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.

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.

This geodatabase serves two purposes: 1) to provide State of Illinois agencies with a fast resource for the preparation of maps and figures that require the use of shape or line files from federal agencies, the State of Illinois, or the City of Chicago, and 2) as a start for social scientists interested in exploring how geographic information systems (whether this is data visualization or geographically weighted regression) can bring new meaning to the interpretation of their data. All layer files included are relevant to the State of Illinois. Sources for this geodatabase include the U.S. Census Bureau, U.S. Geological Survey, City of Chicago, Chicago Public Schools, Chicago Transit Authority, Regional Transportation Authority, and Bureau of Transportation Statistics.

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.

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.

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.

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

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

This data layer contains geothermal resource areas and their technical potential used in long-term electric system modeling for Integrated Resource Planning and SB 100. Geothermal resource areas are delineated by Known Geothermal Resource Areas (KGRAs) (Geothermal Map of California, 2002), other geothermal fields (CalGEM Field Admin Boundaries, 2020), and Bureau of Land Management (BLM) Geothermal Leasing Areas (California BLM State Office GIS Department, 2010). The fields that are considered in our assessment have enough information known about the geothermal reservoir that an electric generation potential was estimated by USGS (Williams et al. 2008) or estimated by a BLM Environmental Impact Statement (El Centro Field Office, 2007). For the USGS identified geothermal systems, any point that lies within 2 km of a field is summed to represent the total mean electrical generation potential from the entire field.

Geothermal field boundaries are constructed for identified geothermal systems that lie outside of an established geothermal field. A circular footprint is assumed with a radius determined by the area needed to support the mean resource potential estimate, assuming a 10 MW/km² power density.

Several geothermal fields have power plants that are currently generating electricity from the geothermal source. The total production for each geothermal field is estimated by the CA Energy Commission’s Quarterly Fuel and Energy Report that tracks all power plants greater than 1 MW. The nameplate capacity of all generators in operation as of 2021 were used to inform how much of the geothermal fields are currently in use. This source yields inconsistent results for the power plants in the Geysers. Instead, an estimate from the net energy generation from those power plants is used. Using these estimates, the net undeveloped geothermal resource potential can be calculated.

Finally, we apply the protected area layer for geothermal to screen out those geothermal fields that lie entirely within a protected area. The protected area layer is compiled from public and private lands that have special designations prohibiting or not aligning with energy development.

This layer is featured in the CEC 2023 Land-Use Screens for Electric System Planning data viewer.

For more information about this layer and its use in electric system planning, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library.

Change Log:

Version 1.1 (January 18, 2024)

• ProtectedArea_Exclusion field was updated to correct for the changes to the Protected Area Layer. A Development Focus Area on Bureau of Land Management (BLM) land that overlays the Coso Hot Springs allows its resource potential to be considered in the statewide estimate.

Data Dictionary:

Total_MWe_Mean: The estimated resource potential from each geothermal field. All geothermal fields, except for Truckhaven, was given an estimate by Williams et al. 2008. If more than one point resource intersects (within 2km of) the field, the sum of the individual geothermal systems was used to estimate the magnitude of the resource coming from the entire geothermal field. Estimates are given in MW.

Total_QFER_NameplateCapacity: The total nameplate capacities of all generators in operation as of 2021 that intersects (within 2 km of) a geothermal field. The resource potential already in use for the Geysers is determined by Lovekin et al. 2004. Estimates are given in MW.

ProtectedArea_Exclusion: Binary value representing whether a field is excluded by the land-use screen or not. Fields that are excluded have a value of 1; those that aren’t have a value of 0.

NetUndevelopedRP: The net undeveloped resource potential for each geothermal field. This field is determined by subtracting the total resource potential in use (Total_QFER_NameplateCapacity) from the total estimated resource potential (Total_MWe_Mean). Estimates are given in MW.

Acres_GeothermalField: This is the geodesic acreage of each geothermal field. Values are reported in International Acres using a NAD 1983 California (Teale) Albers (Meters) projection.

