The map was created to be shared within the Hudson County Place Vulnerability Instant App. Layers from the Hudson County Place Vulnerability Midterm Web Map were originally copied over and unneeded layers were deleted. The resulting map shows additional hazards other than flooding in Hudson County, NJ.LayersWildfire Hazard Potential: Shows the average wildfire hazard potential for the US on a scale of 1-5. The layer was obtained using ESRI's Living Atlas. Source: https://napsg.maps.arcgis.com/home/item.html?id=ce92e9a37f27439082476c369e2f4254 NOAA Storm Events Database 1950-2021: Shares notable storm events throughout the US recorded by NOAA between the years of 1950-2021. The layer was obtained using ESRI's Living Atlas. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=88cc0d5e55f343c28739af1a091dfc91 Category 1 Hurricane Storm Surge: Includes the expected Inundation Height of areas within the US should a Category 1 Hurricane hit the area. The layer was obtained using the ArcGIS Online Portal. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=49badb9332f14079b69cfa49b56809dc Category 2 Hurricane Storm Surge: Includes the expected Inundation Height of areas within the US should a Category 2 Hurricane hit the area. The layer was obtained using the ArcGIS Online Portal. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=b4e4f410fe9746d5898d98bb7467c1c2 Category 3 Hurricane Storm Surge: Includes the expected Inundation Height of areas within the US should a Category 3 Hurricane hit the area. The layer was obtained using the ArcGIS Online Portal. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=876a38efe537489fb3bc6b490519117f U.S. Sea Level Rise Projections: Shows different sea level rise projections within the United States. The layer was obtained via ESRI's Living Atlas. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=8943e6e91c304ba2997d83b597e32861

In response to Executive Order 13817 of December 20, 2017, the U.S. Geological Survey (USGS) coordinated with the Bureau of Land Management (BLM) to identify 35 nonfuel minerals or mineral materials considered critical to the economic and national security of the United States (U.S.). Acquiring information on possible domestic sources of these critical minerals is the basis of the USGS Earth Mapping Resources Initiative (Earth MRI). The program, which partners the USGS with State Geological Surveys, federal agencies, and the private sector, aims to collect new geological, geophysical, and topographic (lidar) data in key areas of the U.S. to stimulate mineral exploration and production of critical minerals. The first phase of Earth MRI focuses on the study of rare-earth elements (REE). The USGS has identified broad areas within the U.S. to target acquisition of geologic mapping, geophysical data, and (or) detailed topographic information to aid research, mineral exploration, and evaluation of REE potential in these areas. Focus areas were defined using existing geologic data on known REE deposits in the U.S. The focus areas are provided as geospatial data supported by tables that summarize what is known about the REE potential and brief descriptions of data gaps that could be addressed by the Earth MRI program. A full discussion of Earth MRI and the rationale and methods used to develop the geospatial data are provided in the following report: Hammarstrom, J.H., and Dicken, C.L., 2019, Focus areas for data acquisition for potential domestic sources of critical minerals—Rare earth elements, chap. A of U.S. Geological Survey, Focus areas for data acquisition for potential domestic sources of critical minerals: U.S. Geological Survey Open-File Report 2019–1023, 11 p., https://doi.org/10.3133/ofr20191023A.

