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Data Entry Outsourcing Service Market was valued at USD 1651.28 Million in 2023 and is projected to reach USD 2515.82 Million by 2030, growing at a CAGR of 6.3% during the forecast period 2024-2030.

Global Data Entry Outsourcing Service Market Drivers

The market drivers for the Data Entry Outsourcing Service Market can be influenced by various factors. These may include:

Cost-Effectiveness: Hiring outside service providers to handle data entry work can drastically save operating expenses. This includes cost reductions on infrastructure, perks, and salaries—all of which are especially advantageous for small and medium-sized businesses. Concentrate on Core Competencies: Businesses can increase overall efficiency and productivity by outsourcing data entry services and concentrating more on their core competencies, which include strategic planning, product development, and customer service. Access to Skilled Workforce: Data entry jobs are the area in which outsourcing offers access to a knowledgeable and experienced workforce. When compared to doing these jobs internally, this can result in higher accuracy and faster turnaround times. Technological Advancements: By increasing efficiency and lowering the risk of error, the incorporation of cutting-edge technology like automation, artificial intelligence, and machine learning in data entry procedures makes outsourcing more alluring. Scalability: Depending on the demands of the business, outsourcing provides the freedom to scale up or down operations. For organizations with varying workloads or seasonal demands, this is especially helpful. Data Security and Compliance: Reputable outsourcing companies guarantee the confidentiality and integrity of sensitive data by adhering to international data protection rules and implementing strong security measures. Globalization and Business Expansion: Effective data management becomes more and more important as firms grow internationally. Businesses can effectively handle massive volumes of data from multiple locations by outsourcing data entry services. Increased Turnaround Time: Since outsourcing companies frequently work in different time zones, continuous workflow and speedier data entering task processing are possible, which can increase overall business efficiency.

Federal land parcels that are withdrawn from settlement, sale, location, or entry under some or all of the general land and mineral laws in order to maintain other public values or purposes. A withdrawal area has one or more associated segregations. A segregation is a specific activity from which the area has been withdrawn such as settlement, sale, location, or entry. Metadata

Dataset Description: Washington Grocery Store Locations

This dataset contains detailed information about the locations and operational status of grocery stores in Washington, spanning multiple years. It includes both spatial and temporal data, offering a comprehensive view of how grocery stores are distributed and have evolved over time. Below is a breakdown of the columns included in the dataset:

1. X, Y: Geographic coordinates (latitude and longitude) representing the store's location in the dataset.

2. STORENAME: The name of the grocery store.

3. ADDRESS: The physical address of the grocery store.

4. ZIPCODE: The ZIP code of the store’s location.

5. PHONE: The contact phone number for the store.

6. WARD: The local government ward in which the store is located.

7. SSL: A unique identifier or code related to the store, possibly referring to specific data collection attributes.

8. NOTES: Additional comments or information about the store.

9. PRESENT: Temporal indicators showing the presence (likely open or closed) of each store across various years. These columns provide insights into the longevity and temporal trends of grocery store operations.

10. GIS_ID: A unique identifier for geographic information system (GIS) data.

11. XCOORD, YCOORD: Coordinates (likely more specific) used for spatial data analysis, providing the exact location of the store.

12. MAR_ID: A unique identifier for marketing or regional analysis purposes.

13. GLOBALID: A global unique identifier for the store data.

14. CREATOR: The individual or system that created the data entry.

15. CREATED: Timestamp showing when the data entry was created.

16. EDITOR: The individual or system that edited the data entry.

17. EDITED: Timestamp showing when the data entry was last edited.

18. SE_ANNO_CAD_DATA: Specific annotation or data related to CAD (computer-aided design), possibly linked to store location details.

