using the NWCG Landowner Category data standard. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State. A value may not be populated for all polygons.JurisdictionalUnitNameThe name of the Jurisdictional Unit. Where an NWCG Unit ID exists for a polygon, this is the name used in the Name field from the NWCG Unit ID database. Where no NWCG Unit ID exists, this is the “Unit Name” or other specific, descriptive unit name field from the source dataset. A value is populated for all polygons.JurisdictionalUnitIDWhere it could be determined, this is the NWCG Standard Unit Identifier (Unit ID). Where it is unknown, the value is ‘Null’. Null Unit IDs can occur because a unit may not have a Unit ID, or because one could not be reliably determined from the source data. Not every land ownership has an NWCG Unit ID. Unit ID assignment rules are available from the Unit ID standard, linked above.LandownerKindThe landowner category value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons.LandownerCategoryThe landowner kind value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons.DataSourceThe database from which the polygon originated. Be as specific as possible, identify the geodatabase name and feature class in which the polygon originated.SecondaryDataSourceIf the Data Source is an aggregation from other sources, use this field to specify the source that supplied data to the aggregation. For example, if Data Source is "PAD-US 2.1", then for a USDA Forest Service polygon, the Secondary Data Source would be "USDA FS Automated Lands Program (ALP)". For a BLM polygon in the same dataset, Secondary Source would be "Surface Management Agency (SMA)."SourceUniqueIDIdentifier (GUID or ObjectID) in the data source. Used to trace the polygon back to its authoritative source.MapMethod:Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality. MapMethod will be Mixed Method by default for this layer as the data are from mixed sources. Valid Values include: GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; DigitizedTopo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Phone/Tablet; OtherDateCurrentThe last edit, update, of this GIS record. Date should follow the assigned NWCG Date Time data standard, using 24 hour clock, YYYY-MM-DDhh.mm.ssZ, ISO8601 Standard.CommentsAdditional information describing the feature.GeometryIDPrimary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature.JurisdictionalUnitID_sansUSNWCG Unit ID with the "US" characters removed from the beginning. Provided for backwards compatibility.JoinMethodAdditional information on how the polygon was matched information in the NWCG Unit ID database.LocalNameLocalName for the polygon provided from PADUS or other source.LegendJurisdictionalAgencyJurisdictional Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.LegendLandownerAgencyLandowner Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.DataSourceYearYear that the source data for the polygon were acquired.Data InputThis dataset is based on an aggregation of 4 spatial data sources: Protected Areas Database US (PAD-US 2.1), data from Bureau of Indian Affairs regional offices, the BLM Alaska Fire Service/State of Alaska, and Census Block-Group Geometry. NWCG Unit ID and Agency Kind/Category data are tabular and sourced from UnitIDActive.txt, in the WFMI Unit ID application (https://wfmi.nifc.gov/unit_id/Publish.html). Areas of with unknown Landowner Kind/Category and Jurisdictional Agency Kind/Category are assigned LandownerKind and LandownerCategory values of "Private" by use of the non-water polygons from the Census Block-Group geometry.PAD-US 2.1:This dataset is based in large part on the USGS Protected Areas Database of the United States - PAD-US 2.`. PAD-US is a compilation of authoritative protected areas data between agencies and organizations that ultimately results in a comprehensive and accurate inventory of protected areas for the United States to meet a variety of needs (e.g. conservation, recreation, public health, transportation, energy siting, ecological, or watershed assessments and planning). Extensive documentation on PAD-US processes and data sources is available.How these data were aggregated:Boundaries, and their descriptors, available in spatial databases (i.e. shapefiles or geodatabase feature classes) from land management agencies are the desired and primary data sources in PAD-US. If these authoritative sources are unavailable, or the agency recommends another source, data may be incorporated by other aggregators such as non-governmental organizations. Data sources are tracked for each record in the PAD-US geodatabase (see below).FWS Data:The FWS Interest layer was crosswalked to the NWCG Jurisdictional Unit/Agency data standard and FWS boundaries were updated from this dataset.NPS Data:Specific NPS