References:

1. Geothermal Map of California, S-11. California Department of Conservation, 2002. https://www.conservation.ca.gov/calgem/geothermal/maps/Pages/index.aspx
2. CalGEM Field Admin Boundaries, 2020. https://gis.conservation.ca.gov/server/rest/services/CalGEM/Admin_Bounds/MapServer
   
3. California BLM State Office GIS Department, California BLM Verified and Potential Geothermal Leases in California, 2010. https://databasin.org/datasets/5ec77a1438ab4402bf09ef9bfd7f04d9/
   
4. Williams, Colin F., Reed, Marshall J., Mariner, Robert H., DeAngelo, Jacob, Galanis, S. Peter, Jr. 2008. "Assessment of moderate- and high-temperature geothermal resources of the United States: U.S. Geological Survey Fact Sheet 2008-3082." 4 p. https://certmapper.cr.usgs.gov/server/rest/services/geothermal/westus_favoribility_systems/MapServer/0
5. El Centro Field Office, Bureau of Land Management (2007). Final Environmental Impact Statement for the Truckhaven Geothermal Leasing Area (Publication Index Number: BLM/CA/ES-2007-017+3200). United States Department of the Interior Bureau of Land Management.
6. Lovekin, James W., Subir K. Sanyal, Christopher W. Klein. 2004. “New Geothermal Site Identification and Qualification.” Richmond, California:

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

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

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.

911 Public Safety Answering Point (PSAP) service area boundaries in the United States According to the National Emergency Number Association (NENA), a Public Safety Answering Point (PSAP) is a facility equipped and staffed to receive 9-1-1 calls. The service area is the geographic area within which a 911 call placed using a landline is answered at the associated PSAP. This dataset only includes primary PSAPs. Secondary PSAPs, backup PSAPs, and wireless PSAPs have been excluded from this dataset. Primary PSAPs receive calls directly, whereas secondary PSAPs receive calls that have been transferred by a primary PSAP. Backup PSAPs provide service in cases where another PSAP is inoperable. Most military bases have their own emergency telephone systems. To connect to such a system from within a military base, it may be necessary to dial a number other than 9 1 1. Due to the sensitive nature of military installations, TGS did not actively research these systems. If civilian authorities in surrounding areas volunteered information about these systems, or if adding a military PSAP was necessary to fill a hole in civilian provided data, TGS included it in this dataset. Otherwise, military installations are depicted as being covered by one or more adjoining civilian emergency telephone systems. In some cases, areas are covered by more than one PSAP boundary. In these cases, any of the applicable PSAPs may take a 911 call. Where a specific call is routed may depend on how busy the applicable PSAPs are (i.e., load balancing), operational status (i.e., redundancy), or time of day / day of week. If an area does not have 911 service, TGS included that area in the dataset along with the address and phone number of their dispatch center. These are areas where someone must dial a 7 or 10 digit number to get emergency services. These records can be identified by a "Y" in the [NON911EMNO] field. This indicates that dialing 911 inside one of these areas does not connect one with emergency services. This dataset was constructed by gathering information about PSAPs from state level officials. In some cases, this was geospatial information; in other cases, it was tabular. This information was supplemented with a list of PSAPs from the Federal Communications Commission (FCC). Each PSAP was researched to verify its tabular information. In cases where the source data was not geospatial, each PSAP was researched to determine its service area in terms of existing boundaries (e.g., city and county boundaries). In some cases, existing boundaries had to be modified to reflect coverage areas (e.g., "entire county north of Country Road 30"). However, there may be cases where minor deviations from existing boundaries are not reflected in this dataset, such as the case where a particular PSAPs coverage area includes an entire county plus the homes and businesses along a road which is partly in another county. 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.

This dataset provides information about the number of programs that have received Agency Review funding; how many of those programs had defined measurable outcome goals (DMOG) specified in the agencies funding request applications; and how many programs achieved their DMOG.

The Agency Review process was developed to distribute human services funds to non-profit agencies. Agency Review funds come from the City of Tempe General Revenue Fund, Federal Community Development Block Grants, and water utility customer donations through Tempe’s Help to Others.

This page provides data for the Human Services Grant performance measure.

Identifies the people served as a result of Agency Review grant funding to non-profit agencies.

The performance measure dashboard is available at 3.10 Human Services Grants.

Additional Information

Source: e-CImpact

Contact: Octavia Harris

Contact E-Mail: octavia_harris@tempe.gov

Data Source Type: Excel

Preparation Method: Data downloaded from e-CImpact, then compiled in spreadsheet to establish yes/no fields for aggregate calculations by population served

Publish Frequency: Annual

Publish Method: Manual

Data Dictionary

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