19. OBJECTID: A unique identifier for the object or record within the dataset.

Insights We Can Extract:

• Geographic Distribution: By analyzing the X and Y coordinates along with ZIP codes and wards, we can identify where grocery stores are concentrated and map areas with high or low store density.
• Temporal Trends: The data in the "PRESENT" columns helps us track the opening and closure patterns of grocery stores over time, providing insights into market trends and store longevity.
• Service Gaps: We can identify areas with no grocery stores, possibly indicating food deserts or underserved communities, by mapping the stores and comparing coverage across ZIP codes and wards.
• Operational Trends: By analyzing the temporal data and comparing store turnover, we can uncover patterns in the longevity or turnover of grocery stores.
• Urban Planning and Accessibility: This dataset could help us assess whether the location of grocery stores aligns with urban infrastructure like transportation routes or population density, which could inform policy decisions to improve grocery access.

This dataset is invaluable for urban planners, policymakers, and business stakeholders looking to improve food access and urban infrastructure.

Neighborhood Map Atlas neighborhoods are derived from the Seattle City Clerk's Office Geographic Indexing Atlas. These are the smallest neighborhood areas and have been supplemented with alternate names from other sources in 2020. They roll up to the district areas. The sub-neighborhood field contains the most common name and the alternate name field is a comma delimited list of all the alternate names.The original atlas is designed for subject indexing of legislation, photographs, and other documents and is an unofficial delineation of neighborhood boundaries used by the City Clerks Office. Sources for this atlas and the neighborhood names used in it include a 1980 neighborhood map produced by the Department of Community Development, Seattle Public Library indexes, a 1984-1986 Neighborhood Profiles feature series in the Seattle Post-Intelligencer, numerous parks, land use and transportation planning studies, and records in the Seattle Municipal Archives. Many of the neighborhood names are traditional names whose meaning has changed over the years, and others derive from subdivision names or elementary school attendance areas.Disclaimer: The Seattle City Clerk's Office Geographic Indexing Atlas is designed for subject indexing of legislation, photographs, and other records in the City Clerk's Office and Seattle Municipal Archives according to geographic area. Neighborhoods are named and delineated in this collection of maps in order to provide consistency in the way geographic names are used in describing records of the Archives and City Clerk, thus allowing precise retrieval of records. The neighborhood names and boundaries are not intended to represent any "official" City of Seattle neighborhood map. The Office of the City Clerk makes no claims as to the completeness, accuracy, or content of any data contained in the Geographic Indexing Atlas; nor does it make any representation of any kind, including, but not limited to, warranty of the accuracy or fitness for a particular use; nor are any such warranties to be implied or inferred with respect to the representations furnished herein. The maps are subject to change for administrative purposes of the Office of the City Clerk. Information contained in the site, if used for any purpose other than as an indexing and search aid for the databases of the Office of the City Clerk, is being used at one's own risk.

Data Entry Service Market size was valued at USD 35.0 Billion in 2024 and is projected to reach USD 60.5 Billion by 2032, growing at a CAGR of 7.2% during the forecast period 2026-2032.• Increasing the Adoption of Digital Transformation Initiatives: Business processes become increasingly digitized, creating a greater demand for precise and effective data entry services to manage expanding volumes of digital data.• Growth of E-commerce and Online Retail Platforms: The rise of e-commerce platforms increased the demand for data entry services, as product listings, customer data, and transaction records must be constantly updated and managed.

Board of Review District based on 2010 census.

Key Features

How to use this dataset

1. Filtering and Subsetting: Depending on your research or analysis objectives, filter and subset the data to focus on specific articles, time periods, or sensor types. This can help you narrow down your analysis to relevant subsets.

2. Temporal Analysis: Utilize the "Publish_Date" column to perform time-based analysis. You can track trends, seasonality, or changes in weather conditions over time, which can be especially useful for weather-related studies.

3. Sensor Data Analysis: If you're interested in the sensor data, explore the different sensor types ("sensorType") and their measurements. Analyze relationships between sensors, temperature, humidity, and other environmental factors.

