Attachment Viewer allows app viewers to explore images stored as feature attachments. Present your photos, videos, and PDF files collected using ArcGIS Field Maps or Survey 123 workflows. Choose an attachment focused layout to display individual images beside your map or a map focused layout to highlight your map beside a gallery of images.Examples:Review photos collected during emergency response damage inspectionsDisplay the results of field data collection and support the downloading of images for inclusion in a reportPresent a map of land parcel along with associated documents stored as attachmentsData RequirementsThis web app includes the capability to view attachments of a hosted feature service or an ArcGIS Server feature service (10.8 or greater). Currently the attachment viewer will display jpeg, jpg, png, gif, mp4, mov, quicktime, pdf in the viewer window. All other attachment types are displayed as a link.Key App CapabilitiesMap focused layout - Display the map in the main panel of the app with a gallery of attachmentsAttachment focused layout - Display one attachment at a time in the main panel of the app with the map on the sideFeature selection - Allow app viewers to select features in the map and view associated attachmentsReview data - Enable tools to review and update existing recordsNavigation boundary - Keep the area in the map in focus by using a navigation boundary or disabling the ability to scrollZoom, pan, download attachments - Allow app viewers to interact with and download attachmentsHome, Zoom Controls, Legend, Layer List, SearchSupportabilityThis web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

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.

Market Overview The global Field Data Collection Software market has witnessed tremendous growth in recent years, driven by the increasing demand for real-time data collection and analysis. The market size was estimated to be XXX million in 2025 and is projected to grow at a CAGR of XX% from 2025 to 2033. Key growth drivers include the rising adoption of mobile devices and cloud-based platforms, the need for improved safety and compliance, and the increasing complexity of field operations. Segmentation and Regional Analysis The market is segmented by deployment type (cloud-based and on-premises) and application (environmental, construction, oil and gas, transportation, mining, and others). The environmental segment held the largest market share in 2025, driven by the growing need for environmental monitoring and compliance. Geographically, North America and Europe are the dominant markets, followed by Asia Pacific and the Middle East & Africa. The market in Asia Pacific is expected to witness significant growth in the coming years due to the rapidly expanding construction and mining industries.

Background: Scientific field observation by members of the public is known as citizen science and has become popular all across the world. Citizen science is advantageous for collecting large amounts of scientific data and can be seen as a crowdsourcing approach to data collection. Information and communications technology is enhancing the availability of citizen science. Mobile devices, such as mobile phones, that have a digital camera with a global positioning system (GPS) are necessities of contemporary life and can be utilized as powerful observation tools in citizen science. New information: We developed a web-based system as a data collection tool for citizen science. Participants submit an e-mail with a photo taken by their mobile phones. The photos contain location information, which can be easily and automatically embedded if the mobile phone is equipped with GPS. We collaborated with regional event managers, such as museum curators, and held citizen science events in each region and for various target taxonomic groups. All photos were stored in our data server, and the organisms were taxonomically identified by citizen scientists, regional managers, and us. In total, 154 species and 843 data records were collected in this project conducted from 2011 to 2016.

Each dataset provides monthly data at the national level of Social Security Retirement Insurance applications filed via the Internet, and Social Security Retirement Insurance applications submitted via telephone, in person through a local SSA field office, or by mail that could be filed via the Internet. Percentage of online applications is derived by dividing the number of retirement insurance applications filed via the Internet by the total number of retirement insurance applications that could be filed via the Internet.

