GIS in the age of community health (Learn ArcGIS Path). Arm yourself with hands-on skills and knowledge of how GIS tools can analyze health data and better understand diseases.

This dataset provides information on Benefits Amounts for Income Supplement and the Allowances according to income level and marital status. This is updated on a quarterly basis. The following tables of amounts will provide you with the amount of your monthly benefit, which will be based on your age, income level and marital status. The dataset is updated for April - June 2025 quarter.

Use this app to examine the known status of structures damaged by the wildfire. If a structure point does not appear on the map it may still have been impacted by the fire. Specific addresses can be searched for in the search bar. Use the imagery and topographic basemaps and photos to positively identify a structure. Photos may only be available for damaged and destroyed structures.

For more information about the wildfire response efforts, visit the CAL FIRE incident page.

The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates

Title: Kids Count Data Center

Summary: The Annie E. Casey Foundation NM Kids Count Data Center, with socioeconomic data including data on food insecurity and social benefits. Query Page.

Notes:

Prepared by: Kids Count Data Center, URL uploaded by EMcRae_NMCDC

Source: This is a link from Kids Count Data Center basic query page, URL is https://datacenter.kidscount.org/data/#USA/1/0/char/0

Feature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=2b63101d72ff4d7783717ba8d60b5853

UID: 73, 98

Data Requested: Family income spent on basic need, and Food security by demo and socioeconomic status, and socioeconomic/population health, and NM Voices for Children data

Method of Acquisition: Linking to Kids Count Data Center webpage.

Date Acquired: Link was uploaded on May 9, 2022. Data is maintained by the Kids Count Data Center page.

Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 6

Tags: PENDING

On March 2, 2022 DC Health announced the District’s new COVID-19 Community Level key metrics and reporting. COVID-19 cases are now reported on a weekly basis. More information available at https://coronavirus.dc.gov. District of Columbia Child and Family Services Agency testing for the number of positive tests, quarantined, returned to work and lives lost. Due to rapidly changing nature of COVID-19, data for March 2020 is limited.General Guidelines for Interpreting Disease Surveillance DataDuring a disease outbreak, the health department will collect, process, and analyze large amounts of information to understand and respond to the health impacts of the disease and its transmission in the community. The sources of disease surveillance information include contact tracing, medical record review, and laboratory information, and are considered protected health information. When interpreting the results of these analyses, it is important to keep in mind that the disease surveillance system may not capture the full picture of the outbreak, and that previously reported data may change over time as it undergoes data quality review or as additional information is added. These analyses, especially within populations with small samples, may be subject to large amounts of variation from day to day. Despite these limitations, data from disease surveillance is a valuable source of information to understand how to stop the spread of COVID19.

This map feeds into a web app that allows a user to examine the known status of structures damaged by the wildfire. If a structure point does not appear on the map it may still have been impacted by the fire. Specific addresses can be searched for in the search bar. Use the imagery and topographic basemaps and photos to positively identify a structure. Photos may only be available for damaged and destroyed structures.

Where does healthcare cost the most? (Learn ArcGIS online lesson).

Mapping incident locations from a CSV file in a web map (YouTube video).

This United States Environmental Protection Agency (US EPA) feature layer represents monitoring site data, updated hourly concentrations and Air Quality Index (AQI) values for the latest hour received from monitoring sites that report to AirNow.

A sample dataset, which anyone can see how the anaysis were done utilizing Collect Earth.

Abstract copyright UK Data Service and data collection copyright owner.

The UK censuses took place on 29th April 2001. They were run by the Northern Ireland Statistics & Research Agency (NISRA), General Register Office for Scotland (GROS), and the Office for National Statistics (ONS) for both England and Wales. The UK comprises the countries of England, Wales, Scotland and Northern Ireland.

Statistics from the UK censuses help paint a picture of the nation and how we live. They provide a detailed snapshot of the population and its characteristics, and underpin funding allocation to provide public services.

Snapshot img

The GIS market share in EMEA is expected to increase to USD 2.01 billion from 2021 to 2026, and the market’s growth momentum will accelerate at a CAGR of 8.23%.

This EMEA GIS market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers GIS market in EMEA segmentation by:

Component - Software, data, and services
End-user - Government, utilities, military, telecommunication, and others

What will the GIS Market Size in EMEA be During the Forecast Period?

Download the Free Report Sample to Unlock the GIS Market Size in EMEA for the Forecast Period and Other Important Statistics

The EMEA GIS market report also offers information on several market vendors, including arxiT SA, Autodesk Inc., Bentley Systems Inc., Cimtex International, CNIM SA, Computer Aided Development Corp. Ltd., Environmental Systems Research Institute Inc., Fugro NV, General Electric Co., HERE Global BV, Hexagon AB, Hi-Target, Mapbox Inc., Maxar Technologies Inc., Pitney Bowes Inc., PSI Services LLC, Rolta India Ltd., SNC Lavalin Group Inc., SuperMap Software Co. Ltd., Takor Group Ltd., and Trimble Inc. among others.