4. Geospatial Analysis: Leverage the "currentLocation" data, which includes geographic coordinates, to perform geospatial analysis. This could involve mapping, spatial clustering, or analyzing how weather conditions vary by location.

If you find this dataset useful, give it an upvote – it's a small gesture that goes a long way! Thanks for your support. 😄

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - No changes v1.0 - Initial public release Description Borders of all Water Management Areas (WMAs) across the 11 western-most states of the coterminous United States are available filtered through a single source. The legal name for this set of boundaries varies state-by-state. The data is provided as two compressed shapefiles. One, stateWMAs, contains data for all 11 states. For 10 of those states, Arizona being the exception, the polygons represent the legal management boundaries used by those states to manage their surface and groundwater resources respectively. WMAs refer to the set of boundaries a particular state uses to manage its water resources. Each set of boundaries was collected from the states individually, and then merged into one spatial layer. The merging process included renaming some columns to enable merging with all other source layers, as well as removing columns deemed not required for followup analysis. The retained columns for each boundary are: basinNum - the state provided unique numerical ID; basinName - the state provided English name of the area, where applicable; state - the state name; and uniID - a unique identifier we created by concatenating the state name, and underscore, and the state numerical ID. Arizona is unique within this collection of states in that surface and groundwater resources are managed using two separate sets of boundaries. During our followup analysis (Grogan et al., in review) we decided to focus on one set of boundaries, those for surface water. This is due to the recommendation of our hydrologists that the surface water boundary set is a more realist representation of how water moves across the landscape, as a few of the groundwater boundaries are based on political and/or economic considerations. Therefore, the Arizona surface WMAs are included within stateWMAs. The Arizona groundwater WMAs are provided as a separate file, azGroundWMAs, as a companion to the first file for completeness and general reference. WMA spatial boundary data sources by state: Arizona: Arizona Surface Water Watersheds; Collected February, 2020; https://gisdata2016-11-18t150447874z-azwater.opendata.arcgis.com/datasets/surface-watershed/explore?location=34.158174%2C-111.970823%2C7.50 Arizona: Arizona Ground Water Basins; Collected February, 2020; https://gisdata2016-11-18t150447874z-azwater.opendata.arcgis.com/datasets/groundwater-basin-2/explore?location=34.158174%2C-111.970823%2C7.50 California: California CalWater 2.2.1; Collected February, 2020; https://www.mlml.calstate.edu/mpsl-mlml/data-center/data-entry-tools/data-tools/gis-shapefile-layers/ Colorado: Colorado Water District Boundaries; Collected February, 2020; https://www.colorado.gov/pacific/cdss/gis-data-category Idaho: Idaho Department of Water Resources (IDWR) Administrative Basins; Collected November, 2015; https://data-idwr.opendata.arcgis.com/datasets/fb0df7d688a04074bad92ca8ef74cc26_4/explore?location=45.018686%2C-113.862284%2C6.93 Montana: Collected June, 2019; Directly contacted Montana Department of Natural Resources and Conservation (DNRC) Office of Information Technology (OIT) Nevada: Nevada State Engineer Admin Basin Boundaries; Collected April, 2020 https://ndwr.maps.arcgis.com/apps/mapviewer/index.html?layers=1364d0c3a0284fa1bcd90f952b2b9f1c New Mexico: New Mexico Office of the State Engineer (OSE) Declared Groundwater Basins; Collected April, 2020 https://geospatialdata-ose.opendata.arcgis.com/datasets/ose-declared-groundwater-basins/explore?location=34.179783%2C-105.996542%2C7.51 Oregon: Oregon Water Resources Department (OWRD) Administrative Basins; Collected February, 2020; https://www.oregon.gov/OWRD/access_Data/Pages/Data.aspx Utah: Utah Adjudication Books; Collected April, 2020; https://opendata.gis.utah.gov/datasets/utahDNR::utah-adjudication-books/explore?location=39.497165%2C-111.587782%2C-1.00 Washington: Washington Water Resource Inventory Areas (WRIA); Collected June, 2017; https://ecology.wa.gov/Research-Data/Data-resources/Geographic-Information-Systems-GIS/Data Wyoming: Wyoming State Engineer's Office Board of Control Water Districts; Collected June, 2019; Directly contacted Wyoming State Engineer's Office