For full FGDC metadata record, please click here.These data represent Staging and Response Locations collected by GPS for Mississippi, Alabama, and the Florida Panhandle prior to the Deepwater Horizon Oil Spill. The locations for the Peninsular portion of Florida, Georgia, South Carolina, Puerto Rico, and the US Virgin Islands have been compiled from numerous sources into this database schema and will at some later date (after Nov. 2010) be verified and validated by GPS. Staging and response locations were identified first by defining the types of locations that fit these descriptions. The broad categories were defined as Boat Ramp, Marina, Staging Area, or any combination of these. A marina may contain a boat ramp as well as a large parking lot with a seawall suitable for deploying equipment into the water. A staging area may contain just a waterfront park with access to the water, but no boat ramp or marina, but perhaps a dock or pier. These categories and attributes were used to design a specific database schema to collect information on these geographic features that could be used on a GPS-enabled field data collection device. Once the categories of information to be collected and the specifics of what types of information to be collected within each category were determined (the database schema), mobile devices were programmed to accomplish this task and area committee volunteers were used to conduct the field surveys. Field crews were given training on the devices. Guided by base maps identifying potential locations, they then traveled into the field to validate and collect specific GPS and attribute data on those locations. This was a cooperative effort between many federal, state, and local entities guided by FWC-FWRI that resulted in detailed and location-specific information on 366 staging area locations within Sector Mobile and a comprehensive GIS data set that is available on the DVD ROM and website as well a being used in the Geographic Response Plan Map Atlas production. Cyber-Tracker was the software used for this field data collection. Cyber-Tracker is a "shareware" software package developed as a data-capture tool designed for use in Environmental Conservation, Wildlife Biology and Disaster Relief. The software runs on numerous types of mobile devices and designing custom data capture processes for these devices requires no programming experience. Funded in large part by the European Commission and patroned by Harvard University, Cyber-Tracker Software has been a very valuable tool in the data collection efforts of this project. Cyber-Tracker Software can be found on the Internet at: http://www.cybertracker.co.za/.