GIS Market in EMEA: Key Drivers, Trends, and Challenges

The integration of BIM and GIS is notably driving the GIS market growth in EMEA, although factors such as data viability and risk of intrusion may impede market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the GIS industry in EMEA. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key GIS Market Driver in EMEA

One of the key factors driving the geographic information system (GIS) market growth in EMEA is the integration of BIM and GIS. A GIS adds value to BIM by visualizing and analyzing the data with regard to the buildings and surrounding features, such as environmental and demographic information. BIM data and workflows include information regarding sensors and the placement of devices in IoT-connected networks. For instance, Dubai's Civil Defense Department has integrated GIS data with its automatic fire surveillance system. This information is provided in a matter of seconds on the building monitoring systems of the Civil Defense Department. Furthermore, location-based services offered by GIS providers help generate huge volumes of data from stationary and moving devices and enable users to perform real-time spatial analytics and derive useful geographic insights from it. Owing to the advantages associated with the integration of BIM with GIS solutions, the demand for GIS solutions is expected to increase during the forecast period.

Key GIS Market Challenge in EMEA

One of the key challenges to the is the GIS market growth in EMEA is the data viability and risk of intrusion. Hackers can hack into these systems with malicious intentions and manipulate the data, which could have destructive or negative repercussions. Such hacking of data could cause nationwide chaos. For instance, if a hacker manipulated the traffic management database, massive traffic jams and accidents could result. If a hacker obtained access to the database of a national disaster management organization and manipulated the data to create a false disaster situation, it could lead to a panic situation. Therefore, the security infrastructure accompanying the implementation of GIS software solutions must be robust. Such security threats may impede market growth in the coming years.

Key GIS Market Trend in EMEA

Integration of augmented reality (AR) and GIS is one of the key geographic information system market trends in EMEA that is expected to impact the industry positively in the forecast period. AR apps could provide GIS content to professional end-users and aid them in making decisions on-site, using advanced and reliable information available on their mobile devices and smartphones. For instance, when the user simply points the camera of the phone at the ground, the application will be able to show the user the location and orientation of water pipes and electric cables that are concealed underground. Organizations such as the Open Geospatial Consortium (OGC) and the World Wide Web Consortium (W3C) are seeking investments and are open to sponsors for an upcoming AR pilot project, which seeks to advance the standards of AR technology at both respective organizations. Such factors will further support the market growth in the coming years.

This GIS market in EMEA analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth st

This dataset series refers to the information on burnt areas and fire severity provided by the European Forest Fire Information System (EFFIS). ▷_How to cite: see below_◁

1 - Burnt areas. The burnt area mapping is a service implemented since 2000 that detects and analyzes the evolution of the fire events during the fire seasons and since 2007 during the whole year. A burnt area monitored in the EFFIS system is an area damaged by a wildfire event; in the system only areas that are about 30 hectares or larger are detected. Fires occurred only on agricultural areas are not mapped. A wildfire event can start either from an agricultural area or from a wildland area. Irrespective of the ignition point, to be considered in EFFIS a fire event must damage a wildland area. This means that the fire was either generated in the natural areas by spontaneous or anthropogenic sources, or sparked in agricultural fields and went out of control up to damage wildland. The mapping provided by EFFIS is on a day-by-day basis, and integrates multiple sources: the fire news, the MODIS and VIIRS satellite thermal anomalies, the near real-time (NRT) fire monitoring based on them, and the MODIS Terra and Aqua images. The NRT Fire Monitoring is useful to obtain an early approximation of the last state of large fires with a short time-lag. A subsequent integrated analysis generates consolidated best estimates of the burnt area. Each day, a semi-automatic procedure takes as input the satellite images and runs an automated classification. The burn scars automatically detected with the thermal anomalies, along with the fire news geolocations, serve as auxiliary data for the final visual check through a computer assisted photointerpretation by a GIS analysts / expert photointerpreter who verifies the reliability of the candidate areas. Once confirmed, the final polygons of the burnt area product contains multiple information fields: affected area in hectares; spatial location (country, province, and municipality); and temporal window (start and end dates of the fires, and date of the last update of the events).

2 - Fire severity.

Fire severity is the degree to which a fire altered the burnt area. It is assessed by EFFIS using the Normalized Burn Ratio (NBR) index (also sensitive to chlorophyll, water content, vegetation, ash), computed for pre-fire and post-fire satellite images. The “differenced NBR” (dNBR) represents the difference between NBR values before and after the event. The estimated “differenced NBR” is remapped into five categories of severity (very low, low, moderate, high, and very high).

