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ArcGIS Field Maps is a mobile app that allows you to view and collect field data using an Android or iOS smartphone or tablet. It is also a web app that allows you to configure web maps for use in the mobile app. The tutorials in this learning path will introduce you to the features of the Field Maps mobile app, how to create and configure web maps in Field Maps Designer that can be used in the Field Maps mobile app in online and offline mode, and how to collect data from a map and in the field with the mobile app.

In this seminar, the presenters will introduce essential concepts of Collector for ArcGIS and show how this app integrates with other components of the ArcGIS platform to provide a seamless data management workflow. You will also learn how anyone in your organization can easily capture and update data in the field, right from their smartphone or tablet.This seminar was developed to support the following:ArcGIS Desktop 10.2.2 (Basic)ArcGIS OnlineCollector for ArcGIS (Android) 10.4Collector for ArcGIS (iOS) 10.4Collector for ArcGIS (Windows) 10.4

In this seminar, you will learn how to use Collector for ArcGIS to download maps, create new GIS features, as well as update existing ones when disconnected from the Internet, and then synchronize changes back to the office when you are connected. In addition, you will learn how to create maps and publish services for devices.This seminar was developed to support the following:Collector for ArcGIS (Android) 10.2Collector for ArcGIS (iOS) 10.2

Find a Sales and Use Tax RateFind a CDTFA office and get directions

The Geographic Information System (GIS) market is experiencing robust growth, driven by increasing adoption across diverse sectors. The market, estimated at $25 billion in 2025, is projected to expand significantly over the forecast period (2025-2033), fueled by a Compound Annual Growth Rate (CAGR) of approximately 8%. Key drivers include the rising demand for location-based services, the increasing use of GIS in urban planning and smart city initiatives, and the proliferation of readily available geospatial data. Furthermore, advancements in cloud computing, artificial intelligence, and big data analytics are enhancing GIS capabilities, leading to wider applications in environmental monitoring, disaster management, and precision agriculture. The government and utilities sector remains a dominant market segment, followed by the business sector, which is rapidly adopting GIS solutions for operational efficiency and strategic decision-making. Android-based GIS systems are currently the most prevalent, reflecting the widespread use of Android devices, although iOS and Windows-based systems maintain significant market shares. Competitive landscape analysis reveals key players such as Environmental Systems Research Institute (Esri), Hexagon, Pitney Bowes, and SuperMap actively innovating and expanding their market presence through strategic partnerships and technological advancements. Regional variations in market growth are expected, with North America and Europe maintaining leading positions due to high technological adoption rates and robust economies. However, Asia-Pacific is projected to witness the fastest growth in the coming years, driven by rapid urbanization, economic development, and increasing government investments in infrastructure projects. Restraints to market growth include the high initial investment costs associated with implementing GIS solutions and the need for specialized technical expertise. Nevertheless, the long-term benefits of GIS, encompassing improved efficiency, better decision-making, and enhanced resource management, are expected to overcome these barriers, resulting in sustained market expansion throughout the forecast period. The continuous development of user-friendly GIS software and services is further expected to fuel broader adoption across diverse user groups.

The global urban planning apps market, valued at $76.9 million in 2025, is projected to experience robust growth, driven by increasing urbanization, the need for efficient resource management, and the rising adoption of digital technologies in urban planning. A Compound Annual Growth Rate (CAGR) of 7.3% from 2025 to 2033 suggests a significant market expansion. Key drivers include the need for sustainable urban development, improved citizen engagement through participatory planning platforms, and the ability of these apps to streamline complex processes like zoning and permitting. Trends like the integration of advanced analytics (e.g., predictive modeling for infrastructure needs), the incorporation of 3D visualization for better stakeholder communication, and the growing use of mobile-first solutions are further fueling market growth. While the market faces restraints such as data security concerns and the need for robust digital infrastructure in certain regions, the overall growth trajectory remains positive. The market segmentation reveals strong demand from both large enterprises and SMEs, with iOS and Android platforms dominating the app landscape. The regional breakdown shows significant market potential across North America and Europe, with emerging markets in Asia Pacific also contributing to the overall growth. The competitive landscape is dynamic, featuring established players like Esri (ArcGIS Collector) and Autodesk (AutoCAD 360) alongside innovative startups offering specialized solutions. The continued advancement of mobile technologies, coupled with increasing government initiatives promoting smart city development, will significantly impact market growth. The rising availability of high-quality geospatial data, and the integration of artificial intelligence (AI) and machine learning (ML) for optimizing urban planning processes, are poised to transform the industry. The focus on creating user-friendly interfaces that cater to various stakeholders, from planners to citizens, will be critical in ensuring broader adoption. Furthermore, collaborations between technology companies and urban planning agencies will be instrumental in driving innovation and accelerating market expansion. The market's future hinges on the successful integration of technology with established urban planning practices, creating a more efficient, sustainable, and participatory urban development process.