Dataset of cattle movement and behavior monitoring collected at the University of Nottingham's Dairy Centre.We performed the field experiments of cattle movement and behavior monitoring at the University of Nottingham's Dairy Centre to collect realistic parameters necessary to develop and evaluate an adequate wireless protocol.date/time of measurement start: 2006-07-04date/time of measurement end: 2006-07-13collection environment: The application of Mobile Ad Hoc Networks to cattle monitoring has the potential to increase the profitability of cattle production and positively impact the everyday live of farm personnel. To realize these possibilities, design of wireless protocols needs to be driven by real experiences. The main research challenges are identifying and refining realistic requirements for a MANET routing protocol and designing such protocol. In order to address this, we performed the field experiments at the University of Nottingham's Dairy Centre. The purpose of these field experiments was collection of realistic parameters necessary to develop and evaluate an adequate wireless protocol. They included cattle movement and behavior monitoring as well as distributing a questionnaire to the farm personnel and researchers working on the farm.network configuration: We installed on each monitored cow a collar comprising a neck strap and an aluminum instrument enclosure containing a Bluetooth GPS and a Bluetooth enabled mobile phone.data collection methodology: In the first field experiment we monitored two of the cows located in one of the divisions of a modern dairy intended for about 100 animals. Cows can move freely in the area with the feeder, water tank, resting bays and milking robots available 24 hours a day. We installed on the monitored cows two collars comprising a neck strap and an aluminum instrument enclosure containing a Bluetooth GPS and a Bluetooth enabled mobile phone. Mobile phones were logging data from the GPS receivers including positions and timestamps. All the cows in the dairy were wearing pedometers. Their measurements were automatically collected by milking robots whenever a cow was milked. The data collection started at 11:10. Both GPS receivers worked until around 14:05. Some of the collected measurements suggested that cows moved with speeds impossible for them, which suggested GPS errors. Concurrently we were filming the part of the dairy where the monitored cows were kept. We placed the camera on the ramp above this area. This location offered the most complete view but some parts of the area were obscured. GPS receivers and filming were utilized only for the purpose of our field experiments. Their utilization is not intended for the target monitoring system.We repeated the previous experiment with five collars mounted on animals and two cameras located at two different ramps to get a more complete view of the area where the monitored cows were kept. We had GPS receivers with better batteries than before and we were logging data about the precision of logged locations. Monitoring started at 11:10. GPS receivers worked until 18:24, 12:23 (probably jammed), 18:51, 15:09, 15:33. We received the plan of the dairy and then captured the coordinates of the characteristic locations on the plan using a handheld GPS receiver.error: Some of the collected measurements suggested that cows moved with speeds impossible for them, which suggested GPS errors.Tracesetnottingham/cattle/mobilityTraceset of cattle movement and behavior monitoring collected at the University of Nottingham's Dairy Centre.file: cattle.tar.gzdescription: We monitored some cows located at the University of Nottingham's Dairy Centre to collect the traces of cattle movement and behavior.measurement purpose: User Mobility Characterization, Routing Protocol for DTNs (Disruption Tolerant Networks), Energy-efficient Wireless Networkmethodology: In the first field experiment we monitored two of the cows located in one of the divisions of a modern dairy intended for about 100 animals. Cows can move freely in the area with the feeder, water tank, resting bays and milking robots available 24 hours a day. We installed on the monitored cows two collars comprising a neck strap and an aluminum instrument enclosure containing a Bluetooth GPS and a Bluetooth enabled mobile phone. Mobile phones were logging data from the GPS receivers including positions and timestamps. All the cows in the dairy were wearing pedometers. Their measurements were automatically collected by milking robots whenever a cow was milked. The data collection started at 11:10. Both GPS receivers worked until around 14:05. Some of the collected measurements suggested that cows moved with speeds impossible for them, which suggested GPS errors. Concurrently we were filming the part of the dairy where the monitored cows were kept. We placed the camera on the ramp above this area. This location offered the most complete view but some parts of the area were obscured. GPS receivers and filming were utilized only for the purpose of our field experiments. Their utilization is not intended for the target monitoring system. We repeated the previous experiment with five collars mounted on animals and two cameras located at two different ramps to get a more complete view of the area where the monitored cows were kept. We had GPS receivers with better batteries than before and we were logging data about the precision of logged locations. Monitoring started at 11:10. GPS receivers worked until 18:24, 12:23 (probably jammed), 18:51, 15:09, 15:33. We received the plan of the dairy and then captured the coordinates of the characteristic locations on the plan using a handheld GPS receiver. error: Some of the collected measurements suggested that cows moved with speeds impossible for them, which suggested GPS errors.nottingham/cattle/mobility Traces2006.07.04: Trace of cattle movement and behavior monitoring collected at the University of Nottingham's Dairy Centre on 2007-07-04.configuration: In the first field experiment we monitored two of the cows located in one of the divisions of a modern dairy intended for about 100 animals. Cows can move freely in the area with the feeder, water tank, resting bays and milking robots available 24 hours a day. We installed on the monitored cows two collars comprising a neck strap and an aluminum instrument enclosure containing a Bluetooth GPS and a Bluetooth enabled mobile phone. Mobile phones were logging data from the GPS receivers including positions and timestamps. All the cows in the dairy were wearing pedometers. Their measurements were automatically collected by milking robots whenever a cow was milked. The data collection started at 11:10. Both GPS receivers worked until around 14:05.format: Directory name - 2006.07.04GPS traces (???.txt):GPS data from Bluetooth GPSes mounted on the wet cows kept in a dairy. We file name - id of the cowPyStumbler 1.0 format, meaning of the important fields: DATE - date of the measurement LAT - latitude LON - longitude TIMEOFFIX - time in GMT (equivalent to British winter time) STATUS - Validity: A-ok, V-invalid SOG - speed over gorund COG - course of the ground MODE - Mode: 1=Fix not available; 2=2D; 3=3D NUMSAT - number of sattelites used for the fix (the more the better) PDOP - Position Dilution of Precision (PDOP) HDOP - Horizontal Dilution of Precision (HDOP) VDOP - Vertical Dilution of Precision (VDOP)DOP (dilution of precision) is an indication of the effect of satellite geometry on the accuracy of the fix. It is a unitless number where smaller is better. For 3D fixes using 4 satellites a 1.0 would be considered to be a perfect number, however for over determined solutions it is possible to see numbers below 1.0.Pedometer data (pedometers.txt):Data from the pedometers mounted on the legs of the wet cows kept in the dairy.Each line represents pedometer reading taken during milking preformed by a robot.1) ID of the cow2) Birth day of the cow3) timestamp in BST (British Summer Time)4) Pedometer reading2006.07.13: Trace of cattle movement and behavior monitoring collected at the University of Nottingham's Dairy Centre on 2007-07-13.configuration: In the second field experiment we monitored five of the cows located in one of the divisions of a modern dairy intended for about 100 animals. We also located two cameras at two different ramps to get a more complete view of the area where the monitored cows were kept. Cows can move freely in the area with the feeder, water tank, resting bays and milking robots available 24 hours a day. We installed on the monitored cows five collars comprising a neck strap and an aluminum instrument enclosure containing a Bluetooth GPS and a Bluetooth enabled mobile phone. Mobile phones were logging data from the GPS receivers including positions and timestamps. All the cows in the dairy were wearing pedometers. Their measurements were automatically collected by milking robots whenever a cow was milked. We had GPS receivers with better batteries than before and we were logging data about the precision of logged locations. Monitoring started at 11:10. GPS receivers worked until 18:24, 12:23 (probably jammed), 18:51, 15:09, 15:33. We received the plan of the dairy and then captured the coordinates of the characteristic locations on the plan using a handheld GPS receiver.format: Directory name - 2006.07.13 GPS traces (???.txt):GPS data from Bluetooth GPSes mounted on the wet cows kept in a dairy.file name - id of the cowPyStumbler 1.0 format, meaning of the important fields: DATE - date of the measurement LAT - latitude LON - longitude TIMEOFFIX - time in GMT (equivalent to British winter time) STATUS - Validity: A-ok, V-invalid SOG - speed over gorund COG - course of the ground MODE - Mode: 1=Fix not available; 2=2D; 3=3D NUMSAT - number of sattelites used for the fix (the more the better) PDOP - Position Dilution of Precision (PDOP) HDOP - Horizontal Dilution of Precision

