This layer presents detectable thermal activity from MODIS satellites for the last 7 days. MODIS Global Fires is a product of NASA’s Earth Observing System Data and Information System (EOSDIS), part of NASA's Earth Science Data. EOSDIS integrates remote sensing and GIS technologies to deliver global MODIS hotspot/fire locations to natural resource managers and other stakeholders around the World.Consumption Best Practices:
As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cacheable relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment.When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cacheable.Source: NASA FIRMS - Active Fire Data - for WorldScale/Resolution: 1kmUpdate Frequency: 1/2 Hour (every 30 minutes) using the Aggregated Live Feed MethodologyArea Covered: WorldWhat can I do with this layer?The MODIS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.Additional InformationMODIS stands for MODerate resolution Imaging Spectroradiometer. The MODIS instrument is on board NASA’s Earth Observing System (EOS) Terra (EOS AM) and Aqua (EOS PM) satellites. The orbit of the Terra satellite goes from north to south across the equator in the morning and Aqua passes south to north over the equator in the afternoon resulting in global coverage every 1 to 2 days. The EOS satellites have a ±55 degree scanning pattern and orbit at 705 km with a 2,330 km swath width.It takes approximately 2 – 4 hours after satellite overpass for MODIS Rapid Response to process the data, and for the Fire Information for Resource Management System (FIRMS) to update the website. Occasionally, hardware errors can result in processing delays beyond the 2-4 hour range. Additional information on the MODIS system status can be found at MODIS Rapid Response.Attribute InformationLatitude and Longitude: The center point location of the 1km (approx.) pixel flagged as containing one or more fires/hotspots (fire size is not 1km, but variable). Stored by Point Geometry. See What does a hotspot/fire detection mean on the ground?Brightness: The brightness temperature measured (in Kelvin) using the MODIS channels 21/22 and channel 31.Scan and Track: The actual spatial resolution of the scanned pixel. Although the algorithm works at 1km resolution, the MODIS pixels get bigger toward the edge of the scan. See What does scan and track mean?Date and Time: Acquisition date of the hotspot/active fire pixel and time of satellite overpass in UTC (client presentation in local time). Stored by Acquisition Date.Acquisition Date: Derived Date/Time field combining Date and Time attributes.Satellite: Whether the detection was picked up by the Terra or Aqua satellite.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel.Version: Version refers to the processing collection and source of data. The number before the decimal refers to the collection (e.g. MODIS Collection 6). The number after the decimal indicates the source of Level 1B data; data processed in near-real time by MODIS Rapid Response will have the source code “CollectionNumber.0”. Data sourced from MODAPS (with a 2-month lag) and processed by FIRMS using the standard MOD14/MYD14 Thermal Anomalies algorithm will have a source code “CollectionNumber.x”. For example, data with the version listed as 5.0 is collection 5, processed by MRR, data with the version listed as 5.1 is collection 5 data processed by FIRMS using Level 1B data from MODAPS.Bright.T31: Channel 31 brightness temperature (in Kelvins) of the hotspot/active fire pixel.FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).DayNight: The standard processing algorithm uses the solar zenith angle (SZA) to threshold the day/night value; if the SZA exceeds 85 degrees it is assigned a night value. SZA values less than 85 degrees are assigned a day time value. For the NRT algorithm the day/night flag is assigned by ascending (day) vs descending (night) observation. It is expected that the NRT assignment of the day/night flag will be amended to be consistent with the standard processing.Hours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.RevisionsJune 22, 2022: Added 'HOURS_OLD' field to enhance Filtering data. Added 'Last 7 days' Layer to extend data to match time range of VIIRS offering. Added Field level descriptions.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!
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European Heat Wave risk in the near, mid-term and distant future, under the RCP4.5 - SSP2 and RCP8.5 - SSP5 scenarios
This layer presents detectable thermal activity from MODIS satellites for the last 7 days. MODIS Global Fires is a product of NASA’s Earth Observing System Data and Information System (EOSDIS), part of NASA's Earth Science Data.
EOSDIS integrates remote sensing and GIS technologies to deliver global
MODIS hotspot/fire locations to natural resource managers and other
stakeholders around the World.
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How to Read the map.This map allows you to visualize the trends over time and cases, recoveries, deaths and testing at the regional health unit. The Map shows the relative state of the COVID-19 outbreak in each region. Colour (red to green) shows the time since a new reported case.
7 Day Hot Spots
The map highlights regions with an active outbreak with a "glowing ball". The size of the ball reflects the average number of new cases in the past 7 days as a rate per 100K population.