AnalysisFEMA's National Flood Hazard Layer (NFHL) and the CDC's Social Vulnerability Index (SVI) were cross referenced to produce a Place Vulnerability Analysis for Hudson County, NJ. Using ArcGIS Pro, the location of interest (Hudson County) was first determined and the Flood Hazard and SVI layers were clipped to this extent. A new feature class, intersecting the two, was then created using the Intersect Tool. The output of this process was the Hudson County Place Vulnerability Layer. Additional Layers were added to the map to assess important special needs infrastructure, community lifelines, and additional hazard risks within the most vulnerable areas of the county.LayersWildfire Hazard Potential: Shows the average wildfire hazard potential for the US on a scale of 1-5. The layer was obtained using ESRI's Living Atlas. Source: https://napsg.maps.arcgis.com/home/item.html?id=ce92e9a37f27439082476c369e2f4254 NOAA Storm Events Database 1950-2021: Shares notable storm events throughout the US recorded by NOAA between the years of 1950-2021. The layer was obtained using ESRI's Living Atlas. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=88cc0d5e55f343c28739af1a091dfc91 Category 1 Hurricane Storm Surge: Includes the expected Inundation Height of areas within the US should a Category 1 Hurricane hit the area. The layer was obtained using the ArcGIS Online Portal. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=49badb9332f14079b69cfa49b56809dc Category 2 Hurricane Storm Surge: Includes the expected Inundation Height of areas within the US should a Category 2 Hurricane hit the area. The layer was obtained using the ArcGIS Online Portal. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=b4e4f410fe9746d5898d98bb7467c1c2 Category 3 Hurricane Storm Surge: Includes the expected Inundation Height of areas within the US should a Category 3 Hurricane hit the area. The layer was obtained using the ArcGIS Online Portal. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=876a38efe537489fb3bc6b490519117f U.S. Sea Level Rise Projections: Shows different sea level rise projections within the United States. The layer was obtained via ESRI's Living Atlas. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=8943e6e91c304ba2997d83b597e32861Power Plants: Includes all New Jersey power plants about 1 Megawatt capacity. The layer was obtained via the NJDEP Bureau of GIS website. Source: https://njdep.maps.arcgis.com/home/item.html?id=282eb9eb22cc40a99ed509a7aa9f7c90Solid & Hazardous Waste Facilities: Includes hazardous waste facilities, medical waste facilities, incinerators, recycling facilities, and landfill sites within New Jersey. Obtained via the NJDEP Bureau of GIS website. Source: https://njdep.maps.arcgis.com/home/item.html?id=896615180fb04d8eafda0df9df9a1d73Solid Waste Landfill Sites over 35 Acres: Includes solid waste landfill sites in New Jersey that are larger than 35 acres. Obtained via the NJDEP Bureau of GIS website. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=2b4eab598df94ffabaa8d92e3e46deb4NJ Transit Rail Lines: A layer showing segments of the NJ Transit Rail System and terminals. Data was obtained via the NJ Transit GIS Department. Source: https://www.arcgis.com/home/item.html?id=e6701817be974795aecc7f7a8cc42f79Medical Emergency Response Structures: Contains emergency response centers within the U.S. based off National Geospatial Data Asset data from the U.S. Geological Survey. The layer was obtained using ESRI's Living Atlas. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=2c36dbb008844081b017da6fd3d0d28bSchools: Shows the location of New Jersey schools, including public, private and charter schools. Obtained via the New Jersey Office of GIS. Source: https://njdep.maps.arcgis.com/home/item.html?id=d8223610010a4c3887cfb88b904545ffChild Care Centers: Shows the location of active child care centers in New Jersey. The layer was obtained via the NJ Bureau of GIS website. Source: https://njdep.maps.arcgis.com/home/item.html?id=0bc9fe070d4c49e1a6555c3fdea15b8aNursing Homes: A layer containing the locations of nursing homes and assisted care facilities in the United States. Obtained via the HIFLD website. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=78c58035fb3942ba82af991bb4476f13cCDC's Social Vulnerability Index (SVI) - ATSDR's Geospatial Research, Analysis & Services Program (GRASP) has created a tool to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event. The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every census tract. The SVI ranks each census tract on 15 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes. Source: https://gisanddata.maps.arcgis.com/home/item.html?id=05709059044243ae9b42f469f0e06642

2019 Novel Coronavirus COVID-19 (2019-nCoV) Visual Dashboard and Map:
https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

• Confirmed Cases by Country/Region/Sovereignty
• Confirmed Cases by Province/State/Dependency
• Deaths
• Recovered

Downloadable data:
https://github.com/CSSEGISandData/COVID-19

Additional Information about the Visual Dashboard:
https://systems.jhu.edu/research/public-health/ncov

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit the following sources:Global: World Health Organization (WHO)U.S.: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.This feature layer contains the most up-to-date COVID-19 cases and the latest trend plot. It covers the US (county or state level), China, Canada, Australia (province/state level), and the rest of the world (country/region level, represented by either the country centroids or their capitals). Data sources are WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, BNO, the COVID Tracking Project (testing and hospitalizations), state and national government health departments, and local media reports. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This feature layer is supported by Esri Living Atlas team, JHU APL and JHU Data Services. This layer is opened to the public and free to share. Contact us.

Analysis of ‘COVID-19 Coronavirus Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/vignesh1694/covid19-coronavirus on 14 February 2022.

--- Dataset description provided by original source is as follows ---

Context

A SARS-like virus outbreak originating in Wuhan, China, is spreading into neighboring Asian countries, and as far afield as Australia, the US a and Europe.