In this seminar, you will learn how to equip field workers with easy-to-use maps that run on a smartphone or tablet using Collector for ArcGIS, an app included with an ArcGIS Online organizational subscriptions or Portal for ArcGIS. You will see how the maps are used to collect accurate data in the field-even when access to a WiFi connection or cellular service is not available-and quickly share data updates with the organization when connected. You will learn how to help your organization reduce errors, increase productivity, and improve data quality by replacing paper-based workflows with maps that feature data-driven, intelligent forms.This seminar was developed to support the following:ArcGIS OnlineArcGIS Online Organizational AccountUser role or equivalentCollector for ArcGIS (Android) 10.4Collector for ArcGIS (iOS) 10.4Collector for ArcGIS (Windows) 10.4

This dataset provides global fixed broadband and mobile (cellular) network performance metrics in zoom level 16 web mercator tiles (approximately 610.8 meters by 610.8 meters at the equator). Data is projected in EPSG:4326. Download speed, upload speed, and latency are collected via the Speedtest by Ookla applications for Android and iOS and averaged for each tile. Measurements are filtered to results containing GPS-quality location accuracy. The data was processed and published to ArcGIS Living Atlas by Esri and is inclusive of the Q1 2025 data download.

Shows the location of all blazed and unblazed trails in all Allegheny County parks. This is the same data used in the Allegheny County Parks Trails Mobile App, available for Apple and Android devices. If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (http://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (http://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below. Category: Recreation Organization: Allegheny County Department: Parks Department Temporal Coverage: present Data Notes: Coordinate System: Pennsylvania State Plane South Zone 3702; U.S. Survey Foot Development Notes: none Other: none Related Document(s): Data Dictionary: none Frequency - Data Change: As needed Frequency - Publishing: As needed Data Steward Name: Eli Thomas Data Steward Email: gishelp@alleghenycounty.us

Geologisk kart over Svalbard (1:250 000).

https://api.npolar.no/dataset/eafafbb7-b3df-4c71-a2df-316e80a7992e/_file/daf3eeae9d3aeb5bdf9a2b9f86ba8bab?key=8ee185b7c7f70470041e8801b3451517+Uyhjrqc9jddVIG52JAZO6t00BYN7eakD" alt="Mobilkart i felt">

Sammendrag (for English see below)

Dette geologiske kartet fra Norsk Polarinstitutt har blitt produsert med tanke på å brukes på smart-telefon, nettbrett eller PC uten nett-tilkobling, for eksempel til feltarbeid eller som et hendig oppslags-kart. Kartet består av 5 raster-filer i GIS-formatet JPEG2000 og er tilgjengelig som nedlasting fra datasenteret til Norsk Polarinstitutt

Informasjon om de geologiske enhetene er plassert som tekst-merkelapper direkte i kartbildet, i motsetning til en vanlig tegnforklaring. Ved å zoome inn på kartet finnes informasjon om geologiske enheter, vist med blå tekst (alder i parentes). I tillegg er hvert enhet (farge) merket med en tilsvarende 4-sifret kode i blå skrift.

I felten kan mobile dingser med GPS vise brukeren sin posisjon på kartet. Avhengig av skjermoppløsning er full detaljgrad i kartet synlig på ca. 1:30 000-skala, men kartet kan også vises på mye større skala for å se f.eks. regionale geologiske trekk.

Kartet kan vises på Android eller iOS-enheter med appen "Geoviewer" fra Extensis (tidligere Lizardtech). På datamaskin fungerer QGIS eller ArcMap bra for å vise kartet. Se forklaring på hvordan overføre kartet til din smart-telefon eller nettbrett lenger nede på sida.

Data

Kartet er laget ved å bruke data fra Norsk Polarinstitutt 1:250 000-skala geologiske kart for Svalbard, opprinnelig publisert i "Geoscience Atlas of Svalbard" av Dallmann (ed.) 2015. Dette kartet er generalisert fra 1:100 000-skala kart-data i hovedkartserien til Norsk Polarinstitutt, og er publisert i Geoscience Atlas of Svalbard (Dallmann 2015).