LMOS_TraceGas_SurfaceMobile_EPA-GMAP_Data_1 is the Lake Michigan Ozone Study (LMOS) trace gas surface mobile data collected via the Environmental Protection Agency (EPA) GMAP mobile platform during the LMOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA, Electric Power Research Institute (EPRI), National Science Foundation (NSF), Lake Michigan Air Directors Consortium (LADCO) and its member states, and several research groups at universities. Data collection is complete. Elevated spring and summertime ozone levels remain a challenge along the coast of Lake Michigan, with a number of monitors recording levels/amounts exceeding the 2015 National Ambient Air Quality Standards (NAAQS) for ozone. The production of ozone over Lake Michigan, combined with onshore daytime “lake breeze” airflow is believed to increase ozone concentrations at locations within a few kilometers off shore. This observed lake-shore gradient motivated the Lake Michigan Ozone Study (LMOS). Conducted from May through June 2017, the goal of LMOS was to better understand ozone formation and transport around Lake Michigan; in particular, why ozone concentrations are generally highest along the lakeshore and drop off sharply inland and why ozone concentrations peak in rural areas far from major emission sources. LMOS was a collaborative, multi-agency field study that provided extensive observational air quality and meteorology datasets through a combination of airborne, ship, mobile laboratories, and fixed ground-based observational platforms. Chemical transport models (CTMs) and meteorological forecast tools assisted in planning for day-to-day measurement strategies. The long term goals of the LMOS field study were to improve modeled ozone forecasts for this region, better understand ozone formation and transport around Lake Michigan, provide a better understanding of the lakeshore gradient in ozone concentrations (which could influence how the Environmental Protection Agency (EPA) addresses future regional ozone issues), and provide improved knowledge of how emissions influence ozone formation in the region.

The size and share of the market is categorized based on Application (Field service technicians, Sales representatives, Inspectors and auditors, Maintenance workers, Logistics and delivery personnel, Healthcare professionals) and Product (Offline forms software, Mobile data collection software, Field service automation software, Inspection forms software, Enterprise mobility software) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

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. Automatically refreshes every five minutes:Fires by Cause - Any incident that has occurred year to date displayed by cause.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.

Tags

   survey, environmental behaviors, lifestyle, status, PRIZM, Baltimore Ecosystem Study, LTER, BES




   Summary


   BES Research, Applications, and Education


   Description


   Geocoded for Baltimore County. The BES Household Survey 2003 is a telephone survey of metropolitan Baltimore residents consisting of 29 questions. The survey research firm, Hollander, Cohen, and McBride conducted the survey, asking respondents questions about their outdoor recreation activities, watershed knowledge, environmental behavior, neighborhood characteristics and quality of life, lawn maintenance, satisfaction with life, neighborhood, and the environment, and demographic information. The data from each respondent is also associated with a PRIZM� classification, census block group, and latitude-longitude. PRIZM� classifications categorize the American population using Census data, market research surveys, public opinion polls, and point-of-purchase receipts. The PRIZM� classification is spatially explicit allowing the survey data to be viewed and analyzed spatially and allowing specific neighborhood types to be identified and compared based on the survey data. The census block group and latitude-longitude data also allow us additional methods of presenting and analyzing the data spatially. 


   The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. 