High
Low
Important InformationNot all data is reported for all regional health units. Data sources are consulted every 24 hours, however not all organizations report on a daily bases. As this data is cumulative, values carry-forward if updates are not provided. Values can go down due to corrected errors as reported. Data SourcesThe source of the data for each regional health unit is listed in the "SourceURL" field.
Looking for the raw data? You can find it here.
Structural characterization of small molecule binding site hotspots within the global proteome is uniquely enabled by photo-affinity labeling (PAL) coupled with chemical enrichment and unbiased analysis by mass spectrometry (MS). MS-based binding site hotspot maps provide structural resolution of interaction sites in conjunction with identification of target proteins. However, binding site hotspot mapping has been confined to relatively simple small molecules to date; extension to more complex compounds would enable the structural definition of new binding modes in the proteome. Here, we extend PAL and MS methods to derive a binding site hotspot map for the immunosuppressant rapamycin, a complex macrocyclic natural product that forms a ternary complex with the proteins FKBP12 and FRB. Photo-rapamycin was developed as a diazirine-based PAL probe for rapamycin, and the FKBP12–photo-rapamycin–FRB ternary complex formed readily in vitro. Photo-irradiation, digestion, and MS analysis of the ternary complex revealed a McLafferty rearrangement product of photo-rapamycin conjugated to specific surfaces on FKBP12 and FRB. Molecular modeling of the ternary complex based on the binding site map revealed a 5.0 Å minimum distance constraint between the conjugated residues and the diazirine carbon. Molecular dynamics further predicted a 9.0 Å labeling radius for the diazirine upon photo-activation that may be useful in the interpretation of binding site measurements from PAL more broadly. Thus, in characterizing the ternary complex of photo-rapamycin by MS, we applied binding site hotspot mapping to a macrocyclic natural product and extracted a precise structural measurement for interpretation of PAL products that may enable the discovery of new ligand space in the “undruggable” proteome.
This web app shows free wifi hotspots (public and commercial) based on an address entered, or point clicked on the map. The purpose of this application is to help increase digital connectivity to the public during the COVID-19 pandemic. Included in this map are:- Public WiFi: Parks, Libraries, WDACS- Commercial WiFi: Starbucks, McDonalds- School District Boundaries: Access to internet may be available to students who attend school and is based on each school district. Students and parents may contact their respective school districts for more information on access.The user-friendly URL http://findwifi.lacounty.gov/ points to this app and replaces https://lacounty.maps.arcgis.com/apps/ZoneLookup/index.html?appid=e6fbcad3b92244cabcb7b2130e5ffae7 Production v3.
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The ten countries/regions with the highest wader biodiversity (ranked by maximum value of species richness), as estimated from maps drawn by BirdLife International experts and aggregated for the purposes of this study.
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In order to minimize the negative impacts of roads on wildlife mortality and fragmentation, ecologists and road managers have been working together on assessing spatial patterns of roadkills, taxa most sensible, and road and landscape characteristics that influence roadkill numbers. Of special concern when analyzing these spatial patterns, is the location of roadkill hostspots, ie. segments of roads with clusters of wildlife mortality. The accuracy in the spatial definition of hotspots is of prime importance not only to conservation biologists, but also to road agencies and planners, as mitigation of roadways is usually expensive. Recently it has been shown that lower frequencies of road monitoring (longer intervals between samplings) may be responsible for losses of more than 50 % of roadkill numbers registered for several taxonomic groups, when compared with a daily sampling. This result highlights the need to account for other possible sources of inaccuracies when monitoring roadkills with varying sampling frequencies. Particularly important is the evaluation of the spatial accuracy of roadkill hotspot locations when different sampling efforts are implemented because inaccurate results may fail to detect “real” roadkill hotspots or can direct highly-cost mitigation measures to the inappropriate road sections. In the present study, we aim to assess the spatial discrepancy of hotspots location using four sampling frequencies (scenarios), and determine for which taxonomic groups is this spatial discrepancy most severe. We used a dataset of a one-year long roadkill daily survey, including 4453 individual records of vertebrate carcasses, for which survival time on the road is known. This dataset was arranged in five data matrices concerning different sampling frequencies: daily sampling (the baseline data), and four scenarios, 2-day interval, weekly, bi-weekly, and monthly sampling. We considered the global species data (all taxonomic groups together) and each of the 13 taxonomic groups considered for the analyses. For analyses, the road was divided in 500-m sections and hotspots were calculated according to Malo's method (using a Poisson distribution). We considered a threshold of 95 % and a corresponding minimum of two observations (roadkilled animals) in order to proceed with the analyses. In order to evaluate spatial discrepancy in hotspot location at road sections (presence/absence of hotspot) between daily and each of the four sampling scenarios, we used the Phi correlation. For global data, spatial discrepancy of hotspots increased most from weekly scenario onwards (phi weekly = 0.66, phi bi-weekly =0.61, phi monthly =0.58), while the 2-day scenario had the lowest discrepancy (phi 2-day =0.89). None of the four scenarios produced a hotspot map identical to the one obtained through daily survey, neither with global data nor with separated taxa. Even for the highest correlated scenario (2-day sampling), a different hotspot map was obtained for all studied taxa. Taxa with higher discrepancy in hotspot maps were bats, toads, salamanders, snakes and small mammals. Birds of prey, hedgehogs, carnivores, and lagomorphs had the lowest spatial discrepancy in hotspot maps. These results must be taken into account when planning roadkill monitoring programs, specially if we are dealing with small species.