On 31 December 2019, the Chinese authorities reported a case of pneumonia with an unknown cause in Wuhan, Hubei province, to the World Health Organisation (WHO)’s China Office. As more and more cases emerged, totaling 44 by 3 January, the country’s National Health Commission isolated the virus causing fever and flu-like symptoms and identified it as a novel coronavirus, now known to the WHO as 2019-nCoV.

The following dataset shows the numbers of spreading coronavirus across the globe.

Content

Sno - Serial number Date - Date of the observation Province / State - Province or state of the observation Country - Country of observation Last Update - Recent update (not accurate in terms of time) Confirmed - Number of confirmed cases Deaths - Number of death cases Recovered - Number of recovered cases

Acknowledgements

Thanks to John Hopkins CSSE for the live updates on Coronavirus and data streaming. Source: https://github.com/CSSEGISandData/COVID-19 Dashboard: https://public.tableau.com/profile/vignesh.coumarane#!/vizhome/DashboardToupload/Dashboard12

Inspiration

Inspired by the following work: https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

--- Original source retains full ownership of the source dataset ---

Analysis of ‘COVID-19 in Turkey’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/gkhan496/covid19-in-turkey on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

COVID-19 data in Turkey. Daily Covid-19 data published by our health ministry.

Content

time_series_covid_19_confirmed_tr
time_series_covid_19_recovered_tr
time_series_covid_19_deaths_tr
time_series_covid_19_intubated_tr
time_series_covid_19_intensive_care_tr.csv 
time_series_covid_19_tested_tr.csv 
test_numbers : Number of test (daily)

Total data

covid_19_data_tr

Github

Github repo : https://github.com/gkhan496/Covid19-in-Turkey/

Acknowledgements

We would like to thank our health ministry and all health workers.

Country level datasets

USA - https://www.kaggle.com/sudalairajkumar/covid19-in-usa Indonesia - https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases France - https://www.kaggle.com/lperez/coronavirus-france-dataset Tunisia - https://www.kaggle.com/ghassen1302/coronavirus-tunisia Japan - https://www.kaggle.com/tsubasatwi/close-contact-status-of-corona-in-japan South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset Italy - https://www.kaggle.com/sudalairajkumar/covid19-in-italy Brazil - https://www.kaggle.com/unanimad/corona-virus-brazil

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2311214%2Feaf61a1cf97850b64aefd52d3de5890b%2FXMhaJ.png?generation=1586182028591623&alt=media" alt="">

Source : https://fastlifehacks.com/n95-vs-ffp/

https://covid19.saglik.gov.tr https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html?fbclid=IwAR0k49fzqTxI4HBBZF7n4hLX4Zj0Q2KII_WOEo7agklC20KODB3TOeF8RrU#/bda7594740fd40299423467b48e9ecf6 http://who.int/ --situation reports https://evrimagaci.org/covid19#turkey-statistics

--- Original source retains full ownership of the source dataset ---

dd_AggAssault

This image reports a Maximum Entropy model that estimates suitable locations for COVID-19 spread, i.e. places that could favour the spread of the virus just in terms of environmental parameters.

The model was trained just on locations in Italy that have reported a rate of new infections higher than the geometric mean of all Italian infection rates. The following environmental parameters were used, which are correlated to those used by other studies:

Average Annual Surface Air Temperature in 2018 (NASA)

Average Annual Precipitation in 2018 (NASA)

CO2 emission (natural+artificial) averaged between January 1979 and December 2013 (Copernicus Atmosphere Monitoring Service)

Elevation (NOAA ETOPO2)

Population per 0.5° cell (NASA Gridded Population of the World)

A higher resolution map, the model file (in ASC format) and all parameters used are also attached.

The model indicates highest correlation with infection rate for CO2 around 0.03 gCm^−2day^−1, for Temperature around 11.8 °C, and for Precipitation around 0.3 kg m^-2 s^-1, whereas Elevation and Population density are poorly correlated with infection rate.

One interesting result is that the model indicates, among others, the Hubei region in China as a high-probability location, and Iran (around Teheran) as a suited location for virus' spread, but the model was not trained on these regions, i.e. it did not know about the actual spread in these regions.

Evaluation:

A risk score was calculated for each country/region reported by the JHU monitoring system (https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6). This score is calculated as the summed normalised probability in the populated locations divided by their total surface. This score represents how much the zone would potentially foster the virus' spread.

We assessed the reliability of this score, by selecting the country/regions that reported the highest rates of infection. These zones were selected as those with a rate higher than the upper confidence of a log-normal distribution of the rates.

