This multi-scale map shows where disconnected youth (i.e. youth 16 -19 who are neither in school nor in the labor force) are. Counts are depicted by size of symbol and percent is depicted by color of symbol. Pop-ups show additional counts of disconnected youth who have not graduated from high school.This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

This map shows predominant sex of disconnected youth (neither in school nor labor force) from the American Community Survey (ACS). These are 5-year estimates shown by tract, county, and state centroids. Estimates here for 'disconnected youth' differ from estimates of 'idle youth' on Census Bureau's website because idle youth includes those unemployed (actively looking for work). This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

This layer shows youth (age 16-19) school enrollment and employment status. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Estimates here for 'disconnected youth' differ from estimates of 'idle youth' on Census Bureau's website because idle youth includes those unemployed (actively looking for work). This layer is symbolized by the percentage of youth who were disconnected. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B14005Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Automatic Disconnect Switches Market Outlook

The global automatic disconnect switches market size is expected to grow from USD 2.1 billion in 2023 to USD 4.5 billion by 2032, at a compound annual growth rate (CAGR) of 8.6%. One of the primary growth factors driving this market is the increasing demand for reliable and safe electrical infrastructure across various sectors. This demand is fueled by rapid industrialization, urbanization, and the expansion of the energy and power sector, which necessitates robust and efficient electrical systems.

A significant growth factor for the automatic disconnect switches market is the rising emphasis on electrical safety and reliability. As industries and residential spaces increasingly depend on electrical equipment, the need for systems that can promptly disconnect faulty circuits to prevent hazards becomes paramount. Automatic disconnect switches, which can isolate parts of an electrical circuit automatically when irregularities are detected, play a crucial role in ensuring safety and minimizing downtime. This is particularly important in industrial settings where any disruption can lead to substantial financial losses.

Technological advancements are also propelling the market forward. Innovations in smart grid technology and the integration of the Internet of Things (IoT) in electrical systems are creating new avenues for the deployment of automatic disconnect switches. These advanced systems offer real-time monitoring and control, enhancing the efficiency and reliability of power distribution. Furthermore, governments and regulatory bodies are implementing stringent safety standards and policies, which is driving the adoption of these switches in both new installations and retrofits.

The increasing investments in renewable energy projects worldwide are another significant driver for the market. As the world moves towards cleaner energy sources like wind and solar power, the need for efficient and reliable power distribution systems becomes critical. Automatic disconnect switches are essential components in these renewable energy setups, ensuring smooth operation and safety. Additionally, the modernization of existing grid infrastructure to accommodate renewable energy sources is further boosting the demand for these switches.

Battery Disconnect Switches are crucial components in both industrial and residential electrical systems. These switches provide an essential function by allowing the safe disconnection of batteries from the electrical system, preventing potential hazards such as short circuits and electrical fires. In industrial applications, battery disconnect switches are vital for maintaining the safety of machinery and equipment, ensuring that power can be safely cut off during maintenance or in emergency situations. In residential settings, these switches offer an added layer of safety, allowing homeowners to easily disconnect power sources when needed, thus protecting their homes and appliances from electrical faults. The growing emphasis on safety and reliability in electrical systems is driving the demand for battery disconnect switches across various sectors.

Regionally, Asia Pacific is anticipated to dominate the automatic disconnect switches market, owing to rapid industrialization and urbanization in countries like China and India. These nations are witnessing a surge in energy demand, driving the need for advanced electrical infrastructure. Moreover, favorable government policies and investments in smart grid projects are supporting market growth in this region. North America and Europe also hold significant market shares due to their well-established industrial sectors and ongoing upgrades to electrical systems to enhance safety and efficiency.

Type Analysis

The automatic disconnect switches market is segmented by type into fused and non-fused switches. Fused disconnect switches integrate a fuse within the switch mechanism, providing an added layer of protection by disconnecting the circuit in case of overloads or faults. These switches are particularly favored in applications where high fault currents are anticipated, ensuring both equipment and personnel safety. The demand for fused switches is driven by their reliability and effectiveness in preventing electrical accidents, making them a crucial component in industrial and commercial settings.