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This map shows police patrols from the past 28 days as represented by a heat map symbology. Patrol areas are logged as points which are placed at the location where the patrol began. This may cause some areas on this map to appear to be un-patrolled, however the City of Alpharetta regularly patrols every street within the City. The incidents and activities represented in the Public Safety data sets are indicated as reported. Current investigation will determine if an actual crime was committed. Data Updated Nightly.
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This marginality hotspot map of Ethiopia uses the lowest quartile as thresholds for the dimensions of marginality. Again, this map shows how many dimension of marginality - as defined by Gatzweiler et al. (2011) - overlap. Quality/Lineage: Indicator - Input - Cut-off point total expenditure at household level - HICE survey data - Total expenditure is defined as all household consumption expenditures as well as non-consumption expenditures; regional level lowest quartile (1671.92 Birr) Prevalence of stunting among children under five, by lowest available subnational administrative unit, varying years (FGGD) - Global raster data layer with 5 arc-minutes resolution. Data compilation by FAO including the prevalence of stunting, LandScan global population database and the percentage of children under five - Percentage of children below 3 standard deviations of WHO growth standards (18.85%) Travel time to major cities: A global map of Accessibility (by Andrew Nelson) - Infrastructural data (based on data of: populated places, cities, road network, travel speeds, railway network, navigable rivers, major waterbodies, shipping lanes, borders, urban areas, elevation and slope); 30 arc-seconds resolution - More than 12 hours travelling to the next agglomeration with ≥50,000 people. percentage of households having health problem in last 2 months and not going to health institution or traditional healer - WMS survey data; regional level - Lowest quartile (49.11%) Global land area with soil constraints Depth, soil chemical status and natural, fertility, drainage, texture, miscellaneous land; - 5 arc-minutes resolution - Soils that have „very frequent severe“ soil constraints as well as soils “unsuitable for agriculture” according to FAO 2007 (FGGD) definition percent of households getting drinking water from unprotected well or spring - DHS survey data; regional level - Lowest quartile (15.83%) percentage of women saying wife beating is ok if she neglects children - DHS survey data - Lowest quartile (70.75%)
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Geostatistics analyzes and predicts the values associated with spatial or spatial-temporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. It is a practical means of describing spatial patterns and interpolating values for locations where samples were not taken (and measures the uncertainty of those values, which is critical to informed decision making). This archive contains results of geostatistical analysis of COVID-19 case counts for all available US counties. Test results were obtained with ArcGIS Pro (ESRI). Sources are state health departments, which are scraped and aggregated by the Johns Hopkins Coronavirus Resource Center and then pre-processed by MappingSupport.com.
This update of the Zenodo dataset (version 6) consists of three compressed archives containing geostatistical analyses of SARS-CoV-2 testing data. This dataset utilizes many of the geostatistical techniques used in previous versions of this Zenodo archive, but has been significantly expanded to include analyses of up-to-date U.S. COVID-19 case data (from March 24th to September 8th, 2020):
Archive #1: “1.Geostat. Space-Time analysis of SARS-CoV-2 in the US (Mar24-Sept6).zip” – results of a geostatistical analysis of COVID-19 cases incorporating spatially-weighted hotspots that are conserved over one-week timespans. Results are reported starting from when U.S. COVID-19 case data first became available (March 24th, 2020) for 25 consecutive 1-week intervals (March 24th through to September 6th, 2020). Hotspots, where found, are reported in each individual state, rather than the entire continental United States.