Mobile GIS for Pipeline Field Crews Market Outlook

According to our latest research, the global Mobile GIS for Pipeline Field Crews market size reached USD 1.54 billion in 2024 and is projected to grow at a robust CAGR of 13.7% from 2025 to 2033, reaching a forecasted market size of USD 4.49 billion by 2033. This growth is primarily driven by the increasing adoption of digital technologies for real-time pipeline monitoring, the rising demand for efficient asset management, and stringent regulatory requirements for pipeline safety and environmental compliance across the globe.

One of the most significant growth factors in the Mobile GIS for Pipeline Field Crews market is the accelerating digital transformation initiatives within the oil and gas sector. Pipeline operators are increasingly leveraging mobile GIS solutions to enhance field productivity, reduce operational downtime, and improve data accuracy. The integration of advanced geospatial analytics, real-time mapping, and cloud-based data sharing has revolutionized how field crews manage inspection, maintenance, and emergency response activities. These technologies allow for seamless communication between field and office teams, ensuring that critical information is updated and accessible instantly. Furthermore, the growing prevalence of Internet of Things (IoT) sensors and unmanned aerial vehicles (UAVs) is complementing mobile GIS platforms, providing comprehensive situational awareness and enabling predictive maintenance strategies that minimize costly disruptions.

Another pivotal driver is the increasing regulatory scrutiny and emphasis on pipeline safety and environmental protection. Governments and regulatory bodies worldwide are imposing stricter compliance standards, mandating pipeline operators to maintain accurate records, conduct regular inspections, and promptly address potential leaks or hazards. Mobile GIS solutions empower field crews to efficiently document inspection results, capture geotagged images, and instantly report anomalies, thereby streamlining compliance reporting and audit processes. This not only mitigates operational risks but also enhances the reputation of pipeline operators by demonstrating a proactive approach to safety and sustainability. The adoption of mobile GIS is further accelerated by the need to manage aging pipeline infrastructure, where rapid identification and remediation of vulnerabilities are critical to preventing environmental incidents and ensuring uninterrupted energy supply.

The increasing complexity and scale of pipeline networks, coupled with mounting pressure to optimize operational efficiency, are also fueling the demand for mobile GIS solutions. As pipeline assets span vast and often remote geographical areas, traditional paper-based methods and manual data entry are proving inadequate and error-prone. Mobile GIS platforms enable field crews to access, update, and synchronize asset data in real time, regardless of their location. This capability not only reduces administrative overhead but also empowers organizations to make data-driven decisions, prioritize maintenance activities, and allocate resources more effectively. The rise of cloud-based deployment models has further democratized access to sophisticated GIS tools, allowing organizations of all sizes to leverage advanced analytics, improve collaboration, and scale their operations without significant upfront investments in IT infrastructure.

Regionally, North America continues to lead the Mobile GIS for Pipeline Field Crews market, driven by extensive pipeline infrastructure, high technology adoption rates, and stringent regulatory frameworks. However, rapid industrialization and expanding energy networks in Asia Pacific and the Middle East are expected to drive significant market growth in these regions over the next decade. Europe is also witnessing steady adoption, supported by ongoing modernization of utility infrastructure and a growing focus on environmental sustainability. Latin America and Africa are emerging as promising markets, albeit at a slower pace, as governments and industry players gradually embrace digital transformation to improve pipeline management and safety outcomes.

Park Entry Points: A simplified point layer of California State Parks entry points, providing location, Park unit name, street address, links to other information, and other attributes. Current as of October 2024.