The agreement between the two maps (covid_high_rate_vs_high_risk.png, where violet dots indicate high infection rates and countries' colours indicate estimated high risk score) is the following:

Accuracy (overall percentage of correctly predicted high-rate zones): 77.25% Kappa (agreement between the two maps): 0.46 (Good, according to Fleiss' intepretation of the score)

This assessment demonstrates that our map can be used to estimate the risk of a certain country to have a high rate of infection, and indicates that the influence of environmental parameters on virus's spread should be further investigated.

dd_Larceny

Context

According to WHO Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people infected with the COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. Older people and those with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illnesses.

Johns Hopkins University has made an excellent dashboard for tracking the spread of COVID-19. Data is extracted from the Johns Hopkins Github repository associated and made available here.

Content

This dataset has daily level information on the number of confirmed cases, deaths and recovery cases from 2019 novel coronavirus. Please note that this is a time series data and so the number of cases on any given day is the cumulative number. The data is available from 22 Jan, 2020 and updated regularly. Github repository of this clean dataset is here

Columns Description

Filename is covid-19_cleaned_data.csv(updated) - Province/State- Province/State of the observations - Country/Region-Country of observations - Date- Last update - Confirmed - Cumulative number of confirmed cases till that date - Recovered - Cumulative number of recovered till that date - Deaths- Cumulative number of deaths till that date - Lat and Long - Coordinates

Acknowledgements

• Johns Hopkins University -https://github.com/CSSEGISandData/COVID-19
• World Health Organization(WHO) - https://www.who.int/

Inspiration

Some insights could be 1. Mortality rate over time 2. Exponential growth 3. Changes in the number of affected cases over time 4. The latest number of affected cases

Larceny_Contour

Now your students can start editing their templates! It is up to you when, where, and how this happens. We HIGHLY recommend students draft text and collect sources outside of StoryMaps (in Word, GoogleDocs, etc.) and copy material into StoryMaps as they are ready to turn in each part of the template. This protects against browser crashes, accidental edits by instructors or other students, and other inevitable mishaps in the digital space. As written, the template requests different kinds and numbers of sources for each part. You can find a quick list of what students will need to have to hand for each section on the Research Activities and Assignments page .

Forsøg på at klassificere antallet af bekræftede SARS-CoV-2-tilfælde i fransk område efter region.

Dataene kommer fra statens medier og sundhedswebsteder. Formålet med dokumentet er at være så præcist som muligt.

Kilder: — https://solidarites-sante.gouv.fr/soins-et-maladies/maladies/maladies-infectieuses/coronavirus/article/points-de-situation-coronavirus-covid-19 — https://www.santepubliquefrance.fr/maladies-et-traumatismes/maladies-et-infections-respiratoires/infection-a-coronavirus/articles/infection-au-nouveau-coronavirus-sars-cov-2-covid-19-france-et-monde — https://france3-regions.francetvinfo.fr — https://www.ars.sante.fr/ — https://www.facebook.com/MinSoliSante/ — https://geodes.santepubliquefrance.fr/#c=home

Andre kilder på data.gouv.fr interessant:

— https://www.data.gouv.fr/fr/datasets/chiffres-cles-concernant-lepidemie-de-covid19-en-france/ — https://www.data.gouv.fr/fr/reuses/visualisation-et-analyse-covid-19-monde-france-regions-francaises/ — https://www.data.gouv.fr/fr/reuses/tableau-de-bord-de-suivi-de-lepidemie-de-covid19/

Andre varer:

— https://www.arcgis.com/apps/opsdashboard/index.html#/3a278da2d7ab4a8a8e1b4ea8bea7121b — https://www.esrifrance.fr/coronavirus-ressources.aspx — https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6 — https://nextstrain.org/ncov

Purpose Of The Item A feature layer view is used to share emergency resources such as shelters and distribution points for essential items and care. It provides crucial data for managing and coordinating the availability and locations of resources for disaster response. AudienceThis feature layer's primary audience includes emergency managers, public safety officials, and students and staff involved in disaster response and preparedness training. Data Source The data for this feature layer view comes from emergency management scenarios:Distribution Points: Locations designated for distributing essential items during an emergency.Shelters: Locations providing temporary care for individuals affected by an emergency.

Versuche, die Zahl der bestätigten SARS-CoV-2-Fälle auf französischem Territorium nach Regionen einzuordnen.