Archive #2: "2.Geostat. Spatial analysis of SARS-CoV-2 in the US (Mar24-Sept8).zip" – the results from geostatistical spatial analyses only of corrected COVID-19 case data for the continental United States, spanning the period from March 24th through September 8th, 2020. The geostatistical techniques utilized in this archive includes ‘Hot Spot’ analysis and ‘Cluster and Outlier’ analysis.
Archive #3: "3.Kriging and Densification of SARS-CoV-2 in LA and MA.zip" – this dataset provides preliminary kriging and densification analysis of COVID-19 case data for certain dates within the U.S. states of Louisiana and Massachusetts.
These archives consist of map files (as both static images and as animations) and data files (including text files which contain the underlying data of said map files [where applicable]) which were generated when performing the following Geostatistical analyses: Hot Spot analysis (Getis-Ord Gi*) [‘Archive #1’: consecutive weeklong Space-Time Hot Spot analysis; ‘Archive #2’: daily Hot Spot Analysis], Cluster and Outlier analysis (Anselin Local Moran's I) [‘Archive #2’], Spatial Autocorrelation (Global Moran's I) [‘Archive #2’], and point-to-point comparisons with Kriging and Densification analysis [‘Archive #3’].
The Word document provided ("Description-of-Archive.Updated-Geostatistical-Analysis-of-SARS-CoV-2 (version 6).docx") details the contents of each file and folder within these three archives and gives general interpretations of these results.
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Geothermometry Mapping of Deep Hydrothermal Reservoirs in Southeastern Idaho. Project final report with detail appendices.
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The Hotspot 1 map combines the Heat in Place map and the socio-economic index map. It provides areas where the socio-economic potential at the surface and the geothermal reservoir conditions are the most promising.
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Climate change poses a greater threat for more exposed and vulnerable countries, communities and social groups. People whose livelihood depends on the agriculture and food sector, especially in low- and middle-income countries (LMICs), face significant risk. In contexts with gendered roles in agri-food systems or where structural constraints to gender equality underlie unequal access to resources and services and constrain women’s agency, local climate hazards and stressors, such as droughts, floods, or shortened crop-growing seasons, tend to negatively affect women more than men and women’s adaptive capacities tend to be more restrained than men’s. Transformation toward just and sustainable agri-food systems in the face of climate change will not only depend on reducing but also on averting aggravated gender inequality in agri-food systems. In this paper, we developed and applied an accessible and versatile methodology to identify and map localities where climate change poses high risk especially for women in agri-food systems because of gendered exposure and vulnerability. We label these localities climate-agriculture-gender inequality hotspots. Applying our methodology to LMICs reveals that the countries at highest risk are majorly situated in Africa and Asia. Applying our methodology for agricultural activity-specific hotspot subnational areas to four focus countries, Mali, Zambia, Pakistan and Bangladesh, for instance, identifies a cluster of districts in Dhaka and Mymensingh divisions in Bangladesh as a hotspot for rice. The relevance and urgency of identifying localities where climate change hits agri-food systems hardest and is likely to negatively affect population groups or sectors that are particularly vulnerable is increasingly acknowledged in the literature and, in the spirit of leaving no one behind, in climate and development policy arenas. Hotspot maps can guide the allocation of scarce resources to most-at-risk populations. The climate-agriculture-gender inequality hotspot maps show where women involved in agri-food systems are at high climate risk while signaling that reducing this risk requires addressing the structural barriers to gender equality.
This Web Map shows seal stranding reports from 2009-2019 from the Greater Atlantic Marine Mammal Stranding Network. This map displays hotspots, or areas with high densities of stranding reports. The stranding hotspots density heatmap layer shows a heatmap version of the stranding data, while the stranding reports by 5 km hexagon layer shows the number of strandings within the boundaries of the hexagon. This data represents the number of strandings reported to the Stranding Network and does not necessarily represent the total number of seal strandings along the North Atlantic coast.Story Map: Sharing Seal Space by the Seashore
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Io hot spots map GIS shapefile derived by Juno/JIRAM orbits: 10, 11, 16, 17, 18, 20, 24, 25, 26, 27, 32, 33
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Selectivity is a crucial property in small molecule development. Binding site comparisons within a protein family are a key piece of information when aiming to modulate the selectivity profile of a compound. Binding site differences can be exploited to confer selectivity for a specific target, while shared areas can provide insights into polypharmacology. As the quantity of structural data grows, automated methods are needed to process, summarize, and present these data to users. We present a computational method that provides quantitative and data-driven summaries of the available binding site information from an ensemble of structures of the same protein. The resulting ensemble maps identify the key interactions important for ligand binding in the ensemble. The comparison of ensemble maps of related proteins enables the identification of selectivity-determining regions within a protein family. We applied the method to three examples from the well-researched human bromodomain and kinase families, demonstrating that the method is able to identify selectivity-determining regions that have been used to introduce selectivity in past drug discovery campaigns. We then illustrate how the resulting maps can be used to automate comparisons across a target protein family.