Mapping of deicing material storage facilities in the Lake Champlain Basin was conducted during the late fall and winter of 2022-23. 126 towns were initially selected for mapping (some divisions within the GIS towns data are unincorporated “gores”). Using the list of towns, town clerk contact information was obtained from the Vermont Secretary of State’s website, which maintains a database of contact information for each town.Each town was contacted to request information about their deicing material storage locations and methods. Email and telephone scripts were developed to briefly introduce the project and ask questions about the address of any deicing material storage locations in the town, type of materials stored at each site, duration of time each site has been used, whether materials on site are covered, and the type of surface the materials are stored on, if any. Data were entered into a geospatial database application (Fulcrum). Information was gathered there and exported as ArcGIS file geodatabases and Comma Separated Values (CSV) files for use in Microsoft Excel. Data were collected for 118 towns out of the original 126 on the list (92%). Forty-three (43) towns reported that they are storing multiple materials types at their facilities. Four (4) towns have multiple sites where they store material (Dorset, Pawlet, Morristown, and Castleton). Of these, three (3) store multiple materials at one or both of their sites (Pawlet, Morristown, and Castleton). Where towns have multiple materials or locations, the record information from the overall town identifier is linked to the material stored using a unique ‘one-to-many’ identifier. Locations of deicing material facilities, as shown in the database, were based on the addresses or location descriptions provided by town staff members and was verified only using the most recent aerial imagery (typically later than 2018 for all towns). Locations have not been field verified, nor have site conditions and infrastructure or other information provided by town staff.Dataset instructions:The dataset for Deicing Material Storage Facilities contains two layers – the ‘parent’ records titled ‘salt_storage’ and the ‘child’ records titled ‘salt_storage_record’ with attributes for each salt storage site. This represents a ‘one-to-many’ data structure. To see the attributes for each salt storage site, the user needs to Relate the data. The relationship can be accomplished in GIS software. The Relate needs to be built on the following fields:‘salt_storage’: ‘fulcrum_id’‘salt_storage_record: ‘fulcrum_parent_id’This will create a one-to-many relationship between the geographic locations and the attributes for each salt storage site.

Point layer for municipal clerk offices in Vermont. This layer can be referenced in conjunction with the Vermont Geographic Area Names and Codes dataset (last updated July 2022). Municipal clerk offices were extracted from the VT Data - E911 Landmarks layer. For towns without a municipal office (Averill, Avery's Gore, Buels Gore, Essex, Ferdinand, Glastenbury, Lewis, Somerset, Warner's Grant, and Warren Gore), a landmark point with the municipality name was used instead.

Link to City Clerk's records for Sioux Falls, South Dakota.

The 119th Congressional Districts dataset reflects boundaries from January 3rd, 2025 from the United States Census Bureau (USCB), and the attributes are updated every Sunday from the United States House of Representatives and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Information for each member of Congress is appended to the Census Congressional District shapefile using information from the Office of the Clerk, U.S. House of Representatives' website https://clerk.house.gov/xml/lists/MemberData.xml and its corresponding XML file. Congressional districts are the 435 areas from which people are elected to the U.S. House of Representatives. This dataset also includes 9 geographies for non-voting at large delegate districts, resident commissioner districts, and congressional districts that are not defined. After the apportionment of congressional seats among the states based on census population counts, each state is responsible for establishing congressional districts for the purpose of electing representatives. Each congressional district is to be as equal in population to all other congressional districts in a state as practicable. The 119th Congress is seated from January 3, 2025 through January 3, 2027. In Connecticut, Illinois, and New Hampshire, the Redistricting Data Program (RDP) participant did not define the CDs to cover all of the state or state equivalent area. In these areas with no CDs defined, the code "ZZ" has been assigned, which is treated as a single CD for purposes of data presentation. The TIGER/Line shapefiles for the District of Columbia, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands) each contain a single record for the non-voting delegate district in these areas. The boundaries of all other congressional districts reflect information provided to the Census Bureau by the states by May 31, 2024. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529006

Abstract

The energy statistics program has implemented many rounds of the Household Energy Survey during 1999-2011.