Non-fused disconnect switches, on the other hand, do not include an inbuilt fuse but

Fuse Disconnect Switches Market Outlook

The global fuse disconnect switches market size was valued at approximately USD 2.5 billion in 2023, and it is projected to reach USD 4.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 6.5% during the forecast period. The significant growth of the market can be attributed to the increasing demand for reliable and safe electrical infrastructure across various industries, advancements in smart grid technologies, and the ongoing industrial automation trends.

One of the key growth factors for the fuse disconnect switches market is the rising need for robust electrical safety mechanisms in both residential and industrial settings. With electrical systems becoming more complex and widespread, the necessity for reliable protection devices has surged. Fuse disconnect switches provide an essential layer of safety by ensuring that electrical circuits can be quickly disconnected in case of faults, preventing damage to equipment and reducing fire hazards. This growing awareness and demand for enhanced electrical safety are expected to drive market expansion.

Another vital factor contributing to the market growth is the rapid industrialization and urbanization in emerging economies. Countries in the Asia Pacific and Latin America regions are witnessing significant infrastructure development, leading to increased investments in power distribution and transmission networks. The expansion of these networks requires the integration of reliable protection devices such as fuse disconnect switches to ensure uninterrupted power supply and system integrity. This has created a substantial demand for these switches, particularly in fast-developing regions.

Technological advancements in smart grid infrastructure and the increasing adoption of renewable energy sources are also propelling market growth. Fuse disconnect switches are crucial components in modern smart grids, which require advanced protection and control mechanisms to manage distributed energy resources efficiently. The ongoing transition to renewable energy systems, such as solar and wind power, necessitates the use of reliable protection devices to handle the variable nature of these energy sources, further boosting the demand for fuse disconnect switches.

Regionally, the Asia Pacific region is expected to dominate the fuse disconnect switches market during the forecast period, driven by rapid industrialization, urbanization, and infrastructure development in countries like China and India. North America and Europe are also significant markets, owing to the presence of advanced electrical infrastructure and a high emphasis on safety standards. In contrast, the Middle East & Africa and Latin America regions are witnessing steady growth, supported by increasing investments in power generation and distribution projects.

Type Analysis

The fuse disconnect switches market can be segmented by type into fused and non-fused switches. Fused disconnect switches are widely used in applications requiring high safety standards and reliable protection. These switches incorporate a fuse element that provides overcurrent protection, ensuring that electrical circuits are interrupted in case of excessive current flow. The demand for fused disconnect switches is particularly high in industrial and commercial applications where safety and reliability are paramount. The increasing focus on enhancing electrical safety in industrial settings is a significant driver for the growth of fused disconnect switches.

Non-fused disconnect switches, on the other hand, do not include an integral fuse for overcurrent protection. These switches are primarily used in applications where overcurrent protection is provided by other means, such as circuit breakers. Non-fused disconnect switches are preferred in scenarios where space constraints or specific design requirements necessitate the use of separate protection devices. The flexibility and cost-effectiveness of non-fused disconnect switches make them suitable for a wide range of applications, including residential and commercial sectors.

The selection between fused and non-fused disconnect switches depends on the specific requirements of the application and the level of protection needed. While fused disconnect switches offer integrated protection and are ideal for high-risk applications, non-fused disconnect switches provide greater flexibility in design and installation. Both types of switches play a crucial role in ensuring the safety and reliability of electrical systems, contributing to the overall

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show the number and percentages of opportunity to youth by Super District in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

CountyName

County Name

PopAges1619_e

# Population, ages 16-19, 2017

PopAges1619_m

# Population, ages 16-19, 2017 (MOE)

DisconYouth_e

# Disconnected youth: ages 16-19 not in school or in labor force, 2017

DisconYouth_m

# Disconnected youth: ages 16-19 not in school or in labor force, 2017 (MOE)

pDisconYouth_e

% Disconnected youth: ages 16-19 not in school or in labor force, 2017

pDisconYouth_m

% Disconnected youth: ages 16-19 not in school or in labor force, 2017 (MOE)

OwnChildInFam_e

# Own children in families, 2017

OwnChildInFam_m

# Own children in families, 2017 (MOE)

NoParentLabForce_e

# Own children in families with no parent in the labor force, 2017

NoParentLabForce_m

# Own children in families with no parent in the labor force, 2017 (MOE)

pNoParentLabForce_e

% Own children in families with no parent in the labor force, 2017

pNoParentLabForce_m

% Own children in families with no parent in the labor force, 2017 (MOE)

last_edited_date

Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

This map shows the predominant combination of school and work of youth (16-19 year-olds) by state, county, and tract: school only, work only, both, school and looking for work, looking only (unemployed), or neither (disconnected).This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available. Estimates here for 'disconnected youth' differ from estimates of 'idle youth' on Census Bureau's website because idle youth includes those unemployed (actively looking for work).