   The BES 2003 telephone survey was conducted by Hollander, Cohen, and McBride from September 1-30, 2003. The sample was obtained from the professional sampling firm Claritas, in order that their "PRIZM" encoding would be appended to each piece of sample (telephone number) supplied. Mailing addresses were also obtained so that a postcard could be sent in advance of interviewers calling. The postcard briefly informed potential respondents about the survey, who was conducting it, and that they might receive a phone call in the next few weeks. A stratified sampling method was used to obtain between 50 - 150 respondents in each of the 15 main PRIZM classifications. This allows direct comparison of PRIZM classifications. Analysis of the data for the general metropolitan Baltimore area must be weighted to match the population proportions normally found in the region. They obtained a total of 9000 telephone numbers in the sample. All 9,000 numbers were dialed but contact was only made on 4,880. 1508 completed an interview, 2524 refused immediately, 147 broke off/incomplete, 84 respondents had moved and were no longer in the correct location, and a qualified respondent was not available on 617 calls. This resulted in a response rate of 36.1% compared with a response rate of 28.2% in 2000. The CATI software (Computer Assisted Terminal Interviewing) randomized the random sample supplied, and was programmed for at least 3 attempted callbacks per number, with emphasis on pulling available callback sample prior to accessing uncalled numbers. Calling was conducted only during evening and weekend hours, when most head of households are home. The use of CATI facilitated stratified sampling on PRIZM classifications, centralized data collection, standardized interviewer training, and reduced the overall cost of primary data collection. Additionally, to reduce respondent burden, the questionnaire was revised to be concise, easy to understand, minimize the use of open-ended responses, and require an average of 15 minutes to complete. 


   The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data, including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. 


   Additional documentation of this database is attached to this metadata and includes 4 documents, 1) the telephone survey, 2) documentation of the telephone survey, 3) metadata for the telephone survey, and 4) a description of the attribute data in the BES survey 2003 survey.


   This database was created by joining the GDT geographic database of US Census Block Group geographies for the Baltimore Metropolitan Statisticsal Area (MSA), with the Claritas PRIZM database, 2003, of unique classifications of each Census Block Group, and the unique PRIZM code for each respondent from the BES Household Telephone Survey, 2003. The GDT database is preferred and used because

Abstract

The main objective of this project is to collect household data for the ongoing assessment and monitoring of the socio-economic impacts of COVID-19 on households and family businesses in Vietnam. The estimated field work and sample size of households in each round is as follows:

Round 1 June fieldwork- approximately 6300 households (at least 1300 minority households) Round 2 August fieldwork - approximately 4000 households (at least 1000 minority households) Round 3 September fieldwork- approximately 4000 households (at least 1000 minority households) Round 4 December- approximately 4000 households (at least 1000 minority households) Round 5 - pending discussion

Geographic coverage

National, regional

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2020 Vietnam COVID-19 High Frequency Phone Survey of Households (VHFPS) uses a nationally representative household survey from 2018 as the sampling frame. The 2018 baseline survey includes 46980 households from 3132 communes (about 25% of total communes in Vietnam). In each commune, one EA is randomly selected and then 15 households are randomly selected in each EA for interview. Out of the 15 households, 3 households have information collected on both income and expenditure (large module) as well as many other aspects. The remaining 12 other households have information collected on income, but do not have information collected on expenditure (small module). Therefore, estimation of large module includes 9396 households and are representative at regional and national levels, while the whole sample is representative at the provincial level.

We use the large module of to select the households for official interview of the VHFPS survey and the small module households as reserve for replacement. The sample size of large module has 9396 households, of which, there are 7951 households having phone number (cell phone or line phone).

After data processing, the final sample size is 6,213 households.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire for Round 1 consisted of the following sections Section 2. Behavior Section 3. Health Section 4. Education & Child caring Section 5A. Employment (main respondent) Section 5B. Employment (other household member) Section 6. Coping Section 7. Safety Nets Section 8. FIES