Til å produsere dette kartet er topografiske data fra S100 (topografi, vann) og S250 (kystlinje)-datasettene fra Norsk Polarinstitutt brukt. Fjellskygge er konstruert med S0 Terrengmodell med 20 meter pr. pixel oppløsning. Bre og snøflekk-områder er vist med datasettet for 2001-2010 av König mfl. (2013), som gir et mer oppdatert bilde av blotning-situasjonen nær breer og snøflekker. Områder der geologiske polygoner ikke er justert til nye blotninger er vist i brunt. Kystlinjen er i noen tilfeller endret for å tilpasses bre-fronter som ender i sjøen.

Forbehold om datakvalitet Dette er et nytt geologisk kartprodukt, og det kan forekomme feil. Spesielt tegnforklaring, som er skrevet direkte på geologiske enheter, kan være problematisk i noen områder. Vi er interessert i tilbakemelding på mulige forbedringer av kartet. Send gjerne tilbakemeldinger på e-post til Geokart@npolar.no.

Dette er et geologisk kart ment for å formidle vitenskapelige data, og er ikke egnet for navigasjon. Noen områder av Svalbard er ennå ikke kartlagt i detalj, og en del av dataene er av eldre dato, så datakvaliteten for dette kartet er varierende. Kartet kan inneholde feil i grunnlagsdata, kartpresentasjon, kartografi og tekst-beskrivelser. For en stor del er geologien kartlagt for en mindre detaljert skala enn den det er mulig å oppnå med dette kartproduktet, så geologiske trekk og enheter vil i ulik grad fremstå feilplassert ved bruk av god GPS-posisjon og detaljert zoom-nivå. Breer og spesielt bre-fronter er i konstant forandring, og selv om ganske oppdaterte data er brukt for å lage kartet, vil det være feil i en del bre-posisjoner. Vær oppmerksom på at det topografiske grunnlaget som er brukt her i mange tilfeller er av nyere dato enn det som opprinnelig var brukt under kartleggingen i felt. Dette kan også føre til feil i kartet.

Geologiske kart-data vil kontinuerlig være gjenstand for re-tolkning og endring. For en full beskrivelse av kartleggingsprogrammet ved Norsk Polarinstitutt, geologiske kart-data presentert her og referanser, se Dallmann (ed.) 2015, eller besøk npolar.no

Hvordan overføre kartet til mobilenheter

Direkte nedlasting Kartet kan nå lastes ned direkte til mobilenheten via lenker øverst. Det er 5 linker, en for hvert område. Enten lagres filene på enheten, eller du vil få et valg om å åpne fila direkte i Geoviewer. NB: Sørg for at det er nok ledig lagringsplass på mobilenheten og vær oppmerksom på fil-størrelsen (550 MB), spesielt hvis det er et betalt internett-abonnenement.

Via PC, kabel eller Dropbox:

NP_S250_Geologi_mobilkart kan brukes direkte i GIS-systemer på PC, mens for bruk på nettbrett og mobil anbefales gratis-appen Geoviewer fra Lizardtech.

Etter å ha lastet ned til PC og pakket opp ZIP-filene, kan kartene for Android-enheter eksempelvis overføres til ønsket plassering på enheten via USB-kabel. For iOS-enheter kan en bruke f.eks. nettjenesten Dropbox som kanal fra PC til enhet. Når kartene er lagret på enheten, kan en legge til de kartrutene en ønsker fra menyen i Geoviewer.

Referanser Kartdata Svalbard 1:100 000 (S100 Kartdata) (2014). Norwegian Polar Institute (Tromsø, Norway): https://data.npolar.no/dataset/645336c7-adfe-4d5a-978d-9426fe788ee3

M König, J Kohler, C Nuth (2013). Glacier Area Outlines - Svalbard. Norwegian Polar Institute https://data.npolar.no/dataset/89f430f8-862f-11e2-8036-005056ad0004

Dallmann, W.K., (ed.) (2015). Geoscience Atlas of Svalbard, Norsk Polarinstitutt Rapportserie nr. 148

Terrengmodell Svalbard (S0 Terrengmodell) (2014). Norwegian Polar Institute (Tromsø, Norway): https://data.npolar.no/dataset/dce53a47-c726-4845-85c3-a65b46fe2fea

English

Geological map of Svalbard (1:250 000).

Abstract This geological map from the Norwegian Polar Institute has been prepared to be used offline on a smartphone, tablet or computer, for example for field work or a handy reference. It consists of 5 raster-files in the JPEG2000 GIS-format, available to download from the Norwegian Polar Institute data centre data.npolar.no via https://data.npolar.no/dataset/eafafbb7-b3df-4c71-a2df-316e80a7992e/.