Layer used in the Field Maps app for capturing census data on disconnected mode.

All data displayed on this map is near real-time. There are two ways in which this happens: Web service based data and a mobile mapping application called Field Maps. Web services are updated regularly ranging from every minute to once a month. All web services in this map are refreshed automatically to ensure the latest data being provided is displayed. Data collected through the use of Field Maps is done so by firefighters on the ground. The Field Maps application is consuming, creating, and editing data that are stored in ArcGIS Online. These data are then fed directly in to this map. To learn more about these web mapping technologies, visit the links below:Web ServicesArcGIS Field MapsArcGIS OnlineWeb Services used in this map:(visit link to learn more about each service)IRWIN - A central hub that orchestrates data between various fire reporting applications. When a new incident is created and/or updated by a dispatch center or other fire reporting system, it is then displayed on the map using the Integrated Reporting of Wildland-Fire Information (IRWIN) service. All layers below are derived from the same IRWIN service and automatically refresh every five minutes:New Starts (last 24hrs) - Any incident that has occurred within the last rolling 24 hour time period.Current Large Incidents - Incidents that have created an ICS 209 document at the type 3 Incident Commander (IC) level and above and are less than 100% contained.Ongoing - Incidents that do not have a containment, control, or out date.Contained - Incidents with a containment date but no control or out date.Controlled/Out (last 24hrs) - Incidents with a containment, control, and/or out date within the last rolling 24 hour time period.Controlled/Out - Incidents with a containment, control, and/or out date. Layer turned off by default.Season Summary - All incidents year to date. Layer turned off by default.ArcGIS Online/Field Maps - Part of the Esri Geospatial Cloud, ArcGIS Online and Collector enables firefighters to use web maps created in ArcGIS Online on mobile devices using the Collector application to capture and edit data on the fireline. Data may be captured and edited in both connected and disconnected environments. When data is submitted back to the web service in ArcGIS Online, it is then checked for accuracy and approved for public viewing.Fire Perimeter - Must be set to 'Approved' and 'Public' to be displayed on the map. Automatically refreshes every five minutes.NOAA nowCOAST - Provides web services of near real-time observations, analyses, tide predictions, model guidance, watches/warnings, and forecasts for the coastal United States by integrating data and information across NOAA, other federal agencies and regional ocean and weather observing systems (source). All layers below automatically refresh every five minutes.Tornado Warning - National Weather Service warning for short duration hazard.Severe Thunderstorm Warning - National Weather Service warning for short duration hazard.Flash Flood Warning - National Weather Service warning for short duration hazard.Red Flag Warning - National Weather Service warning for long duration hazard.nowCOAST Lightning Strike Density - 15-minute Satellite Emulated Lightning Strike Density imagery for the last several hours.nowCOAST Radar - Weather Radar (NEXRAD) Reflectivity Mosaics from NOAA MRMS for Alaska, CONUS, Puerto Rico, Guam, and Hawaii for last several hours.

The MCAD Tax Parcel Point View was updated in early 2024 to streamline the data schema and improve data accuracy. Instead of including a centroid for every parcel in the county (~300,000 parcels), the updated point layer now only includes parcels with anomalies that may impact mapping or other GIS products. The following types of anomalies qualify a parcel to be included in this view:Multiple Owners: Parcels with more than three listed owners.Example: Parcel at 2827 Waterbend Cove, Spring, TX, with 6 listed owners.Disconnected or Multi-part Parcels: Parcels that are split into multiple parts (more than one polygon).Example: Parcel 51296, which is split into 3 separate parts.Because the point layer is focused on these special cases, the record count for the Tax Parcel Point View has decreased significantly after the May 2024 update. For a complete view of all parcels, users are encouraged to refer to the MCAD Tax Parcel View dataset, which includes all parcels in Montgomery County.Data Includes:Flagged Parcels: Geographic points representing parcels with anomalies (multiple owners, disconnected parcels, multi-part parcels).Anomaly Types: Data on parcels with specific anomalies such as multiple owners or multiple parts.Data Source:The data is maintained and provided by the Montgomery Central Appraisal District (MCAD).Access:The view is available through Montgomery County’s Open Data Portal and can be accessed for public use.Update Frequency:The data is updated monthly, especially after changes to MCAD’s appraisal processes or data schema.