NYC Wi-Fi Hotspot Locations Wi-Fi Providers: CityBridge, LLC (Free Beta): LinkNYC 1 gigabyte (GB), Free Wi-Fi Internet Kiosks Spot On Networks (Free) NYC HOUSING AUTHORITY (NYCHA) Properties AT&T (Free): Wi-Fi access is free for all users at all times. Partners: In several parks, the NYC partner organizations provide publicly accessible Wi-Fi. Visit these parks to learn more information about their Wi-Fi service and how to connect. Cable (Limited-Free): In NYC Parks provided by NYC DoITT Cable television franchisees. ALTICEUSA previously known as “Cablevision” and SPECTRUM previously known as “Time Warner Cable” (Limited Free) Connect for 3 free 10 minute sessions every 30 days or purchase a 99 cent day pass through midnight. Wi-Fi service is free at all times to Cablevision’s Optimum Online and Time Warner Cable broadband subscribers. Wi-Fi Provider: Chelsea Wi-Fi (Free) Wi-Fi access is free for all users at all times. Chelsea Improvement Company has partnered with Google to provide Wi-Fi a free wireless Internet zone, a broadband region bounded by West 19th Street, Gansevoort Street, Eighth Avenue and the High Line Park. Wi-Fi Provider: Downtown Brooklyn Wi-Fi (Free) The Downtown Brooklyn Partnership - the New York City Economic Development Corporation to provide Wi-Fi to the area bordered by Schermerhorn Street, Cadman Plaza West, Flatbush Avenue, and Tillary Street, along with select public spaces in the NYCHA Ingersoll and Whitman Houses. Wi-Fi Provider: Manhattan Downtown Alliance Wi-Fi (Free) Lower Manhattan Several public spaces all along Water Street, Front Street and the East River Esplanade south of Fulton Street and in several other locations throughout Lower Manhattan. Wi-Fi Provider: Harlem Wi-Fi (Free) Network will extend 95 city blocks, from 110th to 138th Streets between Frederick Douglass Boulevard and Madison Avenue is free outdoor public wireless network. Wi-Fi Provider: Transit Wireless (Free) Wi-Fi Services in the New York City subway system are available in certain underground stations. For more information visit http://www.transitwireless.com/stations/. Wi-Fi Provider: Public Pay Telephone Franchisees (Free) Using existing payphone infrastructure, the City of New York has teamed up with private partners to provide free Wi-Fi service at public payphone kiosks across the five boroughs at no cost to taxpayers. Wi-Fi Provider: New York Public Library Using Wireless Internet Access (Wi-Fi): All Library locations offer free wireless access (Wi-Fi) in public areas at all times the libraries are open. Connecting to the Library's Wireless Network •You must have a computer or other device equipped with an 802.11b-compatible wireless card. •Using your computer's network utilities, look for the wireless network named "NYPL." •The "NYPL" wireless network does not require a password to connect. Limitations and Disclaimers Regarding Wireless Access •The Library's wireless network is not secure. Information sent from or to your laptop can be captured by anyone else with a wireless device and the appropriate software, within three hundred feet. •Library staff is not able to provide technical assistance and no guarantee can be provided that you will be able to make a wireless connection. •The Library assumes no responsibility for the safety of equipment or for laptop configurations, security, or data files resulting from connection to the Library's network.