Cleaning operations

Data cleaning began during the data collection process. Inputs for the cleaning process include available interviewers’ note following each question item, interviewers’ note at the end of the tablet form as well as supervisors’ note during monitoring. The data cleaning process was conducted in following steps: • Append households interviewed in ethnic minority languages with the main dataset interviewed in Vietnamese. • Remove unnecessary variables which were automatically calculated by SurveyCTO • Remove household duplicates in the dataset where the same form is submitted more than once. • Remove observations of households which were not supposed to be interviewed following the identified replacement procedure. • Format variables as their object type (string, integer, decimal, etc.) • Read through interviewers’ note and make adjustment accordingly. During interviews, whenever interviewers find it difficult to choose a correct code, they are recommended to choose the most appropriate one and write down respondents’ answer in detail so that the survey management team will justify and make a decision which code is best suitable for such answer. • Correct data based on supervisors’ note where enumerators entered wrong code. • Recode answer option “Other, please specify”. This option is usually followed by a blank line allowing enumerators to type or write texts to specify the answer. The data cleaning team checked thoroughly this type of answers to decide whether each answer needed recoding into one of the available categories or just keep the answer originally recorded. In some cases, that answer could be assigned a completely new code if it appeared many times in the survey dataset.
• Examine data accuracy of outlier values, defined as values that lie outside both 5th and 95th percentiles, by listening to interview recordings. • Final check on matching main dataset with different sections, where information is asked on individual level, are kept in separate data files and in long form. • Label variables using the full question text. • Label variable values where necessary.

Response rate

The target for Round 1 is to complete interviews for 6300 households, of which 1888 households are located in urban area and 4475 households in rural area. In addition, at least 1300 ethnic minority households are to be interviewed. A random selection of 6300 households was made out of 7951 households for official interview and the rest as for replacement. However, the refusal rate of the survey was about 27 percent, and households from the small module in the same EA were contacted for replacement and these households are also randomly selected.

This is a video demonstrating how to connect Survey123 for ArcGIS to an external GNSS receiver.Steps:Connect your mobile device to the external GNSS receiver using bluetooth.Once the connection is successful, open an ArcGIS mobile app for field data collection (e.g., Survey123 for ArcGIS).Go to Settings, and look for Location setting.Click "Add Provider" and choose "External receiver".Once your external GNSS receiver is detected, press it and wait until the app establishes the connection.Author: Esri Indonesia Solution Strategist TeamCopyright © 2020 Esri Indonesia. All rights reserved.

Abstract

The high-frequency phone survey of refugees monitors the economic and social impact of and responses to the COVID-19 pandemic on refugees and nationals, by calling a sample of households every four weeks. The main objective is to inform timely and adequate policy and program responses. Since the outbreak of the COVID-19 pandemic in Ethiopia, two rounds of data collection of refugees were completed between September and November 2020. The first round of the joint national and refugee HFPS was implemented between the 24 September and 17 October 2020 and the second round between 20 October and 20 November 2020.

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was drawn using a simple random sample without replacement. Expecting a high non-response rate based on experience from the HFPS-HH, we drew a stratified sample of 3,300 refugee households for the first round. More details on sampling methodology are provided in the Survey Methodology Document available for download as Related Materials.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The Ethiopia COVID-19 High Frequency Phone Survey of Refugee questionnaire consists of the following sections:

• Interview Information
• Household Roster
• Camp Information
• Knowledge Regarding the Spread of COVID-19
• Behaviour and Social Distancing - Access to Basic Services
• Employment
• Income Loss
• Coping/Shocks
• Social Relations
• Food Security
• Aid and Support/ Social Safety Nets.

A more detailed description of the questionnaire is provided in Table 1 of the Survey Methodology Document that is provided as Related Materials. Round 1 and 2 questionnaires available for download.

Cleaning operations

DATA CLEANING At the end of data collection, the raw dataset was cleaned by the Research team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes. Data cleaning carried out is detailed below.

Variable naming and labeling: • Variable names were changed to reflect the lowercase question name in the paper survey copy, and a word or two related to the question. • Variables were labeled with longer descriptions of their contents and the full question text was stored in Notes for each variable. • “Other, specify” variables were named similarly to their related question, with “_other” appended to the name. • Value labels were assigned where relevant, with options shown in English for all variables, unless preloaded from the roster in Amharic.

Variable formatting: • Variables were formatted as their object type (string, integer, decimal, time, date, or datetime). • Multi-select variables were saved both in space-separated single-variables and as multiple binary variables showing the yes/no value of each possible response. • Time and date variables were stored as POSIX timestamp values and formatted to show Gregorian dates. • Location information was left in separate ID and Name variables, following the format of the incoming roster. IDs were formatted to include only the variable level digits, and not the higher-level prefixes (2-3 digits only.)
• Only consented surveys were kept in the dataset, and all personal information and internal survey variables were dropped from the clean dataset. • Roster data is separated from the main data set and kept in long-form but can be merged on the key variable (key can also be used to merge with the raw data). • The variables were arranged in the same order as the paper instrument, with observations arranged according to their submission time.