How to cite - When using these data, please cite the relevant data sources. A suggested citation is included in the following:

• San-Miguel-Ayanz, J., Houston Durrant, T., Boca, R., Libertà, G., Branco, A., de Rigo, D., Ferrari, D., Maianti, P., Artés Vivancos, T., Schulte, E., Loffler, P., Benchikha, A., Abbas, M., Humer, F., Konstantinov, V., Pešut, I., Petkoviček, S., Papageorgiou, K., Toumasis, I., Kütt, V., Kõiv, K., Ruuska, R., Anastasov, T., Timovska, M., Michaut, P., Joannelle, P., Lachmann, M., Pavlidou, K., Debreceni, P., Nagy, D., Nugent, C., Di Fonzo, M., Leisavnieks, E., Jaunķiķis, Z., Mitri, G., Repšienė, S., Assali, F., Mharzi Alaoui, H., Botnen, D., Piwnicki, J., Szczygieł, R., Janeira, M., Borges, A., Sbirnea, R., Mara, S., Eritsov, A., Longauerová, V., Jakša, J., Enriquez, E., Lopez, A., Sandahl, L., Reinhard, M., Conedera, M., Pezzatti, B., Dursun, K. T., Baltaci, U., Moffat, A., 2017. Forest fires in Europe, Middle East and North Africa 2016. Publications Office of the European Union, Luxembourg. ISBN:978-92-79-71292-0, https://doi.org/10.2760/17690

• San-Miguel-Ayanz, J., Schulte, E., Schmuck, G., Camia, A., 2013. The European Forest Fire Information System in the context of environmental policies of the European Union. Forest Policy and Economics 29, 19-25. https://doi.org/10.1016/j.forpol.2011.08.012

• San-Miguel-Ayanz, J., Schulte, E., Schmuck, G., Camia, A., Strobl, P., Libertà, G., Giovando, C., Boca, R., Sedano, F., Kempeneers, P., McInerney, D., Withmore, C., de Oliveira, S. S., Rodrigues, M., Houston Durrant, T., Corti, P., Oehler, F., Vilar, L., Amatulli, G., 2012. Comprehensive monitoring of wildfires in Europe: the European Forest Fire Information System (EFFIS). In: Tiefenbacher, J. (Ed.), Approaches to Managing Disaster - Assessing Hazards, Emergencies and Disaster Impacts. InTech, Ch. 5. http://doi.org/10.5772/28441

Last updated on 06/17/2022

Overview

The national fire history perimeter data layer of conglomerated Agency Authoratative perimeters was developed in support of the WFDSS application and wildfire decision support for the 2021 fire season. The layer encompasses the final 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 2021 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.

Several data sources were used in the development of this layer:

• Alaska fire history
• USDA FS Regional Fire History Data
• BLM Fire Planning and Fuels
• National Park Service - Includes Prescribed Burns
• Fish and Wildlife Service
• Bureau of Indian Affairs
• CalFire FRAS - Includes Prescribed Burns
• WFIGS - BLM & BIA and other S&L

Fire 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 Authoratative service or a perimeter is missing in both services, please contact the appropriate agency Fire GIS Contact listed in the table below.

For a file geodatabase (.gdb) Click Here (includes files used to create data).

For the final report, full documentation, and metadata Click Here.

Feature Classes are loaded onto tablet PCs and Field crews are sent to label the crop or land cover type and irrigation method for a subset of select fields or polygons. Each tablet PC is attached to a GPS unit for real-time tracking to continuously update the field crew’s location during the field labeling process.

Digitizing is done as Geodatabase feature classes using ArcPro 3.1.0 with Sentinel imagery as a background with other layers added for reference. Updates to existing field boundaries of individual agricultural fields, urban areas and more are precisely digitized. Changes in irrigation type and land use are noted during this process. Cropland Data Layer (CDL) rasters from the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) are downloaded for the appropriate year. https://nassgeodata.gmu.edu/CropScape/

Zonal Statistics geoprocessing tools are used to attribute the polygons with updated crop types from the CDL. The data is then run through several stages of comparison to historical inventories and quality checking in order to determine and produce the final attributes.

Attributes

Landuse– A general land cover classification differentiating how the land is used

• Agriculture: Land managed for crop or livestock purposes

• Other: A broad classification of wildland

• Riparian/Wetland: Wildland influenced by a high water table, often close to surface water

• Urban: Developed areas, includes urban greenspace such as parks.

• Water: Surface water such as wet flats, streams, and lakes.

CropGroup– Groupings of broader crop categories to allow easy access to or query of all orchard or grain types etc.

Description– Attribute that describes/indicates the various crop types and land use types determined by the GIS process.

IRR_Method– Crop Irrigation Method carried over from statewide field surveys ending in 2015 and updated based on imagery and yearly field checks.

• Drip: Water is applied through lines that slowly release water onto the surface or subsurface of the crop

• Dry Crop: No irrigation method is applied to this agricultural land, the crop is irrigated via natural processes.

• Flood: Water is diverted from ditches or pipes upland from the crop in sufficient quantities to flood the irrigated plot

• None: Associated with non-agricultural land

• Sprinkler: Water is applied above the crop via sprinklers that generally move across the field.

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Using the coronavirus infographic template in Business/Community Analyst Web (ArcGIS Blog).