Information about the geological units has been placed as text labels (in blue typescript) directly on the map, as opposed to a regular legend. By zooming in, information about each geological unit on the map can be found, shown in blue text (age in parentheses). In addition, each unit is labelled with a corresponding 4-digit code also in blue typescript.

In the field, GPS-enabled devices can show the user's location on the map. Depending on screen resolution, full detail of the map (including text labels) is best viewed at ca. 1:30 000 scale, but the map can also be viewed at much larger scales to see e.g. regional geological features.

For mobile use, the app "Geoviewer" from Extensis (formerly Lizardtech) can be used. On a computer, QGIS works well to view these maps. See an explanation below on how to transfer the map to your tablet or smartphone.

Data

The map is made using data from the Norwegian Polar Institute 1:250 000-scale geological map for Svalbard, originally published in Dallmann (ed.) 2015. This geological map has been generalised from the 1:100 000-scale main map series published by the Norwegian Polar Institute, and is published in Geoscience Atlas of Svalbard (Dallmann 2015).

For the purpose of this map product, topographic data from the Norwegian Polar Institute S100 Map (topography, water) and S250 (coastline) data sets have been used. Hill shade was created using the NPI S0 Terrengmodell at 20 meters/pixel resolution. Glacier and snow patch outlines are shown using the 2001-2010 dataset of glacier area outlines for Svalbard by König et al. (2013), which gives a more up to date picture of the outcrop situation near glaciers or snow patches. Areas where geology polygons have not been re-adjusted to the new outcrops are shown in brown. The coast line-data has been adjusted in some cases to adapt to glacier fronts ending in the sea.

Disclaimer This is a new geological map product, and errors may occur. In particular the legend, which have been printed directly on the geological units, can be problematic in places. We appreciate feedback on the map that can be used to improve the map in future versions. Please email feedback to Geokart@npolar.no.

This is a geological map meant to convey scientific data, and is not suited for navigation. This map product may contain errors in base data, map presentation, cartography and text descriptions. Much of the geology was originally mapped for a less detailed scale than what is possible to obtain with this map, so geological features will to varying degrees appear out-of place when a good GPS-position and detailed zoom level is used. Glaciers and in particular glaciers fronts are dynamic features, and although using fairly up-to-date data, this map does contain errors in glacier front positions. Note that the topographic base data used here in many cases is of a newer vintage than the data originally used for geological mapping in the field. This may cause some errors in the map. Some areas of Svalbard have not yet been mapped in detail and some of the data are of older origin, so the data quality presented on this map is variable.

Geological map data will be subject to continual re-interpretation and editing. For a full description of the bedrock mapping programme at the Norwegian Polar Institute, the geological map data presented here and

ArcGIS Earth es una herramienta gratuita y fácil de utilizar para fusionar, manipular y colaborar rápidamente datos 3D con cualquier usuario dentro y fuera de una organización desarrollada por ESRI. Está disponible en dispositivos desktop y móviles (iOS y Android), y permite procesar datos en diferentes formatos, incluyendo modelos 3D como KML, KMZ, CSV/TXT, Shapefile, servicios web de ArcGIS (Online y Enterprise), archivos locales o desde una URL externa como servicios OGC (WMS, WFS, WMTS), GeoJSON, entre otros.

This dataset contains mobile wireless download speed test results and areas where the PSD (Vermont Public Service Department) challenged mobile wireless service asserted by wireless carriers.DOWNLOAD SPEED TEST RESULTSResults from download speed tests that were conducted in September-December 2018 are contained by 6 point feature-classes, each with results for a particular carrier.PSD staff employed the android smartphone application G-NetTrack to conduct download speed tests at approximately 300 meter intervals along all federal-aid highways.The point feature-classes are very detailed and more suitable when zoomed into the neighborhood scale. All point feature-classes have the same field schema, which includes these fields: timestamp: Date and time at which the data point was collected. signal_str: Signal strength (RSRP in dBm). download_s: Download speed (in Mbps). latency: The round-trip time for a request to a website, in milliseconds.DRIVE-TEST BLOCKSDrive-test blocks (Utility_DriveTest_poly_Blocks) is a polygon feature-class that is composed of 1-kilometer blocks; it has a field for each of the 6 carriers; the fields show the average download speed recorded in each block for each carrier.The fields also include a composite field (All_) that contains averages of all carriers, masking variation in coverage between individual carriers. "999" indicates no test was conducted for the carrier in that block.Drive-test blocks are generalized information and are suitable when zoomed at various scales. A BLOCK DOES NOT INDICATE SERVICE THROUGHOUT A BLOCK; use the point feature-classes for detailed data and judge accordingly.WIRELESS CHALLENGE BLOCKSWireless Challenge Blocks (Utility_DriveTest_poly_VTMFCIIChallengeBlocks) depicts the status of each block in the submission of the PSD in the FCC Mobility Fund Phase II Challenge process. It shows challenges to mobile wireless service asserted by wireless carriersA value of 0 in the Area_1 field indicates that the challenge was rejected, either because a) the block is already largely eligible, or b) because no tests below 5 Mbps were submitted.DISCLAIMERVCGI and the State of VT make no representations of any kind, including but not limited to the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the data.