Backcheck data review: Results of the backcheck survey are compared against the originally captured survey results using the bcstats command in Stata. This function delivers a comparison of variables and identifies any discrepancies. Any discrepancies identified are then examined individually to determine if they are within reason.

Data appraisal

The following data quality checks were completed: • Daily SurveyCTO monitoring: This included outlier checks, skipped questions, a review of “Other, specify”, other text responses, and enumerator comments. Enumerator comments were used to suggest new response options or to highlight situations where existing options should be used instead. Monitoring also included a review of variable relationship logic checks and checks of the logic of answers. Finally, outliers in phone variables such as survey duration or the percentage of time audio was at a conversational level were monitored. A survey duration of close to 15 minutes and a conversation-level audio percentage of around 40% was considered normal. • Dashboard review: This included monitoring individual enumerator performance, such as the number of calls logged, duration of calls, percentage of calls responded to and percentage of non-consents. Non-consent reason rates and attempts per household were monitored as well. Duration analysis using R was used to monitor each module's duration and estimate the time required for subsequent rounds. The dashboard was also used to track overall survey completion and preview the results of key questions. • Daily Data Team reporting: The Field Supervisors and the Data Manager reported daily feedback on call progress, enumerator feedback on the survey, and any suggestions to improve the instrument, such as adding options to multiple choice questions or adjusting translations. • Audio audits: Audio recordings were captured during the consent portion of the interview for all completed interviews, for the enumerators' side of the conversation only. The recordings were reviewed for any surveys flagged by enumerators as having data quality concerns and for an additional random sample of 2% of respondents. A range of lengths were selected to observe edge cases. Most consent readings took around one minute, with some longer recordings due to questions on the survey or holding for the respondent. All reviewed audio recordings were completed satisfactorily. • Back-check survey: Field Supervisors made back-check calls to a random sample of 5% of the households that completed a survey in Round 1. Field Supervisors called these households and administered a short survey, including (i) identifying the same respondent; (ii) determining the respondent's position within the household; (iii) confirming that a member of the the data collection team had completed the interview; and (iv) a few questions from the original survey.

The size and share of the market is categorized based on Application (Field Research, Surveys, Asset Management, Environmental Monitoring) and Product (Data Collection Software, Mobile Data Collection, Field Data Collection, Cloud-Based Data Collection) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

LISTOS_SurfaceMobile_InSitu_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) surface mobile data collected via mobile platforms during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. Data collection is complete. The New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.

Abstract

Within the frame of PCBS' efforts in providing official Palestinian statistics in the different life aspects of Palestinian society and because the wide spread of Computer, Internet and Mobile Phone among the Palestinian people, and the important role they may play in spreading knowledge and culture and contribution in formulating the public opinion, PCBS conducted the Household Survey on Information and Communications Technology, 2014.

The main objective of this survey is to provide statistical data on Information and Communication Technology in the Palestine in addition to providing data on the following: -

· Prevalence of computers and access to the Internet. · Study the penetration and purpose of Technology use.

Geographic coverage

Palestine (West Bank and Gaza Strip) , type of locality (Urban, Rural, Refugee Camps) and governorate

Analysis unit

Household. Person 10 years and over .

Universe

All Palestinian households and individuals whose usual place of residence in Palestine with focus on persons aged 10 years and over in year 2014.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The sampling frame consists of a list of enumeration areas adopted in the Population, Housing and Establishments Census of 2007. Each enumeration area has an average size of about 124 households. These were used in the first phase as Preliminary Sampling Units in the process of selecting the survey sample.

Sample Size The total sample size of the survey was 7,268 households, of which 6,000 responded.

Sample Design The sample is a stratified clustered systematic random sample. The design comprised three phases:

Phase I: Random sample of 240 enumeration areas. Phase II: Selection of 25 households from each enumeration area selected in phase one using systematic random selection. Phase III: Selection of an individual (10 years or more) in the field from the selected households; KISH TABLES were used to ensure indiscriminate selection.

Sample Strata Distribution of the sample was stratified by: 1- Governorate (16 governorates, J1). 2- Type of locality (urban, rural and camps).