This dashboard is best viewed using a mobile device. For an enhanced viewing experience on a desktop or laptop computer please use the NV Wildfire Info desktop version dashboardAll data displayed on this map is near real-time. There are two ways in which this happens: Web service based data and a mobile mapping application called Field Maps. Web services are updated regularly ranging from every minute to once a month. All web services in this map are refreshed automatically to ensure the latest data being provided is displayed. Data collected through the use of Field Maps is done so by firefighters on the ground. The Field Maps application is consuming, creating, and editing data that are stored in ArcGIS Online. These data are then fed directly in to this map. To learn more about these web mapping technologies, visit the links below:Web ServicesArcGIS Field MapsArcGIS OnlineWeb Services used in this map:(visit link to learn more about each service)IRWIN - A central hub that orchestrates data between various fire reporting applications. When a new incident is created and/or updated by a dispatch center or other fire reporting system, it is then displayed on the map using the Integrated Reporting of Wildland-Fire Information (IRWIN) service. All layers below are derived from the same IRWIN service and automatically refresh every five minutes:New Starts (last 24hrs) - Any incident that has occurred within the last rolling 24 hour time period.Current Large Incidents - Incidents that have created an ICS 209 document at the type 3 Incident Commander (IC) level and above and are less than 100% contained.Ongoing - Incidents that do not have a containment, control, or out date.Contained - Incidents with a containment date but no control or out date.Controlled/Out (last 24hrs) - Incidents with a containment, control, and/or out date within the last rolling 24 hour time period.Controlled/Out - Incidents with a containment, control, and/or out date. Layer turned off by default.Season Summary - All incidents year to date. Layer turned off by default.ArcGIS Online/Field Maps - Part of the Esri Geospatial Cloud, ArcGIS Online and Collector enables firefighters to use web maps created in ArcGIS Online on mobile devices using the Collector application to capture and edit data on the fireline. Data may be captured and edited in both connected and disconnected environments. When data is submitted back to the web service in ArcGIS Online, it is then checked for accuracy and approved for public viewing.Fire Perimeter - Must be set to 'Approved' and 'Public' to be displayed on the map. Automatically refreshes every five minutes.NOAA nowCOAST - Provides web services of near real-time observations, analyses, tide predictions, model guidance, watches/warnings, and forecasts for the coastal United States by integrating data and information across NOAA, other federal agencies and regional ocean and weather observing systems (source). All layers below automatically refresh every five minutes.Tornado Warning - National Weather Service warning for short duration hazard.Severe Thunderstorm Warning - National Weather Service warning for short duration hazard.Flash Flood Warning - National Weather Service warning for short duration hazard.Red Flag Warning - National Weather Service warning for long duration hazard.nowCOAST Lightning Strike Density - 15-minute Satellite Emulated Lightning Strike Density imagery for the last several hours.nowCOAST Radar - Weather Radar (NEXRAD) Reflectivity Mosaics from NOAA MRMS for Alaska, CONUS, Puerto Rico, Guam, and Hawaii for last several hours.

This layer provides long-distance bus network in Southern Ontario, excluding Go Rail, Go Bus and Go stops network which are hosted as individual public facing feature layers in MTO AGOL. Note that some services might fully disconnected, added or re-routed. For more up-to-date information refer to each service provider's website.Ontario Northland: https://ontarionorthland.ca/enNapanee: https://www.greaternapanee.com/en/live-play-and-discover/transportation-and-getting-here.aspx#Niagara falls: https://niagarafalls.ca/living/transit/bus-routes.aspxHammond: https://hammondtransportation.com/Greyhound: https://www.greyhound.ca/Cette couche présente le réseau de transport interurbain par autobus du Sud de l’Ontario, à l’exclusion des réseaux GO Rail, GO Bus et les arrêts de GO, auxquels correspondent des couches destinées au public dans le système ArcGIS Online (AGOL) du MTO. Notez que certains services pourraient être coupés, ajoutés ou déroutés. Pour l’information la plus à jour, consulter le site Web du fournisseur de services.