This data is used for a broadband mapping initiative conducted by the Washington State Broadband Office. This dataset provides global fixed broadband and mobile (cellular) network performance metrics in zoom level 16 web mercator tiles (approximately 610.8 meters by 610.8 meters at the equator). Data is projected in EPSG:4326. Download speed, upload speed, and latency are collected via the Speedtest by Ookla applications for Android and iOS and averaged for each tile. Measurements are filtered to results containing GPS-quality location accuracy. The data was processed and published to ArcGIS Living Atlas by Esri.AboutSpeedtest data is used today by commercial fixed and mobile network operators around the world to inform network buildout, improve global Internet quality, and increase Internet accessibility. Government regulators such as the United States Federal Communications Commission and the Malaysian Communications and Multimedia Commission use Speedtest data to hold telecommunications entities accountable and direct funds for rural and urban connectivity development. Ookla licenses data to NGOs and educational institutions to fulfill its mission: to help make the internet better, faster and more accessible for everyone. Ookla hopes to further this mission by distributing the data to make it easier for individuals and organizations to use it for the purposes of bridging the social and economic gaps between those with and without modern Internet access.DataHundreds of millions of Speedtests are taken on the Ookla platform each month. In order to create a manageable dataset, we aggregate raw data into tiles. The size of a data tile is defined as a function of "zoom level" (or "z"). At z=0, the size of a tile is the size of the whole world. At z=1, the tile is split in half vertically and horizontally, creating 4 tiles that cover the globe. This tile-splitting continues as zoom level increases, causing tiles to become exponentially smaller as we zoom into a given region. By this definition, tile sizes are actually some fraction of the width/height of Earth according to Web Mercator projection (EPSG:3857). As such, tile size varies slightly depending on latitude, but tile sizes can be estimated in meters.For the purposes of these layers, a zoom level of 16 (z=16) is used for the tiling. This equates to a tile that is approximately 610.8 meters by 610.8 meters at the equator (18 arcsecond blocks). The geometry of each tile is represented in WGS 84 (EPSG:4326) in the tile field.The data can be found at: https://github.com/teamookla/ookla-open-dataUpdate CadenceThe tile aggregates start in Q1 2019 and go through the most recent quarter. They will be updated shortly after the conclusion of the quarter.Esri ProcessingThis layer is a best available aggregation of the original Ookla dataset. This means that for each tile that data is available, the most recent data is used. So for instance, if data is available for a tile for Q2 2019 and for Q4 2020, the Q4 2020 data is awarded to the tile. The default visualization for the layer is the "broadband index". The broadband index is a bivariate index based on both the average download speed and the average upload speed. For Mobile, the score is indexed to a standard of 25 megabits per second (Mbps) download and 3 Mbps upload. A tile with average Speedtest results of 25/3 Mbps is awarded 100 points. Tiles with average speeds above 25/3 are shown in green, tiles with average speeds below this are shown in fuchsia. For Fixed, the score is indexed to a standard of 100 Mbps download and 3 Mbps upload. A tile with average Speedtest results of 100/20 Mbps is awarded 100 points. Tiles with average speeds above 100/20 are shown in green, tiles with average speeds below this are shown in fuchsia.Tile AttributesEach tile contains the following adjoining attributes:The year and the quarter that the tests were performed.The average download speed of all tests performed in the tile, represented in megabits per second.The average upload speed of all tests performed in the tile, represented in megabits per second.The average latency of all tests performed in the tile, represented in millisecondsThe number of tests taken in the tile.The number of unique devices contributing tests in the tile.The quadkey representing the tile.QuadkeysQuadkeys can act as a unique identifier for the tile. This can be useful for joining data spatially from multiple periods (quarters), creating coarser spatial aggregations without using geospatial functions, spatial indexing, partitioning, and an alternative for storing and deriving the tile geometry.LayersThere are two layers:Ookla_Mobile_Tiles - Tiles containing tests taken from mobile devices with GPS-quality location and a cellular connection type (e.g. 4G LTE, 5G NR).Ookla_Fixed_Tiles - Tiles containing tests taken from mobile devices with GPS-quality location and a non-cellular connection type (e.g. WiFi, ethernet).The layers are set to draw at scales 1:3,000,000 and larger.Time Period and update Frequency Layers are generated based on a quarter year of data (three months) and files will be updated and added on a quarterly basis. A /year=2020/quarter=1/ period, the first quarter of the year 2020, would include all data generated on or after 2020-01-01 and before 2020-04-01.