This data set is part of an ongoing project to consolidate interagency fire perimeter data. Currently only certified perimeters and new perimeters captured starting in 2021 are included. A process for loading additional perimeters is being evaluated.The Wildland Fire Interagency Geospatial Services (WFIGS) Group provides authoritative geospatial data products under the interagency Wildland Fire Data Program. Hosted in the National Interagency Fire Center ArcGIS Online Organization (The NIFC Org), WFIGS provides both internal and public facing data, accessible in a variety of formats.This service includes perimeters for wildland fire incidents that meet the following criteria:Categorized in the IRWIN (Integrated Reporting of Wildland Fire Information) integration service as a Wildfire (WF) or Prescribed Fire (RX)Is Valid and not "quarantined" in IRWIN due to potential conflicts with other recordsAttribution of the source polygon is set to a Feature Access of Public, a Feature Status of Approved, and an Is Visible setting of YesPerimeters are not available for every incident. This data set is an ongoing project with the end goal of providing a national interagency fire history feature service of best-available perimeters.No "fall-off" rules are applied to this service. The date range for this service will extend from present day back indefinitely. Data prior to 2021 will be incomplete and incorporated as an ongoing project.Criteria were determined by an NWCG Geospatial Subcommittee task group. Data are refreshed every 5 minutes. Changes in the perimeter source may take up to 15 minutes to display.Perimeters are pulled from multiple sources with rules in place to ensure the most current or most authoritative shape is used.Warning: Please refrain from repeatedly querying the service using a relative date range. This includes using the “(not) in the last” operators in a Web Map filter and any reference to CURRENT_TIMESTAMP. This type of query puts undue load on the service and may render it temporarily unavailable.Attributes and their definitions can be found below. More detail about the NWCG Wildland Fire Event Polygon standard can be found here.Attributes:poly_SourceOIDThe OBJECTID value of the source record in the source dataset providing the polygon.poly_IncidentNameThe incident name as stored in the polygon source record.poly_MapMethodThe mapping method with which the polygon was derived.poly_GISAcresThe acreage of the polygon as stored in the polygon source record.poly_CreateDateSystem generated date for the date time the source polygon record was created (stored in UTC).poly_DateCurrentSystem generated date for the date time the source polygon record was last edited (stored in UTC).poly_PolygonDateTimeRepresents the date time that the polygon data was captured.poly_IRWINIDIRWIN ID stored in the polygon record.poly_FORIDFORID stored in the polygon record.poly_Acres_AutoCalcSystem calculated acreage of the polygon (geodesic WGS84 acres).poly_SourceGlobalIDThe GlobalID value of the source record in the source dataset providing the polygon.poly_SourceThe source dataset providing the polygon.attr_SourceOIDThe OBJECTID value of the source record in the source dataset providing the attribution.attr_ABCDMiscA FireCode used by USDA FS to track and compile cost information for emergency initial attack fire suppression expenditures. for A, B, C & D size class fires on FS lands.attr_ADSPermissionStateIndicates the permission hierarchy that is currently being applied when a system utilizes the UpdateIncident operation.attr_ContainmentDateTimeThe date and time a wildfire was declared contained. attr_ControlDateTimeThe date and time a wildfire was declared under control.attr_CreatedBySystemArcGIS Server Username of system that created the IRWIN Incident record.attr_IncidentSizeReported for a fire. The minimum size is 0.1.attr_DiscoveryAcresAn estimate of acres burning upon the discovery of the fire. More specifically when the fire is first reported by the first person that calls in the fire. The estimate should include number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.attr_DispatchCenterIDA unique identifier for a dispatch center responsible for supporting the incident.attr_EstimatedCostToDateThe total estimated cost of the incident to date.attr_FinalAcresReported final acreage of incident.attr_FFReportApprovedByTitleThe title of the person that approved the final fire report for the