Die Daten stammen aus den Medien und Gesundheitsseiten des Staates. Das Ziel des Dokuments ist, so genau wie möglich zu sein.

Quelle: — https://solidarites-sante.gouv.fr/soins-et-maladies/maladies/maladies-infectieuses/coronavirus/article/points-de-situation-coronavirus-covid-19 — https://www.santepubliquefrance.fr/maladies-et-traumatismes/maladies-et-infections-respiratoires/infection-a-coronavirus/articles/infection-au-nouveau-coronavirus-sars-cov-2-covid-19-france-et-monde — https://france3-regions.francetvinfo.fr — https://www.ars.sante.fr/ — https://www.facebook.com/MinSoliSante/ — https://geodes.santepubliquefrance.fr/#c=home

Weitere interessante Quellen auf data.gouv.fr:

— https://www.data.gouv.fr/fr/datasets/chiffres-cles-concernant-lepidemie-de-covid19-en-france/ — https://www.data.gouv.fr/fr/reuses/visualisation-et-analyse-covid-19-monde-france-regions-francaises/ — https://www.data.gouv.fr/fr/reuses/tableau-de-bord-de-suivi-de-lepidemie-de-covid19/

Sonstiges:

— https://www.arcgis.com/apps/opsdashboard/index.html#/3a278da2d7ab4a8a8e1b4ea8bea7121b — https://www.esrifrance.fr/coronavirus-ressources.aspx — https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6 — https://nextstrain.org/ncov

Homewood_HMBuff_CommonAssault

DEPRECATED: This layer has not been maintained and is outdated. We recommend using the NG911 Address Points layer, filtered to shows schools, as a more current data layer.Point locations of schools in Maine. Structures data contains name and location data for selected manmade facilities. These data are designed to be used in general mapping and analysis of structure related activities using a geographic information system (GIS) For mapping purposes, structures can be used with other GIS data themes to produce general reference maps as a base map dataset.

[Redigera 12/09/2020] Du hittar nu i filerna nedan de senaste ** 30 dagarna**, alltför många människor respekterar inte begäran om att inte återställa för ofta datauppsättningen (inget intresse för att återhämta sig varje minut medan filen ändras 4 eller 5 gånger om dagen) Om du vill ha tillgång till hela historiken, kontakta mig

[Redigera 31/03/2020] Sedan igår såg jag till att ha dagens data sedan ESSC, så uppgifterna för samma dag är nu tillgängliga och uppdaterade flera gånger om dagen (ungefär varje timme) när de nya siffrorna faller över hela världen. Uppgifterna för föregående dag är alltid konsoliderade runt 2am (det är inte längre 1h sedan tidsändringen). Om du bara vill ha fullständiga data, ta bara inte hänsyn till den sista dagen (dagens datum)

Här delar jag data som jag sammanställer med den berömda världskartan för coronavirusinfektioner som skapats och underhålls av Johns Hopkins University(https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6) och som tjänar mig att visa ** CoronaVirus statistik över hela världen och efter land**

De delar dagens data varje natt på en GitHub deposition. Mina verktyg sammanställer dessa nya data så snart de är tillgängliga och jag delar resultatet här.

Dessa data används för att visa tabeller och grafer på CoronaVirus webbplats (Covid19) på Politologue.com https://coronavirus.politologue.com/

Dessa data gör att du kan göra dina egna grafer och analyser om du tittar på ämnet.

Jag tvingar dig inte att göra det, men om min sammanställning tillåter dig att göra något åt det och sparade dig tid, kommer en länk till https://coronavirus.politologue.com/ att vara märkbar.

Information i filer (csv och json) — Antal mål — Antal dödsfall — Antal healing — Dödlighet (procentandel) Läkningsgrad (procent) — Infektionsfrekvens (personer som fortfarande är smittade, inte avlidna eller botade) (procent) — Och för data per land hittar du ett fält ”land”

** Om du integrerar klientsidan json eller csv på en webbplats eller applikation, behåll en cache på dina servrar utan att riskera en oväntad belastning på mina servrar.**

Now your students can start editing their templates! It is up to you when, where, and how this happens. We HIGHLY recommend students draft text and collect sources outside of StoryMaps (in Word, GoogleDocs, etc.) and copy material into StoryMaps as they are ready to turn in each part of the template. This protects against browser crashes, accidental edits by instructors or other students, and other inevitable mishaps in the digital space. As written, the template requests different kinds and numbers of sources for each part. You can find a quick list of what students will need to have to hand for each section on the Research Activities and Assignments page .