This dataset represents a high resolution urban land cover classification map across the southern California Air Basin (SoCAB) with a spatial resolution of 60 cm in urban regions and 10 m in non-urban regions. This map was developed to support NASA JPL-based urban biospheric CO2 modeling in Los Angeles, CA. Land cover classification was derived from a novel fusion of Sentinel-2 (10-60 m x 10-60 m) and 2016 NAIP (60 cm x 60 cm) imagery and provides identification of impervious surface, non-photosynthetic vegetation, shrub, tree, grass, pools and lakes.

Land Cover Classes in .tif file: 0: Impervious surface 1: Tree (mixed evergreen/deciduous) 2: Grass (assumed irrigated) 3: Shrub 4: Non-photosynthetic vegetation 5: Water (masked using MNDWI/NDWI)

Google Earth Engine interactive app displaying this map: https://wcoleman.users.earthengine.app/view/socab-irrigated-classification

A portion of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Support from the Earth Science Division OCO-2 program is acknowledged. Copyright 2020. All rights reserved.

The Global Human Footprint dataset of the Last of the Wild Project, version 2, 2005 (LWPv2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1 km grid cells, created from nine global data layers covering human population pressure (population density), human land use and infraestructure (built-up areas, nighttime lights, land use/land cover) and human access (coastlines, roads, navigable rivers).The Human Footprint Index (HF) map, expresses as a percentage the relative human influence in each terrestrial biome. HF values from 0 to 100. A value of zero represents the least influence -the "most wild" part of the biome with value of 100 representing the most influence (least wild) part of the biome.

Gray Basemap for LA County Streets using Vector Tiles.This vector basemap is derived from the LA County Basemap Source vector tile basemap and the ESRI World Hillshade.For cartographic purposes, the Basemap Source, which contains all of the raw vector data, is split into three derived vector tile layers:Labels - enables labels to be turned on and off as necessaryTop Layers - these are 0% transparent to ensure crispness of the cartographyHillshade (shown at 40% transparency to enable the cities and land types in the Bottom Layers to be seen)Bottom Layers - these layers (cities and land types) are shown under the hillshade

Introduction

This group of maps shows relative susceptibility of hill slopes to the initiation sites of rainfall-triggered soil slip-debris flows in southwestern California. As such, the maps offer a partial answer to one part of the three parts necessary to predict the soil-slip/debris-flow process. A complete prediction of the process would include assessments of "where", "when", and "how big". These maps empirically show part of the "where" of prediction (i.e., relative susceptibility to sites of initiation of the soil slips) but do not attempt to show the extent of run out of the resultant debris flows. Some information pertinent to "when" the process might begin is developed. "When" is determined mostly by dynamic factors such as rainfall rate and duration, for which local variations are not amenable to long-term prediction. "When" information is not provided on the maps but is described later in this narrative. The prediction of "how big" is addressed indirectly by restricting the maps to a single type of landslide process soil slip-debris flows.

The susceptibility maps were created through an iterative process from two kinds of information. First, locations of sites of past soil slips were obtained from inventory maps of past events. Aerial photographs, taken during six rainy seasons that produced abundant soil slips, were used as the basis for soil slip-debris flow inventory. Second, digital elevation models (DEM) of the areas that were inventoried were used to analyze the spatial characteristics of soil slip locations. These data were supplemented by observations made on the ground. Certain physical attributes of the locations of the soil-slip debris flows were found to be important and others were not. The most important attribute was the mapped bedrock formation at the site of initiation of the soil slip. However, because the soil slips occur in surficial materials overlying the bedrocks units, the bedrock formation can only serve as a surrogate for the susceptibility of the overlying surficial materials.

The maps of susceptibility were created from those physical attributes learned to be important from the inventories. The multiple inventories allow a model to be created from one set of inventory data and evaluated with others. The resultant maps of relative susceptibility represent the best estimate generated from available inventory and DEM data.

Slope and aspect values used in the susceptibility analysis were 10-meter DEM cells at a scale of 1:24,000. For most of the area 10-meter DEMs were available; for those quadrangles that have only 30-meter DEMs, the 30-meter DEMS were resampled to 10-meters to maintain resolution of 10-meter cells. Geologic unit values used in the susceptibility analysis were five-meter cells. For convenience, the soil slip susceptibility values are assembled on 1:100,000-scale bases. Any area of the 1:100,000-scale maps can be transferred to 1:24,000-scale base without any loss of accuracy. Figure 32 is an example of part of a 1:100,000-scale susceptibility map transferred back to a 1:24,000-scale quadrangle.