incident.attr_FFReportApprovedByUnitNWCG Unit ID associated with the individual who approved the final report for the incident.attr_FFReportApprovedDateThe date that the final fire report was approved for the incident.attr_FireBehaviorGeneralA general category describing the manner in which the fire is currently reacting to the influences of fuel, weather, and topography. attr_FireBehaviorGeneral1A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireBehaviorGeneral2A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireBehaviorGeneral3A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireCauseBroad classification of the reason the fire occurred identified as human, natural or unknown. attr_FireCauseGeneralAgency or circumstance which started a fire or set the stage for its occurrence; source of a fire's ignition. For statistical purposes, fire causes are further broken into specific causes. attr_FireCauseSpecificA further categorization of each General Fire Cause to indicate more specifically the agency or circumstance which started a fire or set the stage for its occurrence; source of a fire's ignition. attr_FireCodeA code used within the interagency wildland fire community to track and compile cost information for emergency fire suppression expenditures for the incident. attr_FireDepartmentIDThe U.S. Fire Administration (USFA) has created a national database of Fire Departments. Most Fire Departments do not have an NWCG Unit ID and so it is the intent of the IRWIN team to create a new field that includes this data element to assist the National Association of State Foresters (NASF) with data collection.attr_FireDiscoveryDateTimeThe date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.attr_FireMgmtComplexityThe highest management level utilized to manage a wildland fire event. attr_FireOutDateTimeThe date and time when a fire is declared out. attr_FireStrategyConfinePercentIndicates the percentage of the incident area where the fire suppression strategy of "Confine" is being implemented.attr_FireStrategyFullSuppPrcntIndicates the percentage of the incident area where the fire suppression strategy of "Full Suppression" is being implemented.attr_FireStrategyMonitorPercentIndicates the percentage of the incident area where the fire suppression strategy of "Monitor" is being implemented.attr_FireStrategyPointZonePrcntIndicates the percentage of the incident area where the fire suppression strategy of "Point Zone Protection" is being implemented.attr_FSJobCodeA code use to indicate the Forest Service job accounting code for the incident. This is specific to the Forest Service. Usually displayed as 2 char prefix on FireCode.attr_FSOverrideCodeA code used to indicate the Forest Service override code for the incident. This is specific to the Forest Service. Usually displayed as a 4 char suffix on FireCode. For example, if the FS is assisting DOI, an override of 1502 will be used.attr_GACCA code that identifies one of the wildland fire geographic area coordination center at the point of origin for the incident.A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.attr_ICS209ReportDateTimeThe date and time of the latest approved ICS-209 report.attr_ICS209RptForTimePeriodFromThe date and time of the beginning of the time period for the current ICS-209 submission.attr_ICS209RptForTimePeriodToThe date and time of the end of the time period for the current ICS-209 submission. attr_ICS209ReportStatusThe version of the ICS-209 report (initial, update, or final). There should never be more than one initial report, but there can be numerous updates, and even multiple finals (as determined by business rules).attr_IncidentManagementOrgThe incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.attr_IncidentNameThe name assigned to an incident.attr_IncidentShortDescriptionGeneral descriptive location of the incident such as the number of miles from an identifiable town. attr_IncidentTypeCategoryThe Event Category is a sub-group of the Event Kind code and description. The Event Category further breaks down the Event Kind into more specific event categories.attr_IncidentTypeKindA general, high-level code and description of the types of incidents and planned events to which the interagency wildland fire community responds.attr_InitialLatitudeThe latitude location of the initial reported point of origin specified in decimal degrees.attr_InitialLongitudeThe longitude location of the initial reported point of origin specified in decimal degrees.attr_InitialResponseAcresAn estimate of acres burning at the time of initial response. More specifically when the IC arrives and performs initial size up. The