This data is used for a broadband mapping initiative conducted by the Washington State Broadband Office.This dataset provides global fixed broadband and mobile (cellular) network performance metrics in zoom level 16 web mercator tiles (approximately 610.8 meters by 610.8 meters at the equator). Data is projected in EPSG:4326. Download speed, upload speed, and latency are collected via the Speedtest by Ookla applications for Android and iOS and averaged for each tile. Measurements are filtered to results containing GPS-quality location accuracy. The data was processed and published to ArcGIS Living Atlas by Esri.AboutSpeedtest data is used today by commercial fixed and mobile network operators around the world to inform network buildout, improve global Internet quality, and increase Internet accessibility. Government regulators such as the United States Federal Communications Commission and the Malaysian Communications and Multimedia Commission use Speedtest data to hold telecommunications entities accountable and direct funds for rural and urban connectivity development. Ookla licenses data to NGOs and educational institutions to fulfill its mission: to help make the internet better, faster and more accessible for everyone. Ookla hopes to further this mission by distributing the data to make it easier for individuals and organizations to use it for the purposes of bridging the social and economic gaps between those with and without modern Internet access.DataTilesHundreds of millions of Speedtests are taken on the Ookla platform each month. In order to create a manageable dataset, we aggregate raw data into tiles. The size of a data tile is defined as a function of "zoom level" (or "z"). At z=0, the size of a tile is the size of the whole world. At z=1, the tile is split in half vertically and horizontally, creating 4 tiles that cover the globe. This tile-splitting continues as zoom level increases, causing tiles to become exponentially smaller as we zoom into a given region. By this definition, tile sizes are actually some fraction of the width/height of Earth according to Web Mercator projection (EPSG:3857). As such, tile size varies slightly depending on latitude, but tile sizes can be estimated in meters.For the purposes of these layers, a zoom level of 16 (z=16) is used for the tiling. This equates to a tile that is approximately 610.8 meters by 610.8 meters at the equator (18 arcsecond blocks). The geometry of each tile is represented in WGS 84 (EPSG:4326) in the tile field.The data can be found at: https://github.com/teamookla/ookla-open-dataUpdate Cadence The tile aggregates start in Q1 2019 and go through the most recent quarter. They will be updated shortly after the conclusion of the quarter.Esri ProcessingThis layer is a best available aggregation of the original Ookla dataset. This means that for each tile that data is available, the most recent data is used. So for instance, if data is available for a tile for Q2 2019 and for Q4 2020, the Q4 2020 data is awarded to the tile. The default visualization for the layer is the "broadband index". The broadband index is a bivariate index based on both the average download speed and the average upload speed. For Mobile, the score is indexed to a standard of 25 megabits per second (Mbps) download and 3 Mbps upload. A tile with average Speedtest results of 25/3 Mbps is awarded 100 points. Tiles with average speeds above 25/3 are shown in green, tiles with average speeds below this are shown in fuchsia. For Fixed, the score is indexed to a standard of 100 Mbps download and 3 Mbps upload. A tile with average Speedtest results of 100/20 Mbps is awarded 100 points. Tiles with average speeds above 100/20 are shown in green, tiles with average speeds below this are shown in fuchsia.Tile Attributes Each tile contains the following adjoining attributes:The year and the quarter that the tests were performed.The average download speed of all tests performed in the tile, represented in megabits per second.The average upload speed of all tests performed in the tile, represented in megabits per second.The average latency of all tests performed in the tile, represented in millisecondsThe number of tests taken in the tile.The number of unique devices contributing tests in the tile.The quadkey representing the tile.QuadkeysQuadkeys can act as a unique identifier for the tile. This can be useful for joining data spatially from multiple periods (quarters), creating coarser spatial aggregations without using geospatial functions, spatial indexing, partitioning, and an alternative for storing and deriving the tile geometry.LayersThere are two layers:Ookla_Mobile_Tiles - Tiles containing tests taken from mobile devices with GPS-quality location and a cellular connection type (e.g. 4G LTE, 5G NR).Ookla_Fixed_Tiles - Tiles containing tests taken from mobile devices with GPS-quality location and a non-cellular connection type (e.g. WiFi, ethernet).The layers are set to draw at scales 1:3,000,000 and larger.Time Period and Update FrequencyLayers are generated based on a quarter year of data (three months) and files will be updated and added on a quarterly basis. A /year=2020/quarter=1/ period, the first quarter of the year 2020, would include all data generated on or after 2020-01-01 and before 2020-04-01.