Sampling deviation

-

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire consists of identification data, quality controls and three main sections: Section I: Data on household members that include identification fields, the characteristics of household members (demographic and social) such as the relationship of individuals to the head of household, sex, date of birth and age.

Section II: Household data include information regarding computer processing, access to the Internet, and possession of various media and computer equipment. This section includes information on topics related to the use of computer and Internet, as well as supervision by households of their children (5-17 years old) while using the computer and Internet, and protective measures taken by the household in the home.

Section III: Data on persons (aged 10 years and over) about computer use, access to the Internet and possession of a mobile phone.

Cleaning operations

Preparation of Data Entry Program: This stage included preparation of the data entry programs using an ACCESS package and defining data entry control rules to avoid errors, plus validation inquiries to examine the data after it had been captured electronically.

Data Entry: The data entry process started on 8 May 2014 and ended on 23 June 2014. The data entry took place at the main PCBS office and in field offices using 28 data clerks.

Editing and Cleaning procedures: Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

Response rate

Response Rates= 79%

Sampling error estimates

There are many aspects of the concept of data quality; this includes the initial planning of the survey to the dissemination of the results and how well users understand and use the data. There are three components to the quality of statistics: accuracy, comparability, and quality control procedures.

Checks on data accuracy cover many aspects of the survey and include statistical errors due to the use of a sample, non-statistical errors resulting from field workers or survey tools, and response rates and their effect on estimations. This section includes:

Statistical Errors Data of this survey may be affected by statistical errors due to the use of a sample and not a complete enumeration. Therefore, certain differences can be expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators.

Variance calculations revealed that there is no problem in disseminating results nationally or regionally (the West Bank, Gaza Strip), but some indicators show high variance by governorate, as noted in the tables of the main report.

Non-Statistical Errors Non-statistical errors are possible at all stages of the project, during data collection or processing. These are referred to as non-response errors, response errors, interviewing errors and data entry errors. To avoid errors and reduce their effects, strenuous efforts were made to train the field workers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, and practical and theoretical training took place during the training course. Training manuals were provided for each section of the questionnaire, along with practical exercises in class and instructions on how to approach respondents to reduce refused cases. Data entry staff were trained on the data entry program, which was tested before starting the data entry process.

Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

The sources of non-statistical errors can be summarized as: 1. Some of the households were not at home and could not be interviewed, and some households refused to be interviewed. 2. In unique cases, errors occurred due to the way the questions were asked by interviewers and respondents misunderstood some of the questions.

Over the past several years, the department has undergone an extensive effort to correct and provide for accurate descriptions of commercial salmon fishing districts, sections, statistical areas, and closed waters. This interactive mobile map allows users to view these areas as well as other information pertinent to Southeast Alaska salmon fisheries management. This map is available for download thru the Esri Field Maps app and can be used offline. This map is for reference purposes only and is still under development. Any feedback is appreciated.The map can be used remotely on mobile devices by downloading the ArcGIS Field Maps app by ESRI and loading the ADF&G SE AK Salmon Mobile – SEAK map. You can find this map by using the search feature within the “Maps” page of the app and typing in “ADF&G”. At this time ArcGIS Field Maps is available for use on Android and iOS platforms,but not compatible with Windows.Recent updates: Yakutat salmon districts and statistical areas for both Yakutat and Southeast Alaska are now included. Check back for updates!Last Update July 10, 2024 (download size 905.36 MB)

The waterways layer is a line geometry representation of rivers, canals and streams in the city of Jakarta, Indonesia. Attributes include river names, length, and geometries representing geographical locations of the line features. The dataset is a geo-processed version of data captured by the PetaJakarta.org project, using field-survey and tracing of aerial imagery. The geo-processing included removal of undershoots and overshoots (duplicate lines), creating line breaks at intersections, and creating line breaks at intersections with points representing pumps and floodgates. The data consists of 618 rows, and is in the WGS 84 / UTM zone 48S (EPSG:32748) projected coordinate reference system. The dataset is network-ready for building both directed and undirected graphs representing hydrological infrastructure network for Jakarta, Indonesia.

DISCOVERAQ_Colorado_Ground_Mobile_Data contains data collected via the Princeton Mobile Lab and NASA Langley LARGE Mobile Lab during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Colorado deployment and data collection is complete. Understanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality. DISCOVER-AQ employed two NASA aircraft, the P3-B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS). The first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.