Because of the importance of the household sector and due to its large contribution to energy consumption in the Palestinian Territory, PCBS decided to conduct a special Household Energy Survey to cover energy indicators in the household sector. To achieve this, a questionnaire was attached to the Labor Force Survey.

This survey aimed to provide data on energy consumption in the household sector and to provide data on energy consumption behavior and patterns in the society by type of energy.

The survey presents data on energy indicators pertaining to households in the Palestinian Territory. This includes statistical data on electricity and other fuel consumption by households covering type of fuel for different activities (cooking, baking, heating, lighting, and water heating).

Geographic coverage

Geographic Coverage

Analysis unit

households

Universe

The target population was all Palestinian households living in the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Frame The sample is a two-stage stratified cluster random sample.

Target Population: The target population was all Palestinian households whom are living in the Palestinian territory.

Sampling Frame: The sample of this survey is a part of the main sample of Labor Force Survey (LFS) which implemented periodically every quarter by PCBS since 1995, so this survey implement every quarter in the year (distributed over 13 weeks), the survey attached with the LFS in the first quarter of 2011, and the sample contain of 6 weeks from the eighth week to the thirteen week from the round 60 of labor force. The sample is two stage stratified cluster sample with two stages, first stage we selected a systematic random sample of 211 enumeration areas for the semi round, then in the second stage we select a random area sample of average 16 households from each enumeration area selected in the first stage.

Sampling Design: The sample of this survey is a sub-sample of the Labor Force Survey (LFS) sample, which has been conducted periodically since September 1995. The sample of LFS is distributed over 13 weeks. The sample of the survey occupies six weeks of the first quarter of 2011 within implementing LFS.

Stratification by number of households: In designing the sample of the LFS, three levels of stratification by number of households were made: Stratification by number of households: Stratification by place of residence which comprises: (a) Urban (b) Rural (c) Refugee camps Stratification by locality size.

Sample Unit: In the first stage, the sampling units are the enumeration areas (clusters) from the master sample. In the second stage, the sampling units are households.

Analysis Unit: The unit of analysis is the household.

Sample Size: The sample size is comprised of (3,313) Palestinian households in the West Bank and Gaza Strip, where this sample was distributed according to locality type (urban, rural and refugee camps).

Mode of data collection

Face-to-face [f2f]

Research instrument

The design of the questionnaire for the Household Energy Survey was based on the experiences of similar countries as well as on international standards and recommendations for the most important indicators, taking into account the special situation of the Palestinian Territory.

Cleaning operations

he data processing stage consisted of the following operations: Editing and coding before data entry: All questionnaires were edited and coded in the office using the same instructions adopted for editing in the field.

Data entry: At this stage, data was entered into the computer using a data entry template developed in Access. The data entry program was prepared to satisfy a number of requirements such as: · To prevent the duplication of the questionnaires during data entry. · To apply integrity and consistency checks of entered data. · To handle errors in user friendly manner. · The ability to transfer captured data to another format for data analysis using statistical analysis software such as SPSS.

Response rate

The survey sample consists of about 3313 households of which 3029 households completed the interview; whereas 1950 households from the West Bank and 1079 households in Gaza Strip. Weights were modified to account for non-response rate. The response rate in the West Bank reached 95. % while in the Gaza Strip it reached 98%.

Non-response cases

No of cases non-response cases
3029 Household completed 22 Traveling households
19 Unit does not exist 56 No one at home
22 Refused to cooperate
139 Vacant Housing unit 1 No available information
25 Other
3313 Total sample size

Sampling error estimates

It includes many aspects of the survey, mainly statistical errors due to the sample, and non statistical errors referring to the workers and tools of the survey. It includes also the response rates in the survey and their effect on the assumptions. This section includes:

Sampling Errors These types of errors evolved as a result of studying a part of the population and not all of it. Because this is a sampled survey, the data will be affected by sampling errors due to using a sample and not the whole frame of the population. Differences appear compared to the actual values that could be obtained through a census. For this survey, variance calculations were made for average household consumption and total consumption for the different types of energy in the Palestinian Territory.