This raster dataset contains LiDAR-derived elevation data flown from Fall 2015 to Spring 2016, with additional reflights through Fall 2016. This dataset encompasses all of the LARIAC4 project, comprised of approximately 4214 square miles.

The NOAA Office for Coastal Management (OCM) downloaded this digital elevation model (DEM) data from the USGS site: ftp://rockyftp.cr.usgs.gov/vdelivery/Data...

This map presents a tour of the City of Redlands, California using the detailed map of Redlands included in the community basemap. The City of Redlands is located in Southern California, about 65 miles east of Los Angeles. The map tour highlights some of the unique features in the history of Redlands as well as several of the places and events that make it a very livable community today.The map features a detailed basemap for the City of Redlands, California, including buildings, parcels, vegetation, land use, landmarks, streets, and more. The map features special detail for areas of high interest within the City, including local parks, landmarks, and the ESRI campus.The map references detailed GIS data provided by the City of Redlands, Department of Innovation and Technology, GIS Division. The map was authored using map templates available from ESRI, including:Topographic Map Template - Large ScalesCampus Basemap TemplateThe map was published as part of ESRI's Community Maps Program and is one of several detailed maps of cities and counties in the World Topographic Map.

This is a tiled collection of the 3D Elevation Program (3DEP) and is one meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard one-meter DEMs are produced exclusively from high resolution light detection and ranging (lidar) source data of one-meter or higher resolution. One-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. The spatial reference used for tiles of the one-meter DEM within the conterminous United States (CONUS) is Universal Transverse Mercator (UTM) in units of meters, and in conformance with the North American Datum of 1983 ...

Aeromagnetic data were collected along flight lines by instruments in an aircraft that recorded magnetic-field values and locations. In the earlier days of surveying, the only way to represent this data was to generate an analog map with contour lines. This dataset is a representation of the digitized contour lines either by following the lines or by choosing the intersection of the contour and flight-line to create a value of the magnetic field. The values presented are latitude, longitude, and map magnetic-field values.

This data set provides spatial data products with identified and classified locations of potential methane (CH4) emitting facilities and infrastructure in the South Coast Air Basin (SoCAB). These data products form a GIS-based mapping database designed to address shortcomings in current urban CH4 source inventories and is known as Vista Los Angeles (Vista-LA). SoCAB is the air shed for the greater Los Angeles urban area, which includes urbanized portions of the Los Angeles, Orange, Riverside, and San Bernardino Counties, California, USA. Vista-LA consists of detailed spatial maps for facilities and infrastructure in the SoCAB that are known or expected sources of CH4 emissions and illustrates the spatial distribution of potential CH4 sources, representing a first step towards developing an urban-scale CH4 emissions gridded inventory for the SoCAB. Vista-LA spatial data sets were created utilizing an assortment of publicly available data sources from local, state, and federal agencies for the years 2012 to 2017. The final Vista-LA database contains over 33,000 entries, which are presented as thirteen CH4 emitting infrastructure maps.

Trip Data Licensing for Map Providers Market Outlook

According to our latest research, the Global Trip Data Licensing for Map Providers market size was valued at $1.82 billion in 2024 and is projected to reach $6.35 billion by 2033, expanding at a CAGR of 14.7% during the forecast period of 2025–2033. One of the major factors driving the robust growth of this market is the accelerating adoption of connected vehicles and smart mobility solutions, which demand precise, real-time, and historical trip data to power navigation, urban planning, and advanced mobility services. As cities and enterprises increasingly rely on data-driven insights to optimize transportation networks, the demand for high-quality trip data licensing is surging globally, underpinning the rapid expansion of this market.

Regional Outlook

North America currently commands the largest share of the global Trip Data Licensing for Map Providers market, accounting for approximately 38% of total revenue in 2024. This regional dominance is attributed to the mature ecosystem of automotive OEMs, advanced telematics infrastructure, and the early adoption of mobility-as-a-service (MaaS) platforms. The United States, in particular, benefits from robust regulatory frameworks supporting data sharing, a high concentration of technology providers, and a thriving ecosystem of ride-hailing and delivery platforms. Major metropolitan areas such as New York, Los Angeles, and Toronto are leveraging licensed trip data to enhance navigation, traffic management, and urban planning initiatives. Furthermore, North American map providers have established strong partnerships with automotive and logistics companies, further cementing the region’s leadership in trip data licensing.