This dataset contains mobile wireless download speed test results and areas where the PSD (Vermont Public Service Department) challenged mobile wireless service asserted by wireless carriers.DOWNLOAD SPEED TEST RESULTSResults from download speed tests that were conducted in September-December 2018 are contained by 6 point feature-classes, each with results for a particular carrier.PSD staff employed the android smartphone application G-NetTrack to conduct download speed tests at approximately 300 meter intervals along all federal-aid highways.The point feature-classes are very detailed and more suitable when zoomed into the neighborhood scale. All point feature-classes have the same field schema, which includes these fields: timestamp: Date and time at which the data point was collected. signal_str: Signal strength (RSRP in dBm). download_s: Download speed (in Mbps). latency: The round-trip time for a request to a website, in milliseconds.DRIVE-TEST BLOCKSDrive-test blocks (Utility_DriveTest_poly_Blocks) is a polygon feature-class that is composed of 1-kilometer blocks; it has a field for each of the 6 carriers; the fields show the average download speed recorded in each block for each carrier.The fields also include a composite field (All_) that contains averages of all carriers, masking variation in coverage between individual carriers. "999" indicates no test was conducted for the carrier in that block.Drive-test blocks are generalized information and are suitable when zoomed at various scales. A BLOCK DOES NOT INDICATE SERVICE THROUGHOUT A BLOCK; use the point feature-classes for detailed data and judge accordingly.WIRELESS CHALLENGE BLOCKSWireless Challenge Blocks (Utility_DriveTest_poly_VTMFCIIChallengeBlocks) depicts the status of each block in the submission of the PSD in the FCC Mobility Fund Phase II Challenge process. It shows challenges to mobile wireless service asserted by wireless carriersA value of 0 in the Area_1 field indicates that the challenge was rejected, either because a) the block is already largely eligible, or b) because no tests below 5 Mbps were submitted.DISCLAIMERVCGI and the State of VT make no representations of any kind, including but not limited to the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the data.

BackgroundIn 1975, the Department of Forestry began a new management inventory designed to provide statistics of forest lands and timber volumes in a form that could be used to develop Forest Management Plans. This involved measuring over 500 Temporary Sample Plots (TSP) and measuring or remeasuring nearly 100 Stand Monitor Plots (SMP) each year. The SMPs were designed to provide information which would allow updating of inventory cover types between subsequent inventories.In 1985, nearing the completion of the second cycle of measuring TSPs, the focus began to change. It was felt that volume estimates acquired through TSPs were adequate for most strata and more emphasis be centered around collecting data on growth and yield. This led to the start of a Permanent Sample Plot (PSP) database. The program focused on establishing PSPs in regenerating and immature stand types. This focus continued between 1985 and 1991.In 1992, an evaluation of the existing PSP program and an understanding of the provinces need for growth and yield information led to the design of a 1,000 plot program focusing on growth and yield data collection. Since 1992, additional measurements have been added to the PSP program at the request of various data users. These include Damman Site Type (soils and vegetation), Hare Pellets, Woody Debris, and Song Birds.In 2007, the Newfoundland Forest Service began to use data loggers for collecting PSP data in the field. This speeds the data input process from the previous paper based system so that the data collected can be used shortly after the field season ends. The program also has controls to aid in avoiding errors during data entry; previously, errors were not detected until subsequent data analysis long after the plot measurements were completed.In 2024, the PSP database underwent a significant overhaul, involving a redesign of the 2007 Microsoft Access database and enabling data collection using more modern smartphone and tablet technology. This required the engineering of the database within the Oracle Forestry Enterprise geodatabase and management of data within the ArcGIS Enterprise environment. Data loggers have been replaced and now use iOS and Android technology to collect and measure PSPs within the ArcGIS Field Maps application.The data within this feature layer is updated daily at 24-hour intervals, beginning at 6:00PM NST. Information within this layer represents the most up-to-date and accurate information currently available.ObjectiveThe objectives of the Permanent Sample Plot Program are to provide stand growth data that can be used to calibrate and validate stand growth projection models and have a network of plots sufficient to sample the important stand conditions at an acceptable intensity. More specifically, the goal is to maintain a PSP program of at least 1,000 plots in natural and managed stands.The PSP Program incorporates measurement of other stand conditions and variables as deemed needed by the users of the data.Establishment and Allocation ProceduresThe allocation is based on proportional representation of stands by Strata (Working Group, Age Class and Site Class in a Management District). The actual plot locations are randomly located within the district and within the stand to avoid bias. As plots are lost to various disturbances, a new plot will be:Re-established at the same siteEstablished as a replacement in the same stratumEstablished in a strata type which is being under-represented, if the lost stratum is already well represented.Data CurrencyThe data within this feature layer is updated daily at 24-hour intervals, beginning at 6:00PM NST. Information within this layer represents the most up-to-date and accurate information currently available.