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As a first step in understanding law enforcement agencies' use and knowledge of crime mapping, the Crime Mapping Research Center (CMRC) of the National Institute of Justice conducted a nationwide survey to determine which agencies were using geographic information systems (GIS), how they were using them, and, among agencies that were not using GIS, the reasons for that choice. Data were gathered using a survey instrument developed by National Institute of Justice staff, reviewed by practitioners and researchers with crime mapping knowledge, and approved by the Office of Management and Budget. The survey was mailed in March 1997 to a sample of law enforcement agencies in the United States. Surveys were accepted until May 1, 1998. Questions asked of all respondents included type of agency, population of community, number of personnel, types of crimes for which the agency kept incident-based records, types of crime analyses conducted, and whether the agency performed computerized crime mapping. Those agencies that reported using computerized crime mapping were asked which staff conducted the mapping, types of training their staff received in mapping, types of software and computers used, whether the agency used a global positioning system, types of data geocoded and mapped, types of spatial analyses performed and how often, use of hot spot analyses, how mapping results were used, how maps were maintained, whether the department kept an archive of geocoded data, what external data sources were used, whether the agency collaborated with other departments, what types of Department of Justice training would benefit the agency, what problems the agency had encountered in implementing mapping, and which external sources had funded crime mapping at the agency. Departments that reported no use of computerized crime mapping were asked why that was the case, whether they used electronic crime data, what types of software they used, and what types of Department of Justice training would benefit their agencies.
This layer presents detectable thermal activity from MODIS satellites for the last 7 days. MODIS Global Fires is a product of NASA’s Earth Observing System Data and Information System (EOSDIS), part of NASA's Earth Science Data. EOSDIS integrates remote sensing and GIS technologies to deliver global MODIS hotspot/fire locations to natural resource managers and other stakeholders around the World.Consumption Best Practices:
As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cacheable relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment.When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cacheable.Source: NASA FIRMS - Active Fire Data - for WorldScale/Resolution: 1kmUpdate Frequency: 1/2 Hour (every 30 minutes) using the Aggregated Live Feed MethodologyArea Covered: WorldWhat can I do with this layer?The MODIS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.Additional InformationMODIS stands for MODerate resolution Imaging Spectroradiometer. The MODIS instrument is on board NASA’s Earth Observing System (EOS) Terra (EOS AM) and Aqua (EOS PM) satellites. The orbit of the Terra satellite goes from north to south across the equator in the morning and Aqua passes south to north over the equator in the afternoon resulting in global coverage every 1 to 2 days. The EOS satellites have a ±55 degree scanning pattern and orbit at 705 km with a 2,330 km swath width.It takes approximately 2 – 4 hours after satellite overpass for MODIS Rapid Response to process the data, and for the Fire Information for Resource Management System (FIRMS) to update the website. Occasionally, hardware errors can result in processing delays beyond the 2-4 hour range. Additional information on the MODIS system status can be found at MODIS Rapid Response.Attribute InformationLatitude and Longitude: The center point location of the 1km (approx.) pixel flagged as containing one or more fires/hotspots (fire size is not 1km, but variable). Stored by Point Geometry. See What does a hotspot/fire detection mean on the ground?Brightness: The brightness temperature measured (in Kelvin) using the MODIS channels 21/22 and channel 31.Scan and Track: The actual spatial resolution of the scanned pixel. Although the algorithm works at 1km resolution, the MODIS pixels get bigger toward the edge of the scan. See What does scan and track mean?Date and Time: Acquisition date of the hotspot/active fire pixel and time of satellite overpass in UTC (client presentation in local time). Stored by Acquisition Date.Acquisition Date: Derived Date/Time field combining Date and Time attributes.Satellite: Whether the detection was picked up by the Terra or Aqua satellite.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel.Version: Version refers to the processing collection and source of data. The number before the decimal refers to the collection (e.g. MODIS Collection 6). The number after the decimal indicates the source of Level 1B data; data processed in near-real time by MODIS Rapid Response will have the source code “CollectionNumber.0”. Data sourced from MODAPS (with a 2-month lag) and processed by FIRMS using the standard MOD14/MYD14 Thermal Anomalies algorithm will have a source code “CollectionNumber.x”. For example, data with the version listed as 5.0 is collection 5, processed by MRR, data with the version listed as 5.1 is collection 5 data processed by FIRMS using Level 1B data from MODAPS.Bright.T31: Channel 31 brightness temperature (in Kelvins) of the hotspot/active fire pixel.FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).DayNight: The standard processing algorithm uses the solar zenith angle (SZA) to threshold the day/night value; if the SZA exceeds 85 degrees it is assigned a night value. SZA values less than 85 degrees are assigned a day time value. For the NRT algorithm the day/night flag is assigned by ascending (day) vs descending (night) observation. It is expected that the NRT assignment of the day/night flag will be amended to be consistent with the standard processing.Hours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.RevisionsJune 22, 2022: Added 'HOURS_OLD' field to enhance Filtering data. Added 'Last 7 days' Layer to extend data to match time range of VIIRS offering. Added Field level descriptions.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!