Asia Pacific emerges as the fastest-growing region in the Trip Data Licensing for Map Providers market, projected to register a remarkable CAGR of 18.9% from 2025 to 2033. This accelerated growth is driven by rapid urbanization, the proliferation of smart city projects, and a surge in connected vehicle adoption across China, India, Japan, and Southeast Asia. Regional governments are investing heavily in digital infrastructure, intelligent transportation systems, and open data initiatives to address mounting traffic congestion and support sustainable urban mobility. The rise of homegrown mobility service providers and the expansion of global map providers into Asian markets are further fueling demand for both real-time and historical trip data. Strategic collaborations between public and private sectors are creating new avenues for data monetization and innovation, positioning Asia Pacific as a key growth engine for the industry.

Emerging economies in Latin America, the Middle East, and Africa are witnessing a gradual but steady uptake of trip data licensing solutions, albeit with unique adoption challenges. These regions face hurdles such as fragmented transportation networks, limited digital infrastructure, and regulatory ambiguities surrounding data privacy and sharing. Nevertheless, localized demand is growing as governments and urban planners recognize the value of trip data in alleviating congestion, improving public transit, and supporting economic development. Pilot projects in cities like São Paulo, Dubai, and Johannesburg are demonstrating the transformative potential of trip data for navigation and urban mobility, though scalability remains contingent on policy reforms, investment in digital infrastructure, and the development of region-specific licensing models.

Report Scope

The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment.

Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994.

Oil and gas plays within province 14 (Los Angeles Basin) are listed here by play number, type, and name:

Number Type      Name
1401 conventional  Santa Monica Fault System and Las Cienegas
           Fault and Block
1402 conventional  Southwestern Shelf and Adjacent Offshore
           State Lands
1403 conventional  Newport-Inglewood Deformation Zone and
           Southwestern Flank of Central Syncline
1404 conventional  Whittier Fault Zone and Fullerton Embayment
1405 conventional  Northern Shelf and Northern Flank of
           Central Syncline
1406 conventional  Anaheim Nose
1407 conventional  Chino Marginal Basin, Puente and San Jose
           Hills, and San Gabriel Valley Marginal Basin

Explore a full description of the map.This map layer shows the 24 time zones commonly used in the Greenwich Mean Time model. The hours added or subtracted from the time in Greenwich are marked on the map. For example, if it is 1:00 p.m. in London, England, United Kingdom, it is 6:30 pm in New Delhi, Delhi, India (+5.50), and 5:00 a.m. in Los Angeles, California, United States (-8.00). CreditsEsri, from National Geographic MapMakerTerms of Use This work is licensed under the Esri Master License Agreement.View Summary | View Terms of Use

The U.S. Geological Survey in cooperation with the University of New Hampshire and the University of New Brunswick mapped the nearshore regions off Los Angeles and San Diego, California using multibeam echosounders. Multibeam bathymetry and co-registered, corrected acoustic backscatter were collected in water depths ranging from about 3 to 900 m offshore Los Angeles and in water depths ranging from about 17 to 1230 m offshore San Diego. Continuous, 16-m spatial resolution, GIS ready format data of the entire Los Angeles Margin and San Diego Margin are available online as separate USGS Open-File Reports.

For ongoing research, the USGS has processed sub-regions within these datasets at finer resolutions. The resolution of each sub-region was determined by the density of soundings within the region. This Open-File Report contains the finer resolution multibeam bathymetry and acoustic backscatter data that the USGS, Western Region, Coastal and Marine Geology Team has processed into GIS ready formats as of April 2004. The data are available in ArcInfo GRID and XYZ formats. See the Los Angeles or San Diego maps for the sub-region locations.

These datasets in their present form were not originally intended for publication. The bathymetry and backscatter have data-collection and processing artifacts. These data are being made public to fulfill a Freedom of Information Act request. Care must be taken not to confuse artifacts with real seafloor morphology and acoustic backscatter.

[Summary provided by the USGS.]

This map shows LA Metro's transit stops in Los Angeles, CA by the maximum wait time for transit and the amount of minority population that lives within a 5-minute walk.This is shown by comparing the two different data patterns. Areas in purple have a high amount of people who are a minority, and wait longer for transit during rush hour times. Areas in pink have a high wait time, but low minority population. Areas in light blue have a high amount of minority population, but don't wait as long for transit. This data comes from the Los Angeles Transit Stops layer from the ArcGIS Living Atlas of the World. The GTFS stops were extracted in 2015.