This dataset contains mobile wireless download speed test results and areas where the PSD (Vermont Public Service Department) challenged mobile wireless service asserted by wireless carriers.DOWNLOAD SPEED TEST RESULTSResults from download speed tests that were conducted in September-December 2018 are contained by 6 point feature-classes, each with results for a particular carrier.PSD staff employed the android smartphone application G-NetTrack to conduct download speed tests at approximately 300 meter intervals along all federal-aid highways.The point feature-classes are very detailed and more suitable when zoomed into the neighborhood scale. All point feature-classes have the same field schema, which includes these fields: timestamp: Date and time at which the data point was collected. signal_str: Signal strength (RSRP in dBm). download_s: Download speed (in Mbps). latency: The round-trip time for a request to a website, in milliseconds.DRIVE-TEST BLOCKSDrive-test blocks (Utility_DriveTest_poly_Blocks) is a polygon feature-class that is composed of 1-kilometer blocks; it has a field for each of the 6 carriers; the fields show the average download speed recorded in each block for each carrier.The fields also include a composite field (All_) that contains averages of all carriers, masking variation in coverage between individual carriers. "999" indicates no test was conducted for the carrier in that block.Drive-test blocks are generalized information and are suitable when zoomed at various scales. A BLOCK DOES NOT INDICATE SERVICE THROUGHOUT A BLOCK; use the point feature-classes for detailed data and judge accordingly.WIRELESS CHALLENGE BLOCKSWireless Challenge Blocks (Utility_DriveTest_poly_VTMFCIIChallengeBlocks) depicts the status of each block in the submission of the PSD in the FCC Mobility Fund Phase II Challenge process. It shows challenges to mobile wireless service asserted by wireless carriersA value of 0 in the Area_1 field indicates that the challenge was rejected, either because a) the block is already largely eligible, or b) because no tests below 5 Mbps were submitted.DISCLAIMERVCGI and the State of VT make no representations of any kind, including but not limited to the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the data.

BUS ROUTES​Stay up to date on bus arrivals & departures, news, events, and much more!Transit Flash: Sign up for BCT's eNewsletter Transit Flash to receive service updates and rider newsDownload the Broward County Transit Mobile App (available in the Apple App Store and for Android on Google Play​) to purchase your ticket on your smartphone

This view only feature layer is used to support the CUSEC Post-Disaster Building Safety Assessment Application, developed by the CUSEC Safety Assessment working group. It allows sharing outside of the assessment group to others that need to view the data but are not allowed to collect or editing existing features.More information:Following a large disaster, thousands of damaged buildings may need to be quickly surveyed to determine their safety and habitability. Traditional survey methods can be time consuming and inefficient. In 2015, the Central U.S. Earthquake Consortium (CUSEC) created a FREE mobile data collection app to address this issue.The "Safety Assessment App" captures post-disaster safety assessments into a geographic information system (GIS) that can be used by emergency managers and decision makers to improve disaster response and recovery efforts. Trained engineers and architects can use the app quickly document whether or not buildings are safe to occupy following an earthquake, wind, or flood event. Instead of using paper forms that are time-consuming and cumbersome to enter into a GIS database, assessment teams can use the app on iOS, Android, or Windows-based smartphones and tablets. Please leave feedback or questions in the Comments section below.