The results of gasoline, wood, charcoal and olive cake suffer from a high variance. This problem should be taken into consideration when dealing with the average household consumption of these types of fuel, keeping in mind that there are no problems in publishing the data at the geographical level (North of the West Bank, Middle of the West Bank, South of the West Bank and Gaza Strip). However, publishing data at the governorate level is not possible due to the high variance, especially for wood, charcoal and olive cake. The variances for the main indicators of this survey are as follows:

95% Confidence Interval C.V % Standard Error Estimate Variable
Upper Lower Value Unit
99.9 99.5 0.001 0.1 99.8 % Main Electricity Source 66.2 61.2 0.020 1.3 63.7 % Use of Solar Heaters
98.5 97.5 0.003 0.2 98.1 % Use of LPG
273 259 0.013 3.44 266 KWh Average Electricity Consumption 264 191 0.081 18.47 228 Kg Average wood Consumption
50.5 41.8 0.047 2.19 46 Liter Average Gasoline Consumption

Non Sampling Errors These errors are due to non-response cases as well as the implementation of surveys. In this survey, these errors emerged because of (a) the special situation of the questionnaire itself, where some parts depend partially on estimation, (b) diversity of sources (e.g., the interviewers, respondents, editors, coders, data entry operator, etc).

The sources of these errors can be summarized as: Some of the households were not in their houses and the interviewers could not meet them. Some of the households did not give attention to the questions in questionnaire.
Some errors occurred due to the way the questions were asked by interviewers. Misunderstanding of the questions by the respondents. Answering the questions related to consumption by making estimations.

Data appraisal

The data of the survey is comparable geographically and over time by comparing the data between different geographical areas to data of previous surveys and census 2007.

The MSB received Precinct lines and boundary descriptions from the Alaska Division of Elections. GIS interpreted these lines to fit the higher-resolution GIS data layers. This step is important to show relative data layers (roads, rivers, schools, etc.). The Clerk used this information to determine which Assembly District individual registered voters fall within. The state sends a file of house ranges per precinct, and the Borough Clerk refines this list by Assembly District. The goal is for every voter to know which precinct, polling place, and Assembly District they live within and to receive the appropriate ballot at election time.

Explore the booming Mobile Data Collection Software market, projected to reach USD 3.5 billion by 2025 with a 12.5% CAGR. Discover key drivers, trends, and leading companies revolutionizing data collection across healthcare, marketing, and logistics.

Boundary geometry for all of the assessed property within the Province of Manitoba. Please visit the Manitoba Property Assessment website for more information www.gov.mb.ca/assessment. The purpose is to provide end users with a digital map of Manitoba's property assessment boundaries and summary assessment data. This data layer is suitable for GIS georeferencing. The Manitoba Property Assessment Information data reflects the most current mapping data available and was originally uploaded to Manitoba Maps as a feature layer on December 23, 2016. Fields Included: OBJECTID: Sequential unique whole numbers that are automatically generated ROLL_NO: Identify property within a municipality ROLL_NO_TXT: Identify property within a municipality. Formatted with 3 decimals PROPERTY_ADDRESS: Civic address if the property has one. Otherwise short legal description-section/township/range or plan MUNI_NO: Manitoba municipality identifier number MUNICIPALITY: Manitoba municipality identifier number and legal name of municipality MUNI_NAME_WITH_TYP: Name of municipality suitable for alphabetical list including type ASMT_ROLL: Tax year and assessment roll version (Preliminary, Final, Tax) DWELLING_UNITS: Number of dwelling units on the property FRONTAGE_OR_AREA: size of property in either acres or feet of frontage TOTAL_VALUE: Assessed value of property as of tax year and assessment roll version