The data set for the Corona North 7.5' quadrangle was prepared under the U.S. Geological Survey Southern California Areal Mapping Project (SCAMP) as part of an ongoing effort to develop a regional geologic framework of southern California, and to utilize a Geographic Information System (GIS) format to create regional digital geologic databases. These regional databases are being developed as contributions to the National Geologic Map Database of the National Cooperative Geologic Mapping Program of the USGS.

This data set maps and describes the geology of the Corona North 7.5' quadrangle, Riverside and San Bernardino Counties, California. Created using Environmental Systems Research Institute's ARC/INFO software, the data base consists of the following items: (1) a map coverage containing geologic contacts and units, (2) a coverage containing structural data, (3) a coverage containing geologic unit annotation and leaders, and (4) attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). In addition, the data set includes the following graphic and text products: (1) a postscript graphic plot-file containing the geologic map, topography, cultural data, a Correlation of Map Units (CMU) diagram, a Description of Map Units (DMU), and a key for point and line symbols, and (2) PDF files of the Readme (including the metadata file as an appendix), and the graphic produced by the Postscript plot file.

The Corona North quadrangle is located near the northern end of the Peninsular Ranges Province. All but the southeastern tip of the quadrangle is within the Perris block, a relatively stable, rectangular in plan area located between the Elsinore and San Jacinto fault zones. The southeastern tip of the quadrangle is barely within the Elsinore fault zone.

The quadrangle is underlain by Cretaceous plutonic rocks that are part of the composite Peninsular Ranges batholith. These rocks are exposed in a triangular-shaped area bounded on the north by the Santa Ana River and on the south by Temescal Wash, a major tributary of the Santa Ana River. A variety of mostly silicic granitic rocks occur in the quadrangle, and are mainly of monzogranite and granodioritic composition, but range in composition from micropegmatitic granite to gabbro. Most rock units are massive and contain varying amounts of meso- and melanocratic equant-shaped inclusions. The most widespread granitic rock is monzogranite of the Cajalco pluton, a large pluton that extends some distance south of the quadrangle. North of Corona is a body of micropegmatite that appears to be unique in the batholith rocks.

Diagonally bisecting the quadrangle is the Santa Ana River. North of the Santa Ana River alluvial deposits are dominated by the distal parts of alluvial fans emanating from the San Gabriel Mountains north of the quadrangle. Widespread areas of the fan deposits are covered by a thin layer of wind blown sand.

Alluvial deposits in the triangular-shaped area between the Santa Ana River and Temescal Wash are quite varied, but consist principally of locally derived older alluvial fan deposits. These deposits rest on remnants of older, early Quaternary or late Tertiary age, nonmarine sedimentary deposits that were derived from both local sources and sources as far away as the San Bernardino Mountains. These deposits in part were deposited by an ancestral Santa Ana River. Older are a few scattered remnants of late Tertiary (Pliocene) marine sandstone that include some conglomerate lenses. Clasts in the conglomerate include siliceous volcanic rocks exotic to this part of southern California. This sandstone was deposited as the southeastern-most part of the Los Angeles sedimentary marine basin and was deposited along a rocky shoreline developed in the granitic rocks, much like the present day shoreline at Monterey, California. Most of the sandstone and granitic paleoshoreline features have been removed by quarrying and grading in the area of Porphyry north to Highway 91. Excellent exposures in highway road cuts still remain on the north side of Highway 91 just east of the 91-15 interchange and on the east side of U.S. 15 just north of the interchange.

South of Temescal Wash is a series of both younger and older alluvial fan deposits emanating from the Santa Ana Mountains to the southeast. In the immediate southwest corner of the quadrangle is a small exposure of sandstone and pebble conglomerate of the Sycamore Canyon member of the Puente Formation of early Pliocene and Miocene age and sandstone and conglomerate of undivided Sespe and Vaqueros Formations of early Miocene, Oligocene, and late Eocene age.

The geologic map data base contains original U.S. Geological Survey data generated by detailed field observation recorded on 1:24,000 scale aerial photographs. The map was created by transferring lines from the aerial photographs to a 1:24,000 scale topographic base. The map was digitized and lines, points, and polygons were subsequently edited using standard ARC/INFO commands. Digitizing and editing artifacts significant enough to display at a scale of 1:24,000 were corrected. Within the database, geologic contacts are represented as lines (arcs), geologic units are polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum.

Multichannel seismic-reflection (MCS) data were collected in the California Continental Borderland as part of southern California Earthquake Hazards Task. Five data acquisition cruises conducted over a six-year span collected MCS data from offshore Santa Barbara, California south to the Exclusive Economic Zone boundary with Mexico. The primary mission was to map late Quaternary deformation as well as identify and characterize fault zones that have potential to impact high population areas of southern California. To meet its objectives, the project work focused on the distribution, character, and relative intensity of active (i.e., Holocene) deformation along the continental shelf and basins adjacent to the most highly populated areas. In addition, the project examined the Pliocene-Pleistocene record of how deformation shifted in space and time to help identify actively deforming structures that may constitute current significant seismic hazards.

The MCS data accessible through this report cover the first four years of survey activity and include data from offshore Malibu coastal area west of Santa Monica, California to the southern survey limit offshore San Diego. The MCS data, which were collected with a 250-m-long, 24-channel streamer used a small gas-injector airgun source. This system provided optimum resolution of the upper 1 to 2 km of sediment for mapping active fault systems. The report includes trackline maps showing the location of the data, as well as both digital data files (SEG-Y) and images of all of the profiles.

For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/s/s197sc/html/s-1-97-sc.meta.html , http://walrus.wr.usgs.gov/infobank/o/o199sc/html/o-1-99-sc.meta.html , http://walrus.wr.usgs.gov/infobank/a/a198sc/html/a-1-98-sc.meta.html , and http://walrus.wr.usgs.gov/infobank/a/a100sc/html/a-1-00-sc.seis.html

These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

This layer depicts the public transportation lines received through the Esri Community Maps program, USDOT, and publicly available datasets from agencies, municipalities and countries around the world. It is designed to be used with the other World Transit layers in the Living Alas. These layers include:World Transit Agency CentroidsWorld Transit StopsWorld Transit Lines by ModalityThese transit layers can be accessed together through the following resources:World Transit Group LayerWorld Transit Web MapWorld Transit Viewer (Web Mapping Application)The public transit lines are symbolized using color codes (six digit hexadecimal) provided by transit agencies. These colors correspond to many agencies" color designations such as in the Washington DC Metro with the Red, Orange, Blue, Green Yellow and Silver lines. The transit data is received in the General Transit Feed Specification (GTFS) format, an open data standard for public transportation data. Each GTFS dataset is a zipped archive of comma-separated files describing the transit services, including the geometry for mapping. Esri converts GTFS datasets into ready-to-use map layers and makes them available as Feature Services in the Living Atlas. Esri Transit map layers include data from Esri Community Maps contributors, the US Department of Transportation, as well as publicly available GTFS datasets. Please note that any data layer fields marked with an "(Esri)" tag have been added by Esri to provide user-friendly translations of GTFS standard values or to add additional context and value. Community Maps GTFS dataThrough the Esri Community Maps Program, transit organizations are able to submit their own GTFS data for use across the ArcGIS platform, giving their data more visibility and accessibility to users. Organizations interested in sharing their data can join the Community Maps Program as a data contributor. Community Maps transit contributors include:Utah Transit AuthorityWeGo Public Transit - Nashville, TNUMass AmherstSpokane Transit Authority - Spokane, WAGrand County, COMETRO HoustonLee County, FLBay of Plenty Regional Council (NZ)Miami-Dade CountyRTC Southern NevadaLA Metro - Los Angeles, CAIMPLAN - Chihuahua, MexicoSunTran - Tucson, AZVIA Metropolitan Transit - San Antonio, TXCape Breton Regional Municipality - Nova Scotia, CanadaWashington County Transit Department - Washington County, MDEastern Panhandle Transit Authority - Martinsburg, WVNew Orleans Regional Transit Authority - New Orleans, LAPark County Transit - Park County, MTMetro Cali S.A. - Cali, Colombia (CO)Lawrence Transit - Lawrence, KS USDOT National Transportation Map DataData is included from the United States Department of Transportation (USDOT), Bureau of Transportation Statistics (BTS) National Transit Map (NTM) National Transportation Atlas Database (NTD) where the data is not available from Esri Community Maps contributors. A full list of NTM contributing entities is available at https://geodata.bts.gov/maps/national-transit-map-agencies. Most agencies" data are in the public domain except for the following, which are licensed under the Creative Commons by Attribution 3.0 (CC BY 3.0) license. Data accessed on Feb 6, 2024 from National Transit Map Routes.USDOT NTM CC BY 3.0 ContributorsGreater Peoria Mass Transit DistrictCity of GlendoraCity of DelanoCity of Sierra VistaCity of AvalonCity of LawndaleChemehuevi Indian TribeVia Mobility ServicesMiddletown Transit DistrictRockland Coaches IncKootenai CountySpokane Tribe of IndiansWaccamaw Regional Transportation AuthorityDillions Bus Service IncUnified Human Services Transportation Systems Inc Publicly Available GTFS DatasetsMetropolitan Council - Metro Transit - Minneapolis/St Paul, MN (Public Domain)Minnesota Valley Transit Authority (Public Domain)AC Transit - Bay Area, CA (Public Domain)OVapi Netherlands GTFS (NL) (Custom open licensing terms)Open Bus Data (UK) (Contains public sector information licensed under the Open Government License v3.0)Swiss Open Transport Data (CH) (Open data license)Japanese Public Transportation Data - Kyoto, Kyoto Prefecture (JP) (Public Transportation Open Data Basic License)Transport for New South Wales (AU) (CC BY 4.0)Victoria Department of Transport and Planning (AU) (CC BY 4.0)Department for Infrastructure and Transport - South Australia (AU) (CC BY 4.0)Otago Regional Council (NZ) (CC BY 4.0)Ireland National Transport Authority (IE) (CC BY 4.0)Metrolink - Southern California (US) (CC BY 3.0)DELFI German GTFS Data (DE) (CC BY 4.0)ENTUR (NO) (Contains data under the Norwegian licence for Open Government data distributed by Entur.org)MITRAMS (ES) (Government of Spain - Ministry of Transport and Sustainable Mobility)Department of Transport, Goa (IN) (Directorate Of Transport, Government Of Goa)Hyderabad Metro Rail Limited (IN) (Contains data provided by Hyderabad Metro Rail Ltd.)Canadian Public Transit Network (CA) (Contains information licensed under the Open Government Licence – Canada)Mexico City | Secretaría de Movilidad (MX) (CC-BY-4.0)Metlink (NZ) (Contains data provided by Greater Wellington Regional Council)

In the late 19th century and into the early 20th century, the world globalized. New technology and more accessible transportation, such as trains, allowed people, ideas, and goods to travel faster and more easily around the world. Time standardization was greatly needed in a world becoming increasingly interconnected.For example, in the United States, the railroad system faced big problems by the late 1800s. Each town and city went by their own time, which was usually regulated by a clock in the town center. Many towns used natural time markers, so whenever they saw the sun highest in the sky, was “high noon.” This caused confusion and some collisions among trains, as different communities were not following the same local time.To prevent further damage, Canadian railway engineer Sir Sandford Fleming devised a globally standardized time system. He proposed to regulate time by dividing the earth into 24 one-hour time zones utilizing longitude lines, each 15 degrees apart. Longitude lines mark the distance east or west of the prime meridian. Fleming’s recommendations led to an international conference held in 1884 to select a common prime meridian, otherwise known as zero degrees longitude, on which to base time zones. Previously, different countries had different prime meridians. However, at the conference, the committee decided that the world should identify an official meridian, and they chose the Greenwich meridian. Although much has changed since the conference in 1884, Fleming’s design has stayed intact, with variations based on political and geographic decisions. For example, China, a very large country, only uses one time zone, while many places in the Middle East use half-hour time zones. This map layer shows the 24 time zones commonly used in the Greenwich Mean Time model. The hours added or subtracted from the time in Greenwich are marked on the map. For example, if it is 1:00 p.m. in London, England, United Kingdom, it is 6:30 pm in New Delhi, Delhi, India (+5.50), and 5:00 a.m. in Los Angeles, California, United States (-8.00). Use this layer to see how time is regulated around the world.

An ArcGIS Blog tutorial that guides you through creating your first dashboard using ArcGIS Dashboards.ArcGIS Dashboards is a configurable web app available in ArcGIS Online that enables users to convey information by presenting interactive charts, gauges, maps, and other visual elements that work together on a single screen.In this tutorial you will create a simple dashboard using ArcGIS Dashboards. The dashboard uses a map of medical facilities in Los Angeles County (sample data only) and includes interactive